Thank you for taking time to read my letter and explore the knowledge center we've created for MedStar Health. 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 MedStar
Northwest healthcare organization Multicare reduced septecemia by 22 percent, leading to a $1.3 million cost savings in the same period. Now the organization is tackling other areas of improvement. Discover what triggered the improvements — and how these resulted in savings.
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.
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.
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.
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.
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.
In the shift to value-based care, Texas Children’s Hospital began to see its bottom line decline and needed an immediate solution to address labor and productivity challenges. The hospital set out to develop a methodology to help them allocate labor resources appropriately to the demand for services.
Quality and efficiency have become even more important to hospitals amid the impending transformation of the reimbursement system from fee-for-service to value-based payments. Learn how this hospital used using analytics technology, team-based processes and evidence-based best practices to drive cultural transformation, improve appendectomy outcomes and reduce costs, and deploy end-to-end workflow optimization. The results are impressive: they reduced postoperative length of stay by 36 percent; they reduced average variable direct costs by 19 percent; they increased order set adoption rates by 36 percent; they increased the percentage of patients receiving recommended antibiotic as first antibiotic by 53 percent.
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.
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.
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%.
Diabetes is the most common life-threatening, chronic illness in children who live in developed countries. With effective management of diabetes, children with diabetes can live long, healthy, and active lives.
Texas Children’s Hospital believes that diabetes patients and their families are most successful in managing their disease if they receive standardized, family-centered, multidisciplinary care in both inpatient and outpatient settings.
Texas Children’s created a new Clinic Care Process Team (CPT) which developed a comprehensive approach to standardizing diabetes care by automating best practice alerts that help clinicians recognize the need for testing, so they order labs more quickly.
Within one month of implementation Texas Children’s saw measurable improvements:
Screening percentages for each test improved to >80 percent.
28.2 percent relative improvement in the percentage of patients receiving recommended annual thyroid-stimulating hormone (TSH) testing, with current performance greater than 90 percent.
23 percent relative improvement in the percentage of patients receiving recommended annual lipid testing, with current performance greater than 90 percent.
54.1 percent relative improvement in the percentage of patients receiving annual retinal examinations, with current performance at 94 percent.
Patient satisfaction is on an upward trend.
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.