Diabetes – Data quality actions

Estimate the impact of data quality

• Access the Imperfect Data Impact Calculator to find out whether the data quality issue(s) you think you have would change your initial decision regarding the need to improve.
• Please see below for more information about this tool.

Increase the quality of the data

…if the “imperfect data impact calculator” shows that the issues in your data may point you to a different decision than suggested in D2D 3.0. You might consider the following:

• Increase awareness of your team about the importance of having “clean” data in your EMR: Project ALIVE shows that having clean data in your EMR allows you to create quick and flexible reports to better inform your team about the needs of patients and which patients require follow-up care.
• Create a diabetes registry: identify patients with diabetes more accurately using the standard queries and processes developed by QIDS Specialists to get started on a diabetes registry
• Track and demonstrate your progress cleaning up your data to improve data quality. Before you start the cleanup process run a “coded” query to capture a baseline, then every few months re-run the query and plot your results over time. You may want to use a tracking form to help you document your progress.
• Consider hiring a student to help you clean up your diabetes data. Check page 26 in this handbook for details about cleaning up diabetes data.
• Once your diabetes registry is clean, run the D2D diabetes queries on an ongoing basis – don’t just wait till the end of the year. Do you notice any changes?
• Consider signing up for CPCSSN or EMRALD to get ongoing, patient-specific reports to help you help your patients manage their diabetes.
• Join an EMR CoP to share new tools and solutions to help you make better use of your EMR.

Additional  information for estimating the impact of data quality for this measure:

The data are almost certainly not a definitive estimate of your team’s actual performance. However, they might be “good enough” to help you decide if your team needs to improve or not. To determine if the data are “good enough” for that, estimate how likely it is that one or more of the issues outlined in the interpretive notes are a problem with your team. Then, run the “imperfect data impact calculator” to see if the issue(s) could lead to a different decision related to the need for improvement. To do this, work with your clinical leaders and staff to establish an approximate impact of data quality – i.e. is the data quality issue causing your performance to look like TWICE or HALF or 10% (or other number) less or more than it actually is? Plug that number into the “imperfect data impact calculator”. It will show you whether the data quality issue(s) you think you have would change your initial decision regarding the need to improve. Click here to access the Imperfect Data Impact Calculator. You may find it hard to generate consensus about the impact of data quality issues on the level of performance shown in the D2D 3.0 report. In that case, consider the following options:

• Track the next 10 (or 20 or other small number) encounters to get a better estimate of the extent of the data quality issue. Perhaps the rate among these patients will shift your team’s overall rate to be TWICE or HALF or 10% (or some other number) of the rate in the report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
• Estimate how many of your patients with diabetes have blood pressure (or HBA1c) recorded properly in the EMR. Perhaps the rate among these patients will shift your team’s overall rate to be TWICE or HALF or 10% (or some other number) of the rate in the report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
• Estimate how many of your patients with diabetes are not coded in a consistent manner in your EMR. Perhaps the rate among these patients will shift your team’s overall rate to be TWICE or HALF or 10% (or some other number) of the rate in the report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
• If none of the above is helpful, consider instead experimenting with possible “error” rates to see how much error (i.e. TWICE or HALF or 10% of some other number) would be needed to change the decision made on the basis of the performance of the indicator in D2D 3.0. If, in the opinion of the team, such an amount of error is reasonable, then it may be worth considering efforts to improve data quality. Alternatively, if that amount of error is considered to be unlikely, then the data are likely good enough to support the initial decision regarding the need to improve, based on the performance shown in D2D 3.0.

If the “imperfect data impact calculator” points to the same decision (e.g. a need to improve or NOT) even after data quality issues are considered, the data are likely “good enough” to base your decision on regarding the need to improve. The next step is to consider strategies to improve, assuming the area of care measured by the indicator is a priority for your team.  If your data are not “good enough”, you may then consider taking action to improve your data quality, before or at the same time as you try to improve processes of care.