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Case Mix Management: A Cautionary Note
Volume 6, No. 3
FalVAutomne 1993
Case Mix Management: A Cautionary Note by Michael J. Long
I
W
ith the increasing propensity for Canadian provinces to implement some form of case mix adjustment in hospital reimbursement, administrators have a growing interest in case mix management. Although sound management practice would dictate that case mix management be undertaken, it is even more sound to be guided in this endeavour by empirical data rather than by intuition, or rule of thumb. For example, it is not uncommon to hear from some who should know better that the incentive now is to admit the patient types with the greatest Resource Intensity Weight (RIW*).To argue this is to ignore the other side of the equation - the cost to treat a given patient type. If there is any incentive inherent in a system that reimburses, to any extent, on the basis of case mix, surely it is in the difference between the reimbursement amount that derives from the patient type weighting system, and the cost to treat that patient type. Also, there is no reason to expect that the most desirable patient types from a financial perspective would be the same for all facilities. Using U.S. data and a macro approach, this paper examines the relationship between the profitability of Diagnostic Related Groups (DRGs) and their DRG
weight and the similarity (or difference) of the most (least)profitable DRGs across hospital types.
Methodology As part of a larger study that evaluated the impact of the DRG-based Prospective Payment System (PPS)?” 509 U.S. acute care hospitals making up the Commission on Professional and Hospital Activities (CPHA)Quality of Care data set that could be linked to the MEDPAR file of the Health Care Financing Administration (HCFA)were identified. The reported charges and the reported reimbursement for each DRG in each hospital was determined using third quarter data for 1984 for Medicare patients from the MEDPAR file. The difference between these two amounts is more accurately referred to as the contract adjustment. However, it is not unreasonable to suggest that since this was done separately within each hospital, the different methods of cost allocation used by different hospitals was not an issue. In other words, charges would represent some measure of relative costliness within the same facility and the contract adjustment would, therefore, represent a measure of relative profitability within the same hospital.
~~
Gestion des soins de santC
47
Volume 6, No. 3
FalUAutomne 1993
Table 7: Five Most and Five Least Profitable DRGs: Minor Teaching Status
TaMe 8: Five Most and Five Least Profitable DRGs: Non-Teaching
Profit
REG
Profit
Banh
~~
Most Profitable 148 110 154 468 116
Banb 1 2 3 4 5
2.5493 2.2912 1.8648 2.0833 1.4868
1 2 4 3 5
6 7 8 9 10
1.2268 ,8979 ,6673 ,9297 1.3855
7 9 10 8 6
1 2 3 4 5
2.5493 2.9328 2.6901 2.1037 2.8665
4 1 3 6 2
Most Profitable 148 209 121 210 197
6 7 8 9 10
,9495 ,9491 2.2200 1.3855 1.6455
9 10 5 8 7
Least Profitable 172 296 15 138 236
Least Profitable 426 131 124 236 125
PBG
= Wt. rank is over all 10 DRGs with greatest weight = 1 rs = ,600
= Wt. rank is over all 10 DRGs with greatest weight = 1 rs = .813
+
Table 9: Rank Order Correlation Matrix of Profitable and Unprofitable DRGs: Number of Beds
Table 10: Rank Order Correlation Matrix of Profitable and Unprofitable DRGs: Teaching Status CATEGORY*
CATEGORY.
C A T E G 0 R Y'
2
3
4
5
1
,587 (.0001) ,500 (.0001) ,321 (.0001) ,167 (.0018)
.346 (.0001)
,181 (.0001)
,118 (.0275)
p
,645 (.0001) ,665 (.0001) ,480 (.0001)
,456 (.0001) ,622 (.oOOi) ,724 (.0001)
,262
0 F I T A
,111 (.0038)
,341 (.OOOl)
,595 (.0001)
3 4 5
* 1 =
2 3 4 5
1
2
= = = =
(.0001) ,464 (.0001) ,722 (.0001) ,781 (.0001)
UNPROFITABLE rsav (profitable) = ,440 rsav (unprofitable) = .469 ( ) = p value
~~~~
For each DRG in each hospital, the sum of reported charges for all patients within the DRG was subtracted from the sum of reported reimbursement for all patients and divided by the number of patients in the DRG. This represents the mean contractual adjustment, or mean profitability. The DRGs were then ranked within each hospital in terms of the magnitude of their profitability. The most profitable was given a rank of 1and the least profitable was given a rank of 468. Hospitals were categorized in various ways, but in this paper only the number of beds and teaching status categories are used. Within each hospital category, the number of times a DRG appeared in a hospital's 10 most and 10 least profitable DRGs was established.
Gestion des soins de sante
C A T E G 0 R Y'
1
1 2
,467 (.0001)
3
.288 (.0001)
P R
2
3
0
,363 (.0001)
,230 (.0001)
F
,724 (.0001)
,757 (.0001)
I T A B L E
UNPROFITABLE
L
0- 99beds 100-199 beds 200-299 beds 300-499 beds 500+ beds ~
~~
1 = Major Teaching
2 = Minor Teaching 3 = Non-Teaching
(profitable) = .439 rsav (unprofitable) = .504 ( ) = p value rsav
Each DRG was then ranked according to the number of hospitals, within the category, in which it appeared in the top lO/bottom 10. The DRG that appeared in the top lO/bottom 10 profitable DRGs in the most hospitals was given the rank of 1. The hospital categories used are: Number of Beds 0-99 beds 100-199 beds 200-299 beds 300-499 beds 500t beds Teaching Status Major Teaching >= -25 residentdbed Minor Teaching c .25 residentshed Non-Teaching no residents The 1984 DRG weights as published in the Federal Register4 are the RIWs that were in use during the 49
Volume 6, No. 3
FaWAutomne 1993
period under examination and are, therefore, the appropriate measure for the comparisons made in this paper. In the interests of brevity, only the five top DRGs and the five bottom DRGs as described above are reported here and are referred to as the five most and five least profitable DRGs. The rank ordered profitability level is compared with the DRG weight and an overall Spearman’s rho (Is)is calculated. This is done separately for each hospital category. The similarity (or difference) in the set of the most/least profitable is then compared across hospital category.
Results Tables 1to 5 show the results of the DRG profitability rank and the DRG weight comparisons for the five categories of hospitals based on the number of beds. Tables 6 to 8 show the results for the three teaching status categories.
Using all DRGs in the data set, a rank order correlation matrix of profitable and unprofitable DRGs by hospital category based on the number of beds and by teaching status was produced as part of the major study and is reported e l s e ~ h e r eThe . ~ matrices are reproduced in Tables 9 and 10 so that the similarity of the relative profitability of DRGs in different hospital categories can be examined over the whole data set rather than only the 10 reported above.
Conclusions When the hospitals are grouped by the number of beds, in all categories the least profitable DRGs tend to have the greatest DRG weights. Although by just listing the top five and the bottom five DRGs in terms of profitability, a great deal of information is lost, the Spearman’s rank order correlations bear out the above conclusion. With negative signs and coefficients that range from -.720 to -.893, it is evident that heavy weights are associated with less profitability. When hospitals are grouped by teaching status, in all categories the least profitable DRGs tend to have the lowest DRG weights. The positive signs and coefficients ranging from .400 to 213 indicate that heavy weights are associated with greater profitability. The results of this macro analysis are not presented as the definitive word on whether greater, or lesser, DRG weights lead to greater, or lesser, profits. They serve to support the thesis of this paper which is:
50
profitability cannot be determined merely by the RIW attached to the patient type. The correlation matrices indicate that the more alike hospitals are in terms of the number of beds or teaching status, the more alike their profitable and unprofitable set of DRGs are. The monotonic decrease in the correlation coefficients as they move from left to right with the profitable DRGs, and right to left with the unprofitable DRGs, strongly supports this conclusion. This provides support for the postulate that there is no reason to expect that the same patient types will necessarily be profitable for all hospitals. The implication for hospital administrators is that the only responsible way to engage in case mix management is to implement a Management Information System that provides the data necessary for determining the cost of treating each patient type within its own institution. To be guided by information derived from other facilities or other systems could spell disaster.
References and notes 1. DesHarnais, S.I., Kobrinski, E.J., Chesney, J.D., Long, M.J., Ament, R.P. and Fleming, S.T. 1987. The early effects of the prospective payment system on inpatient utilization and the quality of care. Inquiry 24: 7-16. 2. Long, M.J., Chesney, J.D., Ament, R.P., DesHarnais, S.I., Fleming, S.T., Kobrinski, E.J. and Marshall, B.S. 1987. The effect of PPS on hospital product and productivity. Medical Care 25: 528-538. 3. Long M.J., Chesney, J.D. and Fleming, S.T. 1990. A reassessment of hospital product and productivity changes over time. Health Care Financing Review 11: 69-77. 4. Health Care Financing Administration. 1983. Rules and regulations. Federal Register 46 (Sept 1): 39876-39886. 5. Long M.J., Chesney, J.D. and Fleming, S.T. Profitable and unprofitable DRGs: the implications for access. Health Services Management Research. In press. *RIW is a registered trademark of the Hospital Medical Records Institute.
Michael J. Long, PhD, is Professor, Department of Health Seruices Administration and Community Medicine, University of Alberta, Edmonton.
Healthcare Management FORUM
FalVAutomne 1993
Case Mix Management: A Cautionary Note by Michael J. Long
I
W
ith the increasing propensity for Canadian provinces to implement some form of case mix adjustment in hospital reimbursement, administrators have a growing interest in case mix management. Although sound management practice would dictate that case mix management be undertaken, it is even more sound to be guided in this endeavour by empirical data rather than by intuition, or rule of thumb. For example, it is not uncommon to hear from some who should know better that the incentive now is to admit the patient types with the greatest Resource Intensity Weight (RIW*).To argue this is to ignore the other side of the equation - the cost to treat a given patient type. If there is any incentive inherent in a system that reimburses, to any extent, on the basis of case mix, surely it is in the difference between the reimbursement amount that derives from the patient type weighting system, and the cost to treat that patient type. Also, there is no reason to expect that the most desirable patient types from a financial perspective would be the same for all facilities. Using U.S. data and a macro approach, this paper examines the relationship between the profitability of Diagnostic Related Groups (DRGs) and their DRG
weight and the similarity (or difference) of the most (least)profitable DRGs across hospital types.
Methodology As part of a larger study that evaluated the impact of the DRG-based Prospective Payment System (PPS)?” 509 U.S. acute care hospitals making up the Commission on Professional and Hospital Activities (CPHA)Quality of Care data set that could be linked to the MEDPAR file of the Health Care Financing Administration (HCFA)were identified. The reported charges and the reported reimbursement for each DRG in each hospital was determined using third quarter data for 1984 for Medicare patients from the MEDPAR file. The difference between these two amounts is more accurately referred to as the contract adjustment. However, it is not unreasonable to suggest that since this was done separately within each hospital, the different methods of cost allocation used by different hospitals was not an issue. In other words, charges would represent some measure of relative costliness within the same facility and the contract adjustment would, therefore, represent a measure of relative profitability within the same hospital.
~~
Gestion des soins de santC
47
Volume 6, No. 3
FalUAutomne 1993
Table 7: Five Most and Five Least Profitable DRGs: Minor Teaching Status
TaMe 8: Five Most and Five Least Profitable DRGs: Non-Teaching
Profit
REG
Profit
Banh
~~
Most Profitable 148 110 154 468 116
Banb 1 2 3 4 5
2.5493 2.2912 1.8648 2.0833 1.4868
1 2 4 3 5
6 7 8 9 10
1.2268 ,8979 ,6673 ,9297 1.3855
7 9 10 8 6
1 2 3 4 5
2.5493 2.9328 2.6901 2.1037 2.8665
4 1 3 6 2
Most Profitable 148 209 121 210 197
6 7 8 9 10
,9495 ,9491 2.2200 1.3855 1.6455
9 10 5 8 7
Least Profitable 172 296 15 138 236
Least Profitable 426 131 124 236 125
PBG
= Wt. rank is over all 10 DRGs with greatest weight = 1 rs = ,600
= Wt. rank is over all 10 DRGs with greatest weight = 1 rs = .813
+
Table 9: Rank Order Correlation Matrix of Profitable and Unprofitable DRGs: Number of Beds
Table 10: Rank Order Correlation Matrix of Profitable and Unprofitable DRGs: Teaching Status CATEGORY*
CATEGORY.
C A T E G 0 R Y'
2
3
4
5
1
,587 (.0001) ,500 (.0001) ,321 (.0001) ,167 (.0018)
.346 (.0001)
,181 (.0001)
,118 (.0275)
p
,645 (.0001) ,665 (.0001) ,480 (.0001)
,456 (.0001) ,622 (.oOOi) ,724 (.0001)
,262
0 F I T A
,111 (.0038)
,341 (.OOOl)
,595 (.0001)
3 4 5
* 1 =
2 3 4 5
1
2
= = = =
(.0001) ,464 (.0001) ,722 (.0001) ,781 (.0001)
UNPROFITABLE rsav (profitable) = ,440 rsav (unprofitable) = .469 ( ) = p value
~~~~
For each DRG in each hospital, the sum of reported charges for all patients within the DRG was subtracted from the sum of reported reimbursement for all patients and divided by the number of patients in the DRG. This represents the mean contractual adjustment, or mean profitability. The DRGs were then ranked within each hospital in terms of the magnitude of their profitability. The most profitable was given a rank of 1and the least profitable was given a rank of 468. Hospitals were categorized in various ways, but in this paper only the number of beds and teaching status categories are used. Within each hospital category, the number of times a DRG appeared in a hospital's 10 most and 10 least profitable DRGs was established.
Gestion des soins de sante
C A T E G 0 R Y'
1
1 2
,467 (.0001)
3
.288 (.0001)
P R
2
3
0
,363 (.0001)
,230 (.0001)
F
,724 (.0001)
,757 (.0001)
I T A B L E
UNPROFITABLE
L
0- 99beds 100-199 beds 200-299 beds 300-499 beds 500+ beds ~
~~
1 = Major Teaching
2 = Minor Teaching 3 = Non-Teaching
(profitable) = .439 rsav (unprofitable) = .504 ( ) = p value rsav
Each DRG was then ranked according to the number of hospitals, within the category, in which it appeared in the top lO/bottom 10. The DRG that appeared in the top lO/bottom 10 profitable DRGs in the most hospitals was given the rank of 1. The hospital categories used are: Number of Beds 0-99 beds 100-199 beds 200-299 beds 300-499 beds 500t beds Teaching Status Major Teaching >= -25 residentdbed Minor Teaching c .25 residentshed Non-Teaching no residents The 1984 DRG weights as published in the Federal Register4 are the RIWs that were in use during the 49
Volume 6, No. 3
FaWAutomne 1993
period under examination and are, therefore, the appropriate measure for the comparisons made in this paper. In the interests of brevity, only the five top DRGs and the five bottom DRGs as described above are reported here and are referred to as the five most and five least profitable DRGs. The rank ordered profitability level is compared with the DRG weight and an overall Spearman’s rho (Is)is calculated. This is done separately for each hospital category. The similarity (or difference) in the set of the most/least profitable is then compared across hospital category.
Results Tables 1to 5 show the results of the DRG profitability rank and the DRG weight comparisons for the five categories of hospitals based on the number of beds. Tables 6 to 8 show the results for the three teaching status categories.
Using all DRGs in the data set, a rank order correlation matrix of profitable and unprofitable DRGs by hospital category based on the number of beds and by teaching status was produced as part of the major study and is reported e l s e ~ h e r eThe . ~ matrices are reproduced in Tables 9 and 10 so that the similarity of the relative profitability of DRGs in different hospital categories can be examined over the whole data set rather than only the 10 reported above.
Conclusions When the hospitals are grouped by the number of beds, in all categories the least profitable DRGs tend to have the greatest DRG weights. Although by just listing the top five and the bottom five DRGs in terms of profitability, a great deal of information is lost, the Spearman’s rank order correlations bear out the above conclusion. With negative signs and coefficients that range from -.720 to -.893, it is evident that heavy weights are associated with less profitability. When hospitals are grouped by teaching status, in all categories the least profitable DRGs tend to have the lowest DRG weights. The positive signs and coefficients ranging from .400 to 213 indicate that heavy weights are associated with greater profitability. The results of this macro analysis are not presented as the definitive word on whether greater, or lesser, DRG weights lead to greater, or lesser, profits. They serve to support the thesis of this paper which is:
50
profitability cannot be determined merely by the RIW attached to the patient type. The correlation matrices indicate that the more alike hospitals are in terms of the number of beds or teaching status, the more alike their profitable and unprofitable set of DRGs are. The monotonic decrease in the correlation coefficients as they move from left to right with the profitable DRGs, and right to left with the unprofitable DRGs, strongly supports this conclusion. This provides support for the postulate that there is no reason to expect that the same patient types will necessarily be profitable for all hospitals. The implication for hospital administrators is that the only responsible way to engage in case mix management is to implement a Management Information System that provides the data necessary for determining the cost of treating each patient type within its own institution. To be guided by information derived from other facilities or other systems could spell disaster.
References and notes 1. DesHarnais, S.I., Kobrinski, E.J., Chesney, J.D., Long, M.J., Ament, R.P. and Fleming, S.T. 1987. The early effects of the prospective payment system on inpatient utilization and the quality of care. Inquiry 24: 7-16. 2. Long, M.J., Chesney, J.D., Ament, R.P., DesHarnais, S.I., Fleming, S.T., Kobrinski, E.J. and Marshall, B.S. 1987. The effect of PPS on hospital product and productivity. Medical Care 25: 528-538. 3. Long M.J., Chesney, J.D. and Fleming, S.T. 1990. A reassessment of hospital product and productivity changes over time. Health Care Financing Review 11: 69-77. 4. Health Care Financing Administration. 1983. Rules and regulations. Federal Register 46 (Sept 1): 39876-39886. 5. Long M.J., Chesney, J.D. and Fleming, S.T. Profitable and unprofitable DRGs: the implications for access. Health Services Management Research. In press. *RIW is a registered trademark of the Hospital Medical Records Institute.
Michael J. Long, PhD, is Professor, Department of Health Seruices Administration and Community Medicine, University of Alberta, Edmonton.
Healthcare Management FORUM