Dataset Information

Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System.

ABSTRACT: Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flow, resource availability, and time taken for registration and clinic processes for public and private patients were sourced from stakeholder discussion and time-motion study (TMS), while arrival times were sourced from the hospital's information system database. Probability distributions were used to fit these input data in the model. Scenario analyses involved configurations on consultation start time/staggered patient arrival. The median registration and clinic turnaround times (TT) were significantly different between public and private patients (p < 0.01). Public patients have longer wait times than private patients in this study's dual practice setting. Scenario analyses showed that early consultation start time that matches patient arrival time and staggered arrival could reduce the overall TT for public and private patients by 40% and 21%, respectively. Similarly, the number of patients waiting at the clinic per hour could be reduced by 10-21% during clinic peak hours. Matching consultation start time with staggered patient arrival can potentially reduce wait times and crowding, especially for public patients, without incurring additional resource needs and help narrow the wait time gap between public and private patients. Healthcare managers and policymakers can consider simulation approaches for the monitoring and improvement of healthcare operational efficiency to meet rising healthcare demand and costs.

SUBMITTER: Fun WH

PROVIDER: S-EPMC8871892 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System.

Fun Weng Hong WH Tan Ee Hong EH Khalid Ruzelan R Sararaks Sondi S Tang Kar Foong KF Ab Rahim Iqbal I Md Sharif Shakirah S Jawahir Suhana S Sibert Raoul Muhammad Yusof RMY Nawawi Mohd Kamal Mohd MKM

Healthcare (Basel, Switzerland) 20220119 2

Long wait times and crowding are major issues affecting outpatient service delivery, but it is unclear how these affect patients in dual practice settings. This study aims to evaluate the effects of changing consultation start time and patient arrival on wait times and crowding in an outpatient clinic with a dual practice system. A discrete event simulation (DES) model was developed based on real-world data from an Obstetrics and Gynaecology (O&G) clinic in a public hospital. Data on patient flo ...[more]

PMID: 35206804

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Project description:ObjectivesPopulation ageing and the rise in chronic diseases place continual stress on healthcare systems. Scarce resources often impede equitable access to healthcare, particularly in rural areas, resulting in prolonged waiting times and heightened risks of morbidity and mortality. Telemedicine has emerged as a promising solution, offering remote and equitable care that could potentially bridge access gaps and enhance health outcomes. This systematic review aims to quantitatively examine the impact of telemedicine implementation on waiting times, defined as the time passed from the booking of a visit for an outpatient to the administration of the service.DesignA systematic review was conducted using studies on telemedicine interventions that specifically addressed waiting times. Bias assessment was performed with three tools: ROBINS-I ("Risk of Bias In Non-Randomized Studies of Interventions"), AXIS ("Appraisal tool for Cross-Sectional Studies") and RoB-2 ("Risk of Bias-2"). A weighted mean approach was used to synthesise results, with medians synthesised using a median approach.Data sourcesArticles in English were retrieved from the PubMed and Scopus databases.Eligibility criteriaStudies were excluded if they did not specifically address waiting times related to telemedicine interventions. Only studies that considered waiting times defined as the time passed from the booking of a visit for an outpatient to the administration of the service and any telemedicine intervention were included.Data extraction and synthesisA total of 53 records were included, encompassing 270 388 patients in both the experimental and control groups. The weighted mean reduction in waiting times was calculated, and bias was assessed. No record was evaluated to be at high risk of bias, with 69.8% of studies evaluated at low risk and 26.4% at moderate risk (3.8% were surveys). Results were synthesised using a weighted mean approach for studies reporting means, and a median approach for studies reporting medians.ResultsOverall, a weighted mean reduction of 25.4 days in waiting times was observed. Focusing on clinical specialties (n=114 042), the weighted mean reduction amounted to 34.7 days, while in surgical patients (n=156 346), telemedicine was associated with a weighted mean of 17.3 days saved.ConclusionsThe implementation of telemedicine solutions may significantly improve waiting times, potentially leading to more efficient and equitable healthcare systems.Prospero registration numberCRD42023490822.

Project description:BackgroundProlonged wait times are known barriers to accessing nephrology care for patients needing more urgent specialist services. Improved process and standardized triage systems are known to minimize wait times of urgent or semi-urgent care in health care disciplines. In Central Zone (CZ) renal clinic, mean wait times for urgent (P1) and semi-urgent (P2) referrals were prolonged before 2014. We also observed prolonged wait times for elective (P3-P5) categories. Improving wait times was identified as an access to care quality improvement focus in CZ renal clinic of the Nova Scotia Health Authority (NSHA).ObjectivesTo describe our new referral process and new triage system, and to examine their effect on number of referrals wait-listed and mean wait times.DesignA quasi-experimental design was used.SettingHalifax, Nova Scotia, Canada.ParticipantsPatients referred to Central Zone Renal Clinic between 2012 and 2018.MeasurementsA time series of referral counts and wait times for each triage category were measured before our interventions and after implementing our interventions.MethodsWe reviewed our referral processes to identify gaps leading to prolonged wait times. On January 1, 2014, we implemented new administrative procedures: pretriage (standardized referral information form and staff training), triage (standardized clinic intake criteria and new triage guidelines), posttriage (protecting clinic spots for urgent and semi-urgent referrals, wait-list maintenance, and increasing new referral clinic capacity). Data were collected prospectively. Descriptive analysis on mean wait times was done using run charts.ResultsA 33% reduction in total number of referrals wait-listed was observed over 4.5 years after intervention. Descriptive analysis of the urgent and semi-urgent categories (P1 and P2) revealed a significant shift of mean wait times on run charts after the interventions. Target wait time was achieved in 94% of P1 category and 78% of P2 category.LimitationsThis type of study design does not exclude confounding variables influencing results. We did not explore stakeholder satisfaction or whether the new referral process presented barriers to resending referrals that had insufficient triage data. The long-term sustainability of adding demand-responsive surge clinics and opportunity cost were not assessed. Our referral process and triage system have not been externally validated and may not be applicable in settings without wait-lists or settings that use electronic, telephone or telemedicine consults.ConclusionOur selective intake of referrals with adequate triage information and referrals needing nephrology consult as defined by our clinic intake criteria reduced number of referrals wait-listed. We saw improved wait times for urgent and semi-urgent referrals with these categories now falling within target wait times for the vast majority of patients. The work of this improvement initiative continues especially for the lower-risk triage categories.Trial registrationNot applicable as this was a Quality improvement initiative.

Project description:IntroductionThe demand for adolescent mental health services has increased significantly in recent years, leading to excessive wait times for adolescents seeking mental health services and poor mental health outcomes. Timely access to mental health services is critical to reducing the risk of symptom chronicity and progression to mental disorder. A better understanding of whether and how interventions to reduce wait times impact mental health outcomes is needed to guide mental health policymakers and service planners in their approach to reducing wait times.Methods and analysisThe scoping review will use Arksey and O'Malley's six-stage framework for scoping reviews and Rayyan to support screening, data extraction and evidence synthesis. The review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. We will search the Cochrane Library, EBSCOhost, MEDLINE (Ovid), PsycArticles (Ovid), PsycINFO (Ovid), EMBASE (Ovid), Web of Science, ProQuest and Scopus databases for peer-reviewed texts published in English between 1 January 2000 and 28 February 2023. We will also search Google Scholar for additional grey literature. To be eligible for inclusion, studies must focus on adolescent populations aged 13-18 years and report on interventions to reduce wait times for any mental health service except crisis and emergency services. Title, abstract and full-text screening will be done by two reviewers. We will extract data describing the interventions and their effects on wait times and adolescent mental health outcomes, and we will identify strengths and limitations in the evidence base to inform recommendations for future research. A youth advisory group with lived experience of mental health difficulties will be consulted throughout the review process.Ethics and disseminationEthics approval is not required. Findings will be disseminated via peer-reviewed publications and presented at conferences.Study registrationThe protocol was registered with the Open Science Framework on 20 February 2023 (https://osf.io/qt4zy).

Project description:BackgroundBreast cancer clinics across the UK have long been struggling to cope with high demand. Novel risk prediction tools - such as the PinPoint test - could help to reduce unnecessary clinic referrals. Using early data on the expected accuracy of the test, we explore the potential impact of PinPoint on: (a) the percentage of patients meeting the two-week referral target, and (b) the number of clinic 'overspill' appointments generated (i.e. patients having to return to the clinic to complete their required investigations).MethodsA simulation model was built to reflect the annual flow of patients through a single UK clinic. Due to current uncertainty around the exact impact of PinPoint testing on standard care, two primary scenarios were assessed. Scenario 1 assumed complete GP adherence to testing, with only non-referred cancerous cases returning for delayed referral. Scenario 2 assumed GPs would overrule 20% of low-risk results, and that 10% of non-referred non-cancerous cases would also return for delayed referral. A range of sensitivity analyses were conducted to explore the impact of key uncertainties on the model results. Service reconfiguration scenarios, removing individual weekly clinics from the clinic schedule, were also explored.ResultsUnder standard care, 66.3% (95% CI: 66.0 to 66.5) of patients met the referral target, with 1,685 (1,648 to 1,722) overspill appointments. Under both PinPoint scenarios, > 98% of patients met the referral target, with overspill appointments reduced to between 727 (707 to 746) [Scenario 1] and 886 (861 to 911) [Scenario 2]. The reduced clinic demand was sufficient to allow removal of one weekly low-capacity clinic [N = 10], and the results were robust to sensitivity analyses.ConclusionThe findings from this early analysis indicate that risk prediction tools could have the potential to alleviate pressure on cancer clinics, and are expected to have increased utility in the wake of heightened pressures resulting from the COVID-19 pandemic. Further research is required to validate these findings with real world evidence; evaluate the broader clinical and economic impact of the test; and to determine outcomes and risks for patients deemed to be low-risk on the PinPoint test and therefore not initially referred.

Project description:Objective: Community addiction treatment agencies have utilized Network for the Improvement of Addiction Treatment (NIATx), a proven implementation strategy, to reduce appointment wait-times. However, its effectiveness at reducing medication access wait-times has not been explored. Thus, we conducted an exploratory analysis to evaluate the impact of the NIATx implementation strategies on reduced wait-times to addiction, psychotropic or both medications for individuals with co-occurring disorders (COD). Methods: In a cluster-randomized waitlist control group design, community addiction treatment agencies (n = 49) were randomized to receive the NIATx strategy (Cohort 1, n = 25) or to a Waitlist control (Cohort 2, n = 24). All agencies had a 12-month active intervention period. The primary outcome was the medication encounter wait-time. A univariate general linear model analysis utilizing a logarithmic (log10) transformation examined medication wait-times improvements. Results: The intent-to-treat analysis for psychotropic medications and both medications (reflecting integrated treatment) showed significant main effects for intervention and time, especially comparing Baseline and Year 1 to Year 2. Conversely, only the main effect for time was significant for addiction medications. Wait-time reductions in Cohort 1 agencies was delayed and occurred in the sustainment phase. Wait-times to a psychotropic, addiction, or both medications encounter declined by 3 days, 4.9 days, and 6.8 days, respectively. For Cohort 2 agencies, reduced wait-times were seen for psychotropic (3.4 days), addiction (6 days), and both medications (4.9 days) during their active implementation period. Same- or next-day medication access also improved. Conclusions: NIATx implementation strategies reduced medication encounter wait-times but timing of agency improvements varied. Despite a significant improvement, a three-week wait-time to receive integrated pharmacological interventions is clinically suboptimal for individuals with a COD in need of immediate intervention. Community addiction treatment agencies should identify barriers and implement changes to improve medication access so that their patients "wait no longer" to receive integrated treatment and medications for their COD.

Dataset Information

Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System.

Publications

Applying Discrete Event Simulation to Reduce Patient Wait Times and Crowding: The Case of a Specialist Outpatient Clinic with Dual Practice System.

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