scholarly journals Electronic medical records in multiple sclerosis research

2018 ◽  
Vol 9 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Vincent Damotte ◽  
Pierre-Antoine Gourraud
Keyword(s):  
Multiple Sclerosis ◽  
Electronic Medical Records ◽  
Medical Records

scholarly journals Perspectives on illness-related stigma and electronically sharing psychiatric health information by people with Multiple Sclerosis.

2020 ◽  
Author(s):  
Eileen Yu ◽  
Alexis Adams-Clark ◽  
Alison Riehm ◽  
Caroline Franke ◽  
Ryoko Susukida ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Health Information ◽  
Electronic Medical Records ◽  
Psychiatric Diagnosis ◽  
Medical Records ◽  
Psychiatric Diagnoses ◽  
Sexual Anxiety ◽  
Related Stigma ◽  
Societal Stigma ◽  
Patient Willingness

Abstract Background: Electronic medical records (EMRs) have transformed the way healthcare professionals manage and share patient data while providing integrated and comprehensive care. However, the rate of EMR use among psychiatrists is generally lower compared to physicians in other medical disciplines, in part due to concerns over patients’ experience of stigma surrounding mental health. This paper explores the willingness to share medical records among people with multiple sclerosis (MS), who experience higher rates of psychiatric comorbidities compared to the general population. It also examines the role that stigma plays in patients’ preferences regarding the sharing of their electronic medical records. Methods: MS patients were surveyed regarding their co-occurring psychiatric and non-psychiatric diagnoses, willingness to share their health information electronically among their treating doctors, and levels of self and societal stigma associated with their various co-occurring diagnoses. Results: 96.44% and 87.14% of participants were willing to share their non-psychiatric and psychiatric diagnoses, respectively; 97.70% and 92.78% were willing to share non-psychiatric and psychiatric medications, respectively. MS patients with a psychiatric co-occurring diagnosis, compared to those without, were significantly more likely to share their psychiatric diagnosis (AOR = 2.59) and psychiatric medications (AOR = 3.19). Those with both non-psychiatric and psychiatric co-occurring diagnoses were significantly more likely to share their psychiatric diagnosis (AOR = 3.84) and psychiatric medications (AOR = 7.02) than patients with no co-occurring diagnosis other than MS. Five (substance use, personality, eating, psychotic, and neurodevelopmental disorders) of the top six diagnoses for which societal stigma was greater than self stigma, and three (sexual, anxiety, and mood disorders) of the top five diagnoses for which self stigma was greater than societal stigma were psychiatric diagnoses. High levels of societal stigma correlated with decreased likelihood in sharing non-psychiatric medications, while high levels of self stigma were associated with a greater decrease in patient willingness to share psychiatric medications. Conclusions: Despite the presence of stigma decreasing patient willingness to share medical records, people with MS who had psychiatric disorders, compared to those without, endorsed greater willingness to share their health records electronically.

scholarly journals Automated extraction of clinical traits of multiple sclerosis in electronic medical records

2013 ◽  
Vol 20 (e2) ◽  
pp. e334-e340 ◽  
Author(s):  
Mary F Davis ◽  
Subramaniam Sriram ◽  
William S Bush ◽  
Joshua C Denny ◽  
Jonathan L Haines
Keyword(s):  
Multiple Sclerosis ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Automated Extraction

Harnessing electronic medical records to advance research on multiple sclerosis

2018 ◽  
Vol 25 (3) ◽  
pp. 408-418 ◽  
Author(s):  
Vincent Damotte ◽  
Antoine Lizée ◽  
Matthew Tremblay ◽  
Alisha Agrawal ◽  
Pouya Khankhanian ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Natural History ◽  
Electronic Medical Records ◽  
San Francisco ◽  
Clinical Data ◽  
Medical Records ◽  
Population Characteristics ◽  
Research Quality ◽  
Quality Data ◽  
Good Concordance

Background: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history. Objectives: To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history. Methods: Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients. Results: We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing–remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration. Conclusion: Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.

scholarly journals Knowledge Retrieval from PubMed Abstracts and Electronic Medical Records with the Multiple Sclerosis Ontology

PLoS ONE ◽  
2015 ◽  
Vol 10 (2) ◽  
pp. e0116718 ◽  
Author(s):  
Ashutosh Malhotra ◽  
Michaela Gündel ◽  
Abdul Mateen Rajput ◽  
Heinz-Theodor Mevissen ◽  
Albert Saiz ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Knowledge Retrieval

scholarly journals Determining Multiple Sclerosis Phenotype from Electronic Medical Records

2016 ◽  
Vol 22 (12) ◽  
pp. 1377-1382 ◽  
Author(s):  
Richard E. Nelson ◽  
Jorie Butler ◽  
Joanne LaFleur ◽  
Kristin Knippenberg ◽  
Aaron W. C. Kamauu ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Electronic Medical Records ◽  
Medical Records
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