Health information technology to support cancer survivorship care planning: A systematic review.

2021 
Objective The study sought to conduct a systematic review to explore the functions utilized by electronic cancer survivorship care planning interventions and assess their effects on patient and provider outcomes. Materials and methods Based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines, studies published from January 2000 to January 2020 were identified in PubMed, CINAHL, EMBASE, PsychINFO, Scopus, Web of Science, and the ACM Digital Library. The search combined terms for cancer, survivorship, care planning, and health information technology (HIT). Eligible studies evaluated the effects of a HIT intervention on usability, knowledge, process, or health-related outcomes. A total of 578 abstracts were reviewed, resulting in 60 manuscripts describing 40 studies. Thematic analyses were used to define meta-themes of system functions, and Fisher's exact tests were used to examine associations between functions and outcomes. Results Patients were the target end users for 18 interventions, while 12 targeted providers and 10 targeted both groups. Interventions used patient-reported outcomes collection (60%), automated content generation (58%), electronic sharing (40%), persistent engagement (28%), and communication features (20%). Overall, interventions decreased the time to create survivorship care plans (SCPs) and supported care planning knowledge and abilities, but results were mixed for effects on healthcare utilization, SCP sharing, and provoking anxiety. Persistent engagement features were associated with improvements in health or quality-of-life outcomes (17 studies, P = .003). Conclusions Features that engaged users persistently over time were associated with better health and quality-of-life outcomes. Most systems have not capitalized on the potential of HIT to share SCPs across a care team and support care coordination.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    87
    References
    0
    Citations
    NaN
    KQI
    []