ReviewJournal of surgical oncology2021
Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.
Review in Journal of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Development and Implementation of Novel Virtual Triage and Exploration of Attitudes Towards the Potential Use of Artificial Intelligence in the Irritable Bowel Syndrome (IBS) Dietetic Pathway.Journal of human nutrition and dietetics : the official journal of the British Dietetic Association · 2026Article
- Utilizing Artificial Intelligence for the Diagnosis, Assessment, and Management of Chronic Pain.Journal of biomedical physics & engineering · 2025Review
- Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy.Journal of personalized medicine · 2024Review
- Machine learning applied in patient-reported outcome research-exploring symptoms in adjuvant treatment of breast cancer.Breast cancer (Tokyo, Japan) · 2024Article
- Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs.Applied clinical informatics · 2023Article
- Patient-Reported Data Augment Prediction Models of Persistent Opioid Use after Elective Upper Extremity Surgery.Plastic and reconstructive surgery · 2023Article
- Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise.Journal of clinical medicine · 2023Article
- Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study.JMIR mHealth and uHealth · 2023Article
- Ten Considerations for Integrating Patient-Reported Outcomes into Clinical Care for Childhood Cancer Survivors.Cancers · 2023Review
- On the importance of interpretable machine learning predictions to inform clinical decision making in oncology.Frontiers in oncology · 2023Review
- Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects.Sensors (Basel, Switzerland) · 2022Review
- The Development of Electronic Health and Artificial Intelligence in Surgery after the SARS-CoV-2 Pandemic-A Scoping Review.Journal of clinical medicine · 2021Article
- Lack of Acceptance of Digital Healthcare in the Medical Market: Addressing Old Problems Raised by Various Clinical Professionals and Developing Possible Solutions.Journal of Korean medical science · 2021Review
- Current Status of Out-of-Hospital Management of Cancer Patients and Awareness of Internet Medical Treatment: A Questionnaire Survey.Frontiers in public health · 2021Article
- Bibliometric analysis on the adoption of artificial intelligence applications in the e-health sector.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.