Health Catalyst® Introduces™: Machine Learning in Healthcare Is Now for Everyone

Machine learning is becoming part of everyday life for many Americans, including navigation app driving estimates, Spotify music recommendations, or Amazon follow-on purchase suggestions. But in healthcare, the use of machine learning has so far been limited to niche science projects in large and academic health systems—those able to afford the highly skilled data scientists and dedicated teams required to turn their data into meaningful performance improvements.

Health Catalyst® is on a mission to change that by embedding the value of machine learning throughout healthcare. Last month, Health Catalyst launched™ to help make machine learning routine, pervasive, and actionable for healthcare organizations of all sizes. Intended for anyone who is interested in machine learning in healthcare, the tool provides topical blog content, weekly, live, hands-on machine learning educational broadcasts, and a collaborative, open source repository of standardized machine learning methodologies and production-quality code—and makes it easy to deploy machine learning in any environment.


Now, to bring this life-saving technology to hospitals and patients everywhere, Health Catalyst is embedding machine learning as a core capability across the company’s entire product line in a technology initiative called™. With optimized machine learning models built into every Health Catalyst application, organizations can leverage the technology for predictions such as identifying patients who are most likely to acquire deadly infections; finding those who may have trouble paying their medical bills; spotting possible canceled appointments before they happen; or launching proactive medical interventions for patients who are at risk for dangerous complications.

Together, and represent the next generation of healthcare analytics. With these machine learning innovations now readily available to organizations large and small, American healthcare will be equipped to exchange today’s limited, retrospective analysis for a new era of powerful, predictive analytics driving an orders-of-magnitude improvement in outcomes.

Predictive analytics powered by machine learning has truly vast potential in healthcare, but lags behind other industries by years—largely because early efforts were extremely expensive one-off models requiring many data scientists to write and test the algorithms behind the technology. solves that problem by lowering the bar for entry and enabling data architects and analysts to become ‘citizen data scientists.’

Another factor limiting the usefulness of machine learning is that healthcare data is far more complex than data in other industries, and difficult to aggregate. Machine learning algorithms are only as good as the volume and quality of data that feeds them. Health Catalyst has invested tens of millions of dollars over the last few years to create high-volume, high-quality data content that these algorithms thrive on, and we are embedding the results in the workflow of clinicians in every department across a hospital or health system.

How a Major Health System Is Using Machine Learning to Reduce Hospital-Acquired Infections

Health Catalyst machine learning has already been deployed at multiple client sites with promising outcomes. A recent article published by Hospitals and Health Networks shares the approach and results of one, integrated health system who is using predictive analytics in its CLABSI prevention initiative. They want to be able to predict which patients are most likely to develop a CLABSI so clinicians can proactively intervene more quickly and deliver optimal care for her or his patient.

They use a Health Catalyst enterprise data warehouse (EDW) and analytics to bridge information gaps in its EMR data and to develop a complete picture of patients’ CLABSI risk. Models on the data were developed and tested by Health Catalyst using machine learning algorithms such as logistic regression and random forest—the workhorses of the machine learning world. This led to the development of a CLABSI risk prediction model that is built into a unit-level dashboard used by nursing staff to identify patient-level care gaps. In addition to the risk score, the top three risk factors for each high-risk patient are displayed, providing immediate insight into specific actions that can reduce the CLABSI risk for these patients.

According to a recent Scottsdale Institute report on the project, the risk-prediction tool—which auto-mines the EHR daily—has successfully predicted 85 percent to 90 percent of CLABSIs

Machine Learning in Healthcare Requires Data to be Successful’s effectiveness is closely tied to Health Catalyst’s proven ability to integrate high-volume data from virtually every internal and external source available. Because multiple sources of data are required for machine learning to develop the models that drive predictive analytics, the technology is more effective the more data is present. Most companies providing machine learning solutions require customers to figure out how to connect up to 100 different data sources to make the technology work. By contrast, Health Catalyst’s EDW integrates 120 different data sources, including the electronic health record (EHR), claims, key financial, operational, and patient satisfaction systems.

Breakthrough: Translating Machine Learning into Widespread Outcomes Improvement

Despite the promise of machine learning technology, most existing machine learning algorithms are largely academic, standalone models that languish beyond their narrow scope, due to a lack of organizational understanding about how to translate the models into meaningful, scalable outcomes. However, at Health Catalyst, we combine this leading technology with our decades-old understanding of improving outcomes. We know how clinicians use data to make decisions. We understand the context in which the machine learning insights need to be delivered, and the right time and modality to deliver those insights. This enables a significant acceleration of improved outcomes across a wide swath of clinical conditions.

Machine Learning Models Available Today

In addition to its CLABSI risk prediction model, Health Catalyst has leveraged to deploy numerous predictive models at launch that are useful for clinical, financial, and operational decision support, including clinical decision support models for:

  • Central line-associated bloodstream infection future risk (CLABSI)
  • Chronic obstructive pulmonary disease (COPD) readmission risk
  • Pre-surgical risk for bowel surgery
  • Diabetes future risk
  • Financial and operational decision support models, such as propensity to pay and appointment no-show risk

In addition to predictive models, Health Catalyst is currently developing algorithms that use machine learning to identify treatment patterns that lead to better outcomes. These algorithms will be used to drive treatment decisions for patients by showing how ‘patients like this’ were treated and what the outcomes were for a cohort of similar patients. On-Demand Webinar PRODUCT SPECIFICATIONS

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