What if I told you that Econometrics can save lives? It is common knowledge that within medicine, every second can make the difference between life or death. To any econometrician, this should generate the question: How do we minimise time spent without treatment to ensure the maximum degree of hospital treatment efficacy? According to Wanis H. Ibrahim, "for every minute without CPR, survival from witnessed VF cardiac arrest decreases by 7–10%", highlighting the importance of speed and ultimately logistics within the medical field. The prior leverages the importance of the following topic: the application of econometric models on the optimisation of hospital logistics. The opportunity of use of econometric models is often narrowed to profit maximisation, leaving crucial sectors untouched by available optimisation techniques. However, in recent years, hospitals have begun to discover how these models can be used to optimise healthcare. This article is meant to open that door for you, through three detailed examples: optimising resource allocation, minimising patient wait times, and forecasting hospital demand.
Principally, let us reflect on how econometric models can be used to optimise resource allocation for hospitals. Resource allocation is a classic econometrics problem that is often neglected within the medical field. An explanation for this is the high healthcare supply chain complexity, playing a supporting role in the goal of hospital management, which is the treatment of patients. However, during recent years, healthcare resource allocation has been profoundly emphasised due to its importance in managing increasing healthcare costs. Research estimates that through efficient logistics management, around half of the logistics-related costs in hospitals can be eliminated. One such example is operating room scheduling. Here, econometric models can be used to help assign surgeries to rooms and times while minimising idle times and maximising resource use. The prior is an example of the classic school timetable problem introduced in combinatorial optimisation, in which the scheduling of teachers (surgeons in this example) is allocated to particular classrooms (operating rooms) to optimise the speed of treatment. Combinatorial optimisation models are crucial econometric models used in logistics around the world, which aid in resource allocation with integer values, making it a vital tool within hospital logistics. While supply chain complexity may make such models inefficient for certain examples, such as for time sensitive pharmaceuticals, they can be crucial in areas such as scheduling.
Secondly, the crucial concept of simulations is introduced, with an application to patient waiting times. Simulations model real life processes, which can be analysed from an econometric lens to make predictions regarding how these processes may react to certain treatments in real life. Simulations allow specialists to answer the "what if” questions without disrupting real life scenarios, proving useful in various fields of econometrics. Aside from proving useful in hospital treatment itself, simulations may be functional in minimising patient waiting times. Within severe cases as mentioned earlier, this can aid in saving lives. Particularly, Discrete Event Simulations (DES), which simulate a sequence of events in time, can be useful within healthcare. For example, a DES model can simulate the impact of different triage and staffing strategies on emergency room waiting times, providing insights that enable hospitals to reduce delays. Especially with emergency rooms, the ability to reduce patient waiting times through econometric models by even a few minutes, has the potential to save lives. Studies have shown that the implementation of simulation model outcome strategies may lead to significant reductions in wait times, proving these tools are vital for modern hospital logistics.
Finally, we introduce one of the most important and well known applications of econometric models, forecasting. Forecasting entails the use of historical data to predict the future. One such forecasting technique used are time-series forecasting models, such as the AutoRegressive Integrated Moving Average model, which may be used to forecast patient volume and resource necessity over time. Such models analyse historical data, such as past patient admissions, seasonality, and fluctuations, to predict crucial resource problems. These include emergency room hospital admissions during public holidays or general hospital admissions during the flu season. This aids in reducing overcrowding, resource allocation, and inefficiencies. All three issues may be treated through hospital demand prediction using time series models. By using such models, hospitals can manage resources such as hospital beds, medical supplies, and staff more accurately, leading to faster and hence more effective treatment. Furthermore, by using historical data on the effects of natural disasters, hospitals can adapt to emergency situations more accurately, which can be significant.
The aforementioned three examples highlight some key econometric methods that have transformed healthcare logistics, especially by reducing crucial waiting times and improving efficiency. As healthcare continues to grow in complexity and evolve, the role of econometrics to analyse such complex data becomes increasingly prominent. Especially through the use of simulations, increasingly complex situations can be modelled and tested to improve treatment of patients rather than purely analysing logistical factors. Evidently, there is a large market for the use of econometric models within healthcare. The presented examples regarding the transformation of hospital logistics gives a minute insight into the opportunities of econometrics within healthcare logistics, indicating the opportunities for econometricians to save lives.