scholarly journals Drug Safety Report Generator

2021 ◽  
pp. 38-49
Author(s):  
Sudhir Dubey ◽  
Pruthviraj Bhamre ◽  
Akshay Patil ◽  
Rahul Kumar
Keyword(s):  
Drug Safety ◽  
Adverse Drug Events ◽  
Digital Health ◽  
Data Extraction ◽  
Individual Case ◽  
Free Text ◽  
Electronic Patient Records ◽  
Health Records ◽  
Signature Test

This document provides an overview on identifying ICSR (Individual case safety reports) & Drug Safety Classification of Adverse Drug Events from free Text Electronic Patient Records and Information. As a remarkable rise is observed in the usage of digital health records the potential for extensive clinical data extraction has drawn much attention. We intend to separate the causes and effects of unfriendly drugs from the records. We have therefore promoted a machine learning-based framework for the planned signature test of hostile drugs or safe phrases in the event of a report. In addition, the framework also uses named substance recognition based on word references to identify drugs and diseases that are present at the same time. The framework evaluation of physical comments in the corpus and a context-related analysis of consumption, which was carried out on preselected drugs, showed convincing results.

scholarly journals Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records

Rheumatology ◽  
2019 ◽  
Vol 59 (5) ◽  
pp. 1059-1065 ◽  
Author(s):  
Sizheng Steven Zhao ◽  
Chuan Hong ◽  
Tianrun Cai ◽  
Chang Xu ◽  
Jie Huang ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Predictive Value ◽  
Area Under The Curve ◽  
Free Text ◽  
Text Data ◽  
Health Records ◽  
Disease Concepts ◽  
Icd Codes ◽  
Electronic Health

Abstract Objectives To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the performance of algorithms incorporating free-text data against approaches using only International Classification of Diseases (ICD) codes. Methods An enriched cohort of 7853 eligible patients was created from electronic health records of two large hospitals using automated searches (⩾1 ICD codes combined with simple text searches). Key disease concepts from free-text data were extracted using NLP and combined with ICD codes to develop algorithms. We created both supervised regression-based algorithms—on a training set of 127 axSpA cases and 423 non-cases—and unsupervised algorithms to identify patients with high probability of having axSpA from the enriched cohort. Their performance was compared against classifications using ICD codes only. Results NLP extracted four disease concepts of high predictive value: ankylosing spondylitis, sacroiliitis, HLA-B27 and spondylitis. The unsupervised algorithm, incorporating both the NLP concept and ICD code for AS, identified the greatest number of patients. By setting the probability threshold to attain 80% positive predictive value, it identified 1509 axSpA patients (mean age 53 years, 71% male). Sensitivity was 0.78, specificity 0.94 and area under the curve 0.93. The two supervised algorithms performed similarly but identified fewer patients. All three outperformed traditional approaches using ICD codes alone (area under the curve 0.80–0.87). Conclusion Algorithms incorporating free-text data can accurately identify axSpA patients in electronic health records. Large cohorts identified using these novel methods offer exciting opportunities for future clinical research.

scholarly journals Identification of Adverse Drug Events from Free Text Electronic Patient Records and Information in a Large Mental Health Case Register

PLoS ONE ◽  
2015 ◽  
Vol 10 (8) ◽  
pp. e0134208 ◽  
Author(s):  
Ehtesham Iqbal ◽  
Robbie Mallah ◽  
Richard George Jackson ◽  
Michael Ball ◽  
Zina M. Ibrahim ◽  
...  
Keyword(s):  
Mental Health ◽  
Adverse Drug Events ◽  
Free Text ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Case Register

Automating classification of free-text electronic health records for epidemiological studies

2012 ◽  
Vol 21 (6) ◽  
pp. 651-658 ◽  
Author(s):  
Martijn J. Schuemie ◽  
Emine Sen ◽  
Geert W. 't Jong ◽  
Eva M. Soest ◽  
Miriam C. Sturkenboom ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Epidemiological Studies ◽  
Free Text ◽  
Health Records ◽  
Electronic Health

Automation in signal management in pharmacovigilance—an insight

2020 ◽  
Author(s):  
Diksha Wadhwa ◽  
Keshav Kumar ◽  
Sonali Batra ◽  
Sumit Sharma
Keyword(s):  
Health Care ◽  
Signal Detection ◽  
Adverse Drug Events ◽  
Data Extraction ◽  
Modern Society ◽  
Individual Case ◽  
Optimal Decision ◽  
Therapeutic Effects ◽  
Quality Of Data ◽  
Ideal System

Abstract Drugs are the imperial part of modern society, but along with their therapeutic effects, drugs can also cause adverse effects, which can be mild to morbid. Pharmacovigilance is the process of collection, detection, assessment, monitoring and prevention of adverse drug events in both clinical trials as well as in the post-marketing phase. The recent trends in increasing unknown adverse events, known as signals, have raised the need to develop an ideal system for monitoring and detecting the potential signals timely. The process of signal management comprises of techniques to identify individual case safety reports systematically. Automated signal detection is highly based upon the data mining of the spontaneous reporting system such as reports from health care professional, observational studies, medical literature or from social media. If a signal is not managed properly, it can become an identical risk associated with the drug which can be hazardous for the patient safety and may have fatal outcomes which may impact health care system adversely. Once a signal is detected quantitatively, it can be further processed by the signal management team for the qualitative analysis and further evaluations. The main components of automated signal detection are data extraction, data acquisition, data selection, and data analysis and data evaluation. This system must be developed in the correct format and context, which eventually emphasizes the quality of data collected and leads to the optimal decision-making based upon the scientific evaluation.

scholarly journals Program for data extraction in primary health records: a valid tool for knowledge production in general practice?

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Martin Holte ◽  
Jostein Holmen
Keyword(s):  
Blood Pressure ◽  
General Practice ◽  
Diastolic Blood Pressure ◽  
Data Extraction ◽  
Free Text ◽  
Inclusion Criteria ◽  
Health Records ◽  
Primary Health ◽  
Correct Data

Abstract Objectives Research in general practice demands it-tools which give the practitioner trusty results. Medrave 4 is a program designed for extraction of data from all areas of the health record. We wanted to do research on the database in a health center, but found no proof of the quality of the data extracted by Medrave 4. Today the database contains about 40,000 records. In this study we wanted to examine if the program could extract correct data. Results From the database 20 records were randomly selected from five different time periods, making a total of 100 records. 14 records did not meet the inclusion criteria, resulting in 86 records included in the study. In phase one these variables were registered manually from the records: Age, gender, systolic and diastolic blood pressure (from free text) and six different laboratory tests. In phase two, Medrave 4 extracted the same variables from the same records. Medrave 4 found correct systolic and diastolic blood pressure values in 79 records (92%). The laboratory results were extracted correct in all 86 records (100%). We conclude that Medrave 4 can be a useful tool in quantifying the work of general practitioners.

scholarly journals ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records

PLoS ONE ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. e0187121 ◽  
Author(s):  
Ehtesham Iqbal ◽  
Robbie Mallah ◽  
Daniel Rhodes ◽  
Honghan Wu ◽  
Alvin Romero ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Adverse Drug Events ◽  
Free Text ◽  
Health Records ◽  
Electronic Health

scholarly journals What is the risk of prostate cancer mortality following negative systematic TRUS-guided biopsies? A systematic review

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e040965
Author(s):  
Sandra Miriam Kawa ◽  
Signe Benzon Larsen ◽  
John Thomas Helgstrand ◽  
Peter Iversen ◽  
Klaus Brasso ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Systematic Review ◽  
Data Extraction ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Population Based ◽  
Free Text ◽  
Medical Subject Headings ◽  
Prognostic Information ◽  
Prostate Biopsies

ObjectiveTo investigate the risk of prostate cancer-specific mortality (PCSM) following initial negative systematic transrectal ultrasound-guided (TRUS) prostate biopsies.DesignSystematic review.Data sourcesPubMed and Embase were searched using a string combination with keywords/Medical Subject Headings terms and free text in the search builder. Date of search was 13 April 2020.Study selectionStudies addressing PCSM following initial negative TRUS biopsies. Randomised controlled trials and population-based studies including men with initial negative TRUS biopsies reported in English from 1990 until present were included.Data extractionData extraction was done using a predefined form by two authors independently and compared with confirm data; risk of bias was assessed using the Newcastle–Ottawa Scale for cohort studies when applicable.ResultsFour eligible studies were identified. Outcomes were reported differently in the studies as both cumulative incidence and Kaplan-Meier estimates have been used. Regardless of the study differences, all studies reported low estimated incidence of PCSM of 1.8%–5.2% in men with negative TRUS biopsies during the following 10–20 years. Main limitation in all studies was limited follow-up.ConclusionOnly a few studies have investigated the risk of PCSM following initial negative biopsies and all studies included patients before the era of MRI of the prostate. However, the studies point to the fact that the risk of PCSM is low following initial negative TRUS biopsies, and that the level of prostate-specific antigen before biopsies holds prognostic information. This may be considered when advising patients about the need for further diagnostic evaluation.PROSPERO registration numberCRD42019134548.

scholarly journals Assessing and piloting interoperability for population health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Estupiñán-Romero ◽  
J Gonzalez-García ◽  
E Bernal-Delgado
Keyword(s):  
Case Studies ◽  
Population Health ◽  
Health Research ◽  
Network Architecture ◽  
Digital Health ◽  
Data Extraction ◽  
Health Data ◽  
Research Network ◽  
Technical Solution ◽  
Privacy And Security

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

scholarly journals Supporting deprescribing in hospitalised patients: formative usability testing of a computerised decision support tool

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Melissa T. Baysari ◽  
Mai H. Duong ◽  
Patrick Hooper ◽  
Michaela Stockey-Bridge ◽  
Selvana Awad ◽  
...  
Keyword(s):  
Decision Support ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Adverse Drug Events ◽  
Digital Health ◽  
Usability Testing ◽  
Routine Care ◽  
Assessment Tools ◽  
Support Tool ◽  
Hospitalised Patients

Abstract Background Despite growing evidence that deprescribing can improve clinical outcomes, quality of life and reduce the likelihood of adverse drug events, the practice is not widespread, particularly in hospital settings. Clinical risk assessment tools, like the Drug Burden Index (DBI), can help prioritise patients for medication review and prioritise medications to deprescribe, but are not integrated within routine care. The aim of this study was to conduct formative usability testing of a computerised decision support (CDS) tool, based on DBI, to identify modifications required to the tool prior to trialling in practice. Methods Our CDS tool comprised a DBI MPage in the electronic medical record (clinical workspace) that facilitated review of a patient’s DBI and medication list, access to deprescribing resources, and the ability to deprescribe. Two rounds of scenario-based formative usability testing with think-aloud protocol were used. Seventeen end-users participated in the testing, including junior and senior doctors, and pharmacists. Results Participants expressed positive views about the DBI CDS tool but testing revealed a number of clear areas for improvement. These primarily related to terminology used (i.e. what is a DBI and how is it calculated?), and consistency of functionality and display. A key finding was that users wanted the CDS tool to look and function in a similar way to other decision support tools in the electronic medical record. Modifications were made to the CDS tool in response to user feedback. Conclusion Usability testing proved extremely useful for identifying components of our CDS tool that were confusing, difficult to locate or to understand. We recommend usability testing be adopted prior to implementation of any digital health intervention. We hope our revised CDS tool equips clinicians with the knowledge and confidence to consider discontinuation of inappropriate medications in routine care of hospitalised patients. In the next phase of our project, we plan to pilot test the tool in practice to evaluate its uptake and effectiveness in supporting deprescribing in routine hospital care.

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