University of Louisville Health (UofL Health) manually extracted COVID-19 vaccine administration data from its EMR and combined it with inventory data to conduct time-consuming reconciliations weekly. It lacked the necessary insights to efficiently model and forecast inventory requirements to administer as many first doses as possible without exhausting its supply of second doses. UofL Health leveraged a vaccine inventory management tool, providing the organization with the necessary data analytics to quickly and accurately model vaccination schedules, forecast vaccine supply needs, identify and resolve quality/safety concerns, and generate required regulatory reports.
Academic Medical Center
The COVID-19 pandemic pushed healthcare to rely on data and analytics for decision making and illustrated the criticality of accurate, real-time data and analytics. Despite having ample patient data available for direct patient care, Albany Med’s analytics platform had a two-day lag for much of the data. The organization quickly recognized that rapid access to COVID-19 analytics was essential and that it was vital for leaders to have real-time access to data for decision making during the COVID-19 crisis.
For each heart failure admission, registered nurses at Guy’s and St Thomas’ NHS Foundation Trust collected data from five different sources, and then filled out a 10-page form for each patient. Information from the forms was then manually entered into the National Institute for Cardiovascular Outcomes Research (NICOR) web portal. This manual process for data collection and reporting was not only time-consuming and resource-intensive—but was also highly susceptible to error. To address these challenges, the organization leveraged the Health Catalyst® Data Operating System (DOS™) to integrate the data from the five source systems and extract data for nearly all of the elements required for heart failure readmissions—streamlining the NICOR submission process and improving data quality and accuracy.
As part of its efforts to improve the timeliness of care for patients undergoing abdominal aortic aneurysm (AAA) repair, Guy’s and St Thomas’ NHS Foundation Trust needed to collect data to guide care redesign, help assess the impact of specific interventions, and gauge progress toward desired outcomes. Guy’s and St Thomas’ implemented the Health Catalyst® Data Operating System (DOS™) platform, including a Referral Pathway analytics application, allowing the organization to aggregate and standardize data across source systems. Improved data and analytics have enabled Guy’s and St Thomas’ to analyze, evaluate, and monitor outcomes for the entire AAA cohort and evaluate operational performance and associated patient outcomes.
Responsible for coding approximately 380,000 episodes annually, clinical coders at Guy’s and St Thomas’ NHS Foundation Trust review documentation across several systems. The overwhelming amount of data, burdensome manual review processes, and limited coding resources made reviewing all data unfeasible. To address its coding challenges, Guy’s and St Thomas’ leveraged its data platform to combine and standardise data across disparate source systems. The organization now has access to data and technology that can be used to augment coders’ work, automating data gathering to better identify patients whose diagnostic coding could be improved.
Texas Children’s Hospital had dramatically improved patient access, yet it recognized that it could advance access further by improving space utilization and proactively reallocating underutilized exam rooms. The organization developed a space visualization analytics application, and conducted a comprehensive space assessment, identifying opportunities to improve utilization of current space to increase clinic offerings—resulting in improved access for patients and families by utilizing space efficiently.
The amount of time a patient may have to wait for a scheduled appointment at Texas Children’s Hospital varied greatly as a result of a lack of standardized processes. After taking a deeper look at its scheduling process with the help of analytics, it was able to develop an improvement strategy aimed at improving access to care—enhancing patient care and boosting revenue.
Virtual Visits and Analytics Enable Continued Delivery of Ambulatory Services During COVID-19 Pandemic
With the emergence of COVID-19, Texas Children’s Hospital was challenged to make data-informed decisions that would allow it to continue offering critically needed healthcare services, while ensuring the safety of its patients, staff, and providers. Texas Children’s dramatically expanded telehealth capacity, converting most in-person primary, specialty, and mental healthcare visits to a phone or video appointment to better meet patient needs. The organization leveraged the Health Catalyst® Data Operating System (DOS™) platform and a virtual health platform to visualize, monitor, and manage the conversion to virtual health, in addition to managing in-person visit volume—enabling the effective management of outpatient capacity, utilization, and financial performance.
Length of stay (LOS) is an essential indicator of hospital operational efficiency. Albany Med compared its performance with benchmark data and determined that it could improve inpatient LOS. By convening a multidisciplinary team of providers committed to decreasing hospital LOS and leveraging its data and analytics platform, Albany Med was able to uncover underlying issues causing unnecessary extended hospital stays and substantially reduce LOS.
Albany Med’s clinical documentation improvement specialists provide high-quality care to complex, acute-care patients; however, Albany Med was experiencing lower reimbursement rates due to gaps in clinical documentation. The organization created a seamless process for clinical documentation with the use of an analytics application as driven by clinical leadership.
Texas Children’s Hospital knew that improving data access was key to driving improvements and sought to improve analytics adoption and democratize its data. By focusing on developing a culture of data access and sharing, Texas Children’s has shifted its data and analytics culture, establishing the foundation required for it to continue to advance its analytics adoption, including engaging in predictive analytics. Leaders and employees are actively investigating and sharing data, and operations are more data-driven than ever before.
To gain more efficient access to data that could reduce unwarranted variation and reduce costs, Dartmouth-Hitchcock Health (D-HH) leveraged a data platform to automate analysis of its financial data. D-HH has substantially improved its ability to use clinical, operational, and financial data to perform opportunity analysis, decrease unwarranted variation, and decrease costs.
Dartmouth-Hitchcock Health (D-HH) was committed to improving its provider performance reporting method so that providers could easily access their performance data and deliver better care to patients. With the right analytics, D-HH was able to implement an affordable solution and reduce the manual reporting required by its financial analysts.
Pediatric sepsis remains a key concern for hospitals due to the serious nature of the disease. Early diagnosis and timely care are a top priority, as this significantly improves a patient’s chance of recovery. With the help of big data and prescriptive analytics, Texas Children’s Hospital developed an early alert system and workflow changes to improve its pediatric sepsis care. The hospital’s investment in new processes, decision support, and analytics has substantially improved pediatric sepsis outcomes.
Improving the management of chronic diseases for patients is crucial for reducing expenses and improving health outcomes. Newton-Wellesley Hospital, a member of the Partners HealthCare system, adopted the population health coordinator role and utilized analytics to help identify variations in chronic disease management across practices and develop standardized best practices aimed at reducing costs through better outcomes for patients.
Increased Visibility into Value-Based Performance Results in $2.1M in Additional Pay for Performance
Data-driven decisions and analytics are critical for organizations and physician practices attempting to thrive under value-based care. With the help of data analytics, UTMB Health was able to focus on improvement efforts for specific patient populations and boost reimbursement based on DSRIP performance.
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.
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.