Integrated Delivery System

Success Stories

Health Catalyst

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 reductionin 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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Machine Learning Automates Outpatient Coding

Accurate service line reporting is necessary for a healthcare organization to understand its total cost of care. Organizations that do not understand the total cost of care cannot be successful in risk-sharing and other forms of value-based payment, resulting in a loss of reimbursement.
In an effort to reduce costs, MultiCare Health System, an integrated delivery system serving Washington, decided to outsource all encounter coding, which eliminated the coding of outpatient encounters, negatively impacting service line reporting. To ensure accurate reporting, MultiCare asked its coders to assign an MS DRG code to all hospital-based outpatient encounters, which brought significant additional costs. To mitigate this, MultiCare utilized data analytics and machine learning to develop an algorithm that predicts the MS DRG code for hospital-based outpatient encounters.
By employing machine learning, MultiCare has achieved impressive results, including:

Successfully restoring service line reporting, enabling the organization to better understand the total cost of care, and supporting future participation in value-based care and risk-sharing agreements.
Ability to avoid additional labor costs that would be required to perform dual coding, saving more than $1M annually.

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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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Widespread Analytics Adoption Improves ACO Measure Performance

On an annual basis, Accountable Care Organizations (ACOs) are required to accurately report data that is used to assess quality performance. This is necessary in order for the ACO to be eligible to share in any savings generated. Improvements in measure performance are often linked with ACOs that have offered providers the skills, tools, and data required to understand and track their own performance, as well as that of their peers.
Mission Health, based in Asheville, North Carolina, is the state’s sixth-largest health system, spanning the 18 counties of western North Carolina. Mission formed one of the largest ACOs in the country, Mission Health Partners (MHP), providing services for nearly 90,000 patients. While MHP had previously achieved success in improving its ACO measure performance, it sought to increase its quality scores even higher. Without access to transparent, actionable data, leadership was unsure if improvements would be sustained, let alone if existing workflows could lead to new improvements. After developing a comprehensive plan that included a massive expansion to data access, Mission practices were able to sustain initial improvements, identify new opportunities, and improve population health quality even further.
Substantial improvement across multiple ACO measures:

29 percent relative improvement in the number of patients receiving colorectal cancer screening.
10 percent relative improvement in the number of patients receiving breast cancer screening.
7 percent relative improvement in the number of patients with blood pressure under control.

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Integrated Data Enables Single Source of Truth and Rapid Adoption

Many healthcare organizations struggle to deliver seamless reporting, advanced visualizations, and end-user self-service models, but these types of analytics are critical to business intelligence and have become a practical and strategic necessity.  There is a lack of trust in data because it can be difficult to access and combine information that is fragmented, coming from multiple, disparate sources such as EMRs, billing, claims, and financial systems. Without an integrated source of clinical and business data in a trusted single source of truth, it is difficult, if not impossible to create a data-driven approach to decision making.
Orlando Health, one of Florida’s most comprehensive private, not-for-profit healthcare networks consisting of eight hospitals and 50 clinics, began its journey to integrate clinical and business data into a single source of truth across the organization when it made the transition from a legacy data warehouse solution that employed an enterprise data model to the Health Catalyst analytics platform, and subsequently to the Health Catalyst® Data Operating System (DOS™) platform.
Integrated data and ability to deliver superior solutions has resulted in a single source of truth, leading to increased adoption. Once customers realized the timeliness, ease of access, and quality of the improved analytics and visualizations available to them, demand and adoption increased and continues to grow.

6,120 queries of the analytics platform each month.
Users access analytic applications and visualizations more than 700 times each month.

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Integrated Healthcare Data Quickly Enables Adaptive, Purpose Driven Analytics

Changes in payment models are putting pressure on clinicians to close gaps in care. To do this, they need instant access to actionable information about their patients and their own performance. However, many electronic health records and business intelligence systems are still grappling with how to deliver the insights necessary to revolutionize the way providers work.
Orlando Health, a Florida-based, not-for-profit health system made up of eight hospitals and 50 clinics, found its enterprise data model difficult to scale, making it challenging to gain insights from its healthcare data. Building upon its analytics platform, Orlando Health recognized the value of immediate access to adaptive, integrated healthcare data that could be rapidly deployed in consumable, actionable visualizations to address a wide spectrum of business needs and use cases, and embraced a next-generation data model.
Results:

Ten data sources loaded into the platform in under six months.
As little as one week to deploy dashboards, visualizations, and analytic insights.
95 percent reduction in work hours required to incorporate system enhancements.
88 days saved in the amount of time required to implement system enhancements.

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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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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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Improving Accuracy of Clinical Documentation Positively Impacts Risk Adjustment Factor and HCC Coding

The Hierarchical Condition Category (HCC) risk adjustment model is used by CMS to estimate predicted costs for Medicare beneficiaries, and the results directly impact the reimbursement healthcare organizations receive for patients enrolled in a Medicare Advantage plan. CMS requires that all qualifying conditions be identified each year by provider organizations. Documentation that is linked to a non-specific diagnosis, as well as incomplete documentation, negatively affects reimbursement.
Allina Health, a not-for-profit integrated healthcare delivery system serving Minnesota and western Wisconsin, needed to improve its HCC coding and clinical documentation in order to ensure the correct risk adjustment factor (RAF) was applied to its patients, since failing to do so would jeopardize its reimbursement and result in lower than expected compensation. After identifying opportunities for improvement by comparing its HCC risk adjustment coding data to other organizations and vendor metrics, Allina Health improved clinical documentation precision, medical diagnoses accuracy, and ensured eligible patients are seen each calendar year.
Results:

10 percent increase in RAF for the target population in one year.
72 percent relative improvement in four key problem list diagnoses.

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Dedication to Quality Improvement Delivers on the Triple Aim: Saves Tens of Millions Annually

Unwarranted variation in clinical care is costly: representing as much as $30 million of actionable savings opportunity for a typical organization. Addressing clinical care at Allina Health, however, was challenging—as a large system with limited resources, the organization struggled to standardize work to impact outcomes and reduce costs.
Allina Health’s executive team understood that, due to market and system demands, it needed sharper focus on increasing clinical value to improve financial margins. In response, the organization launched its Clinical Value Program, a systemwide effort to measure and improve clinical value. The program quantifies the value of clinical change work to improve outcomes, while reducing costs and increasing revenue for reinvestment in care.
With a data-driven, multidisciplinary team effort, Allina Health’s Clinical Value Program has improved care and delivered on the Triple Aim, achieving the following results:

More than $33 million positive margin impact by expense reduction and additional hospital in/outpatient revenue.
Identified $13 million in additional opportunities for cost reductions, which have been integrated into the health system budget plan.

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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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Activity-Based Costing and Clinical Service Lines Team up to Improve Financial and Clinical Outcomes

Healthcare costs continue to increase at a disproportionate rate relative to gross domestic product, and Americans are becoming increasingly aware that they aren’t getting their money’s worth. To build a sustainable healthcare system, healthcare organizations must identify and address waste and reduce the total cost of care.
UPMC recognized that the common denominator to addressing threats to sustainability is to fully understand and effectively manage costs. It implemented activity-based costing (ABC), facilitated by the Health Catalyst CORUS™ Suite, to deliver detailed and actionable cost data across the analytics environment, and support service line reporting, contract modeling, and clinical process improvement. UPMC has used this approach to effectively drive cost savings and improve clinical outcomes in many of its service lines, including Surgical Services, Women’s Health, Orthopedics, and Cardiovascular. For example:

$3M cost savings/avoidance over 2 years through the implementation of the ERAS program.
Increased insight into cost variation and drivers of inefficiency in the operating room setting.
Improved patient outcomes and quality (readmissions, complications, patient reported outcomes, patient satisfaction, etc.) for patients undergoing joint replacement.

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Accuracy of Readmission Risk Assessment Improved by Machine Learning

Hospital readmissions carry significant financial costs and are associated with negative patient outcomes. While the reasons behind patient readmissions are multi-factorial, and the specific rates vary by institution, nearly 20 percent of all Medicare discharges nationwide led to a readmission within 30 days. Preventing even 10 percent of these readmissions could save Medicare $1 billion.
North Carolina’s only not-for-profit, independent community healthcare system, Mission Health, is comprised of seven hospitals, 750 employed/aligned providers, and one of the largest Medicare Shared Savings ACOs in the nation. Mission had been using the LACE index to predict risk for readmission, and while it was helpful, Mission’s patient population was different than the population used to develop the LACE index, leaving the health system with some uncertainty regarding the readmission risk of its patients. With the help of data analytics, Mission developed its own predictive model for assessing readmission risk, aimed at preventing readmissions and improving outcomes for patients.
Results:

The area under the curve (AUC) for Mission’s readmission risk predictor is 0.784, outperforming LACE, and meeting the organization’s goal for performance.
Mission’s readmission rate is 1.2 percentage points lower than its top hospital peers.

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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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Opportunity Analysis Permits Successful Execution of At-Risk Contracts

Growth in the government payer mix and an increased cost burden to the commercial population, decreases in the private payer population, and programs like the Medicare Shared Services Program, have caused joint ventures, partnerships, and co-branding efforts, better known as at-risk contracts, between payers and providers to increase.
Allina Health has three Integrated Health Partnership (IHP) contracts, an accountable care model that incentivizes healthcare providers to take on more financial accountability for the cost of care for Medicaid patients, which cover approximately 90,000 members. To achieve success in its IHP contracts, and avoid losses, Allina Health needed to reduce healthcare costs while improving patient outcomes and experience.
Allina Health has integrated several data sources, including claims and developed the infrastructure required to perform opportunity analysis. Using data and analytics for opportunity analysis has given Allina Health insight into its IHP patient population, supporting the development of interventions to decrease the total cost of care and improve outcomes.

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Clinical and Financial Partnership Reduces Denials and Write-Offs by More than $3 Million

CMS denies nearly 26 percent of all claims, of which up to 40 percent are never resubmitted. The bane of many healthcare systems is the inability to identify and correct the root causes of these denials, which can end up costing a single system tens of millions of dollars. Yet almost two-thirds of denials are recoverable and 90 percent are preventable.1 Despite previous initiatives, The University of Kansas Health System’s denial rate (25 percent) was higher than best practice (five percent), and leadership realized that, to provide its patients with world-class financial and clinical outcomes, it would need to engage differently with its clinical partners.
To effectively reduce revenue cycle and implement effective change, The University of Kansas Health System needed to proactively identify issues that occurred early in the revenue cycle process. To rethink its denials process, it simultaneously increased organizational commitment, refined its improvement task force structure, developed new data capabilities to inform the work, and built collaborative partnerships between clinicians and the finance team.
As a result of its renewed efforts, process re-design, stakeholder engagement, and improved analytics, The University of Kansas Health System achieved impressive savings in just eight months.

$3 million in recurring benefit, the direct result of denials reduction.
$4 million annualized recurring benefit.
Successfully partnered with clinical leadership to transition ongoing denial reduction efforts to operational leaders.

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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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Blood Conservation Program Yields Millions of Dollars in Savings

Every three seconds, someone in the United States will need a blood transfusion, which adds up to nearly 17 million blood components transfused annually. Yet, evidence shows that up to 60 percent of red cell transfusions may not be necessary. In 2011, Allina Health, a healthcare delivery system that serves Minnesota and western Wisconsin, had a wide variation in transfusion practices throughout the system and a transfusion rate that was 25 percent above national benchmarks. In an effort to improve outcomes of high-risk transfusions, Allina Health turned to its data to develop an evidence-based blood conservation program aimed at reducing costs and saving valuable blood resources.
Results:

$3.2M decrease in annual blood product acquisition costs since 2011
30,283 units saved annually
111 units of red cells saved per 1000 inpatient admissions

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