Patient Wait Times Research Articles

BN O02 - Introducing the Cholecystectomy Urgency Index (CUI) – A novel way to visualise and optimise the waiting list for laparoscopic cholecystectomy at a large district general hospital

Abstract Background Gallstone disease effects approximately 10-15% of the adult population, and laparoscopic cholecystectomy is one of the most common surgical procedures in the UK, with over 60,000 performed each year. Delays to surgery from diagnosis has been found to increase patient morbidity and readmission rate. Waiting lists in the UK have been struck with a significant backlog from the COVID-19 pandemic, and navigating this ever-expanding patient population to determine priority for booking cases is complex. We propose a new system to prioritise and visualise this cohort of patients in real time – the Cholecystectomy Urgency Index. Method At one large district general hospital, we had a waiting list of 148 patients for laparoscopic cholecystectomy. We identified the individual underlying pathology and established the waiting time since diagnosis to allocation of operative date. We used excel to formulate a pairwise scatter plot of the waiting list patients based upon the time waiting. We were able then to colour code them based on pathology (biliary colic/cholecystitis/cholangitis/pancreatitis/obstructive jaundice). We were then able to link this directly to our scheduling to create a live, visual representation of our waiting list, by both days waited and causative pathology. Results We found our median wait times for patients varied based on pathology: Biliary Colic = 185 days, Cholecystitis = 57 days, Pancreatitis, Cholangitis and Obstructive Jaundice (OJ) = 65 days. We found that the pairwise scatter plot provided a rapidly accessible, easily identifiable visual representation of our patient cohort by both days waited and diagnosis. This allows relevant staff, surgeons and schedulers to plan elective cases according to both urgency and time waited. Long waiters are easily identified as outliers on the graph and can therefore be flagged for last minute cancellation slots. Conclusion By using this system, we can optimise our waiting lists, improve productivity and patient outcomes. We propose a system to enable this further when booking patients: to give them a colour coded CUI number of 1-3. 1 being biliary colic, 2 – cholecystitis, 3 – pancreatitis/cholangitis/OJ. This will be directly linked to our scatter graph and in doing so, provides us with a readily available, live snapshot in time of the current wait list. This system allows for both clinical urgency prioritisation and easy visualisation of patient cohorts, thus catalysing the elimination of long waiters by means of clear identification.

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times.

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients' service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system's efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.

The Impact of Health Administration on Healthcare Quality: A Systematic Review of Effective Management Practices

Health administration plays a crucial role in shaping the quality of healthcare delivery by implementing effective management practices that enhance patient outcomes and operational efficiency. This systematic review aims to examine the impact of key administrative practices on healthcare quality, focusing on strategies that improve patient satisfaction, reduce errors, and optimize resource utilization. A comprehensive literature search was conducted across databases including PubMed, Scopus, and Google Scholar, reviewing studies published from 2016 onwards. Inclusion criteria targeted research on management practices such as resource allocation, leadership models, operational efficiency, and quality assurance systems. Findings indicate that health administration practices, particularly in leadership and operational efficiency, have a significant positive effect on quality metrics, such as reduced patient wait times, lower readmission rates, and increased patient satisfaction. However, challenges like limited resources and resistance to change often hinder implementation. This review underscores the need for adopting evidence-based administrative strategies and suggests directions for future research to further enhance healthcare quality through effective management practices.

Healthcare Internet of Things system implementations for COVID-19 prevention

BackgroundIn response to the widespread transmission of COVID-19 in Australia, healthcare facilities implemented stringent infection control measures, and mandatory and manual screening procedures were introduced to ensure the safety of patients and healthcare staff. However, these necessary measures resulted in imbalances within the healthcare system, a shortage of front-line workers and impacts on patient experience and wait times. The prioritization of infection control measures shifted resources away from routine care, causing delays in accessing necessary healthcare services.Methods and FindingsTo address these challenges, we developed and implemented an Internet of Things (IoT) Smart Screening eGate solution in partnership with a large metropolitan children’s hospital in Australia. This solution integrated a contactless health self-service web app, thermal camera, and physical barrier to automate the COVID-19 health screening and data recording process. During the 3-month pilot period, we deployed the eGate at multiple entrances to the hospital, and monitored the number of users of the system in different periods. We also used a framework of formative evaluation to classify user design challenges within limited resources and improved the design of the eGate to enhance its effectiveness. Our findings show that the IoT eGate solution improved the efficiency of the screening process and reduced the workload and exposure risks of front-line staff and anyone who required access to the hospital. By automating the screening process, we reduced the need for manual screening and minimized contact between individuals, thus reducing the risk of potentially infected.ConclusionIn conclusion, our pilot study demonstrated the potential of IoT technologies in improving the efficiency and safety of medical facilities during pandemics and provided a series of recommendations for the translation of IoT technologies for medical facilities, including the importance of co-design and collaboration with stakeholders, user-centered design, and ongoing monitoring and evaluation.

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.

Improving Patient Wait Times on the First Day of Radiotherapy Treatment

Long wait times on starting day of radiotherapy (day 1) can cause dissatisfaction among both patients and healthcare providers. Reducing these wait times will decrease stress and decongest hospital facilities especially in current coronavirus disease 2019 times. A multidisciplinary core team was formed during the Stanford-India Collaborative Quality Improvement training to reduce the median wait times on day 1 of treatment from 6 to 4.5 hours (a 25% reduction). Several factors were identified on the fishbone diagram, and key causes were identified using a Pareto chart and action prioritization matrix. The Plan-Do-Study-Act Cycle strategy was undertaken for the identified interventions. The outcome measure was time from arrival at the hospital to entry into a treatment room. Data were obtained from time charts at various stations and electronic records. The secondary measures were visual analog scale (VAS) scores, 80th percentile wait times, and the day-2 delay percentage. The balancing measure was “new errors” due to interventions. The interventions included the completion of all administrative tasks not needing patients' presence on the day before day 1. Baseline data from 198 patients and postintervention data from 160 patients were compared and analyzed. The median wait time at baseline, which was 6 hours, was reduced to 4.2 hours. The VAS score showed 70.4, 67.7, and 71.9% satisfaction for the resident physician, therapists, and patients, respectively. The 80th percentile wait times reduced from 8 to 5.7 hours; and the day 2 starting rate decreased from 22.5 to 2.04%, with no new errors reported. Radiotherapy day 1 wait times can be safely decreased, leading to improved satisfaction among patients and healthcare providers, by utilizing classic quality improvement methods and tools.

Current Capacity for Diagnosing Alzheimer's Disease in Germany and Implications for Wait Times.

Amyloid-targeting therapies for Alzheimer's disease (AD) might become available in Germany soon. The combination of a large pool of prevalent cases and a complex diagnostic process to determine eligibility for these treatments is likely to challenge health systems' capacity. To analyze Germany's healthcare system capacity to identify treatment-eligible patients in a timely and equitable manner. We modeled patients' diagnostic journey and projects wait times due to capacity constraints for AD specialist visits and PET scans from 2024 to 2043. Model parameters were derived from published data and expert input. Wait times would be ∼50 months over the model horizon, if patients were referred to specialists based on a brief cognitive assessment in primary care. Wait times for patients with social health insurance are projected to be 1.9 times those of patients with private insurance, with peak wait times of around 76 and 40 months, respectively. Adding a blood test for the AD pathology as additional triage step would reduce wait times to below 24 months. In spite of having a well-resourced health system, Germany is projected to be unable to cope with the demand for biomarker-based AD diagnosis, if a disease-modifying AD treatment were introduced. As these treatments might become available by the end of 2024, decisive action, in particular dissemination of high-performing AD blood tests for triage in primary care, will be needed to prevent delays in access and potentially avoidable and inequitable disease progression.

HYBRID ARCHITECTURE WITH IMPROVED SCORE LEVEL FUSION FOR PATIENT WAITING TIME PREDICTION

Hospitals are overcrowded in proportion to the massive increase in population, making it difficult for hospitals to manage or arrange or shorten patient wait time during medical care. However, it is difficult to overstate the significance of patient waiting time management and predictability. In most of the hospitals, the clinical workflow is defined and driven by the patient queues. Predicting the incoming patients and waiting times has thus emerged as one of the most crucial clinical management techniques. Our proposed work aims to address the limitations of current waiting time prediction models by providing a more sophisticated, data-driven approach that can adapt to the complexities of healthcare environments. This work focuses on developing a new model for predicting the patient’s waiting time. The primary aim of this research is to create a reliable predictive model capable of accurately anticipating patient waiting times. This innovation aims to enhance both patient satisfaction and operational efficiency within healthcare settings. By offering healthcare administrators a tool to better manage patient flow, allocate resources efficiently, and minimize waiting times, this model seeks to optimize overall healthcare service delivery. The initial process carried out is pre-processing which includes data acquisition and improved [Formula: see text]-score normalization. Subsequently, entropy features, correlation features, and improved mutual information (MI) features are extracted. Then, prediction is carried out using hybrid classifier (HC) that involves a convolutional neural network (CNN) and bidirectional gated recurrent unit (Bi-GRU). Finally, improved score level fusion (ISLF) is introduced to get absolute outcomes on WTP. The analysis outcomes on varied error metrics show the efficacy of the proposed WTP model.

Optimizing infusion experience for patients in GI oncology at Hartford Hospital Cancer Institute.

331 Background: Cancer Treatments have become increasingly complex and the volume of patients requiring infusion treatments have increased in the outpatient setting. As a part of our Clinical Care Redesign Quality Initiative, the Hartford Healthcare Team identified that our GI Oncology patients receiving chemotherapy at the Hartford Hospital Infusion Center were experiencing a mean wait time of 81 minutes from the start of check-in to the start of their first medication, resulting in patient and staff dissatisfaction and inefficient use of resources. Our objective was to reduce the mean wait time to less than 60 minutes from the start of check-in to the start of the first medication by December 2023, which in turn would improve patient and staff satisfaction. Methods: By process mapping, creation of a fishbone diagram, and multi-voting, we demonstrated that waiting for laboratory results for patients who had provider and chemotherapy visits on the same day was a large contributor to long wait times. We implemented an Advanced Preparation Process that included Unlinking infusion visits from Provider visits and obtaining patient labs prior to chemotherapy treatment day to achieve the goal. Results: By Unlinking visits beginning mid-October 2023, we were able to decrease the mean wait time from 81 minutes to an average of 53 minutes, reflecting a 54% reduction in wait time. This allowed Pharmacy and Infusion Nurses additional time to review orders and complete safety checks and spend more time with patients at the chairside. An estimated amount of 12.6 hours of chair time was made available per week, representing a potential revenue increase of $188,021 annually. Most patients appreciated the decrease in wait time even if it meant coming in two separate days. Conclusions: The project aim of reducing wait times for GI Oncology patients was achieved. A reduction in time to first drug for other patients receiving chemotherapy during our pilot period was also noted, which we attributed to Hawthorne effect. Patient and staff satisfaction were improved. Our next steps will include continuing to optimizing the process.

What explains differences in average wait time in the emergency department among different racial and ethnic populations: A linear decomposition approach.

Non-Hispanic Black (NHB) and Hispanic/Latino (Hispanic) patients wait longer in the emergency department (ED) to see practitioners when compared with non-Hispanic White (NHW) patients. We investigate factors contributing to longer wait times for NHB and Hispanic patients using a linear decomposition approach. This retrospective observational study included patients presenting to one tertiary hospital ED from 2019 to 2021. Median wait times among NHW, NHB, and Hispanic were calculated with multivariable linear regressions. The extent to which demographic, clinical, and hospital factors explained the differences in average wait time among the three groups were analyzed with Blinder‒Oaxaca post-linear decomposition model. There were 310,253 total patients including 34.7% of NHW, 34.7% of NHB, and 30.6% of Hispanic patients. The median wait time in NHW was 9 min (interquartile range [IQR] 4‒47 min), in NHB was 13 min (IQR 4‒59min), and in Hispanic was 19 min (IQR 5‒78 min, p<0.001). The top two contributors of average wait time difference were mode of arrival and triage acuity level. Post-linear decomposition analysis showed that 72.96% of the NHB‒NHW and 87.77% of the Hispanic‒NHW average wait time difference were explained by variables analyzed. Compared to NHW patients, NHB and Hispanic patients typically experience longer ED wait times, primarily influenced by their mode of arrival and triaged acuity levels. Despite these recognized factors, there remains 12%‒27% unexplained factors at work, such as social determinants of health (including implicit bias and systemic racism) and many other unmeasured confounders, yet to be discovered.

Kaizen-led utilization improvement at medical oncology daycare unit.

293 Background: Optimizing utilization of oncology day care units is key in throughput, patient experience and minimizing errors. At our oncology center, the medical oncology day care (MODC) unit was at a infusion chair/bed utilization rate of 92% with a turnaround time for multiagent or long-infusion chemotherapeutic regimens of 275 minutes. This resulted in prolonged wait times for patients and delayed treatment initiations appointments. Therefore, we set out to improve the utilization of the treatment chairs and chemotherapy turnaround time at the MODC. Methods: The goal of this project was to increase MODC chair/bed utilization up to 150% and decrease chemotherapy infusion time by 30% by the end of 4/2024. The interventions integrated Agile Project Methodology to ensure adaptability and responsiveness, with DMAIC (Define, Measure, Analyze, Improve, Control) serving as the guiding quality improvement tool for the improvement plan. Interventions began with project charter approval involving stakeholders from all relevant departments, followed by a comprehensive assessment and gap analysis using DMAIC to identify and address wasted steps in the process. This involved process mapping to streamline operations and eliminate unnecessary tasks. Key interventions included pre-chemotherapy blood work 48 hour prior to treatment, patient segmentation based on treatment session-length, proactive communication with patients regarding treatment session times, and coordination with the chemotherapy pharmacy for medication readiness. Furthermore, strict adherence to defined timelines for patient assessment and chemotherapy order placement was enforced. A sensitive Key Performance Indicator (KPI) structure was developed to measure the outcomes of the interventions, aligning with Agile principles for continuous improvement. Sustainability was ensured through the creation of a sustainability plan aligned with a dashboard to maintain success thresholds. Results: The results demonstrated significant improvements; MODC median bed utilization increasing to 168%, single-agent infusion turnaround time reduced to 150 minutes (33% decrease), and long-agent infusion turnaround time decreased to 210 minutes (30% decrease). Sustainability studies are now underway. Conclusions: These findings underscore the effectiveness of interventions in not only increasing chemotherapy infusion center capacity but also decreasing chemotherapy turnaround time utilizing a Kaizen-led approach.Results of these interventions are expected to enhance efficiency and improve patient care within the MODC unit.

Healthcare Service Delivery and Patients’ Satisfaction in Public Hospitals in Akwa Ibom State

This study is centred on healthcare service delivery and patients’ satisfaction in public hospitals in Akwa Ibom State. We formulated two specific objectives and hypotheses to guide this investigation. We also adopted a cross-sectional research approach and selected the respondents using simple random sampling. Given the study's infinite population, we used the Topman formula to determine the sample size. The data assembled from the participants was analysed using the simple linear regression technique. Findings from the study demonstrated that patient waiting time (PWT) and healthcare provider-patient communication (HCP-PC) have positive and significant effects on patient satisfaction in selected public hospitals in Akwa Ibom State, Nigeria. The researcher’s conclusion is that healthcare service delivery has a significant positive effect on patients’ healthcare satisfaction in the studied public hospitals in Akwa Ibom State. Based on these findings, public hospitals should prioritise the proper management of patient administrative concerns, as this could potentially reduce patient waiting times. Given that patients place their trust in healthcare workers, it is important for healthcare providers in the studied public hospitals to respect their private healthcare information.

Comparative Effectiveness of Teledermatology Versus In-person Consultations: A Systematic Review of Diagnostic Information Elements

Context: Teledermatology is delivered through two primary methods: Live video conferencing, which enables real-time interaction between patients and doctors, and the store-and-forward technique, where images of skin lesions are sent over the internet for later examination. Objectives: This systematic review evaluates the concordance between in-person and telemedicine follow-up methods in dermatology. Methods: In February 2024, a comprehensive literature search was conducted across PubMed, Medline, ISI Web of Science, and the ACM Digital Library. Keywords such as "teledermatology," "telemedicine," and "mobile health" were used, following the PICOS criteria. Results: Initially, 716 articles were identified from the databases, along with four studies from the gray literature search. After removing duplicates, screening studies, and applying the inclusion and exclusion criteria, 21 studies were selected for the final review. These studies detailed follow-up procedures and assessed both direct and indirect costs and effectiveness. The consensus indicates that teledermatology reduces costs and satisfies both patients and healthcare professionals. Conclusions: Teledermatology has been shown to potentially increase the efficiency of follow-up care compared to traditional methods. However, further research is needed to develop standardized evaluation protocols and to more accurately measure and validate key variables. Publications suggest that teledermatology is cost-effective, reduces patient wait times, extends access to dermatological care in remote areas, and enhances satisfaction among patients, doctors, and nurses.

A Comprehensive Analysis of Wait Times in Ambulatory Obstetrics and Gynecology Clinic at a Tertiary Care Hospital in Pakistan.

Objective Wait times are an important determinant of patient satisfaction measured objectively and associated significantly with it. It is a measure of time taken from the arrival of patients to the registration desk of a facility to the time they access service. This study focuses on current patient pathways at the Obstetrics and Gynecology Outpatient (OBGYN) Ambulatory Clinic at Shalamar Hospital and assesses wait times by stratifying it at each step of the pathway. Methodology Data on consultation times was obtained from 105 patient processes over three months. We had in-depth discussions with the doctors, nurses, and paramedical staff to better comprehend the medical conditions, the terminologies, the structural hierarchy, and the functions of the department. Based on this the patient journey through the clinic was further dissected into operational steps including the check-in process, patient vitals screening, patient history, physical examination, medical hierarchy, referral for further testing or procedures, and lastly prescription generation. Results The average wait time for obstetrics and gynecology patients is 45.18±39.37 minutes. The average time the patients waited for the check-in process was 1.73±1.80 minutes, for the vitals screening was 3.08±10.07 minutes, for patient history and examination was 19.90±22.71 minutes, and for consultation within the medical hierarchy (SR and Consultants) patients waited 1.31±5.71 minutes. For those patients who had no other tests or procedures recommended, the prescription generation required a further 4.10±3.14 minutes. While those who had tests or procedures recommended waited 5.75±28.27 minutes for voucher generation for the specific test or procedure and 9.31±11.35 minutes for the test or procedure itself. Conclusion The obstetrics and gynecology clinic at Shalamar Hospital has average wait times comparable to others in the region of approximately 45 minutes. The longest wait during the visit is before patient history and examination approximately 20 minutes. To improve wait times, the clinic should implement staggered appointments, use a token system, manage queues better, follow clinic hours, and improve coordination between doctors. This will allow for improved patient experiences, faster patient turnovers, and increased patient inflow.

A - 66 Factors Impacting Appointment No-Shows and Cancellations in Neuropsychology: Predictors for Improving Access to Care for a Diverse Patient Population

Abstract Objective Access to care can be impacted adversely by appointment adherence. We aimed to identify predictors of no-shows and cancellations for neuropsychological services offered by a healthcare system serving diverse communities. Method Demographics and appointment status were documented from a sample of 537 consecutive patients scheduled for neuropsychological evaluation at an outpatient psychiatry clinic. Patients included 220 males and 317 females with an average formal education of 11.01 years (SD = 3.87) and age of 55.64 years (SD = 16.2). Chi-square analyses were used to evaluate the association between appointment status and gender, race/ethnicity, payor, availability of a collateral, and MyChart access. Multivariate analysis of variance was used to assess the relationship between age, education, proxy income, and prior no-show rate with appointment status. Results The overall rate of no-shows or late cancellations was 20%, and some patients no-showed multiple times. No-shows and late cancellations were associated with historical no-show rates and older age (F = 14.60, p &amp;lt; 0.0001), while race/ethnicity and MyChart activation had slight impacts (χ2 = 5.572, p = 0.062 and χ2 = 3.69, p = 0.055, respectively). Being a monolingual Spanish speaker was associated negatively with MyChart activation, but positively with collateral availability (p &amp;lt; 0.05). Conclusions No-show rates, which impact access to care, are high and associated with prior history of no-shows. Other factors, such as MyChart activation and culturally-associated barriers may also impact access. Implementing interventions addressing prior no-show rates and enhancing MyChart access could significantly increase show rates; such improvements could increase patient access to care, reduce the system’s financial burden, and decrease wait times for other patients.

HARNESSING BIG DATA AND MACHINE LEARNING FOR TRANSFORMATIVE HEALTHCARE INFORMATION MANAGEMENT

This study explores the transformative impact of big data and machine learning (ML) on healthcare information management, drawing insights from a comprehensive review of 50 peer-reviewed articles published between 2010 and 2023. The analysis highlights significant advancements in predictive analytics, with ML models improving disease prediction accuracy by up to 40% compared to traditional methods, as demonstrated in multiple studies. Personalized medicine has also benefited, with ML-driven treatment plans showing a 25% improvement in chronic disease management and a 15% reduction in treatment failures, as reported in several case studies. Additionally, integrating big data and ML into healthcare operations has led to a 35% reduction in patient wait times and a 20% decrease in hospital readmissions. However, challenges such as data fragmentation, privacy concerns, and algorithmic bias were identified in over 60% of the reviewed studies, indicating the need for stronger regulatory frameworks. This review underscores the potential of big data and ML to revolutionize healthcare while also calling attention to the ethical and practical challenges that must be addressed to ensure equitable and transparent healthcare solutions.

Advanced practice providers in the medical genetics workforce: A nationwide survey

PurposeThis study characterizes the current landscape of genetics advanced practice providers (APPs) in the United States. MethodsA 35-question survey was emailed to the Genetics APP Listserv in the fall of 2023. Questions represented 5 domains: demographics, practice, onboarding, compensation, and perceptions. ResultsA total of 105 genetics APPs (93%) completed the survey. Genetics APPs evaluate various patient types and populations in multiple settings, working an average of 41.3 hours and seeing 15 patients weekly. Nearly all see new (96%) and follow-up (98%) patients and utilize telemedicine (93%). Half (51%) have only worked in the genetics specialty during their career. Overall, APPs are generally satisfied with their career as a genetics APP (98%) and work-life balance (86%), and most (86%) feel that they function at the top of their scope. ConclusionStudy findings elucidate the current state of genetics APPs. Results define the characteristics and role of an APP in the genetics specialty and will guide employers and genetics organizations to utilize APPs at the top of their scope and recruit new APPs to this exciting field. A collaborative effort is needed to increase the overall genetics workforce, decrease patient wait times, and increase access to genetics care.

Outpatient appointment systems: A new heuristic with patient classification

PurposeThis study aims to develop a heuristic for an outpatient appointment system considering patient classification. Design/methodology/approachThe proposed heuristic was applied in simulations with eighteen scenarios, combining different environmental factors. Total cost was adopted as a performance metric, composed of the patient's wait time and the service provider's idleness and overtime. The patients were divided into two classes according to their no-show probability, in an arrivals sequence with a binomial distribution. As a significance test of the results, Bonferroni-adjusted repeated measures analysis was applied. FindingsHaving Dome rule as baseline, an increase in performance in terms of total cost (TC) was observed, which varied between 0.46 % and 5.94 % among the means of the simulated environments, validated using the proposed significance test. The greatest benefits were obtained in the scenarios with lower ratios between service provider costs and patient costs (CR), as well as lower coefficients of variation for service times (Cv). It was also found that the heuristic is more efficient when patients from the class with the highest no-show rate predominate in the session. OriginalityThe single study identified in the literature that contemplates recalculations adopts deterministic service times to make its model viable. The present research, in turn, makes more realistic assumptions for the simulated environments, considering the variables and probability distributions most commonly observed in practical contexts Practical implicationsThe proposed heuristic provided a significant increase in performance for some combinations of environmental factors analyzed, preserving flexibility in the choice of appointment slots and covering a wide range of healthcare services found in practice.

Improving patient flow in free medical service by introducing a process map: a quality improvement study

Introduction: The tradition of charitable medical treatment can be drawn back to the early years of Christianity when the Church growth closely related to the care for the sick as well as other charitable activities. Though its purpose is charity, free medical service should also improve the quality of service. It can be achieved through the implementation of lean six sigma tools. One of its tools is a process map. Process map in Lean Six Sigma is commonly used as a diagnostic tool to determine the possible bottlenecks in the system. Methods: This is a brief pre and post quality improvement study. Before the implementation of a process map suggested by the researcher (period I), the patient flow was not properly organized. After the lunch break, the researcher introduced a new process map and briefed it to the committee. The patients flow in the afternoon (Period II) was using the process map. Patient numbers and wait times were measured within the time interval of this study. Results: Out of 159 patients who served during the period of this study, 63 (40%) were male and 96 (60%) were female. During Period I of the medical service, 50 patients (31%) were treated with average 17 patients per hour and approximate waiting time 36 minutes. After implementation of process map (Period II) the number of patients served were 109 (69%) with an average 36 patients per hour and an approximate waiting time of 17 minutes. Conclusion: The implementation of process map in free medical service may assist in increasing the number of patients served and reduce the waiting time as a result. Apart from reducing the confusion of where the patients were to go next, the process map also help the committee to identify possible hinderances and address the impediments to efficient charitable medical care.

Why do patients who are triaged as low-acuity visit the emergency department? – A Polish perspective

BackgroundEmergency departments (EDs) worldwide are dealing with overcrowding, system fragmentation, and coordination problems, which impact patient wait times, staff job satisfaction, and patient outcomes. Inappropriate ED visits, particularly those for low acuity conditions, exacerbate these challenges. However, the motivations behind these visits are poorly understood, with limited data from the patient perspective. This study investigated patient-reported motives behind ED visits triaged as low acuity in Poznan, Poland, to propose health care system flow enhancements. Material and methodsA cross-sectional survey and retrospective chart review were conducted in the ED of the Hipolit Cegielski Medical Center in Poznań, Poland, over three months in 2022–23. Patients who were triaged to have low acuity conditions were invited to participate in the survey. The data collected through the questionnaire included patient and ED visit characteristics. Additional information on the visits was extracted from the patient charts. Main resultsThis study involved 293 patients who underwent low-acuity triage. Among them, 58 % were deemed to have conditions that could have been treated in primary care. Most of the patients (74 %) visited the ED of their own volition due to concerns about their health. Other reasons for ED attendance were challenges accessing primary care or a specialist clinic (11 %), system navigation problems (5 %), or a lack of trust in their primary care provider (2 %). ConclusionsThis study showed that of the patients surveyed, the majority had conditions that could have been treated outside of the ED setting. We recommend prioritizing education, particularly among younger adults, to increase awareness about nonurgent care options while improving health care policies.