Assessment of Variation in Electronic Health Record Capabilities and Reported Clinical Quality Performance in Ambulatory Care Clinics, 2014-2017.

Importance: Electronic health records (EHRs) are widely promoted to improve the quality of health care, but information about the association of multifunctional EHRs with broad measures of quality in ambulatory settings is scarce. Objective: To assess the association between EHRs with different degrees of capabilities and publicly reported ambulatory quality measures in at least 3 clinical domains of care. Design, Setting, and Participants: This cross-sectional and longitudinal study was conducted using survey responses from 1141 ambulatory clinics in Minnesota, Washington, and Wisconsin affiliated with a health system that responded to the Healthcare Information and Management Systems Society Annual Survey and reported performance measures in 2014 to 2017. Statistical analysis was performed from July 10, 2019, through February 26, 2021. Main Outcomes and Measures: A composite measure of EHR capability that considered 50 EHR capabilities in 7 functional domains, grouped into the following ordered categories: no functional EHR, EHR underuser, EHR, neither underuser or superuser, EHR superuser; as well as a standardized composite of ambulatory clinical performance measures that included 3 to 25 individual measures (median, 13 individual measures). Results: In 2014, 381 of 746 clinics (51%) were EHR superusers; this proportion increased in each subsequent year (457 of 846 clinics [54%] in 2015, 510 of 881 clinics [58%] in 2016, and 566 of 932 clinics [61%] in 2017). In each cross-sectional analysis year, EHR superusers had better clinical quality performance than other clinics (adjusted difference in score: 0.39 [95% CI, 0.12-0.65] in 2014; 0.29 [95% CI, -0.01 to 0.59] in 2015; 0.26 [95% CI, -0.05 to 0.56] in 2016; and 0.20 [95% CI, -0.04 to 0.45] in 2017). This difference in scores translates into an approximately 9% difference in a clinic's rank order in clinical quality. In longitudinal analyses, clinics that progressed to EHR superuser status had only slightly better gains in clinical quality between 2014 and 2017 compared with the gains in clinical quality of clinics that were static in terms of their EHR status (0.10 [95% CI, -0.13 to 0.32]). In an exploratory analysis, different types of EHR capability progressions had different degrees of associated improvements in ambulatory clinical quality (eg, progression from no functional EHR to a status short of superuser, 0.06 [95% CI, -0.40 to 0.52]; progression from EHR underuser to EHR superuser, 0.18 [95% CI, -0.14 to 0.50]). Conclusions and Relevance: Between 2014 and 2017, ambulatory clinics in Minnesota, Washington, and Wisconsin with EHRs having greater capabilities had better composite measures of clinical quality than other clinics, but clinics that gained EHR capabilities during this time had smaller increases in clinical quality that were not statistically significant.

Trends in Low-Value Health Service Use and Spending in the US Medicare Fee-for-Service Program, 2014-2018.

Importance: Low-value care, defined as care offering no net benefit in specific clinical scenarios, is associated with harmful outcomes in patients and wasteful spending. Despite a national education campaign and increasing attention on reducing health care waste, recent trends in low-value care delivery remain unknown. Objective: To assess national trends in low-value care use and spending. Design, Setting, and Participants: In this cross-sectional study, analyses of low-value care use and spending from 2014 to 2018 were conducted using 100% Medicare fee-for-service enrollment and claims data. Included individuals were aged 65 years or older and continuously enrolled in Medicare parts A, B, and D during each measurement year and the previous year. Data were analyzed from September 2019 through December 2020. Exposure: Being enrolled in fee-for-service Medicare for a period of time, in years. Main Outcomes and Measures: The Milliman MedInsight Health Waste Calculator was used to assess 32 claims-based measures of low-value care associated with Choosing Wisely recommendations and other professional guidelines. The calculator designates services as wasteful, likely wasteful, or not wasteful based on an absence of indication of appropriate use in the claims history; calculator-designated wasteful services were defined as low-value care. Spending was calculated as claim-line level (ie, spending on the low-value service) and claim level (ie, spending on the low-value service plus associated services), adjusting for inflation. Results: Among 21 045 759 individuals with fee-for-service Medicare (mean [SD] age, 77.4 [7.9] years; 12 515 915 [59.5%] women), the percentage receiving any of 32 low-value services decreased from 36.3% (95% CI, 36.3%-36.4%) to 33.6% (95% CI, 33.6%-33.6%) from 2014 to 2018. Uses of low-value services per 1000 individuals decreased from 677.8 (95% CI, 676.2-679.5) to 632.7 (95% CI, 632.6-632.8) from 2014 to 2018. Three services comprised approximately two-thirds of uses among 32 low-value services per 1000 individuals: preoperative laboratory testing decreased from 213.8 (95% CI, 213.4-214.2) to 166.2 (95% CI, 166.2-166.2), while opioids for back pain increased from 154.4 (95% CI, 153.6-155.2) to 182.1 (95% CI, 182.1-182.1) and antibiotics for upper respiratory infections increased from 75.0 (95% CI, 75.0-75.1) to 82 (95% CI, 82.0-82.0). Spending per 1000 individuals on low-value care also decreased, from $52 765.5 (95% CI, $51 952.3-$53 578.6) to $46 921.7 (95% CI, $46 593.7-$47 249.7) at the claim-line level and from $160 070.4 (95% CI, $158 999.8-$161 141.0) to $144 741.1 (95% CI, $144 287.5-$145 194.7) at the claim level. Conclusions and Relevance: This cross-sectional study found that among individuals with fee-for-service Medicare receiving any of 32 measured services, low-value care use and spending decreased marginally from 2014 to 2018, despite a national education campaign in collaboration with clinician specialty societies and increased attention on low-value care. While most use of low-value care came from 3 services, 1 of these was opioid prescriptions, which increased over time despite the harms associated with their use. These findings may represent several opportunities to prevent patient harm and lower spending.

Racial and ethnic disparities in care for health system-affiliated physician organizations and non-affiliated physician organizations.

OBJECTIVE: To assess racial and ethnic disparities in care for Medicare fee-for-service (FFS) beneficiaries and whether disparities differ between health system-affiliated physician organizations (POs) and nonaffiliated POs. DATA SOURCES: We used Medicare Data on Provider Practice and Specialty (MD-PPAS), Medicare Provider Enrollment, Chain, and Ownership System (PECOS), IRS Form 990, 100% Medicare FFS claims, and race/ethnicity estimated using the Medicare Bayesian Improved Surname Geocoding 2.0 algorithm. STUDY DESIGN: Using a sample of 16 007 POs providing primary care in 2015, we assessed racial/ethnic disparities on 12 measures derived from claims (2 cancer screenings; diabetic eye examinations; continuity of care; two medication adherence measures; three measures of follow-up visits after acute care; all-cause emergency department (ED) visits, all-cause readmissions, and ambulatory care-sensitive admissions). We decomposed these "total" disparities into within-PO and between-PO components using models with PO random effects. We then pair-matched 1853 of these POs that were affiliated with health systems to similar nonaffiliated POs. We examined differences in within-PO disparities by affiliation status by interacting each nonwhite race/ethnicity with an affiliation indicator. DATA COLLECTION/EXTRACTION METHODS: Medicare Data on Provider Practice and Specialty identified POs billing Medicare; PECOS and IRS Form 990 identified health system affiliations. Beneficiaries age 18 and older were attributed to POs using a plurality visit rule. PRINCIPAL FINDINGS: We observed total disparities in 12 of 36 comparisons between white and nonwhite beneficiaries; nonwhites received worse care in 10. Within-PO disparities exceeded between-PO disparities and were substantively important (>=5 percentage points or>=0.2 standardized differences) in nine of the 12 comparisons. Among these 12, nonaffiliated POs had smaller disparities than affiliated POs in two comparisons (P < .05): 1.6 percentage points smaller black-white disparities in follow-up after ED visits and 0.6 percentage points smaller Hispanic-white disparities in breast cancer screening. CONCLUSIONS: We find no evidence that system-affiliated POs have smaller racial and ethnic disparities than nonaffiliated POs. Where differences existed, disparities were slightly larger in affiliated POs.

Health system affiliation of physician organizations and quality of care for Medicare beneficiaries who have high needs.

OBJECTIVE: To test the hypothesis that health systems provide better care to patients with high needs by comparing differences in quality between system-affiliated and nonaffiliated physician organizations (POs) and to examine variability in quality across health systems. DATA SOURCES: 2015 Medicare Data on Provider Practice and Specialty linked physicians to POs. Medicare Provider Enrollment, Chain, and Ownership System (PECOS) and IRS Form 990 data identified health system affiliations. Fee-for-service Medicare enrollment and claims data were used to examine quality. STUDY DESIGN: This cross-sectional analysis of beneficiaries with high needs, defined as having more than twice the expected spending of an average beneficiary, examined six quality measures: continuity of care, follow-up visits after hospitalizations and emergency department (ED) visits, ED visits, all-cause readmissions, and ambulatory care-sensitive hospitalizations. Using a matched-pair design, we estimated beneficiary-level regression models with PO random effects to compare quality of care in system-affiliated and nonaffiliated POs. We then limited the sample to system-affiliated POs and estimated models with system random effects to examine variability in quality across systems. PRINCIPAL FINDINGS: Among 2 323 301 beneficiaries with high needs, 52.3% received care from system-affiliated POs. Rates of ED visits were statistically significantly different in system-affiliated POs (117.5 per 100) and nonaffiliated POs (106.8 per 100, P < .0001). Small differences in the other five quality measures were observed across a range of sensitivity analyses. Among systems, substantial variation was observed for rates of continuity of care (90% of systems had rates between 70.8% and 89.4%) and follow-up after ED visits (90% of systems had rates between 56.9% and 73.5%). CONCLUSIONS: Small differences in quality of care were observed among beneficiaries with high needs receiving care from system POs and nonsystem POs. Health systems may not confer hypothesized quality advantages to patients with high needs.

How much is postacute care use affected by its availability?

OBJECTIVE: To assess the relative impact of clinical factors versus nonclinical factors-such as postacute care (PAC) supply-in determining whether patients receive care from skilled nursing facilities (SNFs) or inpatient rehabilitation facilities (IRFs) after discharge from acute care. DATA SOURCES AND STUDY SETTING: Medicare acute hospital, IRF, and SNF claims provided data on PAC choices; predictors of site of PAC chosen were generated from Medicare claims, provider of services, enrollment file, and Area Resource File data. STUDY DESIGN: We used multinomial logit models to predict PAC use by elderly patients after hospitalizations for stroke, hip fractures, or lower extremity joint replacements. DATA COLLECTION/EXTRACTION METHODS: A file was constructed linking acute and postacute utilization data for all medicare patients hospitalized in 1999. PRINCIPAL FINDINGS: PAC availability is a more powerful predictor of PAC use than the clinical characteristics in many of our models. The effects of distance to providers and supply of providers are particularly clear in the choice between IRF and SNF care. The farther away the nearest IRF is, and the closer the nearest SNF is, the less likely a patient is to go to an IRF. Similarly, the fewer IRFs, and the more SNFs, there are in the patient's area the less likely the patient is to go to an IRF. In addition, if the hospital from which the patient is discharged has a related IRF or a related SNF the patient is more likely to go there. CONCLUSIONS: We find that the availability of PAC is a major determinant of whether patients use such care and which type of PAC facility they use. Further research is needed in order to evaluate whether these findings indicate that a greater supply of PAC leads to both higher use of institutional care and better outcomes-or whether it leads to unwarranted expenditures of resources and delays in returning patients to their homes.