U.S. patent application number 10/289711 was filed with the patent office on 2003-08-14 for method and system for healthcare treatment planning and assessment.
This patent application is currently assigned to Dental Medicine International L.L.C., a Maryland corporation. Invention is credited to Martin, John, Nolf, Randy.
Application Number | 20030154109 10/289711 |
Document ID | / |
Family ID | 26824103 |
Filed Date | 2003-08-14 |
United States Patent
Application |
20030154109 |
Kind Code |
A1 |
Martin, John ; et
al. |
August 14, 2003 |
Method and system for healthcare treatment planning and
assessment
Abstract
Methods and systems consistent with the present invention
provide a comprehensive assessment and planning system. Methods and
systems consistent with the present invention employ a preventive
approach to predicting the likelihood of an entity entering a
degraded future state by computing a risk value that reflects that
likelihood. An embodiment of the present invention applies to a
comprehensive healthcare treatment and planning system. This
healthcare system employs a preventive approach to healthcare by
basing healthcare decisions on a multi-factorial computation of
risk. The risk value is computed by evaluation of a finction that
considers a variety of historic, environmental, and systemic
behaviors and conditions. In addition to considering a risk value,
a treatment plan developed in accordance with the healthcare system
considers symptoms and objectives of the treatment from the
perspective of both the patient and the provider. The outcomes
associated with treatment and risk assessment are fed back into the
healthcare system to increase its accuracy and subsequent
effectiveness in computing risk values over time.
Inventors: |
Martin, John; (State
College, PA) ; Nolf, Randy; (Saylorsburg,
PA) |
Correspondence
Address: |
PETER J. DEVLIN
Fish & Richardson P.C.
225 Franklin Street
Boston
MA
02110-2804
US
|
Assignee: |
Dental Medicine International
L.L.C., a Maryland corporation
|
Family ID: |
26824103 |
Appl. No.: |
10/289711 |
Filed: |
November 7, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10289711 |
Nov 7, 2002 |
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09396404 |
Sep 15, 1999 |
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6484144 |
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60125931 |
Mar 23, 1999 |
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Current U.S.
Class: |
705/3 |
Current CPC
Class: |
G06Q 40/08 20130101;
G16H 20/00 20180101; G16H 50/30 20180101; G16H 70/20 20180101 |
Class at
Publication: |
705/3 |
International
Class: |
G06F 017/60 |
Claims
What is claimed is:
1. A method in a data processing system for determining an
appropriate treatment for a patient by a provider, comprising the
steps of: receiving diagnostic data indicating a current state of a
patient; receiving data reflecting treatment objectives of the
patient and the provider indicating a preferred treatment outcome;
receiving a plurality of treatment plans for preventing the patient
from developing a disease; computing a risk value for the patient,
the risk value indicating a likelihood of the patient developing
the disease and being responsive to the treatment plans based on a
subset of the diagnostic data; receiving an indication of a
selected one of the treatment plans; receiving an indication of a
degree of success of the treatment plan in preventing the
development of the disease; comparing the degree of success with
the treatment objectives to assess the effectiveness of the
selected treatment plan; and adjusting the computed risk value to
increase an accuracy in determining the appropriate treatment.
2. The method of claim 1 further including the step of obtaining
insurance pre-authorization for a treatment plan based on the risk
value.
3. The method of claim 1 wherein the disease under examination is
multi-factorial.
4. The method of claim 1 wherein the computed risk value is
adjusted consistent with outcomes of prior treatment.
5. A method in a data processing system for determining an
appropriate treatment for a patient, comprising the steps of:
receiving diagnostic data reflecting a current state of the
patient; and computing a risk value reflecting a likelihood of the
patient developing a disease based on a subset of the diagnostic
data.
6. The method of claim 5 further including the steps of: receiving
a proposed treatment plan that reflects the computed risk value;
and analyzing the proposed treatment plan to determine a likelihood
that the proposed treatment plan will prevent the patient from
developing the disease.
7. The method of claim 6 wherein the analyzing step includes:
receiving patient and provider objectives of treatment indicating a
preferred treatment outcome; and determining whether the proposed
treatment plan is consistent with the patient and the provider
objectives of treatment.
8. The method of claim 6 wherein the analyzing step includes
determining whether the proposed treatment plan is appropriate for
the diagnostic data.
9. The method of claim 6 wherein the analyzing step includes
determining whether the proposed treatment plan is appropriate
based upon prior treatment provided.
10. The method of claim 6 further including the step of adjusting
the computed risk value based on prior outcomes of treatment to
increase an accuracy in determining the appropriate treatment.
11. The method of claim 5 wherein the step of receiving includes
accessing the diagnostic data from a remote location.
12. The method of claim 5 wherein the computing step includes
adjusting the risk value based on prior outcomes of treatment to
increase an accuracy in determining the appropriate treatment.
13. The method of claim 5 further including the step of
transmitting the computed risk value to an external source for
pre-authorization of a treatment for the patient.
14. The method of claim 13 wherein the transmitting step includes
transmitting the computed risk value to an insurance company.
15. The method of claim 13 wherein the transmitting step includes
transmitting the computed risk value to an organization that makes
policy decisions pertaining to healthcare benefits.
16. A method in a data processing system for determining an
appropriate treatment for a patient, comprising the steps of:
receiving diagnostic data reflecting a current state of the
patient; and computing a risk value reflecting a likelihood of the
patient being responsive to treatment based on a subset of the
diagnostic data.
17. The method of claim 16 further including the steps of:
receiving a proposed treatment plan that reflects the computed risk
value; and analyzing the proposed treatment plan to determine a
likelihood that the proposed treatment plan will prevent the
patient from developing the disease.
18. The method of claim 17 wherein the analyzing step includes:
receiving patient and provider objectives of treatment indicating a
preferred treatment outcome; and determining whether the proposed
treatment plan is consistent with the patient and the provider
objectives of treatment.
19. The method of claim 17 wherein the analyzing step includes
determining whether the treatment plan is appropriate for the
diagnostic data.
20. The method of claim 17 wherein the analyzing step includes
determining whether the proposed treatment plan is appropriate
based upon prior treatment provided.
21. The method of claim 17 further including the step of adjusting
the computed risk value based on prior outcomes of treatment to
increase an accuracy in determining the appropriate treatment.
22. The method of claim 16 wherein the receiving step includes
accessing the diagnostic data from a remote location.
23. The method of claim 16 wherein the computing step includes
adjusting the risk value based on prior outcomes of treatment to
increase an accuracy in determining the appropriate treatment.
24. The method of claim 16 further including the step of
transmitting the computed risk value to an external source for
pre-authorization of a treatment for the patient.
25. The method of claim 24 wherein the transmitting step includes
transmitting the computed risk value to an insurance company.
26. The method of claim 24 wherein the transmitting step includes
transmitting the computed risk value to an organization that makes
policy decisions pertaining to healthcare benefits.
27. A data processing system, comprising: a storage device
including patient health information; a memory containing a program
with first code that computes a risk value related to the patient
health information and with second code that updates the risk value
according to subsequent computations of risk; and a processor for
running the program.
28. The data processing system of claim 27 wherein the program
further includes third code for predicting the effectiveness of a
proposed treatment plan.
29. A data processing system including a client and a healthcare
server, comprising: a storage device including patient health
information; a memory including administrative software and a
healthcare system that computes a risk value based on diagnostic
data and that analyzes a proposed treatment plan that reflects the
risk value, the risk value reflecting a likelihood of a patient
developing disease and being responsive to treatment; and at least
one processor for executing the healthcare system and the
administrative software.
30. A data processing system including a client and a healthcare
server, comprising: means for receiving diagnostic data reflecting
a patient's likelihood of developing a disease and being responsive
to treatment; means for computing a risk value based on a subset of
the diagnostic data; and means for analyzing a proposed treatment
plan that reflects the computed risk value.
31. A computer-readable medium containing instructions for
controlling a data processing system to perform a method, the data
processing system having a healthcare server, the method comprising
the steps of: receiving diagnostic data reflecting a current state
of the patient; and computing a risk value reflecting a likelihood
of the patient developing a disease based on a subset of the
diagnostic data.
32. The computer-readable medium of claim 31 further including
instructions for: receiving a proposed treatment plan that reflects
the computed risk value; and analyzing the proposed treatment plan
to determine a likelihood that the proposed treatment plan will
prevent the patient from developing the disease.
33. The computer-readable medium of claim 32 wherein the analyzing
step includes: receiving patient and provider objectives of
treatment indicating a preferred treatment outcome; and determining
whether the treatment plan is consistent with the patient and
provider objectives of treatment.
34. The computer-readable medium of claim 32 wherein the step of
analyzing includes determining whether the treatment plan is
appropriate for the patient's diagnostic data.
35. The computer-readable medium of claim 32 wherein the analyzing
step includes determining whether the proposed treatment plan is
appropriate based upon prior treatment provided.
36. The computer-readable medium of claim 32 further including
instructions for adjusting the computed risk value based on prior
outcomes of treatment.
37. The computer-readable medium of claim 31 wherein the receiving
step includes accessing diagnostic data stored at a remote
location.
38. The computer-readable medium of claim 31 wherein the computing
step includes adjusting the risk value based on prior outcomes of
treatment.
39. The computer-readable medium of claim 31 further including
instructions for transmitting the computed risk value to an
external source for pre-authorization of a treatment for the
patient.
40. The computer-readable medium of claim 31 wherein the
transmitting step includes transmitting the computed risk value to
an insurance company.
41. The computer-readable medium of claim 31 wherein the
transmitting step includes transmitting the computed risk value to
an organization that makes policy decisions pertaining to
healthcare benefits.
42. A method in a data processing system, comprising the steps of:
receiving information reflecting a current state of an entity; and
computing a risk value that indicates a likelihood of the entity
entering an undesirable state.
43. The method of claim 42 further including the steps of:
receiving a proposed strategy for preventing the entity from
entering the undesirable state; and requesting authorization from
an external source before employing the strategy.
44. The method of claim 42 further including the steps of:
receiving a proposed strategy for preventing the entity from
entering the undesirable state; and analyzing the proposed strategy
to determine a likelihood of the entity entering the undesirable
state when the proposed strategy is employed.
45. The method of claim 44 further including the step of receiving
objectives of the entity and determining whether the proposed
strategy is consistent with the objectives of the entity.
46. The method of claim 44 wherein the analyzing step includes
determining whether the proposed strategy is appropriate for the
diagnostic information.
47. The method of claim 46 wherein the determining step includes
determining whether the proposed strategy is appropriate based upon
prior results of executed strategies.
48. The method of claim 44 further including the step of adjusting
the computed risk value based on prior results of strategy
invocations.
49. The method of claim 42 wherein the receiving step includes
accessing the diagnostic information from a remote location stored
on a remote location.
50. The method of claim 42 wherein the computing step includes
adjusting the computed risk value based on prior results of
executed strategies.
51. The method of claim 42 wherein the receiving step includes
receiving information reflecting a current state of a healthcare
patient.
Description
RELATED APPLICATIONS
[0001] The following identified U.S. patent application is relied
upon and is incorporated by reference in this application:
Provisional U.S. Patent Application No. 60/125,931, entitled
"Method and System for Healthcare Treatment Planning and
Assessment," filed on Mar. 23, 1999.
FIELD OF THE INVENTION
[0002] The present invention relates generally to data processing
systems and, more specifically, to an assessment and planning
system that uses a multi-factorial computation of risk to determine
an appropriate strategy for preventing an entity from entering an
undesirable state.
BACKGROUND OF THE INVENTION
[0003] A patient generally seeks medical advice and treatment from
a healthcare provider when the patient experiences a medical
condition that the patient is unable to treat. The term healthcare
as used herein refers generally to any activity directed to the
care and maintenance of a patient (e.g., a human being). A
healthcare provider may thus provide services directed to the
mental, emotional, or physical well-being of a patient.
Accordingly, healthcare providers may include, for example,
psychiatrists, podiatrists, dentists, substance abuse counselors,
etc. A healthcare provider diagnoses a condition, or disease, and
recommends a course of treatment to cure the condition, if such
treatment exists. This model of reparative healthcare treatment
focuses only on healing, or repairing, an existing condition.
[0004] To determine an appropriate treatment for an existing
condition, a healthcare provider runs a series of diagnostic tests
and collects clinical data related to the patient's symptoms. The
term "clinical data" refers to the data measured and observed by a
healthcare provider during examination of a patient, reflecting the
patient's health, or related to a health condition. The clinical
data generally reflects the effects of a disease as determined at a
point in time. For example, if a patient has a tumor, a healthcare
provider may collect clinical data reflecting the tumor's size,
appearance, location, and texture.
[0005] After collecting the clinical data, the healthcare provider
forms hypotheses about the cause of the condition, its severity,
and its impact. Next, the healthcare provider diagnoses the
condition and determines how to treat the condition. The patient
only provides input into this process by enumerating symptoms and
giving background information about the condition or related
conditions.
[0006] Alternatively, a patient or healthcare provider may input
clinical data into a computer program that calculates a value of
risk. The risk value output by the computer program is a quantified
measure indicating a patient's likelihood of currently having a
condition or disease as indicated by the patent's symptoms. This
computed value of risk may be considered in diagnosing a condition
or disease.
[0007] For example, consider a situation where a patient enters a
dentist's office with red, swollen gums, extreme sensitivity to
both hot and cold substances, and pain in several areas of her
mouth. The dentist hypothesizes that the patient has periodontal
disease. Or alternatively, the dentist inputs an enumerated list of
the patient's symptoms into a computer program which outputs a
quantified indicator of the patient's likelihood of either having
periodontal disease, or if the patient's periodontal disease is in
remission, having an exacerbation of the periodontal disease.
[0008] Before proceeding with a diagnosis and plan for treatment,
suppose the dentist runs a series of tests and makes observations
to determine the accuracy of the initial hypothesis or indicator
value. During the examination, suppose the dentist finds
significant bone loss associated with several teeth and decides to
restore the areas of bone loss with a bone graft procedure. After
performiing the bone graft procedure, the dentist submits claim
forms to the patient's insurance carrier for approval. The dentist
may further recommend that the patient initiate a scheme of
improved oral hygiene, including regular professional cleaning
appointments to minimize or retard the effects of periodontal
disease. Absent any obviously related complications, the patient
and dentist consider the treatment a success and continue their
relationship. If the insurance company refuses payment the patient
must absorb the cost of the procedure.
[0009] This reparative model for healthcare treatment fails to
consider how a patient's intended behavior impacts the
effectiveness of the treatment. In the example above, the patient's
condition may have been exacerbated by the patient's smoking habit
that the patient has no intention of ceasing. Additionally, the
patient may be unable or unwilling to improve her oral hygiene.
Both of these factors contribute to the effectiveness and longevity
of a bone graft procedure. The model also fails to direct treatment
towards the prevention of future conditions. For instance, in the
example above, the patient's periodontal disease is likely to
worsen over time, absent any changes in the patient's oral care.
The bone graft procedure used to treat the most severe areas of
bone loss fails to retard, prevent, or otherwise impact other areas
of the patient's gums that have been effected by the periodontal
disease. Therefore, performing a bone graft, an intrusive and
unpredictable procedure, as the only form of treatment, may not be
the best treatment because it fails to address the likely
progression of the disease and a potential need for subsequent
treatment related to a current condition. A reparative treatment
planning scheme fails to consider that current symptoms reflect
only one indicator of the significance or severity of a condition.
Further, the reparative model for treatment fails to consider the
patient's medical history and its impact on the effectiveness and
longevity of treatment.
[0010] Overall, the reparative model for healthcare treatment
planning focuses solely on healing an existing condition as
indicated by diagnostic tests and clinical data. This model fails
to consider various other factors that impact the effectiveness of
treatment. As a result, the most effective and comprehensive
treatment may not be administered. Similarly, because the
reparative model fails to focus on preventing future conditions, it
is likely to result in a higher number of procedures needed on a
long-term basis. Patients and insurance companies experience
inflated economic healthcare costs when healthcare providers
administer unnecessary, overly intrusive, or ineffective treatment,
or treatment that contributes to a new disease. Additionally,
patients absorb high non-economic costs, in the form of emotional,
mental, or physical anxiety, when they are subjected to unnecessary
or overly intrusive procedures, as determined in light of the
patient's overall state of health. It is therefore desirable to
improve healthcare systems.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate an
implementation of the present invention and together with the
description, serve to explain the advantages and principles of the
invention. In the drawings,
[0012] FIG. 1 depicts an exemplary data processing system suitable
for use with methods and systems consistent with the present
invention;
[0013] FIG. 2 depicts a flowchart of the steps performed by the
healthcare system depicted in FIG. 1;
[0014] FIG. 3 depicts a flowchart of the steps performed by the
healthcare system, depicted in FIG. 1, when performing risk
assessment;
[0015] FIG. 4 depicts a flowchart of the steps performed by the
healthcare system, depicted in FIG. 1, when performing guided
treatment planning;
[0016] FIG. 5 depicts a flowchart of the steps performed by the
healthcare system, depicted in
[0017] FIG. 1; when performing assessment of patient outcomes;
[0018] FIG. 6 depicts a flowchart of the steps performed by the
healthcare system, depicted in FIG. 1, when performing assessment
of provider outcomes; and
[0019] FIG. 7 depicts a flowchart of the steps performed by the
healthcare system, depicted in FIG. 1, when updating a computed
risk value.
SUMMARY OF THE INVENTION
[0020] In accordance with a first aspect of the present invention,
as embodied and broadly described herein, a method is implemented
in a data processing system for computing a risk value that
indicates a likelihood of an entity entering an undesirable state.
The system receives data reflecting a current state of an entity
and computes a risk value that reflects a likelihood of the entity
entering the undesirable state, based on a subset of the received
data. The system then analyzes a proposed strategy for preventing
the entity from entering the undesirable state.
[0021] Furthermore, in accordance with a first aspect of the
present invention, a method is implemented where the entity is a
patient.
[0022] Consistent with an embodiment of the first aspect of the
present invention, as embodied and broadly described herein, a
method is implemented in a data processing system for determining
an appropriate treatment for a patient. The system receives data
reflecting a current state of the patient and computes a risk value
that reflects a likelihood of the patient developing a disease,
based on a subset of the diagnostic data. The system then analyzes
a proposed treatment plan, considering the computed risk value and
the received diagnostic data.
[0023] Furthermore, in accordance with an embodiment of the method
of the first aspect of the present invention, as embodied and
broadly described herein, a method is implemented in a data
processing system for determining an appropriate treatment for a
patient. The system receives data reflecting a current state of the
patient and computes a risk value that reflects a likelihood of the
patient being responsive to treatment, based on a subset of the
diagnostic data. The system then analyzes a proposed treatment
plan, considering the computed risk value and the received
diagnostic data.
[0024] Furthermore, in accordance with an embodiment of the method
of the first aspect of the present invention a method is performed
for a plurality of patients to identify an adjustment to the risk
value that will render the risk value more accurate and adjust the
risk value accordingly.
[0025] Furthermore, in accordance with an embodiment of the method
of the first aspect of the present invention the diagnostic data
includes clinical data, objectives of treatment, and data
reflecting factors that may positively or negatively impact the
success of treatment. In accordance with this aspect of the present
invention, the computation of the risk value includes analyzing the
diagnostic information.
[0026] Furthermore, in accordance with an embodiment of the method
of the first aspect of the present invention a treatment plan is
assessed for its suitability in treating a condition based on the
computed risk value and the diagnostic data.
[0027] In accordance with an embodiment of the first aspect of the
present invention, as embodied and broadly described herein, an
apparatus is provided that includes a client and a healthcare
server. The system further includes a storage device including
patient health information, a memory including administrative
software and a healthcare system, and at least one processor for
executing the healthcare system and the administrative
software.
DETAILED DESCRIPTION
[0028] Methods and systems consistent with the present invention
generally provide a comprehensive assessment and planning system
that employs a preventive approach to predicting the likelihood of
an entity entering an undesirable future state. Additionally, based
on a value of an indicator that reflects the likelihood of an
entity entering an undesirable state, methods and systems
consistent with the present invention assess the appropriateness of
a strategy proposed to avoid, or limit, the entity from entering
the undesirable state. One embodiment of a method and system
consistent with the present invention is described below relative
to healthcare.
[0029] A healthcare treatment and planning system (hereinafter
"healthcare system") consistent with the present invention enables
assessment of a patient's current and likely future health and the
effectiveness of healthcare decisions. Unlike current methods and
systems which only assess a patient's likelihood of currently
having a condition or disease, methods and systems consistent with
the present invention employ a predictive approach to healthcare
treatment decision-making. The healthcare system thus provides
healthcare according to a preventive model, where a multi-factorial
computed risk value is used to prescribe an appropriate treatment
for an existing condition, and to prevent a condition from
occurring. Accordingly, methods and systems consistent with the
present invention formulate a health treatment plan by considering
input from various factors that impact a patient's likelihood of
developing disease and the likelihood that a particular course of
treatment will be effective for the patient. This comprehensive,
preventive treatment planning model requires fewer procedures on a
long-term basis, thereby yielding higher quality, more effective,
and lower cost healthcare. The risk value reflects an integrated
computation of environmental factors, current health conditions,
intended patient behavior, and effectiveness of prior treatment
both for a particular patient and for a large group of unrelated
patients. The predictive model of healthcare employed by methods
and systems consistent with the present invention supports early
diagnosis, interceptive treatment, and behavior modification.
[0030] Overview
[0031] Treatment planning directed to maintaining health and
preventing future conditions reduces the long-term costs associated
with healthcare. To achieve a preventive model of healthcare,
treatment planning considers factors beyond current symptoms that
impact a condition. Thus, such a model expands the focus of
healthcare to consider those factors that directly or indirectly
lead to future disease occurrences. Various behavioral elements
contribute to a patient's likelihood of developing a disease and
the likely success of treatment. The objective of comprehensive and
preventive treatment planning is to determine which treatment will
achieve with acceptable predictability the most desirable set of
outcomes based on a patient's desires, conditions, risk factors,
susceptibility, and healing capacity. Such a plan decreases
healthcare costs for patients and insurance companies by increasing
the effectiveness of treatment and treatment planning decisions of
healthcare providers. The impact of a patient's desires,
conditions, susceptibility, and healing capacity on the patient's
health may be expressed as a probability in terms of a risk of the
patient developing disease or responding to treatment.
[0032] Generically speaking, risk is a measure of a loss, expressed
as a probability. A loss occurs as the result of some course of
events that may include interrelated factors and events, possibly
occurring over a long period of time. An event may be perceived as
a loss in one context, but not in another. For example, in
healthcare the extraction of a tooth is a loss for a patient, but
may be a gain for an insurance company because the extraction
allows the insurance company to avoid future losses associated with
the extracted tooth.
[0033] Managing risk requires identification of the conditions,
events, and behaviors that contribute to a loss and a course of
action to mitigate their effects. An effective risk management
strategy thus focuses on identifying and controlling the
conditions, events, or behaviors that contribute to or prevent the
occurrence of a loss. Once the relevant conditions, events and
behaviors have been identified, the effective courses of action to
reduce risk may be determined and analyzed with respect to cost and
outcome.
[0034] As applied to healthcare, a loss may be identified as a
health condition. By considering factors impacting a patient's
health, that patient's risk of experiencing certain health
conditions may be determined. Relevant factors to consider in
determining an appropriate risk value may include systemic,
psychological and environmental conditions, events, and behaviors,
including, for example, age, climate, and marital status. Once a
healthcare provider determines an accurate measure of a
multi-factorial risk value, the provider may develop a treatment
plan directed to curing current conditions and preventing future
ones, while maintaining a specified level of health. Such a
preventive model of healthcare yields lower costs for patients and
insurance companies and increases the effectiveness and
predictability of healthcare overall.
[0035] Methods and systems operating in accordance with the present
invention implement a risk-based approach to healthcare treatment
and treatment planning by providing a healthcare provider with a
risk assessment tool located at the provider's site. During an
examination of a patient, the healthcare provider inputs a variety
of clinical data and data reflecting the patient's behavior into a
risk assessment tool. The tool computes a value of risk that
reflects relationships between the inputs, and is effective for
predicting an appropriate treatment plan. A healthcare provider
develops a treatment plan, taking into account the patient's
symptoms and the computed risk value. The healthcare system
analyzes the plan and evaluates its appropriateness.
[0036] Once a treatment plan has been implemented, the healthcare
system analyzes the outcomes of treatment as a measure of the
effectiveness of the plan. An effectiveness rating reflects a
measure of the actual outcome of treatment against the expected
outcome of the treatment. A value representing the effectiveness of
a particular course of treatment is fed back into the risk
assessment tool, thereby impacting future computations of risk to
allow the system to increase its accuracy in determining risk over
time.
[0037] Implementation Details
[0038] FIG. 1 depicts a data processing system 100 suitable for
practicing methods and systems consistent with the present
invention. Data processing system 100 includes client computer 105
connected to server computer 110 via network 115. Client computer
105 includes a memory 122, a secondary storage device 124, a
central processing unit (CPU) 126, an input device 128, and a video
display 130. A program 132, performing organization management and
administrative functions, operates in memory 122. The program is
suited to the particular client computer. For example, if the
client computer is situated in a healthcare provider's office, the
program may be directed to managing the healthcare provider's
practice, including scheduling and tracking patients. In the
secondary storage device 124 resides a database 134 containing a
subset of a database maintained at the server computer and
including the subset of data needed by a particular client computer
105. For example, if the client computer 105 is situated in a
healthcare provider's office, database 134 may contain patient
records.
[0039] A healthcare server computer 110 includes a memory 138, a
secondary storage device 140, a CPU 142, an input device 146, and a
video display 148. Memory 138 includes administrative software 149
and a healthcare system 150. Administrative software 149 includes,
for example, a module that coordinates access to the healthcare
server computer 110 by various client computers 105. A healthcare
system 150 implements a preventive scheme of treatment planning
where treatment reflects a computed value of risk, updated
regularly by the system to ensure accuracy. Secondary storage
device 140 includes a database 152 that may be accessed by any of
the client computers having appropriate authorization. The ability
of a system consistent with the present invention to distribute
database information among various computers in various locations
further supports maintenance of a central repository of patient
health data, reflecting data collected by various healthcare
providers during the patient's life, accessible to authorized
entities.
[0040] The client computers 162 and 164 are similarly configured to
client computer 105. These computers may be located at an insurance
company or a research organization, and may perform a variety of
data analysis functions. For example, if located at an insurance
company, a client computer may gather and analyze data collected by
the healthcare system for the purpose of determining economically
viable treatment alternatives for various conditions and levels of
risk. Similarly, a client computer at an insurance company may
assess outcomes information collected by the healthcare system to
compare methods and results of treatment planning among providers
to determine which providers to approve. A client computer at a
research organization may perform a similar function of comparing
data collected by the healthcare system to determine treatment
planning trends and provide suggestions regarding effective
treatment plans for specified conditions and levels of risk.
[0041] One skilled in the art will appreciate that client computer
105 and healthcare server computer 110, although depicted with
various components, may contain additional or different components.
Additionally, network 115 may include a wide area network, like the
Internet, or a local area network. Furthermore, although aspects of
the present invention are described as being stored in memory, one
skilled in the art will appreciate that these aspects can also be
stored on or read from other types of computer-readable media, such
as secondary storage devices, like hard disks, floppy disks, a
CD-ROM, or other forms of RAM or ROM. Still further, one skilled in
the art will appreciate that databases 134 and 152 and
administrative software 149 may be stored on or distributed across
other devices on the network 115.
[0042] FIG. 2 depicts a flowchart of the steps performed by the
healthcare system. An exemplary embodiment is described below
relative to a dentist although one skilled in the art will
appreciate that the present invention can be used in other
health-related or non-health-related fields. Each of the steps of
FIG. 2 will be discussed in greater detail relative to the
discussion of FIGS. 3-7.
[0043] First, the healthcare system receives diagnostic information
collected by a healthcare provider during an examination (step
202). This information is provided by a healthcare provider. The
term "diagnostic information" refers to (1) clinical data observed
and measured by a healthcare provider, and (2) personal health
history information. The diagnostic information captures data
reflecting a patient's overall health situation, including
physical, current and historical environmental conditions, events,
and behaviors.
[0044] The clinical data reflects clinical conditions or behaviors,
either existing, or found to have an increased risk of occurring,
that justify treatment beyond routine maintenance as determined by
a healthcare provider during an examination of a patient. A
healthcare provider determines clinical conditions by taking
measurements and making observations. For example, swelling,
appearance, and thermal sensitivity of a surface growth may be
observed and measured by a provider during an examination. Table 1
lists an exemplary set of clinical data considered by the
healthcare system. The system initializes the value of each
clinical condition to 0 indicating that the clinical condition does
not exist.
[0045] If a patient has a specified clinical condition, the
healthcare system quantifies the condition by assigning it an
appropriate value according to the values listed in Table 1. The
"site" column in Table 1 receives input identifying a location,
i.e., a tooth or segment of the mouth, in which the condition
exists. The six segments considered by the healthcare system are
discussed below relative to step 318 of FIG. 3.
1TABLE 1 Clinical Conditions Yes No Site 1. Pain, discomfort,
thermal sensitivity 1 0 2. Swelling, infection 1 0 3. Unacceptable
appearance 1 0 4. Caries 1 0 5. Pulpitis or necrosis 1 0 6
Fractures of clinical crown 1 0 7. Limited embrasure space 1 0 8.
Inadequate remaining tooth structure 1 0 9. Missing teeth 1 0 10.
Restoration with inadequate retention or 1 0 physiologic design 11.
Retained deciduous teeth 1 0 12. Prostheses with inadequate
retention or 1 0 physiologic design 13. Attrition 1 0 14. Erosion 1
0 15. Inadequate access to sound tooth structure 1 0 16. Root
fractures 1 0 17. Root proximity 1 0 18. Mobility 1 0 19.
Periodontal inflammation 1 0 20. Pathologic sulcus deeper than 5mm,
pocket depth 1 0 21. Radiographic evidence of disease, bone loss 1
0 22. Inadequate attached gingiva 1 0 23. Aberrant frena 1 0 24.
Oral lesion, non-periodontal 1 0 25. Inadequate oral hygiene 1 0 NA
26. Non-physiologic bone/gingiva architecture 1 0 27. Malocclusion
- inter-arch tooth alignment 1 0 28. Malocclusion due to
restoration or prosthesis 1 0 29. Tooth position - intra-arch 1 0
30. TMJ dysfunction 1 0 NA 31. Athletic participation 1 0 NA 32.
Impacted teeth 1 0 33. Skeletal or mucosal abnormalities 1 0 34.
Abnormal growth (soft or hard tissue) 1 0 35. Oral habit 1 0 NA 36.
Tobacco use 1 0 NA 37. Diet or eating disorder 1 0 NA 38. Bleeding
1 0 NA 39. Numbness or paresthesia 1 0 NA 40. Inadequate bone
volume 1 0 41. Furcation 1 0
[0046] The term personal health history refers to a standard set of
personal health information collected by a healthcare provider. For
example, information reflecting a patient's current and/or past
medications, laboratory test results, behaviors, environmental
exposures, and family health history may be gathered by a
healthcare provider during an examination of a patient. Table 2,
below, lists an exemplary set of personal health history data and
their corresponding values used by the healthcare system.
2 TABLE 2 Personal Health History Value 1. Parental history of
periodontitis None 0.8 Had periodontitis 1.3 Had tooth loss due to
periodontitis 1.4 Unknown 1 2. Patient's history of diabetes Do not
have 1 Controlled diabetic 1 Uncontrolled diabetic 1.1 Unknown 1 3.
Patient's use of cigarettes Don't use cigarettes 1 Smoke less than
10 per day 1.2 Smoke more than l0 per day 1.3 Unknown 1 4. Number
of Annual Professional Cleanings 1 (or less) per year 0 2 per year
-4 3 (or more) per year -10
[0047] After receiving the diagnostic information, the healthcare
system computes a risk value that reflects a likelihood that a
condition will occur (step 204). In this step, the system
quantifies a subset of the diagnostic information collected
relative to step 202. The subset of the diagnostic information
quantified to compute a value of risk includes the set of data
indicated by scientific data analysis and study as having an impact
on a patient's likelihood of developing disease and being
responsive to treatment. Because the data used to compute a risk
value is a subset of diagnostic information regularly collected by
a healthcare provider, the set of data considered in computing the
risk value may be changed without altering the design of the
healthcare system, i.e. introducing additional variables or risk
factors. The computation of a risk value also considers data
generated during a risk adjustment process, described below with
respect to step 216. This computed risk value contributes to the
decision process of treatment planning. "Treatment restrictions" is
a term used to refer to limitations of treatment that may be
imposed, as necessitated by the computed value of risk. For
example, an aggressive, highly unpredictable procedure may not be
appropriate for a high risk patient. Accordingly, the aggressive,
highly unpredictable procedure serves as a treatment restriction
for that patient. Treatment restrictions may be considered by a
healthcare provider, a healthcare payor (e.g., an insurance
company), or a healthcare policy maker in determining the
appropriate limitations of treatment. A healthcare payor or policy
maker may further consider these restrictions when determining
which benefits are allowable under particular benefit plans.
[0048] The healthcare system then receives information reflecting
patient and provider objectives of care, and information reflecting
events, behaviors, or conditions that may adversely impact the
success of a treatment plan (step 205).
[0049] Patient and provider objectives include data reflecting the
patient's and provider's objectives regarding treatment. Patient
objectives reflect certain of a patient's present and intended
behaviors and the patient's objectives in obtaining treatment.
Provider objectives reflect certain of a provider's objectives in
assigning a course of treatment. By considering the patient and
provider objectives of a treatment plan, the treatment plan may be
tailored to meet those objectives, thereby increasing its
subjective utility. Table 3 categorizes and lists an exemplary set
of patient and provider objectives, and the values assigned to each
by the healthcare system. The healthcare system initializes the
value of each objective to 0.
3TABLE 3 Objectives of Treatment Yes No Patient Objectives 1.
Improve current function (chewing, eating) 1 0 2. Improve current
comfort 1 0 3. Improve current appearance 1 0 4. Repair broken or
diseased structures 1 0 5. Prevention/Reduce risk of disease 1 0 6
Spread treatment over several years to reduce annual cost 1 0 7.
Out-of-pocket cost 1 0 8. Total cost 1 0 9. Minimize cost for
future treatment 1 0 10. Date treatment must be completed by 1 0
11. Appointments - number or time 1 0 12. Alleviate current pain 1
0 13. Control treatment pain and anxiety 1 0 14. Prevent tooth loss
1 0 Behavior Modification 15. Improve oral hygiene 1 0 16. Decrease
or eliminate tobacco use 1 0 17. Improve diet 1 0 18. Improve
exercise 1 0 19. Comply with prescribed drug regimen 1 0 20. Comply
with agreed upon treatment recommendations 1 0 Provider Objectives
- Clinical Characteristics - General 21. Improve appearance 1 0 22.
Create a physiologic occlusion 1 0 23. Resolve the non-periodontal
inflammatory lesion 1 0 24. Satisfy the patient's objectives 1 0
25. Improve function 1 0 26. Maintain health, appearance and
function 1 0 Provider Objectives - Clinical Characteristics -
Periodontal 27. Eliminate clinical signs of inflammation 1 0 28.
Reduce probing depths to less than 5 mm 1 0 29. Improve
accessibility for maintenance 1 0 30. Decrease mobility 1 0 31.
Enhance the zone of attached gingiva 1 0 32. Create adequate
clinical crown length 1 0 33. Slow the inflammatory lesion's
progression 1 0 Provider Objectives - Clinical Characteristics -
Restorative 34. Control caries progression 1 0 35. Create
restorations that have marginal integrity 1 0 36. Create
restorations that have physiologic form 1 0 37. Create restorations
that have proper contacts 1 0 38. Restore vertical dimension of
occlusion 1 0
[0050] The healthcare system also receives diagnostic information
that corresponds to tentative factors that may adversely affect
treatment predictability. By considering these factors during the
treatment planning process, a treatment plan will account for these
factors and is therefore more likely to achieve its expected
result. Table 4, below lists an exemplary set of tentative factors
considered by the healthcare system. The healthcare system
initializes the value of each adverse factor to 0.
4TABLE 4 Adverse Factors Yes No Site 1. Tobacco use 1 0 NA 2. Poor
oral hygiene 1 0 NA 3. Oral habits 1 0 NA 4. Occlusal stress,
bruxism 1 0 NA 5. Diet or eating disorder 1 0 NA 6 Systemic disease
or its treatment 1 0 NA 7. Xerostomia 1 0 NA 8. Radiation therapy 1
0 NA 9. Hormonal changes 1 0 NA 10. Susceptibility to periodontal
disease 1 0 NA 11. Susceptibility to caries 1 0 NA 12. Presence of
pathogenic bacteria 1 0 NA 13. Friable or poor tissue quality 1 0
NA 14. Thin periodontium 1 0 15. Poor healing capacity 1 0 NA 16.
Mental illness or impairment 1 0 NA 17. Physical impairment 1 0 NA
18. Access problems 1 0 19. Anatomical limitations 1 0 20. Tooth
position 1 0 21. Limited embrasure space 1 0 22. Root proximity 1 0
23. Inadequate remaining tooth structure 1 0 24. Canal morphology 1
0 25. Calcified canals 1 0 26. Bone architecture 1 0 27. Excessive
edentulous span length 1 0 28. Crown to root ratio 1 0 29. Root
morphology 1 0 30. Structural strength of the tooth 1 0 31. Bone
volume 1 0 32. Bone quality 1 0 33. Tooth mobility 1 0 34.
Inadequate anchorage 1 0 NA 35. Inter-occlusal space 1 0 36.
Occlusion 1 0 NA
[0051] The healthcare system then receives a proposed treatment
plan, input into the system by a healthcare provider, determines
whether the plan is appropriate, and predicts its effectiveness
(step 206). The proposed treatment plan includes treatment for
healing existing conditions and preventing the occurrence of future
conditions. In proposing a plan, a healthcare provider considers
the computed risk value, the diagnostic information, treatment
restrictions, the patient and provider objectives, and any factors
that may adversely affect treatment. The plan is input into the
healthcare system as a series of treatment codes that correspond to
types of treatment.
[0052] After receiving a proposed treatment plan, the healthcare
system analyzes the series of treatment codes to determine whether
the plan is appropriate. During analysis of a plan, the healthcare
system compares the proposed plan with a set of treatment codes
representing an appropriate treatment plan as determined by the
healthcare system. Although the healthcare system does not provide
a proposed plan, it evaluates each of the relevant treatment codes
relative to the diagnostic data received and risk value computed to
determine if a particular treatment is appropriate. The healthcare
system determines the appropriateness of a plan by analyzing data
contained in its series of tables.
[0053] For example, the healthcare system includes a database that
maintains a list of objectives matched with clinical conditions,
and a database of clinical conditions matched with appropriate
treatment. During analysis of the plan, the healthcare system
determines whether each objective corresponds to a clinical
condition and whether each clinical condition corresponds to an
appropriate treatment, and vice versa. The healthcare system also
includes a database that maintains a list of treatment codes,
matched with conditions, or factors, that may reduce the
predictability of treatment. The healthcare system includes a
predetermination section that notifies a healthcare provider and an
insurance company if a proposed plan fails to appropriately address
objectives, clinical conditions, and treatment. Therefore, for
example, if a proposed plan includes a treatment that is matched
with a condition that has been flagged as potentially reducing the
predictability of treatment, the healthcare system may alert a user
accordingly.
[0054] After reviewing the healthcare system's analysis of a plan,
a healthcare provider may propose a different plan. For example, if
the healthcare system suggests that a plan fails to address a
clinical condition, the healthcare provider may propose a different
plan. Similarly, if the healthcare system suggests that a plan
fails to address a patient's objectives, the healthcare provider
may propose a different plan. The new plan may reflect differing
objectives of treatment, from either the patient or provider. The
healthcare system analyzes the new plan in the same manner as it
analyzed the previous plan. Once the patient and provider decide
that a plan is acceptable, it may be administered to a patient.
[0055] Before being administered, a treatment plan is authorized by
the appropriate entities, including a patient or patient's
guardian, and an insurance company or other entity responsible for
payment of the treatment. A patient agrees to a course of treatment
and any associated risks thereof. The treatment plan provided for
each patient therefore includes customized patient consent forms,
detailing the treatment plan and its associated risks.
[0056] After an approved treatment plan has been administered, the
healthcare system compares the actual outcomes of treatment to the
expected outcomes of treatment to evaluate the effectiveness of the
treatment plan (step 208). In this step, the healthcare system
provides information about how well a treatment plan met its
objectives, the effectiveness of treatment planning decisions, the
effectiveness of treatment in preventing additional treatment, and
the accuracy of risk assessment. An exemplary patient outcomes
assessment report is included in Table 7, below.
[0057] The healthcare system computes outcomes assessment data for
each group of 200 patients, thereby assisting healthcare providers
in tracking their performance on an ongoing basis. Therefore, after
evaluating the effectiveness of the treatment plan, the healthcare
system determines whether more than 200 patients for a specific
healthcare provider have been analyzed by the system (step 210). If
so, the healthcare system calculates outcomes assessment data for
the healthcare provider using the outcomes assessment data for a
provider using the outcomes assessment data for each patient,
groups it by percentile, and reports the information to the
provider (step 212). Additional details of the outcomes assessment
data computed by the healthcare system are described relative to
the discussion of FIG. 5, below. An exemplary provider outcomes
assessment report is included in Table 8, below. This provider
outcomes assessment information may be used to determine a
provider's overall standard of care. If less than 200 patients have
been analyzed by the system, processing continues to step 202.
[0058] The healthcare system periodically adjusts, or updates
computed risk values to increase the accuracy of the system's
computation of risk values. Risk values are updated by the system
for each set of 300 patients. Thus, after performing outcomes
assessment, the healthcare system determines whether the next group
of 300 patients, regardless of provider, has been analyzed by the
system (step 214). If not, processing continues to step 202.
Otherwise, the healthcare system uses the patient outcomes
assessment information to automatically update, i.e., adjust, the
risk value computed during risk assessment (step 216). The risk
update process re-calculates a computed risk value. The risk update
process re-calculates each patient's computed value of risk to make
it consistent with both the actual risk for all patients, based on
values derived from the patient and provider outcomes assessment,
and the patient's risk as determined by outcomes associated with
previous treatment received by that patient. This re-calculation
considers the value of risk adjustment factors that represent
trends in risk values among all patient. Additional details of the
risk adjustment factors are described below relative to the
discussion of FIG. 7.
[0059] This risk adjustment represents one way the healthcare
system evolves over time. For example, during the lifetime of a
patient the patient's risk value changes due to changes in both,
e.g., the patient's diagnostic information and changes in the risk
update factors. By updating the risk value for each patient each
time an additional 300 patients receive treatment plans approved by
the system, the system increases the accuracy of both the risk
update factors and the overall computation of a risk value.
[0060] FIG. 3 depicts, in greater detail, a flowchart of the steps
performed by the healthcare system when performing risk assessment,
as described in step 204 of FIG. 2. First, the healthcare system
receives from a healthcare provider diagnostic data (step 310). The
healthcare system uses a subset of the diagnostic data to compute a
patient's risk value by considering the sum of three components of
risk, including (1) systemic risk, (2) exposure risk, and (3)
experience risk. The subset of data used to compute risk includes
the data determined by scientific analysis to be relevant in
assessing a person's risk of contracting a disease, or being
responsive to treatment.
[0061] Systemic risk reflects a measure of a patient's overall risk
and is not related to a particular segment of the mouth, as are
exposure and experience risk. The healthcare system computes
systemic risk as the product of a pre-defined standard risk value,
parental history of periodontitis, patient's history of diabetes,
and patient's use of cigarettes, all listed in Table 2 as personal
health history information (step 314). The healthcare system
converts this computed value to an integer that represents the
systemic risk. For example, if the computed value for systemic risk
was 19.2 or 19.9, the healthcare system uses 19 as the systemic
risk value. The standard risk value used to compute systemic risk
is initially set to 15 by the healthcare system.
[0062] The healthcare system then calculates exposure and
experience risk values for the six segments of the mouth, including
(1) maxillary and first and second molars and maxillary first
premolars, (2) mandibular first and second molars, (3) maxillary
incisors, (4) mandibular incisors, (5) mandibular first and second
premolars and maxillary second premolars, and (6) canines (step
318). If no teeth are contained in a segment then the total risk
value for that segment is set to zero. The healthcare system's
breakdown of risk values by segment allows for precise calculation
of risk, thereby supporting targeted treatment planning.
[0063] To calculate exposure risk for a segment, the healthcare
system first computes, e.g., the sum of the values corresponding to
periodontal inflammation, pocket depth multiplied by 2, bone loss
multiplied by 4, and mobility times 6. The value for each of these
data points may be found in items 19, 20, 21, and 18 of Table 1,
respectively. This value ranges between 0 and 13. The healthcare
system then converts the value to a 0 to 50 scale by multiplying
the computed value by, e.g., 3.84. To this value, the healthcare
system then adds e.g., the value of the furcations, item 41 of
Table 1, and the value reflecting annual professional cleanings,
item 4 of Table 2. This sum represents the exposure risk and is
done for each segment. An example that uses this series of
calculations to compute an exposure risk value is provided below,
relative to the discussion of Table 6B. If the value of the
exposure risk is less than 0, the healthcare system uses 0 as the
value for exposure risk.
[0064] The healthcare system assigns an experience risk value to
each segment based on the periodontal breakdown values received as
diagnostic data. Specifically, the healthcare system determines
this value by counting the number of years since the last entry in
Table 1 reflecting pathologic sulcus deeper than 5 mm, item 20, or
radiographic evidence of disease, item 21. Table 5 lists an
exemplary set of values the healthcare system assigns to different
levels of periodontal breakdown.
5TABLE 5 Periodontal Breakdown Value bone loss has occurred or if
bone level maintained <2 years 10 bone level maintained >2
years and <5 years 0 bone level maintained >5 years and
<10 years -10 bone level maintained .gtoreq.10 years -20
[0065] After determining the exposure and experience risk values
for each segment containing teeth, and the systemic risk value of a
patient, the healthcare system computes the patient's total risk
according to the following formula (step 322):
[0066] (systemic risk-.vertline.systemic risk.vertline.* "sys")+
(exposure risk-.vertline.exposure risk.vertline. * "xpos")+
(experience risk-.vertline.experience risk.vertline. * "xper")+
"totalnew" where "sys," "xpos," "xper," and "totalnew" are
variables representing risk adjustment factors. Each of these
variables is discussed below with respect to FIG. 7, relative to
the discussion of updating risk values. If the total risk value is
less than 0, the healthcare systems uses 0 for the total risk
value.
[0067] The healthcare system ranks a computed risk value as low,
moderate, or high. The initial value for low risk is 20 or less,
moderate risk is between 20 and 40, and high risk is any value
greater than 40. The low, moderate and high ranges are updated
according to the risk values for all patients considered by the
healthcare system as follows: low risk includes values in the
bottom 20% of all patients' risk values, moderate risk includes
values in the 20-80% range of all patients' computed risk values,
and high risk includes values in the upper 20% of all patients'
risk values.
[0068] Tables 6A and 6B, below, provide values for an exemplary set
of risk data that may be processed by the healthcare system. Table
6A reflects personal health history information, i.e., the data
used to compute a patient's systemic risk value. By using the
formula discussed above the healthcare system computes this
patient's systemic risk value as 19.
[0069] Table 6B reflects an exemplary set of clinical data for each
segment that may be used to compute a patient's exposure and
experience risk values. The risk calculation indicates that this
patient has a total risk value of 21. The value of 21 is obtained
by adding the risk values for the six segments and dividing the sum
by the number of segments having a risk score greater than 0. This
patient's risk values for each segment are as follows: segment
1-exposure 22, experience 10, total risk 51; segment 2-exposure 7,
experience -20, total risk 6; segment 3-exposure 0, experience -20,
total risk 0; segment 4-exposure 0, experience -20, total risk 6;
segment 5-exposure 7, experience -20, total risk 6; segment
6-exposure 0, experience -20, total risk 0.
6TABLE 6A Personal Health History Information Data Point Response
Value parental history of peridontitis unknown 1 patient's history
of diabetes none 1 patient's use of cigarettes >10 per day 1.3
number of professional cleanings per year 2 per year -4
[0070]
7TABLE 6B Clinical Data Per Segment Data Point Segment 1 Segment 2
Segment 3 Segment 4 Segment 5 Segment 6 Number of teeth 6 4 4 4 6 4
Presence of Inflammation 1 1 0 1 1 1 Pocket Depth ("0" if 1 1 0 0 1
0 < 5 mm; "1" if > 5 mm) Bone Loss ("0" if < 2 mm; 1 0 0 0
0 0 "1" if > 2 mm) Existence of Mobility 0 0 0 0 0 0 ("0" if
.ltoreq. 1 degree; "1" if > 1 degree) Existence of Furcations 0
0 0 0 0 0 ("0" if none; "0" if < grade 1, "10" if > grade 1)
Peridontal Breakdown 0 3 3 3 3 3 ("0" if bone loss or bone
maintained < 2 years; "1" if bone level > 2 years + < 5
years; "2" if bone level > 5 years + < 10 years; "3" if bone
level > 10 years
[0071] FIG. 4 depicts, in greater detail, a flowchart of the steps
performed by the healthcare system when performing guided treatment
planning, as described relative to step 206 of FIG. 2. First, the
healthcare system receives a risk value (step 410). A healthcare
provider uses the risk value e.g., to determine a treatment plan.
The risk value is used in this process to determine the best
treatment according to both the patient's diagnostic data and the
computed risk value. Treatment restrictions refer to limitations on
treatment based on the patient's risk value, and considering the
patient's diagnostic information. A healthcare provider considers
treatment restrictions when developing a treatment plan (step
414).
[0072] The healthcare provider then provides a treatment plan,
reflecting treatment restrictions, to the healthcare system (step
422). Continuing with the exemplary set of risk data listed in
Tables 6A and 6B above, suppose the dentist also collects the
following diagnostic data during the examination:
[0073] (1) clinical conditions-heavily filled teeth 2,3, and 4;
root proximity associated with teeth 2 and 3; and generalized
periodontal breakdown including pocket depth measurements greater
than 5 mm (Table 1, items 6, 17, 19, 20, 21, and 26);
[0074] (2) patient objectives-to fix dental problems while avoiding
extraction of any teeth (Table 3, items 4 and 14)
[0075] (3) provider objectives-to satisfy patient objectives;
eliminate clinical signs of inflammation; reduce probing depths to
less than 5 mm; and create restorations having marginal integrity,
physiologic form and proper contacts (Table 3, items 24, 27, 28,
35, 36, and 37);
[0076] (4) adverse factors-susceptibility to periodontal disease;
limited embrasure space; root proximity; and bone architecture of
the segment including teeth 2 and 3 (Table 4, Items 10, 21, 22, and
26).
[0077] The dentist may propose the following treatment plan: root
planing for all teeth, osseous surgery for the segment including
teeth 2-5, endodontics for tooth 3, and crowns for teeth 24.
[0078] After, the provider proposes a plan, the healthcare system
analyzes the plan and provides a report of its analysis (step 426).
The analysis considers the feasibility of the treatment plan
proposed by the provider, given the limitations associated with the
patient's risk value, insurance coverage, and diagnostic
information. The system ensures that objectives correspond to
clinical conditions and vice versa. It also ensures that all
clinical conditions are addressed in the treatment plan. The
healthcare system also identifies additional adverse affects
associated with a proposed treatment plan. Finally, the healthcare
system provides information about the patient's and provider's
prior predictive history regarding effectiveness, adverse effects,
and longevity. This analysis information may be used to help a
patient or provider adjust their objectives in order to adopt a
treatment plan that is likely to yield the most effective long-term
impact at the lowest cost. The healthcare system then prints a
detailed report, analyzing the plan. The report lists the
diagnostic information, adverse effects, estimated cost, and
predictions regarding treatment effectiveness, adverse effects,
longevity, and urgency of treatment.
[0079] Continuing further with the example, suppose the healthcare
system determines that the patient's insurance company would not
approve a bone graft procedure because of the patient's moderate
risk score. The healthcare system provides a report indicating
reasons why the proposed treatment may not be the most appropriate
treatment. For example, the report indicates the adverse factors
that reduce the likelihood of success of the osseous surgery and
crown procedures. It also indicates that although root planning was
done twice in the last six years, neither the surgery nor the
crowns were done in the sites listed. The report may further
indicate that the proposed treatment plan fails to address a root
proximity condition and that the proposed endodontic treatment does
not relate to a clinical condition. The report may further identify
additional potential adverse effects of the proposed treatment plan
to include pulpitis, thermal sensitivity, swelling, or infection,
and an increased susceptibility to caries and periodontal
disease.
[0080] After reviewing the report, if the patient and provider
decide that the plan is not acceptable, they may change their
objectives and develop a new treatment plan. Suppose the new
objectives add prevention (objective 5), including a minimization
of cost for future treatment (objective 9), and eliminate the
avoidance of tooth extraction (objective 14). The new treatment
plan proposes extracting tooth 3 and performing a prosthetic
replacement with a three-unit fixed bridge. The healthcare system
analyzes the new plan and provides a new report. After reviewing
the report of the most recent proposed treatment plan, the patient
and doctor tentatively agree that the plan is acceptable in meeting
the objectives of care.
[0081] After reviewing the healthcare system's analysis of a plan,
a healthcare provider predicts the effectiveness of the treatment
plan by answering value assessment questions (step 432). The term
"value assessment" refers to data indicating a provider's
predictions about a treatment plan.
[0082] During value assessment the healthcare system receives input
from a healthcare provider indicating the provider's predictions
regarding the outcomes of the proposed treatment, including
urgency, longevity, adverse effects, and effectiveness. Urgency
reflects the length of time (in years) treatment may be postponed
before the proposed treatment plan becomes invalid, or treatment
predictability reduces. Longevity reflects the probability that
treatment will last for a specified time period (in years) before
additional treatment is needed. Adverse effects reflects the
probability that treatment not included in the proposed treatment
plan will be needed because the effects of treatment contribute to
a new condition. Effectiveness reflects the probability that
treatment will result in meeting the objectives of care. The
healthcare system displays a value assessment report that includes
the objectives of treatment, proposed treatment plan, the
predictive value assessment values, and the cost of the plan.
[0083] After a provider makes predictions about the plan through
the value assessment process, the patient and provider determine
whether the plan is acceptable (step 436). A plan may be considered
acceptable if, for example, it meets the patient and provider
objectives of treatment. If the plan is not acceptable, processing
continues to step 422; otherwise, processing continues to step
440.
[0084] Once the patient and provider agree upon a treatment plan,
the plan, along with information related to a patient's symptoms
and an analysis of the plan is analyzed by the healthcare system
for insurance company pre-authorization of payment (step 440). The
pre-authorization may be done local to the healthcare server. The
server contains a database of authorized treatments for a
condition, given a specified level of risk and a set of diagnostic
information. Once a plan has been pre-authorized, the healthcare
system provides a report of the plan, along with a customized
consent form, for the patient to review and sign upon acceptance
(step 446).
[0085] During treatment, a healthcare provider reports to the
healthcare system all treatment administered to a patient. This
information is maintained by the system and referred to as
"treatment records."
[0086] FIG. 5 depicts, in greater detail, a flowchart of the steps
performed by the healthcare system when performing patient outcomes
assessment, as described relative to step 208 of FIG. 2. A
healthcare provider may request an outcomes assessment report,
shown below in Table 7, on a per-patient basis, as desired. An
outcomes assessment report provides outcomes assessment data that
may be useful to a patient or a provider in determining the
effectiveness of a treatment plan. Generally, an outcomes
assessment is most valuable if done 1-2 years after a patient has
received treatment.
[0087] First, the healthcare system receives value assessment
information from a provider (step 518). As discussed above,
relative to the discussion of FIG. 4, the value assessment data
collected by the healthcare system corresponds to data reflecting
the following: (1) the probability that a treatment plan will meet
the objectives of care, (2) the probability that a treatment plan
will yield the need for subsequent treatment for the same or a
related condition, and (3) the expected length of time the
treatment will last. After receiving the value assessment data, the
healthcare system receives updated information reflecting whether
patient and provider objectives, listed in Table 3, were met (step
522). For each objective listed in Table 3 corresponding to an
affirmative response as indicated in a treatment plan that was
approved by a patient and a healthcare provider, the patient (or
provider) indicates whether treatment did or did not meet the
objective; the patient (or provider) may also indicate an "unsure"
response. Each of the "yes," "no," and "unsure" responses is
assigned a value of, e.g., "1." The healthcare system calculates
the sum of the "yes," "no," and "unsure" values and divides the sum
for each category by the sum of the total initial "yes," "no," and
"unsure" values.
[0088] The healthcare system then receives updated clinical
information, listed in Table 1, reflecting a patient's current
condition (step 526). The healthcare system captures this
information during outcomes assessment to track a patient's
progress. Ideally, a patient's clinical conditions, reported during
outcomes assessment, will not be the same as the clinical
conditions that fed into a treatment plan.
[0089] Next, the healthcare system receives information
corresponding to treatment records (step 528). The treatment
records include a comprehensive listing of treatment received by a
particular patient. Each time a provider administers treatment to a
patient, the treatment record for that patient is updated.
[0090] After collecting the data related to patient and provider
objectives, patient clinical conditions, and treatment records, the
healthcare system computes a validation score, reflecting the
healthcare system's assessment of the accuracy of the data
collected for a subset of the objectives, for example, 4-5, 15-17,
22-24, 26-28, 30-32, and 35-37 listed in Table 3 (step 530).
[0091] The validation score is computed by a validation procedure
that determines whether patient and provider responses are
consistent. The validation procedure compares data corresponding to
(1) the clinical conditions at the time of outcomes assessment, (2)
the treatment provided, as reported by a provider during treatment,
and (3) values representing assessment scores of the objectives
listed in Table 2 and determined in step 518 above. All of the
relevant objectives are given initial validation scores of "zero."
If the patient or provider outcomes assessment value for a
particular objective corresponds to "unsure" or "no," the
healthcare system assigns that objective a validation score of
negative one; if the outcomes assessment value for a particular
objective is "yes," then the system assigns that objective a
validation score of positive one. For example, if all planned
treatment was administered to a patient then the validation score
corresponds to plus one; if all planned treatment was not
administered, the validation score corresponds to negative one.
[0092] Once the healthcare system determines a validation score for
each objective, it computes the sum of the validation scores and
divides the sum by the number of validation scores having a value
of either positive one or negative one, to convert the scores to a
range between negative one and positive one.
[0093] The system then computes the other outcomes assessment
scores and provides a report of the values (step 534). The outcomes
assessment report provides information reflecting how the actual
outcomes of a treatment plan compare to the expected outcomes of
the plan. Table 7 lists an exemplary outcomes assessment report
provided by the healthcare system. Items 8a-8c of Table 7 are
computed as the sum of the number of periodontal surgical
procedures for each tooth site, less 1 if the number of procedures
is greater than 0. Item 8b represents this value, based on the
number of periodontal procedures performed over the previous 10
years. Item 8c represents this value based on the numbers of
periodontal procedures performed over the previous 5 years.
Similarly, items 9a-9c are computed as the sum of the number of
restorative and prosthetic treatments performed less 1 (if the
number is greater than 0), in total, during the previous 10 years,
and during the previous 5 years, respectively.
[0094] Items 12, 13, and 14 reflect aspects of a healthcare
provider's assessment of a proposed treatment plan. Item 12
reflects a provider's assessment of how well a treatment plan will
meet the objectives of care. The healthcare provider inputs this
information in the form of a percentage. An exemplary set of values
may be assigned as follows:
[0095] "1" if 85% or more of all objectives have an assessment of
"yes" (Table 7, item 1) and the probability of a treatment plan
meeting the objectives of care is moderate or low.
[0096] "0" if 85% or more of all objectives have an assessment of
"yes" (Table 7, item 1) and the probability of a treatment plan
meeting the objectives of care is very high or low; or, if 75%-85%
of all objectives have an assessment of "yes" and the probability
of a treatment plan meeting the objectives of care is high; or, if
65-85% of all objectives have an assessment of "yes" and the
probability of a treatment plan meeting the objectives of care is
moderate.
[0097] "-1" in all other situations.
[0098] Item 13 reflects the probability that a treatment plan will
yield a need for subsequent treatment of the same or a related
condition. The values for this data point may be determined as
follows:
[0099] "1" if the value assessment information provided by a
healthcare provider (in step 518 of FIG. 5) is greater than 25% and
no treatment was needed.
[0100] "0" if the value assessment information provided by a
healthcare provider (in step 518 of FIG. 5) is between 10% and 25%
and no treatment was needed; or, if the value assessment
information provided by a healthcare provider (in step 518 of FIG.
5) is greater than 25% and treatment was needed.
[0101] "-1" in all other situations.
[0102] Item 14 reflects a healthcare provider's assessment of the
value reflecting the expected length of time treatment will last
(i.e. predictive longevity of treatment, entered in step 512 of
FIG. 5). Item 14 also considers the value of repeat periodontal
surgical procedures during a 5-year time period (Table 7, Item 8c).
The values for item 14 may be calculated, for example, as
follows:
[0103] "-1" if the healthcare provider is unsure whether treatment
lasted as long as expected;
[0104] "0" if the healthcare provider believes treatment will last
as long as initially expected, the predictive longevity of
treatment is less than 5 years, and the value for repeat
periodontal surgical procedures is greater than 0;
[0105] "1" if the healthcare provider believes treatment will last
as long as initially expected, the predictive longevity of
treatment is less than 5 years, and the value for repeat
periodontal surgical procedures is 0;
[0106] "-1" if the healthcare provider believes treatment will last
as long as initially expected, the predictive longevity of
treatment is greater than 5 years, and the value for repeat
periodontal surgical procedures is greater than 0;
[0107] "0" if the healthcare provider believes treatment will last
as long as initially expected, the predictive longevity of
treatment is greater than 5 years and the value for repeat
periodontal surgical procedures is 0;
[0108] "1" if the healthcare provider believes treatment will last
as long as initially expected and the predictive longevity of
treatment is greater than 10 years;
[0109] "-1" if the healthcare provider believes that treatment will
last as long as initially expected and the value of the predictive
longevity of treatment is "unsure";
[0110] "1" if the healthcare provider believes that treatment will
not last as long as initially expected and the predictive longevity
of treatment is less than 5 years; and
[0111] "-1" if the healthcare provider believes that treatment will
last as long as initially expected and the predictive longevity of
treatment is not less than 5 years.
8TABLE 7 PATIENT OUTCOMES ASSESSMENT SCORE NO. DESCRIPTION VALUE 1
Percentage of all objectives (yes) with an assessment of % yes.
Range 0-100%. 2 Percentage of all objectives (no) with an
assessment of % no. Range 0-100%. 3 Percentage of all objectives
(unsure) with an assessment % of unsure. Range 0-100%. 4 Percentage
of all patient objectives (#1-14 - yes) with an % assessment of
yes. Range 0-100%. 5 Percentage of all patient objectives (#1-14 -
no) with an % assessment of no. Range 0-100%. 6 Percentage of all
patient objectives (#1-14 - unsure) % with an assessment of unsure.
Range 0-100%. 7 Validation score. The sum of the validation scores
# divided by the sum of the count of scores equal to 1 and the
count of scores equal to -1. Range -1 to +1. 8a Total Periodontal
re-treatment score. # 8b Periodontal re-treatment score - last 10
years # 8c Periodontal re-treatment score - last 5 years # 9a Total
Restorative and prosthetic treatment time # interval score. 9b
Restorative and prosthetic treatment time interval score - # last
10 years. 9c Restorative and prosthetic treatment time interval
score - # last 5 years. 10 Questionable treatment choice score. The
dollar amount $ spent for sites that had endodontic, prosthodontic
or bone graft treatment prior to extraction of a tooth. This score
is the sum of dollar amount for all sites. 11 Questionable
treatment choice site list. The sites that are site included in the
questionable treatment choice score. list 12 Predictive history on
effectiveness. Values are -1, 0, +1 # with -1 = outcome worse than
expected; 0 = outcome as expected; +1 = outcome better than
expected. 13 Predictive history on adverse effects. Values are #
-1, 0, +1 with -1 = outcome worse than expected; 0 = outcome as
expected; +1 = outcome better than expected. 14 Predictive history
on longevity. Values are # -1, 0, +1 with -1 = outcome worse than
expected; 0 = outcome as expected; +1 = outcome better than
expected. 15 Composite predictive history. Sum of values for items
# 12, 13 and 14 above. Value range is -3 to +3 with <0 =
outcomes fell short of predictions; = 0, outcomes matched
predictions; >0 outcomes exceeded predictions
[0112] FIG. 6 depicts, in greater detail, a flowchart of the steps
performed by the healthcare system when performing provider
outcomes assessment, as described relative to step 212 of FIG. 2.
The healthcare system groups the patient outcomes assessment values
corresponding to items 1-6 and 8a-9c, shown above in Table 7, by
percentile range (step 602). The percentile ranges correspond to
the groupings marked "A," "B," and "C" in Table 8, below. Value "A"
represents the 50.sup.th percentile; value "B" represents the
70.sup.th percentile; and value "C" represents the 90.sup.th
percentile. The healthcare system provides a report of the computed
values to the relevant healthcare provider (step 610). This
information reflects a provider's effectiveness as determined
during patient outcomes assessment.
[0113] For items 7 and 12-15, the healthcare system computes the
percentage of outcomes assessment data values falling in the ranges
specified in Table 8. For example, "Value A" of item 7 corresponds
to the sum of all validation score values that are less than zero,
divided by the sum of all validation score values.
9TABLE 8 PROVIDER OUTCOMES ASSESSMENT DATABASE POAS Item # Value A
Value B Value C 1 50.sup.th percentile 70.sup.th percentile
90.sup.th percentile 2 50.sup.th percentile 70.sup.th percentile
90.sup.th percentile 3 50.sup.th percentile 70.sup.th percentile
90.sup.th percentile 4 50.sup.th percentile 70.sup.th percentile
90.sup.th percentile 5 50.sup.th percentile 70.sup.th percentile
90.sup.th percentile 6 50.sup.th percentile 70.sup.th percentile
90.sup.th percentile 7 % <0 % =0 % >0 8a 50.sup.th percentile
70.sup.th percentile 90.sup.th percentile 8b 50.sup.th percentile
70.sup.th percentile 90.sup.th percentile 8c 50.sup.th percentile
70.sup.th percentile 90.sup.th percentile 9a 50.sup.th percentile
70.sup.th percentile 90.sup.th percentile 9b 50.sup.th percentile
70.sup.th percentile 90.sup.th percentile 9c 50.sup.th percentile
70.sup.th percentile 90.sup.th percentile 12 % (-1) % (0) % (+1) 13
% (-1) % (0) % (+1) 14 % (-1) % (0) % (+1) 15 % <0 % =0 %
>0
[0114] The healthcare system tabulates provider outcomes assessment
information in two categories (1) for each set of 200 patients
associated with a provider, and (2) for all patients associated
with a provider. If a provider has more than 200 patients, each set
of 200 patients' outcomes assessment values will be contained in a
separate table. Thus, if a provider has 601 patients, four tables
are computed during provider outcomes assessment: one table is
computed for each group of 200 (including data for patients 1-600),
and one table is computed for the entire group of 600. Data related
to patient 601 is contained in a provider outcomes assessment
report done after the provider treats an additional 199
patients.
[0115] FIG. 7 depicts, in greater detail, a flowchart of the steps
performed by the healthcare system when it adjusts a computed risk
value as described in step 216 of FIG. 2. For each patient having a
risk value and outcomes assessment data, the healthcare system
updates, or adjusts the patient's risk value by: (1) computing an
adjustment factor for each of the three categories, systemic,
exposure, and experience risk by comparing the previously computed
risk values, which are predictions, with quantified values
reflecting actual occurrences at sites that required more than one
surgical treatment (item 8a of Table 7), and (2) adding quantified
values for new factors to be included in the computation of a risk
value. Values of the diagnostic data, including patient health
history, drugs and medications, and laboratory reports may all be
considered in identifying new factors to include in the computation
of a risk value.
[0116] To compute a risk adjustment factor, the healthcare system
first ranks the sum of each patient's number of repeat periodontal
surgical procedures (step 710). To rank the repeat periodontal
surgical procedures, the healthcare system classifies the repeat
periodontal surgical procedure values for each patient listed in
the database by deciles, or groups of 10 percentiles. Next the
healthcare system ranks the systemic, exposure, and experience risk
values for each patient in terms of deciles, or groups of 10
percentiles (step 714). Then, the healthcare system computes the
risk adjustment factors for systemic risk, "sys," exposure risk,
"xpos," and experience risk, "xper," by subtracting the rank for a
patient's repeat periodontal surgical procedure value from the rank
for the patient's risk value for each category of risk (systemic,
exposure, or experience), and converting it to a number ranging
from (-.3) to (+.3) (step 718). This process must be done three
times for each patient to compute "sys," "xper," and "xpos." To
convert the updated value to a range between (-.3) and (+.3), the
healthcare system may, for example, perform the following steps:
subtract the rank for a patient's repeat periodontal surgical
procedure value from the rank for the patient's risk value for each
category of risk (systemic, exposure, or experience), change the
sign of the number to positive (unless it is already positive), add
1 to the number to change the range from 1-10, multiply the number
by .3 to change the range from .3 to 3, round the number up to
change the range from 1 to 3, divide by 10 to change the range from
.1 to .3, and adjust the sign of the update value, changing the
range from -.3 to +.3. This positive sign adjustment is the
positive or negative sign of the value of subtracting the rank for
a patient's repeat periodontal surgical procedure value from the
rank for the patient's risk value.
[0117] The healthcare system further adjusts computed risk values
by idenitying and quantifying additional factors to be included in
computation of a risk value (step 722). To identify additional
factors to be included in the computation of a risk value, the
healthcare system first generates a frequency distribution of
positive occurrences for the diagnostic data collected. "Positive
occurrence" refers to a patient having a designated disease or
laboratory value, or taking a specified drug. The healthcare system
creates this frequency distribution for three categories of
patients, including: (1) all patients, (2) high risk patients, and
(3) low risk patients. The healthcare system defines patients
having risk values in the upper 20% of the range of total risk
values as high risk patients, and patients having risk values in
the lower 20% of the range of total risk values as low risk
patients. The healthcare system compares the frequency distribution
for these categories as follows: If the value of a data point for a
patient having a high risk has a frequency of occurrence of more
than twice the frequency of occurrence of that data point for all
patients in the patient database, and if the value of a data point
for a patient having a low risk has a frequency of occurrence of
less than twice the frequency for all patients in the patient
database, then the healthcare system identifies the data point as
an additional factor to be considered in computing a risk value and
quantifies the value as (+2). On the other hand, if the value of a
data point for a patient having a high risk has a frequency of
occurrence of less than twice the a frequency of occurrence of the
same data point for all patients in the patient database, and if
the value of a data point for a patient having a low risk has a
frequency of occurrence of more than twice the frequency for all
patients in the database, then the healthcare system identifies the
data point as an additional factor to be considered in computing a
risk value and quantifies the value as (-2). The healthcare system
repeats this process of identifying and quantifying additional
factors to include in computing a risk value for each combination
of two data points of items of diagnostic data included in the
frequency distributions of high and low risk patients. The newly
identified factors and their values are maintained in the patient
database. The sum of the newly identified factors corresponds to
the variable "totalnew", discussed above relative to FIG. 3 and the
discussion of computing a risk value.
[0118] After the healthcare system computes the risk adjustment
factors and identifies additional factors to consider in computing
a risk value, it recalculates a patient's total risk value by
plugging the risk update factors and "totalnew" into the equation
used to compute total risk (step 726). More specifically, the
healthcare system recalculates a patient's total risk value by
multiplying the previously computed values of systemic, exposure
and experience risk by the appropriate update values, determining
the sum of those values, and adding to it, the sum of
"totalnew."
[0119] Conclusion
[0120] By considering various factors impacting a patient's risk of
both developing a disease and responding to treatment, a healthcare
system consistent with the present invention assists a healthcare
provider in making more effective healthcare decisions, thereby
decreasing both economic and non-economic costs of healthcare to
patients and insurance companies.
[0121] Additionally, by maintaining a database of patient records
and analysis information related to treatment and associated
results, the healthcare system supports uniform and higher quality
healthcare. The data maintained by the healthcare system may be
used for scientific study and analysis purposes, making it possible
to trace the evolution of a patient's health by analyzing data
maintained at a central repository.
[0122] Although methods and systems consistent with the present
invention have been described with reference to an embodiment
thereof, those skilled in the art will know of various changes in
form and detail which may be made without departing from the spirit
and scope of the invention as described in the appended claims and
the full scope of their equivalents.
* * * * *