Using the Integration of Discrete Event and Agent-Based Simulation to Enhance Outpatient Service Quality in an Orthopedic Department

Abstract

Many hospitals are currently paying more attention to patient satisfaction since it is an important service quality index. Many Asian countries' healthcare systems have a mixed-type registration, accepting both walk-in patients and scheduled patients. This complex registration system causes a long patient waiting time in outpatient clinics. Different approaches have been proposed to reduce the waiting time. This study uses the integration of discrete event simulation (DES) and agent-based simulation (ABS) to improve patient waiting time and is the first attempt to apply this approach to solve this key problem faced by orthopedic departments. From the data collected, patient behaviors are modeled and incorporated into a massive agent-based simulation. The proposed approach is an aid for analyzing and modifying orthopedic department processes, allows us to consider far more details, and provides more reliable results. After applying the proposed approach, the total waiting time of the orthopedic department fell from 1246.39 minutes to 847.21 minutes. Thus, using the correct simulation model significantly reduces patient waiting time in an orthopedic department.

Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models

“…Post-full text screening 11 ABM and 28 SDM papers were retained for analysis, six of which utilised hybrid modelling methods. Three of the hybrid modelling papers integrated SDM with DES [43–45], two integrated ABM with DES [24, 46] and one integrated SDM with ABM [47]. A summary table of selected papers is given in Table 2.…”

“…CTS and ICU are broken down into their respective units such as the high care unit of CTS etc.• An additional ward bed on the CTS ward decreased the frequency of sending pre- and post- operative admissions to other wards by a factor of 3 with minimal cost.• The brute force optimiser indicated that the number of IC high care beds should be increased and number of IC beds decreased to gain optimum throughput of patients in simulation.• Java.Huynh (2012) [20]To assess the impact of redesigning medication administration process (MAP) workflow for registered nurses to improve medication administration safety.• A local (anonymous) medical centre where nurses are administering medication to patients.• Implementing a protocol for the order of MAP tasks to be performed improved the amount of time spent performing tasks.• When registered nurses performed tasks in the most frequently observed order (in the pilot study) this improved MAP task times.• Netlogo.Kittipitta. (2016) c [24]To examine patient flow in an outpatient clinic of an orthopedic department and explore interventions that can improve clinical services to reduce patient waiting times.• Orthopedic department at unidentified community hospital.• Average waiting time for outpatient appointments fell by 32.03% under the new management policy.• AnyLogic.Liu (2014) [21]To develop a tool that can be used as a decision support system for managers of emergency departments (ED) to assess risk, allocation of resources and identify weakness in emergency care service.• ED at Hospital of Sabadell (University tertiary level hospital in Barcelona, Spain). The Department is split into sections A (critical patients) and B (least critical patients).• A tool that can be used simulate the behaviour of agents in ED.• Netlogo.Liu (2016) [25]To explore how accountable care organisations (ACO) can impact payers, healthcare providers and patients under a shared savings payment model for congestive heart failure (CHF) and achieve optimal outcomes.• A generalised simulation of patients (Medicare beneficiary, over 65 years old who has or can develop congestive heart failure) seeking care (hospital or primary care physician facility) in Unites States.• Quality orientated providers yielded higher financial returns to the payer agent (which were then shared between providers) than those that were profit-orientated.• AnyLogic.Viana (2018) c [46]To examine and improve patient flow through a pregnancy outpatient clinic in light of the uncertainty in demand for services from overdue patients.• Overdue pregnancy outpatient clinic, pregnancy clinic and postnatal clinic at Akershus University Hospital, Norway.• As expected increasing the number of midwives in the clinic reduces resource utilisation but combined with an increase in demand led to an increase in doctor utilisation.• Midwives act as a buffer (or bottleneck) to patients seeing doctors.• AnyLogic.Yousefi (2017) [67]To apply group decision-making techniques for emergency department (ED) resource allocation and determine whether this approach improves performance indicators.• A ...…”

“…Nine of the ABM papers assessed the impact of health policy or interventions on the modelled system. Common policy targets included decreasing the time agents spent performing tasks, waiting for a service or residing in parts of the system [20, 22, 24, 67], reducing undesirable patient outcomes [23, 25, 47, 67], reducing the number of patients who left a health facility without being seen by a physician [22, 67] and optimising resource utility (beds and healthcare staff) [46, 66, 67]. The remaining two papers described simulation tools capable of comparing health insurance reimbursement schemes [65] and assessing risk, allocation of resources and identifying weaknesses in emergency care services [21].…”

“…Four of the hybrid simulation papers assessed the impact of policy or intervention on the modelled system. Policy targets included improving access to social support and care services [43], reducing undesirable patient outcomes [47], decreasing patient waiting time to be seen by a physician [24] and improving patient flow through the system by optimising resource allocation [46]. The remaining two papers used hybrid simulation to estimate the future demand for health care from patients with cardiac disease [44] and model patient flow through an integrated care system to estimate impact of patient admission on health care professionals wellbeing [45].…”

A Simulation Approach to Evaluate the Effect of Demographic Changes on Healthcare Demand

“…Basic simulation methods are applied such as Monte Carlo (MC) [11], system dynamics (SD) [12], agent-based (ABS) [13], discrete event (DES) [14], and microsimulation (MSM) [15]. Some authors have proposed hybrid models by adding analytical approaches to the main simulation method [16] or by building a second supportive simulation model that uses another simulation technique [17].…”