Evidence map›Paper›PMID 35508198›Full record

ArticleApplied clinical informatics2022

Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives.

Joshua E Richardson, Luke V Rasmussen, David A Dorr, Jenna T Sirkin, Donna Shelley, Adovich Rivera, Winfred Wu, Samuel Cykert, Deborah J Cohen, Abel N Kho

Abstract read
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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

10 authors.

Joshua E RichardsonCenter for Health Informatics and Evidence Synthesis, RTI International, Chicago, Illinois, United States.
Luke V RasmussenDepartment of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States.
David A DorrDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, United States.
Jenna T SirkinNORC at the University of Chicago, Cambridge, Massachusetts, United States.
Donna ShelleyDepartment of Public Health Policy and Management, New York University School of Global Public Health, New York, New York, United States.
Adovich RiveraInstitute of Public Health and Management, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States.
Winfred WuBureau of Primary Care Information Project, New York City Department of Health and Mental Hygiene, New York, New York, United States.
Samuel CykertDivision of General Medicine and Clinical Epidemiology and the Cecil G. Sheps Center for Health Services Research, the University of North Carolina, Chapel Hill, North Carolina, United States.
Deborah J CohenDepartment of Family Medicine, Oregon Health & Science University, Portland, Oregon, United States.
Abel N KhoCenter for Health Information Partnerships (CHiP), Feinberg School of Medicine, Northwestern University, Chicago, Illinois, United States.

Funding

Midwest Small Practice Care Transformation Research AllianceR18HS023921 · AHRQ · NORTHWESTERN UNIVERSITY AT CHICAGO · PI KHO, ABEL N. · 2015 to 2017
$14.5M
Research Design, Data, and Analytics CoreP30DK092949 · NIDDK · UNIVERSITY OF CHICAGO · PI MILDA Renne SAUNDERS · 2011 to 2026
$10.0M
AHRQ HHS R18 HS023921NIDDK NIH HHS P30 DK092949
6 · The paper itself

Abstract

backgroundElectronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings.

objectiveThis study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices.

methodsA qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors.

resultsCooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs.

conclusionCooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Indexed as

Electronic Health RecordsQuality Indicators, Health CareElectronicsPrimary Health CareQuality Improvement

Identifiers

PMID35508198
PMCPMC9068273

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.