Reduce Patient Wait Times Research Articles

Using artificial intelligence and machine learning to reduce patient wait times and increase patient satisfaction

Madam, Prolonged patient waiting time has long been a significant source of dissatisfaction among patients. Waiting time has been recognized by WHO as one of the key elements of a responsive healthcare system [1]. A study conducted by Bleustein et al. suggested that longer wait times negatively impacted the patient’s confidence in the care provider and the perceived quality of care.[2] Furthermore, in large hospital settings, patients are expected to queue up multiple times before meeting with their physician, which amplifies their stress. We would like to highlight that the incorporation of artificial intelligence (AI) in healthcare can help reduce patient and physician stress. Strategies such as telemedicine and online registration have been acquired in efforts to resolve this issue but these techniques may burden doctors with extra responsibilities. However, introducing an AI-based physician assistance programme, which was able to model itself on the decision-making requirements of the doctor significantly reduced the wait time and the burden on the hospital [3]. Xiaoqing Li et al developed an AI-based model that helped patients automatically order any investigations. Implementation of the model reduces waiting time while also playing a pivotal role in creating alternative methods to improve patient flow. [4] The results of these studies prove that implementation of an AI-based model in daily medicine has helped improve patient experience and minimize hospital burden. The implementation of AI in place of traditional methods, helps enhance patient satisfaction by not only significantly reducing waiting time but also ensuring that patients receive care appropriate to their condition. Moreover, it helps reduce, the workload on the physician and enables them to focus more on the patient. Being a new concept in healthcare AI may not be free of error. This might lead to inaccuracies in medication prescription, investigations, imaging, scheduling of appointments and diagnosis in atypical cases. Patient confidentiality is also an extremely important aspect of healthcare and in an attempt to protect the privacy of the patients, AI models may not have free reign over the patients’ data which may prevent them from assisting in medical practices. Furthermore, there are no organizations that customize the AI models according to the needs of the individual hospital or clinic. Moreover, human interaction is a critical aspect of healthcare which would be significantly impacted by the use of AI which could result in patients being unable to express themselves or feeling a sense of apathy. Furthermore, developing countries are not well equipped to implement AI in daily practice, hence it would not be a practical solution for them. [5] Further research needs to be conducted with regards to the use of AI models in hospitals to understand how they impact patient wait times and satisfaction, alongside their ability to optimize healthcare.

The Proposed Model for Pharmacy Led Med-to-bed Discharge Service in Tertiary Care Hospital

Introduction: The pharmacy-led discharge medication delivery to bedside (Med-to-bed) service in a tertiary care Johns Hopkins Aramco Healthcare (JHAH) hospital aims to optimize medication management during patient transitions from admission to discharge, especially for those with chronic conditions. The service entails the direct delivery of discharge medications to patients at the pharmacy following their discharge from inpatient wards, accompanied by comprehensive medication counseling. The pharmacy-led Med-to-Bed service aims to reduce patient wait times and improve overall clinical outcomes. Methods: This article highlights common challenges in medication reconciliation and post-discharge prescription fulfillment, highlighting the need for standardized protocols and improved hospital adherence to ensure seamless care transitions. It also discusses future considerations for optimizing the Med-to-Bed service, including proposed pharmacy renovations, installing a dedicated robotic dispensing system, and providing private spaces for patient counseling during medication pickup. Additionally, the proposal suggests deploying more pharmacy staff to address delayed discharge orders and making systemic enhancements to the EHR system to streamline prescription pickup. Result: The successful implementation of the proposed pharmacy-led Med-to-Bed service demonstrates the efficacy of interdisciplinary collaboration among nurses, physicians, and pharmacists. Through this collaborative approach, the Med-to-Bed service has shown tangible improvements in operational efficiency and patient outcomes, underscoring its potential to transform healthcare delivery models. Conclusion: Overall, this proposed model provides valuable insights into the transformative impact of integrated care initiatives like the Med-to-bed service in tackling complex medication management and enhancing quality patient care in hospitals.

A paradigm shift in appointment Scheduling: Introducing a decentralized integrated Online booking system

Long wait times, coordination, and integration are persistent issues in many health systems, hindering effective management of client waitlists and delaying treatment. The traditional approach of independent scheduling at each center does not allow for a coordinated response to demand fluctuations.An Integrated Online Booking system (IOB) for outpatient services is designed in this study, presenting the first central intake-based appointment system integrated with an optimization approach and a novel decentralized and distributed appointment system. This system considers the independence of centers while better responding to demand and improving overall system performance.The optimization component of the IOB system is expanded in this paper to include multiple sites, patient preferences, priorities, and wait time targets. It becomes a large-scale problem as all requests from a geographical region are combined into one stream. A decomposable algorithm based on the Alternating Direction Method of Multipliers (ADMM) is used for this system. The study employs real large-scale MRI data from all hospitals in Ontario, Canada, to demonstrate the effectiveness of the IOB system, providing insights into its potential impact on healthcare operations. The results indicate that the IOB system can outperform common appointment scheduling approaches in terms of reducing patient wait times, balancing utilization rates, improving referral patterns, and enhancing system efficiency.The proposed IOB system can bring added value to the market by speeding up diagnosis and treatment processes while improving the efficiency of healthcare systems. This platform can be considered as Software as a Service (SaaS), providing a scalable and accessible solution for managing appointments across various sectors, and leveraging blockchain technology to ensure secure, transparent, and tamper-proof appointment records. This study provides insights for decision-makers, highlighting the importance of effective scheduling in managing client waitlists during treatment in the healthcare sector, such as elective surgery, home healthcare, physical and mental therapies, and other service industries like education, public consultations, and government services. The IOB system can be integrated with Artificial Intelligence (AI)-based tools and machine learning methods and combined with other service software to optimize appointment scheduling and improve system performance.

Enhancing Patient Flow in Emergency Departments: A Machine Learning and Simulation-Based Resource Scheduling Approach

The efficient scheduling of resources within emergency departments (EDs) is crucial to minimizing patient length of stay (LoS) times and maximizing the utilization of limited resources. Reducing patient wait times can enhance the operation of emergency departments and improve patient satisfaction and the quality of medical care. This study develops a simulation model using Discrete Event Simulation (DES) methodology, examining six resource scheduling policies that consider different combinations of general and senior physicians. By leveraging six scheduling policies and machine learning techniques, this model dynamically identifies the most effective scheduling policy, based on a comprehensive dataset of ED visits in South Korea. The ED simulation achieves an accuracy rate of 90% and demonstrates that our proposed integrated machine learning approach reduces average length of stay (LoS) to approximately 322.91 min, compared to 327.10 min under traditional methods. This study underscores the potential of integrating DES and machine learning to enhance resource management in EDs.

IMPLEMENTING LEAN MANAGEMENT PRINCIPLES TO OPTIMIZE HEALTHCARE FACILITY OPERATIONS

This article explores the implementation of Lean management principles to optimize operations in healthcare facilities. Lean management, a philosophy made famous by the Toyota Motor Company, focuses on continuous improvement and the elimination of waste to maximize quality and safety for patients. The primary objective of this paper is to examine how Lean tools and techniques can be adapted to the unique environment of healthcare institutions to enhance their productivity and service quality. The article begins by outlining the core principles of Lean management, emphasizing the importance of value stream mapping, Kaizen, and the 5S methodology. Value stream mapping helps identify and visualize the flow of materials and information required to bring a product or service to a consumer, which in healthcare translates to patient care processes. Kaizen, a practice of continuous improvement, encourages small, incremental changes that collectively lead to significant enhancements in efficiency and quality. The 5S methodology—Sort, Set in order, Shine, Standardize, and Sustain—ensures a clean, organized, and efficient work environment. The paper then delves into specific Lean practices that can be applied within healthcare settings, such as reducing patient wait times, improving the flow of patients and information, and minimizing unnecessary movements and processes. By focusing on these areas, healthcare facilities can reduce bottlenecks, streamline operations, and enhance patient care delivery. For instance, implementing Lean techniques in patient scheduling can lead to more efficient use of clinical resources and reduce the time patients spend waiting for services. Furthermore, the paper provides a critical review of existing literature and studies on the application of Lean management in healthcare, highlighting successful implementations and the challenges faced during the process. It discusses real-world examples where Lean management has significantly improved operational efficiency, patient satisfaction, and overall healthcare outcomes. For example, a hospital that adopted Lean principles reported reduced patient wait times, lower operational costs, and improved staff morale. The article also addresses the potential obstacles and resistance to change that healthcare facilities might encounter when adopting Lean principles. It offers strategic recommendations for overcoming these barriers, including leadership commitment, staff training, and fostering a culture of continuous improvement. Leadership commitment is crucial as it sets the tone for organizational change and ensures that the necessary resources are allocated for Lean initiatives. Staff training is essential to equip employees with the skills and knowledge required to implement Lean practices effectively. Fostering a culture of continuous improvement involves engaging staff at all levels, encouraging open communication, and recognizing and rewarding contributions to Lean projects. In conclusion, the paper argues that Lean management principles, when effectively implemented, can lead to substantial improvements in the operational performance of healthcare facilities. It calls for further research and collaboration between healthcare professionals and Lean management experts to tailor these practices to the specific needs of the healthcare sector. The successful integration of Lean management into healthcare operations not only enhances efficiency and reduces waste but also improves patient care quality and safety. By adopting Lean principles, healthcare facilities can create a more efficient, patient-centered environment that supports high-quality care delivery. This article highlights the importance of a systematic approach to implementing Lean management, considering the unique challenges and opportunities within the healthcare sector. It underscores the need for ongoing evaluation and adaptation of Lean practices to ensure sustained improvements and long-term success.

Premix versus On-Demand Workflow Models in a Veterans Affairs Hematology/Oncology Pharmacy: Impact on Estimated Waste Costs and Patient Wait Time

Background: The balance between reducing patient wait time and mitigating waste of parenteral products has not been well described in literature. Objective: Evaluate the patient wait times and cost-effectiveness of employing a premix versus an on-demand workflow model for compounding parenteral admixtures in a hematology/oncology infusion setting. Methods: This single center, retrospective cost analysis compiled manually documented monthly waste reports and estimated drug pricing for the institution to calculate the cost of waste during both premix and on-demand compounding workflows. Time to administration was audited for one week with both models. Results: Over a period of 28.5 months following the premix model, 564 products were documented as wasted ($1,196,014.01 in estimated drug purchasing cost). Over a period of 3 months following the on-demand model, 12 products were wasted ($34,823.98 in estimated drug purchasing cost). Switching models reduced the monthly average number of wasted products from 20 to 4 per month; the average cost of waste was reduced from $41,965.40 to $11,607.99 per month (P < .0001). Overall patient wait time from clearance until administration, excluding any recommended wait times after premedication administration (if applicable), was similar in both models: an average of 38.26 minutes in the premix model and 40.97 minutes in the on-demand model. Conclusion: Premixing parenteral admixtures was not cost effective at our institution. After resuming an on-demand compounding model, the monthly cost of waste (based on drug pricing alone) was reduced by over 70%. The wait time from clearance to treatment administration was similar in both models.

Workflow efficiencies for flexible cystoscopy: comparing single-use vs reusable cystoscopes

BackgroundFlexible cystoscopy is a common procedure to diagnose and treat lower urinary tract conditions. Single-use cystoscopes have been introduced to eliminate time-consuming reprocessing and costly repairs. We compared the hands-on labor time differences between flexible reusable cystoscopes versus Ambu’s aScope™ 4 Cysto (aS4C) at a large urology Ambulatory Surgery Center (ASC).MethodsReusable and single-use cystoscopy procedures were shadowed for timestamp collection for setup and breakdown. A subset of reusable cystoscopes were followed through the reprocessing cycle. T-tests were calculated to measure the significance between groups.ResultsThe average hands-on time necessary for reusable cystoscope preparation, breakdown, and pre-cleaning was 4′53″. Of this, 2′53″ were required for preparation, while 2′0″ were required for breakdown and pre-cleaning. The average hands-on time for reprocessing for reusable was 7’1” per cycle. The total time for single-use scopes was 2′22″. Of this, 1′36″ was needed for single-use preparation, and 45 s for breakdown. Compared to reusable cystoscopes, single-use cystoscopes significantly reduced pre and post-procedure hands-on labor time by 2’31”, or 48%. When including reprocessing, total hands-on time was 80% greater for reusable than single-use cystoscopes.ConclusionSingle-use cystoscopes significantly reduced hands-on labor time compared to reusable cystoscopes. On average, the facility saw a reduction of 2′31″ per cystoscope for each procedure. This translates to 20 additional minutes gained per day, based on an 8 procedures per day. Utilizing single-use cystoscopes enabled the facility to reduce patient wait times, decrease turnaround times, and free up staff time.

Common-Unique Decomposition Driven Diffusion Model for Contrast-Enhanced Liver MR Images Multi-Phase Interconversion.

All three contrast-enhanced (CE) phases (e.g., Arterial, Portal Venous, and Delay) are crucial for diagnosing liver tumors. However, acquiring all three phases is constrained due to contrast agents (CAs) risks, long imaging time, and strict imaging criteria. In this paper, we propose a novel Common-Unique Decomposition Driven Diffusion Model (CUDD-DM), capable of converting any two input phases in three phases into the remaining one, thereby reducing patient wait time, conserving medical resources, and reducing the use of CAs. 1) The Common-Unique Feature Decomposition Module, by utilizing spectral decomposition to capture both common and unique features among different inputs, not only learns correlations in highly similar areas between two input phases but also learns differences in different areas, thereby laying a foundation for the synthesis of remaining phase. 2) The Multi-scale Temporal Reset Gates Module, by bidirectional comparing lesions in current and multiple historical slices, maximizes reliance on previous slices when no lesions and minimizes this reliance when lesions are present, thereby preventing interference between consecutive slices. 3) The Diffusion Model-Driven Lesion Detail Synthesis Module, by employing a continuous and progressive generation process, accurately captures detailed features between data distributions, thereby avoiding the loss of detail caused by traditional methods (e.g., GAN) that overfocus on global distributions. Extensive experiments on a generalized CE liver tumor dataset have demonstrated that our CUDD-DM achieves state-of-the-art performance (improved the SSIM by at least 2.2% (lesions area 5.3%) comparing the seven leading methods). These results demonstrate that CUDD-DM advances CE liver tumor imaging technology.

Time spent at health facility is a key driver of patient satisfaction, but did not influence retention to HIV care: A serial cross-sectional study in Mozambique.

Patient satisfaction with clinical services can have an effect on retention in HIV care and adherence to antiretroviral therapy. This study assessed patient satisfaction and its association with retention and viral suppression in Zambézia Province, Mozambique. Monthly exit interviews with persons living with HIV were completed from August 2017-January 2019 in 20 health facilities; clinical data were extracted from medical records. Regression analyses assessed the effect of satisfaction scores on retention and viral suppression, adjusting for age, sex, education, civil status, time on treatment, and site. Satisfaction scores were correlated with time spent at health facilities using generalized linear regression models. Data from 4388 patients were analyzed. Overall median satisfaction score was 75% (IQR 53%-84%); median time spent at facilities (from arrival until completion of clinical services) was 2h54min (IQR 1h48min-4h). Overall satisfaction score was not associated with higher odds of retention or viral suppression, but association was seen between satisfaction regarding attention given to patient and respect and higher odds of viral suppression. Patient satisfaction was negatively associated with time spent in facility (Spearman's correlation -0.63). Increased time spent at facility (from 1 to 3 hours) was not associated with lower retention in care (OR 0.72 [95%CI:0.52-1.01] and 0.83 [95%CI: 0.63-1.09] at 6- and 12-months, respectively), nor with a lower odds of viral suppression (OR 0.96 [95%CI: 0.71-1.32]). Strategies to reduce patient wait times at the health facility warrant continued prioritization. Differentiated models of care have helped considerably, but novel approaches are still needed to further decongest crowded health facilities. In addition, a good client-provider communication and positive attitude can improve patient satisfaction with health services, with an overall improved retention.

FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

Ultrasound is a non-invasive method to diagnose and treat medical conditions. It is becoming increasingly popular to use portable ultrasound scanning devices to reduce patient wait times and make healthcare more convenient for patients. By using ultrasound imaging, you will be able to obtain images with better quality and also gain information about soft tissues. The interference caused by tissues reflected in ultrasound waves resulted in intensified speckle sound, complicating imaging. In this paper, a novel Foe-Net has been proposed for segmenting the fetal in ultrasound images. Initially, the input US images are noise removal phase using two different filters Adaptive Gaussian Filter (AGF) and Adaptive Bilateral Filter (ABF) used to reduce the noise artifacts. Then, the US images are enhanced using CLAHE and MSR for smoothing to enhance the image quality. Finally, the denoised images are input to the V-net is used to segment the fetal in the US images. The experimental outcomes of the proposed Multi-Scale Retinex (MSR) is an image enhancement technique that improves image quality by adjusting its illumination and enhancing details. Foe-Net was measured by specific parameters such as specificity, precision, and accuracy. The proposed Foe-Net achieves an overall accuracy of 99.48%, specificity of 98.56 %, and precision of 96.82 % for segmented fetal in ultrasound images. The proposed Foe-Net attains better pre-processing outcomes at low error rates and, high SNR, high PSNR, and high SSIM values.

Complexity-Based Triaging and Scheduling for Reducing Clinic Wait Times in Student-Run Free Clinics

Student-run free clinics are a valuable resource for both patients who lack access to care and undergraduate medical education. Free clinics often face specific challenges, such as long clinic wait times, necessitating the development of strategies to minimize wait time and enhance patient satisfaction. We propose the use of a patient complexity score to facilitate patient scheduling and the proper assignment of students to complex patients based on training level. A numeric score estimating the complexity of each patient may provide valuable information to clinic organizers and ultimately reduce patient wait times.

WTP7.6 Novel ambulatory surgical pathways can reduce operation waiting times whilst maintaining patient outcomes

Abstract Introduction A record number of patients are currently awaiting NHS treatment. A novel ambulatory surgical pathway (Rapid Access Theatre (RAT) lists) can reduce patient wait times for common operations such as cholecystectomies and ease the burden on the traditional elective operating list. However, it is important that this approach does not jeopardise patient safety. Method All non-inpatient cholecystectomies carried out in a large teaching hospital from 01/10/21 – 31/01/22 were analysed. Operations carried out on RAT lists were compared to those on elective lists for time to surgery from listing, post-op length of stay (LOS), number of patient returns within 30 days and rate of post-op complications. Results There were 85 cholecystectomies carried out on RAT lists and 86 carried out on elective lists. The average time to surgery from listing was 70.56 days for RAT lists and 152.19 for elective lists (p = 0.0005). The average post-op LOS was 0.73 for RAT lists and 1.00 for elective lists (p = 0.43). The number of patients who returned to hospital within 30 days of their operation was 8 (10.39%) for RAT lists and 11 (14.86%) for elective lists (p = 0.751). The number of post-op complications was 10 (13.33%) for RAT lists and 13 (17.81%) for elective lists (p = 0.52). Conclusions The introduction of a novel ambulatory surgical pathway (RAT lists) has reduced the waiting time for cholecystectomies whilst maintaining outcomes and patient safety. This pathway is therefore cost effective and an important tool for reducing length waiting lists.

Simulation and evaluation of increased imaging service capacity at the MRI department using reduced coil-setting times.

The wait times for patients from their appointments to receiving magnetic resonance imaging (MRI) are usually long. To reduce this wait time, the present study proposed that service time wastage could be reduced by adjusting MRI examination scheduling by prioritizing patients who require examinations involving the same type of coil. This approach can reduce patient wait times and thereby maximize MRI departments' service times. To simulate an MRI department's action workflow, 2,447 MRI examination logs containing the deidentified information of patients and radiation technologists from the MRI department of a medical center were used, and a hybrid simulation model that combined discrete-event and agent-based simulations was developed. The experiment was conducted in two stages. In the first stage, the service time was increased by adjusting the examination schedule and thereby reducing the number of coil changes. In the second stage, the maximum number of additional patients that could be examined daily was determined. The average number of coil changes per day for the four MRI scanners of the aforementioned medical center was reduced by approximately 27. Thus, the MRI department gained 97.17 min/d, which enabled them to examine three additional patients per month. Consequently, the net monthly income of the hospital increased from US$17,067 to US$30,196, and the patient wait times for MRI examinations requiring the use of flexible torso and head, shoulder, 8-inch head, and torso MRI coils were shortened by 6 d and 23 h, 2 d and 15 h, 2 d and 9 h, and 16 h, respectively. Adjusting MRI examination scheduling by prioritizing patients that require the use of the same coil could reduce the coil-setting time, increase the daily number of patients who are examined, increase the net income of the MRI department, and shorten patient wait times for MRI examinations. Minimizing the operating times of specific examinations to maximize the number of services provided per day does not require additional personnel or resources. The results of the experimental simulations can be used as a reference by radiology department managers designing scheduling rules for examination appointments.

A Study Review of the Appropriateness of Oral Antibiotic Discharge Prescriptions in the Emergency Department at a Rural Hospital in Mississippi, USA.

Antimicrobial therapy in emergency departments (EDs) is usually empiric in nature. Due to workload and a goal to reduce patient wait times, providers often make rapid decisions regarding antibiotic prescriptions for discharge. A review of current empiric prescribing practices would determine the appropriateness of oral antibiotic discharge prescriptions from EDs. A single-center retrospective electronic health record review of all adult patients with an ED visit from 1 June 2019, to 30 June 2021 who received at least one oral antibiotic prescription at discharge from Baptist Memorial Hospital-Golden Triangle was conducted. The primary outcome was the assessment of appropriate antibiotic discharge prescriptions. The parameters for appropriateness included empiric drug selection, dosage, frequency, duration, and subsequent cultures and sensitivities. Of the 18,289 identified records, 421 patients were randomly sampled with 400 patients included in the final analysis. Of these, 190 (47.8%) discharge oral antibiotic prescriptions were assessed as appropriate and 209 (52.3%) discharge oral antibiotic prescriptions were assessed as inappropriate based on the guideline recommendations. With approximately half of the patients receiving discharge antibiotics that did not fully follow the guideline recommendations, there is a need for provider education, pharmacist intervention, and antimicrobial stewardship programs focusing on this practice.

Improving Safety and Quality With an Emergency Department Overcrowding Plan

IntroductionEmergency department overcrowding is a concern that predates the recent coronavirus disease pandemic. Overcrowding in the emergency department continues to worsen internationally. There are multiple combined strategies that help to maintain quality and safety by reducing patient wait times, left-without-being-seen rates, and the length of time a patient stays in the emergency department. The objective of the project was to use an interdisciplinary team to strengthen and revise the emergency department overcrowding plan to reduce the patient wait times, length of stay, and the left-without-being-seen rates. MethodsThe quality improvement team used interprofessional collaboration to focus on 3 areas of the emergency response plan. The team automated an instrument to measure overcrowding in the emergency department, developed a tiered response plan to overcrowding, and implemented a standardized multidisciplinary paging protocol. ResultsThe emergency department overcrowding plan resulted in a 2.7% decrease in the left-without-being-seen rates, a 42-minute (14.5%) decrease in median emergency department length of stay, and a 3.56-hour (33.3%) decrease in daily overcrowding. DiscussionEmergency department overcrowding is influenced by a multitude of factors. The development and implementation of an efficient and effective overcrowding plan have significant value for patient quality and safety as well as health system planning. An effective response to emergency department overcrowding is a pre-established plan that incrementally uses system-wide resources to support emergency department functions as the census and patient acuity fluctuate.

Optimizing Outpatient Oral Contrast Use in Abdominal CT-A Radiology Pandemic Response Initiative to Reduce Patient Time in the Waiting Room and Reduce Costs, While Improving Patient Experience.

Purpose: The aim was to reduce outpatient wait time and improve patient experience by optimising oral contrast use. Methods: Our multidisciplinary stakeholder collaboration implemented two simultaneous interventions: (1) Creation of 'oral contrast policy', limiting recommended indications. (2) Creation of a new shorter oral contrast regime (30 vs 60min). We conducted a retrospective service evaluation of oral contrast use in outpatient (OP) abdominal CT at baseline and post-intervention. Patient wait times were measured and per-patient cost-savings were reported. An image quality review was performed by 2 blinded abdominal radiologists. Patient experience was evaluated with a standard voluntary survey. Statistical analysis was performed comparing baseline and evaluation outcomes using Chi-square or Fisher Exact test for categorical variables and Student's t-test or ANOVA for continuous data. Results: Over 1-month periods, OP CT scans were assessed in baseline (pre-pandemic) n = 575, baseline (pandemic) n = 495 and post-intervention n = 545 groups. Oral contrast use reduced from 420/575, 73.0% at baseline to 178/545, 32.7% post intervention. The turn-around time reduced by 15.8minutes per patient from 70.3 to 54.5minutes, P < .001 (Interventions 1 and 2). The diagnostic quality did not differ between the oral contrast regimes (Intervention 2, P = 1.0, P = .08). No repeat CTs were needed due to lack of oral contrast (Intervention 1) or poor opacification (Intervention 2). There was oral contrast cost reductions of 69.1-78.4% (P < .001). Patients reported their overall experience was improved post-intervention (Interventions 1 and 2). Conclusions: Optimising the CT oral contrast service through judicious use and a shorter regime, reduced patient wait times, improved patient experience and preserved diagnostic quality.

Customizing Treatment Scheduling Windows with a Time Margin Recipe: A Single-institutional Study.

Rising cancer incidences, complex treatment techniques, and workflows have all impacted the radiotherapy scheduling process. Intelligent appointment scheduling is needed to help radiotherapy users adapt to new practices. We utilized van Herk's safety margin formula to determine the radiotherapy department's treatment scheduling window (TSW). In addition, we examined the influence of in-room imaging on linac occupancy time (LOT). Varian Aria™ software version 15.1 was used to collect retrospective data on LOT, treatment site, intent, techniques, special protocol, and in-room imaging. Treatment scheduling windows varied across treatment sites. The mean TSW using van Herk's formalism was 31.5 min, significantly longer than the current TSW of 15 min (P = 0.036), with the pelvic site having the longest (43.8 min) and the brain site having the shortest (12 min). 28% of patients exceeded the in-practice TSW of 15 min. 46.2% of patients had multiple images per fraction, with the proportion being highest in pelvic patients (33%). Patients treated with palliative intent, intensity-modulated radiotherapy, special protocols (bladder protocol and gating), and multiple in-room images per fraction had significantly higher LOT. High treatment time uncertainty was observed in the pelvic and thorax sites, indicating the impact of in-room imaging frequency and on-couch treatment decisions on overall treatment time and indicating that current treatment practices should be reviewed and modified if necessary. The time margin recipe can customize the treatment scheduling window and improve treatment practices. This formalism can help manage the radiotherapy department's workload and reduce patient wait times.

Telehealth Intervention of Cardiovascular Diseases Patients: A Quantitative Study

Telehealth provides medical treatment, patient education, self-care, and health information using digital communication technology. Telehealth involves remote data exchange between a patient and a healthcare provider. Telehealth may enhance health care in underdeveloped countries. This research examines how Pakistani physicians and nurses see telemedicine for cardiovascular disease therapy. We employed non-probability sampling to pick respondents for this quantitative investigation. In this research, 30 individuals completed a well-designed questionnaire. 63 percent were male, 37 percent female. The researchers concluded that telemedicine intervention is useful for cardiovascular disease and possibly many other illnesses and ailments. These findings did not prove it was always useful. International NGOs are helping telehealth expand. These organisations help poor rural Pakistanis improve their health. Telehealth and personal health information has spread via electronic and social media. Telehealth helps battle cardiovascular problems by connecting patients in urban and rural Pakistan to competent hospitals and institutes. Telehealth may reduce patient wait times. Telehealth platform users who continuously address their particular health challenges may outperform the overall population, according to the research. Telehealt make treatment easier and more accessible for patients. There aren't enough physicians, nurses, hospital beds, specialists, or appointments on patients' chosen

Wait times and patient throughput after the implementation of a novel model of virtual care in an outpatient neurology clinic: A retrospective analysis

Introduction Neurology wait times — from referral to consultation — continue to grow, leading to various adverse effects on patient outcomes. Key elements of virtual care can be leveraged to improve efficiency. This study examines the implementation of a novel virtual care model – Virtual Rapid Access Clinics – at the Neurology Centre of Toronto. The model employs a patient-centred care workflow, involving multidisciplinary staff and online administrative tools that are synthesized to expedite care and maintain quality. Methods Virtual Rapid Access Clinic efficacy was studied by determining average wait times and patient throughput, calculated from anonymous data that was extracted from the clinic patient database (n = 1542). Comparative analysis focused on new patient consultations during the 12-month periods prior to (pre-Virtual Rapid Access Clinic, n = 456) and following (post-Virtual Rapid Access Clinic, n = 1086) Virtual Rapid Access Clinic implementation. Results After Virtual Rapid Access Clinic implementation, there was a mean 15-day wait time reduction, and a monthly average 52-patient increase in patient throughput. Wait time reductions and increased patient throughput were observed in all three Virtual Rapid Access Clinic sub-clinics – epilepsy, headache and concussion. Respectively, average wait times reduced significantly by 26.4 and 18.9 days and insignificantly by 1.1 days; monthly average patient throughputs increased by 235%, 95% and 161%. Discussion These findings demonstrated that the Virtual Rapid Access Clinic model of care is effective at reducing patient wait times and increasing patient throughput. While the Virtual Rapid Access Clinic presents a feasible model both during and after pandemic restrictions, further research exploring its scalability in other care contexts, potential changes in care quality and efficiency outside of pandemic restrictions must be performed.

Reducing Wait Times for Radiology Exams Around Holiday Periods: A Monte Carlo Simulation.

Reducing patient wait times is a key operational goal and impacts patient outcomes. The purpose of this study is to explore the effects of different radiology scheduling strategies on exam wait times before and after holiday periods at an outpatient imaging facility using computer simulation.An idealized Monte Carlo simulation of exam scheduling at an outpatient imaging facility was developed based on the actual distribution of scheduled exams at outpatient radiology sites at a tertiary care medical center. Using this simulation, we examined three scheduling strategies: (1) no scheduling modifications, (2) increase imaging capacity before or after the holiday (i.e. increase facility hours), and (3) use a novel rolling release scheduling paradigm. In the third scenario, a fraction of exam slots are blocked to long-term follow-up exams and made available only closer to the exam date, thereby preventing long-term follow-up exams from filling the schedule and ensuring slots are available for non-follow-up exams. We examined the effect of these three scenarios on utilization and wait times, which we defined as the time from order placement to exam completion, during and after the holiday period.The baseline mean wait time for non-follow-up exams was 5.4days in our simulation. When no scheduling modifications were made, there was a significant increase in wait times in the week preceding the holiday when compared to baseline (10.0days vs 5.4days, p < 0.01). Wait times remained elevated for 4weeks following the holiday. Increasing imaging capacity during the holiday and post-holiday period by 20% reduced wait times by only 6.2% (9.38days vs 10.0days, p < 0.01). Increasing capacity by 50% resulted in a 7.1% reduction in wait times (9.28days, p < 0.01), and increasing capacity by 100% resulted in a 13% reduction in wait times (8.75days, p < 0.01). In comparison, using a rolling release model produced a reduction in peak wait times equivalent to doubling capacity (8.76days, p < 0.01) when 45% of slots were reserved. Improvements in wait times persisted even when rolling release was limited to the 3weeks preceding or 1week following the holiday period. Releasing slots on a rolling basis did not significantly decrease utilization or increase wait times for long-term follow-up exams except in extreme scenarios where 80% or more of slots were reserved for non-follow-up exams.A rolling release scheduling paradigm can significantly reduce wait time fluctuations around holiday periods without requiring additional capacity or impacting utilization.