TRATAMIENTO JURIDICO DE LA VIOLENCIA ECONOMICA EN EL PERU:

As mentioned previously, the midnight bed census is one of the most important inpatient metrics, therefore it makes sense to derive its time series from the PA data to check for any long-term trends or seasonality. The midnight census is derived by counting the number of patients whose admission and discharge dates span midnight for each day in the observation period. The resulting series also serves as a benchmark for model validation in the chapters which follow.

Figure 3.3 charts the midnight bed census (or midnight occupancy) over time for the emergency and elective admission types, for all hospital wards, after filtering the data. The AGH clearly has a greater number of emergency patients resident at midnight throughout the observation period, and on average, the ratio of emergencies to electives is approximately 5:1. There also appears to be some non-stationarity in both the emergency and elective series. For the elective patients, a slight downward trend occurs for most of the observation period. In the emergency series, the non-stationarity is more noticeable; with an upward trend in the first four months, and a decline during the last nine months. However, the pattern of decline between October 2011 and February 2012 does not appear to occur in the previous year, suggesting reasons other than seasonality.

54 Figure 3.3: The emergency and elective midnight bed census during the observation period in the PA data.

The existence of longer-term trends in key simulation parameters (such as patient arrival rates) are important considerations when developing an ODES. However, this research is firstly concerned with developing a proof-of-concept model. Additional complexity, such as trends caused by seasonal effects (or other reasons) can be added at a later stage, after demonstrating the model’s capabilities over the planning horizons it is designed for. For this reason, a set of time-based exclusions are also applied to the InpatientStay data set to remove some of the trending behaviour seen in Figure 3.3. The result is a subset containing the inpatient episodes occurring between the 22nd _{of March} 2010 and the 3rd _{of October 2011 (indicated by the period between the vertical} lines in Figure 3.3) which is less likely to be affected by external or systemic factors which change over time.

0 50 100 150 200 250 N u m b er o f O ccup ie d Be d s

Observed Midnight Bed Census

55

The final dataset contains the stay segments which occur during each inpatient episode, with each segment coded to one of 20 wards, and classified by admission type (emergency/elective). Table 3.1 summarises the data by ward, with statistics derived from the midnight census, along with arrival rate and length-of-stay information which is typically used in DES modelling. The summary statistics are in descending order of the average midnight census, with the top five wards contributing to over 70% of the hospital-wide census for emergency and elective patients. In terms of the proportion of occupied beds, these five wards experience occupancy levels of approximately 80% or higher on average, which could indicate a greater likelihood of encountering capacity- related issues. Further down the table, some of the wards exhibit very low occupancy levels, with the bottom eight rows displaying midnight census levels which average less than one patient. This is caused by the exclusions applied earlier (in the case of Ward 4O, Ward 4N and the Renal Units), or by the rarity of overnight stays (in the case of Theatres and the Day Procedure Unit), rather than indicating frequently empty wards. It should be noted that the proportion of occupied beds at midnight (third column) is calculated from the highest midnight bed census observed in the filtered PA data, rather than the total number of physical beds on each ward. Using the total number of beds in the denominator would overestimate the capacity available to the within-scope patients.

56 Table 3.1: Summary statistics for each ward with stay segments in the filtered PA data. The ranges associated with the midnight census, arrival rates and lengths-of-stay are 95% confidence intervals for the means.

The split between emergency and elective patients (fourth column of Table 3.1) shows that for most wards, the emergency patients outnumber the elective patients in terms of average midnight occupancy, although the proportion fluctuates by ward. In terms of absolute occupancy, Ward 5B has the highest number of elective patients, averaging approximately 10 occupied beds at midnight, closely followed by Ward 5A which averages approximately 9 occupied beds. The only ward where elective bed occupancy could outnumber that of the emergency patients is the Day Procedure Unit. However, overnight stays at this location are rare, making it less important from the perspective of inpatient bed management. The other locations with higher elective proportions

57

only have a handful of stay segments in the filtered PA data, therefore no meaningful conclusion can be drawn about the emergency/elective split.

As might be expected from the high proportion of beds occupied by emergency inpatients hospital-wide, the arrival rate at the Emergency Department (ED) is easily the highest among the wards in the PA data (fifth column of Table 3.1), and more than doubles the next highest arrival rate (Ward 5B). It should be noted that the arrival rates to each ward includes internal transfers from other wards, as well as new admissions to the hospital. However, the total row includes only new admissions, since internal transfers would not be classed as arrivals at the whole-hospital level. Therefore, the sum of the ward-level arrival rates is necessarily greater than the whole-hospital arrival in the Total row.

Of the wards exhibiting higher levels of average occupancy, Ward Northside has the highest average length-of-stay (sixth column of Table 3.1) of approximately 11 days. All patients admitted to the Northside ward do so under the Psychiatry specialty, in which patients often require longer hospital stays that those presenting with physical disorders (Mechanic et al., 1998). Two locations exist which have average lengths-of-stay higher than Northside (“Hospital in the Home” and “The Manor Transitional Unit”), however their contribution to overall midnight occupancy is negligible. The Manor Transitional Unit has the highest average length-of-stay of all wards in the filtered PA data. However, the very low number of observed stay segments (along with the influence of potential outliers in the sample) causes the confidence interval to be wide relative to its mean, therefore the estimate may not be reliable.

58

Although 20 wards are referenced in the data, some of them make relatively small contributions to the hospital’s midnight census (on average) based on the patient episodes which are within scope. For this reason, modelling every ward as an individual location in the simulation may not be practical, especially if the number of observed stay segments is small. Since these types of considerations also inform the model’s structure, further discussions about selecting wards to be individually modelled take place in the next chapter (Section 4.4), where other structural elements of the ODES are characterised.

59

Chapter 4