Clinical

Success Stories

Health Catalyst

Machine Learning Improves Accuracy of Risk Predictions and Improves Operational Effectiveness

Hospital readmissions can impact the health outcomes for patients and result in costly readmission penalties from CMS. Learn how the data analytics teams at Westchester Medical Center Health Network and network member Bon Secours Charity Health System utilized its analytics platform, in coordination with a machine learning algorithm, to build a knowledgeable and accurate readmission risk model that better reflected its patient population.

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Health Catalyst

Standard Approach to Early Induction of Labor Successfully Reduces Unnecessary Cesarean Deliveries

In the U.S., nearly one in three women give birth via cesarean delivery. Unnecessary cesarean deliveries can expose mothers and babies to possible harm without providing many benefits. Read how Gunnison Valley Hospital reduced the number of unnecessary cesarean deliveries by standardizing labor and delivery care practices and utilizing data from its analytics platform.

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Enhanced Recovery Program Improves Elective Colorectal Surgical Outcomes

Contemporary colorectal surgery is often associated with long LOS, high costs, and surgical site infections (SSI) approaching 20 percent. Much of the LOS variation is not attributable to patient illness or complications, but most likely represents differences in practice style. Successfully reducing SSI requires a multimodal strategy under the supervision of numerous providers with high compliance across the spectrum.
Allina Health was using established, evidence-based clinical guidelines, yet clinical variation remained high across pre-arrival, preoperative, intraoperative, and postoperative care areas, leading to substantial variation in LOS, cost of care, and the patient experience. To ensure greater consistency, Allina Health developed an enhanced recovery program (ERP) for patients undergoing elective colorectal surgery, which built standard protocols into the EHR to address elements of care from pre-arrival through post-discharge.
To facilitate the program and monitor performance, Allina Health developed an ERP analytics application with an administrative dashboard to easily visualize first-year results:

78 percent relative reduction in elective colorectal SSI rate.
19 percent relative reduction in LOS for patients with elective colorectal surgery.
82.4 percent utilization of preoperative and postoperative order sets, increasing the consistency of care and reducing unwarranted variation.

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Using Data to Spotlight Variation and Transform Total Joint Care

Total Hip (THA) and Total Knee (TKA) Arthroplasty are the most prevalent surgeries for Medicare patients, numbering over 400,000 cases in 2014, costing more than seven billion dollars annually for the hospitalization alone. Today, more than seven million Americans have hip or knee implants, and the number is rising. Furthermore, substantial variation in the cost per case has raised questions about the quality of care. At Thibodaux Regional Medical Center, total joint replacement for hips and knees emerged as one of the top two cost-driving clinical areas with variation in care processes.
To address this, Thibodaux Regional maintained its focus on the IHI Triple Aim while developing organizational and clinical strategies to transform the care of patients undergoing THA and TKA. It commissioned a Care Transformation Orthopedic Team that set multiple outcome goals. Among its many efforts, the team established standard care processes, created an educational program, redesigned order sets and workflows, and deployed a joint replacement analytics application.
Thibodaux Regional reduced variability and decreased costs significantly while maintaining high levels of patient satisfaction:

76.5 percent relative reduction in complication rate for total hip and total knee replacement.
38.5 percent relative reduction in LOS for patients with total hip replacements.
23.3 percent relative reduction in LOS for patients with total knee replacement.
$815,103 cost savings, achieved in less than two years.

 
 

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Machine Learning, Predictive Analytics, and Process Redesign Reduces Readmission Rates by 50 Percent

The estimated annual cost of readmissions for Medicare is $26 billion, with $17 billion considered avoidable. Readmissions are driven largely by poor discharge procedures and inadequate follow-up care. Nearly one in every five Medicare patients discharged from the hospital is readmitted within 30 days.
The University of Kansas Health System had previously made improvements to reduce its readmission rate. The most recent readmission trend, however, did not reflect any additional improvement, and failed to meet hospital targets and expectations.
To further reduce the rate of avoidable readmission, The University of Kansas Health System launched a plan based on machine learning, predictive analytics, and lean care redesign. The organization used its analytics platform, to carry out its objectives.
The University of Kansas Health System substantially reduced its 30-day readmission rate by accurately identifying patients at highest risk of readmission and guiding clinical interventions:

39 percent relative reduction in all-cause 30-day.
52 percent relative reduction in 30-day readmission of patients with a principle diagnosis of heart failure.

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