Thank you for taking time to read my letter and explore the knowledge center we've created for UAB Health System. Below, I've selected resources I believe will resonate with your organization. I hope they will provide you with useful insights and perspectives as you think about how enterprise data warehousing and analytics can transform your organization. Lastly, I’d like to challenge every healthcare system in the country by asking a simple question: “What transformative data driven success stories will you be adding in the next 12 months?”
Suggested Content for UAB Health System
Improving Healthcare Performance through Analytics and Cultural Transformation: One Healthcare Organization’s Journey
OSF HealthCare, a pioneer accountable care organization (ACO), was looking to deliver superior clinical outcomes, improve the patient experience, and enhance the affordability and sustainability of its services. OSF’s leaders recognized that to effectively achieve these goals, they needed to reinvent the organization’s performance improvement measurement and reporting system. In addition to deploying new analytics technology, OSF knew they needed to drive a cultural shift throughout the organization to embrace a data-empowered system. By engaging leadership, aligning the initiative with business strategies, and building data-driven clinical and operational improvement teams, OSF was able to save $9-12 million over three years—through both process improvement and cost avoidance. OSF also drove clinical performance improvements in key areas including heart failure and palliative care.
Studies have shown that elective deliveries before 39 weeks increase the risk of newborn respiratory distress as well as increase the rates of C-sections where there is a higher rate of postpartum anemia and longer lengths of stay for both mothers and babies. Payers are partnering with healthcare organizations to lower elective delivery rates. Learn how this healthcare organization reduced their elective deliveries by 75 percent in just six months and received a six-figure payer partner bonus.
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.
The need to effectively manage the health of populations is largely driven by the fact that 5 percent of the population accounts for 50 percent of healthcare costs. Being able to identify these patients, provide high-quality care and reduce their utilization is a pressing goal for many of today’s primary care providers (PCPs). Learn how this healthcare organization used a healthcare enterprise data warehouse and analytics to better manage their individual patients and patient population, integrate regulatory and performance reporting, and allow PCPs to spend more time with patients and less time collecting data.
Clinical data can be difficult to extract from an EMR, particularly when you want to combine it with other data and use it in a timely manner. But when Texas Children’s Hospital adopted an enterprise data warehouse, they found they were able to extract the data and create near real-time reports that came with an added bonus: a 67% cost savings.
Digitizing healthcare comes with its own set of problems — including how to use all the raw data created and turn it into something meaningful that results in improvements in quality and cost of care. Indiana University Health found a solution that integrated with their Cerner EHR. And the best part? From start to finish, it took just 90 days.
Analytics is a buzzword in healthcare today. You hear it often: “What does an organization need to succeed in a value-based care environment? Robust analytics.” But what exactly does that mean? Anyone who has looked into implementing “analytics” for their organization knows that a multitude of options for healthcare analytics are available—and each vendor touts its approach to analytics as the best. I’d like to take a moment here to summarize four primary analytics options available to healthcare organizations today: 1) hosted analytics service providers, 2) “best of breed” point solutions, 3) EMR vendors, 4) healthcare data warehouse platform providers.
Have you ever had one of those “wake up moments” where you literally learn a lesson that impacts and changes the trajectory of your life? Read this personal story by Dr. Bryan Oshiro of his “wake up” call where he learned the importance of data to save lives. He learned this first-hand when he saw rows of babies on ventilators in the neonatal unit and realized that they had all been electively delivered before 39 weeks. But he didn’t have the data compiled to make a compelling case to his physicians to stop elective pre-39 week deliveries. Working with his technology team, he gathered the data, analyzed it, and successfully engaged his physician team in a quality improvement project to reduce these elective deliveries.
The demand on hospital coders continues to rise – and even more so with the ICD-10 rollout. At the same time, health systems want to make sure professional billing charge captures are accurate. Learn how North Memorial Health System leveraged their hospital enterprise data warehouse – and the Health Catalyst Professional Billing Module – to: a) increase the number of provider notes with sufficient clinical data for billing, b) increase their monthly net income and c) improve their hospital coding staff productivity by 25%.
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.
Up to 50 percent of all hospital deaths in the United States are linked to sepsis. That sepsis mortality statistic was not lost on Piedmont Healthcare, a system of six hospitals and more than 100 physician and specialist offices across greater Atlanta and North Georgia. Sepsis accounted for half of Piedmont’s mortality rate, despite years of progress in sepsis care.
Piedmont leaders recognized that they needed an innovative quality improvement methodology to spread best practices and sustain improvement, supported by an accessible source of timely, reliable, and actionable information. They therefore implemented a “core and spread” team structure to promote enterprise-wide adoption of best practices. The health system also deployed a sepsis prevention analytics application to deliver performance insight to all levels of the organization, and discovered a high correlation between better patient and financial outcomes and the number of bundle elements the patient received. Being able to tie outcomes to interventions, along with the incorporation of nurse driven protocols, resulted in sustained practice change and greater engagement from physicians, nursing and frontline staff, all the way to the Board level.
As a result, Piedmont achieved the following impressive outcomes:
5.8 percent reduction in mortality for all patients with severe sepsis and septic shock, translating to 26 lives saved in one year.
2.5 percent reduction in total inpatient length of stay (LOS).
8.2 percent reduction in variable cost per case, equating to $4.3 million saved in one year.
A stay in the intensive care unit (ICU) is both costly and risky. In a sobering example of the latter, nearly one third of patients admitted to the ICU experience delirium, a state of cognitive impairment that can increase risk of death in the hospital. Still, many cardiovascular patients need intensive care that can only be provided safely in an intensive care unit, requiring hospitals to assure enough beds and skilled ICU staff for these patients—while quickly identifying which patients can receive care as good or better in another unit.
Allina Health has achieved this dual objective with a concerted ICU avoidance strategy for specific complex sub-populations of cardiovascular (CV) patients. The foundation of this strategy is risk-informed decisions about which patients can avoid the ICU; clinical staff education; and an analytics platform and enterprise data warehouse (EDW) from Health Catalyst that enables CV care leaders to monitor safety metrics for those patients who avoid a stay in the ICU. So far, Allina Health’s efforts have resulted in the following achievements:
636 additional ICU days made available for more critically ill patients by employing ICU avoidance strategies
One-day reduction length of stay (LOS) for Transcatheter Aortic Valve Replacement (TAVR) patients
$589,000 cumulative cost savings
Clinical variation can be frustrating for patients and their families, often leaving the impression that healthcare team members are not on the same page and don’t agree on the plan for the patient’s diagnosis or treatment. It is also costly—the Institute of Medicine estimates that $265 billion (30 percent) of healthcare spending is waste that directly results from clinical variation.
To reduce unwanted variation, Texas Children’s Hospital invested considerable resources to develop clinical standards tools, including evidence-based order sets; however, demonstrating the effectiveness and utilization of those guidelines, pathways, and order sets had been daunting. To that end, Texas Children’s deployed an analytics platform from Health Catalyst to aggregate and analyze the data needed to perform both of these critical functions.
$2,401 reduction in cost per patient with order set utilization, and an 8.4-day difference in average length of stay (LOS).
$15 million reduction in total direct variable costs in Fiscal Year 2015, $32 million anticipated reduction in Fiscal Year 2016 at the current order set usage rate, and a potential $64 million annual reduction with a hypothetical 80 percent order set usage rate.
1,629 percent return on investment (ROI).
Introducing the New Health Catalyst Care Management Suite: Solving the Patient Engagement and Outcomes Challenge with Innovative Data-driven Workflow
Earlier this year Russ Staheli, SVP and Product Line Manager – Population Health presented a vision around how Care Management can help drive your system to this triple aim. He is back to discuss the formal release of our brand new suite of tools that represent the first end-to-end care management solution in the industry and the first to enable discovery of an otherwise invisible subset of patients – those who will benefit most from care management and who can be engaged most effectively to lower the cost of care.
Join two of Health Catalyst’s best, Vice President Dan Soule, and Senior Consultant Sam Turman, as they cover important basics including who Health Catalyst is, what we provide and how we deliver our products.
We’ll still make it education-oriented as we just aren’t a pushy, salesy company. We’ll orient around the basics of who we are and what we do.
Dan and Sam will provide an easy-to-understand discussion regarding the key analytic principles of adaptive data architecture.
Some specific items they will cover are:
The industry challenges that warranted the creation of Health Catalyst.
The use of Health Catalyst’s data analysis tools and applications that enable organizations to quickly uncover care improvement and cost reduction opportunities.
Implementation best practices including how the Health Catalyst Platform is delivered, installed, and typical implementation schedules. Attendees will understand who in your organization needs to be involved and the secrets to success and pitfalls to avoid.
The discussion will include the key analytic principles of an adaptive data architecture including data aggregation, normalization, security, and governance. They will also address the basic requirements for implementation of the measurement platform of a data warehouse, such as team creation, roles, and reporting.
Finally, they will demonstrate several of the key tools necessary to move the analytics strategy forward including applications used to organize patient populations, others used to monitor and measure care results and still others that are specific to advanced areas of care.
To tackle the variation and waste that can arise from different treatment decisions, Allina Health developed a solid framework to establish and deploy standard, evidence-based practices across the enterprise. The transition to a standard evidence-based decision-making process required collaboration and buy-in from multiple stakeholders and physicians. Allina’s established quality governance structure reviewed and approved system-wide clinical practice guidelines for Stage 1 lung cancer treatment and IV heparin treatment. To sustain and improve on this new model of care, a comprehensive checklist was developed to ensure that all future guidelines are based on patient subgroups and preferences, available evidence, stakeholder review, and other important criteria including IOM standards. Adherence to guidelines is monitored with metrics based on data extracted from Allina’s enterprise data warehouse and from the electronic health record. Results to date already indicate notable improvements in variation and cost, including the following: the establishment of a system-wide EBDM model and policy, 20 system-wide approved evidence-based guidelines developed months faster, a 5 percent decrease in Stage 1 lung cancer treatment variation, and a 20 percent decrease in the number of heparin protocols.