Healthcare Think Tank

The healthcare industry is expected to undergo significant changes in 2024. Healthcare executives will need to make decisions and resource allocations concerning digital transformation, generative artificial intelligence, ESG, and margin pressures. Healthcare executives will face the challenge of adopting new technologies and business models while under sustained financial pressure. In terms of healthcare costs, estimates suggest that healthcare costs will rise by 7% in 2024, which is higher than the previous two years. This increase is attributed to continued labor shortages, drug price increases,
and new contracts between payers and providers. It is also worth noting that the cost of job-based health care coverage for 2024 is expected to rise at its fastest pace in years as inflation pervades insurance policies. Overall, the healthcare industry is expected to undergo significant changes in 2024, and healthcare executives will need to adapt to the new environment to ensure their organizations’ success.

The terms "Artificial Intelligence" and "Advanced Machine Learning" are often thought of interchangeably. While there is a relationship between AI and AML, to say they are the same thing is an oversimplification and misclassification. Rather, one begets the other with AI being the basic principle upon which AML is developed.

As AI begins to mature and migrate away from purely advanced mathematical operations into decision making paradigms, AML steps forward as the predictive ability of machines to process vast quantities of data. As data and analytics becomes foundational to the way every business operates, AI and AML will become foundational capabilities.

Healthcare employee wellness programs emphasize the special factors that accompany working in a healthcare enterprise, such as long shifts, frequent night shifts, exposure to infectious diseases and hazardous materials, isolation and burnout from working in a high-stakes environment, stress due to the higher responsibility of life or death situations, and trauma and psychological strain from seeing daily suffering. Healthcare employee retention and well-being are arguably more important than that of other professions, given the implications of the job. Health and wellness programs are essential for our most essential workers.

Value-based payment and delivery transformation is not the future, it is the present. Successful health systems, hospitals, medical groups and other providers are those that seek to engage with members to improve their health and total cost of care, rather than simply providing episodic services when a patient is sick. Cost reduction is no longer primarily about per-visit cost, but rather total cost of care per member per month (PMPM). In addition to looking at cost from the payer perspective, cost reduction must also be viewed in terms of the member’s out-of-pocket expenses. Put another way, providers must aim to reduce the totality of medical costs for each member they manage, rather than focusing only on the costs for a member when they show up at a clinic or hospital

AI is being applied within the healthcare field, especially for the tasks of diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities of the healthcare workforce. Seemingly, the best thing about applying AI in healthcare is that it can be used to improve various spheres: from gathering and processing valuable patient data to being used for programming surgeon robots. Let’s take a closer look at how AI is impacting healthcare diagnosis including detecting and classifying disease; improving decision making process and driving treatment solutions.

Workforce stabilization has to be a top priority for healthcare executives. Successful workforce stabilization initiatives will be necessary to develop a consistent, reliable, well-balanced healthcare workforce. Achieving workforce stabilization requires: getting to the root of staff turnover; addressing the reality of clinician burnout; understanding clinician wellness; and addressing a pipeline that is not producing the necessary talent.

Optimization and productivity are crucial in healthcare to ensure that patients receive the best care possible. To cope with these unprecedented challenges, health systems must learn to do more with less, and consolidation and efficiency are the name of the game. Consolidating and integrating business critical software and systems; and aligning the right resources with need based on skills and availability must considered 

Health equity is when everyone has the opportunity to be as healthy as possible. Innovation can be defined as invention + adoption + diffusion. Successful innovations often possess two key qualities: they are both usable and desirable. We will consider working examples of how clinic innovation is favorably impacting health equity, the associated challenges, risks, and how to evaluate success.

Accuracy of diagnosis, data quality and patient centric care are the buzzwords around improving healthcare outcomes. Leveraging artificial intelligence with medical procedures to diagnose disease early, when there is the highest potential for impact. Data quality lives by accuracy, consistency, and relevancy. How can we improve data quality by reducing redundancy and decreasing medical errors? Patient centric care thrives if the partnership among practitioners, patients, and their families align with patients' wants, needs, and preferences. In this session, we will discuss what’s working and what’s not working, as we look to improve diagnosis, data quality and patient centric care.