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.
The University of Kansas Health System
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.
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.