Adaptation of remote symptom monitoring using electronic patient-reported outcomes for implementation in real-world settings
Open accessAbstract
PURPOSE: Despite evidence of clinical benefits, widespread implementation of remote symptom monitoring has been limited. We describe a process of adapting a remote symptom monitoring intervention developed in a research setting to a real-world clinical setting at two cancer centers. METHODS: This formative evaluation assessed core components and adaptations to improve acceptability and fit of remote symptom monitoring using Stirman's Framework for Modifications and Adaptations. Implementation outcomes were evaluated in pilot studies at the two cancer centers testing technology (phase I) and workflow (phase II and III) using electronic health data; qualitative evaluation with semistructured interviews of clinical team members; and capture of field notes from clinical teams and administrators regarding barriers and recommended adaptations for future implementation. RESULTS: Core components of remote symptom monitoring included electronic delivery of surveys with actionable symptoms, patient education on the intervention, a system to monitor survey compliance in real time, the capacity to generate alerts, training nurses to manage alerts, and identification of personnel responsible for managing symptoms. In the pilot studies, while most patients completed > 50% of expected surveys, adaptations were identified to address barriers related to workflow challenges, patient and clinician access to technology, digital health literacy, survey fatigue, alert fatigue, and data visibility. CONCLUSION: Using an implementation science approach, we facilitated adaptation of remote symptom monitoring interventions from the research setting to clinical practice and identified key areas to promote effective uptake and sustainability.
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- Authors
- Rocque, G. B., Dent, D. N., Ingram, S. A., Caston, N. E., Thigpen, H. B., Lalor, F. R., Jamy, O. H., Giri, S., Azuero, A., Young Pierce, J., McGowen, C. L., Daniel, C. L., Andrews, C. J., Huang, C.-H. S., Dionne-Odom, J. N., Weiner, B. J., Howell, D., Jackson, B. E., Basch, E. M., & Stover, A. M.
- Affiliations
- University of Alabama at Birmingham (US)
- University of Alabama at Birmingham School of Nursing, Birmingham, AL
- University of South Alabama Mitchell Cancer Institute, Mobile, AL
- USA Mitchell Cancer Institute (US)
- University of Washington (US)
- Princess Margaret Cancer Centre (CA)
- University of North Carolina at Chapel Hill (US)
- ORCID
- Rocque, Gabrielle B.
- Dent, D’Ambra N.
- Ingram, Stacey A.
- Caston, Nicole E.
- Thigpen, Haley B.
- Jamy, Omer H.
- Giri, Smith
- Azuero, Andres
- McGowen, Chelsea L.
- Daniel, Casey L.
- Andrews, Courtney J.
- Huang, Chao-Hui Sylvia
- Dionne-Odom, J. Nicholas
- Weiner, Bryan J.
- Howell, Doris
- Jackson, Bradford E.
- Basch, Ethan M.
- Stover, Angela M.
- Venue
- JCO Oncology Practice
- Published
- 2022-12
- Volume
- 18
- Issue
- 12
- Pages
- e1943-e1952
- ISSN
- 2688-1527 (ISSN-L)
- 2688-1535
- PMC
- PMC9750550
- OpenAlex
- W4307585142
- Cited by
- 62
- References
- 17
- Citations by year
- 2026: 13 · 2025: 15 · 2024: 22 · 2023: 11 · 2022: 1
- FWCI
- 11.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- JCO Oncology Practice (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- AstraZeneca
- Henry Ford Health System
- Association of Community Cancer Centers
- UroGen Pharma
- National Institute of Nursing Research (R01NR019058)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- remote symptom monitoring, patient-reported outcomes, implementation adaptation, cancer care, symptom monitoring, alarm fatigue, digital health literacy, workflow barriers, nurse training, implementation outcomes, formative assessment
- MeSH terms
- Patient Reported Outcome Measures, Humans, Pilot Projects, Surveys and Questionnaires
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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