The embeddedness of organizational performance: multiple membership multiple classification models for the analysis of multilevel networks

We present a Multiple Membership Multiple Classification (MMMC) model for analysing variation in the performance of organizational sub-units embedded in a multilevel network. The model postulates that the performance of organizational sub-units varies across network levels defined in terms of: (i) direct relations between organizational sub-units; (ii) relations between organizations containing the sub-units, and (iii) cross-level relations between sub-units and organizations. We demonstrate the empirical mer- its of the model in an analysis of inter-hospital patient mobility within a regional community of health care organizations. In the empirical case study we develop, organizational sub-units are departments of emergency medicine (EDs) located within hospitals (organizations). Networks within and across levels are delineated in terms of patient transfer relations between EDs (lower-level, emergency transfers), hospitals (higher-level, elective transfers), and between EDs and hospitals (cross-level, non-emergency transfers). Our main analytical objective is to examine the association of these interdependent and par- tially nested levels of action with variation in waiting time among EDs – one of the most commonly adopted and accepted measures of ED performance. We find evidence that variation in ED waiting time is associated with various components of the multilevel network in which the EDs are embedded. Before allowing for various characteristics of EDs and the hospitals in which they are located, we find, for the null models, that most of the network variation is at the hospital level. After adding these characteris- tics to the model, we find that hospital capacity and ED uncertainty are significantly associated with ED waiting time. We also find that the overall variation in ED waiting time is reduced to less than a half of its estimated value from the null models, and that a greater share of the residual network variation for these models is at the ED level and cross level, rather than the hospital level. This suggests that the covari- ates explain some of the network variation, and shift the relative share of residual variation away from hospital networks. We discuss further extensions to the model for more general analyses of multilevel network dependencies in variables of interest for the lower level nodes of these social structures

The development of a volunteer resource manual in the emergency department

Background & Purpose Satisfaction plays a pivotal role in patients’ overall perception about their health care experience (Ontario Hospital Association, 2010/2011). Patient satisfaction within the Emergency Department (ED) is largely dependent on wait times, awareness regarding wait times, and communication from ED staff (Ontario Hospital Association, 2010/2011). Unfortunately, ED wait times are lengthy and staff are challenged with meeting the communication needs of the patients (Ontario Hospital Association, 2010/2011). The current literature has revealed that volunteer programs in waiting rooms have demonstrated insurmountable improvements in patient satisfaction (Lorhan, van der Westhuizen, & Gossman, 2015; Stone & Lammers, 2012). However, a volunteer program in the HSC ED waiting room is yet to exist due to limited training for the volunteers. Therefore the development of a volunteer resource manual that can be utilized in the training of volunteers in the ED waiting room is a strategy to address this issue. Methods 1.Literature review 2. Consultations with key informants 3. Environmental scan Results & Next Steps The results of the literature review and consultations reiterated the importance of establishing a volunteer program within the HSC ED waiting room to improve patient satisfaction. A needs-based resource manual was developed for the volunteers to utilize during their volunteer experience in the ED waiting room. Future goals include the implementation of a volunteer program within the HSC ED waiting room

Waiting times in emergency departments: A resource allocation or an efficiency issue?

Background: In recent years, the flow of patients to the Emergency Departments (ED) of Western countries has steadily increased, thus generating overcrowding and extended waiting times. Scholars have identified four main causes for this phenomenon, related to: continuity of primary care services; availability of specific clinical pathways for chronic patients; ED's personnel endowment; organization of the ED. This study aims at providing a logical diagnostic framework to support managers in investigating specific solutions to be applied to their EDs to cope with high ED waiting times. The framework is based on the ED waiting times and ED admission rate matrix. It was applied to the Tuscan EDs as illustrative example. Methods: To provide the factors to be analyzed once the EDs are positioned into the matrix, a list of issues has been identified. The matrix was applied to Tuscan EDs. Data were collected from the Tuscan performance evaluation system, integrated with specific data on Tuscan EDs' personnel. The Tuscan EDs matrix, the descriptive statistics for each quadrant and the Spearman's rank correlation analysis among waiting times, admission rates and a set of performance indicators were conducted to help managers to read the phenomena that they need to investigate. Results: The combined reading of the correlations and waiting times-admission rates matrix shows that there are no optimal rules for all the EDs in managing admission rates and waiting times, but solutions have to be found considering mixed and personalized strategies. Conclusions: The waiting times-admission rates matrix provides a tool able to support managers in detecting the problems related to the management of ED services. In particular, using this matrix, healthcare managers could be facilitated in the identification of possible solutions for their specific situation

Predictive analysis in healthcare: emergency wait time prediction

Emergency departments are an important area of a hospital, being the major entry point to the healthcare system. One of the most important issues regarding patient experience are the emergency department waiting times. In order to help hospitals improving their patient experience, the authors will perform a study where the Random Forest algorithm will be applied to predict emergency department waiting times. Using data from a Portuguese hospital from 2013 to 2017, the authors discretized the emergency waiting time in 5 different categories: “Really Low”, “Low”, “Average”, “High”, “Really High”. Plus, the authors considered as waiting time, the time from triage to observation. The authors expect to correctly evaluate the proposed classification algorithm efficiency and accuracy in order to be able to conclude if it is valuable when trying to predict ED waiting times.info:eu-repo/semantics/acceptedVersio

Optimizing Emergency Department Throughput Using Best Practices to Improve Patient Flow

Emergency Department (ED) crowding and bottle necks are the reality of hospitals across the country. Patients seeking care and needing inpatient beds via the emergency rooms are facing delays with attaining the right level of care. Orchestrating a patient through an ED admission requires a multidisciplinary effort to provide safe, effective and efficient care. This quality improvement project conducted in a tertiary acute care hospital focused on Centers for Medicare and Medicaid metrics to measure Emergency Department (ED) throughput. This multidisciplinary initiative focused on reducing time stamps for patient arrival to the ED through departure to hospital or home. Outcomes showed a significant decrease in the time frame for patient arrival to being seen by a qualified provider, left without being seen rates, ED diversion, and ancillary department turnaround times. The interventions can be applied at other hospital based emergency departments

Crowding and Delivery of Healthcare in Emergency Departments: The European Perspective

Emergency department (ED) crowding is a multifactorial problem, resulting in increased ED waiting times, decreased patient satisfaction and deleterious domino effects on the entire hospital. Although difficult to define and once limited to anecdotal evidence, crowding is receiving more attention as attempts are made to quantify the problem objectively. It is a worldwide phenomenon with regional influences, as exemplified when analyzing the problem in Europe compared to that of the United States. In both regions, an aging population, limited hospital resources, staff shortages and delayed ancillary services are key contributors; however, because the structure of healthcare differs from country to country, varying influences affect the issue of crowding. The approach to healthcare delivery as a right of all people, as opposed to a free market commodity, depends on governmental organization and appropriation of funds. Thus, public funding directly influences potential crowding factors, such as number of hospital beds, community care facilities, and staffing. Ultimately ED crowding is a universal problem with distinctly regional root causes; thus, any approach to address the problem must be tailored to regional influences

Gender Differences in Smoking Among an Urban Emergency Department Sample.

BackgroundUrban emergency department (ED) patients have elevated smoking and substance use compared with the general population. We analyzed gender differences in smoking among an urban ED sample and assessed the contribution of substance use, demographic, and couple factors.MethodsWe conducted a secondary analysis of data obtained from a cross-sectional, observational survey (N = 1037 participants) on drinking, drug use, and intimate partner violence (IPV). Gender-specific logistic regression models for current (past 30-day) smoking and multinomial regression models for smoking intensity (light: ⩽5 cigarettes per day [CPD]; moderate: 6 to 10 CPD; heavier: >10 CPD) were estimated.ResultsSmoking prevalence was higher among men than women (35.5% vs 18.9%; P < .001). Substance use (frequency of intoxication, marijuana, amphetamine, and cocaine use), demographic (food insufficiency, unemployment), and couple-related factors (having a spouse/partner who smoked, IPV involvement, being in a same-gender couple) were differentially associated with current smoking and level of intensity among men and women.ConclusionsEmergency department staff should consider the impact of polysubstance use, food insufficiency, unemployment, and whether both partners in the couple smoke when screening patients for smoking and formulating cessation treatment plans. Women in same-gender relationships and those who have experienced IPV involvement may require additional referral

The relationship between in-hospital location and outcomes of care in patients of a large general medical service

Background: The discrepancy between the number of admissions and the allocation of hospital beds means many patients admitted under the care of a general medical service can be placed in other departments’ wards. These patients are called “outliers” and their outcomes are unknown. Aims: To examine the relation between the proportion of time each patient spent in their “home ward” during an index admission and the outcomes of that hospital stay. Methods: Data from Flinders Medical Centre’s (FMC) patient journey database were extracted and analysed. The analysis was carried out on the patient journeys of patients admitted under the General Medicine units. Results: Outlier patients’ length of stay (LOS) was significantly shorter than that of the inlier patients (110.7 hours cf 141.9 hours; p < 0.001).They had a reduced risk of readmission within 28 days of discharge from hospital. Outlier patients’ discharge summaries were less likely to be completed within a week (64.3% cf 78.0%; p < 0.001). Being an outlier patient increased the risk-adjusted risk of in-hospital mortality by over 40%. 50% of deaths in the outlier group occurred within 48 hours of admission. Outlier patients had spent longer in the Emergency Department (ED) waiting for a bed (6.3 hours cf 5.3 hours; p < 0.001) but duration of ED stay was not an independent predictor of mortality risk. Conclusion: Outlier patients had significantly shorter LOS in hospital, but significantly greater in-patient death rates. Surviving outlier patients had lower rates of readmission but lower rates of discharge summary completion

Improving waiting times in the Emergency Department

Waiting times in the Emergency Department cause considerable delays in care and in patient satisfaction. There are many moving parts to the ED visit with multiple providers delivering care for a single patient. Factors that have been shown to delay care in the ED have been broken down into input factors such as triaging, throughput factors during the visit, and output factors, which include discharge planning and available inpatient beds for admitted patients. Research has shown that throughput factors are an area of interest to decrease time spent in the ED that will lead to decrease waiting room times. In this Quality Improvement project, we will develop a systematic check in system with ED providers that will allow providers to identify any outstanding issues that may be delaying care or discharge. We hypothesize that this system will increase throughput in the ED by resolving any lab, radiology, or treatments that were overlooked. Reviewing the results of this QI project will allow us to see if we were effective in our timing of scheduled check-ins. Ultimately, this will reduce time spent in the waiting room by allowing more patients to be seen. In the era of the Affordable Care Act, more patients have access to affordable healthcare and will increase volume in the ED. This check-in system will allow more patients to be seen smoothly and in a timely manner that will improve and increase patient care and satisfaction in the ED