Evidence map›Paper›PMID 41313563›Full record

ArticleJournal of patient-reported outcomes2025

A 10-step guide to standardising patient-reported outcomes data collection in healthcare: insights from the health outcomes observatory (H2O) project on overcoming implementation barriers.

Gemma Galan, Preston Long, Yolima Cossio-Gil, Francesco Patalano, Kathryn Hamilton, Anouk S Huberts, Anouk Neureiter di Torrero, Lisa R Otto, Alizé A Rogge, Liselotte Fierens and 7 more

Abstract read
In one paragraph

Article in Journal of patient-reported outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

17 authors.

Gemma Galan *Health Services Research Group, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Barcelona, Spain. gemma.galan@vhir.org.ORCID http://orcid.org/0009-0002-5252-3738
Preston Long *Institute for Outcomes Research, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Yolima Cossio-GilHealth Services Research Group, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Barcelona, Spain.
Francesco PatalanoNovartis Pharma AG, Basel, Switzerland.
Kathryn HamiltonFlorence Nightingale Faculty of Nursing and Midwifery, King's College London, London, UK.
Anouk S HubertsDepartment of Quality and Patient Care, Erasmus University Medical Center, Rotterdam, The Netherlands.
Anouk Neureiter di TorreroDepartment of Quality and Patient Care, Erasmus University Medical Center, Rotterdam, The Netherlands.
Lisa R OttoCenter for Patient-Centered Outcomes Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Alizé A RoggeCenter for Patient-Centered Outcomes Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany.
Liselotte FierensDepartment of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium.
Rahim LaljiNovartis Pharma AG, Basel, Switzerland.
Belle H de RooijDepartment of Research & Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands.
Ann-Kristin PorthDivision of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.
Carolina E WatsonUniversitat Autònoma de Barcelona, Nursing Department, Faculty of Medicine, Cerdanyola Del Valles, Spain.
Alexandra Kautzky-WillerDivision of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria.
Nadia C W KammingaDepartment of Quality and Patient Care, Erasmus University Medical Center, Rotterdam, The Netherlands.
Tanja StammInstitute for Outcomes Research, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of patient-reported outcome measures (PROMs), including their electronic versions (ePROMs), is increasing in clinical practice and their value for individual patient care and health service delivery is becoming more widely recognized. However, their routine use remains limited due to the lack of structured and standardized systems for collecting and integrating this information into clinical workflows. Developing a sustainable system for ePROM data collection, requires addressing a range of organizational, technical and cultural challenges. The Health Outcome Observatory project (H2O) project aims to standardise and facilitate the concurrent collection of ePROs and clinical outcomes in routine clinical practice. This paper presents the 10-steps ePROM implementation framework to guide ePROM implementation in real-world clinical settings, based on project insights. The framework is primarily designed for implementation within individual clinical practices and hospital systems, while offering adaptable elements that can inform broader, population-level strategies. METHODOLOGY: An implementation taskforce was formed within H2O, involving academic researchers with expertise in PRO measurement, digital and data specialists, Health Care Professionals (HCP), and ePROMs implementation specialists across four countries (Austria, Germany, Netherlands and Spain) and, representing the project’s three disease areas - diabetes, inflammatory bowel disease (IBD) and cancer. Data were iteratively collected from country teams and analysed thematically to identify common challenges, solutions and implementation elements. Internal validation was conducted through member checking with country teams, feedback from Patient Advisory Board (PAB) and expert triangulation within the taskforce.

resultsTen core steps for successful ePROM implementation in clinical practice were derived. In addition, disease-specific and/or site-specific considerations such as clinical population, technology use, Information Technology (IT) literacy, demographic factors and engagement of patient and providers proves important. A significant challenge identified was establishing the required technological infrastructure and data flow needed for implementation.

conclusionsThe ten steps derived provide a scalable framework that supports the integration of ePROMs into routine practice and fostering improvements in patient care by including the patient voice in a structured and useable manner. Additionally, the detailed analysis of challenges and solutions for successful ePROM implementation, based on the H2O initiative, strengthens our contribution to this field.

Indexed as

Data CollectionPatient Reported Outcome MeasuresAustriaDelivery of Health CareDigital HealthGermanyHumansNetherlandsSpainDigital healthEuropeHealth dataImplementationPatient-reported outcomes measuresValue based health care

Identifiers

PMID41313563
PMCPMC12662975

What OpenQuestion holds

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LicenceCC BY
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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.