Archive for the ‘Risk Stratification’ Category

New Population Health Management Strategy for ‘Emergent-Risk:’ Arrest Trajectory of Compounding Conditions, Cost

June 11th, 2015 by Adam Kaufman, PhD, President & CEO, Canary Health

Adam Kaufman, PhD, president and CEO, Canary Health


For many years, healthcare organizations have invested in two approaches to population health management: First, wellness management for healthy populations who want to prevent illness, and second, disease/case management for the very sick who must adhere to physicians’ prescribed medical care.

Now, organizations are filling the gap between the healthy and very sick by investing in the ’emergent-risk’ population—adults with one or more pre-chronic or early-stage chronic conditions. Population goal: arrest the trajectory of compounding chronic conditions that compound declines in quality of life and compound increases in cost of care.

Consumer Engagement with Digital Health Self-Management

For many years, the natural worsening of chronic illness has been the focus of academic institutions, a few digital health innovators, and pioneering health plans. As a result of these efforts, a new class of digital health programs is now proven to arrest the trajectory of chronic illness. It started when Stanford’s Patient Education Research Center discovered that self-efficacy (a person’s belief in his or her own ability to achieve goals) had the strongest correlation to improved health outcomes. With this discovery, the concept of health self-management was born. Thus began the drive to engage and impact consumers.

Over the years, healthcare organizations across the United States began offering Stanford’s in-person workshops with notable improvements in health and with measurable reductions in cost of care. According to Stanford research, participants reduced pain, fatigue, depression, and A1C—and reduced ER visits and days in the hospital for up to one year.1 While results were impressive, the programs had one drawback for managers of population health: due to the cost and complexity of in-person delivery, the programs were hard to scale.

As web-based technologies advanced, the research community began to search for ways to harness digital innovation to scale evidence-based programs. In 2006, researchers at the University of Pittsburgh tested the first digital self-management program aimed at diabetes prevention. This digital translation2 of the NIH’s Diabetes Prevention Program3 (DPP) delivered outcomes that mirrored those achieved with the DPP’s in-person, self-management intervention—but at a fraction of the cost.

Data-Driven Insight: The Compounding Effect

As health plans began to adopt digital health self-management, data revealed deeper insights into individuals with prechronic and early-stage chronic conditions. Data from years of Canary Health research with a pioneering health plan shows that chronically ill patients add, on average, a new chronic condition every two to three years. These compounding conditions drive compounding increases in the cost of care—specifically in the areas of pharmacy, medical equipment, and outpatient care.

And without intervention, according to Advisory Board, each year 15 to 20 percent transition to the high-risk population of very sick individuals who require high-cost medical care. This trajectory of chronic illness translates into an additional $1,000-$3,500 in expenses per person, per year. With 80 million adults in this population, that’s an additional $80-$280 billion in costs each year to the U.S. healthcare system. If healthcare leaders don’t prevent this compounding effect, both health plans and providers will hit a financial tipping point where the cost of care puts both margins and mission at risk.

Proven Outcomes: Arresting the Trajectory of Chronic Illness

As health plans began to measure the ROI of digital interventions, a deeper look at results revealed the broader and longer-term impact4 of digital health self-management programs. For emergent-risk populations, the interventions accomplished the following:

  • Halted the progression of individuals’ preconditions to diabetes, heart disease and other conditions;
  • Slowed the progression of existing conditions, and;
  • Prevented compounding conditions and compounding costs of care.

On the heels of this research, the goal became “trajectory impact” at a population level: programs for the emergent-risk population are now designed to arrest the trajectory of compounding conditions and compounding costs of care. With digital technology’s ability to scale, entire emergent-risk populations can be targeted immediately for outreach and intervention.

And with the lower cost structure of digital technology, health self-management interventions can generate a return beginning one year after the intervention and continuing over the lifetime of each individual.

Citations:
1 Lorig K, Sobel DS, Stewart AL, Brown BW, Bandura A, Ritter P, González VM, Laurent DD, Holman HR. Evidence suggesting that a chronic disease self-management program can improve health status while reducing hospitalization: a randomized trial. Med Care 1999; 37(1):5-14. View the abstract at http://www.ncbi.nlm.nih.gov/pubmed/10413387.

2 The digital translation of the DPP was described in the journal article from McTigue, et al. Using the Internet to Translate and Evidenced Based Lifestyle Intervention into Practice Telemedicine and e-Health Vol 15#9 November 2009. Read more at http://www.ncbi.nlm.nih.gov/pubmed/19919191.

3 The Diabetes Prevention Program (DPP), a major, multicenter clinical research study, discovered that modest weight loss through dietary changes and increased physical activity sharply delayed the onset of type 2 diabetes among pre-diabetic patients. The study showed that taking metformin also reduced risk, although less dramatically. Read more at http://diabetes.niddk.nih.gov/dm/pubs/preventionprogram.

4 A two-year, controlled matched study by Canary Health for GEHA, a self-insured, not-for-profit association providing health and dental plans to federal employees and retirees and their families through the Federal Employees Health Benefits Plan. For a briefing on case study results, contact akaufman.canaryhealth.com

About the Author: Adam Kaufman is a health economist and the president and CEO of Canary Health. He speaks to audiences nationwide on the accelerating trend of chronic illness and the financial tipping point that threatens the margins and mission of American healthcare organizations and advises healthcare senior management teams on making strategic investments in their emergent-risk populations. Prior to serving Canary Health as President and CEO, Adam served as general manager of dLife’s Healthcare Solutions division. Kaufman has served as adjunct assistant professor in the economics department at the University of Southern California, and he is the author of a data analytics patent that predicts consumer engagement.

HIN Disclaimer: The opinions, representations and statements made within this guest article are those of the author and not of the Healthcare Intelligence Network as a whole. Any copyright remains with the author and any liability with regard to infringement of intellectual property rights remain with them. The company accepts no liability for any errors, omissions or representations.

AltaMed Constructs Business Case for Care Coordination Team

May 19th, 2015 by Patricia Donovan

The AltaMed multidisciplinary care team targets dual eligibles with multiple chronic conditions and functional and cognitive impairments.

When the largest FQHC in the country set out to quantify the contributions of its multidisciplinary care team, it found the concept didn’t fit neatly into return on investment models.

So at budget time this year, leaders of AltaMed Health Services Corporation’s care coordination model for its highest risk patients identified seven performance metrics to present to its CFO, explained Shameka Coles, AltaMed’s associate vice president of medical management, during A Comprehensive Care Management Model: Care Coordination for Complex Patients, a May 2015 webinar now available for replay.

The evidence that ultimately secured funding for the care coordination project’s next phase included the model’s impact on specialty costs, emergency room visits, and HEDIS® measures, among other factors.

These were all areas examined early on, back in phase one, when the care coordination team set a number of strategic goals that aligned with the corporation’s five pillars: service, quality, people, community and finance.

Rolled out in four phases beginning in July 2014, the model is aimed at AltaMed’s dually eligible population— Medicare-Medicaid beneficiaries with high utilization, multiple chronic conditions, and multiple functional and cognitive impairments, Ms. Coles explained.

Phase one of the project was devoted to understanding and engaging the duals population via telephonic and print outreach, then developing a care management model reflecting both Triple Aim and patient-centered medical home goals. (The 23-site multi-specialty physician organization in Southern California has earned Joint Commission primary care medical home designation.)

At the heart of the model is a multidisciplinary care team, which counts a care coordinator, clinic patient navigator and care transitions coach among its eleven roles. Patients are stratified as high, moderate or low risk and matched to risk-appropriate interventions.

“Each member is activated based on where the patient is at in the continuum of care,” noted Ms. Coles, who also reviewed team member roles and responsibilities and a host of complementary programs supporting care coordination during the May 2015 program sponsored by the Healthcare Intelligence Network.

In phase two, focused on development of end-to-end workflows, staff assessments and ratios, and team training, AltaMed hired an educator, fleshed out the patient navigator role, and examined integration of behavioral health and long-term services and supports (LTSS).

Phase three triggered a deeper dive into case manager caseloads and utilization patterns as well as several quality improvement activities.

Now in phase four, the goal of AltaMed’s care coordination model is to ensure it can reflect a financial impact. “We’ll look very closely at our per member per month cost and our inpatient metrics,” Ms. Coles concluded.

Top Risk Stratification Tools for Telephonic Case Management

December 18th, 2014 by Patricia Donovan

The case management assessment is the top tool for stratifying candidates for telephonic case management contact, according to market data from HIN’s 2014 survey on Telephonic Case Management.

Sixty-one percent of respondents use a case management assessment to identify high-risk, high-cost individuals who would benefit from telephonic follow-up and care coordination.

Other risk stratification tools reported by survey respondents include the following:

  • Provider referral: 60 percent

  • Claims utilization data: 55 percent

  • Hospital census and discharge reports: 48 percent

  • Predictive modeling: 39 percent

  • Self- or family referrals: 37 percent

In other market data, more than 84 percent of respondents utilize telephonic case managers, with more than half—54 percent—making contact with patients from virtual home offices.

The complex comorbid are the primary targets of telephonic case managers (TCMs), the survey found, but the newly discharged, those in acute stages of chronic illness, frequent utilizers and high-risk, high-cost patients also receive their fair share of telephonic attention from these case managers.

Source: Stratifying High-Risk, High-Cost Patients: Benchmarks, Predictive Algorithms and Data Analytics

http://hin.3dcartstores.com/Stratifying-High-Risk-High-Cost-Patients-Benchmarks-Predictive-Algorithms-and-Data-Analytics_p_4934.html

Stratifying High-Risk, High-Cost Patients: Benchmarks, Predictive Algorithms and Data Analytics presents a range of risk stratification practices to determine candidates for health coaching, case management, home visits, remote monitoring and other initiatives designed to engage individuals with chronic illness, improve health outcomes and reduce healthcare spend.

Each program discussion is supplemented by market data on risk stratification approaches for that care coordination intervention.

11 Statistics about Stratifying High-Risk Patients

November 20th, 2014 by Cheryl Miller

Healthcare organizations use a range of tools and practices to identify and stratify high-risk, high-cost patients and determine appropriate interventions. Most critical to the stratification process is clinical patient data, say an overwhelming 87 percent of respondents to the Healthcare Intelligence Network’s (HIN) inaugural survey on Stratifying High-Risk Patients. However, obtaining and verifying patient data remain major challenges for many respondents. Following are 10 more statistics from our survey.

  • „„Hospital readmissions is the metric most favorably impacted by risk stratification tools, according to a majority of respondents.
  • „„In addition to high utilization, clinical diagnosis is considered a key factor in stratifying high-risk patients, according to 16 percent of respondents.
  • „„Case management as a post-stratification intervention is offered by 83 percent of respondents; health coaching by 56 percent.
  • Reducing heart failure (HF), pneumonia (PN), and atrial myocardial infarction (AMI) are among the greatest successes of risk stratification programs.
  • Diabetes is considered the prominent health condition among high-risk populations, according to 37 percent of respondents; other prominent conditions include hypertension (20 percent) and mental health/psychological issues (15 percent).
  • Physician referrals are cited by 76 percent of respondents as an important input for stratification, followed by case/care manager referrals (71 percent).
  • „„Home health and/or home visits are available to risk-stratified populations of 56 percent of respondents.
  • „„LACE (Length of stay, Acute admission, Charleston Comorbidity score, ED visits) is considered the primary indice and screen to assess health risk, according to 33 percent of respondents.
  • Nearly half of respondents (45 percent) cite high utilization of the emergency department (ED) or hospital as the most critical attribute of high-risk patients.
  • „„While more than half of respondents have a program in place to identify and risk-stratify complex cases, the majority admit it is too early to tell the ROI achieved.

Source: 2014 Healthcare Benchmarks: Stratifying High-Risk Patients

http://hin.3dcartstores.com/2014-Healthcare-Benchmarks-Stratifying-High-Risk-Patients_p_4963.html

2014 Healthcare Benchmarks: Stratifying High-Risk Patients captures the tools and practices employed by dozens of organizations in this prerequisite for care management and jumping-off point for population health improvement — data analytics that will ultimately enhance quality ratings and improve reimbursement in the industry’s value-focused climate.

Ochsner’s Standardized Risk Stratification, Care Coordination Protocols Boost Outcomes across Continuum

October 20th, 2014 by Patricia Donovan

Ochsner Health System’s scaling of a successful transitional care model across one region not only reduced duplication of calls to recently discharged patients but also quadrupled its connect rate—from about 20 percent to nearly 98 percent of discharged patients—and decreased rehospitalizations by about 15 percent.

All while remaining salary-neutral.

To achieve these results and others like them, Ochsner uniformly applied scripts, templates and protocols to processes across its care continuum, even assuming clinical oversight for some providers in external facilities to ensure standardization, explained Mark Green, assistant VP of transition management at Ochsner, during Moving the Metrics: Financial and Quality Returns from System-wide Care Coordination and Risk Stratification, an October 2014 webinar now available for replay.

To replicate these achievements, the nine-hospital system looks up and down its continuum for opportunities to collaborate in care coordination and has elevated its approach to risk stratification. This culture shift is a prerequisite for success in today’s value-based climate, Green estimates.

“A really critical step to understand is managing not only your ‘rising risk’ but also your ‘falling risk’ patient population,” he said, categorizing ‘falling risk’ as those whose conditions are under control and who can be handed off to a lower risk medical home or chronic disease management environment.

Healthcare doesn’t currently do a good job of moving ‘falling risk’ patients down the stratification model, he said, which leaves little room for newly diagnosed ‘rising risk’—an out of control CHF patient, for example.

Risk stratification is scalable, Green emphasized, from single providers without an electronic medical record to a large health system or accountable care organization. As a nine-hospital system, Ochsner’s risk segmentation approach relies heavily on automation and data analytics. For example, every Ochsner hospital patient is assigned a severity of illness (SOI) level that helps to guide individuals to the appropriate level of care. For example, all level 3 patients are automatically referred to complex case management.

During the webinar, Green shared several of Ochsner’s collaborations in risk stratification and care coordination, including an automated post-discharge telephonic follow-up for emergency department patients that replaced its siloed approach and has reduced avoidable ER use in the range of 13 to 15 percent depending on the payor and the location.

“We are very cognizant of and careful that we’re not driving too much business away from our emergency room if it’s appropriate. We’re just letting [staff] manage a higher risk population within their emergency room and giving them time to spend more of it with the patients.”

Listen to an interview with Mark Green.