Let’s look at one measure that is extremely important to hospitals right now: How do you calculate Patient Re-Admission rates?
- Do you break it down by certain characteristics of a Re-Admission like where the patient came from (“Admission Source”)?
- Do you make sure to exclude any patients that expired (“Discharge Disposition” = expired) so your numbers aren’t inflated?
- Do you include certain patient types and exclude others (include “Patient Type” = Inpatient or Psych; exclude “Patient Type” = Rehab)?
- Do you exclude outliers like the patient that has been in your hospital for what seems like forever but goes back and forth from home health and nursing clinics to the hospital?
Ok, let’s say you do take all these nuances into consideration when calculating your Re-Admission rate……now let me ask you this — what source are you using to collect these various data elements?
- Your ADT / registration system?
- Your billing system?
- How about the patient tracking system
- Or the case management system?
- How about your brand new shiny EMR?
The scenario I describe above is very real. It is highly likely that the data elements needed to calculate Re-Admission rates are scattered in multiple systems across the enterprise and to make matters worse, the same data elements like “Discharge Disposition” and “Admission Source” can be found in multiple systems! Now extrapolate this problem over nearly ALL of your quality measures and you have the conundrum that Patient Safety and Quality departments struggle with in every hospital across the country. I consistently hear:
- “How do I know everyone is calculating infection rates [CLABSIs, VAPs, CAUTIs] the same way?”
- “How can I be sure we’re identifying the appropriate Pressure Ulcers stages consistently across units?”
- “How can I standardize the collection and reporting of NDNQI? PQRI? AHRQ PSIs? Across my units and entities (for multi-facility organizations)?”
The answer to these questions often starts with knowing where the most reliable source of the individual data elements needed to calculate these measures sit. It gets trickier though because even when you find the best data source, you then have to be sure that the data is being entered consistently across your user community. That means:
- Is the data being entered at the same time?
- Is each data field restricted to certain values? Are the users entering these values or free-text?
- Is the data being entered by the same person / role? With the same level of experience and expertise (this is especially critical in the case of identifying infections and pressure ulcers)?
- Is the data being entered manually? Is it entered in multiple places?
- Are there spreadsheets and documents that have this data that are paper sources and therefore, not able to be automatically data-mined?
- Do your users understand the importance of standardizing this process? Do they understand the value it provides their organization and thus their patients?
The answer to the last question often eludes many clinicians I run into. It is sometimes difficult for someone who has been clinically trained their entire career to understand the power of discrete data over free-text narrative documentation.
One exercise we have found extremely helpful for our clients is creating a source-to-measure mapping document that identifies the agreed upon sources of the individual data elements (both numerator and denominators) for each quality measure being reported across the enterprise. Once this is created, get your clinicians, nursing, and analysts to bless it and finally publish it for reference so there is no ambiguity moving forward. Now everyone is reporting the same data with the same definitions. The most difficult part, though, comes as you have to hold people accountable for changing their practices to improve patient outcomes once you’re all reporting in the same language.