The Empirical Foundations of Telemedicine Interventions in Primary Care

Bashshur, Rashid L.; Howell, Joel D.; Krupiński, Elizabeth A.; Harms, Kathryn M.; Bashshur, Noura; Doarn, Charles R.

doi:10.1089/tmj.2016.0045

Cited by 272 publications

(219 citation statements)

References 111 publications

(120 reference statements)

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“…In the context of modern disease management programs, default values or values defined by a case manager are assigned as a threshold for the collected parameters to trigger an alert when significant deterioration in patient status occurs. 25 This approach has several major limitations. First, limited evidence exists for optimal threshold identification; thresholds may differ between patients and may change during the course of a chronic illness.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein

Jeong

2016

Annals of the New York Academy of Sciences

146

110

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Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training data sets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.

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Section: Introductionmentioning

confidence: 99%

Machine learning approaches to personalize early prediction of asthma exacerbations

Finkelstein

Jeong

2016

Annals of the New York Academy of Sciences

146

110

View full text Add to dashboard Cite

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“…8,9 A systematic review investigating the merits of telehealth interventions in primary care found that telehealth was generally more acceptable to patients than providers, outcomes were at least as effective as in-office care, and costs were no higher than for in-office care. 10 Improvements, however, are still needed. A recent study by Teladoc, Inc., an independent company offering mobile device-based and Internetbased medical care delivered by a physician who does not have an established relationship with the patient, found that users were not preferentially located in underserved communities, and providers had poorer performance than physician office visits on measures such as ordering diagnostic testing and prescribing appropriate antibiotics for bronchitis.…”

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confidence: 99%

Family Physicians Report Considerable Interest in, but Limited Use of, Telehealth Services

et al. 2017

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Purpose:Little is known about the attitudes toward and adoption of telehealth services among family physicians (FPs), the largest primary care physician group. We conducted a national survey of FPs, randomly sampled from membership organization files, to investigate use of and barriers to using telehealth services.Methods: Using bivariate analyses, we examined how telehealth usage affected FPs' identified barriers to using telehealth services. Logistic regressions show the factors associated both with using telehealth services and with barriers to using telehealth services.Results: Surveys reached 4980 FPs; 1557 surveys were eligible for analysis (31% response rate). Among FPs, 15% reported using telehealth services during 2014. After controlling for the characteristics of the physicians and their practice, FPs who were based in a rural setting, worked in a practice owned by an integrated health system or other ownership structure, and provided hospital/urgent/emergency care were more likely to use telehealth. Physician and practice characteristics by telehealth use status, sex of the physician, practice location, years in practice, care provided, and practice ownership were associated with the barriers identified. As health care delivery in the United States transitions to a patient-centered, value-based system with improved access to services, physician availability is a challenge. Telehealth could help address this problem. Although the term telehealth has been widely applied and well recognized for more than 4 decades, it lacks a singular definition. Broadly, telehealth is the use of technology to deliver health care services and information from a distance. Telehealth usage has evolved from static "store-and-forward" applications in which information, such as radiologic images, is stored and then forwarded for diagnostic review or a second opinion. Today's clinicians are providing virtual visits in real time through secure, interactive video exchange. These telehealth visits address a wide range of issues, from urgent to chronic, from primary care to subspecialty consultation, and from initial diagnosis to follow-up and management.As the largest health care delivery platform in the United States, the primary care setting 1 offers great potential for expanding telehealth use. TeleThis article was externally peer reviewed.

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“…This stepwise approach is significantly correlated with the degree of mobile health adoption by providers, and thus availability to patients . The transition of this technological advancement to the primary‐care arena has yet to be properly facilitated within the healthcare reimbursement structure, which may be the most significant barrier to widespread utilization of mHealth within the current framework …”

Section: Mhealth Technology Interpretation and Adoption By Cliniciansmentioning

confidence: 99%

Harnessing mHealth technologies to increase physical activity and prevent cardiovascular disease

Feldman

Robison

Pacor

et al. 2018

Clinical Cardiology

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Research into prevention of cardiovascular disease has increasingly focused on mobile health (mHealth) technologies and their efficacy in helping individuals adhere to heart-healthy recommendations, including daily physical activity levels. By including the use of mHealth technologies in the discussion of physical activity recommendations, clinicians empower patients to play an active daily role in modifying their cardiovascular risk-factor profile. In this review, we critically evaluate the mHealth and physical activity literature to determine how these tools may lower cardiovascular risk while providing real-time tracking, feedback, and motivation on physical activity levels. We analyze the various domains-including user knowledge, social support, behavioral change theory, and self-motivation-that potentially influence the effectiveness of smartphone applications to impact individual physical activity levels. In doing so, we hope to provide a thorough overview of the mHealth landscape, in addition to highlighting many of the administrative, reimbursement, and patient-privacy challenges of using these technologies in patient care. Finally, we propose a behavioral change model and checklist for clinicians to assist patients in utilizing mHealth technology to best achieve meaningful changes in daily physical activity levels.

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The Empirical Foundations of Telemedicine Interventions in Primary Care

Cited by 272 publications

References 111 publications

Machine learning approaches to personalize early prediction of asthma exacerbations

Machine learning approaches to personalize early prediction of asthma exacerbations

Family Physicians Report Considerable Interest in, but Limited Use of, Telehealth Services

Harnessing mHealth technologies to increase physical activity and prevent cardiovascular disease

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