Beyond section Q: prioritizing nursing home residents for transition to the community

Brant E Fries,Mary L James

doi:10.1186/1472-6963-12-186

Abstract

BackgroundNursing Facility Transition (NFT) programs often rely on self-reported preference for discharge to the community, as indicated in the Minimum Data Set (MDS) Section Q, to identify program participants. We examined other characteristics of long-stay residents discharged from nursing facilities by NFT programs, to “flag” similar individuals for outreach in the Money Follows the Person (MFP) initiative.MethodsThree states identified persons who transitioned between 2001 and 2009 with the assistance of a NFT or MFP program. These were used to locate each participant’s MDS 2.0 assessment just prior to discharge and to create a control sample of non-transitioned residents. Logistic regression and Automatic Interactions Detection were used to compare the two groups.ResultsAlthough there was considerable variation across states in transitionees’ characteristics, a derived “Q + Index” was highly effective in identifying persons similar to those that states had previously transitioned. The Index displays high sensitivity (86.5%) and specificity (78.7%) and identifies 28.3% of all long-stayers for follow-up. The Index can be cross-walked to MDS 3.0 items.ConclusionsThe Q + Index, applied to MDS 3.0 assessments, can identify a population closely resembling persons who have transitioned in the past. Given the US Government’s mandate that states consider all transition requests and the limited staffing available at local contact agencies to address such referrals, this algorithm can also be used to prioritize among persons seeking assistance from local contact agencies and MFP providers.

Highlights

Nursing Facility Transition (NFT) programs often rely on self-reported preference for discharge to the community, as indicated in the Minimum Data Set (MDS) Section Q, to identify program participants
Known as nursing facility transition (NFT) programs, 44 states are engaged in the Centers for Medicare and Medicaid Services’ (CMS) $1.75B Money Follows the Person (MFP) initiative, targeted at long-stay residents, and a number of states have dedicated increasingly scarce general funds to similar NFT efforts [1] for both the short-and long-stay nursing facility population
We hypothesized that specific characteristics of NFT program participants would distinguish them from individuals who remain in NFs

Summary

Introduction

Nursing Facility Transition (NFT) programs often rely on self-reported preference for discharge to the community, as indicated in the Minimum Data Set (MDS) Section Q, to identify program participants. We examined other characteristics of long-stay residents discharged from nursing facilities by NFT programs, to “flag” similar individuals for outreach in the Money Follows the Person (MFP) initiative. Known as nursing facility transition (NFT) programs, 44 states are engaged in the Centers for Medicare and Medicaid Services’ (CMS) $1.75B Money Follows the Person (MFP) initiative, targeted at long-stay residents, and a number of states have dedicated increasingly scarce general funds to similar NFT efforts [1] for both the short-and long-stay nursing facility population. We hypothesized that specific characteristics of NFT program participants would distinguish them from individuals who remain in NFs. We sought an algorithm to “flag” NF residents who would be contacted to discuss potential community transition. This algorithm would target a relatively small percentage of all residents yet would successfully identify a very large percent of those who were transitioned in the past

Methods

Results

Discussion

Conclusion