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Invited Commentary
Cardiology
April 16, 2020

Use of Mobile Health and Patient-Generated Data—Making Health Care Better by Making Health Care Different

Author Affiliations
  • 1Healthcare Delivery Innovation Center, Minneapolis Heart Institute, Minneapolis, Minnesota
  • 2JAMA Network Open Associate Editor
JAMA Netw Open. 2020;3(4):e202971. doi:10.1001/jamanetworkopen.2020.2971

An increasing array of mobile health (mHealth) tools are designed to capture patient-generated data, including more than 250 000 mobile health applications available for download and more than 300 million wearable devices in public use.1 Despite the proliferation of these tools, there is limited evidence to indicate how incorporating patient-generated data in care delivery can improve patient health.2 In fact, some studies designed to leverage patient-generated data have been associated with decrements in measures of health.3 Although the potential for mHealth and patient-generated data to improve health is alluring, a focus on health outcomes alone may detract from other ways in which these tools might improve patient care. A feasibility study by Treskes et al4 offers helpful insights on how mHealth and patient-generated data can make health care better by making health care different.

In a randomized clinical trial, Treskes et al4 evaluated a bundle of care-delivery tools designed to replace and supplement routine follow-up after an acute myocardial infarction.4 This bundle included e-visits in place of in-person clinic visits and smartphone-enabled tools that captured patient-generated data from a blood pressure monitor, step counter, weight scale, and a single-lead electrocardiogram. The resulting patient-generated data was transmitted and reviewed by the study team daily. Designed as a feasibility study, the results do not indicate how mHealth and patient-generated data might affect health outcomes. However, patient acceptance of the intervention was high, with 72.7% of patients agreeing to participate in the study. Among patients who participated, patient satisfaction scores were similar among patients who used e-visits and mHealth compared with those who had usual care. Of those who declined study participation, 40% specified concerns about being confronted by their disease more frequently and 33% felt unable to cope with the accompanying technology. The high degree of overall acceptability and insights on barriers to participation inform the degree to which mHealth and resulting patient-generated data could offer a different approach to longitudinal health care for patients with chronic disease.

Why does health care delivery for chronic disease management need to change? The traditional health care paradigm for chronic disease management focuses on in-person clinic visits with the intent of reviewing a patient’s health status and refining care strategies based on information reviewed during the visit. The time between in-person visits is typically determined by practice norms that lack evidence to inform the specified interval (ie, annual follow-up).5 Additionally, scheduled visits are asynchronous relative to patient health and are thus ill equipped to prevent a decompensation in clinical status. From the perspective of the patient, in-person clinic visits require travel and disrupt schedules for themselves and their loved ones. Alternative strategies for care delivery that meet patients where they are (ie, e-visits) and leverage the automated capture and transmission of patient-generated data to inform the optimal timing of follow-up visits are ways in which mHealth could be different.

Although the capture and review of patient-generated data are not new concepts (eg, use of pen and paper to record blood glucose levels or blood pressure), mHealth can automate the process of data capture and transmission. In the study by Treskes et al,4 63% of patients transmitted patient-generated data in more than 80% of all 52 weeks they participated in the trial, although the details on adherence by type of mHealth tool are not provided. These results suggest that it is feasible to capture and transmit patient-generated data for a substantial proportion of patients with subsequent care delivery informed by data review. Although the mechanism of data review applied in the study by Treskes et al4 is not scalable (ie, daily review of transmitted data by a trained professional), alternative approaches to data management have been proposed, which include automated identification of important outliers and trends in patient-reported data to trigger clinical action.1,6

Improving access to care is another way in which patient-generated data and mHealth could have a positive effect. With the aging US population and the accompanying rise of chronic medical conditions, demand for health care visits is outpacing supply. Wait times for new appointments increased by 30% between 2014 and 2017.7 An alternative model that leverages a combination of data from the patient’s electronic health record and routinely transmitted patient-generated data could obviate the need for many routinely scheduled visits. Electronic communication would provide reassurance to patients that their health is being monitored via their patient-generated data. Furthermore, as the scientific evidence that informs best practices for specific medical conditions evolves, patients affected by changes in the evidence base could be identified via algorithms applied to patient-generated and electronic health record data. This could both shorten the time to optimization of care and eliminate the need for scheduled check-in visits designed to ensure care is up to date.

Patient health remains the yardstick against which changes in health care must be measured. However, when new approaches to health care delivery achieve similar health outcomes, it is important to assess the other ways in which a new care delivery approach might be better or worse. Although evidence suggesting mHealth and patient-generated data improve health is sparse,2 this should not temper enthusiasm for the potential of mHealth and patient-generated data to address shortcomings in traditional health care delivery or for studies of how these tools might make health care better.

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Article Information

Published: April 16, 2020. doi:10.1001/jamanetworkopen.2020.2971

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Bradley SM. JAMA Network Open.

Corresponding Author: Steven M. Bradley, MD, MPH, Healthcare Delivery Innovation center, Minneapolis Heart Institute, 920 E 28th St, Ste 300, Minneapolis, MN 55407 (steven.bradley@allina.com).

Conflict of Interest Disclosures: None reported.

References
1.
Office of the National Coordinator for Health Information Technology. Conceptualizing a data infrastructure for the capture, use, and sharing of patient-generated health data in care delivery and research through 2024. Accessed March 16, 2020. https://www.healthit.gov/sites/default/files/pghd_white_paper_final_formatted_by_onc_508_11-29-17.pdf
2.
Marcolino  MS, Oliveira  JAQ, D’Agostino  M, Ribeiro  AL, Alkmim  MBM, Novillo-Ortiz  D.  The impact of mHealth interventions: systematic review of systematic reviews.   JMIR Mhealth Uhealth. 2018;6(1):e23. doi:10.2196/mhealth.8873PubMedGoogle Scholar
3.
Jakicic  JM, Davis  KK, Rogers  RJ,  et al.  Effect of wearable technology combined with a lifestyle intervention on long-term weight loss: the IDEA randomized clinical trial.   JAMA. 2016;316(11):1161-1171. doi:10.1001/jama.2016.12858PubMedGoogle ScholarCrossref
4.
Treskes  RW, van Winden  LAM, van Keulen  N,  et al.  Effect of smartphone-enabled health monitoring devices vs regular follow-up on blood pressure control among patients after myocardial infarction: a randomized clinical trial.   JAMA Netw Open. 2020;3(4):e202165. doi:10.1001/jamanetworkopen.2020.2165Google Scholar
5.
Javorsky  E, Robinson  A, Boer Kimball  A.  Evidence-based guidelines to determine follow-up intervals: a call for action.   Am J Manag Care. 2014;20(1):17-19.PubMedGoogle Scholar
6.
Bradley  SM, Rumsfeld  JS, Ho  PM.  Incorporating health status in routine care to improve health care value: the VA Patient Reported Health Status Assessment (PROST) System.   JAMA. 2016;316(5):487-488. doi:10.1001/jama.2016.6495PubMedGoogle ScholarCrossref
7.
Merritt Hawkins. 2017 Survey of Physician Appointment Wait Times. Accessed March 16, 2020. https://www.merritthawkins.com/uploadedFiles/MerrittHawkins/Content/Pdf/mha2017waittimesurveyPDF.pdf
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