Dataset Information

Usage of documented pre-hospital observations in secondary care: a questionnaire study and retrospective comparison of records.

ABSTRACT:

Background

The patient handover is important for the safe transition from the pre-hospital setting to secondary care. The loss of critical information about the pre-hospital phase may impact upon the clinical course of the patient.

Methods

University Hospital Emergency Care registrars answered a questionnaire about how they perceive clinical documentation from the ambulance services. We also reviewed patient records retrospectively, to investigate to what extent eight selected parameters were transferred correctly to hospital records by clinicians. Only parameters outside the normal range were selected.

Results

The registrars preferred a verbal handover with hand-written pre-hospital reports as the combined source of clinical information. Scanned report forms were infrequently used. Information from other doctors was perceived as more important than the information from ambulance crews. Less than half of the selected parameters in pre-hospital notes were transferred to hospital records, even for parameters regarded as important by the registrars. Abnormal vital signs were not transferred as often as mechanism of injury, medication administered and immobilisation of trauma patients.

Conclusions

Data on pre-hospital abnormal vital signs are frequently not transferred to the hospital admission notes. This information loss may lead to suboptimal care.

SUBMITTER: Knutsen GO

PROVIDER: S-EPMC3606240 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Usage of documented pre-hospital observations in secondary care: a questionnaire study and retrospective comparison of records.

Knutsen Geir O GO Fredriksen Knut K

Scandinavian journal of trauma, resuscitation and emergency medicine 20130301

<h4>Background</h4>The patient handover is important for the safe transition from the pre-hospital setting to secondary care. The loss of critical information about the pre-hospital phase may impact upon the clinical course of the patient.<h4>Methods</h4>University Hospital Emergency Care registrars answered a questionnaire about how they perceive clinical documentation from the ambulance services. We also reviewed patient records retrospectively, to investigate to what extent eight selected par ...[more]

PMID: 23453123

Similar Datasets

Project description:BACKGROUND:UK "Pathway" teams offer specialist hospital care coordination for people experiencing homelessness. Emergency healthcare use is high among homeless people, yet "homelessness" is not routinely coded in National Health Service (NHS) data. Pathway team records provide an opportunity to assess patterns in admissions and outcomes for inpatients identified as homeless. METHODS:Retrospective analysis of patients referred to "Pathway" homelessness teams in seven UK hospitals to explore the patterns of hospital admission, morbidity, secondary healthcare utilisation and housing status. Each patient was individually identified as experiencing homelessness. Within a six-month period, demographic data, reason for admission, morbidity, mortality and secondary care hospital usage 120-days before and 120-days after the index admission was collected. RESULTS:A total of 1009 patients were referred, resulting in 1135 admissions. Most admissions had an acute physical health need (94.9%). Co-morbid mental illness and/or substance misuse was common (55.7%). Reasons for admission included mental and behavioral disorders (overdose, alcohol withdrawal or depression, 28.3%), external causes of morbidity and mortality (assault or trauma, 18.7%), and injury, poisoning and external causes (head injury, falls and fractures, 12.4%). Unplanned Emergency Department attendances reduced after index admission and unplanned hospital admissions increased slightly. Planned admissions doubled and total bed days increased. Housing status was maintained or improved for over 60% of inpatients upon discharge. Within 12?months of index admission, 50 patients (5%) died, 15 deaths (30%) occurred during the index admission. CONCLUSIONS:Disengagement with health services is common among homeless people. Many deaths are due to treatable medical conditions (heart disease, pneumonia, cancer). Observed increases in planned admissions suggests intervention from Pathway teams facilitates necessary investigations and treatment for homeless people. Equity, parity of care, and value should be inbuilt interventions for inclusion health groups and evaluations need to move beyond simply seeking cost reductions.

Project description:IMPORTANCE:Large-scale DNA sequencing identifies incidental rare variants in established Mendelian disease genes, but the frequency of related clinical phenotypes in unselected patient populations is not well established. Phenotype data from electronic medical records (EMRs) may provide a resource to assess the clinical relevance of rare variants. OBJECTIVE:To determine the clinical phenotypes from EMRs for individuals with variants designated as pathogenic by expert review in arrhythmia susceptibility genes. DESIGN, SETTING, AND PARTICIPANTS:This prospective cohort study included 2022 individuals recruited for nonantiarrhythmic drug exposure phenotypes from October 5, 2012, to September 30, 2013, for the Electronic Medical Records and Genomics Network Pharmacogenomics project from 7 US academic medical centers. Variants in SCN5A and KCNH2, disease genes for long QT and Brugada syndromes, were assessed for potential pathogenicity by 3 laboratories with ion channel expertise and by comparison with the ClinVar database. Relevant phenotypes were determined from EMRs, with data available from 2002 (or earlier for some sites) through September 10, 2014. EXPOSURES:One or more variants designated as pathogenic in SCN5A or KCNH2. MAIN OUTCOMES AND MEASURES:Arrhythmia or electrocardiographic (ECG) phenotypes defined by International Classification of Diseases, Ninth Revision (ICD-9) codes, ECG data, and manual EMR review. RESULTS:Among 2022 study participants (median age, 61 years [interquartile range, 56-65 years]; 1118 [55%] female; 1491 [74%] white), a total of 122 rare (minor allele frequency <0.5%) nonsynonymous and splice-site variants in 2 arrhythmia susceptibility genes were identified in 223 individuals (11% of the study cohort). Forty-two variants in 63 participants were designated potentially pathogenic by at least 1 laboratory or ClinVar, with low concordance across laboratories (Cohen ??=?0.26). An ICD-9 code for arrhythmia was found in 11 of 63 (17%) variant carriers vs 264 of 1959 (13%) of those without variants (difference, +4%; 95% CI, -5% to +13%; P?=?.35). In the 1270 (63%) with ECGs, corrected QT intervals were not different in variant carriers vs those without (median, 429 vs 439 milliseconds; difference, -10 milliseconds; 95% CI, -16 to +3 milliseconds; P?=?.17). After manual review, 22 of 63 participants (35%) with designated variants had any ECG or arrhythmia phenotype, and only 2 had corrected QT interval longer than 500 milliseconds. CONCLUSIONS AND RELEVANCE:Among laboratories experienced in genetic testing for cardiac arrhythmia disorders, there was low concordance in designating SCN5A and KCNH2 variants as pathogenic. In an unselected population, the putatively pathogenic genetic variants were not associated with an abnormal phenotype. These findings raise questions about the implications of notifying patients of incidental genetic findings.

Project description:BackgroundSecondary hypertension is a kind of hypertension with a definite etiology and may be cured. Patients with suspected secondary hypertension can benefit from timely detection and treatment and, conversely, will have a higher risk of morbidity and mortality than those with primary hypertension.ObjectiveThe aim of this study was to develop and validate machine learning (ML) prediction models of common etiologies in patients with suspected secondary hypertension.MethodsThe analyzed data set was retrospectively extracted from electronic medical records of patients discharged from Fuwai Hospital between January 1, 2016, and June 30, 2019. A total of 7532 unique patients were included and divided into 2 data sets by time: 6302 patients in 2016-2018 as the training data set for model building and 1230 patients in 2019 as the validation data set for further evaluation. Extreme Gradient Boosting (XGBoost) was adopted to develop 5 models to predict 4 etiologies of secondary hypertension and occurrence of any of them (named as composite outcome), including renovascular hypertension (RVH), primary aldosteronism (PA), thyroid dysfunction, and aortic stenosis. Both univariate logistic analysis and Gini Impurity were used for feature selection. Grid search and 10-fold cross-validation were used to select the optimal hyperparameters for each model.ResultsValidation of the composite outcome prediction model showed good performance with an area under the receiver-operating characteristic curve (AUC) of 0.924 in the validation data set, while the 4 prediction models of RVH, PA, thyroid dysfunction, and aortic stenosis achieved AUC of 0.938, 0.965, 0.959, and 0.946, respectively, in the validation data set. A total of 79 clinical indicators were identified in all and finally used in our prediction models. The result of subgroup analysis on the composite outcome prediction model demonstrated high discrimination with AUCs all higher than 0.890 among all age groups of adults.ConclusionsThe ML prediction models in this study showed good performance in detecting 4 etiologies of patients with suspected secondary hypertension; thus, they may potentially facilitate clinical diagnosis decision making of secondary hypertension in an intelligent way.

Dataset Information

Usage of documented pre-hospital observations in secondary care: a questionnaire study and retrospective comparison of records.

Background

Methods

Results

Conclusions

Publications

Usage of documented pre-hospital observations in secondary care: a questionnaire study and retrospective comparison of records.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets