OneCare Vermont sought to identify which of its patients were at the highest risk of serious illness or mortality from COVID-19 and which of its patients with chronic healthcare problems needed care for medical issues other than COVID-19. Leveraging its data platform, OneCare Vermont enabled rapid identification of at-risk patients. Providers and care teams use the risk-stratification care coordination tool to proactively conduct patient outreach, including telephone calls and telemedicine virtual visits, ensuring patients receive needed social support and medical care during the pandemic.
The Ohio Health Information Partnership (OHIP) was using its technology to share individual COVID-19 test results to providers at the bedside. The State Department of Health approached OHIP with a request to aid the state by providing a more comprehensive data set for COVID-19. Using the Health Catalyst® Data Operating System (DOS™) platform, OHIP has improved the effectiveness of public health reporting and COVID-19 surveillance.
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.
Thibodaux Regional Health System recognized its patients with Type 2 diabetes had hemoglobin A1C (HbA1c) levels that exceeded the evidence-based guidelines for blood glucose control and sought to improve the health of this patient population. Using a data platform and a consistent improvement methodology, Thibodaux Regional learned more about the challenges to diabetes self-management in its population. The organization was then able to improve its outreach and support for patients with diabetes.
Thibodaux Regional Health System sought to transform its blood transfusion practices. Aware that blood transfusions were often over-used, Thibodaux Regional’s leadership prioritized engaging providers to decrease unnecessary transfusions, reduce potential risks to patients, and improve costs of care. The organization leveraged data and analytics to better understand opportunities for improvement.
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.
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.
Thibodaux Regional Health System had implemented evidence-based stroke care interventions in its emergency department. However, the organization was not meeting its established goals for early identification and treatment. With strong leadership support and the help of analytics, the organization’s stroke care transformation team was able to identify opportunities for improvement, culminating in improved care delivery through facility-wide automated alerts and a reduction in the need to transfer patients to other facilities for treatment.
Thibodaux Regional Health System had improved sepsis mortality rates, yet it recognized the challenges associated with sustaining and further improving care outcomes. Driven by a mission of patient-centered excellence that starts with the chief executive officer, the Board, and leadership, Thibodaux Regional’s sepsis care transformation team utilized its data platform and analytics applications to help facilitate data-driven, sustained improvements.
UnityPoint Health evaluated its percutaneous coronary intervention (PCI) performance and identified the opportunity to further improve. The health system decided to identify ways to improve its PCI outcomes. With its data operating system and a robust suite of analytics tools, UnityPoint Health took a data-driven approach to improving its PCI outcomes.
Colorectal cancer (CRC) accounts for $16 billion in healthcare costs, and with 142,250 new cases annually, it’s the second leading cause of cancer deaths in the U.S. Thibodaux Regional Health System had implemented evidence-based screening and oncology treatment guidelines for colon cancer, yet it still needed to meet organizational goals for early diagnosis and colon cancer survival. With support from the CEO and senior executive leadership, a collaborative approach to tackling CRC diagnosis rates, and a robust suite of analytics applications to deliver accurate data, Thibodaux Regional improved CRC outcomes and patient satisfaction.
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.
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.
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.
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.
To improve the care of patients with Clostridioides difficile (C. diff), Thibodaux Regional Medical Center used data and analytics to monitor completion of early recognition screening, protocol compliance, antimicrobial stewardship rounds, and probiotic administration. Thibodaux Regional’s leadership, culture, evidence-based practices, and data-driven improvement approach have positively impacted its patients.
Pediatric sepsis remains a key concern for hospitals due to the serious nature of the disease. Early diagnosis and timely care are a top priority, as this significantly improves a patient’s chance of recovery. With the help of big data and prescriptive analytics, Texas Children’s Hospital developed an early alert system and workflow changes to improve its pediatric sepsis care. The hospital’s investment in new processes, decision support, and analytics has substantially improved pediatric sepsis outcomes.
Chronic knee and back pain associated with morbid obesity increases the risk for opioid dependence among patients undergoing bariatric surgery. Mission Health sought a comprehensive, data-driven, evidence-based approach to reduce opioid prescribing after bariatric surgery, decreasing the risk for misuse and harm. By using comprehensive ERAS protocols with multimodal pain management interventions, Mission realized substantial reductions in opioid use for pain management among patients undergoing bariatric surgery, including a:
Community Health Network (CHNw) was keenly aware of the needs of the elderly population in its communities of impact. However, despite the development and implementation of a successful geriatric program, the organization lacked access to, and visibility of, meaningful data to quantify program outcomes. The CHNw Geriatric Evaluation and Management (GEM) team used an analytics application to demonstrate the sizeable, positive impact of the GEM team care and interventions on both patient and financial outcomes:
To optimize the impact of its diabetes self-management education program, Allina Health enhanced its service model, aligning resources to proactively meet patient demand, while also maintaining high-quality clinical outcomes. Utilizing analytics in the redesign process has allowed Allina Health to understand patient needs better and monitor the impact of planned changes to the program on patient outcomes.
Seeking to drive down unnecessary cost, Hospital Sisters Health System (HSHS) needed a way to automate risk stratification of patients who may benefit from care management services and eliminate the burdensome manual work its care managers were performing to identify at-risk patients. HSHS utilized a population health analytics platform to accurately risk stratify its care management and identify patients who would benefit from additional care management interventions.
Community Health Network (CHNw) was keenly aware of the impact that opioid prescribing patterns have on potential opioid misuse and set a focus on decreasing opioid prescriptions; however, it lacked access to meaningful data that could be used to understand the volume of opioids that were prescribed postoperatively. CHNw created an orthopedics guidance team and leveraged data within its analytics platform to gain insight into prescribing habits over time.
Improving the management of chronic diseases for patients is crucial for reducing expenses and improving health outcomes. Newton-Wellesley Hospital, a member of the Partners HealthCare system, adopted the population health coordinator role and utilized analytics to help identify variations in chronic disease management across practices and develop standardized best practices aimed at reducing costs through better outcomes for patients.