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“Learning Health System” Pilot Cuts Care Costs While Improving Quality

Posted on January 11, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

As some of you will know, the ONC’s Shared Nationwide Interoperability Roadmap’s goal is to create a “nationwide learning health system.”  In this system, individuals, providers and organizations will freely share health information, but more importantly, will share that information in “closed loops” which allow for continuous learning and care improvement.

When I read about this model – which is backed by the Institute of Medicine — I thought it sounded interesting, but didn’t think it terribly practical. Recently, though, I stumbled upon an experiment which attempts to bring this approach to life. And it’s more than just unusual — it seems to be successful.

What I’m talking about is a pilot study, done by a team from Nationwide Children’s Hospital and The Ohio State University, which involved implementing a “local” learning health system. During the pilot, team members used EHR data to create personalized treatments for patients based on data from others with similar conditions and risk factors.

To date, building a learning health system has been very difficult indeed, largely because integrating EHRs between multiple hospital systems is very difficult. For that reason, researchers with the two organizations decided to implement a “local” learning health system, according to a press statement from Nationwide Children’s.

To build the local learning health system, the team from Nationwide Children’s and Ohio State optimized the EHR to support their efforts. They also relied on a “robust” care coordination system which sat at the core of the EHR. The pilot subjects were a group of 131 children treated through the hospital’s cerebral palsy program.

Children treated in the 12-month program, named “Learn From Every Patient,” experienced a 43% reduction in total inpatient days, a 27% reduction in inpatient admissions, a 30% reduction in emergency department visits and a 29% reduction in urgent care visits.

The two institutions spent $225,000 to implement the pilot during the first year. However, the return on this investment was dramatic.  Researchers concluded that the program cut healthcare costs by $1.36 million. This represented a savings of about $6 for each dollar invested.

An added benefit from the program was that the clinicians working in the CP clinic found that this approach to care simplified documentation, which saved time and made it possible for them to see more patients during each session, the team found.

Not surprisingly, the research team thinks this approach has a lot of potential. “This method has the potential to be an effective complementary or alternative strategy to the top-down approach of learning health systems,” the release said. In other words, maybe bottom-up, incremental efforts are worth a try.

Given these results, it’d be nice to think that we’ll have full interoperability someday, and that we’ll be able to scale up the learning health system approach to the whole US. In the mean time, it’s good to see at least a single health system make some headway with it.

EHRs Can Help Find Patients At High Risk Of Dying

Posted on June 1, 2016 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

Much of the discussion around EMRs and EHRs these days focuses on achieving broad, long-term goals such as improved population health. But here’s some data suggesting that these systems can serve a far more immediate purpose – finding inpatients at imminent risk of death.

A study appearing in The American Journal of Medicine details how researchers from Arizona-based Banner Health created an algorithm looking for key indicators suggesting that patients were in immediate danger of death. It was set up to send an alert when patients met at least two of four systemic inflammatory response syndrome criteria, plus at least one over 14 acute organ dysfunction parameters. The algorithm was applied in real time to 312,214 patients across 24 hospitals in the Banner system.

Researchers found that the alert was able to identify the majority of high-risk patients within 48 hours of their admission to a hospital, allowing clinical staff to deliver early and targeted medical interventions.

This is not the first study to suggest that clinical data analysis can have a significant impact on patients’ health status. Research from last year on clinical decision support tools appearing in Generating Evidence & Methods to Improve Patient Outcomes found that such tools can be beefed up to help providers prevent stroke in vulnerable patients.

In that study, researchers from Ohio State University created the Stroke Prevention in Healthcare Delivery Environments tool to pull together and display data relevant to cardiovascular health. The idea behind the tool was to help clinicians have more effective discussions with patients and help address risk factors such as smoking and weight.

They found that the tool, which was tested at two outpatient settings at Ohio State University’s Wexner Medical Center, garnered a “high” level of satisfaction from providers. Also, patient outcomes improved in some areas, such as diabetes status and body mass index.

Despite their potential, few tools are in place today to achieve such immediate benefits as identifying inpatients at high risk of death. Certainly, clinicians are deluged with alerts, such as the ever-present med interaction warnings, but alerts analyzing specific patients’ clinical picture aren’t common. However, they should be. While drug warnings might irritate physicians, I can’t see them ignoring an alert warning them that the patient might die.

And I can hardly imagine a better use of EMR data than leveraging it to predict adverse events among sick inpatients. After all, few hospitals would spend dozens or hundreds of millions of dollars to implement the system which creates a repository that simply mimics paper records.

In addition to preventing adverse events, real-time EMR data analytics will also support the movement to value-based care. If the system can predict which patients are likely to develop expensive complications, physicians can do a better job of preventing them. While clinicians, understandably, aren’t thrilled will being told how to deliver care, they are trained to respond to problems and solve them.

I’m hoping to read more about technologies that leverage EMR data to solve day-to-day care problems. This is a huge opportunity.