Estudio de localización

Our objective here is to achieve complete information about the arrival times of all the patients under study. In addition for later use, I need to have complete observations on physician initial assessments (PIA) and discharge times as well. The dataset consists of individual time stamps of several service activities for every single patient who flows through the ED service process. Definition and description of the short-form variable names of our dataset that were used to obtain values for arrivals, PIA, and discharges are provided in Table 3.2.

Table 3.2: Definition of the variables on which individual time stamps were obtained.

Variable Names Definition

Received Time at which charge nurse swipes patient’s health card. Triaged Time at which patient was triaged.

TBS Time at which the patient is expected to be seen. SBMD Time at which patient is seen by an MD.

DTD Time at which decision about patient’s discharge was made. DC Time at which patient was actually discharged from the ED. Depart Time at which chart is actually signed out.

TransWard Time at which patient was transfered to another ward. ConsAdm Time at which consultant decides to admit the patient.

LWBS Time at which patient was observed left without been seen by an MD. LABS Time at which patient was observed left after been seen by an MD.

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Imputation is the process of substituting suitable values in places where there are missing values. Missing values are of three types: (i) missing completely at random (no pattern), (ii) missing at random (can be predicted from other values of the variable), and, (iii) missing not at random (depends upon unobserved variables). We assumed missing data occurs at random. In fact the missing data are unlikely to be random, but more likely to relate to the busy periods. However, this is not likely to lead to major problems due to the level of missing data involved in most cases.

I predicted the missing data from the observed values of the same variable after controlling for block of day, day of week, month of year, and triage category. There are several methods available for missing value imputation. I used single mean imputation to impute missing values which is the simplest among all imputation methods. Mean imputation uses the mean of the observed values as a substitute for missing values after matching for similar values of other variables.

Missing values are evident in the ED data collection process. This is due to the fact that providing services to a patient takes precedence over recoding data in an ED. A physician will start the treatment process for a critical patient before record the instant of service time initiation. In addition, while one observation is missing for one patient, it may not be so for others. Therefore, missing values can well be scattered across the dataset although one anticipates a greater preponderance of missing values during congested periods than during calm ones. During analysis, the major problem for having missing values in the dataset is that any standard statistical software disregards individual patients with any of their time stamps missing. Consequently, discarding a large portion of the data may introduce bias and reduces sample size. Since discarding missing values leads to loss of information, replacing missing observations through imputation improves the power of the study.

The algorithms used to obtain complete observations for arrival, PIA, and discharge times using observed and imputed values of the variables described in Table (3.2) are provided below,

3.2. StudySetting 27

There were 42 missing values for Received time stamp. The time stamp that follows Received time stamp is the TBS time stamp. After being received, patients are usually triaged followed by registration. When patients are being registered, they are assigned a TBS time stamp. I used TBS time stamp to impute received time stamp values for 35 patients because 35 of the 42 missing received time stamps have TBS time stamps (3.2). Since TBS time stamps occur a certain time after the Received time stamp, in order to retrieve respective Received time stamps, I subtracted the average of the differences be- tween the TBS and Received time stamps from the TBS time stamps for which complete observations were available. In our study ED, patient volume varied by the day of the week and by triage category. Therefore, further refinement was done by computing the averages of the differences between the TBS and Received time stamps after matching by the day of the week and triage category.

The remaining 7 missing Received time stamps had only corresponding Depart time stamps available, and I imputed those missing observations by subtracting the average of the time differences between the Depart and Received time stamps from Depart time stamps by day of the week and triage category. A schematic diagram of the algorithm described above to obtain arrival times is provided in Figure 3.2.

• Physician First Assessment (PIA):The Seen by MD (SBMD) time stamp was regarded

as a PIA time. However, there are 10,411 missing SBMD time stamp. The closest time stamp is TBS and I approximated 9244 of the missing SBMD time from the correspond- ing TBS time. Since SBMD time stamps occur after TBS time stamps the majority of the time, I added the average of the differences between TBS and SBMD time stamps with TBS time to approximate PIA time. Additionally, as patient volume varied by the day of the week and by triage category, I computed average of the differences after matching by the day of the week and triage category. The similar procedure was applied to impute the remaining 1167 missing SBMD time stamps form DC (272), Depart (619), and DTD

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(276), respectively.

• Discharges: Patients’ are considered discharged from an ED when they are released

home or admitted to other facilities of the hospital. Therefore, observed DC and

TransWard time stamps were considered as discharge times. Among 7660 missing time stamps, 7286 and 374 were imputed from Depart and DTD times, respectively. The 724 of the remaining 768 missing time stamps have ConsAdm time stamps. I imputed discharge times by adding the average of the differences between the ConsAdm and DC times with DC times after matching by the day of the week and triage category. A similar procedure was applied to impute the remaining 44 missing discharge time stamps form LWBS (12), LABS (3), and Received (29) times, respectively.