Evidence map›Paper›PMID 42404032›Full record

ReviewESMO real world data and digital oncology2026

Real-world implementation of electronic patient-reported outcome measures (ePROMs) in routine oncology practice: a scoping review.

K Liao, Q Liu, Y Wang, K H Law, N Takemura, D H Z Lui, Y-K Lo, C Faivre-Finn, T Nuamek, J Price and 2 more

Abstract readReview
In one paragraph

Review in ESMO real world data and digital oncology, 2026. 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

12 authors.

K LiaoSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
Q LiuSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
Y WangSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
K H LawSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
N TakemuraSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
D H Z LuiDepartment of Oncology, Princess Margaret Hospital, Hong Kong, China.
Y-K LoDepartment of Oncology, Princess Margaret Hospital, Hong Kong, China.
C Faivre-FinnDivision of Cancer Sciences, The University of Manchester, Manchester, United Kingdom.
T NuamekThe Christie NHS Foundation Trust, Manchester, United Kingdom.
J PriceDivision of Cancer Sciences, The University of Manchester, Manchester, United Kingdom.
K Y HoSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, China.
J YorkeSchool of Nursing, Hong Kong Polytechnic University, Hong Kong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Routine collection of electronic patient-reported outcome measures (ePROMs) enhances symptom monitoring and improves outcomes in cancer care. We evaluated published reports of real-world ePROM implementation in oncology, focusing on approaches to implementation, types of ePROMs used, completion rates, factors associated with ePROM completion, and impacts on patient and health care process/system-level outcomes. We also aimed to propose a classification of ePROM programmes. Materials and methods: We systematically searched Medline, Embase, CINAHL Complete, and Scopus for articles describing routine ePROM implementation and evaluation. Data charting captured characteristics of the study, study population, characteristics of routinely collected ePROMs, and evaluation of their routine use. We also present a new classification of ePROM programmes into three levels: asynchronous, clinician-supervised, and real-time clinician-supervised, based on clinical pathway integration. Results: Of 10 384 identified manuscripts, 50 met the inclusion criteria, reporting on 39 real-world ePROM programmes. Nearly all ePROM programmes (97.4%) collected data on symptom burden. The median overall completion rate was 61% (interquartile range 47%-76%), with substantial variability over time and across hospitals. Completion was associated with sociodemographic, clinical, and treatment-related factors. Most ePROM programmes (61.5%) were classified as asynchronous models, with five (12.8%) clinician-supervised models and three (7.7%) real-time clinician-supervised models, while seven (17.9%) lacked sufficient detail. The most frequently reported outcomes were fewer hospitalisations or emergency department visits (10.3%) at the patient level and improved symptom identification (17.9%) at the process/system level. Conclusion: ePROMs are increasingly embedded in routine oncology care, demonstrating benefits at both patient and system levels. However, most current programmes adapt asynchronous models with limited rapid clinician interaction.

Indexed as

cancerelectronic patient-reported outcome measuresePROMssymptom monitoring

Identifiers

PMID42404032
PMCPMC13331967

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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.