By Robert Fuller,
Managing Partner, Healthcare at Clarity Insights. Bob is a passionate leader and business-driven technologist who leverages a collaborative approach to deliver deep insights and measurable results. Bob has provided business and technology consulting services for 30+ years to Fortune 500, middle market and start-up companies spanning many industries, including Healthcare, CPG, Financial Services, Manufacturing, and Automotive. Bob has led global teams of more than 250 consultants delivering business-driven analytic solutions with significant financial results. For the past 15 years, Bob has focused on solving complex healthcare challenges by delivering data and analytics solutions.

Artificial intelligence offers many benefits to the healthcare sector – from improved patient experience to more effective decision support for providers – so it’s no surprise that the Artificial Intelligence and Cognitive Computing Systems in the Healthcare market is predicted to continue to grow at 40 percent CAGR through 2021, according to Frost and Sullivan

Since the first year Frost and Sullivan began this prediction – 2016 – a lot has changed. While the sociopolitical environment has raised uncertainty surrounding the ACA, innovation in artificial intelligence has attempted to shine a light on its merits – amidst initial skepticism across various levels of adoption – as it learns and evolves over time. Even the critics are becoming more optimistic, with AI in healthcare funding reaching an historic high in Q2 2019. As artificial intelligence continues to advance, where can it have most immediate impact? In what ways will technology create the most enduring change? 

Many start-ups have recently focused their energies on value-based care improvements, building momentum and illuminating the paths of potential for AI. While data integration and visibility between emerging applications creates new challenges altogether, the emphasis on value-based care is valid: it is ripe with opportunities to see how real-time information can alter the choices made by key stakeholders to make healthcare less painful for all parties.

While the concept is not new, recent investments and the reality of implementation and optimization is as relevant now as it ever was. And now that the hype has worn off, the real work to make value-based care live up to its promise is of utmost importance to everyone involved. Because value-based care at its most basic level seeks to put the patient back at the center of industry decision-making, its advantages speak for themselves: but it relies on data to make connections and to categorize information differently to measure success. You could say that the secret behind how AI is driving better patient care is really no secret at all - just a mix of what we’ve known about from the beginning. 

Incentives are at the heart of enforcing value-based care, creating alignment between patients, payers and providers by shedding fee-for-service excess. But how can quality of care be quantified? Those thinking of the logic of this policy and care delivery model shift are smart to understand its benefits; even so, the system changes require the greatest of care, as well. IT continues to be at the heart of these transitions and their ongoing monitoring, compliance and efficacy.  

Data requires intelligent analysis to keep the benefits of value-based care at the forefront and to allow the systems to operate without shortsighted changes made in the years following ACA rollout hindering long-term performance. And this is where AI can add value to value-based care: ensuring goals are met, automating processes, using algorithms to match physician behaviors to patient outcomes, reimbursement model optimization and more. 

At Clarity Insights, we continue to see trends in how data analytics can improve the patient experience, with key metrics that match value-based care aspirations. Whether reducing length of stay, improving treatment recommendations, or easing the enrollment process (which in turn can translate into higher patient retention and churn reduction for insurers), deep analytics makes new evaluations possible. Artificial intelligence extends the impact of data analytics, creating automated alerts and learning from adjacent sources for patient information that can influence anything from preventive care to patient follow-up to avoid readmission.

And while the patient and insurance benefits of value-based care may be most obvious, AI is equipped to give providers the additional tools they need so that they can meet new care quality requirements while receiving additional assurance that their recommendations have been vetted extensively against the most recent studies, existing patient records, and other environmental factors. Given the demands and problems with modern healthcare, a physician or researcher would never have the time to explore all of the data points that AI can process in moments. In an era where provider burnout is a sign of the times, support for care teams is a crucial way that AI can be a part of the solution.

Want to learn more about how data analytics, AI and change management can create new opportunities for your healthcare organization? Let’s talk

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