The emergence of powerful and user-friendly healthcare data visualization programs has transformed analytical reporting. The amount of information conveyed by all types of graphs, symbols, sizes, and colors is staggering. The ability to “drill down” in real-time with increasing levels of granularity enables all manner of analyses. The downside of this data hunger is the creation of simplified, context-free visualizations which may inadvertently lead to misinterpretations, most often in the form of a false positive (believing a change has occurred that really hasn’t). This often leads to knee-jerk reactions to correct the “change” and unnecessary actions being taken that waste time, effort, and money. Avoiding the most common pitfalls will ensure your organization has the most complete picture to drive meaningful change.
Learn more about Justin Gressel
Justin Gressel joined Health Catalyst in January of 2015 as a senior data scientist. Prior to coming to Health Catalyst, he worked both in industry (Disney and Great Wolf Resorts) and in academia as a marketing professor. Justin has a PhD in Marketing from Purdue, and an MBA and baccalaureate in Statistics from Brigham Young University.
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Patient satisfaction metrics are being put in the spotlight and are becoming more important as healthcare organizations transition from fee-for-service reimbursements to alternative payment models. While healthcare and the entertainment industry may seem disparate on the surface, there is much organizations can learn about improving the patient experience from companies like Disney who utilize data to understand their customers’ wants and needs in order to provide a superior guest experience. Disney creates the idea guest experience in 5 ways: 1. Understanding the guest; 2. Everyone is a performer; 3. Seeking out interactions; 4. Owning the guest; and 5. Accountability
We developed a predictive analytics framework for patient care based upon concepts from airline operations. Using the idea of an aircraft turnaround time where the airline wants to put the aircraft back into operation as soon as possible, we’ve created a way to help patients headed toward poor outcomes, along with their providers, “turnaround” and get the best possible, most cost-effective outcome. For example, in a diabetes patient, we might use variables such as: age, alcohol use, annual eye/foot exam, BMI, etc. to look for patterns that might influence two outcomes: 1) Diabetic control and 2) The absence of progression toward diabetic complications. The notion of our Patient Flight Path is useful at both the conceptual level, as well as the predictive algorithm implementation level.