Financial risk, hospital cost, and complications and comorbidities in medical non-complications and...

COMMENTARY

Financial Risk, Hospital Cost, and Complications and Comorbidities in Medical Non-Complications and Comorbidity-Stratified Diagnosis-Related Groups

ERIC MUr;OZ, M.D., M.B.A.

New Hyde Park, New York New York, New York

and Stony Brook, New York

FRED ROSNER, M.D.

New York, New York

RICHARD FRIEDMAN, D.M.D., M.P.H.

HARRIS STERMAN, M.D.

JONATHAN GOLDSTEIN, M.P.A.

LESLIE WISE, M.D.

New Hyde Park, New York New York, New York

and Stony Brook, New York

From the Departments of Medicine and Surgery, Queens Hospital Center and the Long Island Jew- ish Medical Center, New Hyde Park, New York, State University of New York, Stony Brook, New York, and City University of New York School of Medicine, New York, New York. Requests for reprints should be addressed to Dr. Eric Mufioz, Research Division, Department of Surgery, Long Island Jewish Medical Center, New Hyde Park, New York 11042. Manuscript submitted Novem- ber 13, 1987, and accepted in revised form March 10, 1988.

A number of methods are being studied to modify and improve the accuracy of the Medicare Diagnosis-Related Group (DRG) hospital classification system. This study analyzed resource consumption for 2,431 medical Medicare patients in the 53 non-complicating condition- stratified (i.e., non-complication and comorbidity) medical DRGs. Re- source consumption per patient increased as the number of complications and comorbidities per patient per DRG increased, as measured by total hospital cost, hospital length of stay, the number of diagnoses and procedures per patient, the percent outliers, and mortality. Patients with more than four complications and comorbidities generated significant financial risk ($5,667 loss per patient) under DRG reimbursement. This study raises the question of the equity of DRG reimbursement for the medical non-complication and comorbidity-stratified DRGs. A method for DRG adjustment based on complications and comorbidities should be implemented by Congress to assure equitable payment for patients in these medical DRGs.

The Medicare Diagnosis-Related Group (DRG) prospective hospital payment system appears to be relatively crude regarding hospital resource prediction [I]. A number of health policy questions face the Congress; one of these is improving the accuracy and equity of Medicare DRG prospective payment [2]. One proposal facing the federal government is to stratify certain DRGs not simply by whether or not a complication or comorbidity exists (i.e., a condition present on admission to the hospital, or a condition acquired while in the hospital), but rather to stratify some DRGs by the numbers and types of complications and comorbidities [3].

Medical DRGs are grouped in an attempt to provide appropriate reimbursement to hospitals based mainly on the principal diagnosis. At the present time, 53 of the 188 medical DRGs (i.e., 28.2 percent) are not stratified by the presence or absence of complications and comorbidities [4]. The purpose of this project was to analyze resource consumption in the non-complication and comorbidity-stratified medical DRGs, and to test the hypothesis that financial risk would be generated by patients who had a greater number of complications and comorbidities.

PATIENTS AND METHODS

All Medicare medical patients (n = 2,431) admitted to the Long Island Jewish Medical Center (an 805-bed teaching hospital in suburban New York City) in any of the 53 non-complication and comorbidity-stratified medical

May 1988 The American Journal of Medicine Volume 84 933

MEDICAL DRGs, COMPLICATIONS, AND COMORBIDITIES-MU602 ET AL

APPENDIX Hospital Costing Methodology

Hospital cost per patient was obtained by utilizing per patient charge specific data and converting these charges to hospital costs. Cost to charge ratios utilized by our hospital accounting division were used to convert charges to costs. The following ratios were used for each hospital service category.

Room and board Laboratory Blood (storage, processing, transfusion,

and products) Pathology Diagnostic radiology Therapeutic radiology Operating room Recovery room Respiratory therapy Electrocardiology Electroencephalography Medical supplies Pharmacy Diagnostic ultrasound Computed tomographic scan Emergency department Pulmonary function

0.75 0.40 1.15

2.42 0.94 1.71 1.10 1.15 0.76 0.25 1.40 1.00 1.00 0.46 0.53 0.91 0.08

Thus, to obtain the hospital cost for diagnostic radiology of $100, the following calculation was performed ($100 X 0.94 = $94); medical supplies($lOO X 1.00 = $100); blood($lOO X 1.15 = $115); and so on.

DRGs from January 1, 1985, to December 31, 1986, were studied. Our hospital is categorized as an urban teaching hospital by the DRG reimbursement scheme; standard New York State DRG methodology was used for this analysis. Our assessment as a proxy for complications and comorbidities was the number of ICD-O-CM diagnosis codes present on the discharge of the patient. Thus, complications such as pneumothorax or aspiration pneumonia would be identified, and comorbidities such as hypertension or diabetes melli- tus would also be quantified. Thus, for this analysis, the proxy for the DRG qualifier of complicating-condition (i.e., complication and comorbidity) was the number of ICD-9- CM diagnosis codes per patient. The principal diagnosis for each patient was not counted as a complication and comorbidity; however, each additional ICD-g-CM diagnosis code was counted as a complication and comorbidity. Patients with only one ICD8-CM diagnosis code were counted as having no complications and comorbidities, those with two ICD-9-CM diagnosis codes were counted as having one complication and comorbidity, those with three ICD-O-CM diagnosis codes were counted as having two complications and comorbidities, and so on. Patients were grouped into four categories: all patients with no complications and comorbidities, all patients with one to two complications and comorbidities, all patients with three to four complications and comorbidities, and all patients with more than four complications and comorbidities.

Hospital costs for each complication and comorbidity group (exclusive of physician fees) were computed for each patient by hospital service area to include: room and board, laboratory (urinalysis, hematology, coagulation, biochemistry, and microbiology), radiology, blood and blood products, central supply-pharmacy, and other (electrocardiography, pathology, and respiratory therapy, among others) (Appen- dix). A number of characteristics were analyzed for each patient including: mean total hospital cost per patient, mean hospital cost per patient for each hospital service, mean DRG reimbursement per patient, mean profit/loss per patient, mean hospital length of stay per patient, the mean number of diagnostic codes (ICD-g-CM) per patient, the mean number of procedure codes (ICD-g-CM) per patient, the percent outliers, and mortality. In addition, three clinical characteristics were analyzed for each complication and comorbidity category such as: intensive care unit admission, need for blood transfusion (i.e., red cells), and the requirement for plasma products (i.e., blood products). The analysis of variance test on SAS statistical software was used to test for statistical significance between groups of patients. Simple linear regression analysis was used to calculate the relationship between in-hospital cost per patient versus the number of complications and comorbidities per patient. In order to equalize for DRG weight index differences, the linear regression equation was total hospital cost per patient/DRG weight index per patient = number of complications and comorbidities per patient. The DRG weight index is that value assigned by the DRG formula to predict resource consumption and subsequent prospective hospital payment.

RESULTS The 2,431 medical patients had a mean DRG weight index per patient for all patients of 1.0065 (range, 0.9704 to 1.1103); the highest DRG weight index was for patients with more than four complications and comorbidities, 1.1103 (Table I). The mean hospital length of stay per patient for all patients was 11.3 days; highest length of stay per patient was for patients with more than four complications and comorbidities, 17.8 days. The mean loss per patient for all patients under DRG reimbursement was $1,150; aggregate loss for all patients was $2,795,650. Highest loss per patient was for patients with more than four complications and comorbidities, $5,667 loss per patient. The mean number of diagnoses per patient for all patients was 3.84, and the mean number of procedures per patient was 1.06. Patients with over four complications and comorbidities had the highest number of diagnoses per patient (7.41), and the highest number of procedures per patient (1.9 1). Thus, resource consumption (i.e., hospital length of stay, profit/loss, and number of diagnoses and procedures per patient) increased as the number of complications and comorbidities increased.

Mean total hospital cost per patient for all patients was $8,0 12; mean total hospital cost per patient rose with the

934 May 1988 The American Journal of Medicine Volume 84

MEDICAL DRGs, COMPLICATIONS, AND COMORBIDITIES-MUI; ET AL

TABLE I Non-Complication and Comorbidity-Stratified DRGs: Resource Characteristics* by Complications and Comorbidities

Complication and Comorbidity Category

Sample Size

DRG Weight Index

Hospital Length of Stay (days) ProfitlLosst

None

One to two

Three to four

Over four

282 0.9704 6.79 f0.2914 f7.2

994 0.9597§ 8.76 f0.3433 412.0

683 1.0178 12.5 f0.2993 f15.3

472 1.11038 17.85 f0.3543 f23.7

All 2,431 1.0065 11.3

$1,382§ f4,566

$5115 f5,315

($1,491) f7,970

($5,6‘W5 f16,147

($1,150)

Number of Number of Diagnoses* Procedures*

1 .oo* fO.OO

2.53 f0.50

4.428 f0.49

7.41 Al.44

3.64

0.529 f0.97

0.755 fl.10

1.14 f1.44

1.915 f1.90

1.06

l Mean per patient f SD. t Mean profit/loss per patient; value in parentheses equals loss. t ICD-O-CM codes per patient. 3 Statistically different at p <O.Ol from all patients.

TABLE II Non-Complication and Comorbidity-Stratified DRGs: Cost* by Hospital Service and Complications and Comorbidities

Complication and Comorbidity Category Room and Board Laboratory? Blood Radiography

Central Supply Pharmacy Other Total

None

One to two

Three to four

Over four

$3.4571: f3,877

4,083* f4,564

5,792 f6,653

8,817X f12,771

$611$ f591

736t f664

1,036 f1,061

1,707x 2,010

$2 f7

4 f12

7 f22

31’ f249

$257t f320

311t f410

425 f508

68Oi f766

$210X f272

250x f375

349 f436

495% f891

$540$ f502

579% f755

854 f1,237

1,702x f4,043

$5,077X f4,963

5.963% f6,018

8,462 f9,039

13,432t f18,782

All $5,409 $994 $10 $408 $320 $869 $8,012

l Mean per patient f SD. t Laboratory (urinalysis, hematology, coagulation, biochemistry, and microbiology). t Statistically different at p <O.Ol from all patients.

number of complications and comorbidities (Table II). Hospital cost per patient for each hospital service demonstrated considerable variation among the complication and comorbidity groups. Mean cost by service always rose as the number of complications and comorbidities increased. The largest component of total hospital cost was room and board, followed by laboratory, other, radiology, central supply-pharmacy, and blood.

The percent of outliers rose as the number of complications and comorbidities per patient increased (Figure 1). Patients with more than four complications and comorbidities had the highest percent of outliers. The percent mortality rose as the number of complications and comorbidities per patient increased (Figure 2). Patients with more than four complications and comorbidities had the highest mortality. Intensive care unit, plasma products, and blood utilization rose as the number of complications

and comorbidities per patient increased (Figures 3 to 5). Patients with over four complications and comorbidities had the highest clinical resource utilization for these pa- rameters. Linear regression analysis demonstrated the correlation coefficient (r*) of total hospital cost per patient/DRG weight index per patient versus the number of complications and comorbidities per patient as 0.08 (p <O.OOOl). Thus, by stratifying these medical DRGs by the number of complications and comorbidities per patient, a significant improvement in hospital resource consumption per patient within each DRG could be achieved.

COMMENTS

The purpose of this study was to analyze resource utilization for Medicare medical patients in the non-complication and comorbidity-stratified medical DRGs. Our data demonstrated that resource utilization increased as the num-


MEDICAL DRGs, COMPLICATIONS, AND COMORBIDITIES-MUiOZ ET AL

I I I

t ONE 2wo L THREE muRL?IuE mlRETmN?n5

I I .L , 1

Figure 1. The percent outliers increased as the number of diagnoses per patient accumulated.

F;gure 2. Mortality rose as the number of diagnoses per patient accumulated.

Figure 3. Intensive care unit utilization was highest for patients with more than five diagnoses per patient.


MEDICAL DRGs, COMPLICATIONS, AND COMORBIDITIES-MU);IOZ ET AL

Figure 4. Plasma product utilization was highest for patients with more than five diagnoses.

Figure 5. Blood utilization rose as the number of diagnoses per patient accumulated.

ber of complications and comorbidities (as measured by the number of ICD-S-CM codes) per patient increased. Thus, patients in the DRGs with more complications and comorbidities had higher total hospital costs, a longer hospital length of stay, greater financial risk under DRGs, more procedures per patient, a greater percentage of outliers, a higher mortality, and a more intense clinical resource utilization of services than patients with fewer complications and comorbidities per patient in these same DRGs. These findings raise several questions about the equity of the DRG classification system in these non- complication and comorbidity-stratified DRGs.

If patients with a greater number of complications and comorbidities are randomly distributed across the 6,000

American acute-care hospitals, and if hospitals cannot preselect patients for admission, than these findings pose no great equity problem for the DRG hospital payment system. However, it appears unlikely that this is the case. Certain types of hospitals have already been shown to treat more severely or complexly ill patients (such as teaching and public hospitals); thus, these hospitals may be more “vulnerable” to financial losses from patients in these DRGs with more complications and comorbidities [5,6]. In addition, as pm-admission screening and management become more efficient at integrating information to hospital administration regarding risk factors associ- ated with financial losses, it is likely that patients with a greater number of comorbidities can be identified as


MEDICAL DRGs, COMPLICATIONS, AND COMORBIDITIES-MUiOZ ET AL

financially “undesirable” in these non-complication and comorbidity-stratified medical DRGs.

Thus, if patients with a greater number of complications and comorbidities and a higher financial risk are not randomly distributed and can be identified by the hospital, their access and quality of care may potentially be affect- ed. The difficulty arises at finding a method to better predict and reimburse for hospital cost vis-a-vis the current non-complication and comorbidity-stratified medical DRGs. Several potential solutions, for example, have been proposed by the Prospective Payment Assessment Commission (ProPAC). Recommendation 2 1: The Use of Patient Age in Defining DRGs, contained in the April 1987 report to the Secretary of Health and Human Services states “DRGs should not be defined based on the current variable of age greater than 69 and/or presence of a complication or comorbidity. The Commission believes that the resource use for Medicare patients 70 years or older without a complication and comorbidity is signifi- cantly lower than for cases with complications and comorbidities. DRGs should be defined on the basis of the presence or absence of a complication and comorbidity, regardless of age. The Secretary should implement this change for DRGs that currently split on age and complication and comorbidity, and should determine whether other DRGs should also be split on complication and comorbidity” [7]. Similarly, ProPAC Recommendation 22 of this same report, Improving the Use of Complications and Comorbidities in Defining DRGs, states “The Secretary should revise the current list of complications and comorbidities and its use in defining DRGs, to ensure more appropriate grouping of Medicare cases for payment under PPS [prospective payment system]. The Secretary should evaluate several possible approaches, including the development of Major Diagnostic Category (MDC) or DRGspecific complications and comorbidities on the basis of resource intensity, and the specification of levels of complexity among the complications and comorbidities” [8]. The Health Care Financing Administration (HCFA) has also recommended similar changes along these lines. However, critics of these proposals contend that modify- ing DRGs further along the lines of complications and comorbidities will provide additional incentives for hospitals to “upcode” medical records and identify more complications and comorbidities, resulting in another round of “DRG creep.” Thus, it is unclear whether certain DRGs will be stratified along the lines of complications and comorbidities.

tion (and cost) for hospitals. In addition, some have not been widely studied and may not be mature enough to implement on a national basis for the 11 million annual Medicare acute-care hospitalizations. It is thus likely that no DRG severity modifier will be implemented in the near future.

Physicians and hospitals may be placed between di- minishing hospital resources to treat patients in these non-complication and comorbidity-stratified DRGs and hospital regulations requiring a greater scrutiny of the quality of their medical care. This causes serious conflicts for practicing physicians, and also may require medical allocative decision-making regarding hospital length of stay and the use of ancillary tests with very little data for this decision-making. The DRG Medicare PPS thus appears to make the American acute hospital care delivery system an on-line experiment in input versus output production changes. Our data demonstrate that some groups of patients will be underreimbursed by DRGs; thus, it is likely that their access or quality of medical care may suffer. Physicians and hospitals now need to work at encouraging more equitable DRG reimbursement; simul- taneously, they must study the health production problem intrinsic in the changes under way in hospital payment. Thus, the study of marginal changes on input versus output will become increasingly important to maintain the quality of health care in this new era of limited medical resources.

CONCLUSIONS

This study of 2,431 Medicare medical patients in 53 non- complicating condition-stratified (i.e., complication and comorbidity) medical DRGs demonstrated greater hospital resource consumption per patient as the number of complications and comorbidities per patient per DRG increased. Patients with a greater number of complications and comorbidities had higher total hospital costs, a longer hospital length of stay, more PrOCedUreS per patient, a greater proportion of outliers, and a higher mortality than patients in these same DRGs with fewer complications and comorbidities. These findings suggest that DRG payment may be inequitable for certain groups of patients in the non-complication and comorbidity-stratified medical DRGs. Financial disincentives to treat these patients may affect both their access and quality of medical care in the future.

ACKNOWLEDGMENT

Another type of DRG modification being studied by both We would like to acknowledge James Moyer, Seth War- HCFA and ProPAC is the severity of illness DRG adjusters ren, and Gordan Doletto of the Divisions of Management [9]. It appears that there are several severity of illness Information Systems and the DRG Task Force of the Long systems that could improve the accuracy of DRG re- Island Jewish Medical Center, in addition to Katherine source prediction. However, some of these systems are Mulloy, for their data computation, and Christina Weiss for

relatively complex and would require further data collec- assistance in the preparation of this manuscript.


MEDICAL DRGs, COMPLICATIONS, AND COMORBIDITIES-MUi ET AL

REFERENCES

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2. Jencks SF, Dobson A, Willis P: Evaluating and improving the measurement of hospital case mix. Health Care Financ- ing Rev Ann 1984; l(suppl): 1-l 1.

3. Office of Technology Assessment, Washington, DC.: Re- port and Recommendations to the Secretary, U.S. Depart- ment of Health and Human Services. Prospective Pay- ment Assessment Commission, April 1987; 51-53.

4. Healthcare Financing Management Association: Description and analysis of Medicare prospective price setting. Oak Brook, Illinois: Healthcare Financial Management Associ- ation, 1984.

5. Muiioz E, Soldano R, Laughlin A, Margolis IB, Wise L: Mode

6. Horn SD: Measuring severity of illness comparisons across institutions. Am J Public Health 1983; 73: 35-41.

7. Office of Technology Assessment, Washington, DC.: Re- port and Recommendations to the Secretary, U.S. Depart- ment of Health and Human Services: Prospective Pay- ment Assessment Commission, April 1987; 51-52.

8. Office of Technology Assessment, Washington, DC.: Re- port and Recommendations to the Secretary, U.S. Depart- ment of Health and Human Services. Prospective Pay- ment Assessment Commission, April 1987; 52-53.

9. Jencks SF, Dobson A, Willis P: Evaluating and improving the measurement of hospital case mix. Health Care Financ- ing Rev Ann 1984; l(suppl): l-l 1.


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