For technical notes, please see page 24 of the Data Dictionary.

Interpretive Notes

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

• This indicator has been risk adjusted for age, sex and co-morbidities. Adjustment takes into account the differences among patient populations to allow for more meaningful comparisons between your patients and other populations. Adjusted data are easier and more meaningful to compare between teams. However, unadjusted data may provide an estimate that better reflects what is actually happening in your team and thus might help guide local improvement efforts.
• The readmission rate for a primary care organization is based on the experience of patients on the roster of that organization AND patients who are considered to be “virtually” rostered according to MOHLTC methodology. 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. Primary care organizations may not be aware that patients have been “virtually” rostered to them and thus might think the data related to these patients are erroneously attributed to their team (i.e., “they are not ‘our’ patients”). Hence, your team’s sense of how many readmissions should be attributed to the team may be different than the rate shown in D2D.
• The data refer to hospitalization and readmissions that may have happened as much as 1.5 years ago (on average) because they are based on hospital data submitted to CIHI 2-6 months after discharge (on average), after which they must be compiled and validated prior to release for reporting purposes.
• The current definition may under-estimate actual readmission rates for patients who have preventable readmissions because the denominator includes ALL patients who were hospitalized for any reason.
  • Readmissions may appear to be lower for teams with a higher proportion of child-bearing women because childbirth is one of the most common reasons for hospitalization and thus will increase the denominator, artificially decreasing the overall rate of readmissions.
  • The same is true for teams with high proportions of young, healthy patients needing elective surgeries, which are not nearly as common as birth as a reason for hospitalization, but still would reduce the overall readmissions rate because readmissions in such situations are rare.
• Many primary care providers do not get timely information about recent hospitalizations of their patients. Teams who do not know if their patients have recently been in hospital may therefore have higher readmission rates than teams with timely access to data, who are better able to engage with patients and other providers to prevent readmissions.
• There are many challenges in preventing readmissions, not all of which are solely under the control of primary care providers such as premature discharge from hospital and the natural progression of chronic conditions. Consider the possible impact of these factors on your team’s readmission rates.

Steps to Improvement

Concrete steps you can take to improve care, based on your data. Indicators based on administrative data tend to be the oldest of all indicators in D2D. Improving the timeliness of administrative data is a priority for AFHTO and HQO and others. And in the meantime, there are things teams can do to use these “old” data to fuel current, local efforts to improve. 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:

• Collaboration with HealthLinks.
• Use of Patient Navigators.
• Since some of the reasons for readmission include lack of coordination or delivery of home care supports and poor patient compliance with discharge instructions, follow-up after hospitalization might help reduce readmissions. Ideas to increase follow-up include.
  • Train administrative staff (e.g., reception) to call patients who were recently in hospital or ER to set up follow-up appointments, with or without a triage process informed by clinical staff to exclude those for whom follow-up is not needed.
  • Implement electronic reminders in EMR to prompt clinicians to decide if follow-up is necessary and if so, who should do it (i.e., physician, other clinician) how (i.e., phone or in-person) and when, so that the health team staff can initiate the follow-up process.
  • Share follow-up rates with providers (anonymously or shared within the team) for their review to identify potential areas to intervene to prevent readmissions, if possible.
  • Set up your EMR to be better able to measure follow-up Link to web page about follow -up indicator.
• 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. To that end, consider reviewing the following presentations and posters from past AFHTO conferences to find out more about how teams are keeping people at home and out of hospital.
  • Reducing the revolving-door syndrome: hospital and primary care working together to reduce 30-day readmission rate for COPD and CHF patients (AFHTO 2015).
  • The evolution of telehomecare: targeting more chronic conditions and offering customized approaches (AFHTO 2015).
  • Aging at home: Interprofessional care to keep seniors home and out of hospital (AFHTO 2015).
  • Post-hospital transition of care: : from hospital to family practice (AFHTO 2015).
  • Improving Hospital Readmission Rates & Follow-up After Hospitalization: A Team Based Approach (AFHTO 2016).
  • Primary Care Impact on an Integrated Case Management Model for the Frequent User (AFHTO 2017).

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:

• Sign up with OntarioMD to implement direct EMR access to hospital discharge data via Hospital Report Manager (HRM). Some teams already have this access via HRM or similar regional systems such as SPIRE, POI or TDIS. This process takes some time to implement, so it is worthwhile to act as soon as possible to get the process started. Check out the Ontario MD site for progress reports on HRM implementation and stories about how teams are using it to improve work flow and patient outcomes.
• Make arrangements with the hospitals most commonly visited by your team’s patients to receive extracts of data from hospital discharges and ER visits. You can then manually update the EMR with this information. Many teams are already doing this with the help of their QIDS Specialists. Contact your QIDS Specialist or AFHTO QIDS program staff for help.
• Establish a communication process for direct notification of your team about hospitalizations on an individual-patient basis via fax, phone calls or other method. This also requires manual updating of the EMR. It also may involve some work on the part of the hospital and therefore may involve some negotiation.
• Review hospital information systems to find hospitalizations of your patients and then manually update the EMR. This assumes appropriate permission to access these systems is in place, as is often the case when your team’s physicians also attend at the hospital’s Emergency Department or inpatient units.
• 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 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:

• 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 have chronic disease conditions that could be expected to result in hospitalizations as part of the normal course of the disease. Also, estimate how many of your patients are likely to be hospitalized for issues not usually associated with readmissions (e.g., birth, minor, elective surgeries). Compare to the proportion in other teams to get a sense of whether the difference is likely to lead to readmissions at TWICE or HALF or 10% (or some other number) of the rate in report. Plug that number into the “imperfect data impact calculator” and proceed accordingly.
• Estimate how many hospitalizations your team is notified of and, by extension, how many you are NOT made aware of in a timely way. Consider how many readmissions might be related to your team’s lack of awareness of the initial hospitalization and estimate whether that would lead to readmissions at TWICE or HALF or 10% (or some other number) of the rate in 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.