Using a Cloud-based Electronic Health Record During Disaster Response: A Case Study in Fukushima, March 2011

2013 ◽  
Vol 28 (4) ◽  
pp. 383-387 ◽  
Author(s):  
Takashi Nagata ◽  
John Halamka ◽  
Shinkichi Himeno ◽  
Akihiro Himeno ◽  
Hajime Kennochi ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Disaster Response ◽  
Nuclear Power ◽  
Large Scale ◽  
Health Record ◽  
Publication Type ◽  
Web Based ◽  
Electronic Health ◽  
Medical Teams

AbstractFollowing the Great East Japan Earthquake on March 11, 2011, the Japan Medical Association deployed medical disaster teams to Shinchi-town (population: approximately 8,000), which is located 50 km north of the Fukushima Daiichi nuclear power plant. The mission of the medical disaster teams sent from Fukuoka, 1,400 km south of Fukushima, was to provide medical services and staff a temporary clinic for six weeks. Fear of radiation exposure restricted the use of large medical teams and local infrastructure. Therefore, small volunteer groups and a cloud-hosted, web-based electronic health record were implemented. The mission was successfully completed by the end of May 2011. Cloud-based electronic health records deployed using a “software as a service” model worked well during the response to the large-scale disaster.NagataT, HalamkaJ, KennochiH, HimenoS, HimenoA, HashizumeM. Using a cloud-based electronic health record during disaster response: a case study in Fukushima, March 2011. Prehosp Disaster Med. 2013;28(4):1-5.

Web-based personal health records: the personal electronic health record (pEHR) multicentred trial

2007 ◽  
Vol 13 (1_suppl) ◽  
pp. 32-34 ◽  
Author(s):  
George E Karagiannis ◽  
Vasileios G Stamatopoulos ◽  
Michael Rigby ◽  
Takis Kotis ◽  
Elisa Negroni ◽  
...  
Keyword(s):  
Health Care ◽  
Electronic Health Record ◽  
Health Care Professionals ◽  
Large Scale ◽  
Study Groups ◽  
Third Party ◽  
Health Record ◽  
User Friendliness ◽  
Web Based ◽  
Electronic Health

A multicentre trial of a Web-based personal electronic health record (pEHR) service was conducted in three different European hospitals. A total of 150 patients and 22 health-care professionals were involved. The service was customised according to the needs of three groups of patients who had congenital heart disease, Parkinson's disease and type 2 diabetes. Two structured questionnaires, one for patients and one for health-care professionals, were used to collect their views on the pEHR service. The questions were about usability and user friendliness, safety and trustworthiness, reliability, functionality, satisfaction and the potential revenue model of the service in the case of future deployment. Patients perceived the service as very motivating and felt that it could help them in managing their clinical information. Health-care professionals showed a very positive attitude towards the use of the service and its potential for future large-scale deployment. They were also keen to recommend the service to their patients. Both study groups were unwilling to pay for the service and preferred it to be sponsored by a third party (e.g. the National Health Service).

scholarly journals Characterization and selection of Japanese electronic health record databases used as data sources for non-interventional observational studies

2021 ◽  
Author(s):  
Yumi Wakabayashi ◽  
Masamitsu Eitoku ◽  
Narufumi Suganuma
Keyword(s):  
Electronic Health Record ◽  
Observational Studies ◽  
Large Scale ◽  
Data Sources ◽  
Flow Diagram ◽  
Health Record ◽  
Medical Institutions ◽  
Data Source ◽  
Electronic Health ◽  
Using Data

Abstract Background Interventional studies are the fundamental method for obtaining answers to clinical question. However, these studies are sometimes difficult to conduct because of insufficient financial or human resources or the rarity of the disease in question. One means of addressing these issues is to conduct a non-interventional observational study using electronic health record (EHR) databases as the data source, although how best to evaluate the suitability of an EHR database when planning a study remains to be clarified. The aim of the present study is to identify and characterize the data sources that have been used for conducting non-interventional observational studies in Japan and propose a flow diagram to help researchers determine the most appropriate EHR database for their study goals. Methods We compiled a list of published articles reporting observational studies conducted in Japan by searching PubMed for relevant articles published in the last 3 years and by searching database providers’ publication lists related to studies using their databases. For each article, we reviewed the abstract and/or full text to obtain information about data source, target disease or therapeutic area, number of patients, and study design (prospective or retrospective). We then characterized the identified EHR databases. Results In Japan, non-interventional observational studies have been mostly conducted using data stored locally at individual medical institutions (713/1463) or collected from several collaborating medical institutions (351/1463). Whereas the studies conducted with large-scale integrated databases (195/1463) were mostly retrospective (68.2%), 27.2% of the single-center studies, 46.2% of the multi-center studies, and 74.4% of the post-marketing surveillance studies, identified in the present study, were conducted prospectively. Conclusions Our analysis revealed that the non-interventional observational studies were conducted using data stored local at individual medical institutions or collected from collaborating medical institutions in Japan. Disease registries, disease databases, and large-scale databases would enable researchers to conduct studies with large sample sizes to provide robust data from which strong inferences could be drawn. Using our flow diagram, researchers planning non-interventional observational studies should consider the strengths and limitations of each available database and choose the most appropriate one for their study goals. Trial registration Not applicable.

scholarly journals Stratifying risk for dementia onset using large‐scale electronic health record data: A retrospective cohort study

2020 ◽  
Vol 16 (3) ◽  
pp. 531-540 ◽  
Author(s):  
Thomas H. McCoy ◽  
Larry Han ◽  
Amelia M. Pellegrini ◽  
Rudolph E. Tanzi ◽  
Sabina Berretta ◽  
...  
Keyword(s):  
Cohort Study ◽  
Electronic Health Record ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Large Scale ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health ◽  
Dementia Onset

scholarly journals An Integrated Success Model for an Electronic Health Record: A Case Study of Hakeem Jordan

2015 ◽  
Vol 23 ◽  
pp. 95-103 ◽  
Author(s):  
Dua’ Abdellatef. Nassar ◽  
Marini Othman ◽  
Jamal A. Hayajneh ◽  
Nor’ashikin Ali
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Success Model ◽  
Electronic Health

scholarly journals Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data

JAMIA Open ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 570-579 ◽  
Author(s):  
Na Hong ◽  
Andrew Wen ◽  
Feichen Shen ◽  
Sunghwan Sohn ◽  
Chen Wang ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Language Processing ◽  
Clinical Data ◽  
Large Scale ◽  
Structured Data ◽  
Health Record ◽  
Data Normalization ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Clinical Resource

Abstract Objective To design, develop, and evaluate a scalable clinical data normalization pipeline for standardizing unstructured electronic health record (EHR) data leveraging the HL7 Fast Healthcare Interoperability Resources (FHIR) specification. Methods We established an FHIR-based clinical data normalization pipeline known as NLP2FHIR that mainly comprises: (1) a module for a core natural language processing (NLP) engine with an FHIR-based type system; (2) a module for integrating structured data; and (3) a module for content normalization. We evaluated the FHIR modeling capability focusing on core clinical resources such as Condition, Procedure, MedicationStatement (including Medication), and FamilyMemberHistory using Mayo Clinic’s unstructured EHR data. We constructed a gold standard reusing annotation corpora from previous NLP projects. Results A total of 30 mapping rules, 62 normalization rules, and 11 NLP-specific FHIR extensions were created and implemented in the NLP2FHIR pipeline. The elements that need to integrate structured data from each clinical resource were identified. The performance of unstructured data modeling achieved F scores ranging from 0.69 to 0.99 for various FHIR element representations (0.69–0.99 for Condition; 0.75–0.84 for Procedure; 0.71–0.99 for MedicationStatement; and 0.75–0.95 for FamilyMemberHistory). Conclusion We demonstrated that the NLP2FHIR pipeline is feasible for modeling unstructured EHR data and integrating structured elements into the model. The outcomes of this work provide standards-based tools of clinical data normalization that is indispensable for enabling portable EHR-driven phenotyping and large-scale data analytics, as well as useful insights for future developments of the FHIR specifications with regard to handling unstructured clinical data.

scholarly journals Insulin Infusion Computer Calculator Programmed Directly Into Electronic Health Record Medication Administration Record

2020 ◽  
pp. 193229682096661
Author(s):  
Kristen Kulasa ◽  
Brittany Serences ◽  
Michael Nies ◽  
Robert El-Kareh ◽  
Kirk Kurashige ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Insulin Infusion ◽  
Medication Administration ◽  
University Of California ◽  
Health Record ◽  
Staff Satisfaction ◽  
Web Based ◽  
Period 2 ◽  
Integration Period ◽  
Electronic Health

Background: Computerized insulin infusion protocols have demonstrated higher staff satisfaction, better compliance with protocols, and increased time with glucose in range compared to paper protocols. At University of California San Diego Health (UCSDH), we implemented an insulin infusion computer calculator (IICC) and transitioned it from a web-based platform directly into the electronic medication administration record (eMAR) of our primary electronic health record (EHR). Methods: This is a retrospective analysis of 6306 adult patients at UCSDH receiving intravenous (IV) insulin infusion from March 7, 2013 to May 30, 2019. We created three periods of the study—(1) the pre-eMAR integration period; (2) the eMAR integration period; and (3) the post-eMAR integration period—and looked at the percentage of readings within goal range (90-150 mg/dL for intensive care unit [ICU], 90-180 mg/dL for non-ICU) in patients with and without hyperglycemic emergencies. As our safety endpoints, we elected to look at incidence of blood glucose (BG) readings <70 mg/dL, <54 mg/dL, and <40 mg/dL. Results: Pre-eMAR 69.8% of readings were in the 90-150 mg/dL range compared to 70.2% post-eMAR ( P = .03) and 82.7% of readings were in the 90-180 mg/dL range pre-eMAR versus 82.9% ( P = .09) post-eMAR in patients without hyperglycemic emergencies. Rates of hypoglycemia with BG <70 mg/dL were 0.43%, <54 mg/dL were 0.07%, and <40 mg/dL were 0.01% of readings pre- and post-eMAR. Conclusions: At UCSDH, our IICC has shown to be safe and effective in a wide variety of clinical situations and we were able to successfully transition it from a web-based platform directly into the eMAR of our primary EHR.

The Need for a Socio-Technical Analysis in E-Health

2015 ◽  
pp. 1123-1138
Author(s):  
Imran Muhammad ◽  
Say Yen Teoh ◽  
Nilmini Wickramasinghe
Keyword(s):  
Electronic Health Record ◽  
Technical Analysis ◽  
Healthcare Systems ◽  
Health Record ◽  
Australian Government ◽  
Healthcare Reforms ◽  
Substantial Investment ◽  
Successful Outcomes ◽  
Electronic Health

Healthcare systems around the globe are facing a number of challenges. Thus Increasing focus is being placed on constructing appropriate healthcare reforms which are attempting to address how to tackle these challenges. A critical enabler in these reforms is the adoption of an e-health solution. Such e-health solutions are not only expensive and complex endeavours, but also have far reaching implications. Given that the implementation and adoption of these e-health solutions is so important, not to mention also requiring a substantial investment in various resources such as time and money, it is therefore essential to ensure their success. The following proffers a socio-technical analysis as an appropriate strategy to ensure more successful outcomes. An exemplar case study of the Personally Controlled Electronic Health Record (PCEHR), the chosen e-health solution by the Australian government is provided to illustrate the benefits such an analysis might provide

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