Evidence map›Paper›PMID 38383045›Full record

ArticleAnnals of family medicine2024

Optimization of Electronic Health Record Usability Through a Department-Led Quality Improvement Process.

Adam M Franks, Charles Clements, Tammy Bannister, Adrienne Mays-Kingston, Ashley Beaty, Alperen Korkmaz, John A Parker, Stephen M Petrany

Abstract read
In one paragraph

Article in Annals of family medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Adam M FranksDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia franks1@marshall.edu.
Charles ClementsDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia.
Tammy BannisterDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia.
Adrienne Mays-KingstonDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia.
Ashley BeatyDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia.
Alperen KorkmazDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia.
John A ParkerDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia.
Stephen M PetranyDepartment of Family and Community Health, Joan C. Edwards School of Medicine, Marshall University, Huntington, West Virginia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundElectronic health records (EHR) have become commonplace in medicine. A disconnect between developers and users while creating the interface often fails to create a product that captures clinical workflow, and issues become apparent with implementation. Optimization allows collaboration of clinicians and informaticists after implementation, but documentation of success has only been at the institutional level.

methodsA 4-month, department-wide EHR optimization was conducted with information technology (IT). Optimizations were developed from an intensive quality improvement process involving all levels of clinicians and clinical staff. The optimizations were then categorized as accommodations (department adjusted workflow to EHR), creations (IT developed new workflows within EHR), discoveries (department found workflows within EHR), and modifications (IT changed workflows within EHR). Departmental productivity, defined as number of visits, charges, and payments, was standardized to ratios prior to the COVID-19 pandemic and evaluated by Taylor's change point analysis. Significant improvements were defined as shifts (change points), trends (5 or more consecutive values above/below the mean), and values outside 95% CIs.

resultsThe 124 optimizations were categorized as 43 accommodations, 13 creations, 54 discoveries, and 14 modifications. Productivity ratios of monthly charges (0.74 to 1.28) and payments (0.83 to 1.58) significantly improved with the optimization efforts. Monthly visit ratios increased (0.65 to 0.98) but did not change significantly.

conclusionDepartmental collaboration with organizational IT for EHR optimization focused on detailed analysis of how workflows can impact productivity. Discovery optimization predominance indicates many solutions to EHR usability problems were already in the system. A large proportion of accommodation optimizations reinforced the need for better developer-user collaboration before implementation.

Indexed as

Electronic Health RecordsMedicineHumansPandemicsQuality Improvementelectronic health recordshealth information managementorganizational efficiencyquality improvement

Identifiers

PMID38383045
PMCPMC11237201

What OpenQuestion holds

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Registered trials

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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.