U.S. patent application number 10/202302 was filed with the patent office on 2003-02-06 for database for pre-screening potentially litigious patients.
Invention is credited to Herz, Frederick S. M., Labys, Walter Paul.
Application Number | 20030028406 10/202302 |
Document ID | / |
Family ID | 26897544 |
Filed Date | 2003-02-06 |
United States Patent
Application |
20030028406 |
Kind Code |
A1 |
Herz, Frederick S. M. ; et
al. |
February 6, 2003 |
Database for pre-screening potentially litigious patients
Abstract
Modern medical practitioners face a real risk from frivolous
lawsuits initiated by overly litigious patients. Even if innocent
of any malpractice, a doctor subject to such lawsuits may
experience personal stress and extended periods of time diverted
from practice in addition to greatly increased insurance premiums.
This patent describes a database system that allows medical
professionals to gauge the legal risk presented by new patients,
giving them the opportunity to avoid medical involvement with those
individuals most prone to engaging in unwarranted legal actions.
Other applications of the present system pertain to insurance
companies, legal services and other professional service
providers.
Inventors: |
Herz, Frederick S. M.;
(Warrington, PA) ; Labys, Walter Paul; (Ogden,
UT) |
Correspondence
Address: |
Frederick S. M. Herz
PO Box 67
Warrington
PA
18976
US
|
Family ID: |
26897544 |
Appl. No.: |
10/202302 |
Filed: |
July 24, 2002 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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60307561 |
Jul 24, 2001 |
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Current U.S.
Class: |
705/4 |
Current CPC
Class: |
G16H 10/60 20180101;
G06Q 30/0201 20130101; G06Q 30/02 20130101; G06Q 50/18 20130101;
G06Q 10/0635 20130101; G06Q 40/08 20130101; G06Q 10/10
20130101 |
Class at
Publication: |
705/4 |
International
Class: |
G06F 017/60 |
Claims
1. We claim a method for constructing and implementing the use of a
user profile which is utilized for purposes of determining a
statistical propensity of said user to engage in litigious behavior
against a potential provider of services, products or other
benefits.
Description
[0001] (Conversion of Provisional Application No. 60/307,561 to a
utility application)
CROSS REFERENCE TO RELATED APPLICATIONS
[0002] U.S. Patent Documents
[0003] U.S. Pat. No. 5,325,291 June, 1994 Garrett et al. 705/1.
[0004] U.S. Pat. No. 5,752,237 May, 1998 Cherny 705/4.
[0005] U.S. Pat. No. 5,852,808 December, 1998 Cherny 705/4
[0006] U.S. Pat. No. 5,875,431 December, 1998 Heckman et al.
705/7.
[0007] U.S. Pat. No. 5,895,450 April, 1999 Sloo 705/1.
[0008] Foreign Patent Documents
[0009] WO 9740460 October, 1997 WO.
REFERENCE TO SEQUENCE LISTING, A TABLE
[0010] Title of the Invention--page 1
[0011] Inventors and Addresses--page 1
[0012] Conversion of Provisional Application No. 60/307,561--page
1
[0013] Cross Reference to Related Applications--page 1
[0014] Background of the Invention--page 2
[0015] Brief Summary of the Invention--page 2
[0016] Description of the drawings--page 3
[0017] Description of the Invention--page 3
[0018] Abstract--on a separate sheet of paper
[0019] Claims--on separate sheets
BACKGROUND OF THE INVENTION
[0020] The need for modalities to curb the spiraling costs of
professional services, which is driven in large part by expense
related to legal costs and the cost of insurance protection against
law suits, is widely recognized. This problem is disproportionately
severe in the realm of medico-legal issues and is a major problem
for virtually all providers of professional services and in the
service industry, in general. In many cases, physicians are
relocating, retiring or changing profession. Hospitals are curbing
services at the cost of declining quality of care or are closing
their doors, in many cases after over one hundred years of
community care. Legal defense and extremely high settlements have
created insurmountable debts. Similar high cost of client-initiated
law suits are impacting virtually all professions. Thus the need to
avoid litigious clients and situations is obvious and
identification of multiple client and situational factors by a
system which enables professional service providers to pre-screen
and identify clients who have a greater than average potential for
initiating law suits is important in order to minimize the ultimate
risk of litigation against the physician as well as other
professionals.
BRIEF SUMMARY OF THE INVENTION
[0021] This patent describes a database system that allows medical
and other professionals to gauge the legal risk presented by new
patients/clients, giving them the opportunity to avoid medical
involvement with those individuals most prone to engaging in
unwarranted legal actions. In this way, such efficient knowledge
dissemination ultimately provides the physician with means for
avoiding or reducing the risks of liability litigation through
patient motivated medical malpractice suits before the fact by
enabling him/her to make much more intelligently informed decisions
regarding such questions as acceptance of that patient or
conversely, denial of the associated needed medical services to
that given patient (or acceptance for particular types of medical
services or treatments) as well as to what degree is special
medical attention and/or personalized care directed to the
emotional needs of the patient most significantly warranted in
order to minimize the ultimate risk of litigation against the
physician eventually resulting from that patient. In formation
within this system will allow improved physician-patient matching.
Other applications of the present system pertain to hospitals,
insurance companies, legal services and other professional service
providers. For example, using the information by the present novel
system will enable insurance carriers to more appropriately pro
rate individual premiums based upon more accurate evaluation of
risk profile.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] FIG. 1 illustrates the use of the litigious patient
screening system. First, a user transmits information about the
identity of a potential patient either manually (through a web
interface) or automatically (through patient management software).
This information is then fed through a system that (1) matches the
patient to a database (linking the individual to other doctors,
past lawsuits, related lawyers, etc.), and (2) uses a statistical
model to predict the likelihood of litigation and expected cost any
such lawsuits. This risk assessment is then transmitted back to the
user, and is either displayed on a web page or entered
automatically into the office system, depending on the mode of
initiation.
DETAILED DESCRIPTION OF THE INVENTION
[0023] 1. Problem
[0024] Many of the physicians practicing in urban or suburban areas
(representing perhaps 50% or more of the total population in the
US), and particularly those practicing in urban areas in the
northeastern United States, have a high probability of facing
egregious medical malpractice suits. Whereas an estimated 95% of
patients are essentially non-litigious, with regards to physician
medico-legal liability issues, it is felt that a mechanism to
identify the small percentage of those patients who are litigious
is desperately needed. Certain specialties are especially
predisposed to medical malpractice claims. Some of the most
vulnerable include obstetrics, neurosurgery, vascular surgery and
pediatrics, although there is an increasing incidence of lawsuits
across all surgical specialties. In many cases physicians are
leaving the practice of medicine or relocating to avoid geographic
areas with higher than average rates of medico-legal action and
unreasonably high damage awards. Hospitals and other medical
establishments spend large amounts of money and personnel effort in
defensive countermeasures, since frivolous lawsuits affect their
ability to properly subsidize the delivery of quality health care,
as well as their ability to locate new doctors locally. These
factors are a major cause of the spiraling out of control costs of
medical care, which directly impacts government, industry and
finally economic well being. Parallel problem situations are
impacting paramedical services, non-medical professional providers,
insurance carriers, and even the legal service providers
themselves. And the same type of system as described in this
invention can be used in parallel to the system described in
preemptive measures to avoid the litigious client or situation in
non-medical applications.
[0025] 2. Proposed Solution
[0026] The present service substantially addresses this major
problem by enabling physicians to pre-screen potential patients for
greater than average litigiousness. The system consists of a
computer database, accessed either on a per-use basis or as an
add-on to standard practice management software, computerized
patient registration systems, into which the medical professional
enters the patient's name, address, and social security number and
other demographic data. The system uses a stored history of medical
lawsuits (among other data) in combination with statistical
algorithms to generate a score. Much like a the credit scores used
by loan officers to gauge an individual's likelihood of default,
the score generated by this system gives the medical professional a
quantitative basis for assessing the risk that a given patient will
engage in frivolous litigation. If the risk is too high for the
professional's preference, he/she can choose to not establish a
medical relationship with the patient, which is the practitioner's
legal right. The present system is similarly applied to non-medical
service providers using the above array of data to determine the
client or situation with the greatest potential for lawsuits.
[0027] 3. Database Organization
[0028] The creation of the relational database supporting the
patient tracking system would be complex, in that many different
sources of legal data would need to be compiled; however, the
technical aspects of the database itself would be quite
straightforward. It would simply contain records on the identities
of patients, doctors, expert witnesses, lawyers, and judges. Each
record would contain various forms of medical, legal, and
demographic information, as well as links to other patients,
doctors, expert witnesses, lawyers, and judges.
[0029] In particular:
[0030] Patient records would include:
[0031] Links to family members
[0032] Medical history (including health status and doctors
previously seen)
[0033] Socioeconomic status
[0034] Demographic information (including age)
[0035] Record of the nature of previous disease (by standard code
number) processes and the timing of the disease(s)
[0036] Current disease(s)
[0037] Family history of disease(s) and proximity of blood
relationship to patient
[0038] Nature of disease (litigious disease process) for which
definite degrees and medical malpractice cannot be proven or
disproven objectively and conclusively (e.g., back pain, thoracic
outlet syndrome, certain neuropathies, emotional trauma such as
that associated with suffering, intractable pain syndromes).
[0039] Evidence of instability such as mental records, criminal
background, evidence of previous courses of medical treatment not
followed (checking out of hospitals by signing out against medical
advice, not following prescription plans, present and historical
subjective level of fear of receiving treatment, in general, or of
the present condition, etc.)
[0040] Previous litigation history ((including medico-legal and
non-medico-legal as well as suits initiated by the individual and
those brought against the individual by a third party (e.g., were
the suits of a medico-legal nature, were the suits egregious or
most likely unjustified such as summary judgments in favor of the
defendant).
[0041] number of suits (total)
[0042] number of suits of a medico-legal nature
[0043] types of suits
[0044] doctors, lawyers, and expert witnesses involved
[0045] money demanded
[0046] suit outcomes
[0047] Does the patient have a history of initiating suits, which
are eventually dismissed or consist of frivolous lawsuits?
[0048] The patient's history of initiating (or his/her immediate
family) medico-legal suits (such as number of suits initiated and
awards or settlements recovered).
[0049] Does the patient have a history or suspected history of
feigning injuries or illnesses?
[0050] Does the patient have a history or suspected history of
committing medical or disability insurance fraud?
[0051] Doctor records of referring physicians (typically belonging
to other doctors) would include:
[0052] Educational/professional profile
[0053] Patients seen
[0054] Commendations or condemnations by medical boards and
organizations (including hospital review boards, state medical
organizations).
[0055] Physician ratings services
[0056] Number of malpractice cases already faced, with outcomes and
amounts.
[0057] Demographic information
[0058] Lawyer records would include:
[0059] Educational/professional profile
[0060] Commendations or condemnations by legal boards and
organizations
[0061] Lawyer ratings services
[0062] Number of cases won/lost/dismissed
[0063] Aggressiveness of solicitation (does lawyer "chase
ambulances" or only take on valid cases?)
[0064] Does lawyer have a history of initiating lawsuits which are
eventually dismissed or consist of frivolous lawsuits?).
[0065] If so, what is the lawyer's history of success in this
regard?
[0066] Demographic information
[0067] Involvement with patients, doctors, and judges
[0068] Degree of public notoriety (extracted from on-line
media)
[0069] Judge records would include:
[0070] History of cases seen
[0071] Commendations or condemnations by review boards
[0072] Degree of public notoriety (extracted from on-line
media)
[0073] Expert witness records would include:
[0074] Educational/professional profile
[0075] Demographic information
[0076] Case involvement
[0077] Overall success
[0078] Degree of public notoriety (extracted from on-line
media)
[0079] 4. Implementation and Algorithms
[0080] Simply put, the function of this system is to receive as
input identifying information about a patient (e.g. name, address,
social security number), and to return a value representing the
predicted litigiousness of the given patient, such as the
probability of a lawsuit as a result of treating the present
condition as well as predicted dollar amounts of any ensuing
lawsuits and a breakdown which correlates predicted probability
with ultimate monetary recovery by the plaintiff:
[0081] a. In general as an overall probability statistic,
[0082] b. If litigation were to ensue:
[0083] The system could also reveal the effect that such a law suit
would have on the physician's insurance premiums, and if these
premiums are adjusted in accordance with the physician's adherence
to avoiding certain levels of litigation risk via the present
system, what would be the direct consequences on the physician's
insurance premiums for:
[0084] a. Accepting the present patient and,
[0085] b. Accepting other patients within the same approximate risk
level of the present patient based upon the litigious risk
statistics of the physician's other patients. The system could even
provide a break down of what the direct monetary losses would be in
this regard for accepting the patient compared with the likely
direct monetary gains that the physician would achieve for
accepting the patient for his/her present condition as well as
analogously what the comparative long term effects would be on
direct income from accepting other patients at a similar risk level
compared to the anticipated losses sustained as a result of
insurance premium increases resulting from accepting this similar
higher risk segment of the physician's current typical population
of patient candidates, and this value could also be adjusted in the
event that litigation did occur in accordance with
[0086] a. The estimated associated probability thereof as at the
average predicted plaintiff recovery under the present
conditions,
[0087] b. The predicted probability/plaintiff recovery distribution
based upon all of the relevant variables of the present type of
circumstances (e.g., likely patient condition, general health,
litigiousness factors, etc.).
[0088] In the preferred embodiment of the system, the service is
bundled with a practice management system, which maintains
persistent connections to a central database of medico-legal
information. In particular, when the receptionist in the
physician's office, clinic or hospital (directly or over the phone)
enters patient information after a patient signs in or schedules an
advanced appointment, the system automatically queries the database
remotely and instantaneously delivers the litigation risk profile.
Examples of such practice management systems include WebMd
(ww.webmd.com), CitX's IntramedX Practice Management systems
(ww.intramedX.com) and InfoCure (ww.infocure.com).
[0089] In other variations, the physician could pay by the patient
or alternatively according to a flat fee allowing use of the system
for a set period of time (e.g. $100/month). In this case, the
interface could be through a web page, eliminating the need for any
extra equipment on the part of the physician. In this way a trial
version of the software could even be downloaded to the physician's
practice management system
[0090] (e.g., for x days free). Moreover, there is an additional
service for physicians, which is described in co-pending patent
entitled "Physician's Referral Network". This service enables
physicians to make referrals to one another based essentially upon
barter currency, which is transacted in conjunction with the
referrals. The present system may be used to provide an additional
screening function for the referrals made via the present
approach.
[0091] Internally, the system statistically analyzes the
previously-described variables, using standard descriptive data
mining techniques to determine the degree of relevance of each
associated variable in predicting the likelihood of further future
litigation based upon past behavior. The receptionist or physician
may also enter data relevant to the condition of the patient such
as the general impression of the patient's overall present state of
health or (for the physician exclusively), the patient's symptoms,
complaints, likely diagnosis or potential diagnosis (such as if the
diagnosis is potentially associated with a severe condition) this
information can, in turn, be used to predict the likely disorder(s)
(which could even be broken down by the physician as a probability
value)and its severity; the likelihood of complications from the
disorder (essential precursor of litigation) as well as (in many
cases) the likely ultimate treatment protocol and its associated
likelihood of complications (another essential precursor of
litigation) are thus factored into the system's calculations.
EXAMPLE
[0092] There are obviously a multitude of ways in which the
predictive model could be developed. This example shows one of many
possible approaches:
[0093] First, a large database of patients is scanned for defining
examples of "litigious" or "non-litigious" patients. In the first
case, any patient linked with a criminal record of legal fraud, or
who initiated two or more medical malpractice lawsuits that were
subsequently dismissed because of insufficient evidence, will be
considered a very high litigious risk. In the second case, any
patient who has undergone major levels of medical care (e.g., over
$50,000 or over 5 procedures in the last 10 years) without ever
involving a doctor legally will be considered a very low litigious
risk.
[0094] A set of explanatory vectors is then prepared, containing
all available data linked to the patients selected as being very
high or very low risks. For example, for each patient i we could
define:
Xi={xi1, xi2, xi3, xi4}
[0095] Where
[0096] xi1=dummy variable (0/1) representing association with
Lawyer A.
[0097] xi2=dummy variable (0/1) representing association with
Lawyer B.
[0098] xi3=Income level.
[0099] xi4=Age.
[0100] And we could also define Yi, where
[0101] Yi=1 if patient is very litigious
[0102] Yi=0 if patient is very un-litigious
[0103] In this case, the model will be structured as a logit
regression (a type of linear regression that, while fed with a
range of data, returns an output value ranging between zero and
one).
Prob(Y=1.vertline.Xi,B)=exp(B'X)/(1+exp(B'X))
[0104] Where B=beta, a vector of coefficients that is estimated on
the previously-described data set. The model will therefore assign
a higher probability to Y=1 when B'X is large.
[0105] Suppose the resulting coefficients are as follows:
[0106] B={10, -10, 1}. This indicates that Lawyer A is not
associated with either type of patient (indicating a fairly neutral
lawyer), whereas Lawyer B is strongly associated with litigious
patents. Moreover, a high income is linked with those patients less
likely to sue, whereas age doesn't have much impact (although its
small positive value indicates aged patients are mildly correlated
with litigation).
[0107] Now, when operating, the system will operate in two stages.
After patient identifying information has been provided for patient
Xj,
[0108] Stage 1: Rule-based filter: Does patient Xj fit into either
the highly litigious or highly non-litigious categories, as
previously defined? If so, simply return a litigation probability
of zero or one.
[0109] Stage 2: Statistical Model. Using the previously-calculated
value for coefficient vector B, calculate
exp(B'Xj)/(1+exp(B'Xj))--this will be a value ranging between zero
and one, indicating the likely litigiousness of the patient. Note
that vector B is multiplied value by value into the patient's data
vector, which allows all the different factors to be taken into
consideration. Thus, even if the patient is somewhat aged, a high
income and association with Lawyer A will push the overall score
down, indicating the patient is a low risk venture for the
physician.
[0110] The system could be further enhanced through the offering of
supplemental medical malpractice insurance: if the physician uses
the present service and does not accept patients who fall above a
certain probability value for litigation (verified by a secure
agent associated with the physician's billing software), the
insurance would cover any claims over and above those covered by
standard malpractice insurance policy and the physician's CAT fund.
In a variation, the present system could actually be used as a
lower premium version of the CAT fund. The present service could
even be used as a reduced premium form of the physician's basic
medical malpractice insurance in which premiums arc set based upon
the system's predicted litigation-based monetary risk to the
physician. It should be noted that the system incorporates those
variables already used in standard medical malpractice actuarial
models. Thus the present service could incorporate an extended
version of the service for those physicians who are interested in
lower medical malpractice insurance rates, e.g., as part of a
special policy for users of the system who follow certain
recommendation criteria. One novel business model, in fact, could
even involve the creation and development of a special new
insurance company, which is developed entirely for physicians who
incorporate the use of the present system (in which case, it would
likely be implemented as a proprietary system).
[0111] 5. Data Sources and Collection
[0112] Several important issues must be considered in the design of
the present system. One of these relates to the means for
collecting and updating the data, which is provided to the system.
It is important to first determine whether and where the desired
data exists in digitized form (or, if not, it may be necessary to
access it and enter it into the system via manual means, (e.g.,
from court house records)). There are a variety of services
available in which it is possible to access on-line databases (for
a fee) which contain considerable personal information about
individuals. Such databases particularly in aggregate may contain a
history of such individuals. Legal databases containing case
histories for legal professionals may also provide a useful
resource, as would any available on-line county courthouse records,
which happen to be stored in database format. A very important
aspect of the above is given the potentially variable heterogeneous
data formation, it is important to enable each of the various
heterogeneous database formats to be able to communicate with each
other. This requires translation software, which is specific to
each type of heterogeneous database software. In many cases, the
software itself must be further customized to each individual
database to the extent that it has certain uniquely definable
characteristics
[0113] Sources of data might include:
[0114] a) Standard legal databases, with names of plaintiffs and
defendants involved in medical litigation.
[0115] b) Court transcripts, which would include such further
details as the names of expert witnesses. One potentially valuable
data aggregation of this information is a commercial vendor called
Knowledge X (ww.knowledgeX.com) which contains complete legal
database information as well).
[0116] c) On-line news sources, such as those provided by
Nexis/Lexis. Natural language processing techniques could scan
these databases of news stories for evidence of past medical
litigation. Once a candidate story is located, the names of the
defendants and plaintiffs could be searched for in tandem, such
that the eventual outcome of the case (settlement, trials,
dismissal by the court, etc.) could be noted. Court cases which
involve the dismissal of a plaintiffs case would be of special
interest, as the plaintiff, lawyers, and professional witnesses
involved would be suspect.
[0117] d) Medical board records, which would provide the names of
doctors either being commended or condemned by other doctors under
various circumstances.
[0118] e) Information from the National Data Bank to the extent
that it is available for access by the present service. This should
also include the physician's entered response to the allegations of
medical malpractice or practice restrictions which are recorded
within the Data Bank.
[0119] f) On-line and printed legal advertisements. The names of
lawyers observed being overly aggressive in their solicitation of
malpractice cases could be recorded. In other words, certain
lawyers would be flagged as "ambulance chasers", and patients who
are also clients of those lawyers (or likely to become clients,
given their locale), would experience an adverse impact on their
score.
[0120] g) Insurance records. These would hold evidence of previous
lawsuits, and would be useful for linking family groups.
[0121] h) Medical records.
[0122] i) Demographic and income databases.
[0123] j) Courthouse records.
[0124] Additional Potential Applications
[0125] 1. Incorporation into Patient Referral Forms
[0126] The information used in the present prescreening process can
readily be incorporated into the current mechanism widely used by
managed care specialty referral forms. In this case the Health
Maintenance Organization (HMO) would implement the use of the
present system to screen patients being referred to specialists for
specialty medical services. The issuance of the patient referral
form by the HMO would then also be subject to medico-legal
clearance via the above system and this information would be
entered directly on to the existing patient referral form as an
additional prerequisite for HMO approval of the referral.
[0127] It is worthy to note that this additional HMO screening of
patients according to degree of litigiousness would put additional
pressure upon the referring physician to implement the present
system, in order to insure that their patients who need quality
specialty care are able to receive it subject to referral approval
by the HMO. Thus, it is certainly conceivable in this scenario,
that patients who are likely to be very litigious, who are
accordingly screened out by the HMO and denied medico-legal
clearance for referral are likely to need a higher premium form of
insurance provided either by the same insurer or by a separate high
risk specialty insurer (as described below). It is also worth
noting that highly litigious patients are likely to become apparent
to employers who offer insurance benefits through group plans to
their employees inasmuch as they will typically not pass the
initial application level screening by the HMO for that group plan
policy. Moreover, in such cases employers may further consider
employees who are high risk from a medico-legal litigiousness
standpoint to also be high risk for potential litigation against
the present prospective employer who may, in turn, consider not
hiring that employee. Accordingly this propensity on the part of
employers could readily become a further dissuading factor for
patients to sue physicians in the first place.
[0128] 2.High Risk Premium Patient Insurance--
[0129] It is entirely plausible to assume that HMOs would implement
the present system to screen patients at all levels of HMO patient
approval, i.e., at the time of application for enrollment, the
applicant would, of necessity, have to be approved through the
system as implemented by the insurer. Both primary and secondary
(or subsequent insurers) may wish to independently implement the
present system for purposes of assuring that the proper screening
has occurred and because each insurer is likely to have differing
criteria for acceptance, rejection and associated premiums
categories. In this way the actuarial formula of the insurer may
incorporate additional attributes which are relevant to overall
medico-legal litigation risks instead of purely medical data alone,
i.e., predicted patient litigiousness in addition to present and
past medical conditions such as those attributes detailed within
the present invention. In addition, the present improved actuarial
model may also be used for patient insurance renewal in the same
fashion as is used in the patient application process. Unless
regulatory agencies place restrictions on which types of variables
related to the patient (and to what degree) these variables can be
used in determining insurability and premiums of the patient, the
same revised actuarial model which incorporates the attributes of
the present invention in order to determine over all litigation
risk for purposes of insurability and rate setting should also be
used for HMO approved medico-legal clearance referrals. Of course,
rejection of the referral would have to be superseded by a doctor's
judgment if the case is determined to be a medical emergency. For
patients who are considered "high litigation risk" the insurer,
instead of denying insurance coverage altogether, the insurer may,
at the application stage, or at the insurance renewal stage, in
many cases place the patient in a higher risk category (for which
there may be multiple high-risk categories). Or another insurer who
specializes in high-risk insurance may be available to provide
coverage for those cases, which do not pass the acceptance criteria
of standard HMOs. Thus, a higher premium form of insurance whether
provided by a specialized carrier or as a higher risk category of
the standard insurer would have to be provided by the primary
insurer and probably by the secondary and tertiary insurer as
well.
[0130] 3. Minimizing Medico-Legal Risk by Optimizing the
Appropriateness of the Match between the Physician and the
Patient.
[0131] Although the primary goal in minimizing the chances of
medico-legal litigation is to initially and preemptively screen out
the highest risk patients for litigation, there are additional
further measures that can be taken to additionally MINIMIZE the
overall probability of encountering ultimate medico-legal liability
issues. In particular, it would be in the interest of hospitals and
clinics to be sure that once a patient has been appropriately
screened for an unnecessarily high degree of litigiousness, to be
sure that there is also a good match between the patient and the
physician based upon the specific detailed initial complaints and
symptoms (as well as medical history) which together would be
suggestive of the likely type of disorder or system involved which
could be valuable data for purposes of improving and, in turn,
optimizing selection of the physician(s) who based upon their
specific skill sets and the associated clinically demonstrated
proficiency thereof would be most appropriately suited for that
particular patient. Accordingly, such an approach further ensures
that physicians who are not optimally (or at a minimum not
adequately) skilled and proficient with regards to certain system
disorders, disease processes (or even diagnoses) which are likely
to be associated with the present patient symptoms and medical
history actually do not ultimately treat the patient
(notwithstanding emergency or other potential extenuating
circumstances). Currently, the standard protocol by which certain
physicians have rights to perform certain procedures is very crude
and is based upon each individual "delineation of hospital
privileges" (or commonly known as "hospital privileges"). Within
its own particular venue, each hospital has the inherent right to
dictate which particular medical procedures and treatments
(delineation of privileges) are performed and by whom. Typically,
the chief of each department is assigned the responsibility of
determining this delineation of privileges for each physician
practicing at that hospital under his/her jurisdiction. However,
this approach unlike the aforementioned which is herein proposed is
often based largely upon subjective opinion and is often even
influenced heavily by politics which occur internal to that
specific hospital. Moreover, in accordance with the presently
accepted protocols, there is no consideration whatsoever given to
the unique physical conditions and associated medical history of
the patient or whether the physician has specific medical knowledge
or expertise which matches these medical profiles of the patient.
There is thus a substantial and unrecognized need in the attempt to
further reduce medico-legal risk for a more sophisticated scheme
which applies detailed knowledge of each patient including present
condition(s) as well as past medical history and family history in
combination with a detailed history of each physician's experience
and the associated success and shortcomings related to this
experience. Typically review of delineation of clinical privileges
occurs only every two years on cursory review of a department
chief. There is currently little objective physician volume/success
data available for review in granting clinical privileges. Data
presently available is incomplete and, in most cases, no data is
available nor is it requested at the time of the review and
granting of clinical privileges. Hospitals and regional medical
societies will have available internal data banks which will
represent an ongoing evaluation of all physicians and all disease
processes treated in respect to staged severity of disease and in
respect to success/failure rate (which is relative to this
determined staged severity of the disease) on a case-by case basis
as well as category specific, case type specific (predictive
success/failure rate for any given newly introduced or developing
case), and overall success/failure rates. Variations of the present
statistical algorithm as above described will be implemented to
calculate from this data the optimum predicted conditions of
physician and medical practice and/or medical center for optimum
treatment of each patient. It will include complete medical
practice history of all physicians subscribing to the service such
as success/failure statistics, complete litigation history, etc.
and other variables as described above. Particularly valuable
attributes for medical centers, hospitals and clinics may include
the profiles of the physician who would be treating the patient
(typically a specialist in referral cases), the profiles of the
other physician(s) who would be (or would likely be) treating the
patient (either for other specific medical care or the likely
attending physician), general quality ratings or reputation of
hospital support staff, medical testing and treatment equipment and
facilities which are relevant to the patient's medical needs and
their associated quality and degree of overall importance to the
patient's present medical needs.
[0132] This statistical algorithm will also determine which point
in the progression of the medical status, as well as which point in
the treatment process is the most optimally appropriate
circumstances to refer the patient to another physician or medical
center, in as much as the present statistical algorithm is able to
consider both where an optimally suitable physician for the present
medical status of the patient is located as well as consider where
the most opportune medical support staff is located, as well as
other relevant attributes such as more subjective aspects of this
algorithm such as the appropriateness and quality of the testing
and treatment equipment available at the center as well as
determine the quality of the staff overall. Regional and personal
financial interests and political considerations must be set aside
in deference to objective optimum patient care. As a result of the
predictive nature of the use of the present algorithm in a data
mining application, somewhat more subjective data will be gleaned
from the algorithm which will efficiently direct educational and
training resources to determine which geographic and specialty
areas to emphasize for training programs by determining the
relative distribution of trained medical specialists in each
specialty area. Ideally such an algorithm would incorporate longer
term predictions based upon such data as predicted demographic
changes, anticipated technical advances in each field (determining
the relative need for newly trained professionals) as well as
present staff admissions and areas of training emphasis of other
hospitals, clinics and teaching medical centers and the emphasis
and profiles of regional independent medical practitioners (which
would be indicative of type and quality thus effective competition
for referrals by the present system on a given locality basis).
[0133] The present system would be of considerable interest to
hospitals, insurance companies, clinics, or private practitioners.
For example, hospitals may use such a scheme as an improved model
for approving, denying or redirecting physical referrals to other
doctors. It is of value to apply the same basic data model as
described above in order to accurately predict the associated risks
of complications for each patient (and with this data determine the
medico-legal risks by also considering the patient's degree of
litigiousness) based upon that physician's history of clinical
treatment to other patients who are most similar to that of the
present one. The statistical algorithm could, for example,
determine across a large data set of physicians and patients which
key features of the physician are most predictive of success (thus
ultimately non-litigation) fort hat particular patient's medical
status.
[0134] Physician data sources include those relevant ones to
physician quality and expertise such as physicians training and
history of cases performed (which historically is data submitted to
the hospital by the physician) including, of course, most
relevantly how many of the same types of cases were seen and the
percentage of those treated which were successful cases. This data
should also include the litigation statistics. The information
relating to the patient is available through similar sources.
[0135] Typically such detailed patient data is available through
digitized hospital records, insurance data bases, physician medical
records such as patient charts (including practice management
databases) and other data detailed above in section 3
[0136] Some of these patient records would include present medical
status and conditions, medical history, medical history of family
members and previous litigation history.
[0137] Patient data includes court transcripts and legal databases
as well as a variety of other data sources such as those described
above in section 3. It is important to note that physician data not
only incorporates attributes representing qualitative data
indicating the type of experience (degree of similarity of the
experience to that of the patient's present medical status which is
currently being presented) and quantitative data (number of
previous cases seen which are of a relevant nature to the present
one) but also the relative degree of overall success in treating
the relevant patients seen and overall relative degree of success
for all patients previously treated overall, where relative degree
of success may be a numeric percentage score of how the present
physician's success compares historically as a ratio to other
physicians on a given similar case by case basis which is, of
course, in turn, averaged overall for each physician. "Success" may
be determined by such variables as nature and severity of
complication and morbidity as well as mortality rates and
subjective assessment by the physician during past treatment cases
and follow-up visits. Medico-legal activity may be another useful
variable provided that these actual statistical values are
normalized by the predicted degree of medico-legal litigiousness of
the patients which actually sue and in this statistical model
details of the nature of the medico-legal complaint are considered
as well as the ultimate outcomes of the suits. Effectively, a
matching score between the physician and the patient is calculated
as well as that of the other physicians who are also presently
viable alternatives to the present physician. For purposes of
hospital clinic or physician specific implementation, a number of
rules for example could be constructed automatically or manually
based upon data analysis of overall success/failure rates for
various types of physician/patient statistical correlation. For
example, as a physician/patient matching score (below X may not be
suitable under any circumstances notwithstanding emergency, etc.)
Or, if another available physician presently is (or becomes) higher
than the present physician presently treating the patient and this
amount exceeds the score by amount Y (or amount Y if the present
physician's score is at or above not unacceptably low (however,
nonetheless sub-optimal) score within range Z, the patient could be
instead referred onto another physician who is better or more
specifically experienced with regards to the patient's present
medical needs. Geographic variables could also be incorporated into
such rules as well as such factors as the degree of the matching
score of the hospital staff (if relevant) to that of the patient's
needs.
[0138] Medico-legal pressures and insurance company pressures will
represent the primary motivating factors which will compel the
medical providers to adapt the presently described protocol. As a
result, it is anticipated that one potential consequence of wide
spread use of the present system is that very qualified physicians
and particularly qualified and focused specialists are likely to
receive a large number of patients via the present system. The same
is true of very high quality medical centers such as those with a
particular medical focus and emphasis. As such, it is likely that
such a resulting quality based demand scenario, once it emerges
within the healthcare field, will drive such high quality primary
physicians, specialists, clinics and medical centers to not only
preferentially select patients of low liability risk but also those
who are able and willing to pay independently for higher quality
healthcare (in addition to or even independently of HMO coverage).
Those who are able to still justify some of their services to be
paid by medical insurance may offer certain routine services while
also providing premium services for an additional fee which is
charged at a higher rate than insurance would cover (that is if it
would even cover it in the first place). Moreover, it is likely in
this scenario that extremely high demand physicians, clinics and
medical centers may offer services exclusively at a rate which
requires additional fees to be covered by the patient directly for
the care of a surgeon, preferentially select those patients who
appear to require complicated, unusual or lengthy surgical
procedures as well as those who are willing to pay for non-HMO
covered specialty treatments such as preventive treatment and
therapeutic regimens and also patients who choose to pay for
non-HMO covered diagnostic tests involving advanced technology,
technical skills and equipment. Because the types of treatments
which a physician offers patients affects litigation risk, the
optimum price which the physician should charge for each treatment
is influenced by overall demand of the patient population (more
particularly the segment of the potential patient population which
the physician actually provides that particular treatment for) as
well as litigation risk of that patient population being treated.
This value may be determined by an optimization technique which is
designed for this type of multi-variable problem techniques are
well known in the field of statistics.
[0139] In certain cases in which the decision as to the most
appropriate treatment regimen is not entirely clear cut or is of a
somewhat subjective nature, because certain risks or some
complications associated with each potential treatment regimen (as
well as the risks associated with resulting litigation) will tend
to be different, the system may provide the physician with a
comparative predicted estimate of the various risks associated with
this potential for resulting litigation for each relevant
additional treatment regimen. Using optimization techniques, the
present methodology may also be also tailored to identify an
optimal relative volume of different kinds of patients, based on
the size of the pool of potential patient selection available to
the physician (which is a function of litigation risk probability
and probability/potential for monetary profit which are also
subtractive variables). The optimization technique may also use
data from numerous other physician's billing systems in order to
predictively suggest this optimal volume distribution of different
patient types (and ultimately treatment types). In light of
attempting to achieve an optimal price (for optimal profitability)
the physician may wish to charge for these non-HMO covered (or
patient supplemented) medical services in order to optimize for
example, likelihood and degree of profitability or to optimize this
value while also maintaining risk of litigation of the type which
could harm his/her practice within an acceptably low level such
that long-term probability and degree of profitability is
optimized. In an analogous application HMOs within reasonable or
regulatory limits may wish to set rates for certain treatments
based upon the same types of variables. It would even be possible
to adjust premiums based upon consideration of the variables
associated with which patients are actually treated and which
treatments are actually given to those patients. This approach
would further incentivize the physician to choose to accept those
types of patients who are not only the most profitable in light of
their overall low risk of litigation as well as those treatments
which represent the lowest risk of litigation and the highest
returns from a profitability standpoint. Because the profitability
potential of certain treatments (and on perhaps certain types of
patients) may represent a different (in some cases opposing) long
term monetary value for the physician compared with that of the
insurer, it may be in the insurer's best interest to adjust for
this factor by setting the rates, e.g., by further accentuating the
cost of premiums for those treatments which are not only higher
risk but also higher profitability potential for the physician.
[0140] At a more general level, the presently described scheme
embodies a profound paradigm shift which would indeed represent a
much more efficient commercial model for healthcare which is
quality-market driven largely exists as the pro-quota for most
other industries within capitalist countries. Moreover, it is
worthy to note that the mere introduction of the present system
will drive the further and ongoing demand for its use in health
care.
[0141] For the implementation within HMOs, the present
physician/patient appropriateness score could be the most accurate
model for determining HMO based medico-legal clearance for patient
referrals as described in sub-section 1 within the present section
(in as much as a very accurate determination of medical risk is
factored into the overall medico-legal liability prediction scheme
and finally the physician/patient appropriateness matching score is
likely to be an extremely valuable metric in malpractice actuarial
models used by HMOs for use in the approval of policy renewal
procedures and as well as risk category allocation for the
patient.
[0142] Similarly, this matching score between the patient and the
prospective physician is an appropriate additional variable to be
added to the HMOs algorithm used to determine medico-legal
clearance for referral of the patient to a particular identified
specialist, and if the matching criteria is inadequate or even
sub-optimal, the present system may recommend another local
physician who is more suitable for that particular patient (e.g.,
is on the same hospital staff, or has associated hospital
privileges) or for non-hospital patients such referral
recommendations by the insurer (or physician for physician
practices or clinic) could be based upon locality including degree
thereof using zip code information of the patient based
implementations) compared to office or hospital locations at which
each physician practices. It should be noted this particular
approach would be ideal for a large scale automated referral system
which is described in co-pending patent application entitled
"Physicians Referral Network", in as much as a very large pool of
physicians with profiles of expertise available via the network and
at any given relevantly required physical locality or hospital.
[0143] 4. Commercial Marketing--Certainly, there are useful
applications of the present system to commercial marketing.
Hospitals, HMOs, physicians, clinics, pharmacies and pharmaceutical
companies spend billions of dollars per year on consumer
advertising. For example, areas which have a high degree of
litigiousness based upon demographic data should be weighed against
the profit opportunity of those areas minimizing a market campaign
by geographic area. In the case of pharmaceutical companies in
particular, litigation is a major problem, however, the litigation
predictions generated by the data model would, of course, be for
litigation against the drug maker and some slightly different
variable may be important compared to predicted litigation against
health care providers (although not to minimize the potential
relevance of litigation history, particularly against health care
providers). An example is, the likelihood and likely degree of
severity of health risks and potential harm to the patient
associated with a drug. This may include, of course, anecdotal
evidence such as chemical and biochemical similarities with the
nature and physiological actions of the drug (respectively) as well
as (if available side effects and health problems associated with
preliminary trials on humans and animal studies as well as if the
drug has already been released commercially) the documentation of
medical side effects, complications and mortality regarding their
numbers and . . . all variables associated with rates of occurrence
as well as (importantly) completed litigation history. Accordingly,
targeted direct marketing via marketing database lists are also an
important form of advertising for each of the above commercial
categories. The present system would be usefully employed as a tool
for screening out those individuals and households, which are
demonstrated or predicted to have litigious propensities.
[0144] The present invention for screening litigious clients is
certainly extensible into other paramedical and non-medical
professional domains including but not limited to legal services,
financial planning and advisory services, tax advisory services,
stock brokers, investment brokers and dealers, and engineering
firms. In these alternative professional domains for which the
present system may be adaptively modified, it would be obvious to
the artfully skilled reader that the features as applied to
physicians for purposes of predicting future probability of
litigation for a given service to a particular user can be
appropriately applied to analogously similar features which are,
however, instead relevant to the specific professional domains of
the particular professional service provider (e.g., professional
credentials, previous litigation for particular types of services
rendered, etc.).
[0145] The present system could also be used by employers to screen
potential employees for litigious propensities. In this latter
example the general inherent risks for monetary loss to the
employer associated with ultimate litigation could be a useful
variable within the data model, e.g., is the position associated
with certain litigation prone risks (such as occupational hazards)
and if so, to what degree? Again, direct marketing initiatives
within other professional services domains (as well as by the way
potentially any/all direct marketing initiatives wherein the
associated potential service to be rendered or product to be sold
carries with it the potential for certain recognized consumer
liability risks) could benefit by implementing variations of the
present invention as a screening tool (in which the variables used
in the predictive litigiousness risk model are adapted
appropriately to the particular domain to which it is applied).
Again as in the medical case, the monetary risks associated with
litigation could be weighed against the predicted monetary profits
on a case by case basis.
* * * * *