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Medical Care:
doi: 10.1097/MLR.0000000000000109
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Algorithm for Identifying Nursing Home Days Using Medicare Claims and Minimum Data Set Assessment Data.

Wei, Yu-Jung PhD; Simoni-Wastila, Linda PhD; Zuckerman, Ilene H. PharmD, PhD; Brandt, Nicole Pharm D; Lucas, Judith A. EdD, RN, GCNS-BC

Published Ahead-of-Print
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Background: No consensus exists about methods of measuring nursing home (NH) length-of-stay for Medicare beneficiaries to identify long-stay and short-stay NH residents.

Objectives: To develop an algorithm measuring NH days of stay to differentiate between residents with long and short stay (>=101 and <101 consecutive days, respectively) and to compare the algorithm with Minimum Data Set (MDS) alone and Medicare claims data.

Research Design: We linked 2006-2009 MDS assessments to Medicare Part A skilled nursing facility (SNF) data. This algorithm determined the daily NH stay evidence by MDS and SNF dates. NH length-of-stay and characteristics were reported in the total, long-stay, and short-stay residents. Long-stay residents identified by the algorithm were compared with the NH evidence from MDS-alone and Medicare parts A and B data.

Results: Of 276,844 residents identified by our algorithm, 40.8% were long stay. Long-stay versus short-stay residents tended to be older, male, white, unmarried, low-income subsidy recipients, have multiple comorbidities, and have higher mortality but have fewer hospitalizations and SNF services. Higher proportions of long-stay and short-stay residents identified by the MDS/SNF algorithm were classified in the same group using MDS-only (98.9% and 100%, respectively), compared with the parts A and B data (95.0% and 67.1%, respectively). NH length-of-stay was similar between MDS/SNF and MDS-only long-stay residents (mean+/-SD: 717+/-422 vs. 720+/-441 d), but the lengths were longer compared with the parts A and B data (approximately 474+/-393 d).

Conclusions: Our MDS/SNF algorithm allows the differentiation of long-stay and short-stay residents, resulting in an NH group more precise than using Medicare claims data only.

(C) 2015 by Lippincott Williams & Wilkins.


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