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.
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.
Healthcare-associated infections (HAIs) remain one of the greatest risks patients face while hospitalized. Read how The University of Kansas Health System used lean management methodologies and its analytics platform to reduce HAIs.
There are more deaths from lung cancer than from any other type of cancer—more than 155,000 deaths annually. Learn how Mission Health utilized its analytics platform to improve the screening and outcomes for patients with lung cancer.
In the U.S., 5.7 million adults have heart failure (HF), costing the nation an estimated $30.7 billion each year. Learn how MultiCare leveraged AI and machine learning to more accurately predict the readmission risk for patients with HF.
Improving transitions of care from hospital to home is key to reducing readmissions for patients with pneumonia. Learn how Piedmont Healthcare used data to effectively manage care transitions and reduce readmissions in less than one year.
Every year, almost 51,000 patients die from pneumonia with pneumonia ranking as the fourth leading cause of death for the elderly. After implementing a pneumonia care pathway and analytics application, Piedmont Healthcare reduced its pneumonia mortality rate.
In the U.S., nearly one in three women give birth via cesarean delivery. Unnecessary cesarean deliveries can expose mothers and babies to possible harm without providing many benefits. Read how Gunnison Valley Hospital reduced the number of unnecessary cesarean deliveries by standardizing labor and delivery care practices and utilizing data from its analytics platform.
For healthcare organizations, sustaining improvements that have been adopted in more than one part of an organization remains a serious challenge. Learn how MultiCare has sustained its elective colon surgery improvement efforts while identifying new opportunities.
Shared decision making can help patients with breast cancer make the best surgical choices. Learn how the Virginia Piper Cancer Institute, part of Allina Health, implemented shared decision making, helping patients choose the surgical option that meets their personal preferences and medical needs.
Patients shouldn’t have to make difficult medical decisions on their own, nor should they feel coerced into making a specific choice; it’s a fine balance. Read how Allina Health’s shared decision-making program has helped patients deal with this delicate process.
In the U.S., sepsis impacts more than 1.5 million people annually, of which about 250,000 will die. Learn how Health Quest established a multidisciplinary sepsis committee to lead improvement efforts, including the use of analytics to combat sepsis mortality rates and improve patient outcomes.
Contemporary colorectal surgery is often associated with long LOS, high costs, and surgical site infections (SSI) approaching 20 percent. Much of the LOS variation is not attributable to patient illness or complications, but most likely represents differences in practice style. Successfully reducing SSI requires a multimodal strategy under the supervision of numerous providers with high compliance across the spectrum.
Allina Health was using established, evidence-based clinical guidelines, yet clinical variation remained high across pre-arrival, preoperative, intraoperative, and postoperative care areas, leading to substantial variation in LOS, cost of care, and the patient experience. To ensure greater consistency, Allina Health developed an enhanced recovery program (ERP) for patients undergoing elective colorectal surgery, which built standard protocols into the EHR to address elements of care from pre-arrival through post-discharge.
To facilitate the program and monitor performance, Allina Health developed an ERP analytics application with an administrative dashboard to easily visualize first-year results:
78 percent relative reduction in elective colorectal SSI rate.
19 percent relative reduction in LOS for patients with elective colorectal surgery.
82.4 percent utilization of preoperative and postoperative order sets, increasing the consistency of care and reducing unwarranted variation.
Total Hip (THA) and Total Knee (TKA) Arthroplasty are the most prevalent surgeries for Medicare patients, numbering over 400,000 cases in 2014, costing more than seven billion dollars annually for the hospitalization alone. Today, more than seven million Americans have hip or knee implants, and the number is rising. Furthermore, substantial variation in the cost per case has raised questions about the quality of care. At Thibodaux Regional Medical Center, total joint replacement for hips and knees emerged as one of the top two cost-driving clinical areas with variation in care processes.
To address this, Thibodaux Regional maintained its focus on the IHI Triple Aim while developing organizational and clinical strategies to transform the care of patients undergoing THA and TKA. It commissioned a Care Transformation Orthopedic Team that set multiple outcome goals. Among its many efforts, the team established standard care processes, created an educational program, redesigned order sets and workflows, and deployed a joint replacement analytics application.
Thibodaux Regional reduced variability and decreased costs significantly while maintaining high levels of patient satisfaction:
76.5 percent relative reduction in complication rate for total hip and total knee replacement.
38.5 percent relative reduction in LOS for patients with total hip replacements.
23.3 percent relative reduction in LOS for patients with total knee replacement.
$815,103 cost savings, achieved in less than two years.
Machine Learning, Predictive Analytics, and Process Redesign Reduces Readmission Rates by 50 Percent
The estimated annual cost of readmissions for Medicare is $26 billion, with $17 billion considered avoidable. Readmissions are driven largely by poor discharge procedures and inadequate follow-up care. Nearly one in every five Medicare patients discharged from the hospital is readmitted within 30 days.
The University of Kansas Health System had previously made improvements to reduce its readmission rate. The most recent readmission trend, however, did not reflect any additional improvement, and failed to meet hospital targets and expectations.
To further reduce the rate of avoidable readmission, The University of Kansas Health System launched a plan based on machine learning, predictive analytics, and lean care redesign. The organization used its analytics platform, to carry out its objectives.
The University of Kansas Health System substantially reduced its 30-day readmission rate by accurately identifying patients at highest risk of readmission and guiding clinical interventions:
39 percent relative reduction in all-cause 30-day.
52 percent relative reduction in 30-day readmission of patients with a principle diagnosis of heart failure.