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

文献信息 ​

DOI10.3322/caac.21608
PMID32311776
期刊CA: a cancer journal for clinicians
影响因子232.4
JCR 分区Q1
发表年份2020
被引次数66
关键词肿瘤、患者生成的健康数据、生活质量、远程医疗
文献类型Journal Article, Research Support, N.I.H., Extramural, Review
ISSN0007-9235
页码182-199
期号70(3)
作者Heather 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

一句话小结 ​

本综述探讨了患者生成的健康数据（PGHD）在肿瘤学中的应用背景、现状和未来前景，强调了其在监管决策和护理质量评估中的重要性，尤其是自我报告结果（PROs）的实时监测。尽管在电子病历整合和大数据分析中面临挑战，但PGHD的潜力使其在改善肿瘤研究和临床护理中的应用前景广阔。

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肿瘤 · 患者生成的健康数据 · 生活质量 · 远程医疗

摘要 ​

患者生成的健康数据（PGHD），即从患者收集的与健康相关的数据，用于帮助解决健康问题，越来越多地应用于肿瘤学领域，以支持监管决策和评估护理质量。PGHD包括自我报告的健康和治疗历史、患者报告的结果（PROs）以及生物传感器数据。无线技术、智能手机和物联网的进步促进了在门诊就诊和日常生活中收集PGHD的新方式。本次综述的目标是提供与肿瘤研究和护理中PGHD相关的当前临床、监管、技术和分析环境的概述。综述首先阐述了美国食品药品监督管理局、医学研究所及其他监管和科学组织所描述的PGHD的合理性。在此基础上，描述了利用PGHD进行基于门诊和远程症状监测的证据基础，特别强调了PROs。接着，概述了当前数字表型技术或基于设备的生物、行为、自我报告和表现数据的实时评估方法。关于PGHD的分析机会在医学大数据和人工智能的背景下被设想。最后，提出了将PGHD整合到临床护理中的挑战和解决方案。这些挑战包括PROs和生物数据的电子病历整合、大型复杂生物数据集的分析以及潜在的门诊工作流程重新设计。此外，当前关于生物数据相对于PROs的使用证据相对有限。尽管面临这些挑战，PGHD的潜在益处使其在肿瘤研究和临床护理中越来越有可能被整合。

英文摘要 ​

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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主要研究问题 ​

  1. 在使用患者生成健康数据（PGHD）进行肿瘤学研究时，哪些具体的创新方法可以提高数据的有效性和可靠性？
  2. 如何评估患者报告结果（PROs）在不同类型癌症患者中的适用性和影响力？
  3. 随着无线技术和物联网的发展，PGHD的收集方式是否有新的趋势或方法，特别是在日常生活中的应用？
  4. 在整合PGHD进入临床护理时，如何解决电子病历系统中数据兼容性和整合性的问题？
  5. 针对PGHD在大数据和人工智能背景下的分析机会，有哪些新的研究方向和应用案例？

核心洞察 ​

研究背景和目的 ​

近年来，患者生成健康数据（PGHD）在肿瘤学领域的应用日益增多。这些数据包括患者自我报告的健康和治疗历史、患者报告的结果（PROs）以及生物传感器数据。研究的目的是综述PGHD在肿瘤研究和临床护理中的现状，探讨其在监管、技术和分析方面的应用。

主要方法/材料/实验设计 ​

本研究采用文献综述的方法，分析了PGHD的临床应用、监管框架和技术发展。重点介绍了数字表型学（数字健康监测）和生物传感器的应用。以下是技术路线的流程图：

Mermaid diagram

关键结果和发现 ​

  1. PGHD的有效性：研究表明，PROs能够提供补充临床评估的重要信息，尤其在肿瘤患者中，患者报告的症状往往被临床医生低估。
  2. 生物传感器的应用：生物传感器在收集患者的健康数据方面显示出良好的可行性，但其临床应用仍处于早期阶段。
  3. 数字健康技术的兴起：智能手机和可穿戴设备的普及为PGHD的收集提供了新的可能性，这些设备能够实时监测患者的健康状况。

主要结论/意义/创新性 ​

PGHD的整合有潜力改善癌症患者的治疗效果，增强患者的生活质量。尽管存在技术、数据整合和临床工作流程的挑战，PGHD在癌症研究和临床护理中的应用前景广阔。尤其是，数字健康技术的进步为早期干预和症状管理提供了新的机会。

研究局限性和未来方向 ​

  1. 局限性：目前关于生物传感器数据的证据基础相对较弱，且PGHD的整合面临数据隐私和系统兼容性等挑战。
  2. 未来方向：未来研究应集中于优化PGHD的临床应用，提升电子健康记录系统的兼容性，并开发有效的临床决策支持工具。此外，还需关注如何避免健康差异的加剧，确保所有患者都能平等受益于PGHD的使用。

参考文献 ​

  1. The impact of patient-reported outcome (PRO) data from clinical trials: a systematic review and critical analysis. - Samantha Cruz Rivera;Derek G Kyte;Olalekan Lee Aiyegbusi;Anita L Slade;Christel McMullan;Melanie J Calvert - Health and quality of life outcomes (2019)
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  4. SIS.NET: a randomized controlled trial evaluating a web-based system for symptom management after treatment of breast cancer. - Alyse E Wheelock;Meredith A Bock;Eva L Martin;Jimmy Hwang;Mary Lou Ernest;Hope S Rugo;Laura J Esserman;Michelle E Melisko - Cancer (2015)
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  8. Update on Prevalence of Pain in Patients With Cancer: Systematic Review and Meta-Analysis. - Marieke H J van den Beuken-van Everdingen;Laura M J Hochstenbach;Elbert A J Joosten;Vivianne C G Tjan-Heijnen;Daisy J A Janssen - Journal of pain and symptom management (2016)
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引用本文的文献 ​

  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)

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