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.
For patients, safety in hospitals and health systems remains a serious concern as medical errors are now the third leading cause of death in the U.S. Determined to improve patient safety, Allina Health turned to predictive analytics to standardize and expand safety event reporting.
Medical errors account for 10 percent of all deaths. To improve patient safety, Allina Health utilized its machine learning, analytics platform, and a trigger-based data-driven surveillance tool to identify and investigate a broader base of harm events, enabling the organization to:
Learn how Allina Health leveraged its analytics platform and Health Catalyst professional services to perform an analysis demonstrating the impact of pharmacist-led medication therapy management (MTM).
Partners HealthCare utilized technology—including its analytics platform, analytics applications, and EMR—to collect data about serious illness conversations and to evaluate the impact of those conversations on trends at the end of life.
By leveraging data from its analytics platform along with a risk predictive model to identify patients who would benefit from its home-based palliative care, Partners HealthCare has improved the end of life care for patients and reduced costs.
Five percent of patients account for half of healthcare spending in the U.S., and patients with multiple chronic conditions cost up to seven times more than those with only one. Read how Partners HealthCare has maintained its integrated care management program (iCMP) and is continuing to decrease costs while improving outcomes.
Learn how Mission Health used data and analytics to gain a comprehensive view of sepsis outcomes so that improvement efforts that help clinicians identify and provide early intervention for patients who may be septic could be effectively implemented and sustained.
The positive impacts of community health workers (CHWs) have been well documented, yet in general, CHWs remain underutilized and have not been fully integrated into care teams. Read how Partners HealthCare successfully integrated CHWs into its integrated care management program (iCMP) care team to improve patient outcomes and reduce cost.
Read how Mission Health used a comprehensive data-driven approach to facilitate early sepsis identification and standardize the treatment of sepsis.
In the U.S., over 1.5 million people are treated for sepsis annually, and one in four people with sepsis die. Read how Allina Health utilized its analytics platform to identify opportunities for improvement and develop evidence-based processes for sepsis identification and treatment.
Read how Memorial Hospital at Gulfport embraced the challenge of reducing LOS to lower costs and improve outcomes for its patients. Its commitment to a data-driven, multi-pronged approach to reducing LOS has produced impressive results in one year.
Hospital readmissions can impact the health outcomes for patients and result in costly readmission penalties from CMS. Learn how the data analytics teams at Westchester Medical Center Health Network and network member Bon Secours Charity Health System utilized its analytics platform, in coordination with a machine learning algorithm, to build a knowledgeable and accurate readmission risk model that better reflected its patient population.
Over the past twenty years, the U.S. has experienced a national opioid misuse and abuse crisis. By utilizing data and analytics, Allina Health has improved its opioid prescribing practices and further reduced the number of opioids prescribed for acute pain.