Apparatus and method for categorizing health care utilization

An apparatus and method for categorizing health care utilization provides an efficient aid in identifying patients who are seeking inappropriate care. The invention involves a computer system having a neural network responsive to several input variables to categorize the utilization characteristics of the patient. The input variables define selected characteristics of a patient. In one embodiment, a screening process identifies patients who are at high risk to an immediate threat to their health and eliminates those least likely to be seeking inappropriate care.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
In general, the present invention involves performing an analysis based on 
several factors relating to a patient's lifestyle and health care 
utilization to identify inappropriate health care utilization. In one 
embodiment, the analysis is performed using a neural network processor 
trained to map the several factors to different categories of health care 
utilization. In particular, the present invention provides a method and 
apparatus to help distinguish appropriate use from inappropriate use of 
the health care system, based on selected characteristics and medical 
usage of patient driven care and illness driven care utilizers. The 
selected characteristics and patterns provide guidelines which indicate 
the presence of underlying disorders such as depression, alcoholism, and 
somatization. 
Preferably, several factors representing the selected characteristics and 
medical usage patterns become input variables, often called an input 
vector, for the neural network processor. A category of care for the 
individual represented by each input vector becomes the corresponding 
target output for the neural network. The collection of input and 
corresponding output pairs comprise the training set for the neural 
network. Based upon a large training set, the neural network "learns" to 
identify patterns which help distinguish categories of health care 
utilization of the patients in the training set. 
In one embodiment, the neural network classifies patients' utilization of 
health care into at least two categories: patient driven care and illness 
driven care. In a preferred embodiment, four categories are identified: 
(1) patient driven care, (2) a mix of patient driven and illness driven 
care, (3) illness driven care, and (4) possible patient driven care. 
Although additional categories may also be established, for present 
purposes, these four categories are disclosed. 
FIG. 1 illustrates a processing system 10 for use in the present invention. 
The processing system 10 comprises a computer 12, which is adapted to 
receive input data from an operator by means of a keyboard 14 or from 
other sources such as a patient database (not shown) stored on mass 
storage media 16. The storage media 16 may also be used to store output 
data from the computer 12. In addition, the computer 12 is coupled to a 
display module 18. The display module may be a computer monitor or similar 
device. The categorization system 10 further comprises a printer 19. 
The computer 12 is conventional and executes a simulation of a neural 
network 20. Typical computers include a general purpose desk-top computer 
such as a Hewlett-Packard computer, a SUN computer or an IBM computer. The 
computer 12 may also be a mainframe computer, a server or a workstation. 
In one embodiment, the computer 12 is connected to a Local Area Network 
("LAN") or a Wide Area Network ("WAN"). Thus, the information generated by 
the processing system 10 may be accessible from any computer on the LAN or 
WAN and may be accessed or assimilated by existing database management 
software. The processing system 10 may also include various other 
input/output ("I/O") and peripheral modules which are coupled to the 
computer 12, as known in the art. 
The neural network 20 used in the present invention is a software 
simulation of a collection of processing elements, as is well understood 
in the art. Inputs are provided to each processing element, which in turn 
generates a single output. This single output may then be provided along 
numerous pathways as an input to other processing elements, connecting the 
processing elements into a network. Each input is assigned a relative 
weight, as well understood in the art. Any one of several different neural 
network models which use supervised training can be used. 
The neural network 20 is trained to process information through exposure of 
the network to input data and corresponding target output data (referred 
to above as the "training set"). During the training phase, the network is 
supplied with input data with corresponding answers (the expected or 
target output) as examples. For each input vector in the training set of 
input vectors, the network compares its output from processing the vector 
with the expected output and adjusts its weights to minimize the error in 
the response. 
In one embodiment, back-propagation training is used. With 
back-propagation, the error signal is generated and back propagated 
through the levels of the network. During back-propagation, the weights 
given to each input at each level are adjusted in an attempt to compensate 
for the error. Another input vector and corresponding expected output 
value from the training set is passed to the network, and the resultant 
error signal is back propagated through the network. Alternatively, the 
weight adjustments may be accumulated and applied after all the training 
examples have been presented to the neural network, as is understood in 
the art. 
This process is iterated until the output error reaches an acceptable 
level, or no further improvement is noted. The size of the training set is 
generally chosen to provide an acceptable level of confidence in the 
neural network output for the given application. 
In general, in the present embodiment, the training set comprises a large 
number of input vectors, each input vector representing the selected 
characteristics and medical usage of a selected patient, and a 
corresponding expected output (target output vector) for the neural 
network 20. The neural network "learns" by adjusting its weights in a 
manner which is targeted to produce the correct response for each of the 
samples in the training set. The objective is to have the network produce 
the correct response (correct classification) for new objects which it has 
not analyzed during training. After training, the neural network may be 
used to process new information. 
The neural network 20 utilized in the present invention may be implemented 
in different ways known in the art. The neural network may be implemented 
in software. Neural network programs are available from several suppliers. 
One such supplier is NeuralWare, Inc. located in Sewickley, Pennsylvania. 
A neural network may also be written in conformance to well understood 
neural network models, such as those described in James A. Freeman and 
David M. Skapura "Neural Networks, Algorithms, Applications and 
Programming Techniques," 1992. In one embodiment, the neural network of 
the present invention comprises a multi-layer artificial neural network of 
the back-propagation type, as well understood in the art. The neural 
network 20 may also comprise a dedicated processor, as understood in the 
art. Advantageously, the neural network 20 of the present invention 
comprises non-linear activation functions in at least the hidden layers. 
Linear or non-linear activation functions for the output layers of the 
neural network can be used depending upon the desired type of response. 
In order to train the neural network 20 of the present invention, 
appropriate input variables are selected. In general these input variables 
are discussed with reference to a Lifestyle Questionnaire and data from a 
patient's medical utilization records. An exemplary Lifestyle 
Questionnaire, designed to determine the selected lifestyle 
characteristics (i.e., the psychological make-up) of the patient, is 
provided in Appendix A. Details of the Questionnaire are discussed below. 
In the present embodiment, the selected input variables are provided to the 
categorization system 10 depicted in FIG. 1 in one of two ways: by 
operator input via the keyboard 14 or from an existing patient database 
stored on the storage media 16. In one embodiment, the patient database 
comprises a plurality of patient files, each of which contains the input 
vector for the represented patient. This information is processed by the 
neural network 20 (or other means) to categorize health care utilization, 
as will be described in detail below. The categorization results are 
presented on display module 18. Alternatively, the results may be stored 
in a patient database on the storage media 16, or printed on the printer 
19 in an organized format. 
In a preferred embodiment, the categorization system 10 may be utilized to 
prescreen patients prior to processing of the patient data by the neural 
network 20. In this embodiment, patients deemed at high risk (of having 
patient driven care tendencies) or patients who engage in extremely 
frequent utilization of medical services are identified and forwarded to a 
case manager for review. 
Some of the input variables taken from the patient medical records relate 
to the type of diagnoses a patient has been given. Appendix B (microfiche) 
depicts a list which provides a proposed classification of well known 
patient diagnoses into the following four categories: (1) illness driven 
diagnoses, (2) definite patient driven diagnoses, (3) possible patient 
driven diagnoses and (4) diagnoses of no interest. In each category, a 
number of diagnoses associated with each class of diagnoses are listed and 
coded numerically. These codes are utilized in a patient's medical records 
to facilitate the classification of that patient's utilization of health 
care, as described in detail below. 
As mentioned above, in the present invention, the neural network developed 
during the training phase utilizes a number of input variables taken from 
information in the patient's medical database as well as information from 
the Lifestyle Questionnaire. The input variables used to form input 
vectors for the present embodiment are listed in Table 1. 
TABLE 1 
__________________________________________________________________________ 
INPUT VECTOR VARIABLES 
Variable 
Number 
Variable Name Range Format.sup.1 
__________________________________________________________________________ 
1 Anxiety 0-5 d 
2 Depression 0-5 d 
3 Somatization 0-5 d 
4 Total ADS 0-15 dd 
5 Age 18-100+ ddd 
6 Sex M = 0, F = 1 
b 
7 Gross Charges Total 0-99999.sup.2 
ddddd 
8 Total Count 0-9999 dddd 
9 Gross Charges Out-Patient 
0-99999.sup.2 
ddddd 
10 Out-Patient Count 0-9999 dddd 
11 Gross Charges DOV 0-99999.sup.2 
ddddd 
12 DOV Count 0-9999 dddd 
13 Gross Charges Non-DOV 0-99999.sup.2 
ddddd 
14 Non-DOV Count 0-9999 dddd 
15 Number of different PCP's seen 
0-99 dd 
16 Number of different specialists seen 
0-99 dd 
17 Number of different Out-Patient Diagnoses 
0-999 ddd 
18 Number of in-patient or out-patient diagnostic ranges 
0-19 dd 
19 Patient Driven Definite yes = 1; no = 0 
b 
20 Illness Driven yes = 1; no = 0 
b 
21 Patient Driven Possible yes = 1; no = 0 
b 
__________________________________________________________________________ 
.sup.1 "d" denotes one decimal digit (0-9); "b" denotes one binary digit 
(0-1) 
.sup.2 hundreds of dollars, rounded to the nearest $100.00 
Table 1 also provides information on the organization of each patient input 
vector data file. As shown in Table 1, each input variable is assigned by 
a variable number. Each input variable also has a range and a data format. 
The format column describes the representative format for the variable in 
the input vector data files. For example, the input variable 1, "Anxiety," 
may have a value ranging from -5 (see "Range" column), whereby "0" 
indicates no anxiety and "5" indicates extreme anxiety. This value is 
represented in an input vector data file by a single decimal digit ranging 
from 0-5. This is illustrated in Table 1 by the single "d" reference in 
the "Format" column. 
In the present embodiment, twenty-one variables have been selected for the 
input vectors for the neural network 20 for categorization purposes. In 
the present embodiment, each of the input vector variables is scaled to a 
value ranging from "-1" to "1." Alternatively, the input vector variables 
are scaled to values ranging from "0" to "1" or "-1" to "0." For instance, 
in the embodiment where the scaling is for the range of -1 to 1, and the 
range of raw values for a variable is 1-5, the raw value "1" will scale to 
-1 and the raw value "5" will scale to 1. This type of scaling for neural 
networks is well understood in the art. 
These twenty-one input vector variables, each of which represents a 
particular characteristic of a patient, are: Anxiety ("ANX"), Depression 
("DEP"), Somatization ("SOM"), Combined Values of Anxiety, Depression and 
Somatization ("TOTAL ADS"), Age, Sex, Total Gross Charges, Total number of 
line item charges ("Total Count"), the Total Out-Patient Gross Charges, 
the total number of Out-Patient Visits ("Out-Patient Count"), the Total 
Gross Charges attributable to Doctor Office Visits ("Gross Charges DOV"), 
the number of Doctor Office Visits ("DOV Count"), the Total Gross charges 
attributable to Non-Doctor Office Visits ("Gross Charges Non-DOV ), the 
number of non-Doctor Office Visits ("Non-DOV Count"), the number of 
different Primary Care Physicians ("PCP") seen, the number of different 
specialists seen, the number of different Out-Patient Diagnoses, the 
number of inpatient or out-patient diagnostic ranges, whether definite 
patient driven care diagnoses are present ("Patient Driven Definite"), 
whether illness driven care diagnoses are present ("Illness Driven"), and 
whether possible patient driven care diagnoses are present ("Patient 
Driven Possible"). 
The values of the first four input vector variables (Anxiety, Depression, 
Somatization, and total ADS) are obtained from various answers provided by 
the patient to the Lifestyle Questionnaire. In the Questionnaire, five 
questions relating to the patient's anxiety, depression and somatization 
are listed. These questions are designed to provide some information about 
the psychological make-up of the patient. 
With reference to the Lifestyle Questionnaire in Appendix A, five questions 
(questions 21-25) in the Lifestyle Questionnaire are designed to determine 
the anxiety level of a patient. Each affirmative response to questions 21 
through 25 indicates that the patient experiences a certain level of 
anxiety. A score of "1" is assigned to each affirmative response and a 
score of "0" is assigned to each negative response. A sum of the values 
for questions 21-25 becomes the value for the input variable number 1, the 
Anxiety variable. Thus, as explained above, the value of the input 
variable for Anxiety may range from 0-5, whereby a score of 0 indicates 
that the patient experiences no or very little anxiety and a score of 5 
indicates that a patient experiences substantial anxiety. 
As shown in the Lifestyle Questionnaire in Appendix A, questions regarding 
depression and somatization are listed to identify the existence of these 
psychological ailments in a patient. Questions 26-30 of the Lifestyle 
Questionnaire in Appendix A relate to the Depression input variable 
depicted in Table 1. Questions 31-35 relate to the Somatization input 
variable from Table 1. As with the Anxiety variable, because there are 
five questions which relate to depression and to somatization, the 
Depression input variable and the Somatization input variable each has a 
range from 0-5, as depicted in Table 1. 
The score of the Anxiety, Depression, and Somatization input variables are 
summed to generate the Total ADS variable listed in Table 1. Thus, the 
Total ADS variable has a range of 0-15. 
The values of the variable numbers 5 and 6, Age and Sex, are also obtained 
from information listed in the Lifestyle Questionnaire or can be obtained 
from patient medical records. The Age input variable, variable number 5, 
ranges from 18 to 100+. This value is represented in the data file by 
three decimal digits. The Sex input vector variable, variable number 6, is 
defined as having a value of 0 for male and 1 for female. This variable is 
represented with a single binary digit (0 or 1) in the patient input 
vector data file (represented by the single "b" in the Format column). 
The values of variable numbers 7-18 are advantageously obtained from the 
patient's medical records. The "Gross Charges" variable, variable number 
7, refers to the gross dollar amount of charges that the patient has 
incurred through his or her use of health care. The value for this 
variable is recorded in hundreds of dollars and ranges from 0-99999. As 
illustrated in Table 1, this value is represented with five decimal digits 
in the data file for each patient. 
The "Total Count" variable, variable number 8, refers to the total number 
of line item charges that the patient has incurred. This value has a 
defined range from 0-9999 and is represented with four decimal digits in 
each data file. 
The "Gross Charges Out-Patient" variable, variable number 9, represent the 
Total Out-Patient Gross Charges incurred. The value of this variable is 
recorded in each patient data file in hundreds of dollars ranging from 
0-99999 in a field in the data file having five decimal digits. 
The "Out-Patient Count" variable, variable number 10, represents the total 
number of out-patient visits of the patient. In the present embodiment, 
out-patient visits refers to the combined number of doctor office visits 
and non-doctor office visits (any medical encounter without visiting the 
attending physician, such as lab work). The value of this variable is 
listed in the data file with a from 0-9999 in a field of having four 
deemed digits. 
The "Gross Charges DOV" variable, variable number 11, represents the total 
dollar charges attributable to doctor office visits by the patient. The 
value of this variable is listed in the patient data file in hundreds of 
dollars, ranging from 0-99999 in a field in the data file of five decimal 
digits. 
The "DOV Count" variable, variable number 12, refers to the number of times 
a patient has visited a doctor at his or her office. The value of this 
variable is listed as a number in the patient data file ranging from 
0-9999, in a field having four decimal digits. 
The "Gross Charges Non-DOV" variable, variable number 13, represents the 
total charges attributable to non-doctor office visits. In the present 
embodiment, a non-doctor office visit is any medical encounter which did 
not involve the attendance of a physician, including visits to 
laboratories for medical testing. The value of this variable is listed in 
the data file in hundreds of dollars, ranging from 0-99999, in a field 
having five decimal digits. 
The "Non-DOV Count" variable, variable number 14, represents the number of 
non-doctor office visits by a patient. The value of this variable is 
listed in the patient data file, ranging from 0-9999, in a field having 
four decimal digits. 
The "number of PCP's seen" variable, variable number 15, represents the 
number of different primary care physicians seen by a particular patient. 
The value of this variable is listed in the data file, ranging from 0-99, 
in a field having two decimal digits. 
The "number of different specialists seen" variable, variable number 16, 
represents the number of different specialists seen by a patient. The 
value of this variable is listed in the data file, ranging from 0-99, in a 
field having two digits. 
The "number of different Out-Patient Diagnoses" variable, variable number 
17, refers to the number of outpatient diagnoses made by physicians seen 
by the patient. The value of this variable is listed in the data file, 
ranging from 0-999, in a filed having three digits. 
The "number in-patient or out-patient diagnostic ranges" variable, variable 
number 18, represents the number of predetermined ranges into which 
in-patient or out-patient diagnoses for a patient are classified. As well 
understood in the art, there are several predefined diagnostic ranges for 
different organ systems in the human body (e.g., Infectious and Parasitic 
Disease, Neoplasms Endocrine, nutritional and metabolic diseases, etc.). 
The diagnostic ranges of the present embodiment are listed in Appendix C. 
The diagnoses that fall into each range are recorded in each patient's 
medical records. The patient receives a count of "1" for each different 
range in which the patient has diagnoses. The total number of ranges into 
which diagnoses for a patient are categorized is the value for variable 
number 18. The value of this variable is listed in the data file, ranging 
from 0-19, in a field having two decimal digits. 
The "Patient Driven Definite" variable, variable number 19, represents 
whether any definite patient driven diagnoses are present. An exemplary 
list of definite patient driven diagnoses is provided in Appendix B. The 
value of this variable is represented in a patient data file with a binary 
digit, where yes=1 and no=0. 
The "Illness Driven" variable, variable number 20, represents whether any 
illness driven diagnoses are present. An exemplary list of illness driven 
diagnoses is provided in Appendix B. The value of this variable is 
represented in a patient data file with a binary digit, where yes=1 and 
no=0. 
The "Patient Driven Possible" variable, variable number 21, represents 
whether any possible patient driven diagnoses are present. An exemplary 
list of possible patient driven diagnoses is provided in Appendix B. The 
value of this variable is represented in a patient data file with a binary 
digit, where yes=1 and no=0. 
TABLE 2 
______________________________________ 
Variable Variable 
Number Name Range Format.sup.1 
______________________________________ 
1 category 1 Yes = 1, No=0 b 
2 category 2 Yes = 1, No=0 b 
3 category 3 Yes = 1, No=0 b 
4 category 4 Yes = 1, No=0 b 
______________________________________ 
As briefly explained above, in order to train a neural network a correct 
output or output vector is provided for each input vector in a training 
set. TABLE 2 is a format chart of patient output vectors for the neural 
network 20. The output vector basically describes the category of care for 
the patient. In the present embodiment, the output vector comprises the 
values corresponding to the four categories of health care utilization 
described above. Category 1 represents Patient Driven Care, Category 2 
represents a combination of Patient Driven Care and Illness Driven Care, 
Category 3 represents Illness Driven Care and Category 4 represents 
Possible Patient Driven Care. It will be understood that additional 
categories are also envisioned, and the particular categories selected do 
not limit the scope of the present invention. 
As illustrated in TABLE 2, each category of care is assigned a value of 0 
or 1, where 0 indicates that a particular category of care is inapplicable 
and 1 indicates that the patient is classified in that category of care. 
Preferably, for any given input vector, only one category in the output 
vector has a value of 1. In other words, only one category of the four 
categories listed applies for any given patient. 
TABLE 3 
__________________________________________________________________________ 
Column Number 
00000000011111111112222222222333333333344444444445555555555666666666677777 
777778888888 
12345678901234567890123456789012345678901234567890123456789012345678901234 
567890123456 
5 2 4 22 41 1 1129 473 378 468 21 26 355 422 4 3 23 10 0 1 1 0 1 0 0 
Input Var Number Out. Var Number 
1 2 3 44 555 6 77777 8888 99999 1111 11111 1111 11111 1111 11 11 111 11 1 
2 2 1 2 3 4 
0000 11111 2222 33333 4444 55 66 777 88 9 0 1 
__________________________________________________________________________ 
As mentioned above, information for each patient is represented in a 
patient data file which is configured to be read by the computer 12. For 
training purposes, the data file for each patient in the training set 
includes the expected output vector. For patients to be analyzed after 
training, the output vectors are unknown, and provided by the neural 
network 20. An exemplary patient data file format is illustrated in TABLE 
3. In TABLE 3, the Column Number, Input Var Number and Output Var Number 
labels, and corresponding reference indexes are provided for ease of 
description. The column numbers in TABLE 3 are read from top to bottom. 
Therefore, the top two rows of numbers make up the column numbers. The 
third row is the variable value. The fourth and fifth rows of numbers, 
read vertically, identify the variable number. 
For example, the value of input variable number 1 (identified by "1" in the 
fourth row of numbers in the first column), listed in column 01 
(identified by the "0" in row one and the "1" in row two in the first 
column) is 5 (provided in the third row of numbers, column one). Because 
variable number 1 is Anxiety, as described above, the 5 indicates that the 
patient is experiencing significant anxiety. The next column, column 02 
serves as a field separator (i.e., it provides a separator between the 
data fields for input variables 1 and 2). In the present embodiment, each 
data field in the patient file is separated by a separator as depicted in 
TABLE 3. Other field separators, such as commas, are well understood in 
the art. 
The third column, column 03 contains the value of input variable number 2, 
which represents Depression. The value of this variable is 2, which 
indicates that the patient is suffering from some depression. 
In one embodiment, in an actual patient data file, the patient data (i.e., 
the actual variable value depicted in the third row of numbers in TABLE 3) 
is recorded without the column number or variable number indexes. These 
indexes may prove helpful to human operators, but the computer 12 uses the 
actual variables as input. Additionally, the "Column Number", "Input Var 
Number" and "Out. Var Number" headings are removed. 
The remainder of the input variables are represented in columns 05-77. 
Columns 79-85 depicted in TABLE 3 contain the values for the output 
variables. For example, the value of output variable number 1, listed in 
column 79 is 0. This indicates that this category of care, Category 1, is 
inapplicable to the patient. The value of output variable number 2, listed 
in column 81, is 1. This indicates that the patient is identified as a 
utilizer of this category of care, Category 2 (i.e., the patient is 
seeking a combination of patient driven care and illness driven care). The 
remaining output variable columns (Columns 83 and 85) contain a "0" in the 
example depicted in TABLE 3. 
As explained above, in order to train the neural network 20, expected 
output vectors corresponding to input vectors are provided. Accordingly, 
training the neural network 20 requires that a judgment be made with 
respect to categorizing the health care utilization of the training set of 
patients. Advantageously, the training set is substantially representative 
of the patient population. Typically, the more vectors (each vector 
representing one patient) provided in the training set, and the more 
accurate the associated expected output vectors for each input vector, the 
more reliable the training of the neural network. 
FIG. 2 illustrates the general factors used in the present embodiment to 
ascertain an expected classification of particular patients into one of 
four categories of care for purposes of training the neural network 20. In 
other words, these factors were selected to generate the original output 
vectors for the training set. It should be noted that the parameters used 
to generate the training set may or may not correlate to the function 
ultimately provided by the neural network 20 based upon the 21 input 
variables illustrated in TABLE 1. In the present embodiment, Category 1 
represents Patient Driven Care, Category 2 represents a combination of 
Patient Driven Care and Illness Driven Care, Category 3 represents Illness 
Driven Care and Category 4 represents Possible Patient Driven Care. 
For purposes of generating expected output vectors for the training set, a 
particular patient's utilization of health care is classified as Patient 
Driven Care, Category 1 utilization, if the patient displays all the 
following characteristics: 
(1) the patient receives high scores on the Lifestyle Questionnaire. In the 
present embodiment, a patient's questionnaire scores are considered high 
if the patient receives: 
(a) a score equal to or greater than 3 for any of the ANX, DEP or SOM input 
vector variables; or 
(b) a Total ADS Score equal to or greater than 6. 
(2) no illness driven diagnosis is present; 
(3) The patient may have had a high number of different diagnoses 
categories. In the present embodiment, a patient is considered as having a 
high number of different diagnostic categories if the patient: 
(a) has diagnoses in six of more diagnostic ranges and is younger than 60 
years old; or 
(b) has diagnoses in 8 or more diagnostic ranges, and is 60 years old or 
older. 
(4) The patient may be diagnosed as having a definite patient driven care 
disorder or two or more possible patient driven care disorders. 
A listing of illness driven diagnoses and definite and possible patient 
driven diagnoses is provided in Appendix B. 
As depicted in FIG. 2, a patient's utilization is classified mixed patient 
driven care and illness driven care, Category 2 utilization, if the 
patient has the following characteristics: 
(1) the patient scores highly on the questionnaire, as described above; 
(2) an illness driven diagnosis is present; 
(3) the patient may be diagnosed in a high number of diagnostic ranges; and 
(4) The patient may be diagnosed as having a definite patient driven care 
disorder or two or more possible patient driven care disorders. 
A patient's utilization is classified as illness driven care, Category 3 
utilization, if the patient exhibits the following characteristics: 
(1) the patient has low scores in the Lifestyle Questionnaire. In the 
present embodiment, a patient scores low on the questionnaire if 
(a) he receives a score of less than 3 on each of the ANX, DEP or SOM input 
vector variables; and 
(b) his Total ADS Score is less than 6. 
(2) an illness driven diagnosis is present; 
(3) the patient may be diagnosed in a high number of diagnostic ranges; and 
(4) no definite patient driven diagnosis is present, and less than two 
possible patient driven care diagnoses are present. 
A patient's utilization is classified as Possible Patient Driven Care, 
category 4 utilization, if the patient exhibits the following 
characteristics: 
(1) patient scores low in the questionnaire, as discussed above for 
category 3; 
(2) no illness driven diagnosis is present; 
(3) the patient has been diagnosed in a high number of diagnostic ranges; 
and 
(4) The patient may be diagnosed as having a definite patient driven care 
disorder or two or more possible patient driven care disorders. 
In training the neural network, many presentations of the training set and 
weight adjustments are required for the network to converge (become 
stable). During training, the neural network 20 is periodically tested to 
determine how well it generalizes from specific examples it has learned. 
To test the network, the trained net is presented with novel patient 
vectors, and the accuracy of the responses of the net is recorded. 
Training and testing proceed, until the neural network achieves the 
desired degree of accuracy on new input vectors or until accuracy no 
longer improves. 
A generalized training process 30 for the neural network 20 of the present 
invention is depicted in the flow diagram of FIG. 3. The training process 
30 begins, as represented in a start block 32. First, input and output 
vectors are scaled to the -1 to 1 range discussed above, and any other 
preprocessing of the training data is completed, as represented in an 
action block 33. The next step is to initialize the neural network 20, as 
represented in an action block 34. In this step, variables used in the 
neural network are initialized. In particular, the weights used for the 
neural network inputs and between layers are selected as random values, 
and the learning rate, network architecture, activation function, and 
other parameters are also selected. 
Input vectors and their corresponding target output vectors are then 
presented to the neural network in random order, as represented in an 
action block 38. The input vectors and target output vectors are 
preferably read from a file created either manually by an operator via the 
keyboard 14 or by providing commands to the processing system 10 to 
retrieve the appropriate patient data files from the training set on the 
storage media 16. In the present embodiment, the data in the patient data 
files is preferably stored in a conventional ASCII format. Each data file 
in the training set consists of two vectors of numbers: an input vector 
and a target output vector. Each vector consists of a series of numbers 
separated by one or more blank spaces, as explained above with reference 
to TABLE 3. The input vector represents the patient data which is 
presented to the network, and the target output vector represents the 
target response of the network (i.e., the output which should be produced 
by the network in response to this input vector). 
Based on the input vector, and the weights initially assigned, the neural 
network calculates the output response, as represented in an action block 
40. Next, the neural network 20 performs a comparison between the 
calculated output response with the target output to provide an error 
signal, as represented in an action block 42; and the weights from each 
input are adjusted to partially compensate for the error signal, as 
represented in an action block 44. 
The amount of compensation in the adjustment of the weights is dependent 
upon the selected learning rate for the neural network 20. If the learning 
rate is too fast, the compensation is too great for each error signal; and 
the neural network 20 loses what the neural network "learned" from each 
vector when the subsequent vector is processed. Accordingly, a learning 
rate between 0 and 1 is selected in the present embodiment to provide less 
than complete adjustment for each error signal, as well understood in the 
art. 
Next, a determination is made whether this iteration is the last iteration 
before testing, as represented in a decision step 46. In other words, 
several iterations with the training set are generally completed before 
testing. If the current iteration is the last iteration, the training 
process 30 proceeds to testing, as represented in the action block 48. If 
the iterative process is not complete, the neural network 20 repeats the 
steps represented in the action blocks 38-46, until all vectors in the 
training set have been presented to the neural network 20 a number of 
times. 
When the iterative process depicted by steps 38-46 has completed (decision 
block 46), the neural network 20 is tested, as represented in the test 
network action block 48. In this step, the neural network 20 receives new 
input vectors, not part of the training set. The output response of the 
neural network 20 is then calculated and compared to a target response. If 
the neural network 20 produces accurate results, the operator may 
determine that training is adequate, as represented in a decision block 
50, and the training process is complete, as represented in an end block 
52. If the result is unacceptable and further improvement is desired, the 
neural network 20 repeats steps 38-46 for a new training set, or for the 
original training set, or a combination of some or all of the original 
training set with additional new input vectors. 
A variety of network models may be used in the neural network 20. Typical 
models include the back-propagation network, the radial basis function 
network and the learning vector quantization network, as known in the art. 
For each neural network type, the appropriate training processes is 
utilized, as well understood in the art. 
After the neural network 20 is trained, it may be used to process new 
information (new input vectors). In one embodiment of the present 
invention, prior to utilizing the trained neural network 20 to analyze 
health care utilization of patients, patient information may be 
preprocessed to identify patients who are at "high risk." In the present 
embodiment, high risk patients are those patients considered to be 
suffering from an immediate threat to their health. 
The flow diagram of FIGS. 4A and 4B illustrate one possible embodiment of a 
prescreening process 60 in accordance with the present invention. 
Beginning at a start block 62, the process proceeds to a read patient data 
action block 64. Patient data is accepted via keyboard input and/or via 
data files. As represented in a decision block 66, a determination is made 
whether the patient has "high" Lifestyle Questionnaire scores, a 
represented in a decision block 66. In the present embodiment, a patient 
is considered to have a high Lifestyle Questionnaire score if a "CAGE" 
score is greater than or equal to 2, a MEBS score is greater than or equal 
to 8, or a score is equal to or greater than 3 on any one of the ANX, DEP 
or SOM input variables or a total ADS score is equal to or greater than 6. 
The CAGE score is obtained from the Lifestyle Questionnaire, and relates 
generally to psychological problems due to alcohol addiction. With 
reference to the Lifestyle Questionnaire in Appendix A, four questions in 
the Questionnaire relate to the CAGE score. Specifically, Questions 16 
through 19 inquire whether (1) the patient has ever felt that he should 
Cut Back on his drinking, (2) if the patient has ever been Annoyed by 
others' criticisms of his drinking, (3) if the patient has ever felt 
Guilty about his drinking habits and (4) if the patient has ever consumed 
alcohol as soon as he wakes up so as to steady his nerves or to overcome a 
hangover. A score of "1" is assigned to each affirmative response and a 
score of "0" is assigned to each negative response. CAGE stands for "Cut 
back, Annoyed, Guilty, Eye Opener," which are attributes related to 
alcoholism. The total from the four questions provides the patient's total 
CAGE score. If the CAGE score is equal to or greater than 2, the patient 
is identified as having a high Lifestyle Questionnaire score. 
The MEBS score is also obtained from the Lifestyle Questionnaire. As seen 
in the Lifestyle Questionnaire, ten questions (questions 5 through 14) 
relate to MEBS. These questions relate to a patients caffeine usage. If 
the patient answers yes to eight or more of questions 5 through 14, the 
patient is identified as having a high Lifestyle Questionnaire score. 
The patient scores for ANX, DEP and SOM are also obtained from the 
Lifestyle Questionnaire. As seen in the Lifestyle Questionnaire, questions 
21-25 relate to anxiety (ANX), questions 26-30 relate to depression (DEP) 
and questions 31-35 related to somatization (SOM). As explained above, if 
a patient has three or more yes answers in any of the ANX, DEP or SOM 
categories of questions, or has a combined total of six or more for all 
three categories, the patient is identified as having a high Lifestyle 
Questionnaire score. 
Any patient identified as having a high Lifestyle Questionnaire score is 
identified as being at high risk, as represented in an action block 68. 
This information is then forwarded to a case manager, as represented in an 
action block 70. The case manager may automatically receive this 
information via a computer network, or the operator for the pre-processing 
system may provide this information to the case manager. 
It should be understood that data for all patients could be pre-processed 
before the case manager is notified. For instance, each patient that is to 
be forwarded to a case manager could be identified in the pre-processing; 
and a list of all such patients, with corresponding recommendations, 
forwarded to the case manager. 
However, if a patient's Lifestyle Questionnaire score is not high (decision 
block 66), or the high risk identification is complete (action block 68), 
those patients who appear to have a narcotic dependency problem are 
identified, as represented in a decision block 76. If a patient has been 
provided with two or more narcotic prescriptions in two or more 
consecutive quarters by two or more doctors at two or more pharmacies, the 
patient is identified as being at high risk. as represented in the action 
block 68. This information is then forwarded to the case manager, as 
illustrated in the action block 70. 
It should be understood that the Lifestyle Questionnaire data and pharmacy 
(narcotic) data from all patients could be pre-processed before the case 
manager is notified. 
If the patient's records do not suggest a narcotic dependence (decision 
block 74), a determination is made whether more patients are available for 
pre-screening, as represented in a decision block 78. If more patients are 
available for screening, the steps represented in block 64-78 are 
repeated. 
If no further patient files are available, patients are ranked according to 
the total cost of claims paid for the patient group under consideration, 
as represented in an action block 80 (FIG. 4B, via continuation point A). 
Next, patients with the least frequent utilization are eliminated as 
unlikely candidates for inappropriate utilization. In the present 
embodiment, the top 10% (dollar amount) for total claims paid for the 
group under consideration are kept for consideration. The bottom 90% (by 
total dollars of claims paid) are removed from consideration. 
In one embodiment, only the top 10% of utilizers are processed by the 
neural network 20, as represented in an action block 84. Alternatively, 
all patients are passed to the neural network 20 for processing. 
Pre-screening for high risk may be advantageous even when all patients are 
analyzed with the neural network 20, in that the case manager receives a 
quick indication of the likelihood of problems. 
The flow diagram of FIG. 5 illustrates a generalized operational process 90 
for the trained neural network 20. Beginning at a start block 92, control 
proceeds to an action block 94. As represented in the action block 94, the 
patient input vector is presented to the trained neural network 20. The 
neural network 20 calculates the output vector for the selected patient, 
as represented in an action block 96. Next, a determination is made 
whether all patients, from a group of patients to be analyzed, have been 
analyzed by the neural network 20, as represented in a decision block 98. 
The neural network repeats the processing until all patients have been 
analyzed. The process completes in an end block 100. 
The output vector for each patient is advantageously stored in the 
patient's data file, or in a separate file categorizing patients by their 
category of health care utilization. Those patients who have been 
classified in Category 1, Category 2 or Category 4 are forwarded to case 
managers for further examination in an attempt to identify inappropriate 
usage, and to treat the underlying problems for the patients. 
It will be understood, that for each patient continually utilizing health 
care in an inappropriate way, many health care dollars will be spent in 
treatments which will not assist in the underlying problem. Thus, by 
characterizing patients as having the potential of inappropriate 
utilization, the case managers can assist in obtaining proper diagnoses, 
and eliminating the unnecessary expenses of treating ailments which merely 
stem from the underlying problem. 
Although the present invention has been described in terms of certain 
preferred embodiments, other embodiments can be readily devised by one 
skilled in the art in view of the foregoing. For instance, processing 
methods other than neural networks may be appropriate given the factors 
suggesting utilization characteristics depicted in FIG. 2. Additionally, 
further factors may also be considered in making the above determinations. 
Accordingly, the scope of the present invention is defined by reference to 
the appended claims. 
APPENDIX C 
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OUTPATIENT 
ICD 9 CODES &