Abstract

Low-value health care remains prevalent in the US despite decades of work to measure and reduce such care. Efforts have been only modestly effective in part because the measurement of low-value care has largely been restricted to the national or regional level, limiting actionability. To measure and report low-value care use across and within individual health systems and identify system characteristics associated with higher use using Medicare administrative data. This retrospective cohort study of health system-attributed Medicare beneficiaries was conducted among 556 health systems in the Agency for Healthcare Research and Quality Compendium of US Health Systems and included system-attributed beneficiaries who were older than 65 years, continuously enrolled in Medicare Parts A and B for at least 12 months in 2016 or 2017, and eligible for specific low-value services. Statistical analysis was conducted from January 26 to July 15, 2021. Use of 41 individual low-value services and a composite measure of the 28 most common services among system-attributed beneficiaries, standardized to distance from the mean value. Measures were based on the Milliman MedInsight Health Waste Calculator and published claims-based definitions. Across 556 health systems serving a total of 11 637 763 beneficiaries, the mean (SD) use of each of the 41 low-value services ranged from 0% (0.01%) to 28% (4%) of eligible beneficiaries. The most common low-value services were preoperative laboratory testing (mean [SD] rate, 28% [4%] of eligible beneficiaries), prostate-specific antigen testing in men older than 70 years (mean [SD] rate, 27% [8%]), and use of antipsychotic medications in patients with dementia (mean [SD] rate, 24% [8%]). In multivariable analysis, the health system characteristics associated with higher use of low-value care were smaller proportion of primary care physicians (adjusted composite score, 0.15 [95% CI, 0.04-0.26] for systems with less than the median percentage of primary care physicians vs -0.16 [95% CI, -0.27 to -0.05] for those with more than the median percentage of primary care physicians; P < .001), no major teaching hospital (adjusted composite, 0.10 [95% CI, -0.01 to 0.20] without a teaching hospital vs -0.18 [95% CI, -0.34 to -0.02] with a teaching hospital; P = .01), larger proportion of non-White patients (adjusted composite, 0.15 [95% CI, -0.02 to 0.32] for systems with >20% of non-White beneficiaries vs -0.06 [95% CI, -0.16 to 0.03] for systems with ≤20% of non-White beneficiaries; P = .04), headquartered in the South or West (adjusted composite, 0.28 [95% CI, 0.14-0.43] for the South and 0.22 [95% CI, 0.02-0.42] for the West compared with -0.09 [95% CI, -0.26 to 0.08] for the Northeast and -0.44 [95% CI, -0.60 to -0.28] for the Midwest; P < .001), and serving areas with more health care spending (adjusted composite, 0.23 [95% CI, 0.11-0.35] for areas above the median level of spending vs -0.24 [95% CI, -0.36 to -0.12] for areas below the median level of spending; P < .001). The findings of this large cohort study suggest that system-level measurement and reporting of specific low-value services is feasible, enables cross-system comparisons, and reveals a broad range of low-value care use.

Highlights

  • Across 556 health systems serving a total of 11 637 763 beneficiaries, the mean (SD) use of each of the 41 low-value services ranged from 0% (0.01%) to 28% (4%) of eligible beneficiaries

  • Low-value health care services are still widely delivered in the US despite decades-long efforts to curb them through measurement and reporting.[1]

  • We found a substantial number of positive, significant within-system correlations between and within clinical measure groups, consistent with prior work showing modestly positive associations between service categories within Veterans Affairs medical centers and within provider organizations.[27,28]

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Summary

Objectives

To measure and report low-value care use across and within individual health systems and identify system characteristics associated with higher use using Medicare administrative data

Methods
Results
Discussion
Conclusion

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