For UPDATED technical notes, please see page 26 of the Data Dictionary.

Interpretive Notes

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

• The Diabetes Care Score represents the % of diabetes measures (aspects of care) that a team’s patient population has achieved. For example, if your team’s score is 68, this means that your population or registry of patients with diabetes has achieved 68% of the 4 measures included in the calculation (HbA1C testing, HbA1C level, blood pressure level and statin therapy). In future iterations of D2D, the composition of the indicator will be modified to include other measures of diabetes care like foot and eye exams, based on increasing EMR maturity/data quality and capacity to access data on personalized targets.
• Your score may be low if you have a lot of patients with diabetes that have only one process/outcome measure within the appropriate target.
• Your score may also be low if you have patients with no measures in range, even though others have most of the measures in range.
• How you document and are able to access blood pressure, HBA1c and medication data in your EMR will affect the numerator – i.e., your score will be low if documentation is an issue for your team.
• The way your team documents diabetes diagnoses in the EMR affects your denominator (i.e., number of patients with diabetes). Your diabetes score may be over- or understated depending on how “clean” your diabetes registry is.

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:

• Discuss with your team how to collaborate more extensively with  IHPs to improve diabetes care.
• Get started on a diabetes program: Leverage the program planning resources developed by your peers to apply to your diabetes program.
• Encourage your clinicians to use the healthcare provider tools developed by the Canadian Diabetes Association.
• Check out self-management resources from the Ministry of Health and Long-Term Care to help you support your patients in managing their own care. There’s a localized program available in each of the 14 LHIN regions!
• Learn from the Sunnybrook FHT about a program they developed to help clinicians understand first hand what patients experience living with diabetes.
• Learn how to transition a gestational diabetes program from a hospital based model of care to primary care.
• Work with patients to help them set their own SMART (Specific, Measurable, Achievable, Realistic, Time-Bound) goals. Consider using a something like the ePRO (electronic patient-reported outcomes) Tool to help with goal-setting and tracking.
• Check out what your peers are already doing to help patients meet their goals through increased physical activity and healthier eating habits – and share your knowledge, too!
  •  Read about Walking Wonders – a seniors’ indoor walking and healthy lifestyle program developed by IHPs at Clinton FHT.
  • Take a look at the resources from Improving Diabetes Care; Improving Diabetes Outcomes and  Get Moving! These one-day workshops brought together teams across Ontario to share ideas and programs that are already in place and ready for spread.
  • Consider joining one of our Interprofessional Communities of Practice. We have one for Diabetes Management and another for Physical Activity in Primary Care. Contact QIDS program staff for information on how to join.
  • Check out these presentations from recent AFHTO conferences:
    • Optimizing the Health of Older Adults with Multiple Chronic Conditions: The Development of KeepWell (AFHTO 2017)
    • Exercise is Medicine: Prescribing Confidence with Unlimited Refills (AFHTO 2017)
    • Lifestyle Change – Big Impact, but How Do You Make It Happen? (AFHTO 2016)
    • Increasing Diabetic Retinopathy Screening Rates: A Rural Northern Ontario Success Story (AFHTO 2016)
• Consider becoming a HealtheSteps coach.
• Use a foot-screening tool such as Inlow’s 60-second diabetic foot-screening tool  to evaluate foot ulcer risk in your patients with diabetes. Developed by the Canadian Association of Wound Care, the assessment is comprised of 12 parameters in three categories – Look, Touch, and Assess. The assessment is usually performed by an RN or an RPN with certification in foot care.
  • This foot- screening tool is also available as a custom form for TELUS PS Suite, developed by Denis Tsang, a registered dietitian at CareFirst FHT.
  • It’s also available from for Accuro, from the publisher (accessible directly from within the EMR).
  • Use a different EMR? If you have an EMR tool for Diabetic Foot Assessments, consider sharing it with your peers. If you would like to help develop one or want to find out if someone else has already done so, consider joining one of our EMR Communities of Practice – contact us for more information.

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:

• Increasing your team’s awareness 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.
• Creating a diabetes registry: Identify patients with diabetes more accurately by using the standard queries and processes developed by QIDS Specialists, to get started on a diabetes registry.
• Tracking and demonstrating your progress cleaning in up your data to improve data quality. Before you start the cleanup process run a “coded” query to capture baseline data, 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.
• Hiring a student to help you clean up your diabetes data. Check page 26 in the “hire  student”  handbook for details about cleaning up diabetes data.
• Once your diabetes registry is clean, running the D2D diabetes queries on an ongoing basis – don’t just wait till the end of the year. This will help you keep track of data quality and progress with diabetes care on an ongoing basis.
• Signing up for CPCSSN or EMRALD to get ongoing, patient-specific reports to help you help your patients manage their diabetes.
• Joining 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 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. 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 improve your data quality, before or at the same time as you try to improve processes of care.