Posts Tagged ‘rising risk patients’

HINfographic: The Rising Risk: Harvesting Population Health’s Low-Hanging Fruit

October 5th, 2016 by Melanie Matthews

Paramount to population health management success under risk-based contracts is strategic oversight of the ‘rising risk’—individuals with two or more unmanaged health conditions. One quarter of respondents to the 2016 Population Health Management survey by the Healthcare Intelligence Network zero in on their own ‘rising risk’ populations.

A new infographic by HIN examines the health risks served by population health management programs and how population health management services are delivered.

2016 Healthcare Benchmarks: Population Health Management2016 Healthcare Benchmarks: Population Health Management analyzes responses of more than 100 healthcare organizations to HIN’s third comprehensive industry survey on PHM trends administered in spring 2016. It delivers the latest metrics on current and future PHM initiatives, providing actionable data on the most effective PHM tools and workflows, risk identification strategies, communication and engagement tools, program delivery modalities, results and challenges, and much, much more.

2016 Healthcare Benchmarks: Population Health Management is supported with more than 50 graphs and tables and describes many successes respondents have achieved with a PHM approach. Participating organizations also weigh in on the sustainability of a population health management approach. Click here for more information.

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Home Visits Validate Predictive Analytics and 10 More 2016 Risk Stratification Trends

August 30th, 2016 by Patricia Donovan

Assuring data integrity is the top challenge to health risk stratification, according to a July 2016 healthcare benchmarks survey.


Two key trends emerging from a July 2016 survey on Stratifying High-Risk Patients highlight the need to occasionally eschew sophisticated tools in favor of basic, face-to-face care coordination.

As one survey respondent noted, “A key element [of stratifying high-risk patients] is building a trusting face-to-face relationship with each patient, knowing what they want to work on, coaching them and activating them.”

The first learning gleaned from the survey’s 112 respondents is that, despite the prevalence of high-end risk predictors, algorithms and monitoring tools, clinicians must occasionally step into the patient’s world—that is, literally enter their home—in order to capture the individual’s total health picture.

Fifty-six percent of respondents make home visits to risk-stratified patients; a half dozen identified the home visit as its most successful intervention for risk-stratified populations.

That inside look at the patient environment illuminates data points an electronic health records (EHRs) might never bring to light, including socioeconomic factors like limited mobility that could prevent a patient from keeping a follow-up appointment.

“I never know until the moment I enter the home and actually see what the environment is like whether we correctly predicted the need for high intervention (and get a return on it),” commented one respondent.

The second trend in risk stratification is the emerging laser focus on ‘rising risk’ patients, an activity reported by 72 percent of respondents. This scrutiny of rising risk populations helps to prevention their migration to high-risk status, where complex and costly health episodes prevail.

Other data points identified by the 2016 Stratifying High-Risk Patients survey include the following:

  • Almost four-fifths of 2016 respondents have programs to stratify high-risk patients, and the infrastructures of more than half of these initiatives utilize clinical analytics, predictive algorithms, EHRs and other IT tools to manage care for high-risk patients.
  • The reigning health risk calculator continues to be the LACE tool (Length of stay, Acute admission, Charleston Comorbidity score, ED visits), used by 45 percent in 2016, versus 33 percent two years ago.
  • For more than a quarter of 2016 respondents, assuring data integrity remains a key challenge to risk prediction.
  • A case manager typically has primary responsibility for risk stratification, say 52 percent of respondents.
  • Diabetes is the most prevalent clinical condition among high-risk patients, say 47 percent.
  • At least 70 percent report reductions in hospitalizations and ER visits related to risk stratification efforts.
  • Improvement in the highly desirable metric of patient engagement is reported by 74 percent of respondents.

Click here to download an executive summary of survey results: Stratifying High-Risk Patients in 2016: As Risk Prediction Prevails, Industry Eyes Social Determinants, Rising Risk.

Appointment Data Opens Door to Population Health Management of Rising Risk Patients

August 9th, 2016 by Patricia Donovan

The rising risk population represents a healthcare organization’s “low-hanging fruit,” says Dr. Adrian Zai, clinical director of population informatics at Massachusetts General Hospital.

Sometimes the most powerful population health management intervention is simply to convince a patient to make an appointment.

This is the first step Dr. Adrian Zai, clinical director of population informatics at Massachusetts General Hospital (MGH), would recommend to any organization hoping to better manage its rising risk population, a group the physician describes as “low-hanging fruit.”

“The appointment does not require significant investment in any health IT or other resources,” said Dr. Zai during Targeting High-Risk and Rising-Risk Patients: A Multi-Pronged Strategy, an August 2016 webinar now available for replay. “All you need is appointment data. The key is to identify existing data you already have in your organization and start there, so that you impact outcomes.”

Dr. Zai, whose hospital has been ranked number one in the nation by U.S. News & World Report, likened the notion of an organization acquiring a sophisticated health data analytics system prior to identifying clinical outcomes to “building a house without an architect.”

However, having done its due data diligence, MGH’s population health management approach embraces technology. The MGH approach, which targets rising- and high-risk patients, has moved far beyond appointment-setting, constructing a safety net program with the goal of improving clinical outcomes for 300,000 patients in its entire primary care network— a network spanning MGH and Brigham and Women’s Hospital.

To this end, MGH developed a new set of clinically meaningful measures, but not before soliciting physician feedback on its existing set. In response, doctors identified more than 200 challenges to the old measures that MGH addressed in its new decision support system.

With new measures in place, MGH then created central population health coordinator teams to support primary care physicians in population health management, freeing clinicians to care for patients.

The selection of technology to support MGH’s primary care safety net presented its own challenges. “Frequently, the tools you end up with—for data aggregation, analytics, care coordination, and patient outreach—don’t actually talk to each other. You need a system to pull all of these functionalities together. That’s the strategy we took,” said Dr. Zai.

The new MGH population health management system enables clinicians to identify and share gaps in care with MGH care coordinators and population health managers, so they can intervene and try and close those gaps, he continued.

The system also tracks outcomes. After using the system for only six months, MGH reported improvement in every one of its newly developed performance framework measures. Not only is the ability to review outcomes appealing to payors, but 85 percent of MGH physicians surveyed also expressed satisfaction with the system—as well as its concurrent financial incentives.

In closing, Dr. Zai reiterated the need for collaboration: between staffers doing the work and the informatics tying those efforts neatly together. “One cannot work without the other. That technology is just a tool. Just as you cannot give a hammer to someone and expect them to build a house, you need the talents working together with technology to make that happen.”

Click here to listen to an interview with Dr. Zai on reducing the natural inertia of low-risk patients to move into the high-risk stratum.