Literature DB >> 29271820

Defining Multiple Chronic Conditions for Quality Measurement.

Elizabeth E Drye1, Faseeha K Altaf, Kasia J Lipska, Erica S Spatz, Julia A Montague, Haikun Bao, Craig S Parzynski, Joseph S Ross, Susannah M Bernheim, Harlan M Krumholz, Zhenqiu Lin.   

Abstract

BACKGROUND/
OBJECTIVE: Patients with multiple chronic conditions (MCCs) are a critical but undefined group for quality measurement. We present a generally applicable systematic approach to defining an MCC cohort of Medicare fee-for-service beneficiaries that we developed for a national quality measure, risk-standardized rates of unplanned admissions for Accountable Care Organizations. RESEARCH
DESIGN: To define the MCC cohort we: (1) identified potential chronic conditions; (2) set criteria for cohort conditions based on MCC framework and measure concept; (3) applied the criteria informed by empirical analysis, experts, and the public; (4) described "broader" and "narrower" cohorts; and (5) selected final cohort with stakeholder input.
SUBJECTS: Subjects were patients with chronic conditions. Participants included 21.8 million Medicare fee-for-service beneficiaries in 2012 aged 65 years and above with ≥1 of 27 Medicare Chronic Condition Warehouse condition(s).
RESULTS: In total, 10 chronic conditions were identified based on our criteria; 8 of these 10 were associated with notably increased admission risk when co-occurring. A broader cohort (2+ of the 8 conditions) included 4.9 million beneficiaries (23% of total cohort) with an admission rate of 70 per 100 person-years. It captured 53% of total admissions. The narrower cohort (3+ conditions) had 2.2 million beneficiaries (10%) with 100 admissions per 100 person-years and captured 32% of admissions. Most stakeholders viewed the broader cohort as best aligned with the measure concept.
CONCLUSIONS: By systematically narrowing chronic conditions to those most relevant to the outcome and incorporating stakeholder input, we defined an MCC admission measure cohort supported by stakeholders. This approach can be used as a model for other MCC outcome measures.

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Mesh:

Year:  2018        PMID: 29271820     DOI: 10.1097/MLR.0000000000000853

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  6 in total

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Authors:  Matthew T Mefford; Michael J Silverberg; Thomas K Leong; Rulin C Hechter; William J Towner; Alan S Go; Michael Horberg; Haihong Hu; Teresa N Harrison; Sue Hee Sung; Kristi Reynolds
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  6 in total

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