Time Well Spent: Patient Industry and Occupation Data Collection in Emergency Departments.
Ontology highlight
ABSTRACT: Objective
No comprehensive national system tracking work-related diseases and injuries exists in the United States. Industry and occupation (I/O) are the missing data elements that would make existing healthcare data useful for occupational health. The authors previously petitioned the National Uniform Billing Committee (NUBC) to adopt I/O standards for states to consider during their healthcare data rulemaking processes.Methods
The NUBC asked for a pilot study to ascertain the potential burden. The time and cost to ask I/O questions in two hospital emergency departments was evaluated.Results
Asking four I/O questions required 48?seconds on average and cost between $520 and $623 per Registrar per year. The annual cost for the two hospitals to gather I/O on every patient was $4160 and $15,000.Conclusions
We conclude no undue burden compared with the estimated $250 billion cost of occupational illnesses and injuries.
SUBMITTER: Taylor JA
PROVIDER: S-EPMC5540352 | biostudies-literature | 2017 Aug
REPOSITORIES: biostudies-literature
Publications
Time Well Spent: Patient Industry and Occupation Data Collection in Emergency Departments.
Taylor Jennifer A JA Widman Shannon A SA James Samantha J SJ Green-McKenzie Judith J McGuire Cydney C Harris Erica J EJ
Journal of occupational and environmental medicine 20170801 8
<h4>Objective</h4>No comprehensive national system tracking work-related diseases and injuries exists in the United States. Industry and occupation (I/O) are the missing data elements that would make existing healthcare data useful for occupational health. The authors previously petitioned the National Uniform Billing Committee (NUBC) to adopt I/O standards for states to consider during their healthcare data rulemaking processes.<h4>Methods</h4>The NUBC asked for a pilot study to ascertain the p ...[more]
Similar Datasets
Project description:The triage process in emergency departments (EDs) relies on the subjective assessment of medical practitioners, making it unreliable in certain aspects. There is a need for a more accurate and objective algorithm to determine the urgency of patients. This paper explores the application of advanced data-synthesis algorithms, machine learning (ML) algorithms, and ensemble models to predict patient mortality. Patients predicted to be at risk of mortality are in a highly critical condition, signifying an urgent need for immediate medical intervention. This paper aims to determine the most effective method for predicting mortality by enhancing the F1 score while maintaining high area under the receiver operating characteristic curve (AUC) score. This study used a dataset of 7325 patients who visited the Yonsei Severance Hospital's ED, located in Seoul, South Korea. The patients were divided into two groups: patients who deceased in the ED and patients who didn't. Various data-synthesis techniques, such as SMOTE, ADASYN, CTGAN, TVAE, CopulaGAN, and Gaussian Copula, were deployed to generate synthetic patient data. Twenty two ML models were then utilized, including tree-based algorithms like Decision tree, AdaBoost, LightGBM, CatBoost, XGBoost, NGBoost, TabNet, which are deep neural network algorithms, and statistical algorithms such as Support Vector Machine, Logistic Regression, Random Forest, k-nearest neighbors, and Gaussian Naive Bayes, as well as Ensemble Models which use the results from the ML models. Based on 21 patient information features used in the pandemic influenza triage algorithm (PITA), the models explained previously were applied to aim for the prediction of patient mortality. In evaluating ML algorithms using an imbalanced medical dataset, conventional metrics like accuracy scores or AUC can be misleading. This paper emphasizes the importance of using the F1 score as the primary performance measure, focusing on recall and specificity in detecting patient mortality. The highest-ranked model for predicting mortality utilized the Gaussian Copula data-synthesis technique and the CatBoost classifier, achieving an AUC of 0.9731 and an F1 score of 0.7059. These findings highlight the effectiveness of machine learning algorithms and data-synthesis techniques in improving the prediction performance of mortality in EDs.
| S-EPMC10497596 | biostudies-literature
Project description:BackgroundThis paper describes and compares patient flow characteristics of adult high system users (HSUs) and control groups in Alberta and Ontario emergency departments (EDs), Canada.MethodsAnnual cohorts of HSUs were created by identifying patients who made up the top 10% of ED users (by count of ED presentations) in the National Ambulatory Care Reporting System during 2011-2016. Random samples of patients not in the HSU groups were selected as controls. Presentation (e.g., acuity) and ED times (e.g., time to physician initial assessment [PIA], length of stay) data were extracted and described. The length of stay for 2015/2016 data was decomposed into stages and Cox models compared time between stages.ResultsThere were 20,343,230 and 18,222,969 ED presentations made by 7,032,655 and 1,923,462 individuals in the control and HSU groups, respectively. The Ontario groups had higher acuity than the Alberta groups: about 20% in the Ontario groups were from the emergent level whereas Alberta had 11-15%. Time to PIA was similar across provinces and groups (medians of 60 min to 67 min). Lengths of stay were longest for Ontario HSUs (median = 3 h) and shortest for Alberta HSUs (median = 2.2 h). HSUs had shorter times to PIA (hazard ratio [HR] = 1.03; 95% confidence interval [CI] 1.02,1.03), longer times from PIA to decision (HR = 0.84; 95%CI 0.84,0.84), and longer times from decision to leaving the ED (HR = 0.91; 95%CI 0.91,0.91).ConclusionsOntario HSUs had higher acuity and longer ED lengths of stay than the other groups. In both provinces, HSU had shorter times to PIA and longer times after assessment.
| S-EPMC7552544 | biostudies-literature
Project description: Not available
| S-EPMC6701034 | biostudies-literature
Project description:BackgroundWith the aging society, the number of emergency transportations has been growing. Although it is important that a patient be immediately transported to an appropriate hospital for proper management, accurate diagnosis in the prehospital setting is challenging. However, at present, patient information is mainly communicated by telephone, which has a potential risk of communication errors such as mishearing. Sharing correct and detailed prehospital information with emergency departments (EDs) should facilitate optimal patient care and resource use. Therefore, the implementation of an app that provides on-site, real-time information to emergency physicians could be useful for early preparation, intervention, and effective use of medical and human resources.ObjectiveIn this paper, we aimed to examine whether the implementation of a mobile app for emergency medical service (EMS) would improve patient outcomes and reduce transportation time as well as communication time by phone (ie, phone-communication time).MethodsWe performed an interrupted time-series analysis (ITSA) on the data from a tertiary care hospital in Japan from July 2021 to October 2021 (8 weeks before and 8 weeks after the implementation period). We included all patients transported by EMS. Using the mobile app, EMS can send information on patient demographics, vital signs, medications, and photos of the scene to the ED. The outcome measure was inpatient mortality and transportation time, as well as phone-communication time, which was the time for EMS to negotiate with ED staffs for transport requests.ResultsDuring the study period, 1966 emergency transportations were made (n=1033, 53% patients during the preimplementation period and n=933, 47% patients after the implementation period). The ITSA did not reveal a significant decrease in patient mortality and transportation time before and after the implementation. However, the ITSA revealed a significant decrease in mean phone-communication time between pre- and postimplementation periods (from 216 to 171 seconds; -45 seconds; 95% CI -71 to -18 seconds). From the pre- to postimplementation period, the mean transportation time from EMS request to ED arrival decreased by 0.29 minutes (from 36.1 minutes to 35.9 minutes; 95% CI -2.20 to 1.60 minutes), without change in time trends. We also introduced cases where the app allowed EMS to share accurate and detailed prehospital information with the emergency department, resulting in timely intervention and reducing the burden on the ED.ConclusionsThe implementation of a mobile app for EMS was associated with reduced phone-communication time by 45 seconds (22%) without increasing mortality or overall transportation time despite the implementation of new methods in the real clinical setting. In addition, real-time patient information sharing, such as the transfer of monitor images and photos of the accident site, could facilitate optimal patient care and resource use.
| S-EPMC9301553 | biostudies-literature
Project description:BackgroundTriage system is a sorting system that categorizes patients on the basis of the severity of their condition and the availability of the resources in the emergency department. There has been little attention in the public literature to triage systems in Sudan. The aim of this study was to explore the triage system and identify the barriers in its application in hospitals in Sudan.MethodsA cross-sectional hospital based study was conducted at eight hospitals in Khartoum during December 2020. A multi-stage cluster sampling was applied. Data were obtained by interviewing emergency department staff using a structured questionnaire. The data were analyzed using statistical package for social sciences to find the association between various variables by chi-square test.ResultsMost of the respondents stated that the triage system was deficient. Most of the participants of this study agreed that the role played by the administration in taking legislative decisions is crucial in improving the triage system. Among the factors found to be significant to a well-functioning triage system were, the need for substantial capital expenditure, p-value: 0.026, prudent legislative decisions, p-value: 0.026, adequate training of staff on means of performing efficient triaging, p-value: 0.007 and raising the awareness of the staff on the correct application of triage guidelines, p-value: 0.017.ConclusionCurrently there is no formal triage system in the State of Khartoum and has yet to be established. Policy making by administrators will play an important role in its implementation. It is suggested that prompt executive orders on improving the current triage system in Khartoum, should be carried out sooner than later, as the ripple effects of a well-functioning triage will decrease the average length of stay, mortality and morbidity rates and will eventually increase the patient's satisfaction.
| S-EPMC8822826 | biostudies-literature
Project description:Acute myeloid leukemia (AML) is the most common type of leukemia found in adults. Identifying jobs that pose a risk for AML may be useful for identifying new risk factors. A matched case-control analysis was conducted using California Cancer Registry data from 1988 to 2007. This study included 8999 cases of AML and 24 822 controls. Industries with a statistically significant increased AML risk were construction (matched odds ratio [mOR] = 1.13); crop production (mOR = 1.41); support activities for agriculture and forestry (mOR = 2.05); and animal slaughtering and processing (mOR = 2.09). Among occupations with a statistically significant increased AML risk were miscellaneous agricultural workers (mOR = 1.76); fishers and related fishing workers (mOR = 2.02); nursing, psychiatric and home health aides (mOR = 1.65); and janitors and building cleaners (mOR = 1.54). Further investigation is needed to confirm study findings and to identify specific exposures responsible for the increased risks.
| S-EPMC4534715 | biostudies-literature
Project description:BackgroundEmergency care in Germany is in transition. Emergency departments (EDs) treat their patients based on symptoms and acuity. However, this perspective is not reflected in claims data. The aim of the AKTIN project was to establish an Emergency Department Data Registry as a data privacy-compliant infrastructure for the use of routine medical data.MethodsData from the respective documentation systems are continuously transmitted to local data warehouses using a standardized interface. They are available for several applications such as internal reports but also multicentre studies, in compliance with data privacy regulations. Based on a 12-months period we evaluate the population with focus on acuity assessment (triage) and vital parameters in combination with presenting complaints.ResultsFor the period April 2018 to March 2019, 436,149 cases from 15 EDs were available. A triage level is documented in 86.0% of cases, and 70.5% were triaged within 10 min of arrival. Ten EDs collected a presenting complaint regularly (82.3%). The respective documentation of vital signs shows plausible patterns.ConclusionsThe AKTIN registry provides an almost real-time insight into German EDs, regardless of the primary documentation system and health insurance claims data. The Federal Joint Committee's requirements are largely met. Standardized presenting complaints allow for symptom-based analyses as well as health surveillance.
| S-EPMC7750913 | biostudies-literature
Project description:ObjectivesThis study aims to describe the use of patient partnership, as defined by the Montreal Model, in emergency departments (EDs) in France and report the perception of patient partnership from both the practitioner and patient perspectives.MethodsThis cross-sectional study was conducted between July 2020 and October 2020. First, a survey was sent to 146 heads of EDs in both teaching hospitals and non-teaching hospitals in France to assess the current practices in terms of patient partnership in service organization, research, and teaching. The perceived barriers and facilitators of the implementation of such an approach were also recorded. Then, semi-structured telephone interviews were carried out with patients involved in a service re-organization project.ResultsA total of 48 answers (response rate 32.9%) to the survey were received; 33.3% of respondents involved patients in projects relating to service re-organization, 20.8% involved patients in teaching projects, and 4.2% in research projects. Overall, 60.4% of the respondents were willing to involve patients in re-organization or teaching projects. The main barriers mentioned for establishing patient partnership were difficulties regarding patient recruitment and lack of time. The main advantages mentioned were the improvement in patient/caregiver relationship and new ideas to improve healthcare. When interviewed, patients mentioned the desire to improve healthcare and the necessity to involve people with different profiles and backgrounds. A too important personal commitment was the most frequently raised barrier to their engagement. All the patients recognized their positive role, and more generally, the positive role of patient engagement in service re-organization.ConclusionAlthough this preliminary study indicates a rather positive perception of patient partnership among heads of EDs in France and partner patients, this approach is still not widely applied in practice.
| S-EPMC10463322 | biostudies-literature
Project description:BackgroundAccident and Emergency Department (AED) is the frontline of providing emergency care in a hospital and research focusing on improving decision-makings and service level around AED has been driving a rising number of attentions in recent years. A retrospective review among the published papers shows that related research can be classified according to six planning modules: demand forecasting, days-off scheduling, shift scheduling, line-of-work construction, task assignment and staff assignment. As patient arrivals demand forecasts enable smooth AED operational planning and help decision-making, this article conducted a systematic review on the statistical modelling approaches aimed at predicting the volume of AED patients' arrival.MethodsWe carried out a systematic review of AED patient arrivals prediction studies from 2004 to 2021. The Medline, ScienceDirect, and Scopus databases were searched. A two-step screening process was carried out based on the title and abstract or full text, and 35 of 1,677 articles were selected. Our methods and results follow the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. We categorise AED methods for modelling patient arrivals into four main classes: regression, time series, artificial intelligence and time series regression. Choice of prediction model, selection of factors and model performance are compared. Finally, we discuss the advantages and limitations of the models and suggest future research directions.ResultsA total of 1,677 papers that fulfilled the initial searching criteria was obtained from the three databases. Based on the first exclusion criteria, 1,603 articles were eliminated. The remaining 74 full text articles were evaluated based on the second exclusion criteria. Finally, 35 articles were selected for full review. We find that the use of artificial intelligence-based model has risen in recent years, from the view of predictive model selection. The calendar-based factors are most commonly used compared with other types of dependent variables, from the view of dependent variable selection.ConclusionsAll AEDs are inherently different and different covariables may have different effects on patient arrivals. Certain factors may play a key role in one AED but not others. Based on results of meta-analysis, when modelling patient arrivals, it is essential to understand the actual AED situation and carefully select relevant dominating factors and the most suitable modelling method. Local calibration is also important to ensure good estimates.
| S-EPMC10006125 | biostudies-literature
Project description:Ethnic minority patients receive lower priority triage assignments in Veteran's Affairs (VA) emergency departments (EDs) compared to White patients, but it is currently unknown whether this disparity arises from generalized biases across the triage assessment process or from differences in how objective and/or subjective institution-level or person-level information is incorporated into the triage assessment process, thus contributing to disparate treatment.The VA database of electronic medical records of patients who presented to the VA ED from 2008 to 2012 was used to measure patient ethnicity, self-reported pain intensity (PI) levels, heart rate (HR), respiratory rate (RR), and nurse-provided triage assignment, the Emergency Severity Index (ESI) score. Multilevel, random effects linear modeling was used to control for demographic and clinical characteristics of patients as well as age, gender, and experience of triage nurses.A total of 359,642?patient/provider encounters between 129,991 VA patients and 774 nurses were included in the study. Patients were 61% non-Hispanic White [NHW], 28% African-American, 7% Hispanic, 2% Asian-American, <1% American Indian/Alaska Native, and 1% mixed ethnicity. After controlling for demographic characteristics of nurses and patients, African-American, Hispanic, and mixed-ethnicity patients reported higher average PI scores but lower HRs and RRs than NHW patients. NHW patients received higher priority ESI ratings with lower PI when compared against African-American patients. NHW patients with low to moderate HRs also received higher priority ESI scoring than African-American, Hispanic, Asian-American, and Mixed-ethnicity patients; however, when HR was high NHWs received lower priority ESI ratings than each of the minority groups (except for African-Americans).This study provides evidence for systemic differences in how patients' vital signs are applied for determining ESI scores for different ethnic groups. Additional prospective research will be needed to determine how this specific person-level mechanism affects healthcare quality and outcomes.
| S-EPMC4998763 | biostudies-literature