Proximity and waiting times in choice models for outpatient cardiological visits in Italy.
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ABSTRACT:
SUBMITTER: Seghieri C
PROVIDER: S-EPMC6117008 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In this work we present a group of theoretical models for reaction times arising from simple-choice task tests. In particular, we argue for the inclusion of a shifted version of the Gamma distribution as a theoretical model based on a mathematical result on first hitting times. We contrast the goodness-of-fit of those models with the Ex-Gaussian distribution, using data from recently published experiments. The evidence of the results obtained highlights the convenience of proposing theoretical models for reaction times instead of models acting exclusively as quantitative distribution measurements.
| S-EPMC6624227 | biostudies-literature
Project description:This paper aims to analyse the variation in the delivery of diagnostic imaging services in order to suggest possible solutions for the reduction of waiting times, increase the quality of services and reduce financial costs. This study provides a logic model to manage waiting times in a regional context. Waiting times measured per day were compared on the basis of the variability in the use rates of CT and MRI examinations in Tuscany for the population, as well as on the basis of the capacity offered with respect to the number of radiologists available. The analysis was performed at the local health authority level to support the decision-making process of local managers. Diagnostic imaging services, in particular the CT and MRI examinations. The study involved all the 12 local health authorities that provide services for 3.7 million inhabitants of the Italian Tuscany Region. the study uses regional administrative data on outpatients and survey data on inpatient diagnostic examinations in order to measure productivity. The study uses the volumes per 1000 inhabitants, the days of waiting times and the number of examinations per radiologist. Variability was measured using the traditional SD measures. A significant variation in areas considered homogeneous in terms of age, gender or mortality may indicate that the use of radiological services is not optimal and underuse or overuse occurs and that there is room for improvement in the service organisation. Considering that there is a high level of variability among district use rates and waiting times, this study provides managers with a specific tool to find the cause of the problem, identify a possible solution, assess the financial impact and initiate the eventual reduction of waste.
| S-EPMC3533122 | biostudies-literature
Project description:Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.
| S-EPMC5504658 | biostudies-other
Project description: Not available
| S-EPMC10835204 | biostudies-literature
Project description:(1) Background: System-level data on waiting time in the outpatient setting in Hungary is scarce. The objective of the study was to explore self-reported waiting time for an appointment and at a doctor's office. (2) Methods: An online, cross-sectional, self-administered survey was carried out in 2019 in Hungary among a representative sample (n = 1000) of the general adult population. Chi-squared test and logistic regression analysis were carried out to explore if socioeconomic characteristics, health status, or residence were associated with waiting times and the perception of waiting time as a problem. (3) Results: Proportions of 90%, 41%, and 64% of respondents were seen within a week by family doctor, public specialist, and private specialist, respectively. One-third of respondents waited more than a month to get an appointment with a public specialist. Respondents in better health status reported shorter waiting times; those respondents were less likely to perceive a problem with: (1) waiting time to get an appointment (OR = 0.400) and (2) waiting time at a doctor's office (OR = 0.519). (4) Conclusions: Longest waiting times were reported for public specialist visits, but waiting times were favorable for family doctors and private specialists. Further investigation is needed to better understand potential inequities affecting people in worse health status.
| S-EPMC7956329 | biostudies-literature
Project description:BackgroundLong waiting times result in low satisfaction. Although several methods are used to shorten the actual waiting time (AWT) in large hospitals of China, the outpatients still have a long actual waiting time. This study aimed to explore whether satisfaction could be improved by extending the expected waiting time (EWT) instead of shortening the AWT.MethodsIn October 2016, 257 students in grade one voluntarily participated in this study. They came from 6 classes, which were randomly divided into two groups: 3 classes comprised the control group (n?=?125) and 3 classes comprised the experimental group (n?=?132). Unfavorable information (UI) was given to the experimental group alone. Six distinct questionnaires were designed to explore the effects of UI on EWT and the effects of an extended EWT on satisfaction. Satisfaction scores ranged from 0 to 100: 0-25, very dissatisfied; 26-50, dissatisfied; 51-75, satisfied; 76-100, very satisfied. Each participant finished one of the 6 questionnaires online. Of the 257 questionnaires, 233 were valid.ResultsBefore UI was given, the initial EWT (T0) was similar between the control and experimental groups (Z?=?-1.924, P?=?0.054). Under the effects of UI, individuals in the experimental group extended their EWT (T1) from 121.0 to 180.0?min (Z?=?-6.367, P?<?0.001). Females prolonged their EWT longer than males did (Z?=?-2.239, P?=?0.025). Then, this study defined T0?=?1.5?h and T1?=?2.5?h, and compared the satisfaction scores between the control and experimental groups: a significant difference was found when AWT =2.0?h (t?=?-?3.568, P?=?0.001), but not when AWT =3.0?h (t?=?-?0.718, P?=?0.475) or when AWT =1.0?h (t?=?-?1.088, P?=?0.280). When AWT =3.0?h, fewer individuals felt "very dissatisfied" in the experimental group (21.2%) than in the control group (44.7%) (?2?=?4.368, P?=?0.037).ConclusionsEWT was found to be extended greatly by UI. An extended EWT could improve satisfaction scores.
| S-EPMC6693195 | biostudies-literature
Project description:The COVID-19 pandemic forced healthcare services organization to adjust to mutating healthcare needs. Not exhaustive data are available on the consequences of this on non-COVID-19 patients. The aim of this study was to assess the impact of the pandemic on non-COVID-19 patients living in a one-million inhabitants' area in Northern Italy (Bologna Metropolitan Area-BMA), analyzing time trends of Emergency Department (ED) visits, hospitalizations and mortality. We conducted a retrospective observational study using data extracted from BMA healthcare informative systems. Weekly trends of ED visits, hospitalizations, in- and out-of-hospital, all-cause and cause-specific mortality between December 1st, 2019 to May 31st, 2020, were compared with those of the same period of the previous year. Non-COVID-19 ED visits and hospitalizations showed a stable trend until the first Italian case of COVID-19 has been recorded, on February 19th, 2020, when they dropped simultaneously. The reduction of ED visits was observed in all age groups and across all severity and diagnosis groups. In the lockdown period a significant increase was found in overall out-of-hospital mortality (43.2%) and cause-specific out-of-hospital mortality related to neoplasms (76.7%), endocrine, nutritional and metabolic (79.5%) as well as cardiovascular (32.7%) diseases. The pandemic caused a sudden drop of ED visits and hospitalizations of non-COVID-19 patients during the lockdown period, and a concurrent increase in out-of-hospital mortality mainly driven by deaths for neoplasms, cardiovascular and endocrine diseases. As recurrencies of the COVID-19 pandemic are underway, the scenario described in this study might be useful to understand both the population reaction and the healthcare system response at the early phases of the pandemic in terms of reduced demand of care and systems capability in intercepting it.
| S-EPMC7984614 | biostudies-literature
Project description:BackgroundReinforcement learning models provide excellent descriptions of learning in multiple species across a variety of tasks. Many researchers are interested in relating parameters of reinforcement learning models to neural measures, psychological variables or experimental manipulations. We demonstrate that parameter identification is difficult because a range of parameter values provide approximately equal quality fits to data. This identification problem has a large impact on power: we show that a researcher who wants to detect a medium sized correlation (r = .3) with 80% power between a variable and learning rate must collect 60% more subjects than specified by a typical power analysis in order to account for the noise introduced by model fitting.New methodWe derive a Bayesian optimal model fitting technique that takes advantage of information contained in choices and reaction times to constrain parameter estimates.ResultsWe show using simulation and empirical data that this method substantially improves the ability to recover learning rates.Comparison with existing methodsWe compare this method against the use of Bayesian priors. We show in simulations that the combined use of Bayesian priors and reaction times confers the highest parameter identifiability. However, in real data where the priors may have been misspecified, the use of Bayesian priors interferes with the ability of reaction time data to improve parameter identifiability.ConclusionsWe present a simple technique that takes advantage of readily available data to substantially improve the quality of inferences that can be drawn from parameters of reinforcement learning models.
| S-EPMC8930195 | biostudies-literature
Project description:BackgroundAntenatal care (ANC) provides a range of critical health services during pregnancy that can improve maternal and neonatal health outcomes. In Mozambique, only half of women receive four or more ANC visits, which are provided for free at public health centers by maternal and child health (MCH) nurses. Waiting time has been shown to contribute to negative client experiences, which may be a driver of low maternity care utilization. A recent pilot study of a program to schedule ANC visits demonstrated that scheduling care reduces waiting time and results in higher rates of complete ANC. This study aims to explore client experiences with waiting time for ANC in standard practice and care and after the introduction of appointment scheduling.MethodsThis study uses a series of qualitative interviews to unpack client experiences with ANC waiting time with and without scheduled care, in order to better understand the impact of waiting time on client experiences. Thirty-eight interviews were collected in May to June 2017 at three pilot study clinics in southern Mozambique, with a focus on two paired intervention and comparison facilities sharing similar facility characteristics. Data were analyzed using inductive thematic analysis methods using NVivo software.ResultsClients described strong motivations to seek ANC, pointing to the need to address convenience of care, and highlighted direct and indirect costs of seeking care that were exacerbated by long waiting times. Direct costs include time and transport costs of going to the clinic, while indirect costs include being unable to fulfill household and work obligations. Other barriers to complete ANC utilization of four or more visits include transport costs, negative provider experiences, and delayed ANC initiation, which limit the potential number of clinic contacts.ConclusionsFindings demonstrate that the scheduling intervention improves the client experience of seeking care by allowing women to both seek ANC and fulfill other productive obligations. Innovation in healthcare delivery should consider adapting models that minimize waiting times.
| S-EPMC6670125 | biostudies-literature
Project description:BackgroundGlobally, reduction of patient waiting time has been identified as one of the major characteristics of a functional health system. In South Africa, 83% of the general population visiting primary healthcare (PHC) facilities must contend with long waiting times, overcrowding, staff shortages, poor quality of care, an ineffective appointment booking system, and a lack of medication. These experiences may, in turn, affect how patients view service quality.MethodsThis scoping review was guided by Arksey and O'Malley methodological framework. The primary literature search of peer-reviewed and review articles was achieved through PubMed/MEDLINE, Google Scholar, Science Direct, and World Health Organization (WHO) library databases, using waiting times, outpatient departments, factors, interventions, and primary healthcare facilities as keywords. Two independent reviewers screened abstracts and full articles, using the set inclusion and exclusion criteria. We used NVIVO® version 10 software to facilitate thematic analysis of the results from included studies.ResultsFrom the initial 250 records screened, nine studies were eligible for inclusion in this scoping review. Seven papers identified the factors contributing to waiting time, and five papers mentioned effective interventions implemented to reduce waiting times within PHC facilities. Our analysis produced three (patient factors, staff factors, and administrative systems) and two (manual-based waiting time reduction systems and electronic-based waiting time reduction systems) main themes pertaining to factors contributing to long waiting times and interventions to reduce waiting times, respectively.ConclusionOur results revealed that the patients, staff, and administrative systems all contribute to long waiting times within the PHC facilities. Patient waiting times recorded a wider and more evenly spread patient arrival pattern after the identified interventions in our study were implemented. There is a need to constantly strategize on measures such as implementing the use of an electronic appointment scheduling system and database, improving staff training on efficient patient flow management, and regularly assessing and optimizing administrative processes. By continuously monitoring and adapting these strategies, PHC facility managers can create a more efficient and patient-centered healthcare experience.
| S-EPMC11338458 | biostudies-literature