Clinical

Success Stories

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Improving Identification of Hospitalized Patients with Sepsis

Patients who are diagnosed with sepsis present on admission (POA) account for nearly 85 percent of cases. However, outcomes for patients with sepsis not on arrival (NPOA) are poorer due to higher acuity of the sepsis at the time of diagnosis. Because of the challenge associated with the early identification of sepsis for hospitalized patients, those with sepsis NPOA have a mortality rate as high as 35 percent, making outcome improvements for this patient population a top priority for hospitals.
Mission Health, a comprehensive healthcare system located in Asheville, North Carolina, sees the early identification of sepsis as a key part of achieving its goal of providing exceptional patient care. However, Mission lacked a mechanism to assist the clinicians in differentiating between sepsis and the patient’s acute illness, making the early diagnosis of sepsis challenging, negatively impacting outcomes. With the help of data analytics, Mission was able 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.
Results:

45.3 percent relative reduction in severe sepsis and septic shock NPOA mortality rate.
14.4 percent relative reduction in length of stay for patients with severe sepsis and septic shock NPOA.

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Integration of Community Health Workers Improves Care Management Effectiveness

While the delivery of healthcare is essential to staying healthy and getting well, it is not the only determinant of health. Other factors such as psychosocial factors and environmental conditions in which people live, work, and age can have a far greater influence. These factors are referred to as social determinants of health. Existing evidence has found that addressing social determinants of health like housing and food, is effective in improving patient health outcomes and decreasing healthcare costs.
Social determinants of health can significantly affect a person’s overall health and quality of life. Patients with social determinants that negatively impact health, such as lack of access to transportation and the place in which they live, can be exceptionally challenging to keep healthy and often rely on the emergency department (ED) for care. Reaching and engaging with patients in primary care settings can be vital to addressing patient needs. The positive influence of community health workers (CHWs) acting as a bridge between vulnerable patients and the healthcare system has shown to decrease emergency department visits and hospital admissions.
Partners HealthCare, a non-profit health system located in Boston, recognized that meeting the needs of vulnerable patient populations was an opportunity to improve patient outcomes as well as reduce cost. Through its integrated care management program (iCMP), the Partners system had some success in improving the care delivered to underserved communities. Partners’ academic medical centers, Brigham and Women’s Hospital and Massachusetts General Hospital, conducted pilots which focused on the CHW role; one model that empowered CHWs to serve as care leads, and one model that incorporated CHWs into the care team.
Results:
Integration of CHWs into the iCMP is yielding positive results for both pilots. Using a pilot conducted at Brigham and Women’s Hospital and Massachusetts General Hospital, when comparing the difference in six months post-program outcomes to six months pre-program outcomes:

When the CHW functions as a lead, results include a:

$664 larger per member per month (PMPM) reduction in total medical expense and an 11 percent larger reduction in ED visits compared to the control group.

When the CHW functions as a part of the care team, results include a:

$635 larger PMPM increase in total medical expense, however, patients with a CHW team member had a 28 percent larger reduction in ED visits, and an 11 percent larger decrease in office no-show rates compared to the control group.

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Optimizing Sepsis Care Improves Early Recognition and Outcomes

Early identification of sepsis is challenging, as the patient’s physical response to this overwhelming infection presents as a syndrome of non-specific symptoms, delaying recognition, diagnosis, and treatment, which increases mortality rates.
Mission Health, North Carolina’s sixth largest health system, had implemented evidence-based sepsis care bundles; however, processes for identifying patients with sepsis and initiation of care was fragmented and varied widely across the system, negatively impacting sepsis outcomes.
Using a comprehensive data-driven approach to facilitate early sepsis identification and standardize the treatment of sepsis, including the addition of evidence-based alerts, Mission has gained insight into sepsis performance to drive improvement. This approach has resulted in:

14.1 percent relative reduction in mortality for patients with severe sepsis and septic shock.
24.9 percent relative difference in mortality for patients that received the evidence-based protocols compared to those who did not—the evidence-based protocols substantially reduce mortality.
6.4 percent relative reduction in emergency department (ED) length of stay (LOS) for patients with severe sepsis and septic shock.
Four percent relative reduction in ICU LOS for patients with severe sepsis and septic shock admitted from the ED.

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Reducing Sepsis Mortality and LOS with Improved Recognition and Treatment Protocols

In the U.S., over 1.5 million people are treated for sepsis annually. One in four people with sepsis die, making improving early identification and providing patients with timely treatment a top priority. Hospitals and health systems continue to look to improve outcomes for patients with sepsis.
Allina Health, a not-for-profit healthcare system of 12 hospitals and 90 clinics, all serving patients throughout Minnesota and western Wisconsin, previously implemented a rapid process improvement project using a three-part bundle focused on the early identification of sepsis. However, sepsis mortality rates remained higher than desired. After turning to an analytics platform to replace its burdensome manual review process, Allina Health was able to identify opportunities for improvement and develop evidence-based processes for sepsis identification and treatment.
Results:

18.6 percent relative reduction in mortality rate and 10.9 percent relative reduction in hospital length of stay (LOS) for all patients with sepsis.
30.3 percent relative reduction in mortality rate and 18.4 percent relative reduction in hospital LOS for patients with severe sepsis and septic shock.
$1.1 million in annual cost savings, the result of efficiencies and substantial reductions in hospital LOS for patients with severe sepsis or septic shock.

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Systematic, Data-Driven Approach Lowers Length of Stay and Improves Care Coordination

Improving and reducing length of stay (LOS) improves financial, operational, and clinical outcomes by decreasing the cost of care for a patient. It can also improve outcomes by minimizing the risk of hospital-acquired conditions.
Faced with declining revenue related to changes in Medicare and Medicaid reimbursements, Memorial Hospital at Gulfport knew additional methods of providing more efficient and cost-effective quality care were needed to maintain long-term success. The organization embraced the challenge of reducing LOS to lower costs and lessen risk for its patients. By adopting a systematic, data-driven, and multi-pronged approach, Memorial has achieved significant results in one year including:

$2 million in cost savings, the result of decreased LOS and decreased utilization of supplies and medications.
47-day percentage point reduction in LOS.

Improved care coordination and physician engagement have successfully reduced LOS.
The 30-day readmission rate has remained stable.

Three percent increase in the number of discharges occurring on the weekend.

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Machine Learning Improves Accuracy of Risk Predictions and Improves Operational Effectiveness

The Centers for Medicare and Medicaid Services (CMS) readmission penalties are a significant concern for healthcare organizations, with over 2,500 hospitals being penalized each year, resulting in CMS withholding more than $500 million in payments.
For Westchester Medical Center Health Network (WMCHealth), hospital readmissions carried more than financial consequences. Care managers had to use multiple systems and time-consuming, manual processes to identify recently discharged patients at risk for readmission. These processes limited the effectiveness of the care management team, as care managers lost valuable time searching patient records for data needed to prioritize their workload and choose the right interventions.
To address this problem, the data analytics teams at WMCHealth and network member Bon Secours Charity Health System leveraged artificial intelligence and machine learning to develop a more accurate readmission risk prediction model that would enable care managers to use their time coordinating and engaging with patients more effectively. Results include:

A risk prediction model that is 17 percent more accurate than widely used readmission risk models in identifying patients at high-risk and low-risk for readmission within 30-days.
Care managers obtain follow-up appointments faster, usually within seven days, and connect patients with the services needed to prevent unnecessary visits to the emergency department and readmissions to the hospital.
1,327 hours per year saved, freeing up care managers to spend more time with patients.

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Reducing Opioid Availability with Improved Prescribing Practices

Historical approaches to the use of opioids in pain management have been associated with overprescribing and have inadvertently contributed to the opioid abuse crisis. Optimizing the use of opioids can help reduce the number of excess pills circulating in the community.
Allina Health, a not-for-profit health system serving Minnesota and western Wisconsin, achieved previous success in reducing opioid prescriptions in outpatient settings through the adoption of standard practices. Though Allina Health had initial success with its opioid prescription reduction efforts, providers still lacked visibility into prescribing practices, leading to variability that made further sustainable improvements challenging. With the help of analytics, Allina Health leveraged its data to develop prescription standards aimed at reducing the oversupply of opioids in the community, while still effectively managing patients’ acute pain after procedures.
Results:

15,730 fewer opioid pills prescribed at discharge in one year.
16 percent relative reduction in the number of opioid pills prescribed per patient.
95 percent of patients that delivered a baby via cesarean section and received opioids at discharge received fewer than 30 opioid pills.

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Analytics Drive Lean Processes to Lower Healthcare-associated Infections

Healthcare-associated infections (HAIs) remain one of the greatest risks patients face while hospitalized. Each day, about one in 25 hospital patients has at least one HAI—with an estimated 722,000 HAIs in U.S. acute care hospitals annually. Approximately 75,000 of the patients with HAIs died during their hospitalization.
The University of Kansas Health System, a large academic medical system with more than 80 locations across two states, experienced organizational central-line associated bloodstream infections (CLABSI) and catheter-associated urinary tract infection (CAUTI) rates that were higher than desired. A lack of consistent uniform evidence-based maintenance of indwelling urinary catheters and central lines led to unintended care variations, which is a challenge to large healthcare organizations.
Developing a reliable system for preventing CAUTI and CLABSI that produced consistent and accurate results would assist The University of Kansas Health System in HAI prevention. To create this solution, the health system chose to implement lean management for addressing both technical and adaptive work, applying data and analytics from its analytics platform to make improvements driven by lean methodologies. These efforts were initiated within a model cell unit resulting in:

Only one CAUTI in 1,861 days. Zero CAUTI in 747 days.
Only one CLABSI in 824 days. Zero CLABSI in 332 days.
95th percentile patient satisfaction ranking.

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Improving Screening for Lung Cancer Enables Early Detection

With one of every four deaths in the U.S. being attributed to cancer, it is the second leading cause of death, surpassed only by heart disease. There are more deaths from lung cancer than from any other type of cancer accounting for more than 155,000 deaths annually.
While new lung cancer screening guidelines were available, few providers were compliant with the guidelines, or fully understood the complex reimbursement requirements, particularly the patient characteristics that qualify a patient to be eligible for low-dose computed tomography (LDCT) screening and the documentation required for reimbursement.
Mission Health, based in Asheville, North Carolina, is the state’s sixth largest health system with six hospitals and numerous outpatient and surgery centers. The organization wanted to increase the number of patients screened for lung cancer to catch the disease at an earlier, more treatable phase. Mission established a care process model improvement team, enhanced its screening program, and utilized its analytics platform to extract and integrate data from various source systems to evaluate the impact of LDCT screening and outcomes for its patients. Results from the enhanced program include:

71 percent relative increase in LDCT screening for people at increased risk for lung cancer.
56 people with lung cancer identified through early screening.
4.3 percent relative increase in people being diagnosed at early stage I or II lung cancer.
21.2 percent relative reduction in people diagnosed with late stage III or IV lung cancer.

 

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Artificial Intelligence Improves Accuracy of Heart Failure Readmission Risk Predictions

A global pandemic, heart failure (HF) affects at least 26 million people worldwide, and its prevalence only continues to increase. Within the U.S. alone, 5.7 million adults live with HF, carrying a cost of nearly $30.7 billion each year. At 55 percent, HF represents the most common cause of Medicare readmissions, and HF accounts for 42 percent of total admissions for Medicare patients.
Readmissions for HF carry a heavy cost for patients and health systems, in addition to reimbursement penalties from CMS. This makes properly assessing the risk for readmission for patients with HF a top priority. MultiCare Health System leveraged artificial intelligence and machine learning to improve the accuracy of readmission risk predictions for patients with HF. Providing a more accurate risk score in a timely fashion gives care teams more time to intervene effectively and prevent avoidable readmissions.
Results: 

85 percent estimated accuracy for heart failure readmission risk predictor. (LACE accuracy around 62 percent)
Three-fold increase in the number of HF readmission risk-predictions made each day.

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Improving Transitions of Care for Patients with Pneumonia

Nationally, the readmission rate for patients over age 65 with pneumonia is 15.8 percent. Though not all hospital readmissions are preventable, high readmission rates may reflect performance on care quality, effectiveness of discharge instructions, and smooth transitioning of patients to their home or other setting.
Piedmont Healthcare wanted to standardize pneumonia care across its entire system but lacked the data it needed to identify patients who could benefit from additional transition support. Piedmont convened a care management steering committee and deployed analytics tools to generate actionable data for appropriate and effective transitions of care for its Medicare patients with pneumonia. In less than one year, it reduced its readmission rate for patients with pneumonia by 26 percent.

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Evidence-Based Care Standardization Reduces Pneumonia Mortality Rates and LOS

Patients with pneumonia account for over 400 thousand emergency room visits, nearly 1.1 million inpatient hospitalizations, and more than 5.7 million inpatient days each year in the U.S. Every year, almost 51,000 patients die from pneumonia. Among the elderly, community-acquired pneumonia is an increasing problem, now ranking as the fourth leading cause of death.
Piedmont Healthcare, a not-for-profit integrated health system serving Georgia, had multiple order sets for disease management, but the health system lacked a uniform care pathway for the treatment of pneumonia. Care provided for the treatment of pneumonia was often not in alignment with evidence-based guidelines, such as antibiotic selections. This lack of consistency increased both LOS and cost, and a lack of case-specific data made the development of a uniform best practice for pneumonia treatment challenging. By accessing detailed case data with the help of analytics, Piedmont was able to identify and develop best practices for the treatment of pneumonia, driving out the variation that increased costs and reduced the overall quality of care.
Results:

56.5 percent relative reduction in pneumonia mortality rate.
$220,000 in savings over one year, the result of a 9.3 percent relative reduction in LOS.

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Standard Approach to Early Induction of Labor Successfully Reduces Unnecessary Cesarean Deliveries

In the U.S., nearly one in three women give birth via cesarean delivery. Rates vary widely by state, ranging from a low of 23 percent to a high of nearly 40 percent. Despite the potential life-saving benefits of a cesarean, this large variation suggests that unnecessary cesarean deliveries are frequently performed and that potentially modifiable factors, such as patient preferences and practice variation among hospitals, systems, and healthcare providers, likely contribute to the high rate.
Gunnison Valley Hospital has a long history of safe obstetric care, delivering more than 150 babies annually, yet the rates of elective early induction (prior to 39 weeks gestation), primary cesarean, and Nulliparous, Term, Singleton, Vertex (NTSV) were somewhat higher than desired. With the help of analytics, Gunnison shined a light on its labor and delivery practices and developed standard procedures aimed at producing better outcomes for patients.
Results:

87 percent relative reduction in the number of elective inductions of labor prior to 39 weeks gestation.
61.1 percent relative reduction in the number of NTSV cesarean deliveries.

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Successfully Sustaining Elective Colon Surgery Outcome Improvements

For healthcare organizations, sustaining improvements that have been adopted in more than one part of an organization remains a serious challenge. After improvement initiatives have been successfully implemented, it is estimated that less than 40 percent of gains are sustained in the long term. Because improvement initiatives are necessary to maintain a high standard of care, sustaining them so that further improvements can be made remains a top priority for health systems.
MultiCare Health System, a not-for-profit healthcare system serving Washington state, successfully implemented improvement efforts for patients undergoing elective colon surgery, which resulted in significant reductions in 30-day readmission, LOS, and surgical site infections (SSIs). However, without ensuring ongoing engagement, accountability, and visibility into performance, MultiCare was concerned improvements could slip away. By supporting continued monitoring powered by insights gained from relevant data, and by closely listening to provider feedback, MultiCare was able to sustain previous improvements while identifying new opportunities.
Results:

32.7 percent relative reduction in 30-day readmission rate for patients having elective colon surgery.
3.4-day median LOS for patients having elective colon surgery, sustaining previous improvement.
Among patients who had the complete enhanced recovery after surgery protocol implemented for elective colon surgery, there were no surgical site infections—for an entire year.

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Enabling Informed Surgical Choices for Breast Cancer Through Shared Decision Making

One out of every eight women in the U.S. will develop breast cancer in her lifetime, and men have a lifetime risk of one in 1,000. This year, over 3.1 million women are currently being treated or have finished treatment for breast cancer.
The Virginia Piper Cancer Institute had clear evidence-based practice guidelines that directed recommendations for early breast cancer treatment options. Even with these evidence-based recommendations, however, the organization’s mastectomy rates were higher than expected.
Recognizing the organization could do better, the breast cancer program committee endorsed the spread of shared decision making for patients with early-stage breast cancer to all Virginia Piper Cancer Institute locations. The spread of shared decision making allowed patients to receive evidence-based information early in their course of care and make informed decisions that aligned with their values and preferences.
Within nine months of implementing a standard process for shared decision-making visits, the Virginia Piper Cancer Institute clinics that have completely adopted the process have made significant progress in engaging patients with early breast cancer in the shared decision-making process:

81 percent of eligible patients (207 people) participated in shared decision-making visits.
62 percent of the shared decision-making visits were in person.
27 percent relative increase in surgical decision of lumpectomy over mastectomy.

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Shared Decision-Making Leads to Better Decisions and Improves Patient Relationships

Shared decision-making is the process by which clinicians and patients work together to make decisions and select tests, treatments, and care plans based on clinical evidence. Shared decision-making balances risk and expected outcomes with patient preferences and values, empowering patients to make informed decisions.
Project leadership at Allina Health didn’t have a way to know if shared decision-making interventions were being applied. By utilizing its analytics platform, Allina Health was able to track whether or not decision support tools were being used consistently and if shared decision-making conversations were happening, if there was variation in how and when they were being used, and if they were making a difference.
Within nine months of implementing the standard shared decision-making process Allina Health substantially increased the number of patients participating in the program:

749 patients have participated in a shared decision-making visit across the system, including:

69 percent of eligible patients with low back pain.
84 percent of eligible patients with early breast cancer.

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Collaborative, Data-Driven Approach Reduces Sepsis Mortality by 54 Percent

In the U.S., sepsis impacts more than 1.5 million people annually, of which about 250,000 will die. This accounts for one-third to one-half of all deaths for hospitalized patients. Health Quest focused on identifying ways to improve these outcomes. Despite instituting several evidence-based recommendations, the organization had not succeeded in reducing sepsis mortality to its desired rate.
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, resulting in a:

54 percent relative reduction in sepsis mortality, saving 92 lives in 10 months.
57.1 percent relative reduction in catheter-associated urinary tract infection (CAUTI) standardized infection ratio (SIR).
30.7 percent relative reduction in C. difficile SIR.

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Enhanced Recovery Program Improves Elective Colorectal Surgical 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.

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Using Data to Spotlight Variation and Transform Total Joint Care

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.

 
 

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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.

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Advancing Health Equity – Data Driven Strategies Reduce Health Inequities

Health equity means that everyone has an equal opportunity to live the healthiest life possible – this requires removing obstacles to health. The U.S. ranks last on nearly all measures of equity, as indicated by its large, disparities in health outcomes. Illness, disability, and death in the United States are more prevalent and more severe for minority groups. Health inequities persist in Minnesota as well, which motivated Allina Health to take targeted actions to reduce inequities.
Allina Health needed actionable data to identify disparities and to reduce these inequities. This came in the form of REAL (race, ethnicity, and language) data, which Allina Health analysts used to visualize how health outcomes vary by demographic characteristics including race, ethnicity, and language.  To understand the root causes of specific disparities as well as to identify solutions within their sphere of influence as a healthcare delivery system, Allina Health consulted the literature and also consulted patients, employees and community members. Then Allina Health created appropriate interventions based on this information.
As a result, Allina Health created an awareness of the health inequities among its patient populations, as well as effective approaches to breach the barriers that were preventing these patients from getting the care they needed. While much work remains in this long journey to achieve health equity, Allina Health has taken some significant steps forward, including:

Three percent relative improvement in colorectal cancer (CRC) screening rates for targeted populations, exceeding national CRC screening rates by more than ten percentage points.
REAL data embedded in dashboards and workflow to easily identify and monitor disparities.

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Unleashing the Data to Sustain Spine Service Line Improvements

Research shows that despite an increase in the number of improvements in clinical, cost, and operational outcomes, there is a lack of sustained improvements. Some of the key challenges can be access to the data and analytics, and adherence to data-driven clinical standards, things the Allina Health Spine Clinical Service Line (CSL) clinical leadership team experienced.
By providing widespread access to the data and analytics, the Spine CSL at Allina Health has been able to continue its reduction in LOS and further improve its reduction in complications, all while increasing cost savings and achieving pay-for-performance incentives.
Results:

$1 million in pay-for-performance incentives received.
More than $2 million in supply chain savings, a result of data-driven clinical standardization.
31 percent of expected complications avoided.
22 percent relative reduction in surgical site infections.

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Comprehensive Approach to CAUTI Prevention Leads to Dramatic Reduction in Infections

Despite being common, healthcare-associated infections are potentially deadly and carry a significant financial cost. Of healthcare associated infections, catheter-associated urinary tract infections (CAUTI) are one of the most common, despite most instances of CAUTI being preventable.
As CAUTI was determined to be one of the top five influential factors in the publicly report quality scores, Piedmont Healthcare looked to data for more visibility into factors that were contributing to CAUTI rate in an effort to permanently reduce the number of infections. By engaging staff for compliance with CAUTI prevention best practices, Piedmont has seen sustainable improvements.
Results:

50.2 percent relative reduction in CAUTI standardized infection ratio (SIR). This translates to 37 fewer patients with infections than expected.
6.7 percent relative improvement in insertion bundle compliance.
Maintenance bundle compliance improved dramatically, with nearly a three-fold increase in the percentage of patients receiving the maintenance bundle.

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Emergency Department Triage Redesign Dramatically Reduces Wait Times, LOS, and Left Without Being Seen Rates

Overcrowding in the emergency department (ED) has been associated with increased inpatient mortality, increased length of stay (LOS), and increased costs for admitted patients. ED wait times and left without being seen (LWBS) rates—patients who present to the ED but leave before receiving a medical evaluation—are indicators of overcrowding.
Mission Health needed to address overcrowding in its ED. The community hospital system confirmed overcrowding when it determined that approximately 4,000 patients were leaving its ED each year without being seen.
Mission implemented an improvement process to address ED overcrowding. The hospital leveraged its analytics platform to develop an ED analytics application that provided actionable, timely ED performance data to focus improvement efforts on four areas: staffing patterns, registration, triage assessment by the registered nurse (RN), and early access to a qualified medical provider.
Mission achieved significant ED performance improvements:

89 percent relative reduction in LWBS rate, with current performance at 0.4 percent.
85 percent relative reduction in percentage of patients who left before treatment complete, with current performance at 0.58 percent.
75 percent relative reduction in median door to assessment by a qualified provider, with current performance under 15 minutes.

 

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Labor and Delivery Transformations Lower Costs and Improve Care

One in three women delivers via cesarean in the U.S., and more than 90 percent of them have repeat operations in subsequent deliveries. Despite numerous evidence-based guidelines and established best practices for labor and delivery, clinical care varies widely for many practices. Labor and delivery care varied at Thibodaux Regional Medical Center, causing the organization to look for ways to standardize care.
To better understand variations in care, and opportunities to reduce its cost, the labor and delivery care transformation team at Thibodaux Regional used the Health Catalyst Labor and Delivery Advanced Application as well as the Financial Management Explorer application, which integrates data from billing and costing, and creates snapshots of current financial metrics.
Informing and educating providers with provider-specific data in conjunction with redesigned workflow, standardized supplies, and new, standardized protocols enabled the labor and delivery care transformation team at Thibodaux Regional to experience cost savings and improved outcomes, including:

24.4 percent relative reduction in the cost of care for uncomplicated vaginal delivery. Projected annual cost savings of $266,067.
22 percent relative reduction in the cost of care for cesarean deliveries. Projected annual cost savings of $346,856.

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