“That data isn’t correct.” Those four words are what every hospital’s data analysts must be prepared to hear and respond to at some point in every presentation they make. What they say next is critical and the stakes are high. How they respond can be the difference between a hospital starting down a path that leads to a cost-saving process improvement or one of continuing inefficiency.
“The cost pressures that hospitals are facing make finding and clarifying opportunities for improvement incredibly important,” said Beth Godsey, senior director, data science, methodology and national imperatives for Vizient. “Nearly 90 percent of the work the data analyst is given is based on an identified problem within the hospital. Sometimes it’s related to a physician suggestion or maybe a patient brings something to their attention. It could also be part of an internal goal. Regardless of the origin, hospital leaders want to understand what the clinical data says about the issue and that’s where the data analyst comes in.”
A good data analyst can glean information out of data that may provide administrators, physicians and clinicians with opportunities and next steps to achieving a key goal. But it can be a tough audience. To win the data conversation, analysts need to frame their findings with methodology transparency instead of just a data dump.
According to Godsey, a winning data conversation includes information about the source of the data, transparency around the methodology for benchmarking and risk adjustment, and actionable takeaways.
“Regardless of the audience, it’s good to spend the first few minutes of a data conversation providing an overview of the data source. Having that framework lets the audience know what levels of insight they will be able to access from the database when they find an improvement opportunity,” said Godsey.
In the data overview, Godsey recommends providing details on:
• The source – or sources – of data
• Size and types of organizations represented in the database
• Time period for the data pull
• Level of transparency available such as type of patient, care unit, individual physicians and outcomes by device
“Providing that level of specificity and granularity in the opportunity makes it more of an actionable conversation,” said Godsey.
Having the right methodology and transparency can make or break a data conversation. Risk adjustment is a methodology that creates an apples-to-apples comparison for outcomes for different levels of patient acuity. For example, hospitals treating patients with higher levels of acuity are credited for longer length of stay and higher mortality than hospitals that don’t manage as many of these types of patients.
“In my experience, hospital leaders, physicians and clinicians want to see the grading scale they have been measured against—that has a lot to do with risk adjustment, benchmarking and compare groups. If there is transparency around the risk adjustment methodology, it makes the improvement opportunity conversation much more productive,” said Godsey.
When presenting data around an improvement opportunity, be prepared to respond to questions like:
• What benchmarks are being used for comparison?
• Is the methodology sound?
• Does the data include appropriate risk adjustment?
• Does the data include patient and hospital information similar to mine?
To illustrate the level of detail needed for actionable takeaways from a winning data conversation, Godsey referenced CMS’ recently released Overall Hospital Star Ratings. “It would be easy for an academic medical center who received a low rating compared to other hospitals due to readmissions to dismiss it by saying ‘they don’t take care of the same type of patients that I do.’”
Godsey recommends starting the conversation about the opportunity to improve by following these steps:
• Show how they are performing in readmissions relative to other “like-me hospitals”
• Show how the data was risk adjusted for patient acuity and population
• Illustrate within the data specifically where the opportunity exists
“Use data to show them where their specific opportunities exist. Identify that it may not be readmissions from heart failure patients, but instead it’s pneumonia patients and it happens in these age groups or patients that are discharged to home health,” said Godsey. “Connect the dots by explaining that you talked to the home health folks and they tell you there are delays in clinician response time when medications need to be adjusted after the patients go to home health. It then becomes a very actionable opportunity to work with these specific patients in specific situations. From there they can monitor performance and see if their readmission rates go down.”
The importance of the role of the data analyst in a hospital is only increasing. With a quality database and sound methodology they will be able to definitively identify an array of opportunities for hospitals to improve processes that reduce costs and improve outcomes. Before you have the data conversation, do your homework. Make sure you can defend the risk adjustment methodology and the benchmarking and then provide the actionable steps toward improvement.