Evidence map›Paper›PMID 40767217›Full record

ArticleInternational journal of health policy and management2025

Co-production of Diagnostic Excellence - Patients, Clinicians, and Artificial Intelligence Comment on "Achieving Diagnostic Excellence: Roadmaps to Develop and Use Patient-Reported Measures With an Equity Lens".

Sumant R Ranji, Benjamin I Rosner

Abstract read
In one paragraph

Article in International journal of health policy and management, 2025. 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

2 authors.

Sumant R RanjiDivision of Hospital Medicine, Department of Medicine, San Francisco General Hospital, San Francisco, CA, USA.ORCID 0000-0002-1941-0005
Benjamin I RosnerDivision of Clinical Informatics and Digital Transformation, Department of Medicine, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0003-3609-6481

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients often experience long journeys within the healthcare system before obtaining a diagnosis. Though progress has been made in measuring the quality of diagnosis, existing measures largely fail to capture the diagnostic process from the patient's perspective. McDonald and colleagues' paper presents 7 overarching goals for the use of patient-reported measures (PRMs) in diagnostic excellence and presents visual roadmaps to guide the development, implementation, and evaluation of these measures. To accelerate the real-world use of PRMs, organizations should initially prioritize the use of patient-reported metrics that are already in development, such as patient-reported experience measures. Pairing PRMs with artificial intelligence (AI) techniques, such as "diagnostic wayfinding" (a dynamic diagnostic refinement process that also includes analysis of electronic health record data and metadata to characterize the diagnostic journey), should also improve diagnostic performance. Ultimately, combining PRMs with technological advancements holds the potential to achieve true co-production of diagnostic excellence.

Indexed as

Artificial IntelligenceDiagnosisPatient Reported Outcome MeasuresQuality of Health CareElectronic Health RecordsHumansDiagnostic ExcellencePatient-Reported MeasuresRoadmaps

Identifiers

PMID40767217
PMCPMC12337162

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.