scholarly journals Systems Medicine 2.0: Potential Benefits of Combining Electronic Health Care Records With Systems Science Models

2015 ◽  
Vol 17 (3) ◽  
pp. e64 ◽  
Author(s):  
Taavi Tillmann ◽  
Alexander R Gibson ◽  
Gregory Scott ◽  
Oliver Harrison ◽  
Anna Dominiczak ◽  
...  
Keyword(s):  
Health Care ◽  
Systems Science ◽  
Systems Medicine ◽  
Science Models ◽  
Potential Benefits ◽  
Electronic Health ◽  
Medicine 2.0 ◽  
Health Care Records

Institutional Trust of Electronic Health Care Records System Measure

2011 ◽  
Author(s):  
José Manuel Ortega Egea ◽  
María Victoria Román González
Keyword(s):  
Health Care ◽  
Institutional Trust ◽  
Electronic Health ◽  
Health Care Records

Intention to Use Electronic Health Care Records System Measure

2011 ◽  
Author(s):  
José Manuel Ortega Egea ◽  
María Victoria Román González
Keyword(s):  
Health Care ◽  
Intention To Use ◽  
Electronic Health ◽  
Health Care Records

Marking Up is Not Enough

1993 ◽  
Vol 32 (04) ◽  
pp. 272-273 ◽  
Author(s):  
A. L. Rector
Keyword(s):  
Health Care ◽  
Intelligent Processing ◽  
Structured Documents ◽  
Electronic Health ◽  
Health Care Records

Response to: Essin DJ. Intelligent processing of loosely structured documents as a strategy for organizing electronic health care records. Meth Inform Med 1993; 32: 265.

scholarly journals Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method (Preprint)

2021 ◽  
Author(s):  
Roselie A Bright ◽  
Summer K Rankin ◽  
Katherine Dowdy ◽  
Sergey V Blok ◽  
Susan J Bright ◽  
...  
Keyword(s):  
Health Care ◽  
William Shakespeare ◽  
Adverse Events ◽  
Comparison Group ◽  
Study Data ◽  
Unstructured Text ◽  
Major Teaching Hospital ◽  
Major Teaching ◽  
Electronic Health ◽  
Health Care Records

BACKGROUND Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome (“attributed”) or state the simple treatment and outcome without an association (“unattributed”). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, “transfusion” and “time-based.” Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians’ documentation of attributed AEs. OBJECTIVE We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs. METHODS We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs. RESULTS Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination. CONCLUSIONS The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process.

scholarly journals rdrugtrajectory: An R Package for the Analysis of Drug Prescriptions in Electronic Health Care Records

2021 ◽  
Author(s):  
Anthony Nash ◽  
Tingyee E Chang ◽  
Benjamin Wan ◽  
M. Zameel Cader
Keyword(s):  
Health Care ◽  
Clinical Practice ◽  
Drug Prescription ◽  
Clinical Information ◽  
R Package ◽  
Drug Prescriptions ◽  
Practice Research ◽  
Clinical Practice Research Datalink ◽  
Electronic Health ◽  
Health Care Records

Primary care electronic health care records are rich with patient and clinical information. Studying electronic health care records has resulted in marked improvements to national health care processes and patient-care decision making, and is a powerful supplementary source of data for drug discovery effort. We present the R package rdrugtrajectory, designed to yield demographic and patient-level characteristics of drug prescriptions in the UK Clinical Practice Research Datalink dataset. The package operates over Clinical Practice Research Datalink Gold clinical, referral and therapy datasets and includes features such as first drug prescriptions analysis, cohort-wide prescription information, cumulative drug prescription events, the longitudinal trajectory of drug prescriptions, and a survival analysis timeline builder to identify risks related to drug prescription switching. The rdrugtrajectory package has been made freely available via the GitHub repository.

Perceived Usefulness of Electronic Health Care Records System Measure

2011 ◽  
Author(s):  
José Manuel Ortega Egea ◽  
María Victoria Román González
Keyword(s):  
Health Care ◽  
Perceived Usefulness ◽  
Electronic Health ◽  
Health Care Records

scholarly journals Recording of hospitalizations for acute exacerbations of COPD in UK electronic health care records

2016 ◽  
Vol Volume 8 ◽  
pp. 771-782 ◽  
Author(s):  
Kieran J Rothnie ◽  
Hana Müllerová ◽  
Sara L Thomas ◽  
Joht S Chandan ◽  
Liam Smeeth ◽  
...  
Keyword(s):  
Health Care ◽  
Acute Exacerbations ◽  
Electronic Health ◽  
Health Care Records

scholarly journals Authors’ Response to Peer Reviews of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”

JMIRx Med ◽  
10.2196/31568 ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. e31568
Author(s):  
Roselie A Bright ◽  
Summer K Rankin ◽  
Katherine Dowdy ◽  
Sergey V Blok ◽  
Susan J Bright ◽  
...  
Keyword(s):  
Health Care ◽  
Adverse Events ◽  
Peer Reviews ◽  
Unstructured Text ◽  
Electronic Health ◽  
Health Care Records

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