|Title||Predicting nursing facility transition candidates using AID: a case study|
|Publication Type||Journal Article|
|Year of Publication||2007|
|Authors||James M.L, Wiley E., Fries B.E|
|Keywords||*Deinstitutionalization/td [Trends], *Disability Evaluation, *Forecasting, *Nursing Homes, Aged, Aged, 80 and over, Arkansas, Community Health Services, Databases as Topic, Female, Humans, Male, Mass Screening, Middle Aged, Organizational Case Studies, Pilot Projects|
PURPOSE: Although the nursing facility transition literature is growing, little research has analyzed the characteristics of individuals so assisted or compared participants to those who remain institutionalized. This article describes an analytic method that researchers can apply to address these knowledge gaps, using the Arkansas Passages nursing facility transition program as a case study. DESIGN AND METHODS: This study employed Arkansas Minimum Data Set 2.0 data for 111 transitioned individuals, a derivation sample of 1,000 other residents, and a validation sample of all residents from the transitioned individuals' nursing facilities. Tree classification techniques identified distinct groups of transitioned and nontransitioned residents. RESULTS: Nearly two thirds of transitionees were part of a group comprising only 1.5% of all Arkansas nursing facility residents. Five characteristics identified this group: age, day of stay (i.e., current day of stay at the time of the assessment), having hemiplegia/paraplegia, cognitive impairment level, and classification into one of eight Resource Utilization Groups (RUG-III) case-mix groups associated with the least nursing staff time. Another group containing 92% of the transitionees comprised 22% of all residents. Two characteristics identified this group: being younger than age 65 or being in the eight low-resource RUG-III groups. IMPLICATIONS: Given that the majority of individuals assisted by this pilot represent a small and unusual nursing facility subpopulation, policy makers may wish to exercise caution in utilizing these data to forecast future transition populations, costs, or outcomes. Replicating this analysis using additional states' data could increase understanding about the characteristics of those assisted across transition programs and could help construct a more robust definition of what constitutes a transition success.