Evidence map›Paper›PMID 33885774›Full record

ArticleJAMA network open2021

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

Paul G Shekelle, Joseph D Pane, Denis Agniel, Yunfeng Shi, Juliet Rumball-Smith, Ann Haas, Shira Fischer, Robert S Rudin, Mark Totten, Julie Lai and 2 more

Abstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Paul G ShekelleDepartment of Health Care, RAND Corporation, Santa Monica, California.
Joseph D PaneDepartment of Economics, Sociology, and Statistics, RAND Corporation, Pittsburgh, Pennsylvania.
Denis AgnielDepartment of Health Care, RAND Corporation, Santa Monica, California.
Yunfeng ShiDepartment of Health Policy and Administration, Pennsylvania State University, University Park.
Juliet Rumball-SmithMinistry of Health, Wellington, New Zealand.
Ann HaasDepartment of Health Care, RAND Corporation, Pittsburgh, Pennsylvania.
Shira FischerDepartment of Health Care, RAND Corporation, Boston, Massachusetts.
Robert S RudinDepartment of Health Care, RAND Corporation, Boston, Massachusetts.
Mark TottenDepartment of Research Programming, RAND Corporation, Santa Monica, California.
Julie LaiDepartment of Research Programming, RAND Corporation, Santa Monica, California.
Dennis ScanlonDepartment of Health Policy and Administration, Pennsylvania State University, University Park.
Cheryl L DambergDepartment of Health Care, RAND Corporation, Santa Monica, California.

Funding

AHRQ HHS U19 HS024067
6 · The paper itself

Abstract

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.

Indexed as

Ambulatory CareElectronic Health RecordsQuality of Health CareAmbulatory Care FacilitiesCross-Sectional StudiesHumansLongitudinal StudiesMinnesotaWashingtonWisconsin

Identifiers

PMID33885774
PMCPMC8063064

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.