scholarly journals Electronic health records in ambulances: the ERA multiple-methods study

2020 ◽  
Vol 8 (10) ◽  
pp. 1-140
Author(s):  
Alison Porter ◽  
Anisha Badshah ◽  
Sarah Black ◽  
David Fitzpatrick ◽  
Robert Harris-Mayes ◽  
...  

Background Ambulance services have a vital role in the shift towards the delivery of health care outside hospitals, when this is better for patients, by offering alternatives to transfer to the emergency department. The introduction of information technology in ambulance services to electronically capture, interpret, store and transfer patient data can support out-of-hospital care. Objective We aimed to understand how electronic health records can be most effectively implemented in a pre-hospital context in order to support a safe and effective shift from acute to community-based care, and how their potential benefits can be maximised. Design and setting We carried out a study using multiple methods and with four work packages: (1) a rapid literature review; (2) a telephone survey of all 13 freestanding UK ambulance services; (3) detailed case studies examining electronic health record use through qualitative methods and analysis of routine data in four selected sites consisting of UK ambulance services and their associated health economies; and (4) a knowledge-sharing workshop. Results We found limited literature on electronic health records. Only half of the UK ambulance services had electronic health records in use at the time of data collection, with considerable variation in hardware and software and some reversion to use of paper records as services transitioned between systems. The case studies found that the ambulance services’ electronic health records were in a state of change. Not all patient contacts resulted in the generation of electronic health records. Ambulance clinicians were dealing with partial or unclear information, which may not fit comfortably with the electronic health records. Ambulance clinicians continued to use indirect data input approaches (such as first writing on a glove) even when using electronic health records. The primary function of electronic health records in all services seemed to be as a store for patient data. There was, as yet, limited evidence of electronic health records’ full potential being realised to transfer information, support decision-making or change patient care. Limitations Limitations included the difficulty of obtaining sets of matching routine data for analysis, difficulties of attributing any change in practice to electronic health records within a complex system and the rapidly changing environment, which means that some of our observations may no longer reflect reality. Conclusions Realising all the benefits of electronic health records requires engagement with other parts of the local health economy and dealing with variations between providers and the challenges of interoperability. Clinicians and data managers, and those working in different parts of the health economy, are likely to want very different things from a data set and need to be presented with only the information that they need. Future work There is scope for future work analysing ambulance service routine data sets, qualitative work to examine transfer of information at the emergency department and patients’ perspectives on record-keeping, and to develop and evaluate feedback to clinicians based on patient records. Study registration This study is registered as Health and Care Research Wales Clinical Research Portfolio 34166. Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 10. See the NIHR Journals Library website for further project information.

2019 ◽  
Vol 48 (Supplement_1) ◽  
pp. i27-i30
Author(s):  
L C Blomaard ◽  
B Korpershoek ◽  
J A Lucke ◽  
J de Gelder ◽  
J Gussekloo ◽  
...  

2020 ◽  
pp. 108705472094327 ◽  
Author(s):  
Eugene Merzon ◽  
Iris Manor ◽  
Ann Rotem ◽  
Tzipporah Schneider ◽  
Shlomo Vinker ◽  
...  

Background: ADHD limits the ability to comply with Covid-19 prevention recommendations. We hypothesized that ADHD constitutes a risk factor for Covid-19 infection and that pharmacotherapy may lower that risk. Methods: Study population included all subjects ( N = 14,022) registered with Leumit Health Services between February 1st and April 30, 2020, who underwent at least one Covid-19 test. Data were collected from the electronic health records. Purchasing consecutively at least three ADHD-medication-prescriptions during past year was considered drug-treatment. Results: A total of 1,416 (10.1%) subjects (aged 2 months–103 years) were Covid-19-positive.They were significantly younger, and had higher rates of ADHD (adjOR 1.58 (95% CI 1.27–1.96, p < .001) than Covid-19-negative subjects. The risk for Covid-19-Positive was higher in untreated-ADHD subjects compared to non-ADHD subjects [crudeOR 1.61 (95% CI 1.36–1.89, p < .001)], while no higher risk was detected in treated ones [crudeOR 1.07 (95% CI 0.78–1.48, p = .65)]. Conclusion: Untreated ADHD seems to constitute a risk factor for Covid-19 infection while drug-treatment ameliorates this effect.


Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 386
Author(s):  
Sheikh S. Abdullah ◽  
Neda Rostamzadeh ◽  
Kamran Sedig ◽  
Amit X. Garg ◽  
Eric McArthur

Acute kidney injury (AKI) is a common complication in hospitalized patients and can result in increased hospital stay, health-related costs, mortality and morbidity. A number of recent studies have shown that AKI is predictable and avoidable if early risk factors can be identified by analyzing Electronic Health Records (EHRs). In this study, we employ machine learning techniques to identify older patients who have a risk of readmission with AKI to the hospital or emergency department within 90 days after discharge. One million patients’ records are included in this study who visited the hospital or emergency department in Ontario between 2014 and 2016. The predictor variables include patient demographics, comorbid conditions, medications and diagnosis codes. We developed 31 prediction models based on different combinations of two sampling techniques, three ensemble methods, and eight classifiers. These models were evaluated through 10-fold cross-validation and compared based on the AUROC metric. The performances of these models were consistent, and the AUROC ranged between 0.61 and 0.88 for predicting AKI among 31 prediction models. In general, the performances of ensemble-based methods were higher than the cost-sensitive logistic regression. We also validated features that are most relevant in predicting AKI with a healthcare expert to improve the performance and reliability of the models. This study predicts the risk of AKI for a patient after being discharged, which provides healthcare providers enough time to intervene before the onset of AKI.


2016 ◽  
Vol 9 (2) ◽  
pp. 19
Author(s):  
Xu Tingting ◽  
Ma Cunhong ◽  
Shang Yulian

<p><span style="font-size: 10.5pt; font-family: 'Times New Roman','serif'; mso-bidi-font-size: 12.0pt; mso-fareast-font-family: 宋体; mso-font-kerning: 1.0pt; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;" lang="EN-US">Objective: To standardize and improve the management of health records in community health services in Tai’an city, also to improve community health service level, and enhance residents' awareness of health. Method: taking the residents within the service range of TaiQian Community Health Center in Taian city and the relevant medical staff as research respondent, 120 questionnaires have been sent out to the residents and 15 questionnaires to the staff, then statistical analysis would be made according to the survey results. Results: at current, there are two main ways to establish personal health records, i.e. residents health examination and community personnel pay visit, which are effective; community residents seldom use their personal records, only 93.5% for once a year; 35% of community doctor hold the opinion that the health records have no big value, so there is no need to read, reflecting that the community medical personnel lack of the awareness of use it; the security and completeness of electronic health records are poor. Conclusion: Government should play a leading role in establishing and managing the community health record, improve awareness of community medical personnel in using health records through training, seminars, On-line advertising and other forms, and strengthen and improve the comprehensive management of electronic health records.</span></p>


BMJ Open ◽  
2014 ◽  
Vol 4 (12) ◽  
pp. e005654 ◽  
Author(s):  
Felicity Callard ◽  
Matthew Broadbent ◽  
Mike Denis ◽  
Matthew Hotopf ◽  
Murat Soncul ◽  
...  

2020 ◽  
pp. 0272989X2095440
Author(s):  
Glen B. Taksler ◽  
Jarrod E. Dalton ◽  
Adam T. Perzynski ◽  
Michael B. Rothberg ◽  
Alex Milinovich ◽  
...  

Electronic health records (EHRs) offer the potential to study large numbers of patients but are designed for clinical practice, not research. Despite the increasing availability of EHR data, their use in research comes with its own set of challenges. In this article, we describe some important considerations and potential solutions for commonly encountered problems when working with large-scale, EHR-derived data for health services and community-relevant health research. Specifically, using EHR data requires the researcher to define the relevant patient subpopulation, reliably identify the primary care provider, recognize the EHR as containing episodic (i.e., unstructured longitudinal) data, account for changes in health system composition and treatment options over time, understand that the EHR is not always well-organized and accurate, design methods to identify the same patient across multiple health systems, account for the enormous size of the EHR, and consider barriers to data access. Associations found in the EHR may be nonrepresentative of associations in the general population, but a clear understanding of the EHR-based associations can be enormously valuable to the process of improving outcomes for patients in learning health care systems. In the context of building 2 large-scale EHR-derived data sets for health services research, we describe the potential pitfalls of EHR data and propose some solutions for those planning to use EHR data in their research. As ever greater amounts of clinical data are amassed in the EHR, use of these data for research will become increasingly common and important. Attention to the intricacies of EHR data will allow for more informed analysis and interpretation of results from EHR-based data sets.


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