Remote symptom monitoring integrated into electronic health records: A systematic review

Research output: Contribution to journalArticlepeer-review



People with long-term conditions (LTCs) require serial clinical assessments. Digital patient-reported symptoms collected between visits can inform these, especially if integrated into electronic health records (EHRs) and clinical workflows. This systematic review identified and summarized EHR-integrated systems to remotely collect patient-reported symptoms and examined their anticipated and realized benefits in LTCs.

Materials and Methods

We searched Medline, Web of Science, and Embase. Inclusion criteria were symptom reporting systems in adults with LTCs; data integrated into the EHR; data collection outside of clinic; data used in clinical care. We synthesized data thematically. Benefits were assessed against a list of outcome indicators. We critically appraised studies using the Mixed Methods Appraisal Tool.


We included 12 studies representing 10 systems. Seven were in oncology. Systems were technically and functionally heterogeneous, with the majority being fully integrated (data viewable in the EHR). Half of the systems enabled regular symptom tracking between visits. We identified three symptom report-guided clinical workflows: Consultation-only (data used during consultation, n=5), alert-based (real-time alerts for providers, n=4) and patient-initiated visits (n=1). Few author-described anticipated benefits, primarily to improve communication and resultant health outcomes, were realized based on the study results, and only supported by evidence from early stage qualitative studies. Studies were primarily feasibility and pilot studies of acceptable quality.

Discussion and Conclusions

EHR-integrated remote symptom monitoring is possible, but there are few published efforts to inform development of these systems. Currently there is limited evidence that this improves care and outcomes, warranting future robust, quantitative studies of efficacy and effectiveness.
Original languageEnglish
Pages (from-to)1752-1763
Number of pages12
JournalJournal of the American Medical Informatics Association
Issue number11
Publication statusPublished - 23 Sep 2020


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