COVID-19 is causing many hospitals and health systems to face resource and capacity restrictions, making the accurate estimation of COVID-19 requirements crucial. Carle Health System needed the ability to anticipate the impact COVID-19 would have on its organization and community. After analyzing national COVID-19 capacity planning resources, Carle chose a model that was customized for its organization. Carle leveraged its analytics platform and data science tools, using local data and infection rates to forecast the impact of COVID-19 locally. The organization now has critical insight into when surges will occur and can determine if it has enough available resources.
Integrated Delivery System
Advanced payment models incentivize Accountable Care Organizations (ACOs) to deliver high-quality care and close gaps in care for members, thereby earning shared savings and increasing profits. However, in order to succeed and identify gaps in care, ACOs must be able to rely on solid data and analytics to avoid losing income that could be invested back into patient care. Utilizing its analytics platform and a quality measures solution has allowed Hospital Sisters Health System to close care gaps, improve ACO quality measures performance, and enhance reporting accuracy and effectiveness.
The COVID-19 pandemic pushed healthcare to rely on data and analytics for decision making and illustrated the criticality of accurate, real-time data and analytics. Despite having ample patient data available for direct patient care, Albany Med’s analytics platform had a two-day lag for much of the data. The organization quickly recognized that rapid access to COVID-19 analytics was essential and that it was vital for leaders to have real-time access to data for decision making during the COVID-19 crisis.
The ambulatory practice data for Community Health Network (CHNw) were located throughout various systems, leading to unproductive operations, slow reporting, and insufficient actionable information. CHNw leveraged its analytics platform to integrate the data and provide the ambulatory analytics required for effective practice management. The organization can quickly and efficiently visualize key performance indicators, including ambulatory income statements, productivity, access measures, costing, revenue cycle performance, and payer contracting data.
Despite having an in-depth, individualized heart failure (HF) program, Billings Clinic’s 30-day readmission rate was higher than desired and negatively impacted the costs of care. The organization leveraged comprehensive data and analytics to create an analytical approach for evaluating and caring for patients with HF, successfully enhancing HF care and improving clinical outcomes.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
COVID-19 required that Billings Clinic manage a disease outbreak and its organizational response to the outbreak. To respond effectively, Billings Clinic needed to integrate widely dispersed data into accurate epidemiological information. The organization utilized data and analytics to quickly visualize the data required to monitor and respond to the disease outbreak effectively.
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.
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.
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.
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.
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.
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.
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.
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.