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How to leverage AI to boost care management success

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Sixty percent of American adults are living with at least one chronic condition, and 12 percent are living with five or more. They spend exponentially more on healthcare than people without chronic conditions. For example, 32% of adults with five or more chronic conditions make at least one emergency room visit each year. Additionally, 24% have at least one inpatient visit, in addition to an average of 20 outpatient visits — up to 10 times more than those without chronic conditions. According to the Centers for Disease Control and Prevention (CDC), 90% of the US $4 trillion in healthcare spending goes to people with chronic and mental illnesses.

The fundamental way healthcare organizations reduce these costs, improve 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 conditions manage their health. Nurse managers proactively reach out to the patients they care for and offer preventative interventions to reduce the number of hospital admissions to the emergency department. Despite best efforts, many of these initiatives deliver suboptimal results.

Why current care management initiatives are ineffective

Much of today’s care management is based on data from the past

For example, care managers identify patients with the highest costs in the past 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 had low costs in the prior year. Without proper outreach, large numbers of at-risk patients are left unattended with the reactive care management approach.

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

These models are not localized, so understanding the social determinants of individual locations is not considered.

The main focus of care management is mainly on the care transition and the avoidance of readmissions

Our experience working with various clients also indicates that readmissions account for only 10-15% of the total intake. There is a lack of focus on proactive care management and avoiding future avoidable emergency admissions and hospital admissions. This is the key to the success of value-based care models.

Costly patients can become cost-effective patients every year

Without such a granular understanding, outreach efforts to contain care costs may be ineffective.

How AI can increase success in care management

Advanced analytics and artificial intelligence (AI) open up a significant opportunity for care management. Health risks are complex and driven by a variety of factors that go well beyond physical or mental health. For example, a person with diabetes is at greater risk if they also have a low income and limited access to medical services. Therefore, when determining the needs of at-risk patients, additional factors must be considered to include those most in need of care.

Machine learning (ML) algorithms can evaluate a complex set of variables such as patient history, previous 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 care managers to tailor their outreach to be effective for those who need it most.

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At an individual level, an AI-enabled care management platform can provide a holistic view of each patient, including their past care, current medications, risks, and accurate recommendations for their future course of action. For the patient in the example above, the AI ​​can arm care managers with HbA1C readings, medication ownership percentage, and predictive risk scores to ensure appropriate care at the right time. It can also guide the care manager on how often to contact each patient for maximum impact.

In contrast to conventional risk stratification mechanisms, modern AI-supported care management systems are self-learning. When care managers enter new information about the patient – such as E.g. last hospital visit, medication change, new habits, etc. – the AI ​​adjusts its risk stratification and recommendation engine to provide more effective results. This means ongoing care for each patient improves over time.

Why payers and providers are reluctant to use AI in care management

In theory, the impact of AI on care management is significant – both governments and the private sector are optimistic about the possibilities. In practice, however, there seems to be reluctance, especially among those who use the technology on a daily basis, i.e. the care managers. For good reason.

Lack of localized models

First of all, many of today’s AI-based care management solutions are not patient-centric. Nationalized models are ineffective for most local populations and throw predictions by a considerable margin. Without accurate predictions, care managers lack reliable tools, leading to further skepticism. Carefully designed localized models are fundamental to the success of any AI-based care management solution.

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Not driven by the needs of the care manager

On the other hand, AI today is not “care manager-driven” either. A “risk score,” or the number that indicates a patient’s risk, means little to the care manager. AI solutions need to speak the user’s language so that they become familiar with the suggestions.

Healthcare is too complex and critical to be left to the black box of an ML algorithm. There must be transparency as to why each decision was made – there must be explainability that is accessible to the end user.

ROI cannot be proven

At the organizational level in healthcare, AI solutions must also demonstrate an ROI. They need to influence the company by moving the needle on its Key Performance Indicators (KPIs). This could include reducing care costs, relieving 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 does the future of AI in care management look like?

Despite current challenges and failures in some early AI projects, the industry is only experiencing teething problems. As a rapidly evolving technology, AI is adapting to the needs of the healthcare industry at an unprecedented pace. With continued innovation and receptivity to feedback, AI can become a superpower in the armor of healthcare organizations.

AI can play an important role, especially in proactive care management. It can help identify at-risk patients and provide care that prevents complications or emergencies. It can allow care managers to monitor progress and provide ongoing support without patients ever having to visit a hospital to receive them. This, in turn, will significantly reduce the cost of care for providers. It will empower patients to lead healthy lives in the long term and promote the general health of the population.

Pradeep Kumar Jain is Chief Product Officer at HealthEM AI.

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