U.S. patent application number 16/292893 was filed with the patent office on 2019-09-05 for system and method for review of automated clinical documentation.
The applicant listed for this patent is Nuance Communications, Inc.. Invention is credited to Kumar Abhinav, Guido Remi Marcel Gallopyn, Matthias Helletzgruber, Jeremy Martin Jancsary, Uwe Helmut Jost, Donald E. Owen, Mehmet Mert Oz, Joel Praveen Pinto, PAUL JOSEPH VOZILA.
Application Number | 20190272902 16/292893 |
Document ID | / |
Family ID | 67767674 |
Filed Date | 2019-09-05 |
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United States Patent
Application |
20190272902 |
Kind Code |
A1 |
VOZILA; PAUL JOSEPH ; et
al. |
September 5, 2019 |
SYSTEM AND METHOD FOR REVIEW OF AUTOMATED CLINICAL
DOCUMENTATION
Abstract
A method, computer program product, and computing system for
obtaining, by a computing device, encounter information of a
patient encounter, wherein the encounter information may include
audio encounter information obtained from at least a first
encounter participant. The audio encounter information obtained
from at least the first encounter participant may be processed. An
alert may be generated to obtain additional encounter information
of the patient encounter.
Inventors: |
VOZILA; PAUL JOSEPH;
(Arlington, MA) ; Gallopyn; Guido Remi Marcel;
(Newburyport, MA) ; Jost; Uwe Helmut; (Groton,
MA) ; Helletzgruber; Matthias; (Wien, AT) ;
Jancsary; Jeremy Martin; (Vienna, AT) ; Abhinav;
Kumar; (Montreal, CA) ; Pinto; Joel Praveen;
(Aachen, DE) ; Owen; Donald E.; (Orlando, FL)
; Oz; Mehmet Mert; (Baden, AT) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Nuance Communications, Inc. |
Burlington |
MA |
US |
|
|
Family ID: |
67767674 |
Appl. No.: |
16/292893 |
Filed: |
March 5, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62803193 |
Feb 8, 2019 |
|
|
|
62638809 |
Mar 5, 2018 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/0482 20130101;
G16H 15/00 20180101; G10L 15/22 20130101; G06F 3/0362 20130101;
G16H 40/60 20180101; G06F 3/0485 20130101; G06F 3/0334 20130101;
G10L 15/26 20130101; G16H 10/20 20180101; G06F 3/0489 20130101;
G06N 20/20 20190101; G06F 3/167 20130101; G16H 10/60 20180101; G06N
20/00 20190101; G06F 40/169 20200101; G06F 3/165 20130101 |
International
Class: |
G16H 10/60 20060101
G16H010/60; G10L 15/26 20060101 G10L015/26; G16H 40/60 20060101
G16H040/60 |
Claims
1. A computer-implemented method comprising: obtaining, by a
computing device, encounter information of a patient encounter,
wherein the encounter information includes audio encounter
information obtained from at least a first encounter participant;
processing the audio encounter information obtained from at least
the first encounter participant; and generating an alert to obtain
additional encounter information of the patient encounter.
2. The computer-implemented method of claim 1 wherein processing
the first audio encounter information includes defining linkages
between each of a plurality of layers associated with the audio
encounter information.
3. The computer-implemented method of claim 1 further comprising
generating a user interface displaying the plurality of layers
associated with the audio encounter information obtained from at
least the first encounter participant.
4. The computer-implemented method of claim 3 wherein the first
layer of the plurality of layers is an audio signal associated with
the audio encounter information, wherein the second layer of the
plurality of layers is a transcript associated with the audio
encounter information, and wherein the third layer of the plurality
of layers is a medical report associated with the audio encounter
information.
5. The computer-implemented method of claim 4 wherein processing
the first audio encounter information includes comparing at least
one layer of the plurality of layers with at least one data
source.
6. The computer-implemented method of claim 5 wherein the at least
one data source includes at least one of physical exam finding
information, lab order information, medical condition checklist
information, and compliance information.
7. The computer-implemented method of claim 1 wherein the alert is
at least one of an audio alert and a visual alert.
8. A computer program product residing on a computer readable
medium having a plurality of instructions stored thereon which,
when executed by a processor, cause the processor to perform
operations comprising: obtaining encounter information of a patient
encounter, wherein the encounter information includes audio
encounter information obtained from at least a first encounter
participant; processing the audio encounter information obtained
from at least the first encounter participant; and generating an
alert to obtain additional encounter information of the patient
encounter.
9. The computer program product of claim 8 wherein processing the
first audio encounter information includes defining linkages
between each of a plurality of layers associated with the audio
encounter information.
10. The computer program product of claim 8 wherein the
instructions further comprise generating a user interface
displaying the plurality of layers associated with the audio
encounter information obtained from at least the first encounter
participant.
11. The computer program product of claim 10 wherein the first
layer of the plurality of layers is an audio signal associated with
the audio encounter information, wherein the second layer of the
plurality of layers is a transcript associated with the audio
encounter information, and wherein the third layer of the plurality
of layers is a medical report associated with the audio encounter
information.
12. The computer program product of claim 11 wherein processing the
first audio encounter information includes comparing at least one
layer of the plurality of layers with at least one data source.
13. The computer program product of claim 12 wherein the at least
one data source includes at least one of physical exam finding
information, lab order information, medical condition checklist
information, and compliance information.
14. The computer program product of claim 8 wherein the alert is at
least one of an audio alert and a visual alert.
15. A computing system including a processor and memory configured
to perform operations comprising: obtaining encounter information
of a patient encounter, wherein the encounter information includes
audio encounter information obtained from at least a first
encounter participant; processing the audio encounter information
obtained from at least the first encounter participant; and
generating an alert to obtain additional encounter information of
the patient encounter.
16. The computing system of claim 15 wherein processing the first
audio encounter information includes defining linkages between each
of a plurality of layers associated with the audio encounter
information.
17. The computing system of claim 15 wherein the instructions
further comprise generating a user interface displaying the
plurality of layers associated with the audio encounter information
obtained from at least the first encounter participant.
18. The computing system of claim 17 wherein the first layer of the
plurality of layers is an audio signal associated with the audio
encounter information, wherein the second layer of the plurality of
layers is a transcript associated with the audio encounter
information, and wherein the third layer of the plurality of layers
is a medical report associated with the audio encounter
information.
19. The computing system of claim 18 wherein processing the first
audio encounter information includes comparing at least one layer
of the plurality of layers with at least one data source.
20. The computing system of claim 19 wherein the at least one data
source includes at least one of physical exam finding information,
lab order information, medical condition checklist information, and
compliance information.
Description
RELATED APPLICATION(S)
[0001] This application claims the benefit of the following U.S.
Provisional Application No. 62/803,193 filed on 8 Feb. 2019 and
62/638,809 filed on 5 Mar. 2018, the contents of which are all
incorporated herein by reference.
BACKGROUND
[0002] Automated Clinical Documentation (ACD) may be used, e.g., to
turn transcribed conversational (e.g., physician, patient, and/or
other participants such as patient's family members, nurses,
physician assistants, etc.) speech into formatted (e.g., medical)
reports. Such reports may be reviewed, e.g., to assure accuracy of
the reports by the physician, scribe, etc.
SUMMARY OF DISCLOSURE
[0003] In one implementation, a computer-implemented method
executed by a computer may include but is not limited to obtaining,
by a computing device, encounter information of a patient
encounter, wherein the encounter information may include audio
encounter information obtained from at least a first encounter
participant. The audio encounter information obtained from at least
the first encounter participant may be processed. An alert may be
generated to obtain additional encounter information of the patient
encounter.
[0004] One or more of the following features may be included. The
alert may be at least one of an audio alert and a visual alert.
Processing the first audio encounter information may include
defining linkages between each of the plurality of layers
associated with the audio encounter information. A user interface
may be generated displaying a plurality of layers associated with
the audio encounter information obtained from at least the first
encounter participant. The first layer of the plurality of layers
may be an audio signal associated with the audio encounter
information, wherein the second layer of the plurality of layers
may be a transcript associated with the audio encounter
information, and wherein the third layer of the plurality of layers
may be a medical report associated with the audio encounter
information. Processing the first audio encounter information may
include comparing at least one layer of the plurality of layers
with at least one data source. The at least one data source may
include at least one of physical exam finding information, lab
order information, medical condition checklist information, and
compliance information.
[0005] In another implementation, a computer program product
resides on a computer readable medium and has a plurality of
instructions stored on it. When executed by a processor, the
instructions cause the processor to perform operations including
but not limited to obtaining encounter information of a patient
encounter, wherein the encounter information may include audio
encounter information obtained from at least a first encounter
participant. The audio encounter information obtained from at least
the first encounter participant may be processed. An alert may be
generated to obtain additional encounter information of the patient
encounter.
[0006] One or more of the following features may be included. The
alert may be at least one of an audio alert and a visual alert.
Processing the first audio encounter information may include
defining linkages between each of the plurality of layers
associated with the audio encounter information. A user interface
may be generated displaying a plurality of layers associated with
the audio encounter information obtained from at least the first
encounter participant. The first layer of the plurality of layers
may be an audio signal associated with the audio encounter
information, wherein the second layer of the plurality of layers
may be a transcript associated with the audio encounter
information, and wherein the third layer of the plurality of layers
may be a medical report associated with the audio encounter
information. Processing the first audio encounter information may
include comparing at least one layer of the plurality of layers
with at least one data source. The at least one data source may
include at least one of physical exam finding information, lab
order information, medical condition checklist information, and
compliance information.
[0007] In another implementation, a computing system includes a
processor and memory is configured to perform operations including
but not limited to obtaining encounter information of a patient
encounter, wherein the encounter information may include audio
encounter information obtained from at least a first encounter
participant. The audio encounter information obtained from at least
the first encounter participant may be processed. An alert may be
generated to obtain additional encounter information of the patient
encounter.
[0008] One or more of the following features may be included. The
alert may be at least one of an audio alert and a visual alert.
Processing the first audio encounter information may include
defining linkages between each of the plurality of layers
associated with the audio encounter information. A user interface
may be generated displaying a plurality of layers associated with
the audio encounter information obtained from at least the first
encounter participant. The first layer of the plurality of layers
may be an audio signal associated with the audio encounter
information, wherein the second layer of the plurality of layers
may be a transcript associated with the audio encounter
information, and wherein the third layer of the plurality of layers
may be a medical report associated with the audio encounter
information. Processing the first audio encounter information may
include comparing at least one layer of the plurality of layers
with at least one data source. The at least one data source may
include at least one of physical exam finding information, lab
order information, medical condition checklist information, and
compliance information.
[0009] The details of one or more implementations are set forth in
the accompanying drawings and the description below. Other features
and advantages will become apparent from the description, the
drawings, and the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is a diagrammatic view of an automated clinical
documentation computer system and an automated clinical
documentation process coupled to a distributed computing
network;
[0011] FIG. 2 is a diagrammatic view of a modular ACD system
incorporating the automated clinical documentation computer system
of FIG. 1;
[0012] FIG. 3 is a diagrammatic view of a mixed-media ACD device
included within the modular ACD system of FIG. 2;
[0013] FIG. 4 is a flow chart of one implementation of the
automated clinical documentation process of FIG. 1;
[0014] FIG. 5 is a user interface of the automated clinical
documentation process of FIG. 1;
[0015] FIG. 6 is a user interface of the automated clinical
documentation process of FIG. 1;
[0016] FIG. 7 is a user interface of the automated clinical
documentation process of FIG. 1;
[0017] FIG. 8 is a user interface of the automated clinical
documentation process of FIG. 1;
[0018] FIG. 9 is a flow chart of one implementation of the
automated clinical documentation process of FIG. 1;
[0019] FIG. 10 is a flow chart of one implementation of the
automated clinical documentation process of FIG. 1;
[0020] FIG. 11 is a diagrammatic view of example peripheral devices
that may be used with the automated clinical documentation process
of FIG. 1;
[0021] FIG. 12 is a flow chart of one implementation of the
automated clinical documentation process of FIG. 1;
[0022] FIG. 13 is a user interface of the automated clinical
documentation process of FIG. 1;
[0023] FIG. 14 is a user interface of the automated clinical
documentation process of FIG. 1; and
[0024] FIG. 15 is a flow chart of another implementation of the
automated clinical documentation process of FIG. 1.
[0025] Like reference symbols in the various drawings indicate like
elements.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
System Overview:
[0026] Referring to FIG. 1, there is shown automated clinical
documentation process 10. As will be discussed below in greater
detail, automated clinical documentation process 10 may be
configured to automate the collection and processing of clinical
encounter information to generate/store/distribute medical
records.
[0027] Automated clinical documentation process 10 may be
implemented as a server-side process, a client-side process, or a
hybrid server-side/client-side process. For example, automated
clinical documentation process 10 may be implemented as a purely
server-side process via automated clinical documentation process
10s. Alternatively, automated clinical documentation process 10 may
be implemented as a purely client-side process via one or more of
automated clinical documentation process 10c1, automated clinical
documentation process 10c2, automated clinical documentation
process 10c3, and automated clinical documentation process 10c4.
Alternatively still, automated clinical documentation process 10
may be implemented as a hybrid server-side/client-side process via
automated clinical documentation process 10s in combination with
one or more of automated clinical documentation process 10c1,
automated clinical documentation process 10c2, automated clinical
documentation process 10c3, and automated clinical documentation
process 10c4.
[0028] Accordingly, automated clinical documentation process 10 as
used in this disclosure may include any combination of automated
clinical documentation process 10s, automated clinical
documentation process 10c1, automated clinical documentation
process 10c2, automated clinical documentation process 10c3, and
automated clinical documentation process 10c4.
[0029] Automated clinical documentation process 10s may be a server
application and may reside on and may be executed by automated
clinical documentation (ACD) computer system 12, which may be
connected to network 14 (e.g., the Internet or a local area
network). ACD computer system 12 may include various components,
examples of which may include but are not limited to: a personal
computer, a server computer, a series of server computers, a mini
computer, a mainframe computer, one or more Network Attached
Storage (NAS) systems, one or more Storage Area Network (SAN)
systems, one or more Platform as a Service (PaaS) systems, one or
more Infrastructure as a Service (IaaS) systems, one or more
Software as a Service (SaaS) systems, a cloud-based computational
system, and a cloud-based storage platform.
[0030] As is known in the art, a SAN may include one or more of a
personal computer, a server computer, a series of server computers,
a mini computer, a mainframe computer, a RAID device and a NAS
system. The various components of ACD computer system 12 may
execute one or more operating systems, examples of which may
include but are not limited to: Microsoft Windows Server.TM.;
Redhat Linux.TM., Unix, or a custom operating system, for
example.
[0031] The instruction sets and subroutines of automated clinical
documentation process 10s, which may be stored on storage device 16
coupled to ACD computer system 12, may be executed by one or more
processors (not shown) and one or more memory architectures (not
shown) included within ACD computer system 12. Examples of storage
device 16 may include but are not limited to: a hard disk drive; a
RAID device; a random access memory (RAM); a read-only memory
(ROM); and all forms of flash memory storage devices.
[0032] Network 14 may be connected to one or more secondary
networks (e.g., network 18), examples of which may include but are
not limited to: a local area network; a wide area network; or an
intranet, for example.
[0033] Various IO requests (e.g. IO request 20) may be sent from
automated clinical documentation process 10s, automated clinical
documentation process 10c1, automated clinical documentation
process 10c2, automated clinical documentation process 10c3 and/or
automated clinical documentation process 10c4 to ACD computer
system 12. Examples of IO request 20 may include but are not
limited to data write requests (i.e. a request that content be
written to ACD computer system 12) and data read requests (i.e. a
request that content be read from ACD computer system 12).
[0034] The instruction sets and subroutines of automated clinical
documentation process 10c1, automated clinical documentation
process 10c2, automated clinical documentation process 10c3 and/or
automated clinical documentation process 10c4, which may be stored
on storage devices 20, 22, 24, 26 (respectively) coupled to ACD
client electronic devices 28, 30, 32, 34 (respectively), may be
executed by one or more processors (not shown) and one or more
memory architectures (not shown) incorporated into ACD client
electronic devices 28, 30, 32, 34 (respectively). Storage devices
20, 22, 24, 26 may include but are not limited to: hard disk
drives; optical drives; RAID devices; random access memories (RAM);
read-only memories (ROM), and all forms of flash memory storage
devices. Examples of ACD client electronic devices 28, 30, 32, 34
may include, but are not limited to, personal computing device 28
(e.g., a smart phone, a personal digital assistant, a laptop
computer, a notebook computer, and a desktop computer), audio input
device 30 (e.g., a handheld microphone, a lapel microphone, an
embedded microphone (such as those embedded within eyeglasses,
smart phones, tablet computers and/or watches) and an audio
recording device), display device 32 (e.g., a tablet computer, a
computer monitor, and a smart television), machine vision input
device 34 (e.g., an RGB imaging system, an infrared imaging system,
an ultraviolet imaging system, a laser imaging system, a SONAR
imaging system, a RADAR imaging system, and a thermal imaging
system), a hybrid device (e.g., a single device that includes the
functionality of one or more of the above-references devices; not
shown), an audio rendering device (e.g., a speaker system, a
headphone system, or an earbud system; not shown), various medical
devices (e.g., medical imaging equipment, heart monitoring
machines, body weight scales, body temperature thermometers, and
blood pressure machines; not shown), and a dedicated network device
(not shown).
[0035] Users 36, 38, 40, 42 may access ACD computer system 12
directly through network 14 or through secondary network 18.
Further, ACD computer system 12 may be connected to network 14
through secondary network 18, as illustrated with link line 44.
[0036] The various ACD client electronic devices (e.g., ACD client
electronic devices 28, 30, 32, 34) may be directly or indirectly
coupled to network 14 (or network 18). For example, personal
computing device 28 is shown directly coupled to network 14 via a
hardwired network connection. Further, machine vision input device
34 is shown directly coupled to network 18 via a hardwired network
connection. Audio input device 30 is shown wirelessly coupled to
network 14 via wireless communication channel 46 established
between audio input device 30 and wireless access point (i.e., WAP)
48, which is shown directly coupled to network 14. WAP 48 may be,
for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n, Wi-Fi,
and/or Bluetooth device that is capable of establishing wireless
communication channel 46 between audio input device 30 and WAP 48.
Display device 32 is shown wirelessly coupled to network 14 via
wireless communication channel 50 established between display
device 32 and WAP 52, which is shown directly coupled to network
14.
[0037] The various ACD client electronic devices (e.g., ACD client
electronic devices 28, 30, 32, 34) may each execute an operating
system, examples of which may include but are not limited to
Microsoft Windows.TM., Apple Macintosh.TM., Redhat Linux.TM., or a
custom operating system, wherein the combination of the various ACD
client electronic devices (e.g., ACD client electronic devices 28,
30, 32, 34) and ACD computer system 12 may form modular ACD system
54.
[0038] Referring also to FIG. 2, there is shown a simplified
example embodiment of modular ACD system 54 that is configured to
automate clinical documentation. Modular ACD system 54 may include:
machine vision system 100 configured to obtain machine vision
encounter information 102 concerning a patient encounter; audio
recording system 104 configured to obtain audio encounter
information 106 concerning the patient encounter; and a computer
system (e.g., ACD computer system 12) configured to receive machine
vision encounter information 102 and audio encounter information
106 from machine vision system 100 and audio recording system 104
(respectively). Modular ACD system 54 may also include: display
rendering system 108 configured to render visual information 110;
and audio rendering system 112 configured to render audio
information 114, wherein ACD computer system 12 may be configured
to provide visual information 110 and audio information 114 to
display rendering system 108 and audio rendering system 112
(respectively).
[0039] Example of machine vision system 100 may include but are not
limited to: one or more ACD client electronic devices (e.g., ACD
client electronic device 34, examples of which may include but are
not limited to an RGB imaging system, an infrared imaging system, a
ultraviolet imaging system, a laser imaging system, a SONAR imaging
system, a RADAR imaging system, and a thermal imaging system).
Examples of audio recording system 104 may include but are not
limited to: one or more ACD client electronic devices (e.g., ACD
client electronic device 30, examples of which may include but are
not limited to a handheld microphone, a lapel microphone, an
embedded microphone (such as those embedded within eyeglasses,
smart phones, tablet computers and/or watches) and an audio
recording device). Examples of display rendering system 108 may
include but are not limited to: one or more ACD client electronic
devices (e.g., ACD client electronic device 32, examples of which
may include but are not limited to a tablet computer, a computer
monitor, and a smart television). Examples of audio rendering
system 112 may include but are not limited to: one or more ACD
client electronic devices (e.g., audio rendering device 116,
examples of which may include but are not limited to a speaker
system, a headphone system, and an earbud system).
[0040] As will be discussed below in greater detail, ACD computer
system 12 may be configured to access one or more datasources 118
(e.g., plurality of individual datasources 120, 122, 124, 126,
128), examples of which may include but are not limited to one or
more of a user profile datasource, a voice print datasource, a
voice characteristics datasource (e.g., for adapting the automated
speech recognition models), a face print datasource, a humanoid
shape datasource, an utterance identifier datasource, a wearable
token identifier datasource, an interaction identifier datasource,
a medical conditions symptoms datasource, a prescriptions
compatibility datasource, a medical insurance coverage datasource,
and a home healthcare datasource. While in this particular example,
five different examples of datasources 118, are shown, this is for
illustrative purposes only and is not intended to be a limitation
of this disclosure, as other configurations are possible and are
considered to be within the scope of this disclosure.
[0041] As will be discussed below in greater detail, modular ACD
system 54 may be configured to monitor a monitored space (e.g.,
monitored space 130) in a clinical environment, wherein examples of
this clinical environment may include but are not limited to: a
doctor's office, a medical facility, a medical practice, a medical
lab, an urgent care facility, a medical clinic, an emergency room,
an operating room, a hospital, a long term care facility, a
rehabilitation facility, a nursing home, and a hospice facility.
Accordingly, an example of the above-referenced patient encounter
may include but is not limited to a patient visiting one or more of
the above-described clinical environments (e.g., a doctor's office,
a medical facility, a medical practice, a medical lab, an urgent
care facility, a medical clinic, an emergency room, an operating
room, a hospital, a long term care facility, a rehabilitation
facility, a nursing home, and a hospice facility).
[0042] Machine vision system 100 may include a plurality of
discrete machine vision systems when the above-described clinical
environment is larger or a higher level of resolution is desired.
As discussed above, examples of machine vision system 100 may
include but are not limited to: one or more ACD client electronic
devices (e.g., ACD client electronic device 34, examples of which
may include but are not limited to an RGB imaging system, an
infrared imaging system, an ultraviolet imaging system, a laser
imaging system, a SONAR imaging system, a RADAR imaging system, and
a thermal imaging system). Accordingly, machine vision system 100
may include one or more of each of an RGB imaging system, an
infrared imaging systems, an ultraviolet imaging systems, a laser
imaging system, a SONAR imaging system, a RADAR imaging system, and
a thermal imaging system.
[0043] Audio recording system 104 may include a plurality of
discrete audio recording systems when the above-described clinical
environment is larger or a higher level of resolution is desired.
As discussed above, examples of audio recording system 104 may
include but are not limited to: one or more ACD client electronic
devices (e.g., ACD client electronic device 30, examples of which
may include but are not limited to a handheld microphone, a lapel
microphone, an embedded microphone (such as those embedded within
eyeglasses, smart phones, tablet computers and/or watches) and an
audio recording device). Accordingly, audio recording system 104
may include one or more of each of a handheld microphone, a lapel
microphone, an embedded microphone (such as those embedded within
eyeglasses, smart phones, tablet computers and/or watches) and an
audio recording device.
[0044] Display rendering system 108 may include a plurality of
discrete display rendering systems when the above-described
clinical environment is larger or a higher level of resolution is
desired. As discussed above, examples of display rendering system
108 may include but are not limited to: one or more ACD client
electronic devices (e.g., ACD client electronic device 32, examples
of which may include but are not limited to a tablet computer, a
computer monitor, and a smart television). Accordingly, display
rendering system 108 may include one or more of each of a tablet
computer, a computer monitor, and a smart television.
[0045] Audio rendering system 112 may include a plurality of
discrete audio rendering systems when the above-described clinical
environment is larger or a higher level of resolution is desired.
As discussed above, examples of audio rendering system 112 may
include but are not limited to: one or more ACD client electronic
devices (e.g., audio rendering device 116, examples of which may
include but are not limited to a speaker system, a headphone
system, or an earbud system). Accordingly, audio rendering system
112 may include one or more of each of a speaker system, a
headphone system, or an earbud system.
[0046] ACD computer system 12 may include a plurality of discrete
computer systems. As discussed above, ACD computer system 12 may
include various components, examples of which may include but are
not limited to: a personal computer, a server computer, a series of
server computers, a mini computer, a mainframe computer, one or
more Network Attached Storage (NAS) systems, one or more Storage
Area Network (SAN) systems, one or more Platform as a Service
(PaaS) systems, one or more Infrastructure as a Service (IaaS)
systems, one or more Software as a Service (SaaS) systems, a
cloud-based computational system, and a cloud-based storage
platform. Accordingly, ACD computer system 12 may include one or
more of each of a personal computer, a server computer, a series of
server computers, a mini computer, a mainframe computer, one or
more Network Attached Storage (NAS) systems, one or more Storage
Area Network (SAN) systems, one or more Platform as a Service
(PaaS) systems, one or more Infrastructure as a Service (IaaS)
systems, one or more Software as a Service (SaaS) systems, a
cloud-based computational system, and a cloud-based storage
platform.
[0047] Referring also to FIG. 3, audio recording system 104 may
include directional microphone array 200 having a plurality of
discrete microphone assemblies. For example, audio recording system
104 may include a plurality of discrete audio acquisition devices
(e.g., audio acquisition devices 202, 204, 206, 208, 210, 212, 214,
216, 218) that may form microphone array 200. As will be discussed
below in greater detail, modular ACD system 54 may be configured to
form one or more audio recording beams (e.g., audio recording beams
220, 222, 224) via the discrete audio acquisition devices (e.g.,
audio acquisition devices 202, 204, 206, 208, 210, 212, 214, 216,
218) included within audio recording system 104.
[0048] For example, modular ACD system 54 may be further configured
to steer the one or more audio recording beams (e.g., audio
recording beams 220, 222, 224) toward one or more encounter
participants (e.g., encounter participants 226, 228, 230) of the
above-described patient encounter. Examples of the encounter
participants (e.g., encounter participants 226, 228, 230) may
include but are not limited to: medical professionals (e.g.,
doctors, nurses, physician's assistants, lab technicians, physical
therapists, scribes (e.g., a transcriptionist) and/or staff members
involved in the patient encounter), patients (e.g., people that are
visiting the above-described clinical environments for the patient
encounter), and third parties (e.g., friends of the patient,
relatives of the patient and/or acquaintances of the patient that
are involved in the patient encounter).
[0049] Accordingly, modular ACD system 54 and/or audio recording
system 104 may be configured to utilize one or more of the discrete
audio acquisition devices (e.g., audio acquisition devices 202,
204, 206, 208, 210, 212, 214, 216, 218) to form an audio recording
beam. For example, modular ACD system 54 and/or audio recording
system 104 may be configured to utilize audio acquisition device
210 to form audio recording beam 220, thus enabling the capturing
of audio (e.g., speech) produced by encounter participant 226 (as
audio acquisition device 210 is pointed to (i.e., directed toward)
encounter participant 226). Additionally, modular ACD system 54
and/or audio recording system 104 may be configured to utilize
audio acquisition devices 204, 206 to form audio recording beam
222, thus enabling the capturing of audio (e.g., speech) produced
by encounter participant 228 (as audio acquisition devices 204, 206
are pointed to (i.e., directed toward) encounter participant 228).
Additionally, modular ACD system 54 and/or audio recording system
104 may be configured to utilize audio acquisition devices 212, 214
to form audio recording beam 224, thus enabling the capturing of
audio (e.g., speech) produced by encounter participant 230 (as
audio acquisition devices 212, 214 are pointed to (i.e., directed
toward) encounter participant 230). Further, modular ACD system 54
and/or audio recording system 104 may be configured to utilize
null-steering precoding to cancel interference between speakers
and/or noise.
[0050] As is known in the art, null-steering precoding is a method
of spatial signal processing by which a multiple antenna
transmitter may null multiuser interference signals in wireless
communications, wherein null-steering precoding may mitigate the
impact off background noise and unknown user interference.
[0051] In particular, null-steering precoding may be a method of
beamforming for narrowband signals that may compensate for delays
of receiving signals from a specific source at different elements
of an antenna array. In general and to improve performance of the
antenna array, in incoming signals may be summed and averaged,
wherein certain signals may be weighted and compensation may be
made for signal delays.
[0052] Machine vision system 100 and audio recording system 104 may
be stand-alone devices (as shown in FIG. 2).
Additionally/alternatively, machine vision system 100 and audio
recording system 104 may be combined into one package to form
mixed-media ACD device 232. For example, mixed-media ACD device 232
may be configured to be mounted to a structure (e.g., a wall, a
ceiling, a beam, a column) within the above-described clinical
environments (e.g., a doctor's office, a medical facility, a
medical practice, a medical lab, an urgent care facility, a medical
clinic, an emergency room, an operating room, a hospital, a long
term care facility, a rehabilitation facility, a nursing home, and
a hospice facility), thus allowing for easy installation of the
same. Further, modular ACD system 54 may be configured to include a
plurality of mixed-media ACD devices (e.g., mixed-media ACD device
232) when the above-described clinical environment is larger or a
higher level of resolution is desired.
[0053] Modular ACD system 54 may be further configured to steer the
one or more audio recording beams (e.g., audio recording beams 220,
222, 224) toward one or more encounter participants (e.g.,
encounter participants 226, 228, 230) of the patient encounter
based, at least in part, upon machine vision encounter information
102. As discussed above, mixed-media ACD device 232 (and machine
vision system 100/audio recording system 104 included therein) may
be configured to monitor one or more encounter participants (e.g.,
encounter participants 226, 228, 230) of a patient encounter.
[0054] Specifically, machine vision system 100 (either as a
stand-alone system or as a component of mixed-media ACD device 232)
may be configured to detect humanoid shapes within the
above-described clinical environments (e.g., a doctor's office, a
medical facility, a medical practice, a medical lab, an urgent care
facility, a medical clinic, an emergency room, an operating room, a
hospital, a long term care facility, a rehabilitation facility, a
nursing home, and a hospice facility). And when these humanoid
shapes are detected by machine vision system 100, modular ACD
system 54 and/or audio recording system 104 may be configured to
utilize one or more of the discrete audio acquisition devices
(e.g., audio acquisition devices 202, 204, 206, 208, 210, 212, 214,
216, 218) to form an audio recording beam (e.g., audio recording
beams 220, 222, 224) that is directed toward each of the detected
humanoid shapes (e.g., encounter participants 226, 228, 230).
[0055] As discussed above, ACD computer system 12 may be configured
to receive machine vision encounter information 102 and audio
encounter information 106 from machine vision system 100 and audio
recording system 104 (respectively); and may be configured to
provide visual information 110 and audio information 114 to display
rendering system 108 and audio rendering system 112 (respectively).
Depending upon the manner in which modular ACD system 54 (and/or
mixed-media ACD device 232) is configured, ACD computer system 12
may be included within mixed-media ACD device 232 or external to
mixed-media ACD device 232.
[0056] As discussed above, ACD computer system 12 may execute all
or a portion of automated clinical documentation process 10,
wherein the instruction sets and subroutines of automated clinical
documentation process 10 (which may be stored on one or more of
e.g., storage devices 16, 20, 22, 24, 26) may be executed by ACD
computer system 12 and/or one or more of ACD client electronic
devices 28, 30, 32, 34.
[0057] As discussed above, automated clinical documentation (ACD)
process 10 may be configured to automate the collection and
processing of clinical encounter information to
generate/store/distribute medical records. Accordingly and
referring also to FIGS. 4-8, ACD process 10 may be configured to
obtain 300 encounter information (e.g., machine vision encounter
information 102 and/or audio encounter information 106) of a
patient encounter (e.g., a visit to a doctor's office) of at least
a first encounter participant, wherein the encounter information
may include audio encounter information obtained from at least a
first encounter participant (e.g., encounter participant 228, 226,
230, and/or 242). ACD process 10 may further be configured to
process 302 the encounter information (e.g., machine vision
encounter information 102 and/or audio encounter information 106)
obtained from at least the first encounter participant, e.g., to
generate an encounter transcript (e.g., encounter transcript 234)
and/or generate 304 a user interface displaying a plurality of
layers associated with the audio encounter information obtained
from at least the first encounter participant. In some
implementations, ACD process 10 may process at least a portion of
the encounter transcript (e.g., encounter transcript 234) to
populate at least a portion of a medical record (e.g., medical
record 236) associated with the patient encounter (e.g., the visit
to the doctor's office). Encounter transcript 234 and/or medical
record 236 may be reviewed by a medical professional involved with
the patient encounter (e.g., a visit to a doctor's office) to
determine the accuracy of the same and/or make corrections to the
same.
[0058] As noted above, ACD process 10 may process 302 the audio
encounter information obtained from at least the first encounter
participant. In some implementations, processing 302 the first
audio encounter information may include defining 306 linkages
between each of the plurality of layers associated with the audio
encounter information. For example, the first layer of the
plurality of layers may be an audio signal associated with the
audio encounter information (e.g., complete audio of the encounter,
encompassing and clearly delineating each participant), wherein the
second layer of the plurality of layers may be a transcript
associated with the audio encounter information (e.g., a diarized
audio transcript (verbatim) for each participant in the encounter),
and wherein the third layer of the plurality of layers may be a
medical report associated with the audio encounter information
(e.g., a draft medical report in the appropriate clinical output
format). In some implementations, additional layers may include,
e.g., the above-noted machine vision-based recording of the
encounter, including various signal formats and features, and
discrete, standardized, actionable data resulting from the
encounter, including, but not limited to medication plans (for
example, in RxNorm) or lab orders (for example, LOINC) or diagnoses
(for example, ICD10, CPT etc). In the example, the signals captured
from the encounter information may be processed 304 into at least
the above-noted three separate, yet closely linked and
interdependent layers.
[0059] In some implementations, ACD process 10 may include an ASR
portion that may process 302 the audio encounter information
producing an approximate (e.g., diarized) verbatim transcript along
with alignment information indicating the audio interval
corresponding to each transcript word. In some implementations, a
deep learning (e.g., sequence to sequence) model associated with
ACD process 10 may convert the transcript to a medical report. It
will be appreciated that various attribution techniques may be
employed by ACD process 10 that may effectively softly assign
responsibility for a given output (e.g., medical report) word to
input (e.g., conversation transcript) words (e.g. attention
weights, integrated gradient, etc.) according to the model. As a
result, this may provide a soft mapping from the transcript word
positions to report word positions. In some implementations, the
input word position assigned maximal attribution for a given output
word may be interpreted as being aligned (linked) to that output
(e.g., when a hard mapping is required). Based on the ASR time
alignment, a word in the draft medical report, aligned to a word in
the ASR conversation transcript, may now be associated with an
audio time interval of the associated audio signal of the audio
encounter information.
[0060] In some implementations, ACD process 10 may also may link
(i.e., align) the ASR conversation transcript words with the draft
medical report words. For transcript words that may have maximal
attribution value for some set of medical report words, ACD process
10 may link them with the first word in that set. For the remaining
transcript words, ACD process 10 may link them to the same word
that the nearest preceding (or if none, nearest subsequent)
conversation transcript word is linked to.
[0061] In some implementations, a visual recording (e.g., video
stream of the patient encounter), if available, may also be a layer
and may be time indexed and thus a given point in the recording may
be associated with the same time in the audio recording and thus a
conversation transcript word and draft report word. In some
implementations, if discrete, standardized, actionable data is
produced as a second (parallel) output sequence of the sequence to
sequence model, then a similar model output attribution technique
may be used to align tokens in this actionable data with the ASR
conversation transcript words, and thus the audio intervals.
[0062] In some implementations, ACD process 10 may generate 304 a
user interface displaying a plurality of layers associated with the
audio encounter information obtained from at least the first
encounter participant. For example, and referring at least to the
example implementation of FIG. 5, an example user interface (e.g.,
user interface 500) is shown. It will be appreciated that UI 500 is
shown merely for example purposes only, and that more or less
features and arrangements of features may be used without departing
from the scope of the present disclosure.
[0063] As can be seen from FIG. 5, UI 500 includes the first layer
of the plurality of layers (e.g., first layer 502 which may be a
visualization and/or direct play-back of an audio signal associated
with the audio encounter information (e.g., complete audio of the
encounter, encompassing and clearly delineating each participant),
second layer 504 of the plurality of layers which may be a
transcript (e.g., encounter transcript 234) associated with the
audio encounter information (e.g., a diarized audio transcript
(verbatim) for each participant in the encounter), and third layer
506 of the plurality of layers which may be a medical report (e.g.,
medical report/record 236) associated with the audio encounter
information (e.g., a draft medical report in the appropriate
clinical output format). It will be appreciated that that each
layer (audio, transcript, draft report, etc.) may be rendered in
multiple different ways, and in the most appropriate way for a
given use/preference of the end-user. As such, the specific
rendering of layers should be taken as example only and not to
limit the scope of the disclosure.
[0064] As noted above, encounter transcript 234 (via second layer
504) and/or medical record/record 236 (via third layer 506) may be
reviewed by a medical professional involved with the patient
encounter (e.g., a visit to a doctor's office) to determine the
accuracy of the same and/or make corrections to the same. For
example, a scribe involved with (or assigned to) the patient
encounter (e.g., a visit to a doctor's office) may review encounter
transcript 234 and/or medical record 236 to confirm that the same
was accurate and/or make corrections to the same. In the event that
corrections are made to encounter transcript 234 and/or medical
record 236, automated clinical documentation process 10 may utilize
these corrections for training/tuning purposes (e.g., to adjust the
various profiles associated the participants of the patient
encounter) to enhance the future accuracy/efficiency/performance of
automated clinical documentation process 10.
[0065] Alternatively/additionally, a doctor involved with the
patient encounter (e.g., a visit to a doctor's office) may review
encounter transcript 234 and/or medical record 236 to confirm that
the same was accurate and/or make corrections to the same. In the
event that corrections are made to encounter transcript 234 and/or
medical record 236, automated clinical documentation process 10 may
utilize these corrections for training/tuning purposes (e.g., to
adjust the various profiles associated the participants of the
patient encounter) to enhance the future
accuracy/efficiency/performance of automated clinical documentation
process 10.
[0066] As such, and referring at least to the example
implementation of FIG. 6, assume for example purposes only that a
scribe is reviewing the encounter information using UI 500. In some
implementations, ACD process 10 may receive 308 a selection of a
first portion of the audio encounter information at a first layer
of the plurality of layers on the user interface, and ACD process
10 may display 310 an annotation of at least one of a second layer
of the plurality of layers and a third layer of the plurality of
layers corresponding to the first portion of the audio encounter
information of the first layer of the plurality of layers selected
on the user interface. For instance, the scribe may use, e.g.,
cursor 508 or other known input techniques, to select a portion of
first layer 502 (i.e., the audio information of the audio encounter
information), which may be received 308 by ACD process 10. As a
result, in some implementations, ACD process 10 may display 310
some kind of annotation of either the second layer (e.g., the
portion of the transcript associated with the selected portion of
the first layer) and/or the third layer (e.g., the portion of the
medical report associated with the selected portion of the first
layer). In the example, ACD process 10 has displayed an annotation
(e.g., bold font) of the second layer (e.g., the portion of the
transcript associated with the selected portion of the first layer)
and the third layer (e.g., the portion of the medical report
associated with the selected portion of the first layer). It will
be appreciated that any type of annotation may be used (e.g., bold,
italics, underline, highlights, shading, transparency, etc.)
without departing from the scope of the present disclosure. As
such, the use of bolding as the annotation should be taken as
example only and not to otherwise limit the scope of the
disclosure.
[0067] Similarly, in some implantations, ACD process 10 may receive
312 a selection of the first portion of the audio encounter
information at one of the second layer of the plurality of layers
and the third layer of the plurality of layers on the user
interface, and ACD process 10 may provide 314 audio of the first
layer corresponding to the first portion of the audio encounter
information of one of the second layer of the plurality of layers
and the third layer of the plurality of layers selected on the user
interface. For example, and referring to the example implementation
of FIG. 7, the scribe may use, e.g., cursor 508 or other known
input techniques, to select a portion of second layer 504 (e.g.,
the transcript), and/or the third layer (e.g., the medical report)
which may be received 312 by ACD process 10. As a result, in some
implementations, ACD process 10 may provide 314 the audio of first
layer 502 (e.g., the audio information of the audio encounter
associated with the selected portion of second layer 504).
[0068] As such, ACD process 10 may leverage the above-noted defined
linkages, such that the encounter recording as captured above may
be provided to a human scribe (or physician) in such a fashion, so
that they may navigate through each of these layers independently
and be always in sync. For instance, if the scribe navigates
through first layer 502 (audio) by listening, the part of the
transcript that corresponds to that part of the audio may be
bolded, highlighted, etc. accordingly, as well as the resulting
medical report (and if relevant actionable data) from that
information. Additionally/alternatively, if the scribe selects a
part of the report, the audio/transcript sections that affected
that part of the report may be bolded, highlighted, emphasized,
etc. This annotation also may correspond to the audio and report
"cursor" (e.g., audio cursor 510) moving, e.g., audio may next play
from that position and typing/keyboard-navigation may commence from
that position in the report. As a result, since each of the
plurality of layers may be synchronized, a scribe (or physician,
etc.) may later playback the whole patient encounter, navigate
(e.g., "clicking" a mouse cursor of other pointing device at any
position) in any portion of any of the layers, make edits, and
would know which edit/correction belongs to the other sections
(layers). More generally, "clicking" (i.e. resetting the cursor) in
one viewed layer may annotate and update the cursor in the linked
points/excerpts in the other layers.
[0069] In some implementations, at least a portion of the audio
encounter information determined to lack relevance to the medical
report may be one of sped up and skipped 316 by ACD process 10. For
example, in some implementations, it may be possible for the scribe
to "playback" the whole encounter and see the draft medical report
"emerge" (or simply see each associated report words highlighted,
in sync with the cursor) so that they may ascertain which signals
from the encounter caused/triggered which parts of the report, and
react (e.g., correct/edit, etc.) accordingly. However, ACD process
10 may sped up or skip 316 the audio not only where no speech is
detected by the ASR portion of ACD process 10, but also where the
model (e.g., sequence to sequence/abstractive summarization, etc.)
does not attribute any significant responsibility for the draft
report to that excerpt of the conversation transcript's aligned
audio (e.g., accumulated attribution across all outputs (e.g.,
medical report text) for an input (e.g., ASR word/excerpt) being
below some threshold). For instance, assume for example purposes
only that Mrs. Smith (i.e., the patient in this example) asks the
Doctor about his golf game, which the Doctor spends about 30
seconds discussing. In the example, ACD process 10 may determine
that the discussion of the Doctor's golf game is not relevant for
the draft report to that excerpt of the conversation transcript's
aligned audio, and may either skip or speed up (fast forward)
through that portion of the audio (and the associated
transcript).
[0070] Similarly, at least a portion of the audio encounter
information determined to lack relevance to the medical report may
be annotated 318 by ACD process 10. For instance, and referring to
the example implementation of FIG. 8, UI 500 is shown. In the
example, ACD process 10 may annotate 318 the portion of the audio
encounter information (e.g., transcript and/or medical report)
where the model (e.g., sequence to sequence/abstractive
summarization, etc.) model does not attribute any significant
responsibility for the draft report to that excerpt of the
conversation transcript's aligned audio. For instance, assume for
example purposes only that Mrs. Smith (i.e., the patient in this
example) asks the Doctor "By the way, how has your golf game been
lately?", to which the Doctor replies "Oh it's been terrible.
People are starting to call me "captain Hook!" In the example, ACD
process 10 may determine that the discussion of the Doctor's golf
game is not relevant for the draft report to that excerpt of the
conversation transcript's aligned audio, and may annotate 318
(e.g., bold) that portion of the transcript. This may be especially
advantageous for direct editing by a physician, who typically would
not listen to the audio if reviewing/finalizing the report directly
subsequent to the patient encounter. Thus, ACD process 10 may
annotate 318 the subset of the transcript for which the model does
not (significantly) attribute any portion of the medical report,
allowing the physician to quickly scan for clinically relevant
information potentially inadvertently missed by the model (e.g.,
not captured in some form in the report).
[0071] As discussed above, automated clinical documentation (ACD)
process 10 may be configured to automate the collection and
processing of clinical encounter information to
generate/store/distribute medical records. Accordingly, as
discussed above and referring also at least to FIG. 9, ACD process
10 may be configured to obtain 300 encounter information (e.g.,
machine vision encounter information 102 and/or audio encounter
information 106) of a patient encounter (e.g., a visit to a
doctor's office) of at least a first encounter participant, wherein
the encounter information may include audio encounter information
obtained from at least a first encounter participant (e.g.,
encounter participant 228, 226, 230, and/or 242). ACD process 10
may further be configured to process 302 the encounter information
(e.g., machine vision encounter information 102 and/or audio
encounter information 106) obtained from at least the first
encounter participant, e.g., to generate an encounter transcript
(e.g., encounter transcript 234) and/or generate 305 a user
interface displaying a plurality of layers associated with the
audio encounter information obtained from at least the first
encounter participant, wherein at least one of the plurality of
layers is one of exposed to the user interface and not exposed to
the user interface based upon, at least in part, a confidence
level. In some implementations, ACD process 10 may process at least
a portion of the encounter transcript (e.g., encounter transcript
234) to populate at least a portion of a medical record/report
(e.g., medical record 236) associated with the patient encounter
(e.g., the visit to the doctor's office). Encounter transcript 234
and/or medical record 236 may be reviewed by a medical professional
involved with the patient encounter (e.g., a visit to a doctor's
office) to determine the accuracy of the same and/or make
corrections to the same.
[0072] In some implementations, processing 302 the first audio
encounter information may include defining 306 linkages between
each of the plurality of layers associated with the audio encounter
information. For example, the first layer of the plurality of
layers may be a visualization and/or direct play-back of an audio
signal associated with the audio encounter information (e.g.,
complete audio of the encounter, encompassing and clearly
delineating each participant), wherein the second layer of the
plurality of layers may be a transcript associated with the audio
encounter information (e.g., a diarized audio transcript (verbatim)
for each participant in the encounter), and wherein the third layer
of the plurality of layers may be a medical report associated with
the audio encounter information (e.g., a draft medical report in
the appropriate clinical output format). In some implementations,
additional layers may include, e.g., the above-noted machine
vision-based recording of the encounter, including various signal
formats and features, and discrete, standardized, actionable data
resulting from the encounter, including, but not limited to
medication plans (for example, in RxNorm) or lab orders (for
example, LOINC) or diagnoses (for example, ICD10, CPT etc). In the
example, the signals captured from the encounter information may be
processed 304 into at least the above-noted three separate, yet
closely linked and interdependent layers.
[0073] In some implementations, ACD process 10 may include an ASR
portion that may process 302 the audio encounter information
producing an approximate (e.g., diarized) verbatim transcript along
with alignment information indicating the audio interval
corresponding to each transcript word. In some implementations, a
deep learning (e.g., sequence to sequence) model associated with
ACD process 10 may convert the transcript to a medical report. It
will be appreciated that various attribution techniques may be
employed by ACD process 10 that may effectively softly assign
responsibility for a given output (e.g., medical report) word to
input (e.g., conversation transcript) words (e.g. attention
weights, integrated gradient, etc.) according to the model. As a
result, this may provide a soft mapping from the transcript word
positions to report word positions. In some implementations, the
input word position assigned maximal attribution for a given output
word may be interpreted as being aligned (linked) to that output
(e.g., when a hard mapping is required). Based on the ASR time
alignment, a word in the draft medical report, aligned to a word in
the ASR conversation transcript, may now be associated with an
audio time interval of the associated audio signal of the audio
encounter information.
[0074] In some implementations, ACD process 10 may also may link
(i.e., align) the ASR conversation transcript words with the draft
medical report words. For transcript words that may have maximal
attribution value for some set of medical report words, ACD process
10 may link them with the first word in that set. For the remaining
transcript words, ACD process 10 may link them to the same word
that the nearest preceding (or if none, nearest subsequent)
conversation transcript word is linked to. As a result, since each
of the plurality of layers may be synchronized, a scribe (or
physician, etc.) may later playback the whole patient encounter,
navigate in any of the layers, make edits, and would know which
edit/correction belongs to the other sections (layers).
[0075] In some implementations, a visual recording (e.g., video
stream of the patient encounter), if available, may also be a layer
and may be time indexed and thus a given point in the recording may
be associated with the same time in the audio recording and thus a
conversation transcript word and draft report word. In some
implementations, if discrete, standardized, actionable data is
produced as a second (parallel) output sequence of the sequence to
sequence model, then a similar model output attribution technique
may be used to align tokens in this actionable data with the ASR
conversation transcript words, and thus the audio intervals.
[0076] In some implementations, ACD process 10 may generate 305 a
user interface displaying a plurality of layers associated with the
audio encounter information obtained from at least the first
encounter participant, wherein at least one of the plurality of
layers is one of exposed to the user interface and not exposed to
the user interface based upon, at least in part, a confidence
level. For example, and referring again at least to the example
implementation of FIG. 5, an example user interface (e.g., user
interface 500) is shown. It will be appreciated that UI 500 is
shown merely for example purposes only, and that more or less
features and arrangements of features may be used without departing
from the scope of the present disclosure.
[0077] As can be seen from FIG. 5, UI 500 includes the first layer
of the plurality of layers (e.g., first layer 502 which may be a
visualization and/or direct play-back of an audio signal associated
with the audio encounter information (e.g., complete audio of the
encounter, encompassing and clearly delineating each participant),
second layer 504 of the plurality of layers which may be a
transcript (e.g., encounter transcript 234) associated with the
audio encounter information (e.g., a diarized audio transcript
(verbatim) for each participant in the encounter), and third layer
506 of the plurality of layers which may be a medical report (e.g.,
medical report/record 236) associated with the audio encounter
information (e.g., a draft medical report in the appropriate
clinical output format). It will be appreciated that that each
layer (audio, transcript, draft report, etc.) may be rendered in
multiple different ways, and in the most appropriate way for a
given use/preference of the end-user. As such, the specific
rendering of layers should be taken as example only and not to
limit the scope of the disclosure.
[0078] As noted above, encounter transcript 234 (via second layer
504) and/or medical record/record 236 (via third layer 506) may be
reviewed by a medical professional involved with the patient
encounter (e.g., a visit to a doctor's office) to determine the
accuracy of the same and/or make corrections to the same. For
example, a scribe involved with (or assigned to) the patient
encounter (e.g., a visit to a doctor's office) may review encounter
transcript 234 and/or medical record 236 to confirm that the same
was accurate and/or make corrections to the same. In the event that
corrections are made to encounter transcript 234 and/or medical
record 236, automated clinical documentation process 10 may utilize
these corrections for training/tuning purposes (e.g., to adjust the
various profiles associated the participants of the patient
encounter) to enhance the future accuracy/efficiency/performance of
automated clinical documentation process 10.
[0079] Alternatively/additionally, a doctor involved with the
patient encounter (e.g., a visit to a doctor's office) may review
encounter transcript 234 and/or medical record 236 to confirm that
the same was accurate and/or make corrections to the same. In the
event that corrections are made to encounter transcript 234 and/or
medical record 236, automated clinical documentation process 10 may
utilize these corrections for training/tuning purposes (e.g., to
adjust the various profiles associated the participants of the
patient encounter) to enhance the future
accuracy/efficiency/performance of automated clinical documentation
process 10.
[0080] As noted above, at least one of the plurality of layers is
one of exposed to the user interface and not exposed to the user
interface based upon, at least in part, a confidence level. For
example, ACD process 10 may employ a confidence model that may only
expose (e.g., display) at least one layer (e.g., the medical report
layer 506 or a section of the medical report layer section) to the
user interface if ACD process 10 (via the confidence model)
determines there is sufficient confidence, such as a threshold
confidence level, that the draft report (or draft report section)
will be faster than being typed from scratch. For instance,
exposing (e.g., displaying) the medical report/section to UI 500
(e.g., for editing) may enable the ability to change some
incorrectly labeled terms/sentences, which may be faster than
manually typing out the entire section from the beginning; however,
if so many terms/sentences are incorrect (and therefore need to be
changed), it may be faster to simply type out the entire section
from the beginning, rather than making substantial changes to the
draft report section. As such, in the example where ACD process 10
determines an estimated time that it will take to make corrections
compared to an estimated time that it will take to type out the
entire section from the beginning, and determines it may be faster
to simply type out the entire section from the beginning (rather
than make corrections), ACD process 10 may not display the
report/section by not exposing the report/section to UI 500 in the
first place. In some implementations, the use of the confidence
level for exposure may be only for outputs expected to be part of
the actual case record, e.g., the medical report layer and the
optionally concurrently generated discrete/actionable structured
data. In some implementations, the decision to expose or not expose
may be per section of the medical report (or structured data),
e.g., Exam section (report) or Lab Orders (structured), etc.
[0081] In some implementations, ACD process 10 may leverage various
features of the confidence model, including the statistics (e.g.,
min, max, standard deviation, etc.) of the difference in
probability between the first and second most likely word
hypothesized at each position in the report/section via, e.g., a
model (e.g., sequence to sequence model), the log likelihood of the
draft report/section per the model (e.g., in addition to a length
normalized version of this), confidence values from the ASR portion
of ACD process 10 (e.g., particularly for segments to which the
report/section content is attributed) and information regarding the
typical (e.g., mean/standard deviation) number of edits required
for a relevant physician and typical number of edits made by a
relevant scribe. For draft reports/sections that ACD process 10 may
actually expose to be edited (rather than typed from scratch), ACD
process 10 may collect the number of edits made and time required
to make them, which may be added to training and improvement of the
confidence model over time.
[0082] For example, in some implementations, ACD process 10 may
train 408 a model that maps the transcript to the medical report
from pairs of prior transcripts and medical reports. For instance,
the core model (e.g., sequence to sequence model) of ACD process 10
that may map the conversation transcript to the medical reports may
be trained 408 from pairs of (e.g., ASR approximate) transcripts
and medical reports. For example, as additional reports are
generated/edited/typed by scribes/physicians, these additional
reports may be automatically (or manually) added to the training
pool for the sequence to sequence model, which may be automatically
intermittently retrained. In some implementations, the model may be
metadata-dependent (e.g., physician, institution, scribe, etc.),
either by (e.g., regularized) adaptation to appropriate subsets of
the training pool or by using this metadata as inputs or
combination thereof. That is, the training pool may be specific to
the user, type of user, or institution of the user making the
changes to the medical report. In some implementations, the number
and types of edits made by the scribe (or other user) may also be
used by the confidence model to better predict the number of edits
(and thus editing efficiency) for subsequent drafts.
[0083] In some implementations, e.g., towards the end of the
patient encounter, as the confidence of the information regarding
the medical report section rises, more items may be documented
(e.g., added) in this section. For instance, assume for example
purposes only that one of the encounter participants (e.g., the
physician) mentions "examination." Using the techniques described
above, ACD process 10 may recognize this fact, and may display some
associated points about the physical examinations under the
"Examination" section (e.g., in the medical report layer 506 shown
at least in FIG. 5), which may be first grayed. In the example, as
the physician continues to verbalize further associated points that
make the confidence level of what was entered into the Examination
section higher, the greyed points may become solid, and further
adjustments/edits may be made by ACD process 10 shortly thereafter
(e.g., in the following seconds). Similarly, related to the
"Assessment/Plan" section (e.g., in the medical report layer 506)
after the physician verbalizes his assessment, ACD process 10 may
start documenting that section, first grayed, but then the grayed
words may become more solid (e.g., darker or normal solid color)
with more points being added as ACD process 10 knows more and the
confidence level rises. In some implementations, showing the
transition from grey to solid wording may help train scribes (or
other users) to use ACD process 10 and recognize how it works.
[0084] In some implementations, ACD process 10 may update 410 an
output of the user interface based upon, at least in part, one or
more modifications made at least one layer of the plurality of
layers. For instance, rather than the output being static during
the editing process, ACD process 10 may update 410 the output based
on the modifications made by the editor so far (e.g., based on the
decoder being autoregressive in nature). In particular, the decoder
(e.g., sequence to sequence decoder) output may depend on its
preceding output. As such, if the scribe (or other user) makes a
correction in one part of the draft report, ACD process 10 may
update a best guess at the subsequent content. In some
implementations, this may be distracting to the user, and so to
make it less distracting, it may be optionally limited to a
toggle-able mode (e.g., online/synchronous vs. global review mode)
and/or limited to only modifying the draft output for subsequent
sections of the report. In some implementations, there may be at
least four ways that corrections to case-record-persistent system
outputs (e.g., medical reports, structured data) may be utilized.
For example, pairs of ASR transcripts and corrected reports may be
used for offline (sequence to sequence, transcript->report)
model training. If in the typing acceleration mode as opposed to
draft-report-correction mode, as the user types, the prediction for
next sentence may be updated, which may not be model
training/adaptation, but rather a reflection of the
(auto-regressive) model predicting next output based on report
content so far. If in the draft-report-correction mode, as the user
makes corrections, prediction of subsequent content (sentences or
perhaps less distracting, sections) and thus the draft report
content (in this report) may be updated, again utilizing the same
auto-regressive nature of the sequence to sequence model (and not
some result of model training). Tuples of ASR transcripts (along
with ASR confidence information), draft reports and noted user
edits (e.g., corrections) may be used for an offline confidence
model (e.g., required number of edits) training.
[0085] In some implementations, ACD process 10 may update 412 a
prediction for a next sentence to be typed based upon, at least in
part, current content of at least one layer of the plurality of
layers, where in some implementations, the at least one layer of
the plurality of layers may be the medical report. For example, for
reports that the above-noted confidence model (via ACD process 10)
determines are best typed from scratch rather than editing a draft,
ACD process 10 may leverage the summarization model to accelerate
composition by having it predict the next sentence to type and
updating that prediction based on, and to be consistent with, what
the scribe (or other user) has typed so far. For instance, ACD
process 10 (e.g., via UI 500) may allow the user to select the
model's prediction of the current/next sentence being presented to
the user in the report by selecting the tab key (or other shortcut)
to complete the sentence (e.g., auto-completion based typing
accelerant). In this way, ACD process 10 (e.g., via the model) may
only predict the draft report a sentence at a time based on the
scribes typing/feedback. In some implementations, the prediction
may be based not just upon what the scribe is currently writing,
but may additionally (or alternatively) be based upon the context
of what the scribe has previous written in previous sentences of
the report (or based upon a training pool specific to the scribe).
In some implementations, the sentence prediction may be based on
the same model that produces the draft medical report. In some
implementations, when based on the confidence model and it is
decided to suppress the draft medical report (section), the ACD
process 10 based auto-completion typing accelerant may allow the
salvage of some efficiency improvement.
[0086] As discussed above, automated clinical documentation (ACD)
process 10 may be configured to automate the collection and
processing of clinical encounter information to
generate/store/distribute medical records. Accordingly and
referring also at least to FIGS. 10-11, ACD process 10 may be
configured to obtain 300 encounter information (e.g., machine
vision encounter information 102 and/or audio encounter information
106) of a patient encounter (e.g., a visit to a doctor's office) of
at least a first encounter participant, wherein the encounter
information may include audio encounter information obtained from
at least a first encounter participant (e.g., encounter participant
228, 226, 230, and/or 242). ACD process 10 may further be
configured to process 302 the encounter information (e.g., machine
vision encounter information 102 and/or audio encounter information
106) obtained from at least the first encounter participant, e.g.,
to generate an encounter transcript (e.g., encounter transcript
234) and/or generate 304 a user interface displaying a plurality of
layers associated with the audio encounter information obtained
from at least the first encounter participant. In some
implementations, ACD process 10 may receive 307 a user input from a
peripheral device to navigate through each of the plurality of
layers associated with the audio encounter information displayed on
the user interface. In some implementations, ACD process 10 may
process at least a portion of the encounter transcript (e.g.,
encounter transcript 234) to populate at least a portion of a
medical record (e.g., medical record 236) associated with the
patient encounter (e.g., the visit to the doctor's office).
Encounter transcript 234 and/or medical record 236 may be reviewed
by a medical professional involved with the patient encounter
(e.g., a visit to a doctor's office) to determine the accuracy of
the same and/or make corrections to the same.
[0087] As noted above, ACD process 10 may process 302 the audio
encounter information obtained from at least the first encounter
participant. In some implementations, processing 302 the first
audio encounter information may include defining 306 linkages
between each of the plurality of layers associated with the audio
encounter information. For example, the first layer of the
plurality of layers may be a visualization and/or direct play-back
of an audio signal associated with the audio encounter information
(e.g., complete audio of the encounter, encompassing and clearly
delineating each participant), wherein the second layer of the
plurality of layers may be a transcript associated with the audio
encounter information (e.g., a diarized audio transcript (verbatim)
for each participant in the encounter), and wherein the third layer
of the plurality of layers may be a medical report associated with
the audio encounter information (e.g., a draft medical report in
the appropriate clinical output format). In some implementations,
additional layers may include, e.g., the above-noted machine
vision-based recording of the encounter, including various signal
formats and features, and discrete, standardized, actionable data
resulting from the encounter, including, but not limited to
medication plans (for example, in RxNorm) or lab orders (for
example, LOINC) or diagnoses (for example, ICD10, CPT etc). In the
example, the signals captured from the encounter information may be
processed 304 into at least the above-noted three separate, yet
closely linked and interdependent layers.
[0088] In some implementations, ACD process 10 may include an ASR
portion that may process 302 the audio encounter information
producing an approximate (e.g., diarized) verbatim transcript along
with alignment information indicating the audio interval
corresponding to each transcript word. In some implementations, a
deep learning (e.g., sequence to sequence) model associated with
ACD process 10 may convert the transcript to a medical report. It
will be appreciated that various attribution techniques may be
employed by ACD process 10 that may effectively softly assign
responsibility for a given output (e.g., medical report) word to
input (e.g., conversation transcript) words (e.g. attention
weights, integrated gradient, etc.) according to the model. As a
result, this may provide a soft mapping from the transcript word
positions to report word positions. In some implementations, the
input word position assigned maximal attribution for a given output
word may be interpreted as being aligned (linked) to that output
(e.g., when a hard mapping is required). Based on the ASR time
alignment, a word in the draft medical report, aligned to a word in
the ASR conversation transcript, may now be associated with an
audio time interval of the associated audio signal of the audio
encounter information.
[0089] In some implementations, ACD process 10 may also may link
(i.e., align) the ASR conversation transcript words with the draft
medical report words. For transcript words that may have maximal
attribution value for some set of medical report words, ACD process
10 may link them with the first word in that set. For the remaining
transcript words, ACD process 10 may link them to the same word
that the nearest preceding (or if none, nearest subsequent)
conversation transcript word is linked to.
[0090] In some implementations, a visual recording (e.g., video
stream of the patient encounter), if available, may also be a layer
and may be time indexed and thus a given point in the recording may
be associated with the same time in the audio recording and thus a
conversation transcript word and draft report word. In some
implementations, if discrete, standardized, actionable data is
produced as a second (parallel) output sequence of the sequence to
sequence model, then a similar model output attribution technique
may be used to align tokens in this actionable data with the ASR
conversation transcript words, and thus the audio intervals.
[0091] In some implementations, ACD process 10 may generate 304 a
user interface displaying a plurality of layers associated with the
audio encounter information obtained from at least the first
encounter participant. For example, and referring again at least to
the example implementation of FIG. 5, an example user interface
(e.g., user interface 500) is shown. It will be appreciated that UI
500 is shown merely for example purposes only, and that more or
less features and arrangements of features may be used without
departing from the scope of the present disclosure.
[0092] As can be seen from FIG. 5, UI 500 includes the first layer
of the plurality of layers (e.g., first layer 502 which may be a
visualization and/or direct play-back of an audio signal associated
with the audio encounter information (e.g., complete audio of the
encounter, encompassing and clearly delineating each participant),
second layer 504 of the plurality of layers which may be a
transcript (e.g., encounter transcript 234) associated with the
audio encounter information (e.g., a diarized audio transcript
(verbatim) for each participant in the encounter), and third layer
506 of the plurality of layers which may be a medical report (e.g.,
medical report/record 236) associated with the audio encounter
information (e.g., a draft medical report in the appropriate
clinical output format). It will be appreciated that that each
layer (audio, transcript, draft report, etc.) may be rendered in
multiple different ways, and in the most appropriate way for a
given use/preference of the end-user. As such, the specific
rendering of layers should be taken as example only and not to
limit the scope of the disclosure.
[0093] As noted above, encounter transcript 234 (via second layer
504) and/or medical record/record 236 (via third layer 506) may be
reviewed by a medical professional involved with the patient
encounter (e.g., a visit to a doctor's office) to determine the
accuracy of the same and/or make corrections to the same. For
example, a scribe involved with (or assigned to) the patient
encounter (e.g., a visit to a doctor's office) may review encounter
transcript 234 and/or medical record 236 to confirm that the same
was accurate and/or make corrections to the same. In the event that
corrections are made to encounter transcript 234 and/or medical
record 236, automated clinical documentation process 10 may utilize
these corrections for training/tuning purposes (e.g., to adjust the
various profiles associated the participants of the patient
encounter) to enhance the future accuracy/efficiency/performance of
automated clinical documentation process 10.
[0094] Alternatively/additionally, a doctor involved with the
patient encounter (e.g., a visit to a doctor's office) may review
encounter transcript 234 and/or medical record 236 to confirm that
the same was accurate and/or make corrections to the same. In the
event that corrections are made to encounter transcript 234 and/or
medical record 236, automated clinical documentation process 10 may
utilize these corrections for training/tuning purposes (e.g., to
adjust the various profiles associated the participants of the
patient encounter) to enhance the future
accuracy/efficiency/performance of automated clinical documentation
process 10.
[0095] Therefore, and referring at least to the example
implementation of FIG. 11, to help review encounter transcript 234
and/or medical record 236 to confirm that the same was accurate
and/or make corrections to the same, in some implementations, ACD
process 10 may receive 307 a user input from a peripheral device to
navigate through each of the plurality of layers associated with
the audio encounter information displayed on the user interface. In
some implementations, the peripheral device may include, e.g., a
keyboard (e.g., keyboard 1100), a pointing device (e.g., mouse
1102), a foot pedal (e.g., foot pedal 1104), a dial (e.g., dial
1106), or combination thereof. It will be appreciated that other
peripheral devices may be used without departing from the scope of
the present disclosure. One or more of the peripheral devices may
be physically or wirelessly connected to the computing device
displaying UI 500.
[0096] As such, and referring again at least to the example
implementation of FIG. 6, assume for example purposes only that a
scribe is reviewing the encounter information using UI 500. In some
implementations, receiving 307 the user input from the peripheral
device may include receiving 309, via the user input from the
peripheral device, a selection of a first portion of the audio
encounter information at a first layer of the plurality of layers
on the user interface, and ACD process 10 may display 310 an
annotation of at least one of a second layer of the plurality of
layers and a third layer of the plurality of layers corresponding
to the first portion of the audio encounter information of the
first layer of the plurality of layers selected on the user
interface. For instance, the scribe may use, e.g., any of the
example peripheral devices, to select a portion of first layer 502
(i.e., the audio information of the audio encounter information),
which may be received 309 by ACD process 10. As a result, in some
implementations, ACD process 10 may display 310 some kind of
annotation of either the second layer (e.g., the portion of the
transcript associated with the selected portion of the first layer)
and/or the third layer (e.g., the portion of the medical report
associated with the selected portion of the first layer). In the
example, ACD process 10 has displayed an annotation (e.g., bold
font) of the second layer (e.g., the portion of the transcript
associated with the selected portion of the first layer) and the
third layer (e.g., the portion of the medical report associated
with the selected portion of the first layer). It will be
appreciated that any type of annotation may be used (e.g., bold,
italics, underline, highlights, shading, transparency, etc.)
without departing from the scope of the present disclosure. As
such, the use of bolding as the annotation should be taken as
example only and not to otherwise limit the scope of the
disclosure.
[0097] Similarly, in some implantations, receiving 307 the user
input from the peripheral device may include receiving 313, via the
user input from the peripheral device, a selection of the first
portion of the audio encounter information at one of the second
layer of the plurality of layers and the third layer of the
plurality of layers on the user interface, and ACD process 10 may
provide 314 audio of the first layer corresponding to the first
portion of the audio encounter information of one of the second
layer of the plurality of layers and the third layer of the
plurality of layers selected on the user interface. For example,
and referring again at least to the example implementation of FIG.
7, the scribe may use, e.g., any of the example peripheral devices,
to select a portion of second layer 504 (e.g., the transcript),
and/or the third layer (e.g., the medical report) which may be
received 312 by ACD process 10. As a result, in some
implementations, ACD process 10 may provide 314 the audio of first
layer 502 (e.g., the audio information of the audio encounter
associated with the selected portion of second layer 504).
[0098] As noted above, the physician (or scribe) may need to verify
the correctness of the medical report created by ACD process 10.
This may involve, e.g., checking for correctness of each
sentence/word in the medical report generated by looking for
evidence in the conversation transcript (attribution), checking the
correctness of the conversational transcript generated by an ASR)
by listening to the audio segment. The use of dedicated hardware
(e.g., a peripheral device) may be utilized by ACD process 10 to
improve editing efficiency for medical
transcriptionists/physicians, etc. to help navigate and browse
(e.g., sentence by sentence) through the draft medical report, the
associated conversation transcript excerpt, and/or associated audio
file, annotated (e.g., highlighted, bolded, etc.) with its
corresponding audio cued up for easy playback.
[0099] For example, in some implementations, the user input from
the peripheral device may include a keyboard shortcut when the
peripheral device is keyboard 1100. For example, default and/or
user defined keyboard shortcuts may be used to interact with UI
500. For instance, a combination of keys (e.g., control+shift+S)
may execute a particular command for UI 500. For instance, such a
shortcut may cause UI 500 to, e.g., switch between sentences in an
output of the medical report, switch between sections in the output
of the medical report, switch between the medical report and the
transcript, provide/playback audio of the audio signal, cease/stop
playback of the audio of the audio signal, speeding up playback of
the audio of the audio signal, or slow down playback of the audio
of the audio signal. It will be appreciated that various other
keyboard shortcuts, as well as single key inputs (e.g., arrows,
spacebar, etc.) may execute the above-noted commands for UI 500
without departing from the scope of the disclosure.
[0100] In some implementations, the user input from the peripheral
device may include a pointing device action when the peripheral
device is pointing device 1102. For example, default and/or user
defined mouse actions may be used to interact with UI 500. For
instance, movement of the mouse cursor over a particular portion of
one of the above-noted layers, clicking or double clicking on a
particular portion of one of the above-noted layers, hovering over
a particular portion of one of the above-noted layers, using a
scroll wheel of the mouse, etc. may execute a particular command
for UI 500. For instance, any one of the example uses of the mouse
may cause UI 500 to, e.g., switch between sentences in an output of
the medical report, switch between sections in the output of the
medical report, switch between the medical report and the
transcript, provide/playback audio of the audio signal, cease/stop
playback of the audio of the audio signal, speeding up playback of
the audio of the audio signal, or slow down playback of the audio
of the audio signal. It will be appreciated that various other
keyboard shortcuts, as well as single key inputs (e.g., arrows,
spacebar, etc.) may execute the above-noted commands for UI 500
without departing from the scope of the disclosure.
[0101] In some implementations, the user input from the peripheral
device may include raising and lowering of the foot pedal when the
peripheral device is foot pedal 1104. For example, default and/or
user defined foot pedal actions may be used to interact with UI
500. For instance, raising and lowering of the foot pedal (similar
to how the foot pedal in a vehicle may function) may execute a
particular command for UI 500. For instance, lowering the pedal
(e.g., using the user's foot to press down on pedal 1104) may cause
UI 500 to, e.g., provide/playback audio of the audio signal, and
may speed up playback of the audio of the audio signal depending on
the amount the pedal is lowered. Conversely, raising the pedal
(e.g., lifting the user's foot off the pedal 1104) may cause UI 500
to, e.g., cease/stop playback of the audio of the audio signal, and
may or slow down playback of the audio of the audio signal
depending on the amount the pedal is raised.
[0102] In some implementations, the user input from the peripheral
device may include at least one of a rotating action, an up action,
a down action, a left action, a right action, and a pressing action
of the dial when the peripheral device is dial 1106. For example,
default and/or user defined dial actions may be used to interact
with UI 500. For instance, UI 500 (via ACD process 10) may switch
the focus between sentences in the output medical report by
rotating the dial, e.g., one click clockwise may move the focus to
the next sentence and counter clockwise may move the focus to the
previous sentence. As another example, UI 500 (via ACD process 10)
may switch the focus between different sections of the medical
report using the up/down arrows on dial 1106. Similarly, as
discussed above at least with regard to FIGS. 6 and 7, evidence in
conversational transcripts for the selected sentence in the
report/transcript may be annotated (e.g., highlighted, bolded,
etc.). Evidence may include a word or phrase or sentence and may be
obtained from attention weights (e.g., byproduct of a sequence to
sequence based summarization system) or other dedicated models for
attribution. As another example, UI 500 (via ACD process 10) may
switch focus from the medical report to the conversational
transcript using, e.g., left/right arrows on dial 1106. As another
example, UI 500 (via ACD process 10) may browse may switch the
focus between sentences in the transcript by rotating the dial,
e.g., one click clockwise may move the focus to the next sentence
and counter clockwise may move the focus to the previous sentence.
As yet another example, UI 500 (via ACD process 10) may, when the
annotated sentence in the transcript is selected by pressing dial
1106, play back the corresponding audio chunk (e.g., identified by
time stamps from the ASR decoding).
[0103] It will be appreciated that any of the example peripheral
devices may be used in any combination with each other's functions
without departing from the scope of the disclosure. For example,
using pointing device 1102 to hover over a particular portion of
the transcript, medical report section, or audio signal may be the
starting location where, the functions of e.g., dial 1106 may be
used. As such, the use of any particular peripheral device and its
particular function should be taken as example only and not to
otherwise limit the scope of the disclosure.
[0104] As such, ACD process 10 may leverage the above-noted defined
linkages and peripheral devices, such that the encounter recording
as captured above may be provided to a human scribe (or physician)
in such a fashion, so that they may navigate through each of these
layers independently and be always in sync. For instance, if the
scribe navigates through first layer 502 (audio) by listening, the
part of the transcript that corresponds to that part of the audio
may be bolded, highlighted, etc. accordingly, as well as the
resulting medical report (and if relevant actionable data) from
that information. Additionally/alternatively, if the scribe selects
a part of the report, the audio/transcript sections that affected
that part of the report may be bolded, highlighted, emphasized,
etc. This annotation also may correspond to the audio and report
"cursor" (e.g., audio cursor 510) moving, e.g., audio may next play
from that position and typing/keyboard-navigation may commence from
that position in the report. As a result, since each of the
plurality of layers may be synchronized, a scribe (or physician,
etc.) may later playback the whole patient encounter, navigate
(e.g., "clicking" a mouse cursor of other pointing device at any
position) in any portion of any of the layers, make edits, and
would know which edit/correction belongs to the other sections
(layers). More generally, "clicking" (i.e. resetting the cursor) in
one viewed layer may annotate and update the cursor in the linked
points/excerpts in the other layers.
[0105] As discussed above, automated clinical documentation (ACD)
process 10 may be configured to automate the collection and
processing of clinical encounter information to
generate/store/distribute medical records. Accordingly, as
discussed above and referring also at least to FIGS. 12-14, ACD
process 10 may be configured to obtain 300 encounter information
(e.g., machine vision encounter information 102 and/or audio
encounter information 106) of a patient encounter (e.g., a visit to
a doctor's office) of at least a first encounter participant,
wherein the encounter information may include audio encounter
information obtained from at least a first encounter participant
(e.g., encounter participant 228, 226, 230, and/or 242). ACD
process 10 may further be configured to process 302 the encounter
information (e.g., machine vision encounter information 102 and/or
audio encounter information 106) obtained from at least the first
encounter participant, e.g., to generate an encounter transcript
(e.g., encounter transcript 234) and/or generate 505 a user
interface displaying a plurality of layers associated with the
audio encounter information obtained from at least the first
encounter participant, wherein at least a portion of one of the
plurality of layers is added to a section of the one of the
plurality of layers based upon, at least in part, a confidence
level. In some implementations, ACD process 10 may process at least
a portion of the encounter transcript (e.g., encounter transcript
234) to populate at least a portion of a medical record/report
(e.g., medical record 236) associated with the patient encounter
(e.g., the visit to the doctor's office). Encounter transcript 234
and/or medical record 236 may be reviewed by a medical professional
involved with the patient encounter (e.g., a visit to a doctor's
office) to determine the accuracy of the same and/or make
corrections to the same.
[0106] In some implementations, processing 302 the first audio
encounter information may include defining 306 linkages between
each of the plurality of layers associated with the audio encounter
information. For example, the first layer of the plurality of
layers may be a visualization and/or direct play-back of an audio
signal associated with the audio encounter information (e.g.,
complete audio of the encounter, encompassing and clearly
delineating each participant), wherein the second layer of the
plurality of layers may be a transcript associated with the audio
encounter information (e.g., a diarized audio transcript (verbatim)
for each participant in the encounter), and wherein the third layer
of the plurality of layers may be a medical report associated with
the audio encounter information (e.g., a draft medical report in
the appropriate clinical output format). In some implementations,
additional layers may include, e.g., the above-noted machine
vision-based recording of the encounter, including various signal
formats and features, and discrete, standardized, actionable data
resulting from the encounter, including, but not limited to
medication plans (for example, in RxNorm) or lab orders (for
example, LOINC) or diagnoses (for example, ICD10, CPT etc). In the
example, the signals captured from the encounter information may be
processed 304 into at least the above-noted three separate, yet
closely linked and interdependent layers.
[0107] In some implementations, ACD process 10 may include an ASR
portion that may process 302 the audio encounter information
producing an approximate (e.g., diarized) verbatim transcript along
with alignment information indicating the audio interval
corresponding to each transcript word. In some implementations, a
deep learning (e.g., sequence to sequence) model associated with
ACD process 10 may convert the transcript to a medical report. It
will be appreciated that various attribution techniques may be
employed by ACD process 10 that may effectively softly assign
responsibility for a given output (e.g., medical report) word to
input (e.g., conversation transcript) words (e.g. attention
weights, integrated gradient, etc.) according to the model. As a
result, this may provide a soft mapping from the transcript word
positions to report word positions. In some implementations, the
input word position assigned maximal attribution for a given output
word may be interpreted as being aligned (linked) to that output
(e.g., when a hard mapping is required). Based on the ASR time
alignment, a word in the draft medical report, aligned to a word in
the ASR conversation transcript, may now be associated with an
audio time interval of the associated audio signal of the audio
encounter information.
[0108] In some implementations, ACD process 10 may also may link
(i.e., align) the ASR conversation transcript words with the draft
medical report words. For transcript words that may have maximal
attribution value for some set of medical report words, ACD process
10 may link them with the first word in that set. For the remaining
transcript words, ACD process 10 may link them to the same word
that the nearest preceding (or if none, nearest subsequent)
conversation transcript word is linked to. As a result, since each
of the plurality of layers may be synchronized, a scribe (or
physician, etc.) may later playback the whole patient encounter,
navigate in any of the layers, make edits, and would know which
edit/correction belongs to the other sections (layers).
[0109] In some implementations, a visual recording (e.g., video
stream of the patient encounter), if available, may also be a layer
and may be time indexed and thus a given point in the recording may
be associated with the same time in the audio recording and thus a
conversation transcript word and draft report word. In some
implementations, if discrete, standardized, actionable data is
produced as a second (parallel) output sequence of the sequence to
sequence model, then a similar model output attribution technique
may be used to align tokens in this actionable data with the ASR
conversation transcript words, and thus the audio intervals.
[0110] As noted above, in some implementations, ACD process 10 may
generate a user interface displaying a plurality of layers
associated with the audio encounter information obtained from at
least the first encounter participant. For example, and referring
again at least to the example implementation of FIG. 5, an example
user interface (e.g., user interface 500) is shown. It will be
appreciated that UI 500 is shown merely for example purposes only,
and that more or less features and arrangements of features may be
used without departing from the scope of the present
disclosure.
[0111] As can be seen from FIG. 5, UI 500 includes the first layer
of the plurality of layers (e.g., first layer 502 which may be a
visualization and/or direct play-back of an audio signal associated
with the audio encounter information (e.g., complete audio of the
encounter, encompassing and clearly delineating each participant),
second layer 504 of the plurality of layers which may be a
transcript (e.g., encounter transcript 234) associated with the
audio encounter information (e.g., a diarized audio transcript
(verbatim) for each participant in the encounter), and third layer
506 of the plurality of layers which may be a medical report (e.g.,
medical report/record 236) associated with the audio encounter
information (e.g., a draft medical report in the appropriate
clinical output format). It will be appreciated that that each
layer (audio, transcript, draft report, etc.) may be rendered in
multiple different ways, and in the most appropriate way for a
given use/preference of the end-user. As such, the specific
rendering of layers should be taken as example only and not to
limit the scope of the disclosure.
[0112] As noted above, encounter transcript 234 (via second layer
504) and/or medical record/record 236 (via third layer 506) may be
reviewed by a medical professional involved with the patient
encounter (e.g., a visit to a doctor's office) to determine the
accuracy of the same and/or make corrections to the same. For
example, a scribe involved with (or assigned to) the patient
encounter (e.g., a visit to a doctor's office) may review encounter
transcript 234 and/or medical record 236 to confirm that the same
was accurate and/or make corrections to the same. In the event that
corrections are made to encounter transcript 234 and/or medical
record 236, automated clinical documentation process 10 may utilize
these corrections for training/tuning purposes (e.g., to adjust the
various profiles associated the participants of the patient
encounter) to enhance the future accuracy/efficiency/performance of
automated clinical documentation process 10.
[0113] Alternatively/additionally, a doctor involved with the
patient encounter (e.g., a visit to a doctor's office) may review
encounter transcript 234 and/or medical record 236 to confirm that
the same was accurate and/or make corrections to the same. In the
event that corrections are made to encounter transcript 234 and/or
medical record 236, automated clinical documentation process 10 may
utilize these corrections for training/tuning purposes (e.g., to
adjust the various profiles associated the participants of the
patient encounter) to enhance the future
accuracy/efficiency/performance of automated clinical documentation
process 10.
[0114] As noted above, at least one of the plurality of layers may
be one of exposed to the user interface and not exposed to the user
interface based upon, at least in part, a confidence level. For
example, ACD process 10 may employ a confidence model that may only
expose (e.g., display) at least one layer (e.g., the medical report
layer 506 or a section of the medical report layer section) to the
user interface if ACD process 10 (via the confidence model)
determines there is sufficient confidence, such as a threshold
confidence level, that the draft report (or draft report section)
will be faster than being typed from scratch. For instance,
exposing (e.g., displaying) the medical report/section to UI 500
(e.g., for editing) may enable the ability to change some
incorrectly labeled terms/sentences, which may be faster than
manually typing out the entire section from the beginning; however,
if so many terms/sentences are incorrect (and therefore need to be
changed), it may be faster to simply type out the entire section
from the beginning, rather than making substantial changes to the
draft report section. As such, in the example where ACD process 10
determines an estimated time that it will take to make corrections
compared to an estimated time that it will take to type out the
entire section from the beginning, and determines it may be faster
to simply type out the entire section from the beginning (rather
than make corrections), ACD process 10 may not display the
report/section by not exposing the report/section to UI 500 in the
first place.
[0115] In some implementations, ACD process 10 may leverage various
features of the confidence model, including the statistics (e.g.,
min, max, standard deviation, etc.) of the difference in
probability between the first and second most likely word
hypothesized at each position in the report/section via, e.g., a
model (e.g., sequence to sequence model), the log likelihood of the
draft report/section per the model (e.g., in addition to a length
normalized version of this), confidence values from the ASR portion
of ACD process 10 (e.g., particularly for segments to which the
report/section content is attributed) and information regarding the
typical (e.g., mean/standard deviation) number of edits required
for a relevant physician and typical number of edits made by a
relevant scribe. For draft reports/sections that ACD process 10 may
actually expose to be edited (rather than typed from scratch), ACD
process 10 may collect the number of edits made and time required
to make them, which may be added to training and improvement of the
confidence model over time.
[0116] For example, in some implementations, ACD process 10 may
train 408 a model that maps the transcript to the medical report
from pairs of prior transcripts and medical reports. For instance,
the core model (e.g., sequence to sequence model) of ACD process 10
that may map the conversation transcript to the medical reports may
be trained 408 from pairs of (e.g., ASR approximate) transcripts
and medical reports. For example, as additional reports are
generated/edited/typed by scribes/physicians, these additional
reports may be automatically (or manually) added to the training
pool for the sequence to sequence model, which may be automatically
intermittently retrained. In some implementations, the model may be
metadata-dependent (e.g., physician, institution, scribe, etc.),
either by (e.g., regularized) adaptation to appropriate subsets of
the training pool or by using this metadata as inputs or
combination thereof. That is, the training pool may be specific to
the user, type of user, or institution of the user making the
changes to the medical report. In some implementations, the number
and types of edits made by the scribe (or other user) may also be
used by the confidence model to better predict the number of edits
(and thus editing efficiency) for subsequent drafts.
[0117] As noted above, in some implementations, ACD process 10
generate 505 UI 500 displaying the plurality of layers associated
with the audio encounter information obtained from at least the
first encounter participant, and in some implementations, ACD
process 10 may annotate 509 at least the portion of the one of the
plurality of layers added to the section of the one of the
plurality of layers based upon, at least in part, the confidence
level. For example, in some implementations, e.g., during a patient
encounter, toward the end of the patient encounter, or during
scribe training during playback, as the confidence of the
information regarding the medical report section rises, more items
may be documented (e.g., added) in this section. For instance,
assume for example purposes only that one of the encounter
participants (e.g., the physician) mentions "assessment" or "plan"
or alternatively ACD process 10 may use NLU to determine from the
conversational transcript 234 that the context of what the
physician is saying (e.g., "and come back for a follow up in two
weeks") may apply to the "Assessment/Plan" section (e.g., in
medical report layer 506) of medical report 236. Using the
techniques described above, ACD process 10 may recognize this, and
may add (display) some associated points about the recommendation
under the "Assessment/Plan" section (e.g., in the medical report
layer 506 shown at least in the example implementation of UI 500 in
FIG. 13), which may be first added and grayed based upon a first
confidence level of what was said. That is, "recommending she come
back for a follow up in 2 weeks" (shown at portion 1300a) may be
first be added and grayed based upon the first confidence level
being below a threshold confidence level. In the example, and
referring at least to the example implementation of UI 500 shown in
FIG. 14, as the physician continues to verbalize further associated
points of what was respectively entered into the Assessment/Plan
section, which therefore makes the confidence level (e.g., an
updated confidence level above the threshold confidence level) of
what was entered become higher, some of the grayed points (e.g.,
"recommending she come back" shown at portion 1300b) associated
with the higher confidence level may be annotated (e.g., become
solid or other type of annotation), or further adjustments/edits
may be made by ACD process 10 shortly thereafter (e.g., in the
following seconds) to correct the grayed portion based on the
confidence level now taking into account the additional context of
the further verbalization, which may then become solid. As such,
after the physician verbalizes his assessment, ACD process 10 may
start documenting that section, first grayed, but then the grayed
words may become more solid (e.g., darker or normal solid color)
with more points being added and becoming more solid as ACD process
10 knows more and the confidence level of what was said rises. In
some implementations, showing the transition from gray to solid (or
other annotation) wording may help train scribes (or other users)
to use ACD process 10 and recognize how it works; however, it will
be appreciated that the annotation of wording based upon confidence
level may occur at any stage of the patient encounter and/or post
patient encounter during scribe/physician playback/editing.
[0118] In some implementations, ACD process 10 may update 410 an
output of the user interface based upon, at least in part, one or
more modifications made at least one layer of the plurality of
layers. For instance, rather than the output being static during
the editing process, ACD process 10 may update 410 the output based
on the modifications made by the editor so far (e.g., based on the
decoder being autoregressive in nature). In particular, the decoder
(e.g., sequence to sequence decoder) output may depend on its
preceding output. As such, if the scribe (or other user) makes a
correction in one part of the draft report, ACD process 10 may
update a best guess at the subsequent content. In some
implementations, this may be distracting to the user, and so to
make it less distracting, it may be optionally limited to a
toggle-able mode (e.g., online/synchronous vs. global review mode)
and/or limited to only modifying the draft output for subsequent
sections of the report.
[0119] In some implementations, ACD process 10 may update 412 a
prediction for a next sentence to be typed based upon, at least in
part, current content of at least one layer of the plurality of
layers, where in some implementations, the at least one layer of
the plurality of layers may be the medical report. For example, for
reports that the above-noted confidence model (via ACD process 10)
determines are best typed from scratch rather than editing a draft,
ACD process 10 may leverage the summarization model to accelerate
composition by having it predict the next sentence to type and
updating that prediction based on, and to be consistent with, what
the scribe (or other user) has typed so far. For instance, ACD
process 10 (e.g., via UI 500) may allow the user to select the
model's prediction of the current/next sentence being presented to
the user in the report by selecting the tab key (or other shortcut)
to complete the sentence. In this way, ACD process 10 (e.g., via
the model) may only predict the draft report a sentence at a time
based on the scribes typing/feedback. In some implementations, the
prediction may be based not just upon what the scribe is currently
writing, but may additionally (or alternatively) be based upon the
context of what the scribe has previous written in previous
sentences of the report (or based upon a training pool specific to
the scribe). As discussed above,
[0120] As discussed above, automated clinical documentation (ACD)
process 10 may be configured to automate the collection and
processing of clinical encounter information to
generate/store/distribute medical records. Additionally, as
discussed above and referring also at least to the example
implementation of FIG. 15, ACD process 10 may be configured to
obtain 300 encounter information (e.g., machine vision encounter
information 102 and/or audio encounter information 106) of a
patient encounter (e.g., a visit to a doctor's office) of at least
a first encounter participant, wherein the encounter information
may include audio encounter information obtained from at least a
first encounter participant (e.g., encounter participant 228, 226,
230, and/or 242). ACD process 10 may further be configured to
process 302 the encounter information (e.g., machine vision
encounter information 102 and/or audio encounter information 106)
obtained from at least the first encounter participant, e.g.,
generate 602 an alert to obtain additional encounter information of
the patient encounter, to generate an encounter transcript (e.g.,
encounter transcript 234) and/or generate 304 a user interface
displaying a plurality of layers associated with the audio
encounter information obtained from at least the first encounter
participant. In some implementations, ACD process 10 may process at
least a portion of the encounter transcript (e.g., encounter
transcript 234) to populate at least a portion of a medical record
(e.g., medical record 236) associated with the patient encounter
(e.g., the visit to the doctor's office). Encounter transcript 234
and/or medical record 236 may be reviewed by a medical professional
involved with the patient encounter (e.g., a visit to a doctor's
office) to determine the accuracy of the same and/or make
corrections to the same.
[0121] As noted above, ACD process 10 may process 302 the audio
encounter information obtained from at least the first encounter
participant. In some implementations, processing 302 the first
audio encounter information may include defining 306 linkages
between each of the plurality of layers associated with the audio
encounter information. For example, the first layer of the
plurality of layers may be a visualization and/or direct play-back
of an audio signal associated with the audio encounter information
(e.g., complete audio of the encounter, encompassing and clearly
delineating each participant), wherein the second layer of the
plurality of layers may be a transcript associated with the audio
encounter information (e.g., a diarized audio transcript (verbatim)
for each participant in the encounter), and wherein the third layer
of the plurality of layers may be a medical report associated with
the audio encounter information (e.g., a draft medical report in
the appropriate clinical output format). In some implementations,
additional layers may include, e.g., the above-noted machine
vision-based recording of the encounter, including various signal
formats and features, and discrete, standardized, actionable data
resulting from the encounter, including, but not limited to
medication plans (for example, in RxNorm) or lab orders (for
example, LOINC) or diagnoses (for example, ICD10, CPT etc). In the
example, the signals captured from the encounter information may be
processed 304 into at least the above-noted three separate, yet
closely linked and interdependent layers.
[0122] In some implementations, ACD process 10 may include an ASR
portion that may process 302 the audio encounter information
producing an approximate (e.g., diarized) verbatim transcript along
with alignment information indicating the audio interval
corresponding to each transcript word. In some implementations, a
deep learning (e.g., sequence to sequence) model associated with
ACD process 10 may convert the transcript to a medical report. It
will be appreciated that various attribution techniques may be
employed by ACD process 10 that may effectively softly assign
responsibility for a given output (e.g., medical report) word to
input (e.g., conversation transcript) words (e.g. attention
weights, integrated gradient, etc.) according to the model. As a
result, this may provide a soft mapping from the transcript word
positions to report word positions. In some implementations, the
input word position assigned maximal attribution for a given output
word may be interpreted as being aligned (linked) to that output
(e.g., when a hard mapping is required). Based on the ASR time
alignment, a word in the draft medical report, aligned to a word in
the ASR conversation transcript, may now be associated with an
audio time interval of the associated audio signal of the audio
encounter information.
[0123] In some implementations, ACD process 10 may also may link
(i.e., align) the ASR conversation transcript words with the draft
medical report words. For transcript words that may have maximal
attribution value for some set of medical report words, ACD process
10 may link them with the first word in that set. For the remaining
transcript words, ACD process 10 may link them to the same word
that the nearest preceding (or if none, nearest subsequent)
conversation transcript word is linked to.
[0124] In some implementations, a visual recording (e.g., video
stream of the patient encounter), if available, may also be a layer
and may be time indexed and thus a given point in the recording may
be associated with the same time in the audio recording and thus a
conversation transcript word and draft report word. In some
implementations, if discrete, standardized, actionable data is
produced as a second (parallel) output sequence of the sequence to
sequence model, then a similar model output attribution technique
may be used to align tokens in this actionable data with the ASR
conversation transcript words, and thus the audio intervals.
[0125] In some implementations, ACD process 10 may generate 304 a
user interface displaying a plurality of layers associated with the
audio encounter information obtained from at least the first
encounter participant. For example, and referring again at least to
the example implementation of FIG. 5, an example user interface
(e.g., user interface 500) is shown. It will be appreciated that UI
500 is shown merely for example purposes only, and that more or
less features and arrangements of features may be used without
departing from the scope of the present disclosure.
[0126] As can be seen from FIG. 5, UI 500 includes the first layer
of the plurality of layers (e.g., first layer 502 which may be a
visualization and/or direct play-back of an audio signal associated
with the audio encounter information (e.g., complete audio of the
encounter, encompassing and clearly delineating each participant),
second layer 504 of the plurality of layers which may be a
transcript (e.g., encounter transcript 234) associated with the
audio encounter information (e.g., a diarized audio transcript
(verbatim) for each participant in the encounter), and third layer
506 of the plurality of layers which may be a medical report (e.g.,
medical report/record 236) associated with the audio encounter
information (e.g., a draft medical report in the appropriate
clinical output format). It will be appreciated that that each
layer (audio, transcript, draft report, etc.) may be rendered in
multiple different ways, and in the most appropriate way for a
given use/preference of the end-user. As such, the specific
rendering of layers should be taken as example only and not to
limit the scope of the disclosure.
[0127] As noted above, encounter transcript 234 (via second layer
504) and/or medical record/record 236 (via third layer 506) may be
reviewed by a medical professional involved with the patient
encounter (e.g., a visit to a doctor's office) to determine the
accuracy of the same and/or make corrections to the same. For
example, a scribe involved with (or assigned to) the patient
encounter (e.g., a visit to a doctor's office) may review encounter
transcript 234 and/or medical record 236 to confirm that the same
was accurate and/or make corrections to the same. In the event that
corrections are made to encounter transcript 234 and/or medical
record 236, automated clinical documentation process 10 may utilize
these corrections for training/tuning purposes (e.g., to adjust the
various profiles associated the participants of the patient
encounter) to enhance the future accuracy/efficiency/performance of
automated clinical documentation process 10.
[0128] Alternatively/additionally, a doctor involved with the
patient encounter (e.g., a visit to a doctor's office) may review
encounter transcript 234 and/or medical record 236 to confirm that
the same was accurate and/or make corrections to the same. In the
event that corrections are made to encounter transcript 234 and/or
medical record 236, automated clinical documentation process 10 may
utilize these corrections for training/tuning purposes (e.g., to
adjust the various profiles associated the participants of the
patient encounter) to enhance the future
accuracy/efficiency/performance of automated clinical documentation
process 10.
[0129] As such, and referring again at least to the example
implementation of FIG. 6, assume for example purposes only that a
scribe is reviewing the encounter information using UI 500. In some
implementations, ACD process 10 may receive a selection of a first
portion of the audio encounter information at a first layer of the
plurality of layers on the user interface, and ACD process 10 may
display an annotation of at least one of a second layer of the
plurality of layers and a third layer of the plurality of layers
corresponding to the first portion of the audio encounter
information of the first layer of the plurality of layers selected
on the user interface. For instance, the scribe may use, e.g.,
cursor 508 or other known input techniques, to select a portion of
first layer 502 (i.e., the audio information of the audio encounter
information), which may be received by ACD process 10. As a result,
in some implementations, ACD process 10 may display some kind of
annotation of either the second layer (e.g., the portion of the
transcript associated with the selected portion of the first layer)
and/or the third layer (e.g., the portion of the medical report
associated with the selected portion of the first layer). In the
example, ACD process 10 has displayed an annotation (e.g., bold
font) of the second layer (e.g., the portion of the transcript
associated with the selected portion of the first layer) and the
third layer (e.g., the portion of the medical report associated
with the selected portion of the first layer). It will be
appreciated that any type of annotation may be used (e.g., bold,
italics, underline, highlights, shading, transparency, etc.)
without departing from the scope of the present disclosure. As
such, the use of bolding as the annotation should be taken as
example only and not to otherwise limit the scope of the
disclosure.
[0130] Similarly, in some implantations, ACD process 10 may receive
a selection of the first portion of the audio encounter information
at one of the second layer of the plurality of layers and the third
layer of the plurality of layers on the user interface, and ACD
process 10 may provide audio of the first layer corresponding to
the first portion of the audio encounter information of one of the
second layer of the plurality of layers and the third layer of the
plurality of layers selected on the user interface. For example,
and referring to the example implementation of FIG. 7, the scribe
may use, e.g., cursor 508 or other known input techniques, to
select a portion of second layer 504 (e.g., the transcript), and/or
the third layer (e.g., the medical report) which may be received by
ACD process 10. As a result, in some implementations, ACD process
10 may provide the audio of first layer 502 (e.g., the audio
information of the audio encounter associated with the selected
portion of second layer 504).
[0131] As such, ACD process 10 may leverage the above-noted defined
linkages, such that the encounter recording as captured above may
be provided to a human scribe (or physician) in such a fashion, so
that they may navigate through each of these layers independently
and be always in sync. For instance, if the scribe navigates
through first layer 502 (audio) by listening, the part of the
transcript that corresponds to that part of the audio may be
bolded, highlighted, etc. accordingly, as well as the resulting
medical report (and if relevant actionable data) from that
information. Additionally/alternatively, if the scribe selects a
part of the report, the audio/transcript sections that affected
that part of the report may be bolded, highlighted, emphasized,
etc. This annotation also may correspond to the audio and report
"cursor" (e.g., audio cursor 510) moving, e.g., audio may next play
from that position and typing/keyboard-navigation may commence from
that position in the report. As a result, since each of the
plurality of layers may be synchronized, a scribe (or physician,
etc.) may later playback the whole patient encounter, navigate
(e.g., "clicking" a mouse cursor of other pointing device at any
position) in any portion of any of the layers, make edits, and
would know which edit/correction belongs to the other sections
(layers). More generally, "clicking" (i.e. resetting the cursor) in
one viewed layer may annotate and update the cursor in the linked
points/excerpts in the other layers.
[0132] As noted above, ACD process 10 may generate 602 an alert
(e.g., an audio alert or a visual alert such as a pop-up message,
text message, email, etc.) to obtain additional encounter
information of the patient encounter. For example, ACD process 10
may be aware of the relevant sections of the above-noted medical
report sections when processing the obtained encounter information,
as well as explicit data points (such as physical exam findings,
lab orders, etc.). In the example, as will be discussed below, ACD
process 10 may determine that, e.g., additional information may be
needed from the patient, since some of the current encounter
information obtained so far may be vague, missing, could not be
documented/verified (e.g., neurological section, extremities,
etc.), or information which would make the patient encounter more
compliant to clinical/administrative guidelines. In some
implementations, such as the above example, ACD process 10 may
generate 602 an alert (e.g., for the physician) to obtain
additional encounter information of the patient encounter to
address the encounter information deficiencies.
[0133] In some implementations, the alert may be generated 602 and
provided to the physician (or other healthcare professional) while
the patient is still in the examination room to make it easier to
then obtain the additional encounter information; however, the
alert may be generated at any time. In some implementations, a
virtual assistant (e.g., virtual assistant 238 from FIG. 2) may
generate and provide the alert to inform any medical professional
(e.g., doctors, nurses, physician's assistants, lab technicians,
physical therapists, scribes/transcriptionist, etc.) and/or staff
members involved in the patient encounter) to gather the additional
encounter information. In some implementations, the alert may be
provided during a pre-visit portion of the patient encounter (e.g.,
patient check in) and/or a post-visit portion of the patient
encounter (e.g., check out).
[0134] In some implementations, processing 302 the first audio
encounter information may include comparing 606 at least one layer
of the plurality of layers with at least one data source, which may
include at least one of physical exam finding information, lab
order information, medical condition checklist information, and
compliance information. For example, as discussed above with regard
to at least FIG. 2, ACD process 10 may be configured to access one
or more data sources 118 (e.g., plurality of individual data
sources 120, 122, 124, 126, 128), examples of which may include but
are not limited to one or more a medical conditions symptoms data
source, a prescriptions compatibility data source, a medical
insurance coverage data source, a data source storing past/present
physical exam findings of the patient, past/present lab order
information for the patient, a medical compliance data source,
combination thereof, etc.
[0135] Continuing with the above example, assume for example
purposes only that the information documented in the medical report
section of UI 500 denotes that the physician has conducted a
standard physical examination for the patient. In the example,
assume that the standard physical examination has nine medical
points that should be checked for a standard physical examination
(e.g., eyes, throat, reflexes, blood pressure, etc.). Further
assume in the example that only eight of the nine medical points
that should be checked for a standard physical examination have
been completed by the physician. In the example, ACD process 10 may
use the patient encounter information (e.g., from the medical
report) to determine that a standard physical examination has been
conducted with only eight medical points being documented, obtain
one of the above-noted data sources that indicates that there are
nine medical points that should be checked for a standard physical
examination, compare 606 the information in the medical report
(e.g., the eight documented medical issues) with one of the
obtained data sources that indicates that there are nine medical
points that should be checked for a standard physical examination,
determine that one of the medical points that should be checked for
a standard physical examination has not been documented in the
medical report (e.g., blood pressure), and generate an alert for
the physician to obtain the missing ninth medical point (e.g.,
blood pressure). In some implementation, the encounter information
in the medical report section that was used (at least in part) to
determine that an alert should be generated may be annotated (as
discussed above) to help the physician confirm that additional
information is indeed needed.
[0136] As another example, assume that for example purposes only
that the information documented in the medical report section of UI
500 denotes that the physician has conducted a standard physical
examination for the patient. In the example, assume that the
standard physical examination includes a mandatory blood test.
Further assume in the example that the physician has not ordered a
blood test. In the example, ACD process 10 may use the patient
encounter information (e.g., from the medical report) to determine
that a standard physical examination has been conducted without a
blood test being documented, obtain one of the above-noted data
sources that indicates that a blood test should be included for a
standard physical examination, compare 606 the information in the
medical report (e.g., showing a lack of blood being drawn or blood
test being ordered) with one of the obtained data sources that
indicates that a blood test should be included for a standard
physical examination, determine that the patient's blood should be
drawn and a blood test should be ordered for a standard physical
examination that has not been documented in the medical report
(e.g., blood pressure), and generate an alert for the physician to
obtain the missing information (e.g., blood draw/test).
[0137] It will be appreciated that while the above example involves
an indication of missing information from a medical examination,
other examples of missing (or vague) information may be used
without departing from the scope of the present disclosure. For
instance, ACD process may similarly generate an alert if the
information in the medical report indicates additional information
may need to be obtained from the patient encounter to be more
compliant with clinical/administrative guidelines. As another
example, ACD process may similarly generate an alert if the
information in the medical report indicates a particular
prescription has not been filled/refilled where one of the
above-noted data sources indicates that the particular prescription
should be filled/refilled. As another example, ACD process may
similarly generate an alert if the information in the medical
report indicates symptoms of a particular illness that could be
verified by examining some part of the patient that has been
examined. As such, the specific disclosure of generating an alert
for a missed medical examination point should be taken as example
only and not to otherwise limit the scope of the present
disclosure.
General:
[0138] As will be appreciated by one skilled in the art, the
present disclosure may be embodied as a method, a system, or a
computer program product. Accordingly, the present disclosure may
take the form of an entirely hardware embodiment, an entirely
software embodiment (including firmware, resident software,
micro-code, etc.) or an embodiment combining software and hardware
aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, the present
disclosure may take the form of a computer program product on a
computer-usable storage medium having computer-usable program code
embodied in the medium.
[0139] Any suitable computer usable or computer readable medium may
be utilized. The computer-usable or computer-readable medium may
be, for example but not limited to, an electronic, magnetic,
optical, electromagnetic, infrared, or semiconductor system,
apparatus, device, or propagation medium. More specific examples (a
non-exhaustive list) of the computer-readable medium may include
the following: an electrical connection having one or more wires, a
portable computer diskette, a hard disk, a random access memory
(RAM), a read-only memory (ROM), an erasable programmable read-only
memory (EPROM or Flash memory), an optical fiber, a portable
compact disc read-only memory (CD-ROM), an optical storage device,
a transmission media such as those supporting the Internet or an
intranet, or a magnetic storage device. The computer-usable or
computer-readable medium may also be paper or another suitable
medium upon which the program is printed, as the program can be
electronically captured, via, for instance, optical scanning of the
paper or other medium, then compiled, interpreted, or otherwise
processed in a suitable manner, if necessary, and then stored in a
computer memory. In the context of this document, a computer-usable
or computer-readable medium may be any medium that can contain,
store, communicate, propagate, or transport the program for use by
or in connection with the instruction execution system, apparatus,
or device. The computer-usable medium may include a propagated data
signal with the computer-usable program code embodied therewith,
either in baseband or as part of a carrier wave. The computer
usable program code may be transmitted using any appropriate
medium, including but not limited to the Internet, wireline,
optical fiber cable, RF, etc.
[0140] Computer program code for carrying out operations of the
present disclosure may be written in an object oriented programming
language such as Java, Smalltalk, C++ or the like. However, the
computer program code for carrying out operations of the present
disclosure may also be written in conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The program code may execute
entirely on the user's computer, partly on the user's computer, as
a stand-alone software package, partly on the user's computer and
partly on a remote computer or entirely on the remote computer or
server. In the latter scenario, the remote computer may be
connected to the user's computer through a local area network/a
wide area network/the Internet (e.g., network 14).
[0141] The present disclosure is described with reference to
flowchart illustrations and/or block diagrams of methods, apparatus
(systems) and computer program products according to embodiments of
the disclosure. It will be understood that each block of the
flowchart illustrations and/or block diagrams, and combinations of
blocks in the flowchart illustrations and/or block diagrams, may be
implemented by computer program instructions. These computer
program instructions may be provided to a processor of a general
purpose computer/special purpose computer/other programmable data
processing apparatus, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or
blocks.
[0142] These computer program instructions may also be stored in a
computer-readable memory that may direct a computer or other
programmable data processing apparatus to function in a particular
manner, such that the instructions stored in the computer-readable
memory produce an article of manufacture including instruction
means which implement the function/act specified in the flowchart
and/or block diagram block or blocks.
[0143] The computer program instructions may also be loaded onto a
computer or other programmable data processing apparatus to cause a
series of operational steps to be performed on the computer or
other programmable apparatus to produce a computer implemented
process such that the instructions which execute on the computer or
other programmable apparatus provide steps for implementing the
functions/acts specified in the flowchart and/or block diagram
block or blocks.
[0144] The flowcharts and block diagrams in the figures may
illustrate the architecture, functionality, and operation of
possible implementations of systems, methods and computer program
products according to various embodiments of the present
disclosure. In this regard, each block in the flowchart or block
diagrams may represent a module, segment, or portion of code, which
comprises one or more executable instructions for implementing the
specified logical function(s). It should also be noted that, in
some alternative implementations, the functions noted in the block
may occur out of the order noted in the figures. For example, two
blocks shown in succession may, in fact, be executed substantially
concurrently, or the blocks may sometimes be executed in the
reverse order, not at all, or in any combination with any other
flowcharts depending upon the functionality involved. It will also
be noted that each block of the block diagrams and/or flowchart
illustrations, and combinations of blocks in the block diagrams
and/or flowchart illustrations, may be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
[0145] The terminology used herein is for the purpose of describing
particular embodiments only and is not intended to be limiting of
the disclosure. As used herein, the singular forms "a", "an" and
"the" are intended to include the plural forms as well, unless the
context clearly indicates otherwise. It will be further understood
that the terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0146] The corresponding structures, materials, acts, and
equivalents of all means or step plus function elements in the
claims below are intended to include any structure, material, or
act for performing the function in combination with other claimed
elements as specifically claimed. The description of the present
disclosure has been presented for purposes of illustration and
description, but is not intended to be exhaustive or limited to the
disclosure in the form disclosed. Many modifications and variations
will be apparent to those of ordinary skill in the art without
departing from the scope and spirit of the disclosure. The
embodiment was chosen and described in order to best explain the
principles of the disclosure and the practical application, and to
enable others of ordinary skill in the art to understand the
disclosure for various embodiments with various modifications as
are suited to the particular use contemplated.
[0147] A number of implementations have been described. Having thus
described the disclosure of the present application in detail and
by reference to embodiments thereof, it will be apparent that
modifications and variations are possible without departing from
the scope of the disclosure defined in the appended claims.
* * * * *