How to leverage AI to boost care management success

WhatsApp
Telegram
Facebook
Twitter
LinkedIn
Pinterest
Reddit


Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.


Sixty percent of American adults live with at least one chronic condition, and 12% with five or more. They spend exponentially more on healthcare than those without any chronic conditions. For instance, 32% of adults with five or more chronic conditions make at least one ER visit each year. On top of that, 24% have at least one inpatient stay, in addition to an average of 20 outpatient visits — up to 10 times more than those without chronic conditions. In fact, 90% of America’s $4 trillion healthcare expenditures are for people with chronic and mental health conditions, according to the Centers for Disease Control and Prevention (CDC).

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. Care managers proactively reach out to patients under their care and offer preventative interventions to reduce hospital ER admissions. Despite their best efforts, many of these initiatives provide suboptimal outcomes.

Why current care management initiatives are ineffective

Much of care management today is performed based on past data

For instance, care managers identify patients with the highest costs over the previous year and begin their outreach programs with them. The biggest challenge with this approach, according to our internal research, is nearly 50-60% of high-cost patients were low-cost in the previous year. Without appropriate outreach, a large number of at-risk patients are left unattended with the reactive care management approach. 

Event

MetaBeat 2022

MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.


Register Here

The risk stratification that the care management team uses today is a national model

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

The care management team’s primary focus is chiefly on transition of care and avoiding readmissions

Our experience while working with different clients also points to the fact that readmissions contribute only 10-15% of total admission. The focus on proactive care management and avoiding future avoidable emergency room and hospital admission is lacking. This is key to success in value-based care models.

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

Without such granular understanding, outreach efforts can be ineffective in curbing the cost of care.

How AI can boost care management success

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 well beyond just one’s physical or mental health. For example, a person with diabetes is at higher risk if they also have low-income and limited access to medical services. Therefore, identifying at-risk patients’ needs to consider additional factors to encompass those most in need of care.

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

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

Unlike traditional risk stratification mechanisms, modern AI-enabled care management systems are self-learning. When care managers enter new information about the patient — such as latest hospital visit, change in medication, new habits, etc. — AI adapts its risk stratification and recommendations engine for more effective outcomes. This means that the ongoing care for every patient improves over time.

Why 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 bullish on the possibilities. Yet, in practice, especially among those who use the technology every day, i.e., care managers, there appears to be reluctance. With good reason.

Lack of localized models

For starters, many of today’s AI-based care management solutions aren’t patient-centric. Nationalized models are ineffective for most local populations, throwing predictions off by a considerable margin. Without accurate predictions, care managers lack reliable tools, creating further skepticism. Carefully designed localized models are fundamental to the success of any AI-based care management solution.

Not driven by the care manager’s needs

On the other hand, AI today is not ‘care manager-driven’ either. A ‘risk score’ or the number indicating the risk of any patient gives little to the care manager. AI solutions need to speak the user’s language, so they become comfortable with the suggestions. 

Healthcare delivery is too complex and critical to be left to the black box of an ML algorithm. It needs to be transparent about why each decision was made — there must be explainability that is accessible to the end-user. 

Inability to demonstrate ROI

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

What is the future of AI in care management?

Despite current challenges and failures in some early AI projects, what the industry is experiencing is merely teething troubles. As a rapidly evolving technology, AI is adapting itself to the needs of the healthcare industry at an unprecedented pace. With ongoing innovation and receptiveness 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 at-risk patients and offer care that prevents complications or emergencies. It can enable care managers to monitor progress and give ongoing support without patients ever visiting a hospital to receive it. This will, in turn, significantly reduce the cost of care for providers. It will empower patients to lead healthy lives over the long term and promote overall population health.

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

DataDecisionMakers

Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation.

If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers.

You might even consider contributing an article of your own!

Read More From DataDecisionMakers



Source link

Share it with your friends

WhatsApp
Facebook
Twitter
LinkedIn
Pinterest
Reddit
Crowded Hell

Crowded Hell

Leave a Reply

Your email address will not be published.