AI has become a hot topic in the healthcare space, from reducing physician burnout to aiding healthcare managers in the transition from traditional healthcare payment models to alternative payment models.

Traditionally, healthcare payment models rely on the fee for service approach, where patients are charged on a per service basis, such as a singular doctor’s visit, blood test, or vaccine. However, there are many downsides to this approach, as providers will prioritize quantity over quality in attempts to make the most money, leading to hastier decisions and a lower quality patient outcomes. Many times, providers may prescribe treatments that may not be the most valuable to the patient, leading to more out-of-pocket costs for little to no benefit.

To try to combat these tactics, the healthcare industry has developed extremely complex procedure codes to try to track all work being done and manage the costs associated with each patient, leading to further physician burnout from attempting to manage this complex payment ecosystem. On average, 2-3 staff members are needed per physician to simply handle the revenue cycle for a single doctor, leading to huge overhead per doctor.

These ballooning costs have pushed many organizations to look for new models to optimize patient health and cost, broadly called Value Based Care or Alternative Payment Models. Value Based Care looks at the cost of care from a holistic perspective, allowing providers to be able to optimize overall patient wellbeing in the most efficient manner possible.

What is value-based care?

Value-based care is a new approach to healthcare that places a strong emphasis on delivering high-quality, patient-centered services while controlling costs. Unlike the traditional fee-for-service model, value-based care shifts the focus from the quantity of services provided to the quality of outcomes achieved. It promotes a holistic approach to patient care, incorporating preventive measures, early intervention, and coordinated care across different healthcare settings. By aligning incentives with desired patient outcomes, value-based care encourages healthcare providers to prioritize efficiency, effectiveness, and patient satisfaction. This approach fosters a collaborative environment where providers, payers, and patients work together to achieve better health outcomes, improve population health, and enhance the overall healthcare experience.

These models have shown the potential to improve patient outcomes and lower healthcare expenditures. However, the complexity and scale of these models call for new technologies to optimize their effectiveness. Artificial intelligence (AI) presents an exciting opportunity to revolutionize shared savings models, offering advanced analytics, predictive capabilities, and automation.

The Role of AI

With AI, provider organizations can unlock various benefits to managing their population’s health while being able to deploy shared savings models more efficiently.

AI-powered analytics platforms can process vast amounts of healthcare data, including electronic health records (EHRs), claims data, and socio-demographic information. By leveraging machine learning algorithms, AI can identify patterns and insights that are beyond human capacity, enabling precise risk stratification, population health management, and fraud detection. Furthermore, AI algorithms can analyze historical data to identify high-risk patients, predict adverse events, and estimate future healthcare utilization. For example, AI can predict the correct course of action for a specific patient based on the data from providers on that patient’s current tests and medical history. These predictive capabilities enable early intervention and personalized care plans, leading to improved patient outcomes and cost savings.

AI can also automate administrative routines, allowing healthcare professionals to focus on patient care. For instance, AI can automate tasks such as claims processing and billing, reducing administrative overhead and freeing up resources for more value-added activities. Furthermore, AI can assist in identifying potential areas for resource allocation optimization, such as reducing unnecessary tests and procedures, optimizing key diagnostic usage, and predicting patient flow patterns. AI-powered chatbots and virtual assistants can provide timely information and support, improving patient engagement and satisfaction. Overall, this can reduce costs spent in operations and workflows, leading to an improved financial future.

AI also enables the implementation of precision medicine by harnessing patient data, real-time monitoring, and predictive analytics. This powerful combination allows for a personalized approach to care, tailoring treatment plans to individual patients. AI algorithms can analyze vast amounts of patient data to identify patterns and make accurate predictions about the most effective treatment options. For example, AI can aid in the application of the shared savings plan to patients, allowing for the most accurate prediction of how payments are made. This not only optimizes treatment plans but also reduces the occurrence of adverse events, ultimately leading to improved patient satisfaction. Additionally, by taking into account cost considerations, AI can ensure that personalized care is delivered in the most cost-effective manner possible.

In analyzing and processing large amounts of data, AI enables the identification of population health trends and the evaluation of social determinants of health. For example, health can be impacted by not only the home environment a patient is in, but also their income bracket, the neighborhood they live in, as well as their work environment. By understanding these factors, healthcare providers can develop targeted interventions and strategies to address specific health challenges within communities. The proactive nature of this approach helps mitigate health disparities and fosters improved overall community health. Through AI-powered analytics, healthcare organizations can gain insights into disease prevalence, identify high-risk populations, and allocate resources accordingly, thereby optimizing healthcare delivery and resource allocation.

Applications of AI Today 

The integration of AI in shared savings models holds immense potential for the future of healthcare financing and management. By leveraging AI technology, several aspects of healthcare can be enhanced and optimized, leading to improved outcomes and cost savings.

  1. Personalized Medicine: The incorporation of AI enables the implementation of precision medicine by harnessing genetic data, real-time monitoring, and predictive analytics. This powerful combination allows for a personalized approach to care, tailoring treatment plans to individual patients. AI algorithms can analyze vast amounts of patient data to identify patterns and make accurate predictions about the most effective treatment options. This not only optimizes treatment plans but also reduces the occurrence of adverse events, ultimately leading to improved patient satisfaction. Additionally, by taking into account cost considerations, AI can ensure that personalized care is delivered in the most cost-effective manner possible.
  2. Population Health Management: AI algorithms possess the capability to analyze and process large volumes of healthcare data. This enables the identification of population health trends and the evaluation of social determinants of health. By understanding these factors, healthcare providers can develop targeted interventions and strategies to address specific health challenges within communities. The proactive nature of this approach helps mitigate health disparities and fosters improved overall community health. Through AI-powered analytics, healthcare organizations can gain insights into disease prevalence, identify high-risk populations, and allocate resources accordingly, thereby optimizing healthcare delivery and resource allocation.
  3. Cost Optimization: The integration of AI-powered systems in healthcare financing presents numerous opportunities for cost optimization. AI algorithms can analyze data from various sources, including clinical records, operational processes, and financial systems. By doing so, these algorithms can identify inefficiencies, streamline administrative processes, and pinpoint cost-saving opportunities. For instance, AI can automate tasks such as claims processing and billing, reducing administrative overhead and freeing up resources for more value-added activities. Furthermore, AI can assist in identifying potential areas for resource allocation optimization, such as reducing unnecessary tests and procedures, optimizing inventory management, and predicting patient flow patterns. By leveraging AI technology to optimize resource allocation and streamline operations, healthcare organizations can achieve significant reductions in healthcare expenditures while maintaining high-quality care.

‍Conclusion

Artificial intelligence has the potential to revolutionize value-based care models in healthcare financing, changing the way providers can manage health outcomes of their population. By employing advanced analytics, predictive modeling, and process optimization, AI can reduce physician burnout, improve patient outcomes and satisfaction, and reduce costs. Looking ahead, AI promises personalized care, proactive population health management, and optimized cost allocation. As the healthcare industry continues to embrace technology, AI will undoubtedly play a pivotal role in shaping the future of healthcare financing and delivering high-quality care to patients worldwide.

Limmi’s proprietary healthcare platform is designed from the ground up to handle the most complex healthcare data your organization has to offer, and provide rich insights and predictions from that data.As your trusted partner in this journey of integrating AI into healthcare, we offer a variety of ways in which you can utilize our platform to manage metrics and analyze the improvement of patient outcomes. Request a demo with our team today.