Posts Tagged ‘rising risk’

Use Annual Wellness Visit to Screen for Social Determinants of Health in High-Risk Medicare Population

December 13th, 2016 by Patricia Donovan

The social determinant of social isolation carries the same health risk as smoking, and double that of obesity.

With about a third of health outcomes determined by human behavior choices, according to a Robert Wood Johnson Foundation study, improving population health should be as straightforward as fostering healthy behaviors in patients and health plan members.

But what’s unstated in that data point is that the remaining 70 percent of health outcomes are determined by social determinants of health—areas that involve an individual’s social and environmental condition as well as experiences that directly impact health and health status.

By addressing social determinants, healthcare organizations can dramatically impact patient outcomes as well as their own financial success under value-based care, advised Dr. Randall Williams, chief executive officer, Pharos Innovations, during Social Determinants and Population Health: Moving Beyond Clinical Data in a Value-Based Healthcare System, a December 2016 webinar now available for replay.

“The challenge is that few healthcare systems are currently equipped to identify individuals within their populations who have social determinant challenges,” said Dr. Williams, “And few are still are structured to coordinate both medical and nonmedical support needs.”

The Medicare annual wellness visit is an ideal opportunity to screen beneficiaries for social determinants—particularly rising and high-risk patients, who frequently face a higher percentage of social determinant challenges.

Primary social determinants include the individual’s access to healthcare, their socio- and economic conditions, and factors related to their living environment such as air or water quality, availability of food, and transportation.

Dr. Williams presented several patient scenarios illustrating key social determinants, including social isolation, in which individuals, particularly the elderly, are lonely, lack companionship and frequently suffer from depression. “Social isolation carries the same health risk as smoking and double that of obesity,” he said.

While technology is useful in reducing social isolation, studies by the Pew Research Center determined that segments of the population with the highest percentage of chronic illness tend to be least connected to the Internet or even to mobile technologies.

“Accountable care organizations (ACOs) and other organizations managing populations must continue to push technology-enhanced care models,” said Dr. Williams, “But they also have to understand and assess technology barriers and inequalities in their populations, especially among those with chronic conditions.”

In another patient scenario, loss of transportation severely hampered an eighty-year-old woman’s ability to complete physical rehabilitation following a knee replacement.

Dr. Williams then described multiple approaches for healthcare organizations to begin to address social determinants in population health, including patients’ cultural biases, which may make them more or less open to specific care options. This fundamental care redesign should include an environmental assessment to catalog available social and community resources, he said, providing several examples.

“This is not the kind of information you’re going to find in a traditional electronic health record or even care management platforms,” he concluded.

Health Risk Stratification Model: How Well Do You Manage ‘Falling Risk’ Populations?

November 3rd, 2015 by Patricia Donovan

Health risk stratification is scalable, according to the experience of Ochsner Health System, whose scaling and centralization of risk stratification and care coordination protocols across its nine-hospital system drive ROI and improve clinical outcomes and efficiency.

Here, Mark Green, system AVP of transition management at Ochsner Health System, explains why health plans and providers need better control over ‘falling risk’ patients.

Regardless of your patient population, no matter how small or large, you’ve got a segment of your population that are healthy patients. You’ve got a certain percentage, about 40 percent, who are at very low risk.

About 20 percent of your population falls into a ‘rising risk’ segment. Those are patients with chronic diseases who are somewhat adherent and compliant. You’ve got some that are newly diagnosed with depression, and a comorbidity. Then you’ve got this very top 3 to 5 percent, which are your poorly controlled multiple comorbidities that need your absolute highest touch, whether it’s through complex case managers or other programs that are the highest touch of those patients.

That is the typical model in the United States where you see this segmentation. In this country, we do a relatively good job of understanding ‘rising risk’ patients. Those are your patients that are diabetic, and suddenly you see their A1C go out of control. You know they’re going off-track for some reason, whether it’s compliance, adherence, needing medication adjustments, or some other social interactions happening outside your care model. These are your ‘rising risk’ patients.

As the country begins to understand this risk stratification, it understands the ‘falling risk’ patients, too. For example, we had a congestive heart failure (CHF) clinic that was pretty successful in managing patients; they had approximately 100 patients in their CHF clinic. They were taking these complex CHF patients and sending them through education and hooking them up with complex case managers. Pretty soon they filled their entire clinic up and didn’t have any more access for new patients. It failed pretty quickly because they weren’t able to churn these patients.

As we began to do a root cause analysis of why this happened, to understand why we didn’t see the sustainability in this program, we realized it was because we never moved patients down that risk stratification model. We kept them in there forever. We received them, we managed them and we got them better. But we never moved them down, so we never had room for another newly diagnosed, out-of-control CHF patient.

That’s a really critical step to understand: managing not only your rising risk but also your falling risk patient population within the sub-categories of your overall risk segmentation. It’s a living organism moving in and out of these different components.

Source: Scalable Models in Health Risk Stratification: Results from Cross-Continuum Care Coordination

http://hin.3dcartstores.com/Rethinking-Readmissions-Patient-Centered-Collaborations-in-Care-Transition-Management_p_4646.html

Scalable Models in Health Risk Stratification: Results from Cross-Continuum Care Coordination explores Ochsner’s approach, in which standardized scripts, tools and workflows are applied along the care continuum, from post-hospital and ER discharge telephonic follow-up to capture of complex cases for outpatient management.