Machine Learning / Predictive Analytics

Success Stories

Health Catalyst

Accuracy of Readmission Risk Assessment Improved by Machine Learning

Hospital readmissions carry significant financial costs and are associated with negative patient outcomes. While the reasons behind patient readmissions are multi-factorial, and the specific rates vary by institution, nearly 20 percent of all Medicare discharges nationwide led to a readmission within 30 days. Preventing even 10 percent of these readmissions could save Medicare $1 billion.
North Carolina’s only not-for-profit, independent community healthcare system, Mission Health, is comprised of seven hospitals, 750 employed/aligned providers, and one of the largest Medicare Shared Savings ACOs in the nation. Mission had been using the LACE index to predict risk for readmission, and while it was helpful, Mission’s patient population was different than the population used to develop the LACE index, leaving the health system with some uncertainty regarding the readmission risk of its patients. With the help of data analytics, Mission developed its own predictive model for assessing readmission risk, aimed at preventing readmissions and improving outcomes for patients.
Results:

The area under the curve (AUC) for Mission’s readmission risk predictor is 0.784, outperforming LACE, and meeting the organization’s goal for performance.
Mission’s readmission rate is 1.2 percentage points lower than its top hospital peers.

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Health Catalyst

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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Health Catalyst

DKA Risk Prediction Tool Helps Reduce Hospitalizations

Each year, more than 12,700 pediatric patients are diagnosed with diabetic ketoacidosis (DKA), a life threatening complication of diabetes. Texas Children’s Hospital sought a way to accurately predict risk of DKA in time for care team members to intervene before these patients suffered a severe episode.
The health system ultimately formed a multidisciplinary high risk diabetes team to devise pre- and post-discharge strategies, and DKA risk prediction tools aided by the Health Catalyst Analytics Platform built using the Late-BindingTM Data Warehouse.
Results:

30.9 percent relative reduction in recurrent DKA admissions per fiscal year.
90 percent of all patients with new onset type 1 diabetes at the Medical Center Campus have a documented RIPGC in their medical chart.
100 percent of patients with type 1 diabetes have a risk index for DKA documented every 6 months.

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Health Catalyst

Reduce Readmissions with Predictive Analytics and Process Redesign

With nearly 20 percent of elderly patients released from a hospital being readmitted within 30 days, Allina Health is focused on providing patients optimum care and support post discharge to minimize readmissions. Focusing on 30-day potentially preventable readmissions (PPRs) as its global outcome measurement, Allina Health used key clinical variables to derive the clinical relationships between hospitalizations that determine PPRs. It further built analytic capabilities to identify opportunities for improvement in care management and to test quality improvement ideas.
Allina Health’s multipronged solution included redesigning care management processes, implementing predictive analytics to identify at-risk patients, using analytics to measure the impact of its interventions, and educating patients, families, and clinicians.
These efforts are driving measurable improvements including: 10.3 percent overall reduction in PPRs, 27 percent reduction in PPRs for patients with clinic follow-up within 5 days, and $3.7 million reduction in variable costs due to avoided readmissions.

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How to Improve Sepsis Diagnosis and Outcomes through Innovative Healthcare Analytics

Sepsis affects more than 750,000 hospitalized patients and results in 570,000 ED visits per year. Learn how this large medical center is tackling their sepsis care challenges by leveraging their EDW and healthcare analytics. They defined and built a sepsis registry and analytics platform in 10 weeks to measure 6 interventions and 4 outcome measures — including mortality rates, length of stay, total hospital stay and 30-day readmits.

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