Principales características psicosociales y mecanismos psicológicos del suicidio

As mentioned before, the goal of this research was to design a decision support tool that accepts two points as input, i.e., sending and receiving facilities, and presents the user with estimated transfer times. As discussed previously, the transfer times that are of

interest to the decision makers are time to definite care, time to sending facility, and patient out-of-hospital time.

There are two possible approaches to analyze the data and design the application. One is to conduct the analysis at the level of each sending receiving facility combination. The other approach is to split up the total time to definite care to meaningful intervals and then provide an estimate for each of these intervals according to the requested combination of sending and receiving facilities. The following example demonstrates and compares these two methods.

Example:

Consider A and B as the sending facilities in one city and C and D as the receiving facilities in another city; both sending facilities have helipads. However, receiving facilities do not have helipads, so the patients are required to be transferred to one of the landing sites (X or Y) close to the receiving facilities and then transferred to C or D using a local land ambulance. Thus, the first leg of travel would be an air transfer and the second leg would be a land transfer (Figure 4-1).

For simplification, assume that the goal is to estimate patient out-of-hospital time for each possible combination of facilities and landing sites (e.g., A-X-C, B-Y-D, etc). The limitation is that, there is not enough historical data available for some combinations. Integrated Model: One approach is to look at the historical data for each route and provide the estimated out-of-hospital time for that. For example, assume that in the dataset, there were 50 transfers from A to C. Twenty seven of these cases used X as the receiving landing site (A-X-C) and 23 of the cases used Y as the receiving landing site (A-Y-C). The estimated travel time for A-X-C was 37 min (air leg=25 min, land leg=12

min), and for A-Y-C was 98 min (air leg=43 min, land leg= 55 min). However, if there are no data available for a specific combination, this model cannot provide any estimates (e.g., B-X-D or B-Y-D). This approach is shown on the left in Figure 4-1.

Modular Model: The other approach is to split up the combination (e.g., A-X-C) to two legs (e.g., A-X and X-C) and look at the historical data for each leg separately and provide the estimated times for those legs. For example, assume that, in the dataset, there were 627 transfers from A to X (air leg) and the travel time estimate for this interval was 21 min. Also, there were 177 transfers from X to C (land leg) and the travel time estimate for this interval was 15 min. Thus, the estimated travel time for A-X-C using this method would be 36 min (36 min = 21 min + 15 min). This method enables us to provide estimates for the combinations that do not have enough observations in the dataset. For example, the combination of B-X-D has never happened in the past, but B-X and X-D have happened. The travel time estimate for B-X based on 150 observations was 60 min and the estimate for X-D based on 600 observations was 30 min. Thus, the travel time for B-X-D should be around 90 min. This approach is shown on the right in Figure 4-1.

The modular model solves the common problem of lack or scarcity of data in many cases. However, there might still be a few legs that do not have enough observations in the data. In that case, we can utilize general models derived from statistical analyses to estimate the travel time for those legs based on predictive factors such as distance. Let’s say in our example, there is another landing site called Z, and we want to estimate the time for A-Z-C. We have 200 observations for Z-C but only three observations for A-Z. Since three observations would not be reliable to generate an estimate, we use a statistical model built on the entire dataset or a subset of it to estimate

the travel time for A-Z, based on explanatory factors such as distance and time of day. For Z-C, we still use the estimate obtained from the 200 observations which were collected from transfers conducted between Z-C. The out-of-hospital time would then be the sum of the estimated times for A-Z and Z-C.

This approach (i.e., splitting the total time into sub-intervals) complies with the principles of a modular design [25], where the out-of-hospital time in this example consists of two modules; sending facility-landing site and landing site-receiving facility (Figure 4-2). The subintervals are produced independently of one another, but will function together as a whole to provide an estimate for the out-of-hospital time. Splitting up the transfer time to subintervals (modules) also makes the complexity of the large data manageable and allows module estimates to be changed and improved over time independently.

Figure 4‐1 Comparison of Two Possible Approaches to Manage the Data; without Data Aggregation  (left), with Data Aggregation/ Modularity Concept (right) 

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Integrated Design Modular Design

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modules can be connected together to produce  a variety of time estimates for different  combinations.

Accuracy: As discussed previously, the modular design of the tool is a good approach to deal with the combinations that do not have enough observations. However, one might argue that adding the estimated times of subintervals might not be correct as summing up the estimates of individual intervals may give a result that might be different than generating an overall estimate from the entire process. To compare the two methods, the sum of the estimated subinterval times (∑ ) can be compared with the estimated total times (∑ ). Figure 4-3, shows the estimated total time to definite care for a commonly serviced route in southern Ontario (i.e., Peterborough Regional Health Centre - University Health Network General Site). With the modular method the sum of the estimated times of subintervals was 135 min (∑ 135min). With the integrated design, based on the 145 observations available for this particular route, the estimated total time was 140 min (∑ 140min .

Figure 4‐3 The Comparison of Total Time Estimates for a Particular Route: Modular Design (Top),  Integrated Design (Bottom) 

Using the dependent t-test, the outputs of the two models were then compared for 36 routes that had more than 30 observations. The results revealed that, on the average

the output of the integrated model (M=117.62, SE=20.23) was 3 minutes greater than the output of the modular model (M=114.67, SE=22.15). This difference is statistically significant t(35)=3.16, p<.001, however, it is not be significant from a practical perspective.

It is worth mentioning that in this analysis, the sample median was chosen as the estimate of central tendency. The reasons for this choice will be discussed in section 6.1. Further, it was assumed that the intervals are independent of each other. This assumption was based on consultations with the paramedics and transport physicians.

Summary: In sum, there were several reasons for splitting the estimation times into intervals instead of calculating travel times for the entire duration of the transfer:

 First of all, the planners tend to make their estimates by breaking down the transfer process into components which are related to these intervals. Thus, the suggested approach is compatible with operators’ mental model of the process.

 Splitting each transfer into individual intervals allows for the use of larger datasets for estimating intervals that were common between different sending- receiving facility combinations.

 As mentioned previously, there are a number of different variables that a physician considers in light of patient acuity: time to bedside, out-of-hospital time, and time to definite care. These variables can be readily calculated from individual interval estimates.

 Separate estimates would allow dispatchers to experiment with different combinations of routes between the sending and receiving facilities. Route

properties such as the base providers, pick-up, and drop-off sites can be modified depending on the current status of resources and other environmental factors. These properties would then change specific intervals while keeping other intervals constant, simplifying the decision making process by allowing for the easy calculation of transfer times for the different alternatives.