Clinical Analytics Success Stories in Healthcare

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Reducing Mortality Through Earlier Sepsis Intervention

Community Health Network had implemented evidence-based care; however, the sepsis mortality rate remained higher than desired. To address this situation, the health system established a sepsis council to coordinate a sepsis improvement plan and implemented an analytics platform to gain insight into sepsis care performance.

Featured Outcomes

120 lives saved.
50 percent relative reduction in mortality for patients with emergency department (ED) present on admission (POA) severe sepsis.
14 percent relative reduction in mortality for patients with ED POA septic shock.
$5.5 million in savings, the result of reducing average variable cost of care for ED POA and inpatient cohort with sepsis, while also capturing patients for downstream care delivery.

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Certified Analytics Platform Enables Electronic Quality Measures Reporting

Organizations that participate in various Centers for Medicare and Medicaid Services sponsored programs must use a certified health IT product to electronically submit performance data. Acuitas Health partnered with Health Catalyst to develop a solution that would enable the organization to meet the electronic clinical quality measures (eCQMs) reporting requirements.

Featured Outcomes

2,080 hours saved annually in some practices.
Eliminated the need to maintain two different quality reporting systems.

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Machine Learning and Feature Selection for Population Health

Christiana Care Health System (CCHS) had used a machine learning model to inform population segmentation. The initial model used “black box” algorithms to predict risk that care managers didn’t have input on or understand. CCHS leaders and experts wanted an efficient model that they understood and trusted to predict 90-day inpatient admission. CCHS used a feature selection process to build the simplest model possible—and AI insight tools for selecting the best model, setting triggers for action, and explaining how the model worked.

Featured Outcomes

Feature selection reduced the model complexity from 236 data features to just 16 data features (7 percent of the original data set).
Both models, the one with 236 data features and the one with 16 data features, had an AUROC of 0.78 and an AUPR of 0.15, suggesting no degradation of predictive performance due to the lower number of features selected.
CCHS care managers have confidence in the predictive model, and they are successfully using the output of the machine learning tool to engage with an average of 857 distinct members each week, completing more than 2,520 tasks for those members.

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Analytics-Driven Clinical Documentation Improvement Efforts Positively Impact Reimbursement

Albany Med’s clinical documentation improvement specialists provide high-quality care to complex, acute-care patients; however, Albany Med was experiencing lower reimbursement rates due to gaps in clinical documentation. The organization created a seamless process for clinical documentation with the use of an analytics application as driven by clinical leadership.

Featured Outcomes

50 percent relative improvement in appropriate coding, as demonstrated in the reduction in the potential opportunity in the emergency department (ED).
10.8 percent relative improvement in DRG group captured for ED visits.

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Medically Integrated Wellness Program Reduces Modifiable Risk Factors

Thibodaux Regional Medical Center conducted a community health needs assessment (CHNA) as part of its vision for a healthier community. The CHNA reinforced the hospital’s identified need for a data-driven, medically integrated wellness program designed to benefit the health of the community by educating, advocating, and supporting healthy lifestyle changes to address modifiable health risks. Using a wellness analytics application, Thibodaux Regional is able to track program outcomes.

Featured Outcomes

16.1 percent relative improvement between pre-and-post physical PROMIS scores.
8.6 percent relative improvement between pre-and-post mental PROMIS scores.
16 percent relative improvement between pre-and-post distances walked.
2.1 percent relative reduction between pre-and-post weight—participants lost an average of 5.1 pounds.

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