Integrated Delivery System

Success Stories

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Denials Management Analytics Reduces Denials by Nearly $5M

On average, claim denials cost each healthcare provider $5 million every year. This loss of revenue resulting from claim denials is a concern for healthcare providers. Billings Clinic sought to determine the cause of claim denials and realized that it needed an analytics solution that could integrate data from multiple sources. The health system leveraged its data platform and analytics applications to pinpoint the sources of the denials, allowing the organization to implement prevention plans and procedures for recovering the denials. Billings Clinic achieved significant results, including:

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Data and Analytics Helps Deliver Safe Care During the Pandemic

Renown Health was prepared to safely provide care to patients with COVID-19. As the pandemic emerged, the organization used data in its EMR to monitor COVID-19 activity, but quickly identified that it needed more robust disease surveillance and reporting. By leveraging the Health Catalyst® Data Operating System (DOS™) platform and Rapid Response Analytics Solution, Renown Health expanded its COVID-19 data beyond the data in the EMR. It now has the integrated data and analytics required to plan and manage a comprehensive COVID-19 response effectively.

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Integrated Data and Analytics Critical for COVID-19 Response and Recovery Planning

Allina Health needed integrated data and analytics to manage its organizational response and recovery to COVID-19. The organization had a substantial amount of actionable information in its EMR, but some data like supply and equipment were not available. Allina Health leveraged the Health Catalyst® Data Operating System (DOS™) platform and Instant Data Entry Application to capture and visualize the data required to respond to COVID-19 effectively.

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LOS Improvement Efforts Saves $24M Annually

At MultiCare Health System (MultiCare), inconsistent application of improvement methods, differing competencies, misaligned projects, and inefficient performance data collection were impeding the organization’s ability to improve, leaving quality and operational metrics below expectations. Using a data platform and a robust suite of analytics applications, MultiCare has integrated analytics support into its improvement teams, reducing hospital length of stay (LOS), and achieving significant cost savings.

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Population Builder Makes Data Available to the Masses

Renown Health’s data were contained in disparate sources, delaying access to data, and resulting in different data definitions and interpretations of data. Renown Health integrated its data, creating one source of truth for the organization with the Health Catalyst® Data Operating System (DOS™), enabling it to deliver timely, quality care, which is especially needful to address the COVID-19 pandemic.

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Improving Population Health: Data-Driven Approach to Identifying and Engaging Patients with High Risk of Mortality from COVID-19

Leveraging the Health Catalyst® Data Operating System (DOS™) platform and the ACO Risk Stratification Dashboard, MemorialCare developed and implemented an algorithm to identify and risk-stratify members at the highest risk of mortality from COVID-19. Care managers can visualize at-risk members and the specific factors contributing to the increased risk, allowing them to quickly prioritize member lists for outreach—improving population health and decreasing mortality.

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Analytics Driven Improvement Efforts Reduces Surgical Supply Costs by $3.2M

Banner Health identified considerable variation in surgical supply use across its facilities. The health system desired a collaborative, data-driven strategy that would allow it to maintain high-quality outcomes while simultaneously decreasing costs across all procedures systemwide. To standardize supply use, Banner Health implemented an analytics application to help identify high-volume, high-cost surgical procedures that varied across the system. It then built standardized surgical preference cards for the high-volume procedures.

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Virtual Care Analytics Supports Rapid Change in Ambulatory Care Delivery

Community Health Network (CHNw) rapidly adjusted to COVID-19, changing primary care appointments to virtual care. With this change came the need for new data and analytics to assess the impact on the number of completed appointments, and the implications for provider productivity and reimbursement rates. CHNw is using data and analytics to effectively manage the transition from in-person visits to virtual visits, safely meeting patient needs while also ensuring ongoing financial viability.

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Analytic Insights Help Drive COVID-19 Financial Recovery Plan

Community Health Network (CHNw) desired to understand the financial, provider, and overall impact of COVID-19 related declines in elective surgeries. The data needed to understand the impact and prioritize the organization’s elective surgery restart plan resided in disparate systems, requiring hundreds of hours of manual data review. Using data and analytics, CHNw is able to plan how to optimally meet its patients’ needs and effectively recover from COVID-19 revenue loss.

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Analytics Accelerates COVID-19 Elective Surgery Restart

Banner Health, regarded as a top health system in the country for clinical quality care, was well prepared to respond to the COVID-19 pandemic and had suspended elective surgeries to help keep resources available for patients with COVID-19. Leveraging Health Catalyst’s data platform and analytics, Banner Health has the integrated clinical, financial, and operational data required to develop the organization’s elective surgery financial plan.

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COVID-19 Dashboard Enables Effective Incident Command

MultiCare Health System activated its incident command structure and set out to use the EMR to support its critical data and analytics needs to manage a systemwide organizational response to COVID-19. The organization quickly identified that the EMR could not integrate data from disparate sources or provide a systemwide dashboard. It leveraged data and analytics to create a COVID-19 dashboard, allowing the organization to quickly visualize the data required to effectively plan for, and manage, the health system’s response to COVID-19.

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Reducing Clinic No-Show Rates Increases Revenue by $1M Annually

Memorial Hospital at Gulfport (Memorial) knew that decreasing clinic no-show rates was an opportunity to increase revenue, eliminate delays in care, and improve care coordination for its patients. With a robust data platform, Memorial leveraged its data and analytics to better understand the reasons behind its high no-show rates. With actionable data, the organization implemented measures to effectively improve its no-show rates and increase revenue.

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Evidence-Based Strategies Reduce Voluntary Turnover and Labor Costs

Caregiver satisfaction and voluntary turnover at Community Health Network (CHNw) were negatively impacted by a lack of standard processes, which resulted in rework and communication gaps regarding procedures and increased labor costs, reducing CHNw’s environmental services (EVS) team’s ability to deliver core services. In an effort to reduce voluntary turnover rates, CHNw convened an improvement team to improve processes, training, and communication related to cleaning requests, which has led to reduced voluntary turnover and labor costs.

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Decreasing Durable Medical Equipment Variation Yields Nearly $1M in Additional Revenue

Community Health Network identified that inconsistent oversight of durable medical equipment (DME), and process variation, were a likely source of waste and lost revenue. The health network sought a systemwide, data-driven process for the purchasing, dispensing, and billing of DME. A data platform and analytics applications were utilized to understand organizational performance, identify opportunities for improvement, and evaluate the impact of these changes on patient, financial, and organizational outcomes.

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Reducing Hospital Onset Clostridioides Difficile Infections Decreases Costs by $855K

Community Health Network, a hospital system in Indiana, discovered that its hospital-acquired C. diff infection (HA-CDI) rate was higher than the national benchmark. The organization knew it needed to decrease infection rates, but without timely, meaningful data, leaders couldn’t identify the right areas to focus improvement efforts. With the use of a high-level, robust analytics system that allowed better access to data, team members were able to determine where to focus their efforts.

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Data-Informed Decisions Drive Revenue Improvement by More Than $48M

MultiCare Health System’s Pulse Heart Institute (Pulse Heart) recognized that better care coordination was required for patients receiving cardiac, thoracic, and vascular care. The organization wanted to further improve quality outcomes, provider engagement and recruitment, and its own economic health. To meet these objectives, Pulse Heart focuses on clinician engagement and organizational alignment, ensuring widespread access to meaningful, actionable data and analytics to inform decisions and drive improvement.

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Trigger-Based Analytics Application Recognizes More Wrong-Patient Order Errors

This healthcare organization, comprised of a specialty hospital and multiple clinics, sought to improve safety for its patients, focusing on identifying wrong-patient order errors. To better understand and improve patient safety, the organization needed to move beyond passive surveillance. By using multiple detection methods for identifying wrong-patient errors and establishing triggers that identify when a wrong-patient order may have occurred, hospital and clinic staff are able to investigate instances.

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Mothers and Babies Benefit From Cost-Saving Substance Use Disorder Program

Community Health Network (CHNw) observed higher than national rates of maternal substance use disorder, with a higher number of pregnant women having positive drug screens for opioids, cocaine, amphetamines, barbiturates, and benzodiazepines. It developed a care coordination and substance use program to help reduce the incidence of substance use disorders among pregnant women. Using its data platform and analytics applications, CHNw was able to evaluate the impact of various process measures on patient outcomes.

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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.

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$17.4M Saved Through Analytics-Driven Patient Blood Management Improvements

UnityPoint Health created a task force to develop and implement a plan for maximizing blood management. The task force incorporated decision support to improve transfusion ordering in alignment with the transfusion standards. An analytics platform has also been leveraged, which monitors the utilization of blood products, including predictive modeling to risk-adjust blood utilization specific to patient case-mix, and data down to the ordering provider level.

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Removing Discharge Barriers Reduces Hospital Length of Stay

Length of stay (LOS) is an essential indicator of hospital operational efficiency. Albany Med compared its performance with benchmark data and determined that it could improve inpatient LOS. By convening a multidisciplinary team of providers committed to decreasing hospital LOS and leveraging its data and analytics platform, Albany Med was able to uncover underlying issues causing unnecessary extended hospital stays and substantially reduce LOS.

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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.