Academic Medical Center

Success Stories

Health Catalyst

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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Gaining a Competitive Advantage with an Advanced Analytics Team

The ability to effectively use data for strategic decision making is imperative for healthcare organizations in this era of increasing at-risk payment models and stiff competition. Yet, healthcare still lags behind most other sectors when it comes to the acquisition, storage, and analysis of data.
The University of Kansas Health System is a large academic medical system with more than 80 locations operating in two states, 999 licensed beds, and over 700 employed physicians. Recognizing the need to more effectively utilize data and analytics to help answer important strategic questions, the health system commissioned an advanced analytics team to help unleash the data capabilities needed to have a competitive edge.
Results:

In two short months, the team demonstrated its value, and the success of the pilot in answering important strategic questions which led to making the applied analytics team a permanent part of the new performance improvement department.
The implementation of an advanced analytics team enabled the health system to unleash its data, to probe and answer previously elusive key strategic questions, resulting in new clinical, operational, and financial insights and actionable knowledge the organization can immediately apply to improve its effectiveness.

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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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Care Transitions Improvements Reduces 30-Day All-Cause Readmissions Saving Nearly $2 Million

Researchers estimate that in just one year, $25 to $45 billion is spent on avoidable complications and unnecessary hospital readmissions, the result of inadequate care coordination and insufficient management of care transitions.
While increasing its efforts to reduce its hospital readmission rate, the University of Texas Medical Branch (UTMB) discovered that it lacked standard discharge processes to address transitions of care, leading to a higher than desired 30-day readmission rate. To address this problem, UTMB implemented several care coordination programs, and leveraged its analytics platform and analytics applications to improve the accuracy and timeliness of data for informing decision making and monitoring performance.
This combination of approaches proved successful, resulting in:

14.5 percent relative reduction in 30-day all-cause readmission rate.
$1.9 million in cost avoidance, the result of a reduction in 30-day all-cause readmission rate.

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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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How One Hospital Embraced Patient Satisfaction Transparency

As consumers pay more for their healthcare they are demanding more transparency. In a telling example, it’s estimated that over 84 percent of patients use online provider reviews to help make care decisions. With increased transparency, hospitals need to develop strategies to address patient satisfaction while finding a way to participate for more fully in the patient satisfaction dialogue and social media communications, including the rating process.
One large hospital has done just that by increasing transparency in the patient review process. A key component is providing physician star ratings by patients on the hospital’s own website, with patient survey data sourced from Health Catalyst’s analytics platform. While this strategy took time and effort to win over physician acceptance, it has paid off considerably by taking patient satisfaction to new heights.
The overall patient satisfaction improvement initiative, of which the physician transparency effort was a key component, has proven to be resoundingly successful in supporting physicians and staff in the difficult work of providing outstanding and compassionate care – and has reaped impressive results including,

Improved patient satisfaction scores from 60 percent to over 90 percent
Successfully implemented a physician mitigation strategy with a 98 percent comment acceptance rate
Intensified focus on the patient experience through data and education

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Effective Healthcare Data Governance: How One Hospital System is Managing its Data Assets to Improve Outcomes

As healthcare invests in analytics to meet the IHI Triple Aim, data has become its most valuable asset—and one of the most challenging to manage. Healthcare organizations must integrate data from a complex array of internal and external sources. To establish a single source of truth, The University of Kansas Hospital deployed an enterprise data warehouse (EDW). However, they quickly realized that without an effective data governance program clinicians and operational leaders would not trust the data. Led by senior leadership commitment, The University of Kansas Hospital established processes to define data, assign data ownership and identify and resolve data quality issues. They also have 70+ standardized enterprise data definition approvals planned for completion in the first year and have created a multi-year data governance roadmap to ensure a sustained focus on data quality and accessibility.

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Healthcare Revenue Cycle: How to Improve Data Timeliness and Reduce Manual Work

With cash flows declining, margins tightening and bad debt increasing, it’s more important than ever for healthcare organizations to maintain their bottom line. Efficient, effective revenue cycle management that ensures timely payment is one key to an organization’s financial health. Learn how this healthcare system: a) improved their data timeliness, b) realized an estimated $380K in annual operational savings, and c) reduced manual work.

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Automating and Integrating Patient Satisfaction Data to Achieve IHI Triple Aim Goals

While measuring patient satisfaction has become increasingly important for organizations seeking to improve quality and maximize reimbursement, using patient satisfaction data effectively presents a variety of challenges. Organizations must collect the data, distribute it to multiple audiences and integrate it with data from other sources—efforts that often consume significant time and resources. Automating the patient satisfaction reporting process and creating an analytics foundation enables integration of patient satisfaction data into an organization’s overarching quality and cost improvement initiatives.

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Automating the Executive Healthcare Dashboard: Spend Less Time Collecting and Validating KPI Data

Healthcare executives rely increasingly on executive healthcare dashboards to provide a snapshot of their organization’s performance measured against established monthly and yearly key process indicator (KPI) targets. However, collecting and aggregating the needed data to create the dashboard can be a very time-intensive process and many organizations are using Excel spreadsheets to “cobble together” these dashboards from a variety of sources. Learn how this organization is leveraging a healthcare enterprise data warehouse (EDW) and analytics technology to automate and improve the dashboarding process.

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How to Reduce Heart Failure Readmission Rates: One Hospital’s Story

An estimated 24 percent of patients who are discharged with heart failure (HF) are readmitted to the hospital within 30 days. Learn how this healthcare organization engaged physicians and multidisciplinary teams to improve their outcomes. Deploying evidence best practices—medication reconciliation, follow-up appointments, follow-up phone calls and teach back—they reduced and sustained their 30-day HF readmission rates by 29 percent, and their 90-day HF readmissions by 14 percent. They have seen their process measures increase significantly: 120 percent increase in follow-up appointments; 78 percent increase in pharmacist medication reconciliation; 87 percent increase in follow-up phone calls; 84 percent increase in teach-back interventions.

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How to Improve Sepsis Diagnosis and Outcomes through Innovative Healthcare Analytics

Sepsis affects more than 750,000 hospitalized patients and results in 570,000 ED visits per year. Learn how this large medical center is tackling their sepsis care challenges by leveraging their EDW and healthcare analytics. They defined and built a sepsis registry and analytics platform in 10 weeks to measure 6 interventions and 4 outcome measures — including mortality rates, length of stay, total hospital stay and 30-day readmits.

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The Problem of Measuring Spinal Surgery Outcomes and How Analytics Can Help

Spinal problems are a common issue with a profound impact on healthcare costs. Faced with the high cost of surgical spine care in an industry transitioning to value-based payments, health systems need analytic solutions to evaluate the effectiveness of surgical interventions. Read how this medical center: 1) built a Spine Registry, 2) drove patient engagement through patient portal usage, 3) integrated data from a multitude of quality of life surveys, and 4) developed a healthcare analytics platform to measure spinal surgery outcomes.

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Building a High-quality Cancer Care Delivery System

In a recent report, the Institute of Medicine (IOM) declared that the cancer care delivery system is in crisis—amplified by the complexity of cancer care and historical limitations in quality-improvement tools.
As a result of an aging population, the IOM predicts a 30 percent increase in cancer survivors by 2022 and a 45 percent increase in cancer incidence by 2030. Parallel to this increase in incidence is a trend toward increasing costs. In 2010, $125 billion was spent on cancer care compared to $72 billion in 2004. In fact, the cost of cancer is expected to reach $173 billion by 2020—a 39 percent increase in just seven years.
One renowned health system recently implemented a solution to mitigate this crisis: a high-quality cancer care delivery system based on healthcare analytics and business intelligence. The health system has implemented a framework for data-driven clinical improvement.

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