How to leverage AI to increase success in care management

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60% of American adults live with at least one chronic condition and 12% with five or more. They spend exponentially more on health care than those without chronic conditions. For example, 32% of adults with five or more chronic illnesses have at least one emergency room visit every year. In addition, 24% have at least one hospital stay, as well as an average of 20 outpatient visits, up to 10 times more than those who do not suffer from chronic diseases. In fact, according to the Centers for Disease Control and Prevention (CDC), 90% of the $ 4 trillion in US healthcare spending is for people with chronic and mental health conditions.

The fundamental way healthcare organizations reduce these costs, improve the patient experience, and ensure better population health is through care management.

In short, care management refers to the collection of services and activities that help patients with chronic diseases manage their health. Care managers proactively target patients under their care and offer preventative interventions to reduce hospital emergency room admissions. Despite their best efforts, many of these initiatives provide sub-optimal results.

Why current care management initiatives are ineffective

Much care management today is done on the basis of past data

For example, care managers identify patients with higher costs than the previous year and begin their outreach programs with them. The biggest challenge with this approach, according to our internal research, is that nearly 50-60% of high-cost patients were low-cost the previous year. Without adequate awareness, large numbers of at-risk patients are left unattended with the reactive care management approach.

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The risk stratification used by the care management team today is a national model

These patterns are not localized, so understanding the social determinants of individual localities is not taken into consideration.

The primary focus of the care management team is primarily on transitioning care and avoiding readmissions

Our experience of working with several clients also indicates that readmissions only contribute 10-15% of the total admission. There is a lack of focus on proactive care management and avoiding avoidable emergency room and hospitalization in the future. This is the key to success in value-based care models.

In any given year, high-cost patients can become low-cost

Without such granular understanding, awareness efforts can be ineffective in reducing the cost of care.

How AI can increase the success of care management

Advanced analytics and artificial intelligence (AI) open up a significant opportunity for care management. Health risks are complex, driven by a wide range of factors that go far beyond your physical or mental health. For example, a person with diabetes is more at risk if they also have a low income and limited access to medical services. Therefore, identifying the needs of patients at risk must consider additional factors to understand those who need care most.

Machine learning (ML) algorithms can evaluate a complex range of variables such as patient history, hospital / emergency room admissions, medications, social determinants of health, and external data to accurately identify patients at risk. It can stratify and prioritize patients based on their risk scores, allowing caregivers to design their outreach to be effective for those who need it most.

On an individual level, an AI-enabled care management platform can offer a holistic view of each patient, including past care, current medications, risks, and accurate recommendations for their future course of action. For the patient in the example above, the AI ​​can provide care managers with HbA1C readings, drug possession ratio, and predictive risk scores to deliver appropriate care at the right time. It can also guide the care manager regarding the number of times he should contact each patient for maximum impact.

Unlike traditional risk stratification mechanisms, modern AI-enabled care management systems are self-learning. When care managers enter new patient information, such as the last hospital visit, medication change, new habits, etc., the AI ​​adapts its risk stratification and recommendation engine for better results. effective. This means that ongoing care for each patient improves over time.

Because payers and providers are reluctant to embrace AI in care management

In theory, the impact of AI in care management is significant: both governments and the private sector are optimistic about the possibilities. Yet, in practice, especially among those who use technology on a daily basis, that is, care managers, there seems to be a certain reluctance. With good reason.

Lack of localized models

To begin with, many of today’s AI-powered care management solutions are not patient-centric. Nationalized models are ineffective for most local populations, busting predictions by a considerable margin. Without accurate predictions, care managers lack reliable tools, creating further skepticism. Carefully designed localized models are critical to the success of any AI-based care management solution.

Not guided by the needs of the care manager

On the other hand, AI today is not even “care manager driven”. A “risk score” or number that indicates the risk of any patient gives little to the care manager. AI solutions need to speak the user’s language so that they feel comfortable with suggestions.

Healthcare is too complex and fundamental to be left to the black box of an ML algorithm. It must be transparent about why each decision was made: there must be an explanation accessible to the end user.

Inability to prove ROI

At the healthcare organization level, AI solutions must also demonstrate ROI. They need to impact the business by moving the needle on its key performance indicators (KPIs). This could include reducing the cost of care, easing the burden of the care manager, minimizing emergency room visits, and other benefits. These solutions must provide healthcare leaders with the visibility they need into hospital operations and delivery metrics.

What is the future of AI in care management?

Despite current challenges and failures in some of the earliest AI projects, what the industry is experiencing are just teething problems. As a rapidly evolving technology, AI is adapting to the needs of the healthcare sector at an unprecedented rate. With continuous innovation and responsiveness to feedback, AI can become the superpower in the armor of healthcare organizations.

Especially in proactive care management, AI can play a significant role. It can help identify patients at risk and offer care that prevents complications or emergencies. It can enable care managers to monitor progress and provide ongoing support without patients ever visiting a hospital to receive it. This, in turn, will significantly reduce the cost of support for suppliers. It will enable patients to lead long-term healthy lives and promote the general health of the population.

Pradeep Kumar Jain is the chief product officer of HealthEM AI.

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