Dataset Information

Using performance data to identify preferred hospitals.

ABSTRACT:

Objective

To explore the implications of current approaches used by health plans and purchasers to identify preferred hospitals for tiered networks using cost and quality information.

Data sources/study setting

2002 secondary data from WebMD Quality Services on hospital quality and costs in five markets (Boston, Miami, Phoenix, Seattle, and Syracuse).

Study design

We compared four alternative tiering strategies that combine information on quality and cost to designate "preferred" (defined as ranking in the top quartile) hospitals. Within each market we identified the sets of hospitals designated preferred according to each strategy and examined the overlap in these sets across strategies.

Principal findings

Compared with identifying preferred hospitals based on quality scores only, we found little overlap with the sets of hospitals that would be preferred based on cost scores only, cost scores after applying minimal quality standards, and an equally weighted quality and cost measure. The last two approaches, commonly used and intuitively appealing strategies to identify high-value hospitals, led to substantially different results.

Conclusions

The lack of agreement among alternative strategies to combine cost and quality data for ranking hospitals suggests the need for clear prioritization by payers and the application of more rigorous methods to identify high-value hospitals.

SUBMITTER: Rosenthal MB

PROVIDER: S-EPMC2151404 | biostudies-literature | 2007 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Using performance data to identify preferred hospitals.

Rosenthal Meredith B MB Landrum Mary Beth MB Meara Ellen E Huskamp Haiden A HA Conti Rena M RM Keating Nancy L NL

Health services research 20071201 6 Pt 1

<h4>Objective</h4>To explore the implications of current approaches used by health plans and purchasers to identify preferred hospitals for tiered networks using cost and quality information.<h4>Data sources/study setting</h4>2002 secondary data from WebMD Quality Services on hospital quality and costs in five markets (Boston, Miami, Phoenix, Seattle, and Syracuse).<h4>Study design</h4>We compared four alternative tiering strategies that combine information on quality and cost to designate "pref ...[more]

PMID: 17995555

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Project description:ImportanceThere has been a growth in the use of performance-based payment models in the past decade, but inherently noisy and stochastic quality measures complicate the assessment of the quality of physician groups. Examining consistently low performance across multiple measures or multiple years could potentially identify a subset of low-quality physician groups.ObjectiveTo identify low-performing physician groups based on consistently low performance after adjusting for patient characteristics across multiple measures or multiple years for 10 commonly used quality measures for diabetes and cardiovascular disease (CVD).Design, setting, and participantsThis cross-sectional study used medical and pharmacy claims and laboratory data for enrollees ages 18 to 65 years with diabetes or CVD in an Aetna health insurance plan between 2016 and 2019. Each physician group's risk-adjusted performance for a given year was estimated using mixed-effects linear probability regression models. Performance was correlated across measures and time, and the proportion of physician groups that performed in the bottom quartile was examined across multiple measures or multiple years. Data analysis was conducted between September 2020 and May 2021.ExposuresPrimary care physician groups.Main outcomes and measuresPerformance scores of 6 quality measures for diabetes and 4 for CVD, including hemoglobin A1c (HbA1c) testing, low-density lipoprotein testing, statin use, HbA1c control, low-density lipoprotein control, and hospital-based utilization.ResultsA total of 786 641 unique enrollees treated by 890 physician groups were included; 414 655 (52.7%) of the enrollees were men and the mean (SD) age was 53 (9.5) years. After adjusting for age, sex, and clinical and social risk variables, correlations among individual measures were weak (eg, performance-adjusted correlation between any statin use and LDL testing for patients with diabetes, r = -0.10) to moderate (correlation between LDL testing for diabetes and LDL testing for CVD, r = .43), but year-to-year correlations for all measures were moderate to strong. One percent or fewer of physician groups performed in the bottom quartile for all 6 diabetes measures or all 4 cardiovascular disease measures in any given year, while 14 (4.0%) to 39 groups (11.1%) were in the bottom quartile in all 4 years for any given measure other than hospital-based utilization for CVD (1.1%).Conclusions and relevanceA subset of physician groups that was consistently low performing could be identified by considering performance measures across multiple years. Considering the consistency of group performance could contribute a novel method to identify physician groups most likely to benefit from limited resources.

Dataset Information

Using performance data to identify preferred hospitals.

Objective

Data sources/study setting

Study design

Principal findings

Conclusions

Publications

Using performance data to identify preferred hospitals.

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