Table of Contents
Introduction
Artificial intelligence (AI) and digital health technological innovations from startup companies used in clinical practice can yield better health outcomes, reduce health care costs, and improve patients’ experience. However, the integration, translation, and adoption of these technologies into clinical practice are plagued with many challenges and are lagging.
Methods
A stakeholder focus group workshop was conducted with a sample of 10 early-stage digital health and health care AI founders and executives. Using an inductive thematic analysis approach, transcripts were organized, queried, and analyzed for thematic convergence.
Results
We identified four categories of barriers in the integration of early-stage digital health innovations into clinical practice and health care systems:
Lack of Knowledge on Health Care Systems’ Technology Procurement Protocols and Best Practices
The stakeholders expressed deficiencies in their knowledge of the health care sales cycle and implementation process of digital technologies into clinical practice.
Demanding Regulatory and Validation Requirements
The participants raised concerns about the strenuous regulatory, validation, and technology evaluation evidence that is required for their products to be used in clinical settings.
Challenges Within the Health Care System Technology Procurement Process
The participants were asked to name the top 3 health care system technology procurement barriers experienced by early-stage health care technology entrepreneurs. The overall response to this question was remarkable.
Disadvantages of Early-Stage Digital Health Companies Compared to Large Technology Conglomerates
All participants mentioned that the top barrier was lack of information on the appropriate decision maker and process. Large technology companies and conglomerates have comprehensive marketing departments and more capabilities to hire the best health care enterprise sales talent in comparison to smaller companies.
Discussion
To mitigate the barriers early-stage digital health and health care AI entrepreneurs experience when integrating technologies, several recommendations were made:
- Provide continuing education opportunities on the health care technology procurement process.
- Create opportunities for early-stage digital health technology companies, venture capitalists, health care providers, health care systems, regulatory boards, and insurance companies to interact and develop relationships.
Limitations
The small sample size in comparison to other study designs such as surveys might prevent generalizability of the study results into other contexts. The sampling method of targeting leaders of preprofit companies with a digital health solution for cardiovascular medicine may offer limited generalizability to the entire AI and digital health care technology community.
Conclusion
The barriers that early-stage health care technology entrepreneurs face must be mitigated for these innovations to have their true impact so that they improve clinical care delivery and patient outcomes. Future research should explore best practices and strategies for successful digital health and AI technology integration into clinical care, focusing on their impact on patient outcomes and cost reduction.
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