By Linda Hand, CEO, Prealize Health
Proactive healthcare has become increasingly important, especially as long-standing health challenges have been exacerbated by the COVID-19 pandemic. Through the use of predictive analytics leveraging artificial intelligence (AI) and machine learning techniques, plans and providers can mitigate these challenges by identifying who is most at risk of health problems and how to best engage them before a health concern arises. This in turn will improve health outcomes and provide cost benefits, while reducing the demand for healthcare services, risk of disease, and health inequities.
Treating Mental and Behavioral Health
The need for behavioral healthcare services has increased exponentially due to COVID-19 and its impact on daily life. At the same time, access to necessary care has not kept pace with the demand. The gap between access and demand will continue to grow to dangerous levels this year, unless payers and providers find new ways to optimize the limited behavioral healthcare resources currently available. Health leaders are leaning on AI and machine learning technology to do just that. By leveraging a variety of data sources, including claims, clinical, and social determinants of health, patients at risk for behavioral health problems and those who would benefit from outreach can be identified earlier. In fact, at Prealize we’ve been able to find 36% more members at risk of mental and behavioral health issues before the diagnosis even hits the claim. Early identification leads to more proactive interventions, which in turn lessens the growing gap between need and supply, as engaging patients early can help prevent their conditions from escalating.
Meeting Shifting Member/Patient Expectations
As patients’ needs from their plans and providers increase, the healthcare industry must make a significant shift in how it approaches patients. We must not only focus on meeting consumers’ expectations and needs when they present with symptoms, but also on identifying each individual patient’s needs earlier (before even the patient recognizes a health problem is on their horizon) and creating a personalized outreach and care plan. Plans and providers are shifting from a reactive to proactive approach using AI and machine learning technology to get out in front of patients’ healthcare problems sooner—and engage them more effectively in their care. These analytics also identify the best engagement channels and next best action for each patient, optimizing resource allocation and reducing costs. At Prealize, we’ve enabled 12 months of communications lead time to target outreach and drive action, which has resulted in healthier and happier patients who feel understood and prioritized by their plans and providers.
Improving Health Equity
As payers and providers work to expand healthcare access, improving health equity must be a key element of all initiatives. This has become more apparent due to the pandemic, during which certain populations, including racial and ethnic minorities, have experienced disproportionately worse health outcomes and have higher rates of substance use and suicidal ideation. While there are many approaches to how we can close the care gaps exacerbated by the pandemic that the industry should consider, AI and machine learning technologies must play a key role. These tools can identify individuals at risk of clinical and nonclinical risk factors, such as social determinants of health, that can prevent individuals from receiving the care and services they need. They can also identify how to best engage individuals for better outcomes. That insight is a key asset for plans and providers committed to improving equity and ensuring access to all, by enabling them to identify underserved patients and match them to the treatment approaches that best meet their needs.
These three challenges share a common bond: They can all be mitigated through targeted, early interventions. At Prealize, we know that these interventions will be key for payers and providers to improve health outcomes efficiently and effectively. In fact, by identifying those individuals at highest risk and most likely to respond to engagement, we’ve been able to help health plans increase engagement by 19%, reduce emergency department utilization by 9%, reduce inpatient care by 7%, and reduce overall healthcare spending by 7%. We know the benefits don’t end there. By investing in proactive care methods now, plans and payers can help reduce fundamental challenges in healthcare.