Evidence map›Paper›PMID 23722634›Full record

ArticleInteractive journal of medical research2013

Health care transformation through collaboration on open-source informatics projects: integrating a medical applications platform, research data repository, and patient summarization.

Jeffrey G Klann, Allison B McCoy, Adam Wright, Nich Wattanasin, Dean F Sittig, Shawn N Murphy

Abstract read
In one paragraph

Article in Interactive journal of medical research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

  1. Clinical Data Warehousing: A Scoping Review.Journal of the Society for Clinical Data Management · 2024
    Article
  2. Article
  3. Article
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  5. Article
  6. Research on Clinical Preventive Services for Adolescents and Young Adults: Where Are We and Where Do We Need to Go?The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2017
    Review
  7. Article
  8. Review
  9. Automated methods for the summarization of electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2015
    Review
  10. Article
  11. Article
  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

6 authors.

Jeffrey G KlannHarvard Medical School, Boston, MA, United States. jklann@partners.org.ORCID 0000-0003-2043-1601
Allison B McCoy
Adam Wright
Nich Wattanasin
Dean F Sittig
Shawn N Murphy

Funding

Heartland Institute for Clinical and Translational ResearchUL1TR000001 · NCATS · UNIVERSITY OF KANSAS MEDICAL CENTER · PI BAROHN, RICHARD J. · 2012 to 2015
$13.0M
NCATS NIH HHS UL1 TR000001
6 · The paper itself

Abstract

backgroundThe Strategic Health IT Advanced Research Projects (SHARP) program seeks to conquer well-understood challenges in medical informatics through breakthrough research. Two SHARP centers have found alignment in their methodological needs: (1) members of the National Center for Cognitive Informatics and Decision-making (NCCD) have developed knowledge bases to support problem-oriented summarizations of patient data, and (2) Substitutable Medical Apps, Reusable Technologies (SMART), which is a platform for reusable medical apps that can run on participating platforms connected to various electronic health records (EHR). Combining the work of these two centers will ensure wide dissemination of new methods for synthesized views of patient data. Informatics for Integrating Biology and the Bedside (i2b2) is an NIH-funded clinical research data repository platform in use at over 100 sites worldwide. By also working with a co-occurring initiative to SMART-enabling i2b2, we can confidently write one app that can be used extremely broadly.

objectiveOur goal was to facilitate development of intuitive, problem-oriented views of the patient record using NCCD knowledge bases that would run in any EHR. To do this, we developed a collaboration between the two SHARPs and an NIH center, i2b2.

methodsFirst, we implemented collaborative tools to connect researchers at three institutions. Next, we developed a patient summarization app using the SMART platform and a previously validated NCCD problem-medication linkage knowledge base derived from the National Drug File-Reference Terminology (NDF-RT). Finally, to SMART-enable i2b2, we implemented two new Web service "cells" that expose the SMART application programming interface (API), and we made changes to the Web interface of i2b2 to host a "carousel" of SMART apps.

resultsWe deployed our SMART-based, NDF-RT-derived patient summarization app in this SMART-i2b2 container. It displays a problem-oriented view of medications and presents a line-graph display of laboratory results.

conclusionsThis summarization app can be run in any EHR environment that either supports SMART or runs SMART-enabled i2b2. This i2b2 "clinical bridge" demonstrates a pathway for reusable app development that does not require EHR vendors to immediately adopt the SMART API. Apps can be developed in SMART and run by clinicians in the i2b2 repository, reusing clinical data extracted from EHRs. This may encourage the adoption of SMART by supporting SMART app development until EHRs adopt the platform. It also allows a new variety of clinical SMART apps, fueled by the broad aggregation of data types available in research repositories. The app (including its knowledge base) and SMART-i2b2 are open-source and freely available for download.

Indexed as

clinical information systemsdata displaydiffusion of innovationknowledge basesmedical informaticsuser-computer interface

Identifiers

PMID23722634
PMCPMC3668611

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.