Introduction: This study first aimed to determine the adequacy of the Diagnosis Related Grouping (DRG) model’s ability to explain (1) the variance in the actual length of stay (LOS) of elderly medical inpatients and (2) the LOS difference in the same cohort between the departments of Geriatric Medicine (GRM) and General Medicine (GM). We then looked at how these explanatory abilities of the DRG changed when patients’ function-linked variables (ignored by DRG) were incorporated into the model.Materials and Methods: Basic demographic data of a consecutively hospitalised cohort of elderly medical inpatients from GRM and GM, as well as their actual LOS, discharge DRG codes [with their corresponding trimmed average length of stay (ALOS)] and selected function-linked variables (including premorbid functional status, change in functional profile during hospitalisation and number of therapists seen) were recorded. Beginning with ALOS, function-linked variables that were significantly associated with LOS were then added into two multiple liner regression models so as to quantify how the functional dimension improved the DRGs’ abilities to explain LOS variances and interdepartmental LOS differences. Forward selection procedure was employed to determine the final models. For the interdepartmental analysis, the study sample was restricted to patients who shared common DRG codes.Results: 114 GRM and 118 GM patients were studied. Trimmed ALOS alone explained 8% of the actual LOS variance. With the addition of function-linked variables, the adjusted R2 of the final model increased to 28%. Due to common code restrictions, the data of 79 GRM and 78 GM patients were available for the analysis of interdepartmental LOS differences. At the unadjusted stage, the median stay of GRM patients was 4.3 days longer than GM’s and with adjustments made for the DRGs, this difference was reduced to 3.9 days. Additionally adjusting for the patients’ functional features diminished the interdepartmental LOS discrep-ancy even further, to 2.1 days.Conclusion: This study demonstrates that for elderly medical inpatients, the incorporation of patients’ functional status significantly improves the DRG model’s ability to predict the patients’ actual LOS as well as to explain interdepartmental LOS differences between GRM and GM.
Casemix refers to the numbers and types of patients within a healthcare setting and Diagnosis Related Groupings (DRGs) represent one mode of classifying casemix. In essence, DRGs are categories of clinically meaningful patient conditions which require similar levels of hospital resources for their treatment.
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