For technical notes, please see page 25 of the data dictionary.

Interpretive Notes

Tips to help you understand the data and put it in context.

• The D2D definition fully aligns with Public Health criteria. This means Rotavirus is now included in the definition and EMR queries. It is not part of the preventive care bonus for physicians. Therefore, performance may appear lower in D2D than in your preventive care bonus reports which exclude Rotavirus.
• The D2D definition does not reflect patient choice. Patients who decline immunization and therefore are not immunized may be the reason why your rates are lower than you expect. See “data quality actions” for ideas to examine the extent to which this is affecting your rates.
• Data for patients immunized outside of the primary care team (e.g., at a health unit) might not be recorded consistently in all EMRs and teams. The performance seen in D2D might therefore under-estimate actual immunization rates. See “data quality actions” for ideas to examine the extent to which this is affecting your rates.
• Rates for teams with very few children in their panel may be more variable than rates based on larger eligible patient populations. For example, if 2 less children are immunized this year out of a population of 10 children, your immunization rate will drop by 20%. However, if 2 less children out of 100 are not immunized, your rate will only drop by 2%. Consider how many children are eligible for immunization when interpreting differences for your team year to year or relative to another team.
• D2D includes data for all children as opposed to only rostered children. This might generate different rates than those the team might be used to seeing in reports based on rostered children only (g., the MOHLTC Preventive Care Target Population/Service Report (TPSR)). This report is provided by MOHLTC to eligible physicians in Patient Enrolment Models (PEMs) in April and September by MOHLTC to assist physicians in determining their Target Population and the delivery of preventive care services.
• The timing of D2D reporting may not coincide with the reporting time period for the MOHLTC Preventive Care Target Population/Service Report. There may be differences in rates related to these differences in time periods.
• Although a consistent definition was developed to create queries that were shared among members for purposes of extracting these data for D2D, it is possible that the extent to which these data were consistently recorded and therefore extracted in your team might vary. To explore this issue, look at the processes used in your team to record and extract immunization data and work to align it as much as possible with the standard process being developed by QIDS Specialists.

Steps to Improvement

Concrete steps you can take to improve care, based on your data. Assuming you have established that the data are good enough to direct action AND that improving performance in this area is a priority for your team, you may wish to discuss the following options with your clinical leaders, Quality Improvement committees, team staff and/or patients:

• Talk with Public Health units and others involved in immunization to act together.
  • For ideas, check out this toolkit on collaboration between public health and primary care.
  • Here’s an example of how primary care & public health are working together in the North East LHIN region (AFHTO 2017).
  • You might also want to check out this presentation about a similar initiative in Windsor, with steps on how to plan and implement a partnership program in your team (AFHTO 2016).
• Contact your peers to determine their performance and work with them to either spread any processes they find have helped them or collaboratively test some new changes that might work for you AND your peers. HQO’s QIP Navigator allows teams to query submitted QIPs, this tool is extremely usefully to identify peers who have focused on similar areas for improvement.
  • Learn how the Queen’s FHT used quality improvement planning to exceed provincial targets for pediatric immunizations.
  • Check out how the Markham team deals with measles outbreaks due to patients with out-of-date vaccinations.

Data Quality Actions

Tips to help you understand the quality of your data and, if necessary, take steps to improve it. 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.
• Additional information for estimating the impact of data quality for this measure.

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 action than suggested in the report, you might consider:

• Work with engaged clinicians to increase the consistency of data entry related to immunization. Refer to the Standardized EMR queries for guidance on ways to increase consistency in data entry such that extraction of the data can be more efficient and accurate over time and between teams.
• Track your immunization rates on an ongoing basis vs. just at year-end. Work with QIDS Specialists in the application of consistent queries across teams and EMRs to increase access to these data in an efficient way.
• Ask patients about their immunizations, partly to update the EMR and partly to engage them in an important discussion about preventive strategies. Tilbury District FHT uses a simple paper form for patients to share information regarding flu shots received elsewhere. A similar idea could be applied to childhood immunizations. Better yet, contact Tilbury directly to find out exactly how this works!
• Support AFHTO’s efforts to work for inclusion of a standard process for recording and reporting immunization data aligned with PHAC guidelines as part of the technical specification for EMRs. This is already in progress through the EMR Data Management committee and individual EMR Communities of Practice.
• Develop data-sharing processes with public health units and other partners engaged in immunization to ensure accurate records regarding immunization status, not only for reporting purposes but also for better risk-management for patients.
• 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 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 impact of data quality issues on the level of performance shown in the D2D 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 get their immunizations elsewhere. 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 you are having trouble estimating how many patients are getting immunizations/flu vaccines elsewhere you might want to use 43% as a ball park figure. This was the rate observed by Tilbury District FHT who use a simple paper form for patients to share information regarding flu shots received elsewhere.]
• Estimate how many of your patients decline immunization when offered. 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.

If the “imperfect data impact calculator” points to the same decision (i.e., 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.