The Impact of AI-driven Remote Patient Monitoring on Cancer Care: A Systematic Review
Kent and Medway Medical School, University of Kent, Canterbury
How they used Delve
Oncology researchers at Kent and Medway Medical School and collaborating NHS trusts used Delve to deductively code 11 studies for a systematic review of AI-driven remote patient monitoring in cancer care, highlighting snippets across the articles and nesting 12 codes into six subthemes and two primary themes.
“The process began with the use of Delve, a qualitative analysis application, to code the data (30). Delve facilitated the organization of data into codes related to how patient outcomes were measured in the studies, ensuring a thorough and systematic approach to [the analysis]. [...] Using the Delve application, it was possible to highlight snippets of text from all 11 articles and organise them under different codes.”
- Field
- Oncology and health informatics; AI-driven remote patient monitoring for cancer patients and what it does to outcomes
- Data
- 11 quantitative studies of AI-driven remote patient monitoring in cancer care, selected from 170 papers retrieved across four databases and surviving CASP appraisal and risk-of-bias screening
- Approach
- Qualitative systematic review across Embase OVID, PubMed, PsycInfo and Web of Science, with CASP quality appraisal and a risk-of-bias assessment excluding high-risk studies. Braun and Clarke's six phases were then applied to the included papers, with deductive coding driven by the study's aims and objectives: snippets were highlighted from all 11 articles and organised under preset codes in Delve, producing 12 codes, six subthemes and two primary themes. Certainty of evidence was separately assessed by an independent reviewer.
- Data types
- Documents
Abstract
The coronavirus disease 2019 (COVID-19) pandemic necessitated a shift in healthcare delivery, emphasizing the need for remote patient monitoring (RPM) to minimize infection risks. This review aimed to evaluate the applications of artificial intelligence (AI) in RPM for cancer patients, exploring its impact on patient outcomes and implications for future healthcare practices. A qualitative systematic review was conducted using keyword searches across four databases: Embase OVID, PubMed, PsychInfo, and Web of Science. After removing duplicates and applying inclusion and exclusion criteria, the selected studies underwent quality assessment using the Critical Appraisal Skills Programme (CASP) tools and a risk of bias assessment. A thematic analysis was then performed using Delve, an application that facilitates deductive coding, to identify and explore themes related to AI in RPM. The search yielded 170 papers, from which 11 quantitative studies were selected for detailed analysis. Deductive coding resulted in the generation of 12 codes, leading to the identification of six subthemes and the construction of two primary themes: Efficacy of the RPM intervention and patient factors. AI systems in RPM show significant potential for enhancing cancer patient care and outcomes. However, this review could not conclusively determine that RPM provides superior outcomes compared to traditional face-to-face care. The findings underscore the preliminary nature of AI in medicine, highlighting the need for larger-scale, long-term studies to fully understand the benefits and limitations of AI in RPM for cancer care.
Citation
Fayha Aziz, Diletta Bianchini, David B. Olawade, Stergios Boussios (2025). The Impact of AI-driven Remote Patient Monitoring on Cancer Care: A Systematic Review. Anticancer Research. https://doi.org/10.21873/anticanres.17430