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Data comprise one of the key resources currently used in organizations. High-quality data are those that are appropriate for use by the customer. The quality of data is a key factor in determining the level of healthcare in hospitals, and its improvement leads to an improved quality of health and treatment and ultimately increases patient satisfaction. The purpose of this paper is to assess the quality of emergency patients’ information in a hospital information system.

This cross-sectional study was conducted on 385 randomly selected records of patients admitted to the emergency department of Shahid Beheshti Hospital in Kashan, Iran, in 2016. Data on five dimensions of quality, including accuracy, accessibility, timeliness, completeness and definition, were collected using a researcher-made checklist and were then analyzed in SPSS. The results are presented using descriptive statistics, such as frequency distribution and percentage.

The overall quality of emergency patients’ information in the hospital information system was 86 percent, and the dimensions of quality scored 87.7 percent for accuracy, 86.8 percent for completeness, 83.9 percent for timeliness, 79 percent for definition and 62.1 percent for accessibility.

Increasing the quality of patient information at emergency departments can lead to improvements in the timely diagnosis and management of diseases and patient and personnel satisfaction, and reduce hospital costs.

Emergency services are critical for high‐quality healthcare service provision to support acute illness, trauma and disaster response. The greater availability of emergency services decreases waiting time, improves clinical outcomes and enhances local community well being. This study aims to assess United States (US) acute care hospital staff's ability to provide emergency medical services by evaluating the number of emergency departments and trauma centers.

Data were obtained from the 2003 and 2007 American Hospital Association (AHA) annual surveys, which included over 5,000 US hospitals and provided extensive information on their infrastructure and healthcare capabilities.

US acute care hospital numbers decreased by 59 or 1.1 percent from 2003 to 2007. Similarly, US emergency rooms and trauma centers declined by 125, or 3 percent. The results indicate that US hospital staff's ability to respond to traumatic injury and disasters has declined. Therefore, US hospital managers need to increase their investment in emergency department beds as well as provide state‐of‐the‐art clinical technology to improve emergency service quality. These investments, when linked to other clinical information systems and the electronic medical record, support further healthcare quality improvement.

This research uses the AHA annual surveys, which represent self‐reported data by individual hospital staff. However, the AHA expends significant resources to validate reported information and the annual survey data are widely used for hospital research.

The declining US emergency rooms and trauma centers have negative implications for patients needing emergency services. More importantly, this research has significant policy implications because it documents a decline in the US emergency healthcare service infrastructure.

This article has important information on US emergency service availability in the hospital industry.

This paper aims to report on research undertaken in an National Health Service (NHS) emergency department in the north of England, UK, to identify which patients, with which clinical conditions are returning to the emergency department with an unscheduled return visit (URV) within seven days. This paper analyses the data in relation to the newly introduced Integrated Care Boards (ICBs). The continued upward increase in demand for emergency care services requires a new type of “upstreamist”, health system leader from the emergency department, who can report on URV data to influence the development of integrated care services to reduce further demand on the emergency department.

Patients were identified through the emergency department symphony data base and included patients with at least one return visit to emergency department (ED) within seven days. A sample of 1,000 index visits between 1 January 2019–31 October 2019 was chosen by simple random sampling technique through Excel. Out of 1,000, only 761 entries had complete data in all variables. A statistical analysis was undertaken using Poisson regression using NCSS statistical software. A review of the literature on integrated health care and its relationship with health systems leadership was undertaken to conceptualise a new type of “upstreamist” system leadership to advance the integration of health care.

Out of all 83 variables regressed with statistical analysis, only 12 variables were statistically significant on multi-variable regression. The most statistically important factor were patients presenting with gynaecological disorders, whose relative rate ratio (RR) for early-URV was 43% holding the other variables constant. Eye problems were also statistically highly significant (RR = 41%) however, clinically both accounted for just 1% and 2% of the URV, respectively. The URV data combined with “upstreamist” system leadership from the ED is required as a critical mechanism to identify gaps and inform a rationale for integrated care models to lessen further demand on emergency services in the ED.

At a time of significant pressure for emergency departments, there needs to be a move towards more collaborative health system leadership with support from statistical analyses of the URV rate, which will continue to provide critical information to influence the development of integrated health and care services. This study identifies areas for further research, particularly for mixed methods studies to ascertain why patients with specific complaints return to the emergency department and if alternative pathways could be developed. The success of the Esther model in Sweden gives hope that patient-centred service development could create meaningful integrated health and care services.

This research was a large-scale quantitative study drawing upon data from one hospital in the UK to identify risk factors for URV. This quality metric can generate important data to inform the development of integrated health and care services. Further research is required to review URV data for the whole of the NHS and with the new Integrated Health and Care Boards, there is a new impetus to push for this metric to provide robust data to prioritise the need to develop integrated services where there are gaps.

To the best of the authors’ knowledge, this is the first large-scale study of its kind to generate whole hospital data on risk factors for URVs to the emergency department. The URV is an important global quality metric and will continue to generate important data on those patients with specific complaints who return back to the emergency department. This is a critical time for the NHS and at the same time an important opportunity to develop “Esther” patient-centred approaches in the design of integrated health and care services.

The purpose of this paper is to observe and analyse the effects of the use of telemedicine in care homes on the use of acute hospital resources.

The study was an uncontrolled retrospective observational review of data on emergency hospital admissions and Emergency Department (ED) visits for care home residents in Airedale, Wharfedale and Craven. Acute hospital activity for residents was observed before and after the installation of telemedicine in 27 care homes. Data from a further 21 care homes that did not use telemedicine were used as a control group, using the median date of telemedicine installation for the “before and after” period. Patient outcomes were not considered.

Care homes with telemedicine showed a 39 per cent reduction in the costs of emergency admissions and a 45 per cent reduction in ED attendances after telemedicine installation. In the control group reductions were 31 and 31 per cent, respectively. The incremental difference in costs between the two groups of care homes was almost £1.2 million. The cost of telemedicine to care commissioners was £177,000, giving a return on investment over a 20-month period of £6.74 per £1 spent.

The results should be interpreted carefully. There is inherent bias as telemedicine was deployed in care homes with the highest use of acute hospital resources and there were some methodological limitations due to poor data. Nevertheless, controlling the data as much as possible and adopting a cautious approach to interpretation, it can be concluded that the use of telemedicine in these care homes was cost-effective.

There are very few telemedicine studies focused on care homes.

This paper aims to show that identification of expectations and software functional requirements via consultation with potential users is an integral component of the development of an emergency department patient admissions prediction tool.

Thematic analysis of semi‐structured interviews with 14 key health staff delivered rich data regarding existing practice and future needs. Participants included emergency department staff, bed managers, nurse unit managers, directors of nursing, and personnel from health administration.

Participants contributed contextual insights on the current system of admissions, revealing a culture of crisis, imbued with misplayed communication. Their expectations and requirements of a potential predictive tool provided strategic data that moderated the development of the Emergency Department Patient Admissions Prediction Tool, based on their insistence that it feature availability, reliability and relevance. In order to deliver these stipulations, participants stressed that it should be incorporated, validated, defined and timely.

Participants were envisaging a concept and use of a tool that was somewhat hypothetical. However, further research will evaluate the tool in practice.

Participants' unsolicited recommendations regarding implementation will not only inform a subsequent phase of the tool evaluation, but are eminently applicable to any process of implementation in a healthcare setting.

The consultative process engaged clinicians and the paper delivers an insider view of an overburdened system, rather than an outsider's observations.

The purpose of this paper is to determine whether trends in arrests for heroin, amphetamine‐type substances (ATS) and cocaine can be used as indicators of trends in the use of these drugs.

The question was addressed using ARIMA models to analyse the relationship between arrests and emergency department (ED) admissions for narcotics, amphetamine type substances (ATS) and cocaine.

Strong positive correlations were found for the narcotics and cocaine series between arrests and EDs in the same month (contemporaneous correlation) and between arrests in the current month and overdoses in earlier months (lagged correlation). The contemporaneous correlation between ATS arrests and EDs was slightly less strong than the lagged correlations at two and four months. A jump in ATS EDs, was followed by a jump in arrests in the same month and then two and four months later.

Arrests for narcotics use/possession, ATS use/possession and cocaine use/possession may in some circumstances provide useful intelligence about drug trends and/or a basis for evaluating the impact of police drug law enforcement activity on the use of narcotics, ATS and cocaine when other stronger measures of drug use are not available.

Efforts to evaluate local drug law enforcement activity on illicit drug use have been hampered by poor measures of trends in illicit drug use at small area levels. This is the only study the authors are aware of that has examined the long‐term relationship between illicit drug arrests and emergency department admissions for illicit drug use.

Predicting a patient's length of stay (LOS) in a hospital setting has been widely researched. Accurately predicting an individual's LOS can have a significant impact on a healthcare provider's ability to care for individuals by allowing them to properly prepare and manage resources. A hospital's productivity requires a delicate balance of maintaining enough staffing and resources without being overly equipped or wasteful. This has become even more important in light of the current COVID-19 pandemic, during which emergency departments around the globe have been inundated with patients and are struggling to manage their resources.

In this study, the authors focus on the prediction of LOS at the time of admission in emergency departments at Rhode Island hospitals through discharge data obtained from the Rhode Island Department of Health over the time period of 2012 and 2013. This work also explores the distribution of discharge dispositions in an effort to better characterize the resources patients require upon leaving the emergency department.

Objective. To quantify the association between obesity and somatic hospital costs and number of overall somatic hospital contacts – number of inpatient admissions, number of outpatient visits, and number of emergency department visits – based on anthropometric measurements of waist circumference (WC) and information from The National Patient Registry and The Danish Case-Mix System (DRG).

Participants. The study population consisted of two sub-samples from the Inter99 study at Research Centre for Prevention and Health in 1999–2001. One sub-sample used WC as an indicator for obesity (n=5,151), whereas the other used BMI as an indicator for obesity (n=4,048). Using WC, obesity was defined as WC > 102cm for men and > 88cm for women. Normal weight was defined as circumference < 94cm for men and < 80cm for women. Using BMI, obesity was defined as BMI > 30kg/m2, whereas individuals with BMI=18.5–24.9kg/m2 were defined as normal weight. Individuals with BMI < 18.5kg/m2 were excluded from both sub-samples.

Design. We undertook a 3-year retrospective study of the relationship between obesity and use of hospital resources. Data on hospital contacts and costs were obtained from The National Patient Registry and DRG. Analyses were performed using two-part models and Poisson regression. Outcome variables were costs and hospital contacts.

Results. This study has demonstrated that obese individuals have a greater use of hospital services and greater hospital costs compared with normal weight individuals. When using WC as an indicator for obesity, mean hospital costs were 33.8% greater among obese women and 45.3% greater among obese men in a 3-year period but the differences were not significant. When using BMI to measure obesity, obese men had significantly greater costs (57.5%) than normal weight men.

Furthermore, obese men and women (indicated byWC) had an increased number of hospital contacts compared with normal weight individuals (rate ratio 1.32, 95% CI 1.21–1.43 for men and 1.20, 95% CI 1.11–1.28 for women) including inpatient admissions, outpatient visits, and emergency department visits. The same trends were seen when obesity was indicated by BMI.

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions.

A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department’s performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients.

Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds.

This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

The purpose of this study/paper is to identify the importance of the partnership between the public and private health-care institutions to improve interhospital patient transfers. Scientific research and statistical data show the increased number of interhospital transportation services; therefore, timely and qualified patient transportation between different health-care institutions must be considered, the activity that directly and significantly impacts the patient’s health status and overall quality of the health-care services. The successful patient transportation from the smaller hospitals to the health-care institutions with advanced intensive care or urgent care units can be enhanced through the partnership between private and public health-care institutions.

The methodology included quantitative method, statistical data analysis and theoretical data generalization. Both primary and secondary data were collected and analyzed during the research. Expert quantification was performed using the survey research method. The survey was conducted in Lithuania. The respondents were selected to be the general managers of the health-care and urgent care institutions, the chief doctors of the reanimation and intensive care department also the chief doctors of the emergency department.

Because of the centralization and regionalization of health-care services, the number of patients transferred between hospitals by the emergency medical services (EMS) and personal health-care institutions has increased. University hospitals are not sufficiently prepared to accept an increasing flow of patients in accordance with the Ministry of Health orders. Not all regional or district hospitals have the right to provide such assistance, which increases transportation time and costs as well as requires additional human resources. The five EMS categories could be used to improve the patient transfer between different levels of health-care institutions. To increase partnership between private and public health-care organizations, incentives should be provided for the development of private health-care organizations, as well as encouraging actions should be taken to increase the demand for private health-care services by Lithuanian patients.

Five EMS categories identified in this paper could be used to ensure a smooth mechanism for the patient transfer between different levels of the personal health-care institutions. The proposed categories should also be used in the pre-stationary emergency phase (for reducing the interhospital patient transportation amount).

Properly organized secondary and tertiary interhospital patient transfers influence the availability and quality of the EMS and reduce inequalities in the provided services and social exclusion.

This paper presents the classification of the interhospital transfer issues, determines the main reasons for the patient interhospital transfer, creates the model for the EMS patient process flows and defines five EMS categories for the assessment of patient conditions. Therefore, the research conducted and the results obtained have both theoretical and social-practical value.