Dr. Lonny Reisman is the founder and former CEO of HealthReveal. He previously served as Aetna’s Chief Medical Officer for six years. Lonny is a recognized leader in health information technology, patient safety and evidence-based medicine and has published numerous clinical, peer-reviewed articles. Prior to his CMO position, Lonny was CEO of ActiveHealth Management. Lonny was also an attending physician at New York Hospital and St. Luke’s-Roosevelt Hospital Center, and was a cardiology fellow at the University of Chicago.
Roger V, Go A, Lloyd-Jones D, Benjamin E, et al. Heart Disease and Stroke Statistics–2012 Update. Circ. 2012; 125(1): e2-e220.
Treating advanced disease of any kind is costly, and candidly, often late. But imagine the impact of identifying high risk patients with chronic disease like diabetes or hypertension and intervening with lifestyle modifications and optimal diagnostics and therapeutics, in advance of dire complications like heart failure, critical limb ischemia or stroke.
Dr. Lonny Reisman believes that the disease burden carried by the elderly is far in excess of what it should be, and that early and optimal management of modifiable risks like obesity, diabetes, hypertension, hyperlipidemia and other chronic risks/diseases, could significantly mitigate the suffering and costs of patients entering Medicare at age 65. Neglect of hypertension would no longer result in stroke, renal failure, and heart failure, along with attendant costs and morbidity. Extending this notion to other chronic diseases and risks would address policy goals around health equity while avoiding suffering and preempting the worst consequences of chronic disease. How then can providers, payers and industry work with individual patients to address this opportunity?
According to Dr. Reisman, in fee for service environments where volume is rewarded, we generally see greater resource utilization that is not evidence based, and therefore not associated with a commensurate improvement in outcomes. Similarly, we often see underutilization of high value services that are not adequately reimbursed. For a patient with low risk and symptoms attributable to a less serious diagnosis than, say, coronary artery disease, a diagnosis of musculoskeletal pain can be arrived at without an array of CV diagnostics administered for reassurance. Similarly, for patients with known coronary disease, established evidence regarding revascularization and pharmacotherapy should be deployed and coupled with suitable follow up. The variability in care that has been well documented across disease states has likely led to avoidable costs and morbidity as patients get older. A system designed to reward high value services to patients across the age continuum, would significantly impact the disease burden CMS encounters as patients become Medicare eligible.
We need to start by screening populations for known modifiable risk factors that are associated with significant morbidity and death. What are we doing about obesity, and associated consequences like hypertension, diabetes, and hyperlipidemia? The Beyond Intervention research revealed that the observed high variability in screening is substantially affected by knowledge deficits among communities and providers regarding prioritization of screening, access to providers, and varying access among providers to the tools needed. For coronary artery disease (CAD) and peripheral artery disease (PAD), common in patients with metabolic derangements, 1 in 4 physicians feel that “lack of technology or equipment to accurately diagnose CAD/PAD” is a key barrier. 1 in 3 healthcare leaders believe that a “lack of standardized approaches for diagnosing CAD/PAD” is a key barrier to accurate diagnosis. Missed therapeutic opportunities, and worse outcomes, naturally occur as a consequence.
Per Dr. Reisman, “there’s no system today that bridges a public health orientation to a level of personalization that considers the social, clinical and financial imperatives impacting a patient.” His point: if we don’t provide personalized care that meets the patient’s preferences, they are likely to ignore advice that is not trusted or simply can’t be followed. As we contemplate the huge volumes of patient data to be considered in personalizing care, Dr. Reisman also wonders if physicians like him, have the analytic capabilities needed to convert those data to personalized interventions. This challenge underscores the opportunity for AI and machine learning to play a transformative role in advancing personalized, and precise, care.
Translating patient data that physicians can trust: Although the adoption of AI tools is gaining momentum, many physicians remain appropriately wary of the reliability of insights generated. Dr. Reisman suggests the need for “open, transparent environments, where data sources, algorithmic content, and applicability to heterogenous populations is considered.” The AI industry has already been plagued by concerns regarding bias, implicit and explicit, which need to be reconciled as we leverage the promise of these technologies.
Perhaps a greater challenge lies in translating insights derived from AI, or the evidentiary standards embedded in guidelines, to the specific context representing individual patients. How are the data needed to depict patient circumstances aggregated from multiple relevant sources like the EMR, claims systems, wearables and implantables, and perhaps most importantly, from the patient directly. Given the complex substrate of data potentially avaialble for every patient, how we translate and deploy with precision, the personalized insights around therapeutics, diagnostics and lifestyle that AI and clinical research have revealed. How are those insights then accommodated by overworked physicians with inadequate workflow systems and patients who may face financial barriers resulting from flawed plan designs that confuse patient skin in the game with the desire to limit utilization of low value services? Where are the incentives for providers to adopt value based care when volume is more richly reward than outcomes?
Dr. Reisman defines pre-emptive care as sitting somewhere between preventive medicine and catastrophic case management. Consider the common scenario of a patient with multiple risk factors like obesity and smoking, or a stable chronic disease like hypertension or diabetes. Without optimal management those risks and “stable’ chronic diseases will advance to irreversible, perhaps catastrophic harms manifesting as end-stage heart, renal, or brain disease. As those patients age they will suffer, and incur exorbitant cost. Why aren’t we anticipating the inevitable deterioration of these patients and in advance of permanent damage, preempting their disease progression?
Fee for service (FFS) continues as the predominant payment mechanism in the US despite years of bemoaning the perverse incentive it drives toward greater volume of services, without adequate reward for better outcomes. More doesn’t mean better, and certainly, the tendency to reward procedures rather than cognitive support for disease prevention and preemption requires adjustment. Rather than focusing on discounts and rebates, our system needs to reward a comprehensive team orientation that leads to greater levels of patient satisfaction and outcomes. Leveraging advanced data analytics and the remarkable progress we’ve seen in preventing and managing disease, we can mitigate the cost increases that have grown inexorably as we combat the tragedy of advanced disease. With properly structured and risk adjusted shared savings arrangements and less burdensome plan designs regarding out-of-pocket costs for patients, incentives can be introduced that will induce patients, providers, and communities to optimize health and control medical cost overruns.