ArticleDigital health
Developing and validation of a smartphone app for post-discharge early follow-up after colorectal cancer surgeries.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- PRECEDE-PROCEED model for discharge preparation in first-cycle colorectal cancer chemotherapy: a quasi-experimental study.Frontiers in oncology · 2026Article
- Comparative effectiveness of remote perioperative telemonitoring in cancer surgery: a randomized trial.NPJ digital medicine · 2025Article
- A randomized controlled trial of a digital lifestyle intervention involving postoperative patients with colorectal cancer.NPJ digital medicine · 2025Article
- Artificial Intelligence Chatbots in Surgical Care: A Systematic Review of Clinical Applications.Journal of artificial intelligence for medical sciences · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal surgeries are complex procedures associated with high rates of complications and hospital readmission. Objective: This study aimed to develop an electronic post-discharge follow-up plan to remotely monitor patients' symptoms in the postoperative period of colorectal surgeries and evaluate the outcomes of emergency department visits and the rate of severe complications within 15 days after hospital discharge. Design: We developed a digital tool capable of remotely assessing symptoms that could indicate complications related to colorectal surgical procedures and directing early management. This project was divided into two stages. The first was platform development with an algorithm for identifying symptoms and directing conduct, and the second was clinical validation of the program and evaluation of patient's experience. Patients who underwent elective oncological colorectal surgery were invited to participate in this study. We used commercial software (CleverCare) that was adjusted according to the clinical algorithm developed in this study, predicting complications and directing conduct with minimal human intervention using a Chatbot with Natural Language Processing (NPL) and artificial intelligence. Results: We planned three Interim Analyses to evaluate the outcomes of complications, referrals to the Emergency Department (ED), ED visits, adherence, and patient satisfaction. After each analysis, specialists validated the changes before implementation. A total of 92 eligible participants agreed to participate in the study. The ability to detect complications increased with each adjustment phase, and after the third and last phase, the digital solution identified 3(4.8%) real complications, with a sensitivity of 75%, specificity of 83%, accuracy of 82%, positive predictive value of 27%, and negative predictive value of 97%. Complete adherence to the monitoring program was 83.7% with an NPS score of 94 in the last evaluation phase. Conclusion: The digital platform is safe with high adherence rates and good patient acceptance.
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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.