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Predictive Analytics: Making Patients Safer Through Event Reporting and Prediction

For patients, safety in hospitals and health systems remains a serious concern as medical errors are now the third leading cause of death in the U.S. Determined to improve patient safety, Allina Health turned to predictive analytics to standardize and expand safety event reporting.

Featured Outcomes

  • Successfully identified more safety events than were identified by voluntary reporting alone.
  • Uncovered opportunities for improving patient care.
  • Further improved the identification of near misses in addition to safety events.
  • The analytics application has provided the ability to organize data by multiple factors such as severity, location, and harm type, which could not be done before.
  • The committee also gained a systemwide view of performance with standardized definitions, and up-to-date information much closer to real-time data than what was previously available.
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Consistent Improvement Methodology Accelerates Healthcare Outcomes

For healthcare organizations, the ability to analyze problems and implement timely, effective improvements is necessary to maintain a competitive advantage, requiring a consistent, systematic approach to introduce and implement change. By developing a new strategy focused on uniform adoption, education, and ongoing oversight, Community Health Network changed the way it approached all organizational improvement efforts.

Featured Outcomes

  • $15.5 million in savings, including:
    • $8.2 million in sepsis cost savings.
      • 34.5 percent relative reduction in mortality for patients with sepsis prior to admission, saving 124 lives.
    • $3.2 million in orthopedic service line savings.
      • $1 million in LOS reductions and $1 million in increased revenue from a newly designed outpatient total joint arthroplasty program.
    • $2.32 million integrated primary care savings.
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Increasing Collections by Acting on Predictions of Propensity to Pay

With patients responsible for an increasing amount of their healthcare costs, self-pay accounts are now the top contributor to bad debt for hospitals and health systems—accounting for more than $55 billion annually. Allina Health partnered with Health Catalyst, using catalyst.ai™, to create a predictive model that could successfully support a propensity to pay strategy.

Featured Outcomes

  • $2 million increase in overall collections in just one year, including more than $660,000 in additional patient payments being collected by phone in the first two months following implementation of the propensity to pay machine learning algorithm and collections strategy, a 43.2 percent relative improvement.
  • 37.5 percent relative improvement in the number of outbound calls.
  • 21 percent relative improvement in the number of inbound calls.
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Increased Visibility into Value-Based Performance Results in $2.1M in Additional Pay for Performance

Data-driven decisions and analytics are critical for organizations and physician practices attempting to thrive under value-based care. With the help of data analytics, UTMB Health was able to focus on improvement efforts for specific patient populations and boost reimbursement based on DSRIP performance.

Featured Outcomes

  • $2.1 million in additional pay for performance dollars achieved after the analytics application was implemented.
  • 23 of 32 performance measures—nearly 72 percent—demonstrated improvement.
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Rapid Response Analytics Solution Enables Quick, Accurate Identification of Patients

Learn how The Queen’s Health Systems (QHS) and The Queen’s Clinically Integrated Physician Network (QCIPN) adopted a Rapid Response Analytics Solution, enabling the accelerated implementation of custom algorithms to better identify patients.

Featured Outcomes

After successfully implementing the algorithms QHS and QCIPN were able to identify:
  • Undiagnosed diabetes in adults.
  • Undiagnosed pre-diabetes in adults.
  • Diabetes/pre-diabetes screening that is past due.
  • Undiagnosed hypertension in adults.
In addition, implementation of the algorithms resulted in:
  • Use of the analytics application to identify the patients and generate a patient list in real-time.
  • For the first time, the organization has a true population health registry based on timely, meaningful clinical data that is valued by its providers.
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