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Review
. 2020 Feb 3:3:14.
doi: 10.1038/s41746-019-0212-z. eCollection 2020.

Beyond validation: getting health apps into clinical practice

William J Gordon  1   2   3 , Adam Landman  2   3   4 , Haipeng Zhang  3   5   6 , David W Bates  1   3
Affiliations
Review

Beyond validation: getting health apps into clinical practice

William J Gordon et al. NPJ Digit Med. .

Abstract

Fueled by advances in technology, increased access to smartphones, and capital investment, the number of available health "apps" has exploded in recent years. Patients use their smartphones for many things, but not as much as they might for health, especially for managing their chronic conditions. Moreover, while significant work is ongoing to develop, validate, and evaluate these apps, it is less clear how to effectively disseminate apps into routine clinical practice. We propose a framework for prescribing apps and outline the key issues that need to be addressed to enable app dissemination in clinical care. This includes: education and awareness, creating digital formularies, workflow and EHR integration, payment models, and patient/provider support. As work in digital health continues to expand, integrating health apps into clinical care delivery will be critical if digital health is to achieve its potential.

Keywords: Diagnostic markers; Health services; Signs and symptoms.

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Conflict of interest statement

Competing interestsW.J.G. reported research funding from IBM, for work unrelated to this manuscript. A.L. reported receiving personal fees from Abbott Medical Device Cybersecurity Council outside the submitted work. H.Z. reported employment at Cake, an end-of-life planning technology company. DWB reported consulting for EarlySense, which makes patient safety monitoring systems. He receives cash compensation from CDI-Negev Ltd, which is a not-for-profit incubator for health information technology startups. He receives equity from ValeraHealth, which makes software to help patients with chronic diseases, from Clew, which makes software to support clinical decision-making in intensive care, and from MDClone, which produces deidentified versions of clinical data.

References

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