A little more than a decade ago, the Institute for Healthcare Improvement established healthcare’s Triple Aim, providing health systems with three ultimate goals:
- Improve the patient experience
- Reduce the costs of healthcare
- Improve the health of populations
As the healthcare industry continues to adopt value-based models, managing population health has become more important. Health systems are placing more emphasis on effective care management—a team-based, patient centric approach is designed to improve care while reducing the need for medical services—in their efforts to achieve the Triple Aim, with substantial financial incentives to do so.
Traditional care management efforts have seen some early success, but they aren’t taking advantage of all the rich patient data available in medical charts. Providers and payers are investing in machine learning technology, which has the potential to transform unstructured healthcare data like doctor’s notes into actionable insights to guide more effective care management activities.
How Does Maching Learning Optimize Care Management?
Machine learning is a computational technique that uses algorithms to learn from historical datasets and make accurate predictions about the future. These algorithms are trained to look at specific variables, then develop models that predict events or find patterns in new data.
In healthcare, machine learning models can be applied to a wide variety of datasets. For example, machine learning could be used to predict which hospital inpatients are most likely to be readmitted, flag patients who haven’t received appropriate preventive screenings, or identify patterns in prescribing behaviors within a specific medical system.
Machine Learning Empowers Care Managers To Make More Intelligent Suggestions
Care managers play a vital role in ensuring that patients receive appropriate and high-quality care. Focused on individual patients, care managers help identify specific care needs and route patients to the right services. The more information care managers can obtain on their patients, the better equipped they’ll be to guide people appropriately throughout the healthcare system.
As more patient data becomes available than ever before, it’s imperative that care managers are able to leverage machine learning capabilities to surface relevant trends and insights.
Machine learning is able to sort through the massive volumes of care management data quickly so care managers are able to efficiently extract useful intelligence from the data. Equipped with this information, care managers can provide suggestions tailored to the unique needs of each patient. As a result, patients receive more personalized care and positive outcomes, and care managers are better positioned to help more patients.
Machine Learning Better Identifies Potential Chronic Issues And Comorbidities
Each year, the U.S. healthcare system spends approximately $1.65 trillion on treating patients with one or more chronic diseases, with expected expenditures potentially reaching $6 trillion by 2050. With chronic disease affecting 133 million Americans (40% of the nation), the need for disease management and prevention is immense.
Based on a study from the U.S. Agency for Healthcare Research and Quality (AHRQ), an estimated 4.4 million hospital admissions totaling $30.8 billion could have been prevented. Of that $30.8 billion, nearly half of the costs were attributed to heart disease and diabetes complications – both chronic diseases.
Through machine learning, chronic care delivery can shift from reactive to proactive, focusing less on a one-size-fits-all approach and more on individualized care. By leveraging patient data, machine learning algorithms can quickly determine personalized interventions for each patient, including everything from increased monitoring to specified treatment plans. With these steps in place, interventions can occur before chronic issues appear or become acute that result in costly hospital stays, preventable emergency care, and an overall lower quality of life.
For example, Boston University’s Center for Information and Systems Engineering conducted a study that used patients’ electronic health records (EHRs) and machine learning to predict hospitalizations due to diabetes and heart diseases. In the study, hospitals provided patients’ anonymized EHR data, which included demographics, diagnoses, admissions, procedures, medications prescribed, and lab results. Machine learning algorithms were then used to process the large set of data and predict who might need to be hospitalized. Boston University was able to predict hospitalizations due to heart disease and diabetes about a year in advance with an 82% accuracy rate. These predictions allowed them to intervene sooner by treating the disease in an outpatient setting and avoiding costly hospitalizations or emergency room visits.
Machine Learning Is A Worthwhile Investment
As value-based care increasingly becomes the primary focus of today’s healthcare industry, technologies like machine learning are emerging as invaluable resources for healthcare organizations to provide a more personalized care management solution.
Healthcare organizations have been reticent to adopt new and complex technologies like machine learning, but the unique opportunities to leverage the influx of big data through machine learning further cements its value to providers. Whether through internal analytic teams or external technology vendors, machine learning is a necessary and valuable investment. Late adopters will miss out on opportunities to leverage their patient data to drive more intelligent, effective patient care.
Machine learning applications have the potential to save the healthcare system billions of dollars and simultaneously help provider systems enhance care coordination, eliminate duplicative care, and help patients stay healthier. These financial and clinical benefits stretch across the entire healthcare spectrum and can benefit providers, health plans and patients alike.
FAQs
What is an AI care coordination platform?
An AI care coordination platform is a technology solution that leverages artificial intelligence to streamline and enhance the coordination of care for patients. It uses advanced algorithms and data analysis to automate tasks, improve communication, and provide valuable insights to healthcare providers, ultimately leading to better patient outcomes and reduced healthcare costs.
How does an AI-powered care coordination system work?
An AI-powered care coordination system utilizes artificial intelligence algorithms to analyze patient data, identify care gaps, and facilitate communication between healthcare providers. It can integrate with electronic health records (EHRs) and other healthcare systems to collect and process relevant information in real-time. The AI algorithms can then generate personalized care plans, monitor patient progress, and make recommendations to optimize care coordination.
What are the benefits of using AI in care coordination?
The use of AI in care coordination brings numerous benefits. It enhances efficiency by automating administrative tasks, reduces errors by providing real-time data analysis, improves communication among healthcare providers, and enables personalized care plans based on individual patient needs. AI-powered care coordination also helps in identifying high-risk patients, predicting health outcomes, and facilitating preventive interventions, resulting in better patient outcomes and cost savings.
How can an AI-powered care coordination platform improve patient outcomes?
An AI-powered care coordination platform can improve patient outcomes in several ways. By analyzing patient data, it can identify high-risk individuals and intervene early to prevent complications. It can also ensure that patients receive appropriate follow-up care, medication reminders, and timely interventions. Additionally, AI algorithms can identify patterns and trends, enabling healthcare providers to make data-driven decisions and optimize treatment plans for better outcomes.
What challenges do AI care coordination platforms face?
AI care coordination platforms face challenges such as data privacy and security concerns, integration with existing healthcare systems, data interoperability, and the need for training and education of healthcare staff to effectively use the technology. Other challenges include the potential for bias in AI algorithms, ensuring accurate data input, and addressing any resistance to change among healthcare providers.
What strategies can healthcare organizations employ to master the challenges of AI-powered care coordination?
Healthcare organizations can master the challenges of AI-powered care coordination by prioritizing data privacy and security, ensuring seamless integration with existing systems through interoperability standards, and providing comprehensive training to healthcare staff. They can also establish governance frameworks to address algorithm bias and ensure ethical use of AI. Collaborating with technology vendors and involving stakeholders in the implementation process can also contribute to successful adoption and mastery of AI care coordination.
What is risk adjustment in the context of MSSP?
In the context of the Medicare Shared Savings Program (MSSP), risk adjustment is a mechanism used to account for the health status and cost differences among patients attributed to Accountable Care Organizations (ACOs). It adjusts the financial benchmarks and payments to ACOs based on the risk profile of their patient population. Risk adjustment ensures that ACOs are not unfairly penalized or rewarded for factors beyond their control, such as the health conditions of their patients.
What are effective risk management strategies for ACOs?
Effective risk management strategies for ACOs include accurate and thorough documentation of patient conditions, implementation of care coordination programs, utilization of data analytics to identify high-risk patients, proactive preventive care initiatives, and effective communication and collaboration among healthcare providers. ACOs should also invest in robust technology solutions, such as AI-powered care coordination platforms, to improve risk identification, patient monitoring, and care coordination efforts.
What services are related to ACO risk adjustment?
Services related to ACO risk adjustment include risk adjustment coding and documentation improvement, predictive modeling and risk stratification, analytics and reporting, care coordination and case management, and provider education and training. These services help ACOs accurately capture and document patient conditions, identify high-risk patients, optimize care coordination efforts, and ensure compliance with risk adjustment requirements. By leveraging these services, ACOs can improve their risk scores and capture appropriate reimbursements.