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Healthcare Visualizations: Are You Getting the Entire Story

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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.

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Healthcare Visualizations: Are You Getting the Entire Story

  1. 1. Healthcare Visualizations: Are You Getting the Entire Story – Justin Gressel Lisa Lendway Jack Thompson
  2. 2. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Data Visualization The emergence of powerful and user-friendly data visualization programs (e.g., Tableau, Qlikview, Spotfire, Power BI, etc.) has transformed analytical reporting. The amount of information conveyed by all types of graphs, symbols, sizes, and colors is staggering, and the ability to “drill down” on the fly to increasing levels of granularity allows for all manner of analyses.
  3. 3. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Data Visualization The power and ease of creating these visualizations, combined with the increased emphasis on making evidence-based decisions, pressures leaders to request large amounts of data and graphics in order to make the most informed decision possible. The downside of this data hunger is that it leads to the creation of simplified, context-free visualizations which may inadvertently lead to misinterpretations.
  4. 4. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Data Visualization Cramming as many visualizations as possible into a report or dashboard results in dumbed down graphs with critical information missing. It’s similar to reading a story where key details have been left out, forcing the reader to mentally fill in the blanks and complete the story. Without all the relevant details, it’s impossible to grasp the full story.
  5. 5. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Data Visualization The importance of providing context for visualizations within the domain of patient satisfaction is emphasized in the following slides, although the lessons can be applied to any area of data-driven decision making. Tips are shared to overcome common pitfalls to ensure that the entire story is being told.
  6. 6. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Include a Comparative Frame of Reference When a change does occur or a true trend appears, it may be difficult to know whether the change is expected or unplanned. There may be seasonal (e.g., day of week, month, quarter) or market- based (e.g., political/regulatory, economic, or social/cultural) effects influencing the metrics. Having a frame of reference enables the organization to accurately, and confidently, evaluate current performance.
  7. 7. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Include a Comparative Frame of Reference Seasonal effects should be accounted for via inclusion of performance over a comparable time period (e.g., last year). This frame of reference can be compared and if a consistent dip or upturn during a comparable time period is noticed, it can infer the possibility of a seasonal effect. A downward trend may not be of concern if the rest of the industry is heading in the same direction.
  8. 8. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Include a Comparative Frame of Reference Figure 1 appears to show an upward trend in patient satisfaction up through the midpoint of the year, followed by a decline. Before drawing any conclusions, the performance should be compared to a similar time frame in the past and/or relative to peers. Figure 2 shows the same trend with the prior year’s data (orange line), indicating a seasonal effect where satisfaction peaks midyear. The increases do not represent actual improvement based on historical performance. What would be worrisome is if the organization didn’t show increases at all.
  9. 9. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Account for Natural Variability If average patient satisfaction is higher during the current time period compared to the previous, can we say that satisfaction increased? Not necessarily. The averages for any given time period are based on samples of a patient population. To definitively say whether a change has occurred, the natural variability of the data must be taken in to account.
  10. 10. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Account for Natural Variability The best way to account for natural variability in a graph is to include upper and lower control limits - horizontal lines that depicts where the data is expected to be. The inclusion of control limits effectively turns the graph into a control chart, a popular tool used within quality control.
  11. 11. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Account for Natural Variability Figure 3 shows monthly satisfaction data rising and falling sharply across 12 months. There’s a lot going on in that story, except that nothing is happening. Keep in mind that the data used here was simulated around a known average, so there is no pattern despite what you think you see. When looking at the same data in the format of a control chart in figure 4, it becomes clear that the ups and downs are just random variability, or white noise, because the data fit comfortably within the control limits.
  12. 12. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Conclusion Data visualization is a form of story-telling, so it’s important the visualizations make the story clear through the graphics and details. All graphics need to have a proper frame of reference, whether it be a properly sized axis, visual cues like control limits that show the typical range of data values, data from comparable time periods, or peer performance.
  13. 13. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Conclusion The ability to enact meaningful changes within healthcare is predicated on the ability to accurately describe the healthcare environment and detect trends. Therefore, much care must be put into how this information is presented to and interpreted by decision-makers within healthcare organizations. Otherwise, resources may be misallocated and opportunities to conduct change are lost.
  14. 14. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic How an EDW Enables the Best Healthcare Visualizations Chris Rains – Data Architect, Michael McCuistion – Technical Director How Healthcare Visualizations Can Improve Organizational Buy-In Dan Hopkins – Data Architect Why Healthcare Requires an EDW, Analytics Applications, and Visualization Tools for Quality Improvement Initiatives Chris Rains – Data Architect, Michael McCuistion – Technical Director 3 Keys for Creating Effective and Insightful Executive Dashboards Russ Staheli – Data Architect Does your solution support reporting dashboards? ― Health Catalyst Link to original article for a more in-depth discussion. Healthcare Visualizations: Are You Getting the Entire Story?
  15. 15. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. For more information:
  16. 16. © 2014 Health Catalyst www.healthcatalyst.com Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. 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. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Lisa Lendway joined Health Catalyst in January 2015 as a Senior Stastician. Prior to coming to Health Catalyst, she worked for Allina Health as a Senior Statistician. Lisa has a PhD in Statistics from the University of Minnesota. Jack Thompson joined Health Catalyst in January 2015 as Data Analyst, as a part of the Health Catalyst and Allina Health partnership. Jack’s primary concentration at Allina Health has been patient experience analytics, and in the future he is eager to get involved with other data sets. Jack has a Bachelor of Business Administration in Health Care Management, and a Bachelor of Arts in Philosophy – Applied Ethics from the University of Minnesota Duluth.

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