Adoption Barriers

The mobile health industry as a whole is complex and comprises a diverse network of stakeholders who have different needs and priorities. To unlock mHealth opportunities, technology innovators first need to understand and address the major barriers that hinder broad adoption of mobile technology. In general, we can categorize the barriers into two groups: consumer-oriented adoption barriers and provider-oriented adoption barriers (see Table 1). Consumers are individuals who show a consciousness towards their health and are either healthy or need to manage a health condition. Providers include professionals that provide health or wellness services to consumers, which include physicians, nurses, or health and wellness coaches.

Table 1 – Major Adoption Barriers
Table 1 – Major Adoption Barriers
Adoption barriers


Usability and perceived usefulness

Data security and privacy

App overload and diversity

Payment and reimbursement

Lack of provider acceptance



Disparate data silos

Data security and privacy

EHR connectivity and integration

Clinical workflow integration

Lagging reimbursement models

Regulatory uncertainties

Cost-effective solutions

Lack of scientific evidence

Inability to reach the most vulnerable cohort

Both consumer-oriented and provider-oriented barriers have common characteristics (e.g. data security and privacy) and individual barriers can be linked to each other creating a network with a feedback loop. For example, increasing adoption at the consumer level would require solving the lack of provider acceptance, which in turn would require addressing provider-oriented barriers as outlined above [5][6].

The health care industry, policy makers, and legislation are fully committed to addressing adoption barriers that relate to payment and reimbursement reform, regulatory hurdles, interoperability, and clinical effectiveness. Meanwhile some of the key adoption barriers can be addressed via technological solutions, such as (1) data security and privacy, (2) the integrated collection and management of data, and (3) meaningful analysis and presentation of data to be consumed and leveraged by consumers and providers.

Data security and privacy - Health information is considered to be the most sensitive type of information related to an individual. For mHealth to assume a fully integrated role in the delivery of health care it must be delivered in an environment in which consumers have confidence that the security and privacy of their data will be protected and the confidentiality be guarded with the highest standards (e.g. HIPAA – Health Insurance Portability and Accountability Act).

Data integration - Health data is heterogeneous and resides in separate data silos. At the consumer level a variety of sensors and devices have independent data standards making data analysis and understanding difficult. Data integration at the population level is even more complex requiring properly crafted data architectures to consolidate across the population and ensure accurate and efficient analysis.

Meaningful, data-driven insights - Data are of limited value if not properly analyzed and presented to derive meaningful actionable insights. From the individual consumer perspective, device fatigue, limited perceived value, absence of an engaging user experience, and the lack of personalization hinder adoption and limit long-term engagement. From the provider perspective, being able to analyze the data at the population level and then drill down to a concise summarized form are key challenges that hinder effective care support, acceptance and workflow integration.

5. HIMSS, Remote Patient Monitoring (RPM) – Security and Other Adoption Barriers, 9 December 2014. 6 April, 2016.

6. HIMSS, mHealth App Essentials: Patient Engagement, Considerations, and Implementation, 20 February 2015. 6 April 2016.