Author: sitesuper

Updated as of January 22, 2016

• This indicator has been risk adjusted for age, sex and co-morbidities. Risk adjustment takes into account the differences among patient populations to allow for fairer comparisons between your patients and other populations. Risk 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 happened 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.

Updated as of January 22, 2016 Colorectal: Please see PAGE 17 of the D2D 3.0 Data Dictionary VERSION 4 Cervical: Please see PAGE 18 of the D2D 3.0 Data Dictionary VERSION 4

7-Day Follow-Up Follow-up in primary care after hospitalization is an important goal of primary care.  However, the current definition of the indicator, as presented in the Health Data Branch (HDB) report, is not useful to AFHTO members.  A few of the issues with the definition are:

• Excludes follow-up by anyone other than the physician; therefore it violates a principle of team-based care
• Excludes follow-up by any method other than office visit; therefore it is not consistent with best practice re: patient centeredness and access via email, phone and/or house calls
• A persistent lack of real-time hospital data prevents health teams from measuring & improving follow-up
• Fails to exclude patients managed in hospital by their primary care physician (and therefore may not need seven-day follow-up once discharged)

AFHTO members remain committed to measuring and improving follow-up, and have, in fact, made considerable progress on this at the team level.  7-day follow-up is presented here as an exploratory indicator to facilitate knowledge transfer and exchange, with the goal of informing a more appropriate definition of this indicator for subsequent iterations of D2D. This indicator was populated only by teams who are already tracking 7-day follow-up in a formal way, at the time of data submission to D2D 2.0.  Teams submitted the most recent data available for this indicator based on the definition or process in place in their team.  Each team also included a brief commentary about the definition, the range of patients included, and the process used for tracking follow-up.  Many teams also provided the rate of follow-up, as defined by HDB, to illustrate the gap between the existing definition and current team-level processes. The range of values is quite wide, as is the variety of approaches to doing, and documenting follow-up.  The quantitative and qualitative data are intended to be interpreted together to develop a more relevant and meaningful version of this indicator for use in subsequent iterations of D2D. Click here to download the PDF version of the Exploratory Indicator for 7-Day Follow-Up graph. This will allow you to hover over the graph and see the stories that each team provided. If you have any problems with the PDF please contact improve@afhto.ca. Click here to download a PDF of all stories submitted by teams.    

Updated as of January 22, 2016 Please see PAGE 16 of the D2D 3.0 Data Dictionary VERSION 4

Updated as of January 22, 2016 Please see PAGE 14 of the D2D 3.0 Data Dictionary VERSION 4

Updated as of January 22, 2016 Please see PAGE 11 of the D2D 3.0 Data Dictionary VERSION 4