Digital health solutions continue to grow in both number and capabilities. Despite these advances, the confidence of the various stakeholders — from patients and clinicians to payers, industry and regulators — in medicine remains quite low. As a result, there is a need for objective, transparent, and standards-based evaluation of digital health products that can bring greater clarity to the digital health marketplace. We believe an approach that is guided by end-user requirements and formal assessment across technical, clinical, usability, and cost domains is one possible solution. For digital health solutions to have greater impact, quality and value must be easier to distinguish. To that end, we review the existing landscape and gaps, highlight the evolving responses and approaches, and detail one pragmatic framework that addresses the current limitations in the marketplace with a path toward implementation.
As the volume of data that is electronically available promliferates, the health-care industry is identifying better ways to use this data for patient care. Ideally, these data are collected in real time, can support point-of-care clinical decisions, and, by providing instantaneous quality metrics, can create the opportunities to improve clinical practice as the patient is being cared for. The business-world technology supporting these activities is referred to as business intelligence, which offers competitive advantage, increased quality, and operational efficiencies. The health-care industry is plagued by many challenges that have made it a latecomer to business intelligence and data-mining technology, including delayed adoption of electronic medical records, poor integration between information systems, a lack of uniform technical standards, poor interoperability between complex devices, and the mandate to rigorously protect patient privacy. Efforts at developing a health care equivalent of business intelligence (which we will refer to as clinical intelligence) remains in its infancy. Until basic technology infrastructure and mature clinical applications are developed and implemented throughout the health-care system, data aggregation and interpretation cannot effectively progress. The need for this approach in health care is undisputed. As regional and national health information networks emerge, we need to develop cost-effective systems that reduce time and effort spent documenting health-care data while increasing the application of knowledge derived from that data.
While digital health solutions continue to grow in number and in complexity, the ability for stakeholders in healthcare to easily discern quality lags far behind. This challenge is in part due to the lack of a transparent and standardized approach to validation. Evaluation of mobile health applications (apps) is further burdened by low barriers to development and direct-to-user marketing, leading to a crowded and confusing landscape. In this context, we investigated the pragmatic application of a previously described framework for digital health validation, the Digital Health Scorecard, in a cohort of 22 popular mobile health oncology apps. The apps evaluated using this framework performed poorly, scoring 49.4% across all evaluation criteria as a group. Performance across component domains varied considerably with cost scoring highest at 100%, usability at 56.7%, technical at 37.3%, and clinical at 15.9%. satisfaction of prospectively determined end-user requirements derived from patient, family, and clinician consensus scored 37.2%. While cost outperformed consistently and usability was adequate, the results also suggested that apps suffered from significant technical limitations, were of limited clinical value, and generally did not do what end users wanted. These large gaps further support the need for transparent and standardized evaluation to help all stakeholders in healthcare improve the quality of mobile health.
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