Three Ways Clinical Surveillance is Helping Prevent Patient Care Problems

Clinical surveillance can help healthcare systems more quickly identify clinical care problems, such as those related to medication errors and poor usage of antibiotics. But it can also help healthcare systems prevent problems from occurring in the first place.

That’s according to Bart Abban, PhD, director of analytics and data science at VigiLanz, who spoke about predictive analytics and clinical surveillance at the recent HIMSS Conference in Orlando.

“We’ve fully integrated a machine-learning platform into our real-time system,” said Abban, during his presentation. “We can look forward [at potential events], look in real time, and look back at historical data. It’s a fully closed-loop.”

Firm foundation needed

Much of the foundation of predictive analytics, Abban noted, comes from creating rules and alerts based on historical data. “You need a data repository,” he said. “We have vast amount of rich data, years and years of data.”

To view Abban’s full presentation, click here.

Health systems have many predictive analytics solutions to choose from. But Abban said VigiLanz stands apart because it provides a broader spectrum of alerts and customizations related to all types of patient care problems.

“With our platform you can do readmissions today, tomorrow you can start looking at mortality risk, you can look at adverse events, glycemic control,” he said. “We are currently developing a model on C. diff to alert clinicians to patients who are at high risk of getting C. diff. The sky is the limit.”

For more on how health systems are using clinical surveillance to enhance patient care, read “Clinical Surveillance: The Next Step in Value-Based Care.”

Three applications

Abban shared three examples of how VigiLanz is providing predictive analytics to health systems: 

  1. Sepsis

Since many hospitalized patients have symptoms that resemble sepsis, it can be difficult for providers to identify sepsis cases or identify patients who are most at risk. VigiLanz created a rules engine so that hospitals can use certain criterion—such as vital signs, labs, microbiology, comorbidities, and medications—to identify patients at risk. VigiLanz can then alert providers when patients are at risk, and providers can monitor these patients more closely and treat them proactively. Since data is updated in near real time, providers always have access to the most up-to-date information related to patients at risk.

  1. Acute kidney injury

Certain medications are essential for patients, but they can also lead to acute-kidney injury. The conventional approach is to monitor patients via labs, however, that means providers can only intervene after injury has already occurred. VigiLanz created a series of risk factors that can help providers more closely monitor patients, and proactively adjust medications as needed.

Risk factors include:

  • High risk nephrotoxic medications
  • Other drugs administered
  • Age, race, chronic morbidities, ICU stay or surgery
  • Specific labs
  1. Anomaly detection

VigiLanz created a rules-based engine to help health systems monitor the likelihood of a cluster or outbreak of hospital-acquired infections.. VigiLanz created alerts based on algorithms for various types of infections, and health systems are able to configure their own alert thresholds depending on their needs, areas of concern, and priorities.

Health systems using the VigiLanz platform to improve patient care and prevent patient care problems can rest assured that critical alerts won’t be missed by providers, said Abban. He noted that VigiLanz can track whether providers are taking actions related to the alerts they receive. If not, alerts can be reissued or the health system can follow up with providers directly. “It’s a whole ecosystem where you can extract the value of predictive analytics,” he said.

How Orlando Health Deploys Predictive Analytics to Fight Sepsis

As a result of the prevalence of sepsis in the healthcare setting and new reimbursement models that reward value and outcomes, many health systems and hospitals are looking for ways to take full advantage of all technological resources available in order to protect patients and reduce costs by working to properly manage and prevent sepsis.

While CMS assesses hospital performance by monitoring 30-day readmissions following hospitalizations for heart attack, heart failure, chronic obstructive pulmonary disease and pneumonia, none of these conditions take the same toll on hospitals and their patients in terms of readmissions as sepsis, according to a recent study published in JAMA. In the study, researchers found sepsis — a condition caused by the body’s immune response to life-threatening infection — accounted for 12.2 percent of readmissions, followed by 6.7 percent for heart failure, 5 percent for pneumonia, 4.6 percent for COPD and 1.3 percent for heart attack.

During a Jan. 26 webinar sponsored by VigiLanz — a provider of real time intelligence and predictive analytics — and hosted by Becker’s Hospital Review, Eric Rose, Pharm. D, manager in corporate clinical decision support at Orlando (Fla.) Health, Adam Klass, chief technology officer with VigiLanz, and Bart Abban, PhD, director of analytics and data science with VigiLanz, discussed the development of predictive models for sepsis risk and how such models can improve outcomes by alerting physicians to the early signs of sepsis and enabling them to intervene sooner.

The VigiLanz intelligence platform conducts automated, continuous surveillance to provide clinicians with solutions and insights in real time to ensure adherence to best practices, increase patient safety and improve reimbursement.

“We’re a layer that sits on top of the EMR and really our goal is to drive all of the value out of all of that great data that’s in the EMR and really affect patient care in real time,” said Mr. Klass during the webinar.

The VigiLanz platform is designed to deliver clinicians predictive analytics tailored to a hospital’s patient population. Patient attributes like age, clinical history, comorbidities and lab results can be continuously introduced into the model. The VigiLanz model also analyzes the potential effects of a patient’s unique history and attributes on their clinical outcomes.

Additionally, models are constructed from the ground up through collaboration between the VigiLanz team and the hospital, tested against a hospital’s historical data and validated within a staging environment.

“We have a very collaborative process between your experts and the VigiLanz team,” said Dr. Abban, during the discussion. “Based on the literature available, we’ve built great base models. When we begin working with [a hospital’s] team, we look at the important differences that are in your institution, things that might impinge on the foundational models and we incorporate those into the models.”

The pilot model designed to predict the risk of sepsis in patients at Orlando Health — a six-campus health system with 1,700 beds and a Level I trauma center — involves a multi-layer process. First, efforts are made to accurately identify patients already presenting sepsis upon admission. Second, patient data like vital signs, microbiology and clinical history are entered into the model. Then the platform conducts a clinical evaluation and attributes a sepsis risk score to the patient. Based on the score, hospitals can decide when medical interventions are warranted and more effectively triage the patient population.

At Orlando Health, the VigiLanz model has a positive sepsis predication rate of 70 percent and has been particularly well received in the med-surgery units.

Additionally, Orlando Health’s Dr. Rose and the VigiLanz team were able to establish more accurate time frames for organ dysfunction pertaining to sepsis. According to Dr. Rose, the newly established time frames “helped us very quickly tighten up the predictive power of the algorithm.”

“It’s really been a pleasure to partner with them [VigiLanz] on this new venture in predictive analytics,” said Dr. Rose.

To view the webinar, click here.

Article reposted with permission from Becker’s Healthcare. See the original here.