For technical notes, please see page 31 of the Data Dictionary.

Interpretive Notes

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

• The D2D definition is a different definition from the Ministry of Health and Long-Term Care (MOHLTC) indicator which is available on the Health Data Branch (HDB) portal. D2D data includes follow-up by any member of the team (by phone or in person). The MOHLTC indicator is based on physician billing data, so it does not accurately reflect the follow-up care that patients get from health teams. As teams get better at providing the right kind of follow-up care, from the right team member, in the right way, their performance on the MOHLTC indicator will look worse. For this reason, we are asking teams to report follow-up after hospitalization using this more inclusive indicator.
• Follow-up of patients by primary care providers after hospitalization is a valuable way to improve patient outcomes, but these do not always need to be in-person visits by physicians. In some cases, it was the patient’s own physician who discharged them. Many patients receive follow-up from a pharmacist, who makes sure their medications are in order, or by a social worker, who makes sure they are adjusting to being home. Teams do this because it is what their patients want and need. It is also more efficient, freeing up physician appointment time.

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:

• Using Patient-Oriented Discharge Summaries (PODS) to increase the consistency of getting hospital discharge data and to give your patients more agency in getting the follow-up care they need. So far, PODS have been implemented in 27 hospitals across the province. These simple forms were co-designed with patients and caregivers and include important information about follow-up needs, in language that patients can understand. Get in touch with your local hospital to find out if they’re using PODS, and if not, encourage them to start. Provide blank PODS forms to patients who are being admitted to hospital and ask them to make sure the discharging physician completes it with them, and to bring it back to you.
• Contacting your peers 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 them.
  • Now that teams can choose to “unmask” themselves in D2D, you can see who is performing well on this indicator and reach out to them. Or find a peer whose team structure is similar to yours, and ask them how they are doing.
  • HQO’s QIP Navigator allows teams to query submitted QIPs. This tool is can help you find peers who have focused on similar areas for improvement.
  • Learn how the Woodbine FHT and Tilbury District FHT improved their 7-day post hospital discharge follow-up rates using the Model for Improvement.
  • Check out the Markham FHT’s Transitions Program, which helps to reduce hospital-based care while affording the patient a better health care experience.
  • Learn about the Algonquin FHT’s Discharge Patient Program which supports transition back into primary care.
  • Read about how  Health for All FHT’s interprofessional approach has helped them provide follow-up care to more patients.

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.

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 use the Imperfect Data Impact Calculator, work with your clinical leaders and staff to establish an approximate impact of data quality.  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.
• Experimenting with possible “error” rates to see how much error (e., TWICE or HALF or 10% of some other number) would be needed to change the decision made based on 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 unlikely, then the data are probably good enough to support the initial decision regarding the need to improve care, 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” for you to whether you need to improve care.

• If your data ARE “good enough,” the next step is to consider strategies to improve, assuming this area of care 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.

Increase the quality of the data

Tracking follow-up after hospitalization requires 2 bits of data: date of discharge from hospital, and date of follow-up by primary care provider. It is necessary for primary care providers to become proficient at tracking patient encounters with all members of the team in all modes (e.g., phone, in person), no matter what the state of hospital data-sharing is. If the “imperfect data impact calculator” shows that your data may not be good enough for you to decide to change processes of care, you might consider:

• Having a conversation with your team on how you are tracking phone encounters. Learn how others have done it.
• Using the EMR Tools and queries for phone encounters. These tools and queries will help inform the development of new queries for follow-up after hospitalization.
• Submitting data for the D2D indicator Follow-Up after Hospitalization, using whatever hospital discharge data you have, combined with your follow-up data to move the conversation in your team along regarding tracking follow-up.
• Working with engaged cliniciansto increase the consistency of data entry related to follow-up after hospitalization.
• Using longer reporting periods, such as 12 months. The reporting period for this indicator is self-defined, and longer periods will yield more stable/reliable data. However, we recognize that you may have only recently started recording follow-up visits in your EMR and may only be able to submit for a short period. This should not be a barrier to participation.
• Working to develop data-sharing processes with local hospitals, to improve the flow of data from hospitals to primary care.
• The definition and tools were developed to address data quality issues responsible for the exclusion of this indicator from D2D 1.0. Please share ideas on how the definitions or tools can be improved.
• Other ideas: please share!