- Traditional Change Models
- Disparate Change Groups
- Uncontained Change
- No Standard Change Approach
- Tools Focus
- Reliance on Benchmarking
- Changes Are Not Based On Data, Good Data, Or The Right Data
- Changes Made Based On Symptoms, Not Causes
- Systems Versus Processes
- Focus On People, Not On Process
- Lack of Context for Solutions
- Adding Versus Subtracting (Patching)
- Poor Implementation
- No Emphasis On Control
- Management Versus Leadership
- Summary
This chapter is from the book
This chapter is from the book
This chapter is from the book
Changes Are Not Based On Data, Good Data, Or The Right Data
When first starting in process improvement in healthcare, one is generally and genuinely surprised at the sheer volume of data available, much more so than in any other industry. On closer scrutiny, though, it becomes apparent that the data and related measurement systems are invalid or unreliable. For example, it is commonplace for emergency departments to measure length of stay (LOS) for patients. In practice, the LOS data collected represents only a fraction of the true duration from when patients arrive at the hospital site to when they leave (typically the captured measure runs only from registration to disposition). Similarly, when asked to provide data for leadership presentations, analysts often ask, “What do you want it to show?” In a recent surgery project, patient data was stored in 16 (sixteen) databases, none of which were in sync.
There is a lot of data, but not much valuable information.
With poor measurement systems and the resultant data they produce, it becomes very difficult to understand with any real confidence what drives process performance and subsequently what could make breakthrough change. With little in the way of supporting evidence, managers often believe they have to be the ones to come up with all the solutions, and usually no one will challenge them. Even if they were to make decisions based on the data available, the statistical validity would be questionable.
Simple Measurement Systems Analysis (MSA) studies on data systems thought to be robust quickly show a different picture. For example, in one hospital’s analysis of the charge capture and subsequent coding of cath lab procedures, it was discovered that coders were all in complete agreement with each other less than 10% of the time and even with themselves only 60% of the time.
Even when improvements are made, without good measurement systems (and therefore data) any change in performance is difficult to detect (due to being shrouded in measurement system noise), reliably verify, or attribute to the changes made.
Quite often it’s just the wrong data or the wrong focus. We’re simply asking the wrong question. A useful example here is one of a project leader trying to improve the access for pregnant women to prenatal education. By asking a number of times in succession, “Why do we care?” the true underlying problem is revealed.5 Mothers need better access to education prior to the delivery visit. Why do we care? Because if they are educated during delivery, they tend to forget things in the stressful environment and retention is not good. Why do we care? Because mothers need to be educated in how to care for themselves and their newborn. Why do we care? Because, after they leave the hospital, informed mothers can successfully prevent complications and avoid an unnecessary return. By digging in this way, the project leader recognized that the real goal (and hence the data required) related to the reduction in the number of unnecessary postnatal readmissions. By focusing on this as the needed data, the team managed to improve how the education was delivered and what was delivered as well as improve access to the education to ensure the best retention and subsequent care.