From retail to travel to education, artificial intelligence (AI) has created new efficiencies and experiences that continue to improve daily lives and business workflows. Research shows there is no sign of AI slowing down. In fact, according to a Gartner report in early 2020, 40% of organizations planned to deploy AI solutions by the end of the year. It’s clear that businesses continue to place big bets on AI, so how can healthcare better leverage this technology?
While there are multiple important use cases for AI in healthcare, one application that deserves more attention is encounter management. Today, many healthcare organizations, especially Medicare Advantage (MAO) plans are underutilizing the rich insights from encounter data and associated risk analytics. Why? Some plans are simply stuck in the past. These plans still use multiple, highly manual encounter submission systems where one small error could cause large scale revenue loss. Others still lack the ability to connect encounter, risk and clinical data together to reveal any meaningful insights about populations served. And finally, some health plans are still making the shift to FHIR (Fast Healthcare Interoperability Resources) standards, which is setting back their ability to experience the potential of applying AI to their data sets.
It is time health plans develop a risk adjustment strategy that incorporates AI in encounter submissions to accelerate compliance and risk-adjusted revenue accuracy and improve member outcomes. Below are just a few of the benefits of integrating AI into encounter management systems:
- Smarter clinical analytics for providers to choose the right cost-effective care that improves outcomes.
Imagine if a doctor diagnoses a patient with a heart condition but has no access to prior patient documentation on that condition. That provider could then choose from a few options such as offering in-home care, scheduling a telehealth appointment, inviting the patient into the office, or conducting a retroactive chart review. Each of these options has a specific cost and might produce a different outcome. What should they choose? By integrating AI into an encounter workflow, a health plan can better support the provider with deciding what to do and when to do it based on prior patterns with this patient and a similar population. It’s not about telling the doctor what to do – the AI-enabled recommendations empower the doctor with rich insight to make the best decision for that patient at the right time.
- Improved population health and value-based program performance.
While AI helps improve care for the individual, it also can enhance a plan’s population health and value-based care strategy by making the system more proactive. Today, a member might receive care only when something is wrong, which can be costly. Through smarter encounter management systems, plans can leverage predictive analytics to proactively piece together data to reach out to members before a major healthcare event occurs. AI can do this by linking encounter data directly to financial incentives and better outcomes.
- More efficient workflows.
Today, health plans are challenged to run analytics across multiple lines of business and will need to adapt to the mountain of data that is coming. Any error can be incredibly costly, affect risk adjustment factor (RAF) scores, and ultimately impact plans’ ability to compete in a highly competitive market. Data inconsistencies can also put a strain on an organization’s internal resources, including IT professionals’ valuable time for an error that could have been avoided with smart technology. Rather than setting up processes to find and fix encounter errors, AI allows health plans to process complex data sets more efficiently and effectively with more informed insight.
In a dynamic healthcare market, the health plans that invest in an intelligent encounter management system, driven by AI, will be best positioned to drive better health outcomes and generate revenue. Don’t be the plan catching up – lead the industry forward with a more informed encounter strategy.
Baker is Associate Vice President of Analytics at Edifecs.