Milestoning estimators of dissipation in systems observed at a coarse resolution.

Many nonequilibrium, active processes are observed at a coarse-grained level, where different microscopic configurations are projected onto the same observable state. Such "lumped" observables display memory, and in many cases, the irreversible character of the underlying microscopic dynamics becomes blurred, e.g., when the projection hides dissipative cycles. As a result, the observations appear less irreversible, and it is very challenging to infer the degree of broken time-reversal symmetry. Here we show, contrary to intuition, that by ignoring parts of the already coarse-grained state space we may-via a process called milestoning-improve entropy-production estimates. We present diverse examples where milestoning systematically renders observations "closer to underlying microscopic dynamics" and thereby improves thermodynamic inference from lumped data assuming a given range of memory, and we hypothesize that this effect is quite general. Moreover, whereas the correct general physical definition of time reversal in the presence of memory remains unknown, we here show by means of physically relevant examples that at least for semi-Markov processes of first and second order, waiting-time contributions arising from adopting a naive Markovian definition of time reversal generally must be discarded.

The impact of the COVID-19 pandemic in the clinical assistance to breast cancer patients.

PURPOSE: We aimed to disclose the impact of the pandemic on breast cancer patients in a specialized breast cancer center (BCC). METHODS: A total of 501 breast cancer patients with a first appointment in the BCC from April 1st, 2019 to March 31st, 2021 were divided into four consecutive periods of 6 months. Data from the homologous semesters was compared. Patients with an appointment in the BCC during the study period were eligible for the secondary aim of our study (BCC workload). RESULTS: After the pandemic declaration (period 3), we found a decrease in the referral by screening programs (p = 0.002) and a reduction in the waiting time between the primary care referral and the first BCC appointment (p < 0.001). There were higher rates of palpable axillary nodes (p = 0.001), an increase in N stage 2 and 3 (p = 0.050), and a trend for primary endocrine therapy as the first treatment (p = 0.021) associated with higher rates of complete axillary node dissection (p = 0.030). In period 4, there were more outward diagnoses (p = 0.003) and a higher rate of surgery as the first treatment (p = 0.013). CONCLUSION: COVID-19 pandemic implied a more advanced nodal stage, which may be related to the delay in breast cancer screening.

Impact of changes in skeletal muscle mass and quality during the waiting time on outcomes of lung transplantation.

INTRODUCTION: The association of changes in skeletal muscle mass and quality during the waiting time with outcomes of lung transplantation (LT) remains unclear. We aimed to examine the association of changes in skeletal muscle mass and quality during the waiting time, as well as preoperative skeletal muscle mass and quality, with outcomes of LT. METHODS: This study included individuals who underwent LT from brain-dead donors. Skeletal muscle mass (cm2 /m2 ) and quality (mean Hounsfield units [HU]) of the erector spinae muscle at the 12th thoracic level were evaluated using computed tomography. Preoperative skeletal muscle mass and quality, and their changes during the waiting time were calculated. We evaluated the associations among mechanical ventilation (MV) duration, intensive care unit (ICU) length of stay (LOS), hospital LOS, 6-minute walk distance at discharge, and 5-year survival after LT. RESULTS: This study included 98 patients. The median waiting time was 594.5 days (interquartile range [IQR], 355.0-913.0). The median changes in skeletal muscle mass and quality were -4.4% (IQR, -13.3-3.1) and -2.9% (IQR, -16.0-4.1), respectively. Severe low skeletal muscle mass at LT was associated with prolonged ICU LOS (B = 8.46, 95% confidence interval [CI]: .51-16.42) and hospital LOS (B = 36.00, 95% CI: 3.23-68.78). Pronounced decrease in skeletal muscle mass during the waiting time was associated with prolonged MV duration (B = 7.85, 95% CI: .89-14.81) and ICU LOS (B = 7.97, 95% CI: .83-15.10). CONCLUSION: Maintaining or increasing skeletal muscle mass during the waiting time would be beneficial to improve the short-term outcomes of LT.

Effects of private health insurance on waiting time in public hospitals.

The Australian government pays $6.7 billion per year in rebates to encourage Australians to purchase private health insurance (PHI) and an additional $6.1 billion to cover services provided in private hospitals. What is the justification for large government subsidies to a private industry when all Australians already have free coverage under Medicare? The government argues that more people buying PHI will relieve the burden on the public system and may reduce waiting times. However, the evidence supporting this is sparse. We use an instrumental variable approach to study the causal effects of higher PHI coverage in the area on waiting times in public hospitals in the same area. The instrument used is area-level average house prices, which correlate with average income and wealth, thus influencing the purchase of PHI due to tax incentives, but not directly affecting waiting times in public hospitals. We use 2014-2018 hospital admission and elective surgery waiting list data linked at the patient level from the Victorian Center for Data Linkage. These data cover all inpatient admissions in all hospitals in Victoria (both public and private hospitals) and those registered on the waiting list for elective surgeries in public hospitals in Victoria. We find that one percentage point increase in PHI coverage leads to about 0.34 days (or 0.5%) reduction in waiting times in public hospitals on average. The effects vary by surgical specialities and age groups. However, the practical significance of this effect is limited, if not negligible, despite its statistical significance. The small effect suggests that raising PHI coverage with the aim to taking the pressure off the public system is not an effective strategy in reducing waiting times in public hospitals. Alternative policies aiming at improving the efficiency of public hospitals and advancing equitable access to care should be a priority for policymakers.

Impact of time from diagnosis to chemotherapy on prognosis in advanced pancreatic cancer.

BACKGROUND: Due to the aggressive nature and poor prognosis of advanced pancreatic cancer, prompt initiation of treatment is critical. We investigated the effect of the interval between cancer diagnosis and initiation of chemotherapy on survival in patients with advanced pancreatic cancer. METHODS: In this retrospective, single-centre study, consecutive patients with advanced pancreatic cancer between April 2013 and March 2022 were analyzed. Data were extracted from the electronic medical records of patients who received chemotherapy for metastatic, locally advanced or resectable pancreatic cancer or who received chemotherapy due to either being intolerant of or declining surgery. We compared overall survival between two groups: the early waiting time group (waiting time ≤30 days from diagnosis to chemotherapy initiation) and the elective waiting time group (waiting time ≥31 days). Prognostic factors, including biliary drainage, were considered. The impact of waiting time on survival was assessed by univariate and multivariate analyses with Cox proportional hazard models. A 1:1 propensity score matching approach was used to balance bias, accounting for significant poor prognosis factors, age and sex. RESULTS: The study involved 137 patients. Overall survival exhibited no statistically significant difference between the early and elective waiting time groups (207 and 261 days, P = 0.2518). Univariate and multivariate analyses identified poor performance status and metastasis presence as predictors of worse prognosis. This finding persisted post propensity score matching (275 and 222 days, P = 0.8223). CONCLUSIONS: Our study revealed that initiating chemotherapy ˃30 days later does not significantly affect treatment efficacy compared to within 30 days of diagnosis.

Dissatisfaction-considered waiting time prediction for outpatients with interpretable machine learning.

Long waiting time in outpatient departments is a crucial factor in patient dissatisfaction. We aim to analytically interpret the waiting times predicted by machine learning models and provide patients with an explanation of the expected waiting time. Here, underestimating waiting times can cause patient dissatisfaction, so preventing this in predictive models is necessary. To address this issue, we propose a framework considering dissatisfaction for estimating the waiting time in an outpatient department. In our framework, we leverage asymmetric loss functions to ensure robustness against underestimation. We also propose a dissatisfaction-aware asymmetric error score (DAES) to determine an appropriate model by considering the trade-off between underestimation and accuracy. Finally, Shapley additive explanation (SHAP) is applied to interpret the relationship trained by the model, enabling decision makers to use this information for improving outpatient service operations. We apply our framework in the endocrinology metabolism department and neurosurgery department in one of the largest hospitals in South Korea. The use of asymmetric functions prevents underestimation in the model, and with the proposed DAES, we can strike a balance in selecting the best model. By using SHAP, we can analytically interpret the waiting time in outpatient service (e.g., the length of the queue affects the waiting time the most) and provide explanations about the expected waiting time to patients. The proposed framework aids in improving operations, considering practical application in hospitals for real-time patient notification and minimizing patient dissatisfaction. Given the significance of managing hospital operations from the perspective of patients, this work is expected to contribute to operations improvement in health service practices.

Outcome evaluation of technical strategies on reduction of patient waiting time in the outpatient department at Kilimanjaro Christian Medical Centre-Northern Tanzania.

BACKGROUND: The Tanzania healthcare system is beset by prolonged waiting time in its hospitals particularly in the outpatient departments (OPD). Previous studies conducted at Kilimanjaro Christian Medical Centre (KCMC) revealed that patients typically waited an average of six hours before receiving the services at the OPD making KCMC have the longest waiting time of all the Zonal and National Referral Hospitals. KCMC implemented various interventions from 2016 to 2021 to reduce the waiting time. This study evaluates the outcome of the interventions on waiting time at the OPD. METHODS: This is an analytical cross-sectional mixed method using an explanatory sequential design. The study enrolled 412 patients who completed a structured questionnaire and in-depth interviews (IDI) were conducted among 24 participants (i.e., 12 healthcare providers and 12 patients) from 3rd to 14th July, 2023. Also, a documentary review was conducted to review benchmarks with regards to waiting time. Quantitative data analysis included descriptive statistics, bivariable and multivariable. All statistical tests were conducted at 5% significance level. Thematic analysis was used to analyse qualitative data. RESULTS: The findings suggest that post-intervention of technical strategies, the overall median OPD waiting time significantly decreased to 3 h 30 min IQR (2.51-4.08), marking a 45% reduction from the previous six-hour wait. Substantial improvements were observed in the waiting time for registration (9 min), payment (10 min), triage (14 min for insured patients), and pharmacy (4 min). Among the implemented strategies, electronic medical records emerged as a significant predictor to reduced waiting time (AOR = 2.08, 95% CI, 1.10-3.94, p-value = 0.025). IDI findings suggested a positive shift in patients' perceptions of OPD waiting time. Problems identified that still need addressing include, ineffective implementation of block appointment and extension of clinic days was linked to issues of ownership, organizational culture, insufficient training, and ineffective follow-up. The shared use of central modern diagnostic equipment between inpatient and outpatient services at the radiology department resulted in delays. CONCLUSION: The established technical strategies have been effective in reducing waiting time, although further action is needed to attain the global standard of 30 min to 2 h OPD waiting time.

Effect of cancer waiting time standards in the English National Health Service: a threshold analysis.

BACKGROUND: The English National Health Service has multiple waiting time standards relating to cancer diagnosis and treatment. Targets can have unintended effects, such as prioritisation based on targets instead of clinical need. In this case, a `threshold effect' will appear as a spike in hospitals just meeting the target. METHODS: We conducted a retrospective study of publicly available cancer waiting time data, including a 2-week wait for a specialist appointment, a 31-day decision to first treatment and a 62-day referral to treatment standard that attracted a financial penalty. We examined the performance of hospital trusts against these targets by financial year to look for threshold effects, using Cattaneo et al. manipulation density test. RESULTS: Trust performance against cancer waiting targets declined over time, and this trend accelerated since the start of the Covid-19 pandemic. Statistical evidence of a threshold effect for the 2-week and 31-day standard was only present in a few years. However, there was strong statistical evidence of a threshold effect for the 62-day standard across all financial years (p < 0.01). CONCLUSION: The data suggests that the effect of threshold targets alters hospital behaviour at target levels but does not do so equally for all standards. Evidence of threshold effects for the 62-day standard was particularly strong, possibly due to some combination of a smaller volume of eligible patients, a larger penalty, multiple waypoints where hospitals can intervene, baseline performance against the target and where the target is set (i.e. how much headroom is available). RCTs of the use of threshold targets and of different designs for such targets in the future would be extremely informative.

Process reengineering using DMAIC framework for reduction of waiting time in daycare infusion therapy for better patient experience.

Daycare infusion therapy is an integral aspect of oncology, but increased waiting time raises concerns for patients. Patient-reported experience measures prompted the need to evaluate reasons for prolonged appointment delays. This study seeks to analyze and address patients' concerns, to streamline the process flow and reduce waiting time for daycare infusion therapy thereby enhancing patient experience. The define, measure, analyze, improve, and control methodology was implemented, and its impact on reducing waiting times was evaluated. The objective is to ensure that >85% of patients enter the daycare infusion unit within an hour of their appointment time in 6 months. The baseline data for patient waiting times was measured for a period of 2 months, and the average waiting time was determined. Potential causes contributing to prolonged waiting times were identified through time-motion analysis, with a fishbone diagram categorizing potential causes and a Pareto chart prioritizing them. Plan, do, study, and act cycles were conducted for implementing the changes, and a new process flow mapped. Baseline data showed 32% average adherence to the defined turnaround time of 1 hour, with an average waiting time of 108 minutes. Forty causes were identified for increased waiting time, of which eight were key. Adherence to waiting time turnaround time improved from 32% to 89% and the average waiting time decreased by 59 minutes from 108 minutes, increasing patient satisfaction index by 7.5%. The balancing measures include an increase in operational efficiency and throughput of the unit and the inventory levels of oncology medicine were decreased, leading to a 50% reduction in inventory value, while medication error declined by 0.62%, improving patient safety. The project gained tangible and intangible benefits impacting staff, patients, and relatives while improving operational efficiency. This study, with its scientific and systematic approach, enhanced patient satisfaction, patient safety, and better utilization of resources.

An Electronic Health Record-Based Automated Self-Rescheduling Tool to Improve Patient Access: Retrospective Cohort Study.

BACKGROUND: In many large health centers, patients face long appointment wait times and difficulties accessing care. Last-minute cancellations and patient no-shows leave unfilled slots in a clinician's schedule, exacerbating delays in care from poor access. The mismatch between the supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations. OBJECTIVE: We evaluated an electronic health record (EHR)-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled. METHODS: In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer. RESULTS: From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million. CONCLUSIONS: Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.

Changes in participatory and societal outcomes during the waiting period for cochlear implantation - an observational study.

INTRODUCTION: Various factors, including an aging population and expanding eligibility criteria, may increase the demand for cochlear implants (CIs), potentially resulting in longer waiting times. In most Dutch CI centers, the time between referral and surgery exceeds 6 months. Clinical experience suggests that during the waiting period for cochlear implantation, hearing and communication difficulties increase. Simultaneously, there is an interest in outcomes more closely aligned with patient values and needs, which resulted in the SMILE (Societal Merit of Interventions on hearing Loss Evaluation) study. This paper presents results on observed changes in societal and participatory outcomes during waiting time in participants with a time to CI surgery exceeding 6 months. METHODS: SMILE is a prospective multi-center study including 232 individuals who were referred for unilateral CI. Continuous and nominal data from multiple questionnaires, sent immediately after referral and shortly before surgery, were analyzed by computing differences, Cohen's D, and odds ratios. RESULTS: Of the total 232 participants, 102 had a time between inclusion and surgery exceeding 6 months. Of these, 89 had (partially) filled out surveys at both time points. Of all the domain scores 55% did not show differences between timepoints. All Cohen's D estimates were relatively small, ranging from - 0.298 to 0.388 for those outcomes that showed noteworthy changes. CONCLUSION: Waiting time from referral to surgery, even though exceeding 6 months, was observed to not seriously affect non-clinically-prioritized patients in an adverse way. Future investigations should identify subgroups on tolerable waiting times regarding short- and long-term outcomes. TRIAL REGISTRATION: Trial registration number at ClinicalTrials.gov: NCT05525221, 25-8-2022.

Waiting Times in Prostate Cancer Diagnosis and Treatment: A Ten-Year Experience in A Nigerian Teaching Hospital.

INTRODUCTION: Prostate cancer is still the leading male cancer and the leading cause of cancer deaths in Nigeria, and other low- and middle-income countries (LMIC) in Sub-Saharan Africa. Early diagnosis is essential to ensuring prompt treatment and reducing morbidity and mortality. Reducing the waiting times for diagnosis and treatment is therefore important. AIMS AND OBJECTIVES: To study prostate cancer management waiting times, to serve as a baseline in improving the quality of cancer care in the Nigerian populace. PATIENTS AND METHODS: This was a ten-year retrospective study of waiting times of all histologically-confirmed prostate cancer patients seen at Alex-Ekwueme Federal Teaching Hospital, Abakaliki, Ebonyi State, Nigeria. Statistical analysis was done SPSS version 26. A P-value less than 0.05 was considered statistically significant. RESULTS: A total of 189 patients presented with prostate cancer; however, 73 patients with complete data were analysed. The mean age of the patients was 71.48±8.16 years. The median duration of symptoms before presentation was 6 months. The mean total prostate-specific antigen was 82.08±54.9ng/mL. The mean duration between the first visit to the definitive diagnosis was 6.53±11.68 months with a median of 1 month. The median duration from visit to treatment was 3 months with a mean of 9.71±13.4 months. There were no associations between occupation, highest educational level, financial constraints, and the different waiting times studied (P>0.05). CONCLUSION: The waiting times for prostate cancer management were unduly prolonged in this study; patient-related factors did not influence this wait. INTRODUCTION: Le cancer de la prostate est toujours le principal cancer chez les hommes et la principale cause de décès par cancer au Nigéria et dans d'autres pays à revenu faible et intermédiaire (PFR) en Afrique subsaharienne. Un diagnostic précoce est essentiel pour garantir un traitement rapide et réduire la morbidité et la mortalité. Il est donc important de réduire les délais d'attente pour le diagnostic et le traitement. OBJECTIFS: Étudier les délais d'attente dans la prise en charge du cancer de la prostate, afin de servir de référence pour améliorer la qualité des soins contre le cancer dans la population nigériane. PATIENTS ET MÉTHODES: Il s'agit d'une étude rétrospective de dix ans sur les délais d'attente de tous les patients atteints de cancer de la prostate confirmé histologiquement et traités à l'hôpital universitaire fédéral Alex-Ekwueme, à Abakaliki, dans l'État d'Ebonyi, au Nigéria. L'analyse statistique a été réalisée avec la version 26 du logiciel SPSS. Une valeur de P inférieure à 0,05 a été considérée comme statistiquement significative. RÉSULTATS: Un total de 189 patients ont présenté un cancer de la prostate ; cependant, seuls les 73 patients avec des données complètes ont été analysés. L'âge moyen des patients était de 71,48±8,16 ans. La durée médiane des symptômes avant la présentation était de 6 mois. La concentration moyenne d'antigène spécifique de la prostate (PSA) total était de 82,08±54,9 ng/mL. La durée moyenne entre la première visite et le diagnostic définitif était de 6,53±11,68 mois, avec une médiane de 1(1) mois. La durée médiane entre la visite et le traitement était de 3 mois, avec une moyenne de 9,71±13,4 mois. Aucune association n'a été observée entre l'occupation, le plus haut niveau d'éducation, les contraintes financières et les différents délais d'attente étudiés (P>0,05). CONCLUSION: Les délais d'attente pour la prise en charge du cancer de la prostate étaient anormalement prolongés dans cette étude ; les facteurs liés au patient n'ont pas influencé cette attente. MOTS-CLÉS: Cancer de la prostate, Délai d'attente, Délai, Diagnostic, Traitement.

Forecasting emergency department waiting time using a state space representation.

The provision of waiting time information in emergency departments (ED) has become an increasingly popular practice due to its positive impact on patient experience and ED demand management. However, little scientific attention has been given to the quality and quantity of waiting time information presented to patients. To improve both aspects, we propose a set of state space models with flexible error structures to forecast ED waiting time for low acuity patients. Our approach utilizes a Bayesian framework to generate uncertainties associated with the forecasts. We find that the state-space models with flexible error structures significantly improve forecast accuracy of ED waiting time compared to the benchmark, which is the rolling average model. Specifically, incorporating time-varying and correlated error terms reduces the root mean squared errors of the benchmark by 10%. Furthermore, treating zero-recorded waiting times as unobserved values improves forecast performance. Our proposed model has the ability to provide patient-centric waiting time information. By offering more accurate and informative waiting time information, our model can help patients make better informed decisions and ultimately enhance their ED experience.

How to adjust the expected waiting time to improve patient's satisfaction?

BACKGROUND: Long waiting time in hospital leads to patient's low satisfaction. In addition to reducing the actual waiting time (AWT), we can also improve satisfaction by adjusting the expected waiting time (EWT). Then how much can the EWT be adjusted to attribute a higher satisfaction? METHODS: This study was conducted though experimental with hypothetical scenarios. A total of 303 patients who were treated by the same doctor from August 2021 to April 2022 voluntarily participated in this study. The patients were randomly divided into six groups: a control group (n = 52) and five experimental groups (n = 245). In the control group, the patients were asked their satisfaction degree regarding a communicated EWT (T0) and AWT (Ta) under a hypothetical situation. In the experimental groups, in addition to the same T0 and Ta as the control group, the patients were also asked about their satisfaction degree with the extended communicated EWT (T1). Patients in five experimental groups were given T1 values with 70, 80, 90, 100, and 110 min respectively. Patients in both control and experiment groups were asked to indicate their initial EWT, after given unfavorable information (UI) in a hypothetical situation, the experiment groups were asked to indicate their extended EWT. Each participant only participated in filling out one hypothetical scenario. 297 valid hypothetical scenarios were obtained from the 303 hypothetical scenarios given. RESULTS: The experimental groups had significant differences between the initial indicated EWT and extended indicated EWT under the effect of UI (20 [10, 30] vs. 30 [10, 50], Z = -4.086, P < 0.001). There was no significant difference in gender, age, education level and hospital visit history (χ2 = 3.198, P = 0.270; χ2 = 2.177, P = 0.903; χ2 = 3.988, P = 0.678; χ2 = 3.979, P = 0.264) in extended indicated EWT. As for patient's satisfaction, compared with the control group, significant differences were found when T1 = 80 min (χ2 = 13.511, P = 0.004), T1 = 90 min (χ2 = 12.207, P = 0.007) and T1 = 100 min (χ2 = 12.941, P = 0.005). When T1 = 90 min, which is equal to the Ta, 69.4% (34/49) of the patients felt "very satisfied", this proportion is not only significantly higher than that of the control group (34/ 49 vs. 19/52, χ2 = 10.916, P = 0.001), but also the highest among all groups. When T1 = 100 min (10 min longer than Ta), 62.5% (30/48) of the patients felt "very satisfied", it is significantly higher than that of the control group (30/ 48 vs. 19/52, χ2 = 6.732, P = 0.009). When T1 = 80 min (10 min shorter than Ta), 64.8% (35/54) of the patients felt "satisfied", it is significantly higher than that of the control group (35/ 54 vs. 17/52, χ2 = 10.938, P = 0.001). However, no significant difference was found when T1 = 70 min (χ2 = 7.747, P = 0.052) and T1 = 110 min (χ2 = 4.382, P = 0.223). CONCLUSIONS: Providing UI prompts can extend the EWT. When the extended EWT is closer to the AWT, the patient's satisfaction level can be improved higher. Therefore, medical institutions can adjust the EWT of patient's through UI release according to the AWT of hospitals to improve patient's satisfaction.

Characteristics and Admission Preferences of Pediatric Emergency Patients and Their Waiting Time Prediction Using Electronic Medical Record Data: Retrospective Comparative Analysis.

BACKGROUND: The growing number of patients visiting pediatric emergency departments could have a detrimental impact on the care provided to children who are triaged as needing urgent attention. Therefore, it has become essential to continuously monitor and analyze the admissions and waiting times of pediatric emergency patients. Despite the significant challenge posed by the shortage of pediatric medical resources in China's health care system, there have been few large-scale studies conducted to analyze visits to the pediatric emergency room. OBJECTIVE: This study seeks to examine the characteristics and admission patterns of patients in the pediatric emergency department using electronic medical record (EMR) data. Additionally, it aims to develop and assess machine learning models for predicting waiting times for pediatric emergency department visits. METHODS: This retrospective analysis involved patients who were admitted to the emergency department of Children's Hospital Capital Institute of Pediatrics from January 1, 2021, to December 31, 2021. Clinical data from these admissions were extracted from the electronic medical records, encompassing various variables of interest such as patient demographics, clinical diagnoses, and time stamps of clinical visits. These indicators were collected and compared. Furthermore, we developed and evaluated several computational models for predicting waiting times. RESULTS: In total, 183,024 eligible admissions from 127,368 pediatric patients were included. During the 12-month study period, pediatric emergency department visits were most frequent among children aged less than 5 years, accounting for 71.26% (130,423/183,024) of the total visits. Additionally, there was a higher proportion of male patients (104,147/183,024, 56.90%) compared with female patients (78,877/183,024, 43.10%). Fever (50,715/183,024, 27.71%), respiratory infection (43,269/183,024, 23.64%), celialgia (9560/183,024, 5.22%), and emesis (6898/183,024, 3.77%) were the leading causes of pediatric emergency room visits. The average daily number of admissions was 501.44, and 18.76% (34,339/183,204) of pediatric emergency department visits resulted in discharge without a prescription or further tests. The median waiting time from registration to seeing a doctor was 27.53 minutes. Prolonged waiting times were observed from April to July, coinciding with an increased number of arrivals, primarily for respiratory diseases. In terms of waiting time prediction, machine learning models, specifically random forest, LightGBM, and XGBoost, outperformed regression methods. On average, these models reduced the root-mean-square error by approximately 17.73% (8.951/50.481) and increased the R2 by approximately 29.33% (0.154/0.525). The SHAP method analysis highlighted that the features "wait.green" and "department" had the most significant influence on waiting times. CONCLUSIONS: This study offers a contemporary exploration of pediatric emergency room visits, revealing significant variations in admission rates across different periods and uncovering certain admission patterns. The machine learning models, particularly ensemble methods, delivered more dependable waiting time predictions. Patient volume awaiting consultation or treatment and the triage status emerged as crucial factors contributing to prolonged waiting times. Therefore, strategies such as patient diversion to alleviate congestion in emergency departments and optimizing triage systems to reduce average waiting times remain effective approaches to enhance the quality of pediatric health care services in China.

Time ratio disparities among ED patients undergoing head CT.

PURPOSE: To assess if patients who underwent head computed tomography (CT) experienced disparities in the emergency department (ED) and if the indication for head CT affected disparities. METHODS: This study employed a retrospective, IRB-approved cohort design encompassing four hospitals. All ED patients between January 2016 and September 2020 who underwent non-contrast head CTs were included. Furthermore, key time intervals including ED length of stay (LOS), ED assessment time, image acquisition time, and image interpretation time were calculated. Time ratio (TR) was used to compare these time intervals between the groups. RESULTS: A total of 45,177 ED visits comprising 4730 trauma cases, 5475 altered mental status cases, 11,925 cases with head pain, and 23,047 cases with other indications were included. Females had significantly longer ED LOS, ED assessment time, and image acquisition time (TR = 1.012, 1.051, 1.018, respectively, P-value < 0.05). This disparity was more pronounced in female patients with head pain complaints compared to their male counterparts (TR = 1.036, 1.059, and 1.047, respectively, P-value < 0.05). Black patients experienced significantly longer ED LOS, image acquisition time, and image assessment time (TR = 1.226, 1.349, and 1.190, respectively, P-value < 0.05). These disparities persisted regardless of head CT indications. Furthermore, patients with Medicare/Medicaid insurance also faced longer wait times in all the time intervals (TR > 1, P-value < 0.001). CONCLUSIONS: Wait times for ED head CT completion were longer for Black patients and Medicaid/Medicare insurance holders. Additionally, females experienced extended wait times, particularly when presented with head pain complaints. Our findings underscore the importance of exploring and addressing the contributing factors to ensure equitable and timely access to imaging services in the ED.

A Two-Stage Multi-Agent EV Charging Coordination Scheme for Maximizing Grid Performance and Customer Satisfaction.

Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact the performance of the electrical network in terms of power losses, voltage deviations and transformer overloads. This paper presents a two-stage multi-agent-based scheme for the coordinated charging scheduling of EVs. The first stage uses particle swarm optimization (PSO) at the distribution network operator (DNO) level to determine the optimal power allocation among the participating EV aggregator agents to minimize power losses and voltage deviations, whereas the second stage at the EV aggregator agents level employs a genetic algorithm (GA) to align the charging activities to achieve customers' charging satisfaction in terms of minimum charging cost and waiting time. The proposed method is implemented on the IEEE-33 bus network connected with low-voltage nodes. The coordinated charging plan is executed with the time of use (ToU) and real-time pricing (RTP) schemes, considering EVs' random arrival and departure with two penetration levels. The simulations show promising results in terms of network performance and overall customer charging satisfaction.

Non-Stationary Characteristics of AQM Based on the Queue Length.

We performed a non-stationary analysis of a class of buffer management schemes for TCP/IP networks, in which the arriving packets were rejected randomly, with probability depending on the queue length. In particular, we derived formulas for the packet waiting time (queuing delay) and the intensity of packet losses as functions of time. These results allow us to observe how the evolution of the waiting time and losses depend on initial conditions (e.g., the full buffer) and system parameters (e.g., dropping probabilities, load, packet size distribution). As side results, the stationary waiting time and packet loss probability were obtained. Numerical examples demonstrate applicability of the theoretical results.

Delay-Aware and Link-Quality-Aware Geographical Routing Protocol for UANET via Dueling Deep Q-Network.

In an unmanned aerial vehicles ad hoc network (UANET), UAVs communicate with each other to accomplish intricate tasks collaboratively and cooperatively. However, the high mobility of UAVs, the variable link quality, and heavy traffic loads can lead to difficulties in finding an optimal communication path. We proposed a delay-aware and link-quality-aware geographical routing protocol for a UANET via the dueling deep Q-network (DLGR-2DQ) to address these problems. Firstly, the link quality was not only related to the physical layer metric, the signal-to-noise ratio, which was influenced by path loss and Doppler shifts, but also the expected transmission count of the data link layer. In addition, we also considered the total waiting time of packets in the candidate forwarding node in order to decrease the end-to-end delay. Then, we modeled the packet-forwarding process as a Markov decision process. We crafted an appropriate reward function that utilized the penalty value for each additional hop, total waiting time, and link quality to accelerate the learning of the dueling DQN algorithm. Finally, the simulation results illustrated that our proposed routing protocol outperformed others in terms of the packet delivery ratio and the average end-to-end delay.

Impact of a Targeted Project for Shortening of Imaging Diagnostic Waiting Time in Patients with Suspected Oncological Diseases in Hungary.

Background and Objectives: Medical imaging is a key element in the clinical workup of patients with suspected oncological disease. In Hungary, due to the high number of patients, waiting lists for Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were created some years ago. The Municipality of Budapest and Semmelweis University signed a cooperation agreement with an extra budget in 2020 (HBP: Healthy Budapest Program) to reduce the waiting lists for these patients. The aim of our study was to analyze the impact of the first experiences with the HBP. Material and Methods: The study database included all the CT/MRI examinations conducted at Semmelweis University with a referral diagnosis of suspected oncological disease within the first 13 months of the HBP (6804 cases). In our retrospective, two-armed, comparative clinical study, different components of the waiting times in the oncology diagnostics pathway were analyzed. Using propensity score matching, we compared the data of the HBP-funded patients (n = 450) to those of the patients with regular care provided by the National Health Insurance Fund (NHIF) (n = 450). Results: In the HBP-funded vs. the NHIF-funded patients, the time interval from the first suspicion of oncological disease to the request for imaging examinations was on average 15.2 days shorter (16.1 vs. 31.3 days), and the mean waiting time for the CT/MRI examination was reduced by 13.0 days (4.2 vs. 17.2 days, respectively). In addition, the imaging medical records were prepared on average 1.7 days faster for the HBP-funded patients than for the NHIF-funded patients (3.4 vs. 5.1 days, respectively). No further shortening of the different time intervals during the subsequent oncology diagnostic pathway (histological investigation and multidisciplinary team decision) or in the starting of specific oncological therapy (surgery, irradiation, and chemotherapy) was observed in the HBP-funded vs. the NHIF-funded patients. We identified a moderately strong negative correlation (r = -0.5736, p = 0.0350) between the CT/MR scans requested and the active COVID-19 case rates during the pandemic waves. Conclusion: The waiting lists for diagnostic CT/MR imaging can be effectively shortened with a targeted project, but a more comprehensive intervention is needed to shorten the time from the radiological diagnosis, through the decisions of the oncoteam, to the start of the oncological treatment.