Evidence map›Paper›PMID 23131180›Full record

ArticleBMC medical informatics and decision making2012

Adaptation of a web-based, open source electronic medical record system platform to support a large study of tuberculosis epidemiology.

Hamish S F Fraser, David Thomas, Juan Tomaylla, Nadia Garcia, Leonid Lecca, Megan Murray, Mercedes C Becerra

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Prevalence of pyrazinamide resistance and Wayne assay performance analysis in a tuberculosis cohort in Lima, Peru.The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease · 2017
    Article
  8. Article
  9. Review
  10. Utilization of open source electronic health record around the world: A systematic review.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2014
    Review
  11. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hamish S F FraserDivision of Global Health Equity, Brigham and Women's Hospital, Boston, MA, USA. hamish_fraser@hms.harvard.edu
David Thomas
Juan Tomaylla
Nadia Garcia
Leonid Lecca
Megan Murray
Mercedes C Becerra

Funding

Real-time surveillance to support dynamic and predictive models of MDR/XDR TBU19AI076217 · NIAID · BRIGHAM AND WOMEN'S HOSPITAL · PI FARMER, PAUL EDWARD · 2007 to 2011
$13.5M
Epidemiology of multidrug-resistant tuberculosis in PeruU01AI057786 · NIAID · HARVARD MEDICAL SCHOOL · PI BECERRA, MERCEDES C · 2009 to 2010
$1.2M
NIAID NIH HHS U01 AI057786NIAID NIH HHS U19 AI076217
6 · The paper itself

Abstract

backgroundIn 2006, we were funded by the US National Institutes of Health to implement a study of tuberculosis epidemiology in Peru. The study required a secure information system to manage data from a target goal of 16,000 subjects who needed to be followed for at least one year. With previous experience in the development and deployment of web-based medical record systems for TB treatment in Peru, we chose to use the OpenMRS open source electronic medical record system platform to develop the study information system. Supported by a core technical and management team and a large and growing worldwide community, OpenMRS is now being used in more than 40 developing countries. We adapted the OpenMRS platform to better support foreign languages. We added a new module to support double data entry, linkage to an existing laboratory information system, automatic upload of GPS data from handheld devices, and better security and auditing of data changes. We added new reports for study managers, and developed data extraction tools for research staff and statisticians. Further adaptation to handle direct entry of laboratory data occurred after the study was launched.

resultsData collection in the OpenMRS system began in September 2009. By August 2011 a total of 9,256 participants had been enrolled, 102,274 forms and 13,829 laboratory results had been entered, and there were 208 users. The system is now entirely supported by the Peruvian study staff and programmers.

conclusionsThe information system served the study objectives well despite requiring some significant adaptations mid-stream. OpenMRS has more tools and capabilities than it did in 2008, and requires less adaptations for future projects. OpenMRS can be an effective research data system in resource poor environments, especially for organizations using or considering it for clinical care as well as research.

Indexed as

Electronic Health RecordsInternetChecklistComputer SystemsEpidemiologic StudiesGeographic Information SystemsHumansInformation SystemsPeruProgram DevelopmentProspective StudiesTuberculosis, Multidrug-Resistant

Identifiers

PMID23131180
PMCPMC3531253

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.