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Analytics Helps Population Health Coordinators to Better Treat Chronic Conditions

Improving the management of chronic diseases for patients is crucial for reducing expenses and improving health outcomes. Newton-Wellesley Hospital, a member of the Partners HealthCare system, adopted the population health coordinator role and utilized analytics to help identify variations in chronic disease management across practices and develop standardized best practices aimed at reducing costs through better outcomes for patients.

Featured Outcomes

  • Improved effectiveness of diabetes management:
    • 6.9 percent more patients with diabetes whose hemoglobin A1c was within the recommended guidelines.
    • 4.6 percent more patients with diabetes achieved hypertension control.
    • 8.5 percent more patients with diabetes whose cholesterol was controlled within the recommended guidelines.
  • Improved effectiveness of cardiovascular management:
    • Six percent more patients with hypertension achieved blood pressure control.
    • 7.7 percent more patients with high cholesterol whose cholesterol was controlled within the recommended guidelines.
  • Newton-Wellesley has also achieved a 16.1 percentage point increase in the adult prevention composite score.
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Using Analytics to Improve Patient Safety Surveillance Makes Patients Safer

Medical errors account for 10 percent of all deaths. To improve patient safety, Allina Health utilized its machine learning, analytics platform, and a trigger-based data-driven surveillance tool to identify and investigate a broader base of harm events, enabling the organization to:

Featured Outcomes

  • Develop a trigger tool that identified 216 more cases of hospital-acquired pressure injury (HAPI) than the voluntary reporting process.
  • Improve central line-associated bloodstream infection (CLABSI) documentation and surveillance and promote early identification of patients at risk.
  • Uncover opportunities for improvement, enabling the organization to better understand its performance, and adjust documentation in the EMR to portray a more complete picture of the patient for both CLABSI and HAPI.
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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™, 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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