Leading Healthcare Insights – Artificial Intelligence and Technology Innovation in Healthcare
Expert Panelist – Dr. Ronke Komolafe, Mathew Anderson, MD., Melanie Patton, MD., Amanda Dean Martin, DNP., and Matthew Sakumoto, MD.
Healthcare is experiencing tremendous innovation, redesign, and growth ranging from expansion of digital health, use of artificial intelligence, evidence-based treatment models, and massive investment in at-home clinics, self-care, virtual care, acquisitions, and venture capitalist investment. The current environment is a battleground for intense competition for thought leadership and market and an increase in innovation. What role do technology and innovation play in this healthcare redesign and renovation stage?
Healthcare experts Dr. Ronke Komolafe, Mathew Anderson, MD, Melanie Patton, MD, Matthew Sakumoto, MD, and Amanda Dean Martin, DNP, provided recommendations and insight into the role of artificial intelligence and technology innovation in the evolving healthcare system.
1) Changes in Healthcare Technology and Artificial Intelligence
Across the healthcare industry, technology innovation and artificial intelligence (AI) are changing care delivery and patient engagement. AI and innovative technology are revolutionizing and transforming the healthcare landscape and ecosystem. The continuous change focuses on:
- Lowering cost
- Improving treatment outcomes
- Clinical decision support
- Precision medicine
- Patient data
- Automation to eliminate redundancy
2) Engage Stakeholders – Innovate from Clinician and Patient Perspectives
Technology helps streamline and support clinicians’ workflow and improve patient outcomes. Involving patients, clinicians, and health professionals as partners in developing healthcare innovation is a strategy that results in a provider and patient-centered technology that enhances care delivery and improves outcomes. New and redesigned technologies should include customized clinical, operational, and technical processes that align with end-users’ workflow.
Essentially, improving, adopting, and successfully implementing AI technology will increase ROI and decrease turnover in the long run. The sustainability of a properly constructed AI ensures the long-term success of achieving quadruple aim of lower cost, better outcomes, and improved patient and provider experience. To find a perfect fit for AI Technology, it is best to pilot programs and utilize the provided data to make an informed decision for adoption.
3) Artificial Intelligence and Robotics Improve Population Health & Access to Care
AI and robotics technology help. It can increase productivity and the efficiency of care delivery through automation of processes and allow healthcare systems to provide more and better care to more people. AI can compute massive amounts of information, such as a risk assessment, with more accuracy and speed than one provider. While you still need clinician oversight, AI can direct clinicians to those at risk or needing more specialized care than others. Continuous leverage of AI for predictive, precision and preventative medicine in mental and physical health leads to better population health management.
4) Data, Connected Data, Better Data
Healthcare is a data-driven industry. Data drives financial and clinical decisions and actions in healthcare. It is imperative to curate, analyze, and streamline data into clinicians’ workflow for seamless integration to make informed decisions for better outcomes. AI and big data provide extensive benefits to patients, payers, and providers which include:
- Faster diagnosis of patients through early identification of symptoms.
- Patient empowerment through wearable devices, chatbots, and patient-centered digital health devices.
- Connecting patients to care and resources
- Reduce administrative burden and cost
- Expand treatment access to vulnerable population
5) Barriers to Clinical Adoption and Sustainability of AI & New Technology in Healthcare
There are common delays in adopting AI and technology in healthcare due to the complexities of programs, clinical workflow, unstructured data, technology mismatch, and other factors. Common influences of these delays include:
- Poor use cases and clinical workflow
- Cost and return on investment (ROI)
- Data bias and mismatch
- Regulatory delay
- HIPAA privacy challenges
To address these barriers, it is best to conduct a low-cost pilot of the technology and resolve these barriers before implementing a company-wide adoption.