1

Introduction

In healthcare Systems, timely access to high-quality services is crucial. long waiting times for treatment can worsen health conditions, with patients sometimes waiting months to see a doctor. Hospitals need to identify operational inefficiencies and bottlenecks and determine the most efficient allocation of resources to ensure optimal service delivery. Health centers are evaluated by recognizing the best practices, using measurable techniques, and having a commitment to improvement.

Medical imaging equipment as an important component of healthcare systems plays a key role in the diagnosis and treatment of diseases. Ultrasound as medical imaging equipment has several advantages over other medical imaging techniques. This process eliminates the need to get exposed to ionizing radiation. Also, it is significantly cheaper and more accessible.

Pregnant people wait for hours in the antenatal clinic to receive medical care (waiting before, during, or after service). The timely provision of medical care services to patients is an important element and enhances patient satisfaction. “When” a patient receives treatment is often as important as “what treatment” they receive. A study indicated that pregnant women are not satisfied with the long wait and as a result experience negative effects.

Long waiting times for appointments can increase anxiety in pregnant women and potentially lead to increased risks for maternal and fetal health, as well as higher treatment costs due to complications.

Due to the rising healthcare costs and demand for care, interventions such as process flow changes and resource increases can enhance services. Recommendations should be tested prior to implementation, and computer simulation can evaluate the impact of costly or potentially disruptive interventions on key performance indicators (KPIs).

The literature review indicates that research has been conducted in the areas of fetal health, ^, obstetrics, and gynecology, including both inpatient and outpatient departments. ^, The objectives of these studies include minimizing admission delays, optimizing resource scheduling to enhance patient flow (movement in the entire process from admission to discharge), and increasing operational efficiency. This has been achieved through various methodologies, such as discrete event simulation (DES), agent-based simulation, lean thinking principles. Also, the development of prediction models to distribute the queue length and waiting time of patients in the ultrasound center has been done by machine learning methods.

This study aims to improve the ultrasound Service Process for pregnant by evaluating proposed scenarios using DES. This research is intended to answer these questions:

• 1.
  

  What is the process of ultrasound service for pregnant women?

  
  
• 2.
  

  What factors cause waste in the course of ultrasound work?

  
  
• 3.
  

  What is the best way to improve the workflow?

In this research, some terms have been used, which are defined in the following:

• 1.
  

  Waste: can Include service waste like unnecessary transportation, waiting for prescriptions, modifying prescriptions, requesting unnecessary tests, and retesting due to inaccurate information.

  
  
• 2.
  

  Rework: Leads to waste through unnecessary transportation and movements.

  
  
• 3.
  

  Non-value-added activities: are processes or tasks that consume resources such as time, money, or effort without directly benefiting patient care or the overall quality of services. These activities do not improve patient outcomes, enhance safety, or increase satisfaction, and they can often contribute to inefficiencies within the hospital system.

  
  
• 4.
  

  Bottleneck: A system constraint limiting maximum output, often due to inadequate infrastructure, inefficient equipment, or supply chain delays in perioperative care.

2

Materials and methods

This study aims to analyze patient service flow by identifying factors causing delays and waste, with the goal of reducing waste, waiting times, and length of stay (LOS), while eliminating non-value-added activities.

The research identified several issues in the ultrasound service process through staff interviews and observations, including ( Fig. 1 ):

• •
  

  Long outpatient waiting times due to internet and insurance site issues

  
  
• •
  

  High demand for services and shortage of ultrasound devices, personnel, and space

  
  
• •
  

  High costs of ultrasound equipment

  
  
• •
  

  Poor coordination between departments

  
  
• •
  

  Patient dissatisfaction from delays

  
  
• •
  

  The hospital’s educational focus and shortage of radiologists during morning shifts.

Radiology department’s challenges.

We used a hybrid method combining DES models and lean thinking techniques, including value stream mapping, to analyze the ultrasound service process, identify its issues, and suggest improvements.

DES has a long history in the field of healthcare, The goals of employing simulation in health projects include reducing costs and process time, minimizing risks in new or modified processes, and gaining a better understanding of healthcare pathways among stakeholders. Discrete event simulation in the healthcare system is challenging because healthcare systems exhibit complex behaviors and involve many stakeholders with diverse opinions and objectives.

The SQUIRE guidelines, established in 2008, provide a framework for reporting data-driven healthcare quality improvement initiatives, enhancing clarity and integrity in the field. We have described the method section of the article based on the EQUATOR guideline:

• 
  

  1. Context

This research evaluates the impact of different improvement scenarios on ultrasound services for pregnant people at Arash Women’s Hospital, using BPMN standards, value stream mapping, and discrete event simulation techniques. The study was conducted at the Radiology Department for outpatients, inpatients, and emergency patients.

• 
  

  2. Intervention

This research utilized the Business Process Model and Notation (BPMN) to assess workflows, value stream mapping to identify inefficiencies, and discrete event simulation to model patient flow. The goal was to improve the quality and accessibility of ultrasound services by reducing reworks, and wait times and enhancing the overall patient experience.

The simulation model helped minimize process time, and mitigate risks in new or modified healthcare processes, enabling stakeholders to visualize and interact with complex workflows in a controlled environment.

• 
  

  3. Study of the intervention

Patients and families and its effect on transition out.

Business Process Model and Notation (BPMN): We used BPMN to create diagrams of ultrasound service workflows, enabling process visualization and identifying bottlenecks and improvement opportunities.

Value Stream Mapping: After BPMN, we applied value stream mapping to analyze and eliminate waste in the ultrasound processes, highlighting inefficiencies and proposing improvement solutions.

To assess the impact of improvement solutions on KPIs we used a simulation model: Discrete Event Simulation (DES): A DES was implemented to model patient flow, identify opportunities to reduce process times, and modify healthcare processes.

• 
  

  4. Measures

Key Performance Indicators (KPIs):

• 1.
  

  Average Patient Length of Stay (LOS) in Radiology: This KPI tracks the average time patients spend in the department from admission to discharge.

  
  
  
  
  • •
    

    Rationale: A decrease in the average patient LOS indicates improved operational efficiency and can enhance the availability of resources for other patients.

  
  
  
  
• 2.
  

  Average Waiting Time for Ultrasound Examinations: This KPI measures the average wait time for patients prior to their scheduled ultrasound.

  
  
  
  
  • •
    

    Rationale: Reducing average waiting time is critical for improving patient satisfaction and ensuring timely diagnostic interventions.

  
  
  
  
• 3.
  

  Average Total Non-Value-Added Time: Measures the total time devoted to activities that don’t enhance the patient experience, including delays, excess administrative tasks, and process redundancies.

  
  
  
  
  • •
    

    Rationale: Minimizing non-value-added time directly correlates with enhanced efficiency in service delivery and improved patient satisfaction.

  
  

We conducted a comparative analysis of input data and simulation results using two verification methods. The simulation’s average activity duration closely matches the real system and both within the model’s confidence interval, indicating strong consistency. Additionally, a student’s t -test (α = 0.05) was performed with 83 replications to confirm the stability of the simulation’s average length of stay (LOS).

Behavioral data from the model and system are plotted to assess whether the model’s output meets desired accuracy, using histograms, box plots, and scatter plots. Box plots we drew illustrate the average patient time in both the real system and the simulation model, validated with 30 % of real data. the negligible difference between the averages for outpatients, inpatients, and emergency patients confirms the simulation model’s validity.

• 
  

  5. Analysis

The research used a blend of qualitative methods (staff interviews, observations) and quantitative techniques (DES models, lean thinking) to analyze the ultrasound service process thoroughly and provide improvement recommendations.

To minimize data variation and achieve stability, the simulation model was run for 83 iterations, resulting in consistent average patient stay lengths.

6. Ethical considerations

Ethical Statement: The Research Ethical Committee of Tarbiat Modares University approved the study for ethics approval and consent to participate. Also, all experimental protocols were approved by this Committee. Verbal informed consent was obtained prior to the research from participants.

The research steps are outlined in Fig. 2 :

• 
  

  Step 1: The present research focused on the radiology department of Arash Women’s Hospital, a specialized hospital in obstetrics and gynecology. The study examined the processes from patient admission to the receipt of ultrasound reports. Data collection forms were designed to capture the duration of each activity in the ultrasound service. A total of 238 patients, including outpatients, inpatients, and emergency cases, were included in the analysis.

  
  
• 
  

  Step 2: we identified the process and wastes. We utilized the Business Process Model and Notation (BPMN) standard with Visual Paradigm software for visualization. A cause-and-effect diagram pinpoint the root causes of waste and develop effective improvement solutions. we employed value stream mapping to identify and eliminate non-value-added activities.

  
  
• 
  

  Step 3: DES modelling was employed to observe the effect of scenarios on the KPIs. The process was simulated in Arena simulation software. the model was validated. Next, each of the scenarios was implemented in the simulation model. Four categories of scenarios were presented, including increasing the number of resources, changes in resource planning, elimination of wastes, and combined scenarios of these cases.

A big picture of the research steps.

KPIs are time-based and try to focus on reducing patient waiting time, non-value-added times in the service process, and increasing patient satisfaction. To evaluate proposed scenarios, three types of KPIs have used, which are:

• 1.
  

  Average patient LOS in the system (radiology department)

  
  
• 2.
  

  Average waiting time in the ultrasound examination

  
  
• 3.
  

  Average total non-value-added time in the system

Institutional Review Board (IRB): The Research Ethical Committee of our University approved the study for ethics approval and consent to participate. verbal informed consent was obtained before the interview from participants.

2.2

Mapping the current state of patients’ ultrasound service process

Value stream mapping illustrates the flow of information and materials required to complete a project, product, or service. It helps identify value-added and non-value-added activities, enabling the initiation of improvement measures. ^,

Fig. 3 displays the factors involved in the process, including receptionists, secretaries, radiologists, referring physicians, and patients. some activities are non-value-added, including waste and rework. These wastes include the following:

• •
  

  Impossibility to pay in cash for ultrasound in the radiology department

  
  
• •
  

  Internet disconnection and insurance site outage in the reception

  
  
• •
  

  Referral of the patient without making pre-ultrasound preparations / Lack of knowledge of some patients about the pre-ultrasound preparations

  
  
• •
  

  Incorrect ultrasound request in the system

  
  
• •
  

  Forgetting to write the reason of the ultrasound and the week of pregnancy in the prescription

The current state of ultrasound service’s value stream map.

In Fig. 3 , the upper lines illustrate the average waiting times, which signify non-value-added times, while the lower lines depict the average service times, representing value-added times. The data clearly shows that non-value-added times exceed value-added times. Additional information regarding non-value-added times can be found in Section 2.4 .

2.3

Identifying wastes and rework in the ultrasound service

We categorized each of the waste factors ( Fig. 4 ). The wastes increase the non-value-added times. Some of these wastes occur frequently in the service, leading to patient dissatisfaction. For example, the wastes of the admission include the lack of admission staff and admission by other employees who are not responsible for this work, frequent interruptions of the insurance site while obtaining patient insurance approval, etc.

Factors affecting rework and waste in the ultrasound service.

2.5

Mapping the future state

Improvement suggestions from radiology staff interviews and a literature review on healthcare process improvement led to the creation of a future value stream map for ultrasound service, shown in Fig. 5 .

The future value stream mapping in the ultrasound service.

Fig. 5 highlights problems with the kaizen burst symbol, marking key activities for effective future value stream mapping. The future map allows cash payments at the radiology reception, eliminating the patient flow path to the fund.

Training was provided to employees to minimize human errors and eliminate waste factors, as outlined in Table 2 , thereby reducing non-value-added times in ultrasound services.

Table 2

Suggestions for improving the ultrasound service.

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