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Innovations in research and clinical care using patient-generated health data. ​

Literature Information ​

DOI10.3322/caac.21608
PMID32311776
JournalCA: a cancer journal for clinicians
Impact Factor232.4
JCR QuartileQ1
Publication Year2020
Times Cited66
Keywordsneoplasms, patient-generated health data, quality of life, telemedicine
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Review
ISSN0007-9235
Pages182-199
Issue70(3)
AuthorsHeather S L Jim, Aasha I Hoogland, Naomi C Brownstein, Anna Barata, Adam P Dicker, Hans Knoop, Brian D Gonzalez, Randa Perkins, Dana Rollison, Scott M Gilbert, Ronica Nanda, Anders Berglund, Ross Mitchell, Peter A S Johnstone

TL;DR ​

This review explores the growing role of patient-generated health data (PGHD) in oncology, highlighting its potential to enhance regulatory decisions and quality of care through improved symptom monitoring and integration of self-reported outcomes. While challenges such as data integration and analysis persist, the advancements in technology and the increasing emphasis on PGHD indicate a promising future for its incorporation into oncology research and clinical practice.

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neoplasms · patient-generated health data · quality of life · telemedicine

Abstract ​

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

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Primary Questions Addressed ​

  1. How can patient-generated health data improve patient outcomes in oncology beyond current applications?
  2. What are the potential ethical implications of using patient-generated health data in clinical trials?
  3. In what ways can advancements in artificial intelligence enhance the analysis of patient-generated health data?
  4. What strategies can be implemented to overcome the challenges of integrating biometric data into electronic medical records?
  5. How do regulatory frameworks impact the use of patient-generated health data in clinical decision-making processes?

Key Findings ​

Research Background and Objectives ​

Patient-generated health data (PGHD) refers to health-related information collected directly from patients to address health concerns, and its importance in oncology is rapidly growing. The review aims to provide a comprehensive overview of the clinical, regulatory, technological, and analytical landscapes surrounding PGHD in cancer research and care, emphasizing the integration of patient-reported outcomes (PROs) and biometric data.

Main Methods/Materials/Experimental Design ​

The review systematically examines the collection and utilization of PGHD, including:

  • Types of PGHD: Self-reported health histories, PROs, and biometric sensor data.
  • Technological Advances: Use of smartphones and wearable devices to collect PGHD.
  • Digital Phenotyping: Real-time assessment of biometric and behavioral data through mobile technologies.
Mermaid diagram

Key Results and Findings ​

  • Clinical Integration: PGHD, especially PROs, significantly enhance patient-provider communication and symptom management.
  • Evidence Base: Studies indicate that PROs provide insights complementary to clinician-reported outcomes, improving the detection of treatment-related toxicities and overall patient care.
  • Remote Monitoring: Remote collection of PGHD through digital tools shows potential for better symptom management, though results are mixed regarding effectiveness.

Main Conclusions/Significance/Innovation ​

The review highlights the transformative potential of PGHD in oncology, suggesting that:

  • Integrating PGHD into clinical practice can enhance patient engagement and quality of life.
  • Continued advancements in technology and data analytics will facilitate the effective use of PGHD.
  • Despite existing challenges, the integration of PGHD is likely to improve patient outcomes and support regulatory decision-making in cancer care.

Research Limitations and Future Directions ​

  • Limitations: Current evidence for biometric data usage is less robust compared to PROs, and there are significant challenges related to data integration within electronic medical records (EMRs).
  • Future Directions: Emphasis on developing standardized protocols for PGHD collection, enhancing interoperability of health IT systems, and ensuring that PGHD integration does not exacerbate health disparities in oncology.

Summary Table of Findings ​

AspectKey Points
Types of PGHDSelf-reported histories, PROs, biometric data
Technological ToolsSmartphones, wearables, real-time assessment
Clinical BenefitsImproved communication, symptom management
ChallengesEMR integration, limited evidence for biometric data
Future NeedsStandardization, interoperability, addressing disparities

References ​

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Literatures Citing This Work ​

  1. Digital Health Applications for Pharmacogenetic Clinical Trials. - Hetanshi Naik;Latha Palaniappan;Euan A Ashley;Stuart A Scott - Genes (2020)
  2. Harnessing consumer smartphone and wearable sensors for clinical cancer research. - Carissa A Low - NPJ digital medicine (2020)
  3. The Use of Wearables in Clinical Trials During Cancer Treatment: Systematic Review. - Ulrikke Lyng Beauchamp;Helle Pappot;Cecilie Holländer-Mieritz - JMIR mHealth and uHealth (2020)
  4. Living with Metastatic Cancer: A Roadmap for Future Research. - Danielle B Tometich;Kelly A Hyland;Hatem Soliman;Heather S L Jim;Laura Oswald - Cancers (2020)
  5. Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review. - Prakash Jayakumar;Eugenia Lin;Vincent Galea;Abraham J Mathew;Nikhil Panda;Imelda Vetter;Alex B Haynes - Journal of personalized medicine (2020)
  6. Web-Based Patient Self-Reported Outcome After Radiotherapy in Adolescents and Young Adults With Cancer: Survey on Acceptance of Digital Tools. - Marco M E Vogel;Kerstin A Eitz;Stephanie E Combs - JMIR mHealth and uHealth (2021)
  7. Artificial intelligence in cancer research: learning at different levels of data granularity. - Davide Cirillo;Iker Núñez-Carpintero;Alfonso Valencia - Molecular oncology (2021)
  8. The impact of electronic health record-integrated patient-generated health data on clinician burnout. - Jiancheng Ye - Journal of the American Medical Informatics Association : JAMIA (2021)
  9. Using Biometric Sensor Data to Monitor Cancer Patients During Radiotherapy: Protocol for the OncoWatch Feasibility Study. - Cecilie Holländer-Mieritz;Ivan R Vogelius;Claus A Kristensen;Allan Green;Judith L Rindum;Helle Pappot - JMIR research protocols (2021)
  10. Survivorship Care of Older Adults With Cancer: Priority Areas for Clinical Practice, Training, Research, and Policy. - Erin E Kent;Eliza M Park;William A Wood;Ashley Leak Bryant;Michelle A Mollica - Journal of clinical oncology : official journal of the American Society of Clinical Oncology (2021)

... (56 more literatures)

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