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Closed-Loop Analytics Supports Data-Driven Medical Management

Acuitas Health improved access to data for its partner clinicians by using its data platform and closed-loop analytics to integrate data from more than ten disparate systems. Clinicians receive patient-specific details before the patient visit, allowing them to identify opportunities for health maintenance, improve quality, support data-driven medical decision making, increase adoption of best practices, and improve hierarchical condition category (HCC) coding.

Featured Outcomes

  • In just five months, there have been more than 24,000 patients that have received pre-visit planning using the solution with 200 patient-level data points updated daily (new information for patients with a scheduled appointment).
  • Nearly a 70 percent reduction in the time required by the staff to complete pre-visit planning in the first week

Achieving Self-Service Analytics with Rapid Response Analytics

Billings Clinic had its data located within multiple different source systems, which limited access to the data and decreased trust in the data. The available tools were difficult for non-analysts to use and understand, creating resistance to self-service analytics. To breakdown data silos, ensure a gold standard for metrics, and optimize its analytics use, Billings Clinic deployed a data platform and analytics application across its organization.

Featured Outcomes

  • One source of truth for its data and elimination of data silos.
  • Analysts are now able to spend most of their time analyzing, rather than preparing data.
  • 95 percent relative increase in the number of users accessing the analytics application.

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.

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.

Widespread Data Utilization Ensures Continuous Data-Driven Improvement

To ensure it continues the widespread use of data and analytics, Allina Health needed a plan to ensure ongoing data utilization and continuous, data-driven improvement, increasing the number of people learning from the valuable data in its data platform. By leveraging an advanced data platform and a robust suite of analytics accelerators, the health system observed significant improvements.

Featured Outcomes

  • 107 percent relative improvement in the number of users accessing the data platform each month, achieved in just one year.
  • 351,513 unique sessions in Allina Health’s top ten analytics applications in one year.
  • More than $33M in positive margin impact by expense reduction and additional hospital in/outpatient revenue.
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