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Editorial Spotlights

Written by Anne DeGheest // 14 October 2014 //

Fixing the Health Care System: The Journey to Value-Based Care by Seth Frazier, Chief Transformation Officer Evolent Health

There is broad consensus that the current health care system is under immense pressure, and is especially inadequate for an aging and unwell patient population. With premiums and the cost of delivering care increasing rapidly, and with payment reform well underway, health systems are moving from fee-for-service to a value-based model – managing outcomes and assuming financial risk for populations. There is no question that most health systems are in a tough spot during this fundamental transformation. The industry has one foot in today’s volume-based world, and another in value-based care.

Transformation can’t happen overnight. These changes take time and intensive effort on the part of hospitals and physician groups. But the broad implications are clear – more payments will be derived from value-based formulas, where quality and efficiency of care are rewarded. Those that adapt now will reap great benefits, including the freedom to do what’s right for the patient over the long-term. Those that “wait and see” may stand to find access to patients and/or capture of the premium dollar highly restricted.

As a long-term operating partner to leading health care systems, Evolent Health provides the technology, platforms and on-the-ground resources needed to execute on clinical and financial transformation objectives. Health systems require an efficient and scalable approach to infrastructure and analytics tools, and to established expertise on how best to make the shift. Evolent’s role is to provide a tailored payer-neutral and clinician-centric solution – helping its partner systems thrive under a new clinical and business model.

Engaging the physician enterprise is essential to the success of any value-based care model. Physicians, whether affiliated or employed, must be skillfully aligned not only as part of the effort but as its leaders. Enlisting the physician enterprise involves extending physician reach and re-engineering incentives. A fruitful model outfits the physician with better information, support and tools so they can engage with patients in a meaningful way, and vice versa. It is this relationship that is and will remain central to delivering on the promise of value-based care.

As providers look to improve the quality and experience of care, they must also carefully determine how they can take on financial risk for populations in a sequenced, safe manner. A natural starting point is with an organization’s own employees. Starting this way can deliver immediate benefit, and demonstrate executional skill to the market against future vertical integration plans. Providers should also look to structure value-based contracts as a demonstrated approach to growing total risk lives rapidly, and to capturing the returns generated from effective population health management.

Applying a proven model, one that allows rapid mobilization from a standing start, is critical as organizations look for ways to navigate from an uncertain health care present to a brighter, value-based tomorrow that functions better for patients, providers and the community at large.

Adding Efficacy to Large Scale Diabetes Management By Rick Altinger, CEO Glooko

At Glooko, our mission is to lower costs and create positive outcomes for patients, health systems, and payers through the right combination of patient engagement, provider engagement and population management analytics. We aim to deliver the right care solution, by the right person, at the right time and at the right cost.

Our first challenge is to make things easier for people with diabetes. What we're solving for is finding and helping the high-risk, high-dollar patient - the patient that has diabetes complications and needs more than just two to three face-to-face visits to a clinic per year. To do that, we created a platform that connects to all the major glucose meters and different Android and Apple devices. We are bringing that data together, putting it in the cloud, integrating it into EHR/Care management systems, and then letting providers and payers drill down on their at-risk patients via the Glooko Population Tracker. In addition to connecting the patient to their healthcare team (e.g. physicians, nurses, family members), Glooko also efficiently engages the patient by leveraging their mobile device.

The meter to mobile device connection occurs through the Glooko MeterSync Blue, featuring Bluetooth Smart and our FDA-cleared technology, which provides health systems and payer groups the ability to more cost-effectively enroll diabetes patients into remote monitoring programs without the need for patients to switch to more costly glucose meters. Instead, health systems and payer groups can leverage the tens of millions of meters already deployed to patients worldwide.

From there, we’re transforming the way patients and their healthcare team interact and manage their diabetes. We facilitate remote monitoring by delivering algorithms and flags for providers to easily identify patients experiencing potentially harmful amounts of hyperglycemic (high blood glucose) or hypoglycemic (low blood glucose) events in between clinic visits. By allowing patients to wirelessly download their trusted blood glucose readings, the Glooko diabetes management platform opens the door for health systems and payers to deliver more real-time reminders and recommendations to patients with diabetes. With data also coming from connected blood pressure cuffs, scales and activity devices, (e.g., Fitbit, Moves, etc.), Glooko can also offer additional algorithm-based solutions to help physicians and patients manage the constant dose changes that is inherent in increasingly complex insulin prescriptions.

Moving from Descriptive Data to Predictive Data By Murray Brozinsky, Chief Strategy Officer Healthline

Data analytics in healthcare has come a long way. However, most data and quality reporting tools often don’t go beyond simply reporting outcomes. Measuring outcomes is important, but to really impact healthcare and bend the cost curve, we need to go from understanding what has happened to understanding what will happen.

In order to get to this next stage of predictive healthcare where clinicians can identify risk and deliver targeted interventions before a patient gets sick, we need to take a comprehensive look at both structured and unstructured patient data – not only the clinical information provided in a patient record but also any relevant non-clinical data (i.e., psycho-social, socioeconomic and environmental factors) often found in unstructured formats, such as free-text physician notes, patient histories and hospital admission notes. It’s estimated that 80 percent of today’s health data is in unstructured formats and therefore not being leveraged by current analytics solutions. Clinical natural language processing (NLP) technologies, combined with rich health taxonomies and rules engines that intelligently map medical concepts to clinical terminology, can help clinicians tap into valuable information locked in unstructured data.

This would considerably enhance the power of predictive factors to stratify patient populations by risk level so clinicians can effectively identify those patients who are at high-risk for certain conditions and take appropriate actions. Take Congestive Heart Failure (CHF) for example – CHF is one of the seven conditions that account for 30 percent of potentially preventable readmissions and classified as a “high avoidable readmission” currently penalized by the CMS. Having the ability to “read” the narrative information and use the insights to more accurately identify CHF patients at high risk of being readmitted to the hospital is the first step in matching the right care team with the right interventions to the right patients.

It comes down to being able to turn relevant data into actionable insights that will allow clinicians to make better, more informed decisions and ultimately make a difference in patient outcomes.



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