Recently the South West Local Health Integration Network in Ontario released a video featuring their CEO Michael Barrett discussing highlights from 2011 and a look ahead to 2012. I came across the video in a tweet by Julie White, SWLHIN’s Director, Communications and Customer Service. I replied to Julie’s tweet stating that I liked the video and wished it included more statistics. Naturally, within a couple of minutes, Julie responded with a link to SWLHIN’s Performance section on their website. Power of Social Media aside, in the Performance page, I came across a report titled “Quarterly Stocktake Report” published in November 2011. I found this report interesting and wanted to share my thoughts on parts of it, specifically the section related to ER wait times at Pay for Results sites.

One of the problems addressed the fact that 50% of ER visits were non-urgent or less urgent than to require an ER visit. For that, a goal was set to reduce ER demand. The Bar Graph provided the number of unscheduled ER visits by quarter per 1,000 population. In the first quarter of fiscal year 2010/2011, ER visits were 146 per 1,000 population (or 14.6%) and 149 in 2011/2012 (14.9%). This shows that the goal was not met. However, the comparison might not be valid when comparing exact numbers, especially if the total population around the SWLHIN has changed. This problem is not unique to the SWLHIN, as the figures above were actually taken from CIHI and the MoHLTC.

For example, if the population in that region grew over the past fiscal year; (past population + new immigrants + newborns) – (deaths + relocation to outside southwest), a more appropriate approach would be measuring the percentage of ER visits out of the total population, along with the growth rate of both population and ER visits. So if the population grew by 5%, assuming total population was 1,000 as an example, in Q1 11/12 the population would be 1050, which brings down the % from 14.9 to 14.2%. Meaning that ER visits were reduced by 0.7%, which is less than the total growth of the population (5%) in that region, in this hypothetical scenario of course. However, this example shows the importance of factoring in variables that while tracked separately, can sometimes be overlooked.

Another problem addressed the long ER wait times and the fact that 90% of patients were treated in the ER within 9.4 hours from triage to discharge, compared to the provincial target of 8 hours for high acuity patients and 4 hours for low acuity patients. The graphs show that time spent in ER for high acuity patients dropped from 8.9 hours in Q2 10/11 to 8.6 in Q2 11/12 while it remained the same for low acuity patients at 3.9 hours. Further details are provided on pages 5 and 6 on a number of indicators including Time from Triage to Physician Assessment. Interestingly enough, the time for this indicator has increased from 3.4 hours to 3.7 hours. While a breakdown by hospital (for Pay for Results sites) is provided for all reports on page 6, including information about size of the ER (Staff, rooms, beds, etc), local population served by hospital, etc, would add more context to the report. This would allow us to realistically compare hospitals based on their staffing levels and local population.

I guess what I’m getting at is that in order for a statistical report to provide a reliable representation of data, it must include all the variables that might influence decision making, without making any assumptions that the reader is aware of those variables. In the first example, if we disregard population growth around a hospital, one might think that the ER visits are increasing, which is half the truth. If the population grew but the % of visits dropped, it is an indication of exactly the opposite of what the initial look at the number of ER visits per 1,000 population.  This is not to say that the increase in ER visits should be ignored, as this number can be correlated to expected population growth, and used for planning future expansions & funds.

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