Health plans can reduce costs, with the focus on increasing risks
With the rise in operating costs, significant shifts in Medicaid, and other financial pressures on the health care system, forecasting revenue and effectively allocating resources to health programs has never been more important than today. It is expected that this will never be so challenging in the future.
Health programs have been gradually deploying AI programs and complex analytics for years to improve programs while reducing costs and mitigating financial risks.
But, due to today’s challenges, a step-by-step approach has become a luxury. According to McKinsey, health plans should speed up.
On the AI side, healthcare organizations need to jump in with their feet, as mitigation of risks quickly becomes an existing business problem. Machine learning quickly went from a good choice to an essential one.
According to McKinsey, AI solutions can save $150 million to $300 million in administrative expenses, $380 million to $970 million in medical expenses, and increase revenue by $260 million to $1.24 billion per payment.
Health programs should no longer debate whether to invest in AI and automation, but must completely shift the focus on how to strategically deploy these technologies.
Some health plans are beginning to see AI realize the benefits of predictive analytics, when the program is combined with clinical BPO services, adding expert decisions to care management, providing them with scalable ways to improve members’ health outcomes while helping cut costs. AI-driven nursing coordination simplifies the complex workflow for many payers.
An example of this innovative approach is to better identify and intervene people who are not classified as medically high demand or costly, and they are becoming such people.
The patient population is at increased risk due to rising rates of heart disease, obesity, asthma and mental health. For payers, mastering the rising risk of early clinical support and intervention risks will be a success or failure for payers as the healthcare system continues to cope with increased cost pressures.
Understand the Risk Risks
Patients based on data analysis divide patients into high, medium and low-risk groups have always been the basis for health care for providers and payers. By ensuring that patients get the highest risk they need, this is what ensures the best allocation of resources.
However, by using new technologies to identify and quickly identify medical interventions for dangerous patients, many healthcare organizations may gain more value from the risk stratification process.
Traditionally, insurers have classified patients into risk categories based on claims data, which analyses healthcare utilization after providing and paying for services. This means that health plans are viewing key decisions in the rearview mirror. Now, new technologies and improved access to clinical records are enabling people to expect plans, which is increasingly necessary in the healthcare industry.
The earlier the risk patients are identified, the earlier they are, the health plan can implement strategies to prevent or slow disease progression, reduce travel to hospitals, and reduce long-term costs.
By adding advanced data analytics and AI, risk stratification shifts from responsiveness to prediction. Health plans that have accepted this approach can detect early indicators of the disease before expensive interventions are needed.
The new predictive model analyzes not only authorization data in the EHR data, but also previous authorization trends, historical diagnosis and previous treatment steps, and real-time clinical inputs to identify patients who are most likely to see worsening in their health conditions.
But determining the risk is only a benefit. Taking this insight opens up a range of cost-saving opportunities for healthcare organizations.
Solve the problem
Understanding rising risks patients lays the foundation for active healthcare, which can help patients, families and organizations that care about them.
By combining clinician-led care coordination, automated prior authorization, remote patient monitoring, AI-driven alerting, and other new technologies and approaches, health plans and other managed care organizations can improve access to the most impactful and necessary care, especially for patients with chronic diseases who may become ill over time.
This approach enables health programs to identify high risk or become high risk, create and tailor-made interventions in real time, and adopt strategies to stand out in an escalating health situation.
The ultimate value proposition is complex, automatic support for proactive decisions. Combining predictive insights with an automated prior authorization workflow can be approved without delay by ensuring approval of critical services (expert recommendations, diagnostic tests, and medications). As a result, risk-risk program members can get the care they need to prevent health deterioration, which means better health.
Health plans that have not experienced AI benefits may wonder how to achieve a greater impact at a lower cost than traditional health plans care management plans.
For many, AI-enabled Clinical Business Process Outsourcing (BPO) is Linchpin and can make a model fixed and predictable. Clinical BPO combines clinician expertise and critical management services with AI-enabled population health management platforms to create forward-looking plans for risk management growth. Combining these features can provide population health management services for the administrative costs of the program and the medical costs of the program at a fixed PMPM cost for individuals they manage in these programs.
The benefits of BPO for any risky managed care organization include:
- Obtain clinical expertise in multiple specialties
- Automatic nursing management process
- Agent AI and Predictive Modeling
- Reduced administrative costs
- Share health care cost risks in guaranteed performance arrangements
It is imperative for healthcare organizations that decide to mitigate risks, adopting an active AI support approach. There is no indication that the cost pressures that plague health care will be reduced soon.
Understanding risk is an important first step in controlling it, and rising risk is an area where many health plans need to take action.
Photos: Champion, Getty Images
David Hamilton is the chief growth officer of Zyter | Trucare, who is a leading strategic plan to drive business growth, expand market operations and strengthen partnerships with key payers and provider organizations. With extensive leadership experience in organizations such as Randstad Digital, Datavant/Ciox, DXC/Gainwell and Cognizant, David provides deep expertise in healthcare technology, services and business process solutions.
David’s leadership focuses on enhancing healthcare data interoperability, risk adjustment strategies and collaboration provided by payers to ensure that organizations effectively lead to regulatory changes and operational complexity. At Zyter | Trucare, he leverages this background to provide impactful solutions designed to improve connectivity, simplify management processes, and enhance patient-centered care.
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