Leveraging Live Data & Analytics Beyond Point-of-Care

Mike Maseda
by Mike Maseda
2024-06-10

In the ever-evolving landscape of healthcare, real-time data and advanced AI models are transforming the way we approach patient care. The request we’ve heard from clinicians over the years is for up-to-date risk predictions at ‘the point of care’ (PoC). If doctors have the breadth of all information up to that time, then they can provide the best care for that scenario. While that may be true, there are many more areas in healthcare where ‘just-in-time’ data can make a huge difference.

Point of decision (PoD) care takes into account the periods in the care continuum, separate from patient visits, that can have a large effect on the kind of care patients receive. Below we’ll identify how data, like GenHealth.ai’s predictions, can be used at the PoD to improve outcomes. The main use cases we’ve identified include prior authorization, enrollment, referral management, and hospital discharge.

Prior Authorization

Prior authorization is a critical step in ensuring that patients receive appropriate treatments without unnecessary delays. Traditionally, this process can be time-consuming and frustrating for patients, providers, and even health plans. At GenHealth.ai, we’ve developed AI Automated Prior Auth & built-in our medical predictions. By predicting futures to see what outcomes and costs would look like for both approval and denial of the prior auth, we can help health plans make better decisions faster.

This is on top of automatically matching medical policy rules with incoming requests, minimizing clicks, and reducing the need to enter the same information multiple times for prior auth reviewers.

Health Plan Enrollment & Plan Selection

Patient enrollment in health insurance often involves navigating complex eligibility criteria, administrative hurdles, and understanding plan design. AI-driven predictions can simplify this process by quickly projecting spend and medical events that a patient might have in the future and recommending the appropriate plan. If you’re young, healthy, and GenHealth’s LMM does not predict any high-cost events in the next year, you can feel better about choosing a less benefits-rich plan, which will save you money in the long term since you will be a low utilizer that year.

For employers, understanding projected spend at the population level is critical. Having the right stop-loss policy for level-funded employers can drastically change the amount of company cash that is at risk during the year. By analyzing the population and assigning employees to different groups, you can make sure to offer the plans that fit the needs of all your employees.

GenHealth.ai’s algorithms can sift through vast amounts of patient data to pinpoint those who are most likely to benefit from specific programs or trials, ensuring that patients receive timely and appropriate care options. This targeted approach improves the efficiency of enrollment processes and enhances the overall patient experience.

Referral Management

Effective referral management is essential for ensuring that patients receive the right care at the right time. What if you could find the best specialist for managing your complex patient’s specific needs? That patient who is polychronic and has an uncommon femur fracture – how do you know which surgeon will provide the best care? We can now simulate that with GenHealth.ai. Our LMM can simulate multiple futures to understand the outcomes and costs associated with each surgeon. By ensuring that patients are referred to the most appropriate specialists promptly, healthcare systems can enhance care coordination and patient satisfaction.

Hospital Discharge

Hospital discharge planning is a complex process that requires careful consideration of a patient’s post-discharge needs to prevent readmissions and ensure a smooth transition to home or another care setting. AI predictions can assist in identifying patients at high risk of readmission and suggest tailored discharge plans to mitigate these risks.

There are also those patients where it is unclear if they’ll benefit from a specific medical service. With GenHealth.ai’s models, you can simulate follow-up appointments, home care services, or other interventions. This proactive approach to discharge planning not only improves patient outcomes but also reduces the strain on hospital resources by lowering readmission rates.

Conclusion

The integration of real-time data and advanced AI models at the point of decision (PoD) represents a significant advancement in healthcare delivery. By harnessing the predictive power of AI, healthcare providers can make more informed decisions, streamline administrative processes, and ultimately improve patient outcomes. GenHealth.ai’s cutting-edge AI solutions are at the forefront of this transformation, offering innovative tools to enhance prior authorization, enrollment, referral management, and hospital discharge processes. As healthcare continues to evolve, the ability to make data-driven decisions will be key to providing high-quality, efficient, and patient-centered care.


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