For technical notes, please see page 11 of the Data Dictionary.

Breakdown of Cost Sub-Categories

1. Primary Care

• General Practitioner – Fee-For-Service visits
• FHO/FHN capitation costs

• Non—Fee-For-Service General Practitioner/Family Physician visits

2. Physician, Lab, drug, ED and outpatient costs

• OHIP specialty physician Fee-For-Service costs
• Ontario Drug Benefit drug cost (e.g., seniors, people on disability)
• Home Care Services cost
• National Ambulatory Care Reporting Service ED (e.g., ED visits)
• OHIP lab cost
• OHIP non-physician cost
• Other non—Fee-For-Service visits
• Emergency Department Alternate Funding Arrangement non-Fee-For-Service visits
• Non—Fee-For-Service medical oncologists
• Non—Fee-For-Service radiation oncologists
• National Ambulatory Care Reporting Service cancer (e.g., day surgery or treatment)
• National Ambulatory Care Reporting Service dialysis (e.g., day hospitalization for treatment)

3. Inpatient and same day surgery costs

• Inpatient (CIHI/DAD)
• Same Day Surgery (SDS)
• Inpatient Mental Health

4. Long Term Care, Complex Continuing Care and Rehab costs

• Long Term Care cost
• Complex Continuing Care cost
• Rehab (NRS)

Interpretive Notes

Tips to help you understand the data and put it in context. Cost has been identified as one of the priority measures for system-level performance by HQO and will therefore be eventually included in system-level performance reports. In the meantime, D2D remains the only primary care reporting process to include per capita cost data. D2D 1.0 was the first time cost data were shared with primary care providers at a team level, although these data have been used in research and policy decision-making for several years. The inclusion of cost data fully embodies the intent of D2D to be a “START-egy,” a tool to get started at meaningful measurement in primary care. As such, the main value of these data was to initiate conversations to refine the measure based on the wisdom of frontline primary care providers to make this measure meaningful and actionable over time. Another value of these data was to make it possible to demonstrate the relationship between lower costs and higher quality, based on data from D2D 2.0 and 3.0. While analyses and refinements continue with this indicator, it is possible that it will function more as a system-level indicator than a metric for particular attention at the team-level.

• There has been a change in how cost is calculated between D2D 3.0 and D2D 4.0. Prior to D2D 4.0 costs were calculated over a 2-year period. For D2D 4.0, ICES changed that timeframe to 1 year. Teams looking to compare cost over iterations of D2D should refer to the team-level report, as comparing between D2D iterations will not be meaningful. Efforts are underway to investigate the feasibility to update previous iterations of D2D data with 1-year costs to make comparison over time easier in D2D.
• Unadjusted total costs do not take into account how sick patients are. Consider focusing on ADJUSTED total costs to allow comparisons between teams to be more meaningful.

• Because costs for long term care are considerably higher than costs in most other categories, costs are broken down into 4 categories: primary care; physician, lab, drug, ED and outpatient; inpatient and same day surgery costs; long term care, complex continuing care and rehab (see technical notes). Further exploration with AFHTO members may help clarify the extent to which any of these categories are sensitive to primary care interventions.

Readers are referred to emerging research (Wodchis and Laberge and others, personal communication) on health care system costs which seems to indicate that differences in costs for patient care by different models, pre-dated the implementation of the models and thus may be related to factors beyond the model of care itself.

• For more information, refer to the Health System Performance Research Network’s Guidelines on Person-Level Costing Using Administrative Databases in Ontario.

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:

• 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 useful to identify peers who have focused on similar areas for improvement.
• Consider exploring the Choosing Wisely campaign for change ideas and share your ideas about inclusion of some of the Choosing Wisely metrics in subsequent iterations of D2D.

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:

• Access the Imperfect Data Impact Calculatorto 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. Primary care contributions to improving data quality would therefore be thoughtful reflections on refinements to the definition, to be considered for presentation in subsequent iterations of D2D.
• 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. 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 do this, work with your clinical leaders and staff to establish an approximate impact of data quality – i.e., is the data quality issue 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:

• Explore the proportion of your patients who are long-term care residents to estimate how much impact their costs are having on your overall team cost. 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.

• Alternatively, 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 better understand the issues affecting data quality, before or at the same time as you try to improve processes of care.