Evidence map›Paper›PMID 28357037›Full record

ArticleF1000Research2016

Electronic medical records in humanitarian emergencies - the development of an Ebola clinical information and patient management system.

Kiran Jobanputra, Jane Greig, Ganesh Shankar, Eric Perakslis, Ronald Kremer, Jay Achar, Ivan Gayton

Open access · goldAbstract read
In one paragraph

Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
1.1field-weighted citation impact, top 25% of its field
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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
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 at 4 institutions in 3 countries.

Kiran JobanputraManson Unit, Médecins sans Frontières (MSF), London, UK.
Jane GreigManson Unit, Médecins sans Frontières (MSF), London, UK.
Ganesh ShankarGoogle Crisis Response Team, Mountain View, CA, USA.
Eric PerakslisCentre for Biomedical Informatics and Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, USA.
Ronald KremerMSF, Amsterdam, Netherlands.
Jay AcharManson Unit, Médecins sans Frontières (MSF), London, UK.
Ivan GaytonManson Unit, Médecins sans Frontières (MSF), London, UK.
Médecins Sans Frontières · UGGoogle (United States) · USHarvard University · USMédecins Sans Frontières · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

By November 2015, the West Africa Ebola epidemic had caused 28598 infections and 11299 deaths in the three countries most affected. The outbreak required rapid innovation and adaptation. Médecins sans Frontières (MSF) scaled up its usual 20-30 bed Ebola management centres (EMCs) to 100-300 beds with over 300 workers in some settings. This brought challenges in patient and clinical data management resulting from the difficulties of working safely with high numbers of Ebola patients. We describe a project MSF established with software developers and the Google Social Impact Team to develop context-adapted tools to address the challenges of recording Ebola clinical information. We share the outcomes and key lessons learned in innovating rapidly under pressure in difficult environmental conditions. Information on adoption, maintenance, and data quality was gathered through review of project documentation, discussions with field staff and key project stakeholders, and analysis of tablet data. In March 2015, a full prototype was deployed in Magburaka EMC, Sierra Leone. Inpatient data were captured on 204 clinical interactions with 34 patients from 5 March until 10 April 2015. Data continued to also be recorded on paper charts, creating theoretically identical record "pairs" on paper and tablet. 83 record pairs for 33 patients with 22 data items (temperature and symptoms) per pair were analysed. The overall Kappa coefficient for agreement between sources was 0.62, but reduced to 0.59 when rare bleeding symptoms were excluded, indicating moderate to good agreement. The time taken to deliver the product was more than that anticipated by MSF (7 months versus 6 weeks). Deployment of the tablet coincided with a dramatic drop in patient numbers and thus had little impact on patient care. We have identified lessons specific to humanitarian-technology collaborative projects and propose a framework for emergency humanitarian innovation. Time and effort is required to bridge differences in organisational culture between the technology and humanitarian worlds. This investment is essential for establishing a shared vision on deliverables, urgency, and ownership of product.

Indexed as

Ebolaelectronic medical recordsEMRhumanitarianinnovation

Identifiers

PMID28357037
PMCPMC5357025
OpenAlexW2473327186

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.