Updated as of January 22, 2016

Estimate 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

Increase quality of the data

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

• Most of the work to improve data quality for this indicator lies in refining the definitions as the data are captured via administrative information systems across all health care sectors and thus beyond the influence of primary care providers.  Primary care contributions to improving data quality would therefore be thoughtful reflections on refinements to the definition, to be considered for presentation in subsequent iterations of D2D.
• Other ideas: please share!
  
   

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 above 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 is 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.

You may find it hard to generate consensus about the possible impact of data quality issues on the level of performance shown in the D2D report. In that case, try the following options:

• Explore the proportion of your patients who are long term care residents to estimate how much impact their costs are having on your overall team cost.  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.

• Alternatively, 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. 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.

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 better understand the issues affecting data quality, before or at the same time as you try to improve processes of care.