Resource Intensity Weighing and Case Mix Grouping: Assumptions and Implications for Health Service...

SprinpJPrintemps 1994 Volume 7, No. 1

Original Article

Resource Intensity Weighing and Case Mix Grouping: Assumptions and Implications for Health Service

Performance Evaluation

by Christabel Chu

The use of Resource Intensity Weights (RIWs*) for equity funding and utilization management assumes validity of the cost estimates, reliability of the patient categorization scheme, equivalence of the bases for cost comparison, and equity of the subsequent resource distribution. This paper examines these assumptions, and concludes that caution must be taken when using the current RIWs and Case Mix Groups (CMGs*) for resource allocation and performance evaluation purposes.

RIW has represented a milestone in the history of Canadian health care product costing and management. It would be prudent for health care professionals at the operational level to provide structured and continuing feedback that can contribute to the validation and refinement of these valuable management tools.

Le recours ri la ponde'ration du volume des ressources {PVR) (Resource Intensity Weighting) pour unfinancement juste et la gestion de l'utilisation suppose la validite'de l'estimation des coiits, la fidbilite'du proce'de'de cate'gorisation des patients, I'e'quivalence des bases sur lesquelles on s'appuie pour cornparer les coiits et l'e'quite' d'une distribution subse'quente des ressources. Ce texte e'tudie ces hypothtses et en arrive; la conclusion qu'on doit user de prudence lorsqu'on utilise les donne'es actuelles sur les PVR et sur les groupes composant la clienttle (GCC) li desfins d'allocation de ressources et d'e'valuation de la performance.

La ponddration du volume des ressources marque un point culminant dans l'histoire des coiits et de la gestion des soins de sante' au Canada. Les professionnels de la sante' qui travaillent au niveau ope'rationnel devraient voir iz transmettre de fagon continue des commentaires concis et bien formule's qui pourraient servir iz la validation et a la mise au point de ces pre'cieux outils de gestion.

ince its inception in 1988, the Ontario Transition- al Funding system has gone through various s refinement stages to advance the relationship be-

tween hospital reimbursement schemes and the types of patients being treated.13 In the past four years, over $140 million has been apportioned to the Equity Fund, which was in turn distributed to the hospitals in which inpatient costs per weighted case were lower than the average of its peer group. The Growth Funding formula was also modified to capture the yearly variation of inpatient activities. To enhance further changes in health care financing and delivery, the Transitional Funding Project was replaced by the Joint Planning and Policy Committee in April 1992.

Fundamental to this new Ontario funding methodology is the use of weighted cases, which are determined by classifying patient types into Case Mix Groups (CMGs*) and totalling the Resource Intensity Weights (RIWs*) assigned to the cases. According to a recent survey on the views regarding Transitional Funding, 94 percent of Ontario hospital executives are satisfied with RIWs, and 60 percent indicate that they use RIWs for utilization management.

Several assumptions are implicit in the increasing use of this case mix funding methodology at both the government and institutional levels. First, the use of RIWs for determining resource allocation assumes relative validity of the cost estimates. Second, the adoption

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of CMGs assumes that clinical practice can be captured and structurally represented by this patient categorization scheme. Third, the use of "peer hospitals" assumes an equivalence of the basis for cost comparison and, in this case, the equality of hospital "products" procured by the costs. Also, the funding adjustment assumes equity of the transitional funding distribution.

To what extent can health care executives rely on these assumptions? What are the limitations for using RIWs for approximating resource consumption and conducting health service impact analyses? How would case mix methodology enhance or hinder hospital utilization management? To address these ques- tions, this paper first examines the individual assumptions and their implications. The second part explores how utility of RIWs and CMGs can be op- timized for resource allocation and health service performance evaluation purposes.

Using RIWs and CMGs - assumptions and implications Assumption 1 - validity of RIW estimates

crepancies of estimated resource consumption between Case Mix Group-Diagnosis Related Group (DRG) pairs with identical costs. He concluded that the 1987 RIWs were inconsistent and unreliable despite the very high correlation between U.S. DRGs and Canadian CMGs.

The problem of DRG-CMG matching was eliminated by grouping the New York data directly into CMGs, and comparing the length of stay (LOS), dia oses and age-mix differences among the populations. A total of 549 CMGs have likewise been listed.* Each CMG is in turn classified into "typical" or "atypical" cases according to the homogeneity of their LOS and discharge status. The former constitute 85 percent of all acute care cases, but account for only 61 percent of the total patient days in 1989. The latter are classified further into "transfers," "signouts," "outliers" and "deaths."

Median versus mean LOS for RIW estimation RIWs are intended to reflect the best cost estimates

specific to the t&ical CMGs. To derive these estimates, arithmetic means of both U.S. and Canadian LOS data are extensively analyzed. Cost adjustments are then conducted based on the differences between these means. Ideally, approximately half of the cases falling into a particular CMG would have higher actual costs, and the other half, lower actual costs.

Whereas the means have higher sampling stability, they do not capture the central tendency of populations with skewed distributions. If the mean LOS of a CMG is significantly lower than the median, most cases within this CMG would have an underestimated

In examining the 1987 RIWs, Botz5 revealed the dis-

gn

LOS. The opposite is also true in some high-volume CMGs, in which more than 50 percent of the cases are above the mean LOS. In addition, without any actual cost-per-day data, the average "routine and ancillary costs" are used as surrogates for marginal costs.' In terms of resource consumption, a shift toward a higher median may signal a more significant change than the mean can reflect, especially in the low-volume, high- RIW CMGs.

More precision is therefore required for cost adjustment based on LOS. Arithmetic means are not as predictive as the medians for the high-volume CMGs with skewed populations and for the low-volume CMGs with high resource consumption. This is especially true for CMGs which have a high number of Al- ternative Level of Care (ALC) cases, since the ALC costs were removed from the U.S. data base. In Canada, many of these cases remain nested in the "outliers" biasing the arithmetic means. Statistical adjustments alone may not suffice to guarantee validity of the RIW cost estimates.

The Joint Planning and Policy Committee is well aware of the constraints imposed by the use of U.S. data and the methodology employed. Continuous ef- fort has been made to improve the estimates, and to ex- pedite the establishment of a Canadian cost data base. Meanwhile, users must be cognizant of the inherent limitations, and acknowledge the need for validating some of these estimates.

LOS versus acuity for RlW estimation Horn and colleagues6 examined the Severity of 111-

ness Index (SOII) across 60 hospitals, and concluded that an acuity measurement tool is indispensable for predicting resource requirement. By testing a Com- puterized Severity Index (CSI) on 27 high-volume DRGs sampled from five teaching hospitals, Horn and Horn7 also concluded that failure to include a measure of severity of illness in the assessment of hospital LOS would lead to an erroneous conclusion about resource consumption. Indeed, the U.S. DRGs have been criticized for their potential to create hospital financial pitfalls and discourage care for critically ill patients.8 Bardswich and co-workers' studied a group of general surgical patients and noted that the overall resource consumption is more closely related to acuity than to LOS.

In other words, LOS is only a crude predictor for cost. Whereas severity measurement is imperative for estimating resource requirement, the search for a universal tool is still ongoing. OBrien'' examined six widely known systems (AIM, APACHE, CSI, PMC, Disease Staging, MedisGroups) and concluded that no single acuity measurement tool is superior to the others for general hospital use. In an attempt to address this issue, the Alberta Acute Care Funding

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Project has adopted Refined DRGs (RDGRs) as their measure of case mix. Validity of such measures, however, is very much contingent on the operational definition and cost predictive value of ”severity.”

The use of RIWs to approximate resource consumption, therefore, may be misleading when users are deal- ing with high-volume CMGs which have variable severity levels or clinical outcomes. Higher acuity levels are not necessarily associated with higher cost. Likewise, homogeneity of LOS within a CMG may not mean homogeneity of acuity levels or resource consumption. Without reliable severity of illness measurement tools, the relationship among LOS, acuity and resource weighing remains obscure. Also, it is crucial to differentiate acuity measurement tools from workload instruments which are derived from time studies (e.g., nursing workload tools). The longer the time required for patient care, the higher the workload index and LOS. Since the current RIWs were derived from LOS data, the use of time-based variables for comparing or validating RIWs can be confounding. At this stage, RIWs must be used with caution when ap- plied to impact analyses, resource allocation and health service performance evaluation.

Assumption 2 - reliability of CMGs Three objectives are paramount in the establishment

of a patient categorization system. First, the scheme must be able to capture clinical complexity and diver- sity of patient characteristics in a feasible manner. Second, ambiguity and uncertainty of terminology should be minimized. Third, the scheme should con- sist of a manageable number of categories conducive to program-planning and management purposes.

The Canadian CMG classification scheme has been improved significantly in recent years. CMGs are con- structed using the International Classification of Dis- ease (ICD) coding regime. Unlike the construction of outpatient Ambulatory Patient Groups (APGs), inpatient CMGs are meant to be mutually exclusive and non-additive in nature. Depending on the homogeneity of LOS data and discharge status, each CMG is differentiated into typical and atypical cases.

Typical versus atypical cases A typical patient is defined as ”one who receives a

full, successful course of treatment in a single institu- tion, and is discharged when no longer requiring the services of an acute care hospital.”’ However, patients with multiple problems may sometimes require more than one legitimately typical category to reflect their complexity.

To illustrate, a patient admitted for hernia repair may have a TURP (Transurethral Resection of Pros- tate) done during the same hospitalization. Under the current scheme, the TURP may be deemed as a

complication of the former procedure, and the patient may be classified as atypical if his or her combined LOS exceeds the trim point. Alternatively, this situa- tion may be categorized in ”non-extensive unrelated Operating Room procedure” (CMG 901). In other words, legitimate typical cases may have to be nested within atypical outliers, or coded into a CMG that iden- tifies neither procedure. Similarly, a patient with chest injuries may have a ”respiratory infection and inflam- mation” (RIW 2.47), ”pulmonary edema and distress” (RIW 2.04), or both. If ”major chest trauma” is assigned based on comprehensiveness of the diagnosis, a substantial underestimation of resource requirement may result (RIW 0.82).

sociated RIWs are non-additive. When clinical reality cannot be represented by the classification scheme, a choice has to be made about the best way to assign the case. Users must be cognizant that the clinical logic underlying individual cases may not be congruent with the statistical logic underlying the construction of the classification scheme as a whole.

Complications and age splits As part of the redevelopment project,’ statistical

analyses were carried out on CMGs which historically had been split on age, complication, or both. Splits were implemented if a LOS difference of one day between groups was demonstrated. Unfortunately, the use of age split and co-morbidity description may not reliably reflect the actual level of resource consumption.

For example, infection may occur after a major bowel procedure. An abscess formation that requires surgical drainage and expensive antibiotics would consume many more resources than an infection that requires only a certain modification of wound-caring technique. Despite the discrepancy in resource consumption, both of these conditions are labelled equally as ”complication.” Or, a 17-year-old patient who has septicaemia (RIW 2.64) may consume a much higher level of resource than an uncomplicated disorder in a 75-year-old patient (RIW 3.22). In this case, the ”age- split” criteria have caused an opposite prediction of the actual resource requirement.

RIWs are only average cost estimates. They will bias information about resource use if the overall underestimation is not balanced by the overestimation. The patient population served by a hospital could marked- ly shift the average resource consumption within a particular CMG. To increase precision of these cost estimates, not only age and complication need to be more differentiated, interaction between the two should be considered also.

Coding technique and room for discretion

CMG categories are mutually exclusive, and their as-



How a clinical problem is understood often affects the manner in which it is coded. For example, a patient with inflammatory bowel disease may be admitted under the diagnosis of G.I. obstruction, treated with relatively time-consuming and costly drug and nutri- tional support therapies, which are then followed by a surgical procedure necessitated by failure of the more conservative therapy. In this case, surgical procedure as a CMG versus a procedure embedded within a diagnosis that is most responsible for the LOS would become a source of variance for coding. The discharging physician may not necessarily be the clinician who is responsible for the major portion of a patient’s hospital stay. Similar examples can also be cited for complications, which may be listed as CMGs or embedded within the responsible diagnoses.

Medical nomenclatures and clinical coherence

guishable ”disease,”” and a ”treatment” is the method, technique, therapy and procedure used to manage or combat the disease. A “complication” may occur due to the disease or treatment. Diagnosis, treatment and complication are therefore clinically overlap- ping phenomena. On the other hand, the CMG classification scheme is predicated upon diagnoses (e.g., angina), treatments (e.g., pacemaker implant) and complications (e.g., septicaemia) which are assumed to be mutually exclusive reasons for hospitalization. Labelling these reasons as “most responsible diagnoses” would therefore obscure the distinction of the terminologies in their strict clinical sense.

Depending on practice norms and the extent of tech- nological advancement, numerous treatment choices and outcomes are possible for a given diagnosis. Using statistical criteria to enlist all the conditions possible for each disease, treatment option, complication, and their probable combinations would render the classification scheme inefficient and cumbersome in the long run. Bardswich and co-workers’ noted that it is al- ready taking up to 64 CMGs to classify 544 cardiology patients and 88 CMGs for 532 general surgical cases. An efficient patient classification system is one that is integrative rdher than proliferative. A credible categorization scheme must be established on logic that is clinically sound as well as statistically correct.

Assumption 3 -basis of cost comparisons A valid basis for cost comparisons is vitally impor-

tant to the equity of distributing additional resources to or reallocating existing resources among Ontario hospitals. To evaluate the relative standing of the costs incurred, the Ontario Ministry of Health has grouped hospitals according to their sizes, case mix profiles and patient demographics, as well as teaching and clinical activities within a given program. It is assumed that,

From the clinical perspective, a diagnosis is a distin-

d

2

by using the peer hospital groupings, cost comparisons can be done on a relatively comparable basis.

Whereas cost control may be associated with improved declines in service quality independent of budgetary ramification^.^^'^^ How can we know that lower costs are not at the expense of compromising health care services? How would third party payers know that health outcomes have been attained by paying for the costs? Unless we are certain of the relationship to patient outcomes, comparing prices alone could be rather misleading.

Structures, process or outcomes One approach to assess health care quality is by

means of structure, which is the resources, personnel, facilities or or anizational arrangements in place to

Assessing quality via providers’ characteristics presupposes that the presence of certain structures would automatically lead to the occurrence of quality. Similar quality management a

it may also cause undesirable

deliver care. 18

strong criticism in the past. 1 7 , P roaches have received

An alternative approach to assess health care quality is by scrutinizing the processes, which refer to the activities that physicians and other health care professionals engage in to provide medical care.” Unfortunately, the link between processes and patient outcomes has been established for only relatively few procedures2’ Also, medical knowledge and what is considered appropriate care are constantly evolving. If the presence of structures and processes cannot guarantee the quality of care outcomes, the matching of providers’ characteristics and adoption of the peer hospitals approach would not suffice to guarantee comparable health care products. Regardless of the validity and precision of the methodology used to match Ontario hospitals, it is likely that there would be equal health outcomes despite highly variable providers’ characteristics, and very discrepant health outcomes among similar or peer hospitals. To ensure equity, the definition of hospital products should go beyond the labelling and enlisting of CMGs. It implies the specification of outcome standards for the given CMGs - standards that have not been established for cost comparison purposes.

Assumption 4 - the assumption of equity Equity and equality are two different but intimately

related concepts. Operationally, equity is achieved if similar situations are treated similarly. Equality is thus a necessary condition for equity and, in the case of health care resource allocation, equity is accomplished if services with equal health outcomes are priced equally. Conversely, equity is defied if equal costs are not reimbursed equally, if unequal outcomes are funded

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equally, or if within-peer-group equality is discrepant from between-peer-group equality.

sumes that efficiency is related directly to the number of cases treated. However, health service efficiency may also be achieved by the introduction of preventive measures or streamlined practices that serve to reduce patient volume. In addition, physician administrators and educators may be contributing to the long-term effectiveness of the health care system by engaging in non-clinical activities, thus reducing their caseload volume.

To achieve fairness, we need to ensure that the reimbursement scheme would not have a deleterious effect on indirect activities that contribute to health service efficiency, and to ascertain the equality of patient outcomes for cases that are funded equally. The CMG classification system has provided an excellent means to identify and quantify hospital products; however, we have yet to establish ways for qualifying these products procured by the cost. Without such specifications, the notion of equity would remain relatively intangible.

Discussion

A volume-driven hospital reimbursement scheme as-

To establish a hospital payment system that is fair and equitable, the relationship between hospital funding and hospital products must be rendered explicit. The introduction of RIWs and CMGs has allowed hospital output to be described in a manner far superior to surrogate measures such as patient days or bed counts. However, as these excellent management tools are being refined continuously, the use of RIWs and CMGs for resource allocation and hospital management must be executed with caution and acumen.

The use of RIWs as cost estimates RIWs are the best estimates of average cost for a

specific definition of CMGs. Whereas they may provide information about the relative cost standing, they are never meant to be predictors of actual cost. Other sources of data are necessary to complement the information provided by RIWs.

Information about hospital-specific LOS Tp enhance the understanding of LOS profiles of the

CMGs, a comparison of Hospital Medical Records In- stitute and hospital LOS distributional patterns would be useful. Such a comparison would provide insight into the assumptions about means or medians, and the relative position of hospital-specific data. Unlike the typical cases, actual LOS of the atypical cases will determine their ultimate RIW values. Without ”average” RIWs as the reference points, cases in which the LOS is beyond the trim point would warrant more detailed analyses to clarify the patterns of, and reasons for, their resource consumption.

2-

Information about hospital-specific clinical acuity Information about severity of illness is useful for

four reasons. First, to assess the correlation between RIWs and clinical acuity, we need to examine the relationship between high/low RIWs and high-/low- severity indices. Second, we need to ascertain that high-severity measures are not embedded in the low- RIW CMGs in any significant manner. Third, we need to link severity data and ALC information with the out- lier distribution. Fourth, we need to examine homogeneity of acuity measures within the CMGs, and correlate cost data with both LOS and acuity. To complement the information derived from RIWs, clinical acuity measurement that can reflect the level of diagnostic services or costly intervention would be valuable to provide information that cannot be reflected by LOS, as well as to pave the way for more precise health service product costing.

Information about hospital-specific product costing To date, most Canadian hospitals are still relatively

unaccustomed to precise product-costing. Without a firm definition of health care ”products,” attribution of functional unit costs along product lines has been ar- bitrary. In addition, the large proportion of indirect costs is especially difficult to allocate except on some questionable basis.*’ Cost information can only be as reliable as the data and method used for segregating total hospital operating costs:* and the systematic im- plementation of Management Information System (MIS) guidelines can only partially address these methodological inconsistencies. Whereas non-patient expenses are heavily dependent upon providers’ characteristics, direct patient care expenses are contingent upon the patients’ illness. Comparison of the latter may provide some insights into care delivery practice, as well as variability across peer hospital groups.

Information about episodic events Comparison of the direct costs of salient episodic

events can be useful to reveal discrepancies in practice norms and patient-specific variables. Plausible examples for micro-costing include the following:

Complications (e.g., infection, embolism, bleeding) which may vary greatly in their resource use. Know- ing the typical cost for each major type of complication would contribute to the differentiation of the ”cc” categories. Treatment prototypes (e.g., radiation, surgery, drug use, dialysis). Since mechanical ventilation is one of the most frequently used treatment modalities and admission criteria for high-cost intensive care units, establishing the exclusive, average ventilation treatment cost would be useful for improving the cost estimates for high-acuity outliers.


Volume 7, No. 1 Springh’rintemps 1994

OR/PAR/L&D/ER episodes can provide insight into the similarities or differences between U.S. and Canadian OR/PAR/L&D/ER practice.

lnformation about cost predictors Costs vary over time. Whereas it is difficult to obtain

reliable cost data, it is reasonable to expect reliability of cost predictors. This means that factors that contribute to the costs must be identified, and their validity and reliability as predictors should be assessed, so that cost information can be adjusted on a continuing basis according to its variation over time within each factor. To this end, correlation methodologies or regression analyses can be invaluable.

incorporate methods for delineating the sources of variance and examine the relationship among plausible factors that affect resource consumption. In- formation about these predictors would be helpful for impact analyses, formulation of strategies to offset negative cost variance, and evaluation of alternative resource allocation methods.

The use of CMGs as hospital products The CMG classification scheme provides a valuable

diagnosis-specific method for reviewing hospital activities. However, categorizing medical conditions according to diagnoses, treatment and complications is analogous to classifying crimes according to criminal codes, judicial systems and liabilities - it risks being illogical and incoherent to the practitioners.

To reflect clinical reality, it is necessary to capture ”additivity” and interaction among the elements within the scheme. We need a strategy to deal with the proliferation of CMGs, and a method to study sys- tematically the combination effect of the diagnoses, treatments and complications. To illustrate, the cost estimate (Y) of a CMG can be calculated by an additive model similar to the following:

Future research for micro-costing, therefore, should

(Y) CMG = 1 ,O( A ) t 1 ,O( B ) t 1 ,O( C ) t e whereby A, B and C can be any diagnosis, treatment or complication, and (1,O) are binary codes that denote the presence or absence of the factor. By analyzing the residual error term ’%,” the interactive effect among the factors can be screened or statistically ”decomposed.” This fom’ula can replace the possible combinations of the-same three elements, thus decreasing the number of CMGs required to represent the clinical situations. The value of the cost estimate (Y) would be dependent upon the position that A, B and C occupy in the formula. In other words, the same clinical problem can be the primary, secondary or tertiary cause for hospitalization, and its resource consumption is allowed to vary according to its degree of influence on hospitalization. Such a combination of factors may lead occasionally to a prolonged LOS, in which the cost can in turn be es-

timated using the same formula rather than a separate costing equation for outliers.

In addition, the current patient classification system contains 27 Major Clinical Categories (MCC) based on the various body organ systems. These categories com- prise CMGs with the number of cases ranging from 9 to 189,840. To support hospital management, the volume and degree of CMG differentiation within each of the clinical categories need to be scrutinized. Apart from using statistical criteria, CMG creation should be clinically coherent, volume sensitive and empirically feasible.

The use of peer hospital groupings The ”output” of a hospital is the patient whose

health status has been improved or altered. Unfor- tunately, we have no systematic means to determine across situations how much better or worse patients become after being admitted to a hospital. As a result, patient days, service volumes, workload intensity and other observable means for care delivery provided by the individual departments have been used as surrogates.

Neither health service effectiveness nor efficiency could be attained if cost reduction is at the expense of compromising outcome quality. Quality of care and efficiency are so intimately related that they can be seen as a single issue to be addre~sed.2~ Although RIWs and CMGs have provided a means to label hospital products, a patient categorization system alone is insuf- ficient to define patients’ change of health status. Even with the most vigorous efforts in matching the peer hospitals, cost comparison would be on a questionable basis if patient care outcomes within CMGs are significantly discrepant. On the other hand, the CMG categorization scheme has provided an ideal framework for the establishment of explicit diagnosis- specific criteria. To ensure quality, clinicians and practitioners need to define care delivery objectives clearly, identify the appropriate level of care, review patients’ clinical intensity, and recognize the inherent risks of reduced LOS for the given diagnoses. Professional bodies and health service educational institutes should collaborate with peer hospital groups to develop quality indices that can be compared across institu- tions, as well as used for quality improvement purposes within the organizations.

Quality management is integral to any assessment of hospital cost performance. The introduction of RIWs and CMGs has provided a way to identify and quantify hospital products. To ensure fairness and equity, we may need to invest our efforts in activities that serve to qualify these listed products while capitaliz- ing on the merits of peer hospital grouping. Cost comparison based on matched providers’ characteristics alone will not suffice. By advancing the defini-

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tion and specifications of patient care end products, the notion of equity can be operationalized and realisti- cally pursued, and the drawbacks of peer group categorization can also be addressed.

Incentives and the need for equity By virtue of its utility for funding purposes, case mix

methodology has exerted much impact on health service organizational behaviours. It is essential that these incentives are rendered explicit and subjected to an iterative process that serves to advance the effectiveness and efficiency of health care services.

analyses of the hospital inpatient admitting volumes would be essential to validate any assumed economy of scale. A shift to more outpatient services may not necessarily increase the total funding credits, which can be revealed by assessing the ratio of increased revenue to marginal cost for inpatient and outpatient cases. Also, a comparison of costs versus per diem credits for the atypical cases would be informative about the actual financial consequence^.^^ Patient characteristics can significantly affect the RIW performance within a given CMG - it is essential that hospitals are not placed in a disadvantageous position for deliver- ing exemplary care or treating sicker and older patients who require urgent medical interventions.

When choices must be made among alternative uses of health care resources, trade-offs are inevitable. How- ever, economic incentives implicit to case mix funding methodologies are not substitutes for ethical choices or social decisions about health care resource allocation. Patient care decisions need to be based on their likelihood of augmenting effectiveness and efficiency of the care delivery process. To ensure equity, the mechanism for rewarding and punishing organizational behaviours must be just; any disparity between patient care and financial objectives would need to be revealed and addressed prudently at both the institutional and government levels.

Conclusion

Since RIWs are based on an average number of cases,

The use and interpretation of RIWs and CMGs for hospital management is limited by the ways in which these tools are derived. Statistical criteria cannot suffice taensure validity of the cost estimates, and other souries of data are necessary to complement the information provided by RIWs.

To capture clinical reality, a coherent patient classification scheme should incorporate the additive and interactive effects among diagnoses, treatments and complications. Whereas CMGs have provided a means to identify and quantify hospital products, a mechanism is needed to qualify these listed products for cost comparison purposes. The established peer- hospital groups have provided a convenient means for

subsequent development of such diagnosis-specific criteria.

The development of RIWs has represented a milestone in the history of Canadian health care product- costing. The case mix funding methodology has stimulated much discussion about how best to deliver services and use health care resources. To maximize the utility of the current RIWs and CMG classification system for hospital management purposes, we need to be cognizant of their limitations and exercise caution in our interpretation. To ensure fairness of resource distribution, inequitable situations must be identified and addressed adequately before any increasing use of case mix methodology for funding purposes. In addition, it would be prudent for front-line health care professionals to provide structured and continuing feedback that can contribute to the validation and refinement of these valuable management tools. *CMG and RIW are registered trademarks of the Hospital Medical Records Institute.

References and notes 1.

2.

3.

4.

5.

6.

7

8.

9.

Hospital Medical Records Institute. 1990. HMRI Resource Intensity Weight - 1990 Re-Developmen t Project. Idem. 1992. Resource Intensity Weights - Summary of Methodology. Lave, J.R., Jacobs, P., and Markel, F. 1991. Ontario’s hospital transitional funding initiative: an overview and assessment. Healthcare Management Forum Winter: 3-11. Lave, J.R. and Jacobs, P. 1993. The attitudes of On- tario hospitals toward transitional funding: response to a survey. Healthcare Management Forum Spring: 25-33. Botz, C.K. 1989. Weighing case mix groups: the fatal flaw in Resource Intensity Weights. Healthcare Managemen t Forum Spring: 8-1 1. Horn S.D., Sharkey, P.D., Buckle, J.M., Backofen, J.E., Averill, R.F., Horn, R.A. 1991. The relationship between severity of illness and hospital length of stay and mortality. Medical Care 29(4): 305-317. Horn, S.D. and Horn, R.A. 1986. Reliability and validity of the severity of illness index. Medical Care

Schwab, C.W., Young, G., Civil, I., Ross, S.E., Taluc- ci, R., Rosenberg, L., Shaikh, K., O’Malley, K., Camishion, R.C. 1988. DRG reimbursement for trauma: the demise of the trauma center. Journal of Trauma 28(7): 939-946. Bardswich, E., Davenport, M., Hundert, M., and Stewart, D.Y. 1989. Analysis of patient categorization schemes. Healthcare Management Forum Spring:

24(2): 159-178.

12-17.



10. O’Brien, J.L. 1989. The Hospital Administrator’s Guide to Severity Measurement Systems, Chicago, American Hospital Association Hospital Research and Educa- tional Trust.

11. Dorland, W.A. Newman. 1988. Dorland’s Illustrated Medical Dictionary, 27th ed., Philadelphia, W.B. Saunders Company.

12. Becker, S., Shortell, S., and Neubauser, D. 1980. Management practice and hospital lengths of stay. Inquiry 17 (Winter): 318-330.

13. Longest, G. 1978. Relationships between coordina- tion, efficiency, and quality of care in general hospitals. In Health Services Management, Komer A. and Neuhauser, D (eds.), Ann Arbor, Mich., Health Ad- ministration Press.

14. Feldman, R. and Sloan, F. 1988. Competition among physicians revisited. Journal of Health Politics, Policy and Law 13(2): 239-261.

15. Wedig, G., Mitchell, J.B., and Cromwell, J. 1989. Can price controls induce optimal physician be- havior? Journal of Health Politics, Policy and Law

16. Donabedian, A. 1980. Explorations in Quality Assess- ment and Monitoring, Volume 1: The Definition of Quality and Approaches to Its Assessment, Ann Arbor, Michigan Health Administration Press.

quality of care. Health Care Financing Review (ann.

14(3): 621-625.

17. Idem. 1987. Commentary on some studies of the

Suppl.): 75-88.

18. Berwick, D.M. and Knapp, M.G. 1987. Theory and practice for measuring health care quality. Ibid: 49- 55.

19. Donabedian, A. 1966. Evaluating the quality of medical care. Millbank Memorial Fund Quarterly

20. McAuliffe, W.E. 1979. Measuring the quality of 44(3) Pt. 2: 166-206.

medical care: process versus outcomes. Millbank Memorial Fund Quarterly 57(1): 118-152.

21. Nestman, N.J. 1989. Management Control and Fund- ing Systems: For Canadian Health Service Executives, Ottawa, Canadian College of Health Service Executives.

22. Botz, C.K. 1992. Evolutions in Hospital Funding in Canada. Unpublished.

23. Harrison, F.P., Juzwishin, D., and Roger, R. 1989. Quality of care and utilization management: con- temporary tools and strategies. Healthcare Manage- ment Forum Summer: 18-23.

24. Botz, C.K. 1991. Principles for funding on a case mix basis: construction of case weights (RIWs). Healthcare Management Forum Winter: 22-32.

This paper was originally submitted in 1992.

Christabel Chu is currently working a t LZ Toronto hospital.

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