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 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 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 A Review on the Quality of Colonoscopy Reporting

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Robyn S. Sharma ◽  
Peter G. Rossos
Keyword(s):  
Resource Management ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Information Sources ◽  
Research Quality ◽  
Communication Tools ◽  
Quality Performance ◽  
Current State ◽  
Adoption And Implementation

Colonoscopy reports are important communication tools for providers and patients with potential to serve as information sources for research, quality, performance, and resource management. Despite decades of work, studies continue to indicate that colonoscopy reports are often incomplete. Although electronic medical records (EMRs) and databases can address this problem, costs, workflow, and interoperability (difficulty exchanging information between systems) continue to limit adoption and implementation of endoscopy EMRs in Canada and elsewhere. Quality and reporting guidelines alone have proven to be insufficient. In this review we have derived and applied five key themes to challenges in the current state of colonoscopy reporting and propose strategies to address them.

scholarly journals Cancer: A Computational Disease that AI Can Cure

AI Magazine ◽  
2011 ◽  
Vol 32 (2) ◽  
pp. 14 ◽  
Author(s):  
Jay M. Tenenbaum ◽  
Jeff Shrager
Keyword(s):  
Learning Community ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Molecular Subtypes ◽  
Quality Data ◽  
Individual Treatment ◽  
Grand Challenge ◽  
Search Problem ◽  
High Quality Data ◽  
Cancer Subtypes

Cancer kills millions of people each year. From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of high quality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records, presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a “rapid learning” community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient, and use these results to individualize therapies. Research opportunities include: adaptively-planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge, and to continually update that knowledge to benefit subsequent patients. Achieving this goal is a worthy grand challenge for AI.

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
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