Economic evaluations of eHealth technologies: A systematic review

Sanyal, Chiranjeev; Stolee, Paul; Juzwishin, Don; Husereau, Don

doi:10.1371/journal.pone.0198112

Cited by 80 publications

(60 citation statements)

References 21 publications

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“…Economic evaluations of eHealth interventions among patients with chronic diseases are scarce and mostly performed among patients with diabetes and cardiovascular disease [28,29]. To the best of our knowledge, this study was the first economic evaluation of an eHealth intervention among cancer survivors.…”

Section: Discussionmentioning

confidence: 99%

Cost-utility of an eHealth application ‘Oncokompas’ that supports cancer survivors in self-management: results of a randomised controlled trial

et al. 2020

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Purpose The eHealth self-management application 'Oncokompas' was developed to support cancer survivors in monitoring health-related quality of life (HRQOL) and symptoms, and obtaining personalized feedback and options for supportive care. The aim of this study was to assess the cost-utility of Oncokompas compared with care as usual (CAU) among cancer survivors. Methods Survivors were randomly allocated to the intervention or control group. Direct (non-)medical, indirect non-medical costs, and HRQOL were measured at 3-and 6-month follow-up, using iMTA Medical Consumption and Productivity Costs and the EuroQol-5D questionnaires. Mean cumulative costs and quality-adjusted life-years (QALYs) were compared between both groups. Results In total, 625 survivors were randomized into intervention (n = 320) or control group (n = 305). Base case analysis showed that incremental costs from a societal perspective were − €163 (95% CI, − 665 to 326), and incremental QALYs were 0.0017 (95% CI, − 0.0121 to 0.0155) in the intervention group compared with those in the control group. The probability that, compared with CAU, Oncokompas is more effective was 60%, less costly 73%, and both more effective and less costly 47%. Sensitivity analyses showed that incremental costs vary between − €40 and €69, and incremental QALYs vary between − 0.0023 and − 0.0057. Conclusion Oncokompas is likely to be equally effective on utilities, and not more expensive than CAU, and will therefore contribute to sustainable cancer survivorship care in a (cost-)effective manner. Implications for Cancer Survivors Oncokompas seems to improve HRQOL and reduces the burden of several tumour-specific symptoms, while costs from a societal perspective are similar to CAU.

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Section: Discussionmentioning

confidence: 99%

Cost-utility of an eHealth application ‘Oncokompas’ that supports cancer survivors in self-management: results of a randomised controlled trial

et al. 2020

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“…Therefore, the overall cost-effectiveness of this technology is currently unclear. 69,70 Policy implications of mobile health development…”

Section: Economic Aspectsmentioning

confidence: 99%

Mobile health applications for the detection of atrial fibrillation: a systematic review

et al. 2020

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Aims Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF. Methods and results We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0–100% and a specificity of 93.5–99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients. Conclusion Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.

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“…Economic evaluation of any eHealth technology is still evolving and therefore needs standardization. While the evaluation of pharmaceutical substance can be easily based on randomized control trial, in the field of eHealth it is more complex and thus demanding [37]. The most important part of research assessing the implementation of an e-solution in any healthcare organisation is to clearly define the aim [38].…”

Section: Conslusionsmentioning

confidence: 99%

What are the aims of the implementation of e-solutions in healthcare? Review of the relevant practical studies

Jędrzejczyk¹,

Zarzeczna-Baran²

2019

Eur J Trans Clin Med

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Economic evaluations of eHealth technologies: A systematic review

Cited by 80 publications

References 21 publications

Cost-utility of an eHealth application ‘Oncokompas’ that supports cancer survivors in self-management: results of a randomised controlled trial

Cost-utility of an eHealth application ‘Oncokompas’ that supports cancer survivors in self-management: results of a randomised controlled trial

Mobile health applications for the detection of atrial fibrillation: a systematic review

What are the aims of the implementation of e-solutions in healthcare? Review of the relevant practical studies

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