{"id":5331,"date":"2024-11-21T15:06:16","date_gmt":"2024-11-21T14:06:16","guid":{"rendered":"https:\/\/www.rivistaeco.com\/?p=5331"},"modified":"2024-11-21T15:06:16","modified_gmt":"2024-11-21T14:06:16","slug":"how-much-time-do-we-spend-in-the-emergency-room","status":"publish","type":"post","link":"https:\/\/www.rivistaeco.com\/en\/2024\/11\/21\/how-much-time-do-we-spend-in-the-emergency-room\/","title":{"rendered":"How Much Time Do We Spend in the Emergency Room"},"content":{"rendered":"

Long waiting times at emergency rooms is not only the result of improper use by patients. An analysis of data from the Lombardy Region reveals some surprises. For instance, there has been a significant increase in patients assigned red and yellow triage codes. Furthermore, bottlenecks could be significantly reduced by improving organisation of services.<\/span><\/i><\/p>\n

The general perception is that emergency room waiting times are excessively long. The growing demand for ER care is partly due to limited availability of general practitioners and lengthy waiting lists for specialist consultations. However, long waits can also be influenced by service-related factors, such as the number of active emergency rooms and their ability to manage patient inflows. In the June issue of <\/span>Eco<\/span><\/i>, Matteo Lippi Bruni and Cristina Ugolini highlighted various issues related to ER overcrowding. Here, we delve deeper into the topic by examining aspects of both the demand for and the supply of emergency services.<\/span><\/p>\n

Lombardy\u2019s Data<\/b><\/h3>\n

Lombardy offers an interesting case study for several reasons. First, it is useful to examine the regional level, since in Italy and most European countries, healthcare services are organised geographically. Moreover, Lombardy has a population of over 10 million people, comparable to that of Sweden and Austria, and it boasts one of Italy’s most advanced healthcare systems. The challenges faced by Lombardy can reflect broader trends rather than specific service limitations.<\/span>
\n<\/span>We use data covering all emergency room visits in the region from 2013 to 2021, but we primarily refer to the year 2019 because 2020 and 2021 were marked by the COVID-19 pandemic and thus present an atypical situation deserving separate discussion.<\/span><\/p>\n

The Evolution of Emergency Services<\/b><\/h3>\n

The total time patients spend in the emergency room\u2014from arrival to discharge\u2014is illustrated in the first figure. The average time increased from 203 minutes in 2013 to 272 minutes in 2019, an increase of over one hour. This total includes both waiting time before the first medical visit and the duration of the visit itself. Specifically, waiting time increased from 50 minutes in 2013 to 71 minutes in 2019, while the duration of the medical visit rose from 153 minutes to 202 minutes. Contrary to common belief, most of the time spent in the ER is attributable to the length of the medical visit rather than the wait before it.<\/span><\/p>\n

Time Spent in the Emergency Room (Monthly Average)<\/b><\/p>\n

 <\/p>\n

Note: The solid line shows the total time spent in the ER, while the dashed lines break it down into waiting time (dash-dot) and duration of the medical visit (dashed).<\/span><\/p>\n

Source: Lombardy Region data.<\/span><\/p>\n

How Demand for Emergency Services Changes<\/b><\/h3>\n

Upon arrival at the ER, patients are classified by triage into four possible priority levels: white and green codes indicate non-urgent cases, while yellow and red codes signal critical and urgent situations. The data shows an increase in more severe cases over time, suggesting a more targeted use of emergency services. This could partly explain the rise in the average time spent in the ER, as more critical cases obviously require more time and resources.<\/span><\/p>\n

Even when accounting for priority level, the time needed to treat patients has significantly increased. For example, the time a red code patient spends in the ER has grown by an average of 200 minutes, and yellow code patients by about 100 minutes. The per capita cost of ER visits has also risen, suggesting that longer medical visits are associated with more expensive care. This rise in time and cost may partly stem from reduced efficiency in managing patient flows. Permanent hospital-employed doctors have gradually been replaced by temporary doctors who may be less familiar with the hospital’s IT system, leading to longer patient management times. Additionally, temporary doctors often have less experience, which can lead to more frequent use of diagnostic tests. However, we currently lack data to confirm whether higher patient management times and costs are directly attributable to temporary doctors.<\/span><\/p>\n

Between 2013 and 2019, the total number of ER visits increased by 85,000 (a 23% rise), while the number of facilities remained the same. However, this increase was more than offset by a 33% rise in patients leaving the ER before the first medical visit.<\/span><\/p>\n

The Difficult Encounter Between Supply and Demand in the ER<\/b><\/h3>\n

The operational capacity of the ER depends not only on the quantity and quality of available medical personnel but also on the alignment between supply and demand. For example, if it takes two hours to see a patient and one doctor starts work at 7 a.m. while two more doctors begin at 8 a.m., three patients arriving at 7 a.m. would result in one being seen immediately and the other two waiting an hour, with an average wait time of 40 minutes. However, if the three patients arrived at 8 a.m., they would all be seen immediately, reducing the average wait time to zero.<\/span><\/p>\n

A similar principle applies to diagnostic tests conducted during medical visits. If a technician takes 10 minutes to analyse a blood sample, receiving two samples simultaneously would create a bottleneck: one sample would be processed in 10 minutes, while the second would take 20. If the samples arrived 10 minutes apart, both would be processed in 10 minutes.<\/span><\/p>\n

The left panel of the figure below shows the demand for emergency services based on patient inflows, while the right panel shows the supply of services based on the flow of patients seen by a doctor (average for 2019, calculated across all of Lombardy, with each point representing one minute).<\/span><\/p>\n

 <\/p>\n

Patient Flow into the ER (Demand) and Flow of Patients Seen (Supply) by Hour of the Day<\/b><\/p>\n

<\/span><\/p>\n

 <\/p>\n

Note: The horizontal axis shows the hours of the day. The curve represents the average daily number of patients entering the ER (above) and being seen by a doctor (below) across all ERs in Lombardy. Each point represents one minute.<\/span><\/p>\n

Source: Lombardy Region data.<\/span><\/p>\n

 <\/p>\n

The demand for services, shown on the left, exhibits regularity: visits are lowest during the night and rise sharply between 6 a.m. and 10 a.m., reaching a peak before decreasing. There is another peak between 1 p.m. and 4 p.m., followed by stabilisation between 4 p.m. and 7 p.m., and then a decline to nighttime levels.<\/span><\/p>\n

The supply of services, shown on the right, is much less regular. It peaks around 10 a.m. and 3 p.m., consistent with demand, but the level is insufficient to fully meet patient needs. This mismatch is not surprising, as the productive capacity of an organisation, including a hospital, does not necessarily meet demand at its peak, especially if the peak lasts for only a limited time.<\/span><\/p>\n

The Supply-Demand Mismatch and Patient Accumulation<\/b><\/h3>\n

Patient accumulation is indicated by the ratio of the number of patients seen to the number of incoming patients at any given time. This is shown in the third figure. A horizontal line at level 1 indicates a balance between the number of patients seen and the number entering the ER. A ratio below 1 means that fewer patients are seen than are entering, leading to patient accumulation. A ratio above 1 means that more patients are seen than are arriving, reducing the number of patients in the waiting room.<\/span><\/p>\n

The figure clearly shows patient accumulation at three times of the day: around 8 a.m., when the inflow of patients rises rapidly; at 2 p.m.; and at 8 p.m., when supply decreases. Outside of these times, the supply level remains almost always below the horizontal line, increasing only in the evening when ER visits are minimal.<\/span><\/p>\n

Ratio Between the Number of Patients Seen and the Number of Patients Entering the ER<\/b><\/p>\n

<\/span><\/p>\n

 <\/p>\n

Note: The horizontal axis shows the hours of the day. The curve represents the ratio of visits to arrivals in ERs across Lombardy. Each point represents one minute.<\/span><\/p>\n

Source: Lombardy Region data.<\/span><\/p>\n

 <\/p>\n

ER productivity is thus significantly affected by the system\u2019s ability to maintain stable and adequate service supply throughout the day.<\/span><\/p>\n

Conclusion<\/b><\/h3>\n

The time spent in the ER increased by 40% from 2013 to 2019. During this period, the number of visits rose by 23%, and the rate of patients leaving the ER before the first medical visit increased by 33%, fully offsetting the increase in inflows. The incidence of yellow and red codes increased by 5%, while the time to treat these cases grew by 70% for red codes and over 30% for yellow codes. Finally, the comparison between demand and supply throughout the day highlights how their misalignment leads to patient accumulation in the ER, which only resolves in the evening when new arrivals are minimal.<\/span><\/p>\n

To better align demand and supply, ER productivity would need to be sustained at higher levels for longer periods throughout the day. While the ER is physically capable of reaching high activity levels, this currently only happens for a few hours a day. Part of the issue is the time required to treat more severe patients, who quickly saturate the ER’s capacity. Reducing treatment times for these patients, who represent 20% of total visits, could free up many resources. It would also be useful to reduce service discontinuity during shift changes by introducing more overlap in staff work schedules or hiring administrative personnel to assist doctors with discharge paperwork, which seems to be contributing to reduced ER productivity.<\/span><\/p>\n

 <\/p>\n

Bio<\/span><\/p>\n


\n<\/span>Diego Battiston is a lecturer at the University of Edinburgh. His research interests include labor economics, organization, and development, and he applies data science and machine learning in his work.<\/span><\/p>\n


\n<\/span>Jordi Blanes-i-Vidal is an associate professor at the London School of Economics. His research focuses on how organizations function and the role of organizational and managerial factors in determining performance.<\/span><\/p>\n

 <\/p>\n

Elena Lucchese is a research fellow at the University of Milan-Bicocca. Her research interests lie in health economics, particularly the role of resource and service organization in determining health outcomes.<\/span><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Long waiting times at emergency rooms is not only the result of improper use by patients. 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