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Algorithm for Identifying Nursing Home Days Using Medicare Claims and Minimum Data Set Assessment Data

Bibliographic Data

ID9102911
AuthorsYu-Jung Wei (Pharmaceutical Health Services Research), Yu‐Jung Jenny Wei (0000-0001-8861-6907, West Pharmaceutical Services (United Kingdom), corresponding author), Jane M Simoni (0000-0002-8711-1576, West Pharmaceutical Services (United Kingdom), corresponding author), Linda Simoni-Wastila (Pharmaceutical Health Services Research), Ilene H Zuckerman (Pharmaceutical Health Services Research, corresponding author), Nicole Brandt (0000-0003-2766-6120, Pharmacy Practice and Science, University of Maryland School of Pharmacy, Baltimore, MD), Judith A Lucas (Department of Behavioral and Community Health, Seton Hall University College of Nursing, South Orange)
Year2016
Volume54
Issue11
Pagese73-e77
Publication date2016-11-01
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000000109
PMID25625654
OpenAlexW2081115189
LanguageEN
Citations received3
References cited14

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

Minimum Data Set · Nursing homes · Skilled Nursing Facility · Chronic Disease Management Strategies · Demography · Emergency Medicine · Frailty in Older Adults · Geriatric Care and Nursing Homes · Medicine · Nursing

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Citations per year0.38
Citation span2018 - 2021 (4)
Highly citedNo

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