Study to Assess the Utility of Discrete Event Simulation Software in Projection & Optimization of Resources in OutPatient Department at an Apex Cancer Institute in India: A Simulation Based Study

Background Healthcare is growing more complex with mandate expanding from the primary function of providing care to include economic, legislative and social conditions that has led to the rise of numerous ancillary services. These have necessitated multiple new processes and systems which are closely intertwined. A study was done to create and run a discrete event simulation in OPD of a tertiary care cancer hospital of North India to project and optimize resource deployment. The OPD process & workow as per the expected load at tertiary care cancer hospital were nalized with various stakeholders in a focused group discussion. The nalized OPD process & workow along with the OPD Building plans were utilized to develop a discrete event simulation model for the OPD at tertiary care cancer hospital using a DES. The simulation model thus developed was tested with incremental patient loads in 5 different scenarios/ “What if” situations (Scenario 1-5). The data regarding initial patient load and resources deployed was taken from on ground observations at tertiary care cancer hospital.


Introduction
Healthcare is growing more complex with mandate expanding from the primary function of providing care to include economic, legislative and social conditions that has led to the rise of numerous ancillary services. These have necessitated multiple new processes and systems which are closely intertwined. According to the World Health Organization [1], a good health system requires "a robust nancing mechanism; a well-trained and adequately paid workforce; reliable information on which to base decisions and policies; well-maintained facilities and logistics to deliver quality medicines and technologies." Today, healthcare organizations are challenged by pressures to reduce costs, improve coordination and outcomes, and be more user friendly. Yet, at the same time, the healthcare delivery is increasingly challenged by entrenched ine ciencies and suboptimal outcomes. These have, over the period of time, brought in new concepts/ approaches that can better manage and optimize healthcare systems which are generally referred to as "Healthcare Engineering" [2].
Data is a key driver for any healthcare engineering project. Globally, healthcare organizations are tryingto harness "big data"to create actionable insights.Healthcare Analytics as a way of transforming data into actions is gaining ground. In essence, Data analytics should help in connecting the dots and making sense out of the data which in turn can assist in decision making [3] [4].
There are many operations research [OR] tools that are helpful in this regard. Among them Discrete Event Simulation [Simulation] has the capacity to model complex situations with inherent advantages of interactive visualization. Even users who are not operation researchers or industry engineers can understand, develop, and validate the system better. It can describe a complex real-world system while accurately representing stochastic elements. Users can ask 'what if?' questions and design new systems [5].
Simulation can act as a forecasting tool where the performance of an existing system with changes in operating conditions can be evaluated. This enables hospital administrators to experiment with different management policies in multitude possibilities without interfering with the normal functioning of the healthcare facility [5]. Thus, simulation gives an edge to the administrators.
The parent institution has OPD footfall of around 12000 patients per day [5]. The huge patient load has put tremendous pressure on existing systems and infrastructure. Long waiting times are a common occurrence, affecting overall patient experience, which has also been re ected through the in-house feedback system [6].
The lessons learnt from the parent institution have prompted the use of new tools and techniques for resource optimization in the tertiary care cancer institute. The cancer institute is an apex centre for translational research in prevention &care for India centric cancers and is the agship project of MoHFW. The institute was recently inaugurated and is being operationalized in phases. Patient load is expected to increase rapidly and this makes it pertinent to scienti cally design processes and optimize resource allocation for more e ciency and better outcomes.
DES over the years has found numerous applications including modeling Lean process for reducing patient delays [7], reduce the turnaround time for patients [8], predict and plan for sta ng needs [9] and develop high-delity simulation models for Quality Improvement [QI] [10]. Therefore, it was decided to leverage Discrete Event Simulator [DES] to model processes and functioning of the cancer institute to help timely, e cient deployment of resources. Hence a study was done to run discrete event simulation in outpatient department of cancer institute to project and optimize the resources.

Methodology
The study design is a stimulation model based on focused group discussions. The study setting was a tertiary care hospital which was recently inaugurated. The hospital has around 700 beds dedicated to cancer treatment making it one of the largest cancer hospital of the country and a total of 250 bed were to be started in Phase I.
1. To build a simulation model of out-patient department at the institute using Discrete Event Simulation Software.
Methodology: As part of the research project, the OPD process & work ow as per the expected load at the institutehave been nalized with various stakeholders in a focused group discussion. The various stakeholders were Head, and key faculty from the department of Medical Oncology, Surgical Oncology, Radiotherapy, Onco-anesthesia& Palliative Medicine, Laboratory Medicine, Radiology, Nuclear Medicine and Hospital Administration.
The nalized OPD process & work ow alongwith the OPD Building plans were utilized to develop a discrete event simulation model for the OPD at the institute by using healthcare speci c discrete event simulation software,Flexsim Healthcare.
1. To simulate different scenarios with incremental patient load in OPD at the institute on the simulation model and identify bottlenecks, if any.
2. To suggest possible solutions/ give recommendations for improvements based on ndings from simulation of sequence of scenarios.
The simulation model thus developed was tested with incremental patient loads in 5 different scenarios. The data regarding initial patient load and resources deployed was taken from on ground observations at the institutes OPD. Each scenario tested incremental patient load, identi ed the bottlenecks and thus recommend additional resources to ease the bottlenecks.
These were then simulated in the subsequent scenarios thus giving us a longitudinal picture of how the system has evolved with incremental work load.

Results
Different scenarios/ "What if?" conditions were simulated during the operationalization of NCI OPD to facilitate decision making. In all the scenarios the focus was on improving the overall patient experience by optimally deploying and utilizing resources. The parameters include average patient waiting times, census, throughput, staff utilization parameters, utilization of screening rooms, utilization of DMG [Disease Management Group] rooms and time at which OPD nishes. The dashboards function in the software provides a real time update on these parameters and thus it is easy to obtain a longitudinal trend over period of time.
The decision points were for deployment of staff [different cadres], size of the waiting area andopening up of additional oors for patient care with focus on better patient experience. The simulation model has helped to strike a ne balance to minimize the waiting times by optimally deploying resources. The general tendency towards blanket increase in manpower could be circumvented as the models provided a real time picture of staff utilizations and staff state times. They also helped to identify the main bottlenecks in the overall process.  Figure 2] The peak census at any given point of time was 25, based on which the capacity of waiting area was kept at 30 initially. This helped to provide seating for patients while they wait for consultation. We could see that utilization of DMG rooms [83.58%] and utilization of doctors in DMG rooms [83.5%] was high and was likely to become a bottleneck with any increase in workload. The staff state times also re ected the same. The utilization of receptionists however was barely around 11% which clearly indicated that there was no need to augment in near future. The average waiting time for patients was around 100 minutes towards the end of the OPD.

Scenario 2
In the second scenario patient load was increased to 60 patients per day in the simulation model. With the same resources as in scenario 1, the OPD did not nish at 3pm. Instead 20 patients were yet to be seen with average waiting time of 115 minutes. Utilization of DMG rooms and doctors in DMG rooms was around 89%. The utilization of screening rooms and doctors there was about 83%. The average state times of the doctors and patients re ected the same. The peak census crossed 40 patients. The capacity of the waiting hall was increased to 50 which provided seating arrangements for patients waiting. The OPD got over around 5pm which was corroborated on ground. [ Figure 3]

Scenario 3
In the third scenario patient load the patient load was increased to 100 patients per day with the same resources in scenario 1 and simulation was run. The OPD did not nish at 3pm and 58 patients were still waiting to be seen at 3pm. In the fourth scenario the manpowerwas increased for a patient load of 100 patients per day in the simulation. Based on the staff utilization seen in scenario 3, additional 1 doctor was deployed in screening area and 2 additional doctors deployed in DMG rooms. That makes a total of 3 doctors in screening area and 4 doctors in DMG rooms. There were some improvements with average waiting time coming down to 128 minutes, with about 24 patients remaining to be seen at 3pm in the OPD waiting area.
The utilization of DMG rooms and its doctors cooled down to 81% but utilization of screening room and its doctors continued to be more than 90%. The peak census came down to little above 60 which decreased the waiting area required. The utilization of other staff increased but continued to be around 40%. [ Figure 5]

Scenario 5
In the fth scenario an additional oor was opened to cater to the load of 100 patients per day. Based on utilization gures from scenario 4, the screening room was found to be the bottleneck. Therefore all the DMG rooms were now shifted to the rst oor and capacity of screening rooms increased in ground oor. With the increased manpower and additional oor opened, the OPD nished at 2:10 pm itself with average waiting time dropping dramatically to 81 minutes. The utilization of screening room and its doctors cooled down to around 81% and that of DMG rooms and its doctors hovered around 82%.The peak census came down to a little below 60 which decreased the need for any further expansion of waiting areas. The utilization of other staff slightly increased but still didn't mandate any further augmentation. [ Figure 6] The results are summarized in Table 1 for different scenarios.

Discussion
The simulation models built on the discrete event simulation software, Flexim healthcare, has been helpful in decision making during operationalization of OPD services at the NCI, AIIMS. The model built in scenario 1 with 40 patients per day and the said resources, very closely resembled the on ground situation when the OPD was operationalized.
Subsequently planning was done for periodic deployment of resources to ensure their optimal utilization. Early deployment, especially of manpower would have led to idling and wastage. In scenario 2 the patient load was increased to 60 per day. In this case it was noted that the OPD would go on until 5 pm. The bottleneck was noted to be the DMG room and doctors. The utilization of support staff like the receptionists, lab technicians and PCCs was barely around 11%. The peak census crossed 40. Therefore, these mandated increase in waiting hall capacity and augmentation of DMG rooms.
Subsequently, the simulation was run with higher patient load of 100 patients per day. In scenario 3 this load was tested with existing resources of scenario 1. It clearly re ected the system was at the verge of failing as less than half the patients were seen by 3 pm with long waiting times and utilization of doctors in screening and DMG rooms clearly crossing 90%. The peak census crossed 80. These indicated requirement for augmentation at levels of waiting areas, screening rooms and DMG rooms. Utilization of support staff however remained around 40%.
Therefore it was suggested to augment with two more DMG rooms [with 2 additional doctors] and one more screening room [with 1 additional doctor]. This was simulated and tested on the software in scenario 4. The augmentation of resources slightly eased the system by bringing down waiting times but still 24 patients remained to be seen at 3 pm in the OPD. Besides, utilization of screening rooms and its doctors continued to be more than 90% which turned out to be the bottleneck.
Also since the peak census was high, there was also need for a larger waiting area which put pressure on space as only the ground oor of the OPD block was initially operational.
It was therefore now suggested to increase the screening rooms and its doctors. Considering the need for space, it was decided to operationalize the rst oor of the OPD and shift all 4 DMG rooms there. This was simulated in scenario 5 on the software. It was found that by operationalizing the new oor and adding just one extra screening room [with 1 extra doctor], the average wait time dramatically dropped to around 80 minutes with the OPD nishing by 2:10 pm. The utilization of screening rooms, DMG rooms and its doctors also hovered around a little above 80%, which indicates optimal utilization.
Utilization of support staff has slowly increased with patient load. 4. The bottleneck in the whole process was the screening room and patient load of more than 100 could also be managed with just 2 DEOs [Data Entry Operators].
5. The average staff state times and average patient waiting time also served as indicators to see the impact of increasing deployment/ resources across different scenarios.
6. The simulations enabled us to deploy resources just when it was required, which ensured optimal utilization and better e ciency.
7. Especially the decision for operationalizing an additional oor was crucial, as adding more oors would entail more resources. This necessitated that the additional oor had to be operationalized not too early to avoid wastage of resources and not too late pushing the system's limits. The simulation models helped us to strike the right balance and precisely time the operationalization of an additional oor.
Therefore, discrete event simulation has served as an important tool for decision making. There are many more applications of this tool like integration with Kaizen activities, alignment with Quality Improvement programs among others which needs to be explored.

Declarations
Ethics approval and consent to participate and Consent for publication -Not applicable But still Ethical approval was obtained from the Institute's ethics committee.
Availability of data and materials -All data generated or analysed during this study are included in this published article Competing interests -None Funding -All India Institute of Medical Institute funded for the software used in simulation