Mitigating Emergency Department Crowding With Stochastic Population Models

Environments such as shopping malls, airports, or hospital emergency departments often experience crowding, with many people simultaneously requesting service. Crowding is highly noisy, with sudden overcrowding "spikes". Past research has either focused on average behavior or used context-specific non-generalizable models. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding, using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) reduces severe overcrowding events by 50%. Such forecasting is crucial in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many natural systems.Comment: 21 pages, 6 figures + Supplementary informatio

What Does It Mean to Be a College Student for Youth with Intellectual Disabilities? : A Survey on Department of Rehabilitation Independence of Korea Nazarene University

<strong>Background:</strong> Governments in several countries are facing problems concerning the accountability of regulators in health care. Questions have been raised about how patients’ complaints should be valued in the regulatory process. However, it is not known what patients who made complaints expect to achieve in the process of health-care quality regulation. <strong>Objective:</strong> To assess expectations and experiences of patients who complained to the regulator. Design Interviews were conducted with 11 people, and a questionnaire was submitted to 343 people who complained to the Dutch Health-care Inspectorate. The Inspectorate handled 92 of those complaints. This decision was based on the idea that the Inspectorate should only deal with complaints that relate to ‘structural and severe’ problems. <strong>Results:</strong> The response rate was 54%. Self-reported severity of physical injury of complaints that were not handled was significantly lower than of complaints that were. Most respondents felt that their complaint indicated a structural and severe problem that the Inspectorate should act upon. The desire for penalties or personal satisfaction played a lesser role. Only a minority felt that their complaint had led to improvements in health-care quality. <strong>Conclusions:</strong> Patients and the regulator share a common goal: improving health-care quality. However, patients’ perceptions of the complaints’ relevance differ from the regulator’s perceptions. Regulators should favour more responsive approaches, going beyond assessing against exclusively clinical standards to identify the range of social problems associated with complaints about health care. Long-term learning commitment through public participation mechanisms can enhance accountability and improve the detection of problems in health care. (aut. ref.

Patient-centered insights: using health care complaints to reveal hot spots and blind spots in quality and safety

Health care complaints contain valuable data on quality and safety; however, there is no reliable method of analysis to unlock their potential. We demonstrate a method to analyze health care complaints that provides reliable insights on hot spots (where harm and near misses occur) and blind spots (before admissions, after discharge, systemic and low‐level problems, and errors of omission). Systematic analysis of health care complaints can improve quality and safety by providing patient‐centered insights that localize issues and shed light on difficult‐to‐monitor problems. Context The use of health care complaints to improve quality and safety has been limited by a lack of reliable analysis tools and uncertainty about the insights that can be obtained. The Healthcare Complaints Analysis Tool, which we developed, was used to analyze a benchmark national data set, conceptualize a systematic analysis, and identify the added value of complaint data. Methods We analyzed 1,110 health care complaints from across England. “Hot spots” were identified by mapping reported harm and near misses onto stages of care and underlying problems. “Blind spots” concerning difficult‐to‐monitor aspects of care were analyzed by examining access and discharge problems, systemic problems, and errors of omission. Findings The tool showed moderate to excellent reliability. There were 1.87 problems per complaint (32% clinical, 32% relationships, and 34% management). Twenty‐three percent of problems entailed major or catastrophic harm, with significant regional variation (17%‐31%). Hot spots of serious harm were safety problems during examination, quality problems on the ward, and institutional problems during admission and discharge. Near misses occurred at all stages of care, with patients and family members often being involved in error detection and recovery. Complaints shed light on 3 blind spots: (1) problems arising when entering and exiting the health care system; (2) systemic failures pertaining to multiple distributed and often low‐level problems; and (3) errors of omission, especially failure to acknowledge and listen to patients raising concerns. Conclusions The analysis of health care complaints reveals valuable and uniquely patient‐centered insights on quality and safety. Hot spots of harm and near misses provide an alternative data source on adverse events and critical incidents. Analysis of entry‐exit, systemic, and omission problems provides insight on blind spots that may otherwise be difficult to monitor. Benchmark data and analysis scripts are downloadable as supplementary files

Estimating emergency department crowding with stochastic population models.

Environments such as shopping malls, airports, or hospital emergency-departments often experience crowding, with many people simultaneously requesting service. Crowding highly fluctuates, with sudden overcrowding "spikes". Past research has either focused on average behavior, used context-specific models with a large number of parameters, or machine-learning models that are hard to interpret. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding. We test the model using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) cuts the probability of severe overcrowding events by 50%. Such forecasting is critical in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many systems in nature

Israeli students’ perceptions regarding sperm donation: dilemmas reflections with dominant demographic effect

Abstract Background Sperm donation has undergone significant medical and social transformations in recent decades. This study aimed to explore Israeli students’ perceptions towards sperm donation and investigate the potential influence of demographic characteristics on these perceptions. Design The study encompassed 254 students from Tel-Aviv University, who completed an anonymous online survey in January–February 2021. This cross-sectional quantitative online survey, comprised 35 questions categorized into three sections: demographic data, assessment of prior knowledge, and perceptions of sperm donation (general perceptions related to both positive and negative stigmas associated with sperm donation, the roles and activities of sperm banks, and considerations surrounding identity disclosure versus the anonymity of sperm donors and their offspring). Results Participants exhibited a relatively low level of prior knowledge (mean 31.2 ± 19 of 100). Scores for positive and negative stigmas ranged from 1.3 to 2.2. Notably, the statement “Donors’ anonymity preservation is crucial to maintain sperm donation” received a mean of 3.7. Seeking for anonymous sperm donation identity both by recipients and offspring was ranked with low means (1.5 and 1.7, respectively). However, the pursuit of half-siblings by mothers or siblings themselves received higher ratings ranging from 2.7 to 3. Women’s stigma ranking were notably lower, while men emphasized the importance of donor anonymity. Conclusions Sperm Banks hold a position of medical authority rather than being perceived as being commercial entity. The preservation of donor anonymity is widely accepted as a crucial element, prioritized over the requests for identity disclosure from recipients and offspring. Demographic parameters exhibit a strong and precise effects on participants’ perceptions