Using Electronic Health Records Data to Identify Strong Performers in Healthcare Quality Improvement.

Adam Baus, Andrea Calkins, Cecil Pollard, Craig Robinson, Robin Seabury, Marcus Thygeson, Curt Lindberg, Andrya Durr

Abstract read

In one paragraph

Article in Perspectives in health information managemen, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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 BausSummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Andrea CalkinsSummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Cecil PollardSummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Craig RobinsonSummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Robin SeaburySummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Marcus ThygesonSummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Curt LindbergSummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Andrya DurrSummary: This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Assessing for positive deviance is one method of identifying individuals, teams, or organizations that perform substantially better than their peers. This approach has been used to support quality-of-care improvement processes in healthcare settings by identifying healthcare team members who perform comparatively well within a given environment and sharing their opinions, actions, and practices with others. This case study presents an adaptable, straightforward framework for identifying positive deviance, or strong performers, within the healthcare setting and is intended for any primary care health system tracking quality measures and aiming to understand the performance of their providers, clinic sites, or organization. Moreover, this protocol does not require the use of more time-consuming methods, such as interviews, and is instead based on repurposing data already being documented in the electronic health record.

Indexed as

Electronic Health RecordsQuality ImprovementHumansOrganizational Case StudiesPrimary Health CareElectronic Health Record DataHealthcare ProviderHealth SystemPositive DeviancePrimary CareQuality Improvement

Identifiers

PMID40135695

PMCPMC11102057

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.