Healthcare in 2024: Navigating Higher Costs, New Technologies, and Demanding Regulations
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The key challenge for healthcare in 2024? Scarcity.
As labor costs continue to skyrocket and evolving regulations demand more from payers, providers, and life sciences companies, the healthcare sector will battle limitations in access to quality data, financial resources, and talent. Technologies like automation, artificial intelligence (AI), and machine learning (ML) can help, but many struggle to implement them.
Here’s what industry players should pay attention to in the year to come.
Regulatory focus on diversity, equity, and inclusion (DEI) will create new operational challenges for providers and drug developers
Regulatory agencies are taking steps to spotlight DEI in healthcare. For instance, the Food and Drug Omnibus Reform Act of 2022 (FDORA) provided draft guidance to promote greater diversity in clinical trials. Meanwhile, the Centers for Medicare & Medicaid Services’ new AHEAD Model aims to advance health equity by requiring participating providers to collect demographic and social needs data.
Both FDORA and AHEAD create opportunities and challenges for healthcare organizations, forcing them to rethink how they interact with larger parts of the population. AI/ML and other emerging tools offer creative solutions—be it predictive modeling to improve clinical trial recruiting or analytics to drive more equitable health outcomes—but are not a panacea. Life sciences companies need a validation process in place to ensure solutions are safe and effective for patients. And providers shouldn’t lose sight of the basics: understanding their specific patient populations and tailoring strategies to meet health consumers where they are.
2024 may be the year of generative AI in healthcare
It’s worth being skeptical of the hype surrounding large language models (LLMs) and generative AI. For now, however, it’s important to remember that the technology doesn’t need to be incredibly advanced to be successful in the near term—particularly when it comes to patient communications and other administrative tasks that take up so much of a healthcare worker’s day.
That said, as the industry gains more experience with the technology in years to come, we should start to see increasingly complex applications. For example, an LLM trained on millions of unstructured clinical notes from the NYU Langone hospital system predicted clinical and operational outcomes crucial to patient care, including risk of in-hospital death and hospital length of stay, outperforming traditional clinical predictive models based on structured data. No matter the use case, clinicians should always lead or guide generative AI.
Automation is critical—but healthcare leaders must see the big picture
Much of healthcare work can be automated through process automation, robotic support, AI, real-time analytics, and more. Perhaps most promising, electronic medical record platforms can quickly normalize unstructured data, from physician notes to patient scans, to move seamlessly throughout the health system. In 2024 and beyond, these innovations can help mitigate the challenges of ongoing labor shortages and clinician burnout.
It’s important, however, to take an enterprise approach to automation that cuts across departments, rather than implementing it in siloes. That requires a strong governance structure, effective leadership, and a culture of automation that stem from the highest levels of the organization.
New regulations are coming. By leveraging data, organizations can prepare now
Significant policy shifts are on the horizon, including changes to the Medicare Part D program for drug benefits (e.g., patients will have a cap to what they pay at the pharmacy and be able to pay in installments, while health plans will take on more liability for drug costs); and potential tweaks to the increasingly popular Medicare Advantage program, with an eye on consumer protection.
Fortunately, healthcare executives can take proactive steps to anticipate where these and other industry regulations are headed. After all, regulators and executives are looking at the same types of data—and seeking out the same types of inefficiencies and loopholes. Executives can examine data from their own organizations and their competitors and pair it with sophisticated policy expertise.
