Author Archives: ashleysmith

  1. Improving Customer Experience using FloSim

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    Introduction

    FloInsights Simulator (FloSim) continues to enhance healthcare operations through advanced simulation technologies and deep industry insights. This innovative tool allows FloInsights to transform clinic operations remotely, requiring minimal inputs like a floorplan, patient pathways, and staffing details.

    After our recent successful engagement, where we identified changes that would increase patient capacity by 46.15% and nearly halve the average visit time, we set upon a new project with a second clinic. Here, our focus was on improving the patient experience (i.e. by reducing wait times) and enhancing overall clinic capacity. By deploying FloSim, we achieved our goals – cutting initial wait times by an impressive 96.49% and boosting clinic throughput by approximately 20 – 30%. This blog post explores the unique challenges of this clinic, the tailored solutions we implemented, and the profound impacts of these changes.

    Figure 1: Using our Simulator as a Digital Twin for an Ophthalmology Clinic, we were able to reduce the patients’ wait time by 96.49% and reduce the length of patient journey by 27.74%.

    Simulator 

    Overview of the Process

    Our collaboration involved a multi-step process designed to ensure the simulations were both accurate and practical. This approach not only helped us pinpoint operational inefficiencies but also allowed us to test potential improvements in a controlled environment. The diagram below illustrates the overall methodology:

    Base Scenario

    The clinic operates with 3 doctors and 4 technicians. The doctors can perform examinations and injections, while technicians are equipped to handle Pre-Exam tests including OCT, IOP, visual field, and refraction. Patients typically navigate through multiple pathways, which allows for tailored treatment approaches depending on individual patient needs. The pathways are shown below: 

    • Pre-Exam → Injection
    • Pre-Exam → Exam
    • Exam

    During the information collection phase, FloInsights identified that the clinic handles around 30 patients in a four-hour session. Given that the Pre-Exam step is a common element in many patients’ treatment pathways, we zeroed in on this phase for a closer look. Our initial findings pinpointed the Pre-Exam as a major bottleneck, seriously limiting the clinic’s ability to manage patient flow efficiently. The graphs below illustrate this clearly. For instance, the room usage time hovers around 85% (see Figure 2), indicating that there’s hardly any downtime for accommodating additional patients. Moreover, as shown in Figure 3, the waiting times are not only high on average but also highly variable, sometimes even hitting the 60-minute mark.

    Figure 2: Pie chart showing the utilisation of the Pre-Exam room. The figure shows that it is working at close to full capacity indicating the presence of a bottleneck.

    Figure 3: Histogram showing a distribution of the initial wait times. The average waiting time for the first step of the pathway is close to 30 minutes, with some wait times reaching an hour.

    Proposed Changes

    In order to optimise clinic performance and increase capacity, we decided to implement the following strategic changes:

    1. Separation of OCT Testing: Moving the OCT test equipment from the general Pre-Exam area to a dedicated location. This reorganisation was targeted to reduce congestion at the Pre-Exam stage, thereby decreasing wait times and improving the overall patient experience.
    2. Change to the pathways: Due to the OCT step being required only when performing Injection, we have adjusted the pathways that the clients take to adjust to the above change. Proposed pathways:
      • OCT → Injection
      • Pre-Exam → Exam
      • Exam

    Impact of the changes

    The restructured approach to OCT testing proved highly effective and we have observed:

    • Reduction in Wait Times: The separation of the OCT test significantly reduced initial wait times by 96%, demonstrating a dramatic improvement in patient flow and potential satisfaction.
    • Increased Clinic Capacity: This more efficient handling of the Pre-Exam step allowed the clinic to increase its capacity, managing a larger volume of patients more effectively than in previous setups. The estimated increase is in the range of 20 – 30%.

    To back up these claims, let’s dive into the graphs below. These can be directly compared with those from the base scenario section. Figure 5 highlights the newfound availability in the Pre-Exam area. After relocating the OCT equipment, the percentage of time that the room is occupied has dropped dramatically from 85% to approximately 43%, effectively freeing up a significant amount of capacity.

    Figure 5: Pie chart showing the utilisation split of the Pre-Exam step after introducing a separate OCT step in the clinic. 

    Additionally, while the initial wait times were approaching an hour mark in some cases, the proposed changes were able to completely remove the issue and decrease the average waiting time to just 1 minute.

    Figure 6: Histogram showing the initial wait times after implementing the proposed change.

    When one bottleneck is resolved, attention often shifts to the next. After effectively eliminating delays in the Pre-Exam step, we observed that the Exam and Injection steps have now become the limiting factors. These stages are not currently optimised to handle the increased flow of patients smoothly and will require further analysis and adjustments to continue enhancing the clinic’s overall patient journey times. Despite these new challenges, the modifications have led to a notable reduction in the average journey time. Originally, patients spent about 58 minutes in the clinic, but thanks to our interventions, this has been significantly reduced — patients now complete their visits in roughly 42 minutes on average, as shown in the graph below.

    Figure 7: Histogram of overall patient journey times in the base scenario (above), and proposed scenario (below). The result of the changes is a smaller variation and a smaller average journey time.

    Conclusion and Future work

    Our latest application of FloSim has once again demonstrated its efficacy, this time by achieving a remarkable 96% reduction in patient wait times and expanding clinic capacity. By isolating and addressing specific bottlenecks such as the OCT testing in the Pre-Exam phase, we achieved significant improvements in both patient wait times and clinic throughput, highlighting the effectiveness of pinpoint interventions in critical areas of clinic operations and impact of FloSim simulations in optimising healthcare processes.

    Looking ahead, we are committed to continuing our close collaboration with the clinic to ensure that the improvements we’ve implemented deliver sustained benefits and adapt seamlessly to evolving operational demands. Our ongoing development of FloSim is driven by the valuable insights gained from its healthcare applications, and we are eager to explore how this technology can be adapted for other industries where efficiency and throughput are critical.

    If you’re interested in seeing how FloInsights can revolutionise operations in your sector, or if you’re considering partnership opportunities, please don’t hesitate to contact us at info@floinsights.com. Let’s work together to find innovative solutions that optimise patient flow and expand clinic capacity, ultimately enhancing outcomes of your operations. We can identify and implement strategies that not only streamline patient journeys but also maximise the efficiency of your clinic’s resources, ultimately leading to improved outcomes for both patients and healthcare professionals.

  2. Enhancing Service Productivity with FloSim: A Case Study

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    Introduction

    FloInsights Simulator (FloSim) is an innovative tool designed to simulate the operations of hospital clinics using extensive datasets and years of industry expertise. This powerful capability allows us to enhance clinic throughput through remote simulations, requiring only a floorplan, patient pathway information, and staff availability. 

    Recently, we collaborated with CentroMedico Ophthalmology Clinic in Chiasso to explore how we could optimise their operations and boost capacity. Utilising our proprietary Simulation tool, we were able to create a digital twin in which the clinic’s patient capacity was increased by 46.15% and the average time each patient spent in the clinic was nearly halved (Figure 1). This blog post delves into how we arrived at these conclusions and the steps we took.

    Figure 1: Using our Simulator as a Digital Twin for the CentroMedico Ophthalmology Clinic in Chiasso, we were able to increase the clinic’s capacity by 46.15 % and reduce the length of patient journey by 45.67 %.

    Simulator 

    Overview of the Process

    Our engagement followed a multi-step process to validate the accuracy of the Simulations and ensure any proposed changes were legitimate and feasible. The steps included:

    1. A discovery document to gather information on the current state of the clinic; the floorplan, staffing levels and patient pathways. 
    2. Simulating the current working environment (Base Scenario)
    3. Proposing the changes to the Chiasso Clinic and verifying their feasibility
    4. Simulating the proposed changes to determine what improvements could be accomplished

    Base Scenario

    The Chiasso clinic operates with 3 members of staff (Technician, Nurse and Doctor) whose responsibilities vary due to their respective qualifications. All patients follow the same pathway in which their eyes are imaged using Optical coherence tomography (OCT) by a nurse, then proceeding to an Injection suite where a Technician prepares them before a Doctor administers an Injection. 

    We simulated the clinic’s current operations with (on average) 13 patients being treated within a 2 hour session. The results of our simulations identified two areas in which we could target in order to facilitate improvements in the clinic’s capacity and throughput; a bottleneck at the injection stage of a patient’s journey and an inefficiency in the current appointment schedule. This is depicted in Figure 2 (below), in which we can see that, whilst there is still space for additional OCT images to be performed (A, left image), there is minimal space available for additional Injections (A, right image). Furthermore, we can see from Figure 2B that as the clinic session progresses, on average the patients’ respective journey times increase; indicating a potential inefficiency in the appointment schedule. 

    Figure 2: The clinic currently has additional availability to perform more OCT images (A, left image)  but is limited by the availability in the Injection suites (A, right image). We also identified that the length of the patient journey increases the later in the session their appointment is scheduled.

    Proposed Scenario 

    After analysing the results of our initial simulations we agreed with the team at Chiasso that we would use our Simulator to determine what improvements they would see if they were to make the following 2 changes:

    1. Spitting the Injection suite into 2 distinct spaces (Figure 3), with the Technician and Doctor rotating between patients to prepare them and perform the Injection, respectively.
    2. Optimising the appointment schedule

    Figure 3: The current Chiasso Clinic floorplan (A) shows they have a room for performing OCT imaging and a room for performing injections. One of our proposed changes (B) was to split the injection suite into 2 distinct areas.

    When we simulated these changes with the same number of patients, we saw a drastic increase in availability of Injection spaces from 10.4% (Figure 2A, right image) to  40.7% and the wait times for Injections being almost completely removed, coming down from 19:25 ± 13:06 minutes to 1:56 ± 1:09 minutes. Both of these clearly confirmed that the bottleneck at the Injection stage had been removed. 

    However, the above results were to be expected given we have effectively doubled the number of injection suites, the real question was what capacity increase would this allow and how would this affect the patient’s overall experience with respect to their total journey length?

    By iteratively increasing the number of patients, we determined that the maximum capacity in which the proposed changes could consistently cope with was 19 patients per day; a 46.15 % increase. Beyond this, the session would occasionally over run. 

    Furthermore, we observed that despite almost 50 % increase in the number of patients in the clinic, the patient experience was not adversely affected, in fact the proposed layout changes alongside an optimised appointment schedule resulted in a significant reduction in the average patient journey time from 57:48 ± 12:50 minutes to 31:23 ± 7:26 minutes (Figure 4, below).

    Figure 4: Despite a nearly 50% increase in clinic capacity, the patient experience should be improved as the average journey times are reduced in the proposed changes (B) compared to the current clinic setup (A). 

    Conclusion and Future work

    Using our proprietary Simulation tool we created a digital Twin of the CentroMedico clinic in Chiasso in which we were able to increase clinic capacity by approximately 46%, while also enhancing the patient experience by reducing average journey times. 

    We are continuing to work with the clinic in Chiasso to first verify that our proposed changes have the desired results when implemented in the ‘real world’ and then to continue to identify ways in which we can help them improve going forward. 

    We are continuing to develop the FloInsights Simulator, and whilst it is currently being utilised in healthcare for hospital clinics, we believe it has the potential to be utilised in a variety of other sectors. If you are interested in finding out more or see an opportunity of where we can work together, please do get in touch with us by dropping us an email at: info@floinsights.com