Coding Response to a Case‐Mix Measurement System Based on Multiple Diagnoses

Abstract

Objective. To examine the hospital coding response to a payment model using a casemix measurement system based on multiple diagnoses and the resulting impact on a hospital cost model. Data Sources. Financial, clinical, and supplementary data for all Ontario short stay hospitals from years 1997 to 2002. Study Design. Disaggregated trends in hospital case-mix growth are examined for five years following the adoption of an inpatient classification system making extensive use of combinations of secondary diagnoses. Hospital case mix is decomposed into base and complexity components. The longitudinal effects of coding variation on a standard hospital payment model are examined in terms of payment accuracy and impact on adjustment factors. Principal Findings. Introduction of the refined case-mix system provided incentives for hospitals to increase reporting of secondary diagnoses and resulted in growth in highest complexity cases that were not matched by increased resource use over time. Despite a pronounced coding response on the part of hospitals, the increase in measured complexity and case mix did not reduce the unexplained variation in hospital unit cost nor did it reduce the reliance on the teaching adjustment factor, a potential proxy for case mix. The main implication was changes in the size and distribution of predicted hospital operating costs. Conclusions. Jurisdictions introducing extensive refinements to standard diagnostic related group (DRG)-type payment systems should consider the effects of induced changes to hospital coding practices. Assessing model performance should include analysis of the robustness of classification systems to hospital-level variation in coding practices. Unanticipated coding effects imply that case-mix models hypothesized to perform well ex ante may not meet expectations ex post.

The effect of misclassification errors on case mix measurement

“…Coders then travel to hospitals and re-abstract patient charts on-site. Separate studies of Canadian discharge data have shown consistent results [12][13][14] that are comparable to international results [15].…”

The association of the number of comorbidities and complications with length of stay, hospital mortality and LOS high outlier, based on administrative data

“…The peer review organization system for coding behavior and DPC creep will be in demand, just like in other countries that are already Fig. 2 Hospital mortality (per 1,000 cases) and number of comorbidities and complications, stratified by treatment group (medical or surgery) and major diagnostic category utilizing those kinds of case-mix classification systems [16][17][18][19]. Third, there was a limitation of coding slots in the DPC dataset, whereby only seven secondary diagnoses (four comorbidities and three complications) could be listed.…”

A mixture model approach to updating payment weights with an application to ICD-10 implementation

“…The accuracy of the clinical information reported from Ontario hospitals has been studied [13][14][15] and hospital response to cost weights has been observed in Ontario [3] and resultant bias in cost weights estimated [16]. The accuracy and adherence to micro-costing standards by hospitals is evaluated on an annual basis by the Ontario Ministry of Health and Long-Term Care, although it is known that averaging departmental costs over broader groups of patients introduces attenuation bias in the cost weights [17].…”

“…However, this assumption is not always tenable, borne out by experience in many jurisdictions. For example, dramatic changes over time in the mix of patients within resource groups has been observed when hospitals have optimized coding practices in order to maximize reimbursement under case based payment systems [1][2][3].…”