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Claims  |
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What is claimed is:
1. A process for evaluating effectiveness of service among a set of
treatment facilities, said process comprising the steps of:
gathering data quantifying quality, cost, and access performance
characteristics of each of the treatment facilities;
displaying the quality, cost and access performance characteristics
simultaneously on a graph to indicate thereby strong and weak quality,
cost and access performance characteristics; and
identifying the strong and weak quality, cost and access performance
characteristics of each treatment facility.
2. A process, as defined in claim 1, wherein said displaying step is
performed using a three-dimensional cube using a separate axis for the
quality, cost and access performance characteristics.
3. A process, as defined in claim 2, wherein said displaying step is
performed on said graph by charting the quality performance
characteristics such that:
##EQU4##
where: i is a quality indicator;
.beta.is an adjustment factor based on an average severity indexing for a
particular treatment facility;
I is a quality indicator value at a particular treatment facility;
.mu. is a mean value of the QIP indicator for all treatment facilities;
.sigma. is a standard deviation for a QIP indicator based on all treatment
facilities; and
.epsilon. is a patient perception adjustment factor (-0.1,0,0.1).
4. A process, as defined in claim 3, wherein said displaying step is
performed on said graph by charting the access performance characteristics
such that:
##EQU5##
where i indicates inhouse client;
C is a preselected average daily client load;
D is particular hospital average daily client;
G is the goal for appointments;
R is the number of available appointments; and
.epsilon. is an adjustment for client perception of access (-0.1,0,0.1).
5. A process, as defined in claim 4 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU6##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
6. A process, as defined in claim 3 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU7##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
7. A process, as defined in claim 2, wherein said displaying step is
performed on said graph by charting the access performance characteristics
such that:
##EQU8##
where i indicates inhouse client;
C is a preselected daily client load;
D is a particular hospital average daily client;
G is the goal for appointments;
R is the number of available appointments; and
.epsilon. is an adjustment for client perception of access (-0.1,0,0.1).
8. A process, as defined in claim 7 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU9##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
9. A process, as defined in claim 2 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU10##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
10. A process, as defined in claim 1, wherein said displaying step is
performed on said graph by charting the quality performance
characteristics such that:
##EQU11##
where: i is a quality indicator;
.beta.is an adjustment factor based on an average severity indexing for a
particular treatment facility;
I is a quality indicator value at a particular treatment facility;
.mu. is a mean value of the QIP indicator for all treatment facilities;
.sigma. is a standard deviation for a QIP indicator based on all treatment
facilities; and
.epsilon. is a patient perception adjustment factor (-0.1,0,0.1).
11. A process, as defined in claim 10, wherein said displaying step is
performed on said graph by charting the access performance characteristics
such that:
##EQU12##
where i indicates inhouse client;
C is a preselected average daily client load;
D is a particular hospital average daily client;
G is the goal for appointments;
R is the number of available appointments; and
.epsilon. is an adjustment for client perception of access (-0.1,0, 0.1).
12. A process, as defined in claim 11 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU13##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
13. A process, as defined in claim 10 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU14##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
14. A process, as defined in claim 1, wherein said displaying step is
performed on said graph by charting the access performance characteristics
such that:
##EQU15##
where i indicates inhouse client;
C is one facility average daily client load;
D is another facility daily client load;
G is the goal for appointments;
R is the number of available appointments; and
.epsilon. is an adjustment for client perception of access (-0.1,0,0.1).
15. A process, as defined in claim 14 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU16##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
16. A process, as defined in claim 1 wherein said displaying step is
performed on said graph by charting the cost performance characteristics
such that are given by:
##EQU17##
where: i is an inhouse client indicator;
.beta. is an adjustment factor for case weight, severity, and a ratio of
direct cost to total cost;
I direct cost per catchment area employee; and
.mu. is a benchmark cost against which facility costs are compared.
17. A system for evaluating effectiveness of service among a set of
treatment facilities, said system comprising:
a means for gathering data quantifying quality, cost, and access
performances characteristics of each of the treatment facilities;
a means for displaying the quality, cost and access performance
characteristics simultaneously on a graph to indicate thereby strong and
weak quality, cost and access performance characteristics; and
a means for identifying the strong and weak quality, cost and access
performance characteristics of each treatment facility.
18. A system, as defined in claim 17, wherein said displaying means
comprises a computer monitor which is connected to a computer which is
programmed to depict the quality, cost and access performance
characteristics in a chart that simulates a three-dimensional cube which
has a separate axis for the quality, cost, and access performance
characteristics. |
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Claims |
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Description |
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BACKGROUND OF THE INVENTION
This invention relates generally to a method and system for utilizing
management effectiveness, and more specifically to a method and system for
providing medical care at a reasonable cost for all the nation's citizens.
Existing tools, which focus on single measurement parameters in isolation,
do not convincingly capture the way health care facilities operate. At
least in part for this reason, such tools have failed to inspire
significant practice pattern changes and/or management efficiencies, even
in light of the current furor over U.S. health care spending. Therefore,
we designed and developed a method and system of integrated medical
organizational performance across the parameters of quality, cost, and
access. Without a complete understanding by medical managers of these
underlying issues of medical care, solutions to the medical problems of
this country are not achievable. Large scale improvements in the current
state of medical care require a standard which compels management's
attention to the proper balance between these competing but interrelated
forces.
The task of evaluating the factors of quality, cost, and access in such a
manner as to provide a holistic description of the effectiveness of
medical treatment data, is alleviated, to some extent, by the systems
disclosed in the following U.S. Patents, the disclosures of which are
incorporated herein by reference:
U.S. Pat. No. 5,128,860 issued to Chapman
U.S. Pat. No. 5,117,353 issued to Stipanovich et al.
U.S. Pat. No. 4,992,939 issued to Tyler
U.S. Pat. No. 4,975,840 issued to Detore et al.
U.S. Pat. No. 4,893,270 issued to Beck et al.
U.S. Pat. No. 4,858,121 issued to Barber et al.; and
U.S. Pat. No. 4,667,292 issued to Mohlenbrock et al.
The patent to Mohlenbrock et al. discloses patient billing for hospital
care. The computer billing is reviewed by the physician each day. The
patent to Beck et al. discloses a medical information updating system for
patents. The patent to Tyler discloses a method of producing a narrative
report. The Tyler system analyzes information which has been inputted to a
database and using predetermined phrases intermingled with extracts from
the database, produce a narrative analytical report which describes the
critical aspects of the database. The Tyler system also produces a listing
of questions on those aspects of the database which require explanation of
clarification. The patents to Barber et al., Detore et al., Stipanovich et
al., and Chapman are of interest, but they do not model medical care
facilities (MTFs) based on quality of care, cost, and access.
Currently, medical care is not evaluated in a holistic manner. Instead,
quality is examined in isolation from cost and neither of these is
compared to access which is rarely, if ever, evaluated. In addition, there
is a lack of commonality between the evaluation criteria that do exist,
making comparisons between treatment facilities and medical practitioners
infeasible. As a result, goals for improvements in medical care cannot be
established except in individual hospitals.
In terms of cost, there are many criteria that are used, whether for the
cost of supplies or provider charge rates. Since none of these cost
criteria are universal, it is difficult to compare different hospitals on
the basis of cost. Also, accounting practices differ causing further
complications. To make medical care affordable for all people in this
country, it is imperative that definitions of cost be standardized.
Quality of medical care is almost universally defined in terms of mortality
rates, which has not proven to be very useful. At least one study has
indicated that even the best hospitals can now and then have unfavorable
mortality rates. When using mortality figures to evaluate quality of care,
it is important to separate those that were expected to die from those
that were not. This is not currently done and is not easy to do,
especially in terms of the litigation such a practice would cause in
insurance and medical industries (i.e. lawsuits over those persons that
should not have died). As a result, mortality in and of itself does not
describe "quality" medical care and is not a useful metric to use to try
to solve the medical problems facing this country.
Access to medical care is not directly measurable. Since there are many
hospitals and medical practitioners from which to choose, at least in
urban areas, it would be infeasible to attempt to associate the number of
people that should have access to a particular hospital or doctor. The
only "measurable" criteria for access to medical care are media accounts
and government estimates of people who have little or no medical insurance
and thereby are assumed to have a lack of access to medical care. Again,
definitions are important since medical care is available, its just that
people cannot afford it.
To assess how well a hospital or doctor provides medical care, and to
establish the cost effectiveness of that care, the three factors of
quality, cost, and access must be evaluated simultaneously. Current
methods of measuring these factors are lacking and provide little useful
information to the medical manager. Without an overall perspective of how
these factors interrelate and how an improvement in one can lead to a
change in another, medical managers cannot be expected to achieve
improvements that would lead to a cost effective medical care program for
everyone in the country.
SUMMARY OF THE INVENTION
The invention is a process and system of modeling the factors of quality,
cost, and access in such a manner as to provide a holistic description of
the effectiveness of medical treatment data from a variety of computerized
databases and incorporating patient perceptions of medical care through
the use of surveys. This allows effectiveness of different groups of
medical care facilities to be compared to each other. Deficiencies in
performance are readily identified through this process, permitting goals
and targets to be established that provide direction for medical
administrators to enhance medical care at their treatment facilities.
One embodiment of the invention may be considered a process for evaluating
effectiveness of service among a set of treatment facilities, the process
includes the steps of: gathering data quantifying quality, cost, and
access performances characteristics of each of the treatment facilities;
displaying the quality, cost and access performance characteristic
simultaneously on a graph to indicate thereby strong and weak quality,
cost and access performance characteristics; and identifying the strong
and weak quality, cost and access performance characteristics of each
treatment facility.
To ensure that all medical treatment facilities are considered fairly,
adjustments are made to the data based on patient severity of illness, the
amount of resources used for treatment (case weight), and the ratio of
direct military care costs to CHAMPUS costs. These adjustments are applied
to equations that have been developed for each of the three factors
(quality, cost, and access), providing a quantitative three dimensional
cube permitting managers to assess the overall effectiveness of their
treatment facilities. For simplicity, each factor is divided into "High"
and "Low" regions.
Another embodiment of the invention is a system for evaluating
effectiveness of service among a set of treatment facilities. This system
uses commercially-available computers as a means for gathering data
quantifying quality, cost, and access performances characteristics of each
of the treatment facilities. This system uses a central computer as a
means for identifying the strong and weak quality, cost and access
performance characteristics of each treatment facility. The central
computer is also an ordinary computer with a computer monitor that serves
as a means for displaying the quality, cost and access performance
characteristics simultaneously on a graph to indicate thereby strong and
weak quality, cost and access performance characteristics. The central
computer is programmed to depict the quality, cost and access performance
characteristics in a chart that simulates a three-dimensional cube which
has a separate axis for the quality, cost, and access performance
characteristics.
The object of the invention is to provide a system and process that
evaluates the quality cost and access of treatment facilities, and
identify deficiencies thereby.
This object together with other objects, features and advantages of the
invention will become more readily apparent from the following detailed
description when taken in conjunction with the accompanying drawings
wherein like elements are given like reference numerals throughout.
DESCRIPTION OF THE DRAWINGS
FIG. 1 is an illustration of a cube used in the invention to depict
quality, cost and access graphically;
FIGS. 2A and B are charts depicting how the QIP indicator is adjusted for
patient perception (FIG. 2A) and for the severity index effect (FIG. 2B);
FIGS. 3A, 3B and 3C are charts of adjustments to cost;
FIGS. 4 and 5 respectively illustrates the use of the cube of FIG. 1 to
chart top and bottom performing treatment facilities; and
FIG. 6 is a distributed computer data system.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
The present invention can be regarded as a process and system for
identifying possible deficiencies among a set of treatment facilities. The
process includes the steps of: measuring actual quality, cost and access
performance characteristics of the treatment facilities; establishing
standards of performance values for quality, cost and access for the
treatment facilities; and comparing the actual quality, cost and access
performance characteristics of each of the treatment facilities with the
standards of performance values of quality, cost and access to identify
thereby the possible deficiencies in the treatment facilities.
In this process, each of the treatment facilities is a medical treatment
facility, and the measuring step for quality is performed using a Quality
Indicator Project service which provides the measure of quality
performance characteristics for each medical treatment facility. Also in
this process, the measure of quality and cost performance characteristics
are adjusted for both patient perception of treatment and for severity of
illness to produce thereby the actual quality performance characteristics
of each medical treatment facility. Finally note that the establishing
step is performed by producing the standards of performance values by
averaging the quality, cost and access performance characteristics to
produce a set of average values, and wherein the company step is performed
by counting magnitudes of deviation between the actual quality, cost and
access performance characteristics and their respective averages among the
set of average values.
As discussed below, the present invention can also be considered a system
which performs the steps recited above using a distributed computer
network.
The interrelated factors of quality, cost, and access suggested a three
dimensional relationship, for example a response surface, since each
measure is conceivably continuous. For simplicity, however, criteria were
established so as to access each factor as being favorable or unfavorable.
As such, a cube of eight octants is used to simultaneously depict
graphically the factors of quality, cost, and access as shown by FIG. 1.
For example, a treatment facility that was the most effective would meet
or exceed established criteria for each of these measures (i.e. high
quality, low cost, high access). Treatment facilities that are deficient
in meeting the established performance goal in one or more measures would
indicate that some level of improvement is warranted. Visually, by color
coding the octants of the cube (all favorable="green", one
unfavorable="yellow", two or more unfavorable="red"), the overall
performance of the treatment facility would be readily apparent, making
the representation a useful management tool.
It is important to note, however, that placement in the cube does not
necessarily indicate good effectiveness. For example, a treatment facility
that did not meet the criterion established for "quality" does not mean
that the facility exhibited poor quality, per se. This is only an
indication that performance appears to be below established values. The
reasons for this less than desired performance could then be explored, and
possibly explained satisfactorily by circumstances not encompassed by the
model.
As lower rated treatment facilities improve in quality, the criterion would
be adjusted upward so that it continues to allow discrimination between
higher and lower levels of performance. The treatment facilities,
therefore, should not only attempt to meet established criteria, but
should seek continued improvement since the criteria will eventually
reflect higher overall expected levels of performance.
Conceptually, the positioning of a treatment facility in the cube is
indicative only of relative rating. However, by using data gathered from
several sources, a more complete picture of facility performance can be
shown. Of the three measures of performance, cost and access can be
measured objectively. A quality measure, however, is much more subjective.
Nevertheless, objective data are available that can be useful in this
regard.
Quality is both an actual fact and a perception on the part of the patient.
If a patient actually receives quality care but perceives it otherwise,
the patient is apt not to elect future treatment from the facility or the
provider, adversely affecting the access measure. Nevertheless, the
emphasis should remain with actual quality of care as evidenced by data
from the treatment facility, perhaps with some adjustment for patient
perceptions. The method used here for examining quality of care is the
Maryland Quality Indicator Project (QIP), to which a number of civilian
and Department of Defense (DOD) hospitals are subscribers. Using data from
hospitals nationwide, ten indicators have been selected as measures of
quality. However, these indicators do not account for severity of illness
or resource usage (case mix), which must be considered when performing
comparisons between treatment facilities.
Equation 1, as presented below, denotes the process by which quality is
calculated. Note that it is a summation over all ten QIP quality measures,
adjusted first for severity of illness and then measured in terms of
magnitude of deviation from an average value. This permits different
treatment facilities to be compared on an equal basis.
##EQU1##
where
i is the QIP indicator
.beta. is an adjustment factor based on an average severity indexing for a
particular treatment facility
I is the QIP indicator value at a particular treatment facility
.mu. is the mean value of the QIP indicator for all treatment facilities
.sigma. is the standard deviation of a QIP indicator based on all treatment
facilities
.epsilon. is a patient perception adjustment factor (-0.1,0,0.1)
As mentioned above, the method used here for examining quality of care uses
the Maryland Quality Indicator Project (QIP), to which a number of
civilian and Department of Defense (DOD) hospitals are subscribers. Using
data from hospitals nationwide, ten indicators have been selected as
measures of quality. These indicators are shown in Table 1 but they do not
account for severity of illness or resource usage (case mix), which must
be considered when performing comparisons between treatment facilities.
TABLE 1
______________________________________
QUALITY INDICATORS
______________________________________
I Hospital Acquired Infections
II Surgical Wound Infections
III Inpatient Mortality
IV Neonatal Mortality (1801 grams only)
V Perioperative Mortality
VI Cesarean Sections
VII Unplanned Admissions Following Ambulatory Procedure
IX Unplanned Returns to Special Care Unit
X Unplanned Returns to Operating Room.
______________________________________
Equation 1 denotes the process by which quality is calculated. Note that it
is a summation over all ten QIP quality measures, adjusted first for
severity of illness and then measured in terms of magnitude of deviation
from an average value. This permits different treatment facilities to be
compared on an equal basis.
As indicated by Equation 1, if a quality indicator for a particular
treatment facility is lower than the average of that quality indicator
across all treatment facilities, then the result is a negative number;
otherwise, it is positive. This would indicate that an overall negative
value for the quality measure suggests a facility that exhibits better
than average quality. A positive value would indicate quality that is less
than average. Therefore, it would seem appropriate to place treatment
facilities in the cube based on negative (green) or positive (red) values
of the quality measure. It was decided to treat all ten QIP quality
measures equally (i.e. no one quality measure is more important than
another).
Severity indexing is used to account for differences in patients and
treatments. As severity increases, the differences between a particular
treatment facility's quality indicator and the average quality indicator
becomes more marked. That is, if two facilities have negative indications
of quality, but the second facility treats patients with more severe
illnesses than does the first, then the second facility would be given
credit for higher quality of care.
Shown in FIGS. 2A and B is the process whereby the adjustment factor for
quality (.beta. in Equation 1) is calculated. Each treatment facility
receives a score, denoted by "I" in FIG. 2A for each of the ten quality
indicators used by the Maryland Quality Indicator Project. The value
".mu." represents the average score for all hospitals for a particular
quality indicator. Thus, if a hospital has a lower value than the average
for all hospitals, then this particular hospital would have "better"
quality for this particular quality indicator.
However, other circumstances could affect this comparison. If the hospital
in question treats patients that are not as sick as those that are treated
at other hospitals, it would be expected that the quality of this hospital
would be better since the patients are not as sick. In this instance the
full benefit of being better than average should not be given, but should
be reduced. On the other hand, if the patients seen at this hospital are
sicker than those seen at other hospitals, and this hospital is also
better than average, then extra credit should be given.
Adjustments for severity of illness are represented by the value .beta..
Each of the ten quality indicators can be associated with a Diagnostic
Related Group (DRG), Medical Diagnostic Code (MDC), or other medical
grouping which can be used to assess each patient in the group and thereby
calculate a severity index (SI) for each quality indicator. Then, an
average SI (represented by .mu..sub.SI) can be calculated for each quality
indicator, permitting a comparison of severity levels between a single
hospital and an average severity levels for all hospitals. As indicated in
FIG. 2B, if the severity index of patients at a hospital (represented by
"SI") is greater than the average for all hospitals, then extra credit
will be given to that hospital for treating a more severe case of
patients.
It is assumed that severity levels will follow a Gaussian, or normal,
distribution. By simply using the area under the normal distribution an
adjustment factor for quality can be calculated. When the severity index
(SI) for a hospital for a quality indicator is compared to the average for
all hospitals, a certain amount of area is covered under the normal
distribution. The amount of area will vary between 0 and 1 and is designed
by .alpha.. We have arbitrarily set .beta., the quality adjustment factor,
equal to 1.5 minus .alpha.(.beta.=1.5-.alpha.). Thus, if the severity
index of a hospital was exactly that of the average for all hospitals,
then .alpha.=0.5 which results in .beta.=1 (i.e. no adjustment will be
made). Similarly, if the severity index for a hospital is greater than
average, then .alpha. is greater than 0.5 which would cause .beta. to be
less than one (.beta.<1). This would shift the quality measure of the
hospital ("I") to the left. Referring to FIG. 2, in the case of a hospital
with "better quality" such an adjustment would mean an even better quality
value than indicated by "I" alone. In a similar fashion, if the severity
index of a hospital was less than average (i.e. the hospital treats less
sick patients), then .alpha.<1 which causes .beta.>1, causing a shift to
the right of the hospital quality indicator value. This indicates that
quality at this hospital for this particular quality indicator is not as
good as it seems.
Since there are ten quality indicators, there will be ten .alpha. values
and ten .beta. values for each hospital. For each quality indicator there
will also be a standard deviation. By converting all quality indicator
values to a standard normal value, and by treating all ten indicators
equally in terms of importance, all ten standardized values can be added
together. If the result is a negative value, this indicates that, overall,
the hospital is performing better than average and should be given a
"high" quality rating. If the result is a positive value this indicates
worse performance than average and the hospital would be given a "low"
quality rating.
This procedure is applicable to other than the normal distribution. Initial
indications are that severity and quality indicators follow a normal
distribution, at least in terms of assessing hospitals and not individual
patients or medical practitioners. The process of calculating quality
would not change under conditions of other than a normal distribution. In
this instance, another distribution would need to be substituted for the
normal distribution and a standardized value for each quality indicator
would need to be calculated. The .alpha. value would then correspond to
the distribution that was being used.
A final adjustment is made to the overall quality measurement by including
the patient's perception of quality based on after-treatment patient
surveys. While the proper magnitude of this adjustment has not been
statistically validated, it is necessary, in principle at least, to allow
some form of patient input in quality assessment. While the patient may
not fully comprehend the meaning of "quality" care, and whether or not he
or she is receiving it, these perceptions carry some weight when
determining what provider to use for subsequent treatments. Furthermore,
if a patient is not satisfied with the quality of his or her care, the
chances for a full and complete recovery could be impaired. Since initial
calculations have shown quality values to range from about -2 to +2, the
following patient perception additive values have been assigned (these
account for about 5% of the quality measure):
+0.1 if the patient surveys rate poor or very poor
0 if the patient surveys indicate a neutral response
-0.1 if the patient surveys rate good or very good
Cost is a relatively easy component to determine for civilian hospitals, at
least in terms of how much was spent for x-rays, supplies, physician
charges, and so forth. For military medical facilities, on the other hand,
cost is perhaps the most difficult to define and measure. While it is
known how much is spent overall in a military treatment facility,
financial records do not reflect itemized costs as is done in civilian
hospitals. As a result, only approximate values can be placed on the cost
for providing care for individual patients in military facilities.
To compare one facility to another in terms of cost, it is necessary to
first delineate the cost per patient, adjusted for case complexity and
severity of illness, for both inpatient and outpatient care. These figures
would then provide a foundation for comparing hospitals. In addition, the
cost of CHAMPUS inpatient and outpatient care must also be examined
(CHAMPUS is a military insurance program for care at civilian facilities).
If two facilities have the same internal costs for direct care but the
first has a higher per patient cost for CHAMPUS care, then this
shortcoming needs to be addressed.
In a manner similar to that used by civilian hospitals, and described in
"The Olmstead County Benchmark Project: Primary Study Findings and
Potential Implications for Corporate America," MAYO Clinic Proceedings,
January 1992, the disclosure of which is incorporated herein by reference
the cost per eligible beneficiary is used to compare hospital costs. As
such, these costs need to be differentiated by direct military care and
CHAMPUS care and by inpatient and outpatient care. Equation 2 describes
how these costs are computed.
##EQU2##
where
i is the inpatient/outpatient indicator
.beta. is an adjustment factor for case weight, severity, and the ratio of
direct military cost to total military cost
I direct military cost per catchment area employee
.mu. is the benchmark cost against which facility costs are compared
By assessing both direct military care and CHAMPUS costs across both
inpatients and outpatients, the total cost per beneficiary in a military
medical service area can be computed. Obviously, the lower the cost, the
better. However, direct costs vary between treatment facilities as do
CHAMPUS charges, based partly on the demographics of the beneficiary
population. In addition, insurance costs can differ based on regional
concerns. To more properly balance the costs associated with each military
treatment facility and the military beneficiaries it serves, severity and
case complexity are used to adjust the cost of medical care.
In a manner similar to the adjustment for quality, an adjustment is also
made to cost. Unlike the quality adjustment, however, several factors need
to be considered for cost of medical care. First, severity of illness is
important for the same reasons it was important to quality considerations.
The severity index is applied to cost in the same manner as it was applied
to the quality measure.
In addition, the cost of resources is important. This is termed "case
weight" and is different from severity indexing. For example, a person
could be severely ill (i.e. a terminally ill cancer patient) but require
few resources. On the other hand, an individual with a broken leg could
consume many resources (x-rays, plaster casts, etc.) but would not be
severely ill. An average case weight for all hospitals can be computed as
was done with severity indexing. Again, a normal distribution is assumed
for case weight (initial tests show this assumption to be valid) and a
standard deviation is computed. An .alpha. value is computed based on the
area under a normal distribution using the case weight for each hospital | | |
