Click on the following links to access: 1. Technical notes 2. Interpretive notes 3. Data quality actions – Actions and ideas to consider and discuss with clinical leads and other members of the team 4. Potential actions related to processes of care – Actions and ideas to consider and discuss with clinical leads and other members of the team Note: The files linked above are updated regularly. Please refresh your internet browser to clear its memory, this will ensure that you see the most updated material. Most internet browsers will save a copy of the PDF file and show you an out of date version. Please see the Members-only page of AFHTO web site for online forums specific to each indicator so that members can share with and learn from others working on similar issues.

Information on this indicator related to D2D 3.0 can be found here. For technical notes, please see page 29 of the Data Dictionary.

Interpretive Notes

Tips to help you understand the data and put it in context. The performance shown on these indicators in D2D is based only on patients either currently in the office or having just had a recent visit. It is possible that this may not be representative of patients who did not have appointments either at the time of the survey or chose to go elsewhere (e.g. walk-in clinic, Emergency Department, other provider) or nowhere at all. There appears to be little difference in performance between the 3 questions and all of them show a high percent of patients with positive experiences. This may be real and it may also be what is known as “ceiling effect” (i.e. bunching of responses at the top end of the scale), possibly because patients want to be positive about their experience with their provider (i.e., social bias). Teams that do not feel able to do anything to improve patient experiences in these areas may decline to ask these questions to avoid setting false expectations among patients that their input will prompt changes. As a result, the performance level may represent teams that would consider interventions to improve patient experience in these areas and thus may not be representative of all teams. It could be an over-estimate of actual patient experience if one assumes that teams with good patient experience are more likely to consider interventions. Or it could be an under-estimate if one assumes that teams for whom patient experience is not good are more likely to be considering interventions to improve their performance in this regard.

Data Quality Actions

Tips to help you understand the quality of your data and, if necessary, take steps to improve it.

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.
• Please see below for more information about this tool.

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. Some options include the following:

• Do your patient survey more often to capture input from patients more than once a year and possibly from patients not recently in the office.
• Consider using email or other electronic tools (e.g. scanning of paper forms) to reduce data entry effort for patient surveys, making it easier for you to do the survey more often and/or offer it to more patients.
• Revise your team’s patient survey to align with standard questions and/or add additional (standard) questions, even if it is one question at a time.
• Participate in TRANSFORM
• 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 possible impact of data quality issues on the level of performance shown in the D2D 1.0 report. In that case, consider tracking 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. If this exercise is not possible or 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 1.0. 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 (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.

Potential Actions Related to Processes of Care

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:

• Collaborate with Patients Canada PFCC KPI initiative.
• Convene a patient advisory council, with the help of the toolkit from CCO:
• Connect with teams who report progress with improving patient experience:
  • Thunder Bay Regional Health Center
  • Institute for patient and family centered care
  • Presentations from previous AFHTO conferences
• Contact your peers to determine their performance with patient experience 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.