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.
Academic Medical Center
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.
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 The University of Kansas Health System embraced the implementation of an advanced analytics team to help the healthcare system unleash the data capabilities needed to become a data-driven organization.
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.
It is estimated that $25 to $45 billion is spent annually on avoidable complications and unnecessary hospital readmissions—the result of inadequate care coordination and insufficient management of care transitions. By implementing care coordination programs and leveraging its analytics platform, the University of Texas Medical Branch reduced its readmission rate and achieved significant cost avoidance.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
Discover how a healthcare system went from manually pulling together reports with varying data to having near real-time data that one executive says, “enables our care coordinators to drive preventive care and ultimately lower our population health costs” all thanks to a Late-BindingTM data warehouse.
Adopting an enterprise data warehouse had a number of positive affects for one medical center. Not only did they create way to more effectively discover and treat certain hospital-acquired infections, the organization also developed five steams of quality improvement—infectious disease, population health, cardiovascular, neuroscience and oncology.
Former Stanford CIO Carolyn Byerly talks about Stanford’s journey to build a centralized data warehouse. She shares how they launched a data warehouse in a matter of months and why it was one of the best decisions of her career. Learn more about the technologies and methodologies that are transforming healthcare and driving improvement outcomes.