Evidence map›Paper›PMID 40306630›Full record

ArticleJournal of medical Internet research2025

New Performance Measurement Framework for Realizing Patient-Centered Clinical Decision Support: Qualitative Development Study.

Prashila Dullabh, Courtney Zott, Nicole Gauthreaux, James Swiger, Edwin Lomotan, Dean F Sittig

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2025. 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. Trial
  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

6 authors.

Prashila DullabhHealth Sciences Department, NORC at the University of Chicago, Washington, DC, United States.ORCID https://orcid.org/0000-0003-0241-0225
Courtney ZottHealth Sciences Department, NORC at the University of Chicago, Washington, DC, United States.ORCID https://orcid.org/0000-0002-1734-6652
Nicole GauthreauxHealth Sciences Department, NORC at the University of Chicago, Washington, DC, United States.ORCID https://orcid.org/0000-0001-5743-0640
James SwigerCenter for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, MD, United States.ORCID https://orcid.org/0009-0003-0580-3486
Edwin LomotanCenter for Evidence and Practice Improvement, Agency for Healthcare Research and Quality, Rockville, MD, United States.ORCID https://orcid.org/0009-0007-3783-8922
Dean F SittigInformatics Review, LLC, Lake Oswego, OR, United States.ORCID https://orcid.org/0000-0001-5811-8915

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatient-centered clinical decision support (PC CDS) exists on a continuum that reflects the degree to which its knowledge base, data, delivery, and use focus on patient needs and experiences. A new focus on value-based, whole-person care has resulted in broader development of PC CDS technologies, yet there is limited information on how to measure their performance and effectiveness. To address these gaps, there is a need for more measurement guidance to assess PC CDS interventions.

objectiveThis paper presents a new framework that incorporates patient-centered principles into traditional health IT and clinical decision support (CDS) evaluation frameworks to create a unified guide to PC CDS performance measurement.

methodsWe conducted a targeted literature review of 147 sources on health IT, CDS, and PC CDS measurement and evaluation to develop the framework. Sources were reviewed if they included the sociotechnical components relevant to PC CDS, covered the full IT life cycle of PC CDS, and addressed measurement considerations at different user and system levels. We then validated and refined the measurement framework through key informant interviews with 6 experts in measurement, CDS, and clinical informatics. Throughout the framework development, we gathered feedback from a 7-member expert committee on the methods, findings, and the framework's relevance and application.

resultsThe PC CDS performance measurement framework includes 6 domains: safe, timely, effective, efficient, equitable, and patient centered. The 6 domains contain 34 subdomains that can be selected to assess performance, depending on the type of PC CDS intervention or the specific research focus. In addition, there are 4 levels of aggregation at which subdomains can be measured (individual, population, organization, or IT system) that account for the multilevel impact of PC CDS. We provide examples of measures and approaches to patient centeredness for each subdomain, followed by 2 illustrative use cases demonstrating the framework application.

conclusionsThis framework can be used by researchers, health system leaders, informaticians, and patients to understand the full breadth of performance and impact of PC CDS technology. The framework is significant in that it (1) covers the entire PC CDS life cycle, (2) has a direct focus on the patient, (3) covers measurement at different levels, (4) encompasses 6 independent but related domains, and (5) requires additional research and development to fully characterize all domains and subdomains. As the field of PC CDS matures, researchers and evaluators can build upon the framework to assess which components of PC CDS technologies work; whether PC CDS technologies are being used as anticipated; and whether the intended outcomes of delivering evidence-based, patient-centered care are being achieved.

Indexed as

Decision Support Systems, ClinicalPatient-Centered CareHumansQualitative ResearchAIartificial intelligenceclinical decision supporthealth caremeasurementpatient-centered

Identifiers

PMID40306630
PMCPMC12079060

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LicenceCC BY
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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.