A Simulation Based Study for Managing Hospital Resources by Reducing Patient Waiting Time

Ahmad, Jawad; Iqbal, Javaid; Ahmad, Imran; Khan, Zulfiqar Ahmad; Tiwana, Mohsin I.; Khan, Khalid Mohammed

doi:10.1109/access.2020.3032760

Cited by 14 publications

(9 citation statements)

References 32 publications

(28 reference statements)

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“…A simulation-based study of such essential ancillary services as X-rays, Labs, and Ultrasound concluded the need for an optimisation model to ensure the adjustment of resources in bottleneck areas like the ED and outpatients (Ahmad et al. , 2020).…”

Section: Discussionmentioning

confidence: 99%

Diagnosing patient flow issues in the emergency department: an Australasian hospital case study

Boehme,

Rylands,

Fan

et al. 2024

JHOM

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PurposeThis study investigates how a hospital can increase the flow of patients through its emergency department by using benchmarking and process improvement techniques borrowed from the manufacturing sector.Design/methodology/approachAn in-depth case study of an Australasian public hospital utilises rigorous, multi-method data collection procedures with systems thinking to benchmark an emergency department (ED) value stream and identify the performance inhibitors.FindingsHigh levels of value stream uncertainty result from inefficient processes and weak controls. Reduced patient flow arises from senior management’s commitment to simplistic government targets, clinical staff that lack basic operations management skills, and fragmented information systems. High junior/senior staff ratios aggravate the lack of inter-functional integration and poor use of time and material resources, increasing the risk of a critical patient incident.Research limitations/implicationsThis research is limited to a single case; hence, further research should assess value stream maturity and associated performance enablers and inhibitors in other emergency departments experiencing patient flow delays.Practical implicationsThis study illustrates how hospital managers can use systems thinking and a context-free performance benchmarking measure to identify needed interventions and transferable best practices for achieving seamless patient flow.Originality/valueThis study is the first to operationalise the theoretical concept of the seamless healthcare system to acute care as defined by Parnaby and Towill (2008). It is also the first to use the uncertainty circle model in an Australasian public healthcare setting to objectively benchmark an emergency department's value stream maturity.

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Section: Discussionmentioning

confidence: 99%

Diagnosing patient flow issues in the emergency department: an Australasian hospital case study

Boehme,

Rylands,

Fan

et al. 2024

JHOM

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“…In addition, the number of waiting people at the Inspection is rise before off-hours, which may indicate that patients are eager to complete the medical test before off hours to avoid dragging the test to the next day. Previous studies have shown that sampling examinations are required for the majority of patients, and the waiting time for sampling examinations is long 27 . In Table 3 , concurrence of multiple diseases does not result in a longer diagnostic time for physicians, but significantly increases the length of time used after diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology

Deng

Fan

Gao

et al. 2023

Sci Rep

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How to allocate the existing medical resources reasonably, alleviate hospital congestion and improve the patient experience are problems faced by all hospitals. At present, the combination of artificial intelligence and the medical field is mainly in the field of disease diagnosis, but lacks successful application in medical management. We distinguish each area of the emergency department by the division of medical links. In the spatial dimension, in this study, the waitlist number in real-time is got by processing videos using image recognition via a convolutional neural network. The congestion rate based on psychology and architecture is defined for measuring crowdedness. In the time dimension, diagnosis time and time-consuming after diagnosis are calculated from visit records. Factors related to congestion are analyzed. A total of 4717 visit records from the emergency department and 1130 videos from five areas are collected in the study. Of these, the waiting list of the pediatric waiting area is the largest, including 10,436 (person-time) people, and its average congestion rate is 2.75, which is the highest in all areas. The utilization rate of pharmacy is low, with an average of only 3.8 people using it at the one time. Its average congestion rate is only 0.16, and there is obvious space waste. It has been found that the length of diagnosis time and the length of time after diagnosis are related to age, the number of diagnoses and disease type. The most common disease type comes from respiratory problems, accounting for 54.3%. This emergency department has congestion and waste of medical resources. People can use artificial intelligence to investigate the congestion in hospitals effectively. Using artificial intelligence methods and traditional statistics methods can lead to better research on healthcare resource allocation issues in hospitals.

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“…Not all doctors and nurses engaged in triage are trained in emergency medicine. Triage training is quite variable across the island [48,49].…”

Section: Improving Triage Systemsmentioning

confidence: 99%

Long Wait Times at Hospitals in Jamaica: A Potential Risk to Patient Safety

Davidson,

Fallah,

Curtis

et al. 2024

Contemporary Topics in Patient Safety - Volume 3

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Long wait time in public emergency rooms continues to be a major challenge in the Jamaican healthcare system. The aim of the study is to investigate the risk management strategies implemented by other nations in maintaining patient safety. The research method is a descriptive analytic study using literature review design to examine current and historical data to identify solutions to the long wait time at hospitals in Jamaica. The results revealed that adequate use and evaluation of risk management strategies, continuous upgrading of national emergency care health policy, improving triage systems, decreasing overcrowding and proper use of observation units in the ER, and maximizing the use of current technology to improve patient care do decrease the threat to patient safety. In conclusion, implementation of the risk management approach developed in this study can assist in maintaining patient safety by decreasing long wait time in public hospitals across the globe.

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A Simulation Based Study for Managing Hospital Resources by Reducing Patient Waiting Time

Cited by 14 publications

References 32 publications

Diagnosing patient flow issues in the emergency department: an Australasian hospital case study

Diagnosing patient flow issues in the emergency department: an Australasian hospital case study

Hospital crowdedness evaluation and in-hospital resource allocation based on image recognition technology

Long Wait Times at Hospitals in Jamaica: A Potential Risk to Patient Safety

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