The Future of healthcare: Embracing AI, VBC in 2024
A variety of advanced computing technologies, particularly generative AI, and more movement toward value-based care will accelerate industry changes.
The steady adoption of alternative healthcare delivery and reimbursement models should continue through the coming year as providers and payers seek to improve individual and population health outcomes while reducing costs through the implementation or expansion of value-based care programs.
We can expect healthcare stakeholders – providers, payers, pharmaceutical companies, device manufacturers, research organizations and public health officials – to aggressively integrate artificial intelligence (AI) capabilities in 2024 for value-based care programs, care management, clinical use cases, drug development, resource allocation, supply chain management, predictive analytics and operational efficiency.
Healthcare organizations will nearly double the amount of budget dollars for AI in 2024, to 10.5 percent from 5.5 percent in 2022, according to a recent Morgan Stanley market research report. Among the areas that may benefit most from AI deployments, the analyst firm wrote, are life sciences tools and diagnostics, medical technology, biopharma and healthcare services.
The following five technology-related trends should have a major impact on healthcare in 2024.
More progress on data digitization, interoperability. Look for healthcare organizations to continue modernizing their technology infrastructures to fully leverage the benefits of VBC and alternative payment models. VBC requires the safe exchange of digitized data across stakeholder systems to properly measure outcomes.
AI technologies, in a robust data engineering framework, will be essential to the digital transformation necessary to support the “network of networks” required under value-based models. These technologies can be used to better automate tasks and decision-making processes, helping to provide scalability.
Specifically, AI-based technologies, such as natural language processing (NLP) and computer vision, can drive data digitization by converting unstructured information from notes, charts, and images into structured data. Further, machine learning algorithms can help reduce claims denials by improving error detection in billing and coding.
Preventive care through predictive AI. While predictive AI already is in a mature state, it will continue to evolve and improve in 2024 as more healthcare data digitization occurs, creating more data to feed to predictive AI algorithms.
Historical patient data contains clues about potential risk factors that clinicians may decide to mitigate through interventions designed to improve a patient’s long-term outcome. Predictive AI unlocks the insights buried in patient historical data to forecast future outcomes or classify future events. This provides clinicians with actionable insights and data-driven evidence for decision-making and care strategy formulation.
AI’s predictive capabilities enable healthcare providers to shift their focus from reactive to proactive care. By analyzing historical patient data and identifying risk factors that may indicate prospective health problems, predictive AI enables clinicians to apply preventive measures and interventions. AI-based preventive care has tremendous potential to lower the prevalence of chronic diseases such as cancer, heart disease, stroke and diabetes.
Healthcare embraces GenAI. The general public has been aware of ChatGPT and other generative AI for slightly more than a year, but the technology concept isn’t new. GenAI is far less mature than predictive AI at this stage. In the next year, we can expect rapid improvements in GenAI’s accuracy and reliability, along with an increase in use cases as healthcare organizations begin to explore its capabilities.
GenAI-based tools already are helping to reduce the administrative burden on physicians and nurses, which results in improved patient care and resource utilization while reducing staff burnout.
Pharmaceuticals also are implementing GenAI today, with cancer researchers using the technology for forecasting, generating data for new models, analyzing patient tissue and conducting functional pre-precision oncology analysis. In addition, GenAI can accelerate clinical trials and precision medicine therapies.
Medical device manufacturers in the U.K. are using GenAI to help with 3D printing design, process planning, and production monitoring for implants and prosthetics. GenAI can help companies develop more personalized and patient-centered devices by incorporating software that enables preventive maintenance and repairs.
Google continues to add new capabilities to its Vertex AI Search platform, which helps users build GenAI-powered search engines that enable clinicians to quickly access charts, notes and other patient data across data sources. Vertex AI Search integrates with the Google Cloud Healthcare API and Healthcare data engine, as well as Care Studio, giving providers better searching capabilities across health datasets.
GenAI has direct applications in VBC as well. Contract modeling, performance forecasting and care management processes can be improved significantly in terms of performance and accuracy with GenAI adoption. Next year there should be significant progress on these use cases as well with pilots starting to prove the concept.
New value-based models will take hold. The Centers for Medicare and Medicaid Services (CMS) has unveiled several new reimbursement models that provide incentives and benefits for various stakeholders. The ACO REACH model encourages provider organizations to form accountable care organizations, which are intended to improve outcomes and manage costs by delivering high-quality, coordinated care.
Two other new CMS models are intended to get states more involved in global budget setting under value-based programs. The Making Care Primary (MCP) model incentivizes patient engagement under value-based programs, while the AHEAD model focuses on primary care alignment. The coming year should see greater deployment of tools to increase patient engagement, such as remote monitoring and wearables, as providers continue to expand where they offer care to the community and home.
Value-based programs will scale. The commercial aspects of value-based models should evolve next year, based on increased implementations of administrative solutions on the payer and risk-bearer sides to scale those types of programs. Likewise, more organizations in 2024 should begin to offer value-based care networks direct to employers.
Change in healthcare can be painfully incremental. However, recent advances in large language models, along with the widespread adoption of GenAI, continuing pressure on margins, and ongoing efforts by CMS to promote value-based care models, could set the stage for a year of aggressive innovation and implementation of digitally transformative tools in 2024.
Rahul Sharma is chief executive officer and Lynn Carroll is chief operating officer of HSBlox, an Atlanta-based technology company that offers VBC applications.