The healthcare industry is at a crossroads, grappling with the dual challenges of declining health quality metrics and skyrocketing care costs. The reality is that health outcomes are driven more by what patients do than by what providers or payers do.
It is estimated that 80% of health outcomes are driven by non-medical factors – often social or behavioral risks that impact health and lead to poor outcomes and high costs. Value-based care (VBC) shifts this focus to helping the patient achieve their best outcome by identifying and addressing social and other issues before they become severe health problems later.
The Data Dilemma in Healthcare
Healthcare systems collect vast amounts of data on patients – demographic, clinical, medication, treatment, care plans, and more. Despite this wealth of information, payers and providers have very little insight into the daily lives of their consumers. They have little awareness of what needs to be done, what they need, or the barriers they face that impacts their health. These failures can snowball into major health issues that strain the healthcare system and contribute to high costs.
To bridge this gap, we need to capture a different type of data. Human intelligence from patients and caregivers provides real-time actionable insights. Acting on these insights could improve member experience, knowledge, and enable interventions that prevent future problems, improve outcomes, and drive down healthcare costs.
The Role of Care Navigation Programs
Care navigation programs have demonstrated their ability to do this over the years, showing substantial reductions in costs like hospitalizations. These programs improve outcomes by assigning care navigators to regularly check in with patients, assess their status, and ensure they follow through with necessary actions such as taking medications and attending appointments. However, the scalability of these programs is limited by the availability of care navigators. The model cannot meet the needs of the entire population due to the sheer volume of patients requiring such personalized attention.
AI: The Catalyst for Proactive Engagement
Enter artificial intelligence (AI) technology, which offers the level of hyper-personalization needed for proactive patient engagement on a large scale. AI can effectively augment the work of human care navigators such as using AI to perform ongoing, proactive nudges and reminders for patients, much like a human navigator would. This technology allows for continuous monitoring and engagement, ensuring that patients receive timely reminders, support, and interventions based on their specific needs and situations.
Leveraging today’s health data to inform AI-driven interactions provides the context necessary for meaningful conversations. AI can better understand each patient’s unique circumstances, identify opportunities for intervention, and improve the odds of achieving positive health outcomes. This application of AI enables a scalable approach to proactive care at the population level, a cornerstone of VBC.
AI’s Impact on Healthcare
The potential benefits of integrating AI into healthcare are profound. For instance, a mere 10% reduction in hospitalizations could save the healthcare system billions of dollars annually. Proactively guiding and orchestrating health processes through AI not only enhances patient and population health but also alleviates the burden on healthcare providers and staff by reducing the volume and pressure on the system.
AI’s value in healthcare extends beyond cost savings. By facilitating continuous engagement and personalized care, AI can improve patient adherence to treatment plans, enhance chronic disease management, and enable timely interventions that prevent complications. This holistic approach aligns perfectly with the goals of VBC, where the emphasis is on outcomes rather than the volume of services provided.
Overcoming Barriers to Value-Based Care
Transitioning to VBC involves overcoming several complex barriers, including data integration, resource constraints, and administrative burdens. Effective implementation of VBC requires a strategic approach that leverages advanced technologies to address these challenges.
Data Integration: One of the primary obstacles is the integration of disparate data sources to create a comprehensive view of the patient. Advanced analytics and AI can facilitate seamless data integration, enabling healthcare providers to access and utilize all relevant information to make informed decisions.
Resource Constraints: The healthcare industry faces significant resource limitations, particularly in terms of skilled personnel. AI-driven solutions can help optimize resource allocation by automating routine tasks, freeing up healthcare professionals to focus on more complex and critical aspects of patient care.
Administrative Burdens: The administrative workload in healthcare is substantial, often diverting attention from patient care. AI can streamline administrative processes, reduce paperwork, and enhance operational efficiency, allowing providers to dedicate more time and effort to improving patient outcomes.
Mitigating the Downsides of AI
Using AI entails a level of risk such as hallucinations, errors, security, and trust concerns. This is mitigated by narrowing the use of AI by adding it to processes and use cases that leverage its scale and power without relying on its “reasoning” capability.
For example, using generative AI by entering a prompt into an LLM like ChatGPT renders a response that answers the question, but it may be incorrect. To limit this risk, AI frameworks are used to augment the prompt to the LLM with data from private information systems such as Retrieval Augmented Generation (RAG) to reduce errors. It adds instructions on the use of the information such as to not use the data in LLM training. It limits the AI response such as telling the LLM that if the answer is not in the data, say “I don’t know” to reduce hallucinations.
This approach also enables generative AI answers to be audited for source data and response. This is becoming a requirement to address the concerns around AI in decision-making and bias.
When applied to healthcare workflows and journeys, AI can be very useful to triage a population of people to identify those that need attention the most.
A New Era of Healthcare
As we navigate the challenges of rising healthcare costs and suboptimal outcomes, the transition to VBC presents a promising path forward. AI decisioning and automation, when combined with comprehensive health data and an understanding of patients’ real-time situations, enable a seamless connection between the business of healthcare and the human aspects of health. This integrated approach is crucial for achieving the goals of VBC and delivering high-quality, cost-effective care.
The healthcare industry must embrace innovative technologies like AI to drive the transition to value-based care. By harnessing data and leveraging AI for proactive patient engagement, we can enhance health outcomes, reduce costs, and create a more sustainable healthcare system. The future of healthcare lies in our ability to adapt and innovate, ensuring that every patient receives the care they need to achieve their best possible health outcomes.
About Robert Connely
Robert Connely is the Global Industry Market Leader for Healthcare at Pega, responsible for leading Pega’s strategy of using AI decisioning and automation to activate people, orchestrate processes, and improve outcomes to reduce costs. Robert brings over three decades of entrepreneurial, innovation, and strategic leadership experience in global organizations such as McKesson and Aetna and successful health IT start-ups such as Medicity.