For technical notes (individual), please see page 20 of the Data Dictionary. For technical notes (team), please see page 21 of the Data Dictionary.

Interpretive Notes (Individual)

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

• Virtual rostering assigns patients to the primary care physician that provided the highest dollar amount of services within a defined set of primary care services. Physicians in your team may not be aware of which patients have been virtually rostered to them. Therefore, they may erroneously think that these patients are not “their” patients. As a result, an individual physician’s sense of how many of patients they see that are “their” patients may be different than the rate shown in D2D.
• Visits to health care providers other than physicians are not included in this measure. However, this does not necessarily skew the measure. For example, if a patient visits a primary care team 10 times and sees a physician 8 times, and each time it is their “own” physician, they will score 100% (8 out of 8) for “regular care provider – individual.” If, however, they visit 10 times, receiving care from multiple providers but only saw a physician once and it was their own physician, they could still score 100% on this measure.
• Efforts to improve access to same or next day appointments may result in patients seeing whichever physician is available for appointments at the time. While this may be valuable from the perspective of access, this process may be reflected in poor performance on the “regular care provider – individual” measure.
• Teams with part-time physicians and teaching teams may have developed strong relationships between physicians to jointly care for patients, such that patients may feel equally comfortable and familiar with more than one physician. However, while this might embody team-based care, it may be reflected in poor performance on the “regular care provider – individual” measure.

Steps to Improvement (Individual)

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:

• Explore interventions to increase proportion of patients with regular care provider and/or interventions to improve continuity of care (i.e., increase the chances that they see the same provider each time).
  • Ask patients what is most important to them: Train front reception staff to discuss options with patients as part of the appointment-booking process (i.e., difference in wait times if patient wants to see their regular primary care provider vs. any primary care provider in the team).
  • Improve same day/next day access for all physicians (i.e., ensure all physicians have same day/next day spots available exclusively for their patients).
• 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.

Data Quality Actions (Individual)

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.
• Additional information for estimating the impact of data quality for this measure.

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.
• Consider generating a more local estimate of continuity based on who patients see in your team, as indicated by your EMR data. Consider collaborating with the QIDS Specialist group to improve definitions of encounter types and provider types so a more local, team-based measure can be extracted from the 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 in the area of access. However, they might be “good enough” to help you decide if your team needs to improve access 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. 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:

• If you have part-time physicians in your team, try the following:
  • Extract data from the EMR to determine the number of visits that patients of part-time physicians made to other physicians in the team (i.e., NOT seen by their “own” doctor). 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 not possible to extract all these data from the EMR, extract as much as possible and project mathematically based on the number of patients, the average number of visits for all patients and how many hours the part-time physician normally works. 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.
• Determine how many of the patients visiting your team are not formally rostered but might be virtually rostered and estimate how many of them usually see the same doctor. 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.
• Attempt to estimate how many patients would have seen their own physician in the absence of efforts to increase same/next day access. This will almost certainly be a judgement call, rather than an “estimate” in the truest sense of the word. 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.

Interpretive Notes (Team)

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

• “Team,” in this indicator, refers to the physician group i.e., FHO or FHN, not FHT.
• HQO has removed this indicator from the PCPR for FHTs with multiple physician groups. 
• Virtual rostering assigns patients to the primary care physician that provided the highest dollar amount of services within a defined set of primary care services. Physicians in your team may not be aware of which patients have been virtually rostered to them. Therefore, they may erroneously think that these patients are not “their” patients. As a result, an individual physician’s sense of how many of patients they see that are “their” patients may be different than the rate shown in D2D.
• Visits to health care providers other than physicians are not included in this measure. However, this does not necessarily skew the measure. For example, if a patient visits a primary care team 10 times and sees a physician on this team 8 times, regardless of whether or not it was their “own” physician, they will score 100% (8 out of 8) for “regular care provider – team.” If, however, they visit 10 times, receiving care from multiple providers but only saw a physician once and it was physician on the team, they could still score 100% on this measure.
• Teams with part-time physicians and teaching teams may have developed strong relationships between physicians to jointly care for patients, such that patients may feel equally comfortable and familiar with more than one physician. This principle is at the core of team-based care and is reflected in higher performance on the “regular care provider – team” vs. “regular care provider – individual” measure across most teams in D2D.

Steps to Improvement (Team)

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:

• 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.

Data Quality Actions (Team)

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

• Additional information for estimating the impact of data quality for this measure.

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.
• Consider generating a more local estimate of continuity based on who patients see in your team, as indicated by your EMR data. Consider collaborating with the QIDS Specialist group to improve definitions of encounter types and provider types so a more local, team-based measure can be extracted from the EMR.

Review list of “rostered” patients with each physician and identify patients who are likely “virtually rostered” (i.e., see the doctor frequently but are not formally rostered) for consideration for formal inclusion in the roster.

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 in the area of access. However, they might be “good enough” to help you decide if your team needs to improve access 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. 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:

• Determine how many of the patients visiting your team are not formally rostered but might be virtually rostered and estimate how many of them usually see the same doctor. 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.
• Attempt to estimate how many patients would have seen their own physician, in the absence of efforts to increase same/next day access. This will almost certainly be a judgement call, rather than an “estimate” in the truest sense of the word. 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 (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.