For technical notes, please see page 19 of the Data Dictionary.

Interpretive Notes

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

• There is a chance that patients may not recall how long they waited for an appointment the last time they visited their provider.
  • There could be bias in either direction, with the patient wanting to be positive (i.e., under-estimating the time) or the patient being generally unsatisfied and therefore over-estimating the time.
• There is likely inconsistency in the recall of patients regarding whether the person they saw was their “own” provider or someone else on the team.

• The level of access to care in your team might be lower than indicated by D2D because it only considers the access of patients who eventually had an appointment. Patients who didn’t get an appointment soon enough (or when they wanted it) might never have shown up and instead may have either gone nowhere or to a walk-in clinic, an Emergency Department or another team. Therefore, these patients would never have been included in the survey.

Measurement Error:

• In spite of best efforts, there may be inconsistency in the wording of the question between teams.

Other Issues with Current Definition:

• The measure as it is currently defined does not distinguish between appointments for an urgent issue vs. appointments for more elective concerns such as well-baby check-ups, follow-ups and preventive visits.
• The measure is counter-intuitive to the purpose of interprofessional team-based care when the wording specifies access to an appointment with the patient’s “own” regular provider. Team-based care suggests that anyone in the team could and should be able to help the patient, whenever they need it.

Some may feel that this indicator is “subjective.” This is accurate and intentional. This indicator intentionally does not specify a time frame because it is oriented around the patient’s perspective of what is reasonable. Certainly, this varies between patients and providers. This indicator is an accurate estimate of how many patients felt they had a reasonable wait (whatever they define as reasonable). It is not intended to be a measure of how many patients were able to get an appointment within a specified period.

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:

• Get more current data to move beyond the idea that “the data are too old to make improvements” OR test out a new question that wasn’t previously included in your survey.
  • Step 1: Work with a physician who is interested in improving patient experience or work through your ED to get the conversation started with your team’s Medical Lead.
  • Step 2: Suggest tracking patient experience for a small subset of patients (i.e., the next 10 patients that come in) or for a narrowly defined timeframe (i.e., whatever you get in the next 2 weeks).
  • Step 3: Look at the data to see how this current “snapshot” compares to the “old” data
  • Step 4: Take this opportunity to discuss sample size. See resources on web. You will see that you do NOT need hundreds of surveys to know how your patients are feeling and to start making improvements.
  • Step 5: Consider setting up a frequent, ongoing automated patient experience survey process (See Data Quality Actions – how to increase quality of the data using new tools to capture and track patient experience data).
• Consult these tools to help improve access and efficiency in your team
  • Join machealth – an HQO supported group that works on Advanced Access and Efficiency for Primary Care : review the online modules to better understand the concepts of Access and Efficiency and how to guide improvement efforts and implement sustainable changes in your team.
  • Access similar resources available through HQO`s Learning Community for Advanced Access and Efficiency for Primary Care.
  • Consider the different ways that patients define “access,” as presented at the Quality Improvement and Patient Safety Forum 2016.
• Check out how your peers are doing by looking at the D2D report to determine their performance with access and connect with them to either spread any processes they find helpful 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.
• Check out what your peers are doing to improve their patients’ experience of care.
  • Presentations from past AFHTO conference:
    • Not Doing Everything All the Time: Streamlining Front Desk Staff Roles for More Effective Clinic Administration (AFHTO 2017).
    • Engaging Patients in Their Care (AFHTO 2014).
    • Clinical Innovations in comprehensive primary care (AFHTO 2014).
    • Improving the Patient’s Experience of Care (AFHTO 2012).
• Get your patients involved! It doesn’t end with the Patient Experience survey. Consider sharing your D2D data with them (check out an example of a waiting-room poster here), and getting them involved with your QI initiatives.

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:

• Click here to 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:

• Administering your patient survey more often to capture input from patients more than once a year and possibly from patients not recently in the office.

• Revising 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.
  • See the HQO Primary Care Patient Experience Survey.
  • Tips and Tools to Capture the Patient’s Experience in Primary Care using the NHS’s Experience-Based Design (EBD) Methodology.
  • What are they thinking? Approaches, Ideas and Tools to Measure Patient Experience in Your Practice.
• Using new tools to capture and track patient experience survey data to improve the efficiency of administrating surveys, and especially improve the timing and quality of patient responses (e.g., administer surveys by email, phone, tablets – all integrated with your EMR).
  • Extending the EMR with Patient Tablets: Using Interactive, Point-of-Care Patient Surveys in the Waiting Room to Generate Clinical Content and Save Time.
  • Using an automated patient reminder service and survey to collect information on patient experiences.
  • Add some SaaS to your patient experience survey.
• 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 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 it the data quality issue that 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:

• 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.
• Compare to other sources of data to see if the rate with other/better data is 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. Other sources of data may include your scheduling system (e.g., actual appointment data), your EMR (queries or chart audits on a small number of patients), other pre-existing reports (CPCSSN, EMRALD, SAR, Physician Profile, MOHLTC, QIP, commercially provided or others) or personal interviews with a few patients, to name a few.
• 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.