Posts Tagged ‘predictive modeling’

Infographic: The Current State of Healthcare Predictive Analytics

March 12th, 2018 by Melanie Matthews

Most organizations sit on a wealth of healthcare data but raw data in itself is not enough to drive down costs and reduce risk, according to a new infographic by Advanced Plan for Health.

The infographic examines how to leverage predictive analytics to improve key areas of cost and risk including wellness programs, case management and telehealth use.

Predictive Healthcare Analytics: Four Pillars for SuccessWith an increasing percentage of at-risk healthcare payments, the Allina Health System’s Minneapolis Heart Institute began to drill down on the reasons for clinical variations among its cardiovascular patients. The Heart Institute’s Center for Healthcare Delivery Innovation, charged with analyzing and reducing unnecessary clinical variation, has saved over $155 million by reducing this unnecessary clinical variation through its predictive analytics programs.

During Predictive Healthcare Analytics: Four Pillars for Success, a 45-minute webinar on March 29th at 1:30 p.m. Eastern, Pam Rush, cardiovascular clinical service line program director at Allina Health, and Dr. Steven Bradley, cardiologist, Minneapolis Heart Institute (MHI) and associate director, MHI Healthcare Delivery Innovation Center, will share their organization’s four pillars of predictive analytics success…addressing population health issues, reducing clinical variation, testing new processes and leveraging an enterprise data warehouse. Click here for more information.

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Infographic: Health IT Dives Into Data Lakes

February 10th, 2016 by Melanie Matthews

To speed their transition to value-based care, healthcare organizations are investing in next generation predictive analytics and data lakes, a powerful data architecture with a unified location for mining and analysis by clinical departments, business analysts and data science teams, according to a new infographic by EMC2.

The infographic looks at where predictive analytics can make a difference and steps to navigate a healthcare data lake.

Health IT Dives Into Data Lakes

As quality and value increasingly drive healthcare rewards, the generation, sharing and analysis of patient data is a prerequisite for organizations desiring to enhance clinical outcomes and drive down cost. Yet the burden of creating infrastructure to support health information management deters many payors and providers from fully engaging in a robust business intelligence strategy.

2016 Healthcare Benchmarks: Data Analytics and Integration examines the goals, data types, collection processes, program elements, challenges and successes shared by healthcare organizations responding to the January 2016 Data Analytics survey by the Healthcare Intelligence Network.

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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

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.

Tactics That Curb Hospital Readmissions Can Also Reduce Avoidable ED Use

October 14th, 2014 by Patricia Donovan

To reduce avoidable emergency room use, particularly by perennial high utilizers, many healthcare organizations are replicating post-discharge data mining and care coordination tactics that successfully reduce hospital readmissions—namely, heavy applications of predictive modeling supported with a blend of embedded case management, telephonic outreach and patient education.

The use of predictive modeling in particular for this purpose has jumped from one-quarter of respondents in 2011 to one-third of this year’s respondents, while ED registry and census use has jumped from 16 to 25 percent in the last three years according to results from a third comprehensive survey on reducing avoidable emergency room use by the Healthcare Intelligence Network.

Telephonic outreach to patients recently visiting the ER has almost doubled in the three-year period, with 31 percent making contact within 24 hours and 29 percent within two days.

The percentage of respondents placing either a case manager or social worker in the ED for the purpose of managing ED utilization has risen from 33 percent in 2011 to 50 percent this year.

Melanie Fox is director of the Caldwell Physician Network Embedded Case Management program at Caldwell UNC Health Care, where embedded case managers in both primary care practices and work sites use telephonic outreach to reduce avoidable hospital utilization.

“One of the goals of embedded case management is to reduce ED visits,” she says. “This is one of the harder things to be able to manage but it is a goal. We don’t know our outcomes yet, although I am starting to measure that.

“We do a lot of education with patients when we follow up,” Ms. Fox continues. “We look and see how many times they’ve been to the ER, and if they’re abusing the ER. Maybe they don’t really have a way to get to the hospital or to the doctor’s office; they end up in a hospital because they don’t have a ride to the doctor. We try to get them to be more proactive with their care. If they let us know ahead of time about a health issue, we can help them stay out of the ER.”

Ochsner Health System is also applying its automated and risk-based post-hospital-discharge follow-up approach to its ED population, connecting them to community resources, a nurse advice line, or the opportunity to schedule a follow-up appointment, depending on need. The process has reduced avoidable ER utilization by between 13 and 15 percent, depending upon payor and location, according to Mark Green, system AVP for transition management at Ochsner Health System. Rather than driving business away from Ochsner ERs, “We’re just letting [ER staff] manage a higher risk population within their emergency room and giving them time to spend more of it with the patients,” explains Green.

Excerpted from: 2014 Healthcare Benchmarks: Reducing Avoidable ER Visits

2014 Healthcare Benchmarks: Reducing Avoidable ER Visits delivers actionable metrics from 125 healthcare organizations on their efforts to foster appropriate use of hospital emergency departments.