Evidence map›Paper›PMID 26210361›Full record

ArticleJournal of biomedical informatics2015

Ease of adoption of clinical natural language processing software: An evaluation of five systems.

Kai Zheng, V G Vinod Vydiswaran, Yang Liu, Yue Wang, Amber Stubbs, Özlem Uzuner, Anupama E Gururaj, Samuel Bayer, John Aberdeen, Anna Rumshisky and 3 more

Abstract readComparative StudyEvaluation Study
In one paragraph

Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Using Natural Language Processing of Clinical Notes to Predict Outcomes of Opioid Treatment Program.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022
    Article
  8. Combining human and machine intelligence for clinical trial eligibility querying.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
  9. Article
  10. Article
  11. DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  12. Article
  13. Article
  14. A Neuro-ontology for the neurological examination.BMC medical informatics and decision making · 2020
    Article
  15. Article
  16. Observational
  17. Review
  18. Article
  19. CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  20. 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

13 authors.

Kai ZhengSchool of Public Health Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA; School of Information, University of Michigan, Ann Arbor, MI, USA. Electronic address: kzheng@umich.edu.
V G Vinod VydiswaranDepartment of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
Yang LiuSchool of Information, University of Michigan, Ann Arbor, MI, USA.
Yue WangDepartment of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Amber StubbsSchool of Library and Information Science, Simmons College, Boston, MA, USA.
Özlem UzunerDepartment of Information Studies, University at Albany, SUNY, Albany, NY, USA.
Anupama E GururajThe University of Texas School of Biomedical Informatics at Houston, Houston, TX, USA.
Samuel BayerThe MITRE Corporation, Bedford, MA, USA.
John AberdeenThe MITRE Corporation, Bedford, MA, USA.
Anna RumshiskyDepartment of Computer Science, University of Massachusetts, Lowell, MA, USA.
Serguei PakhomovUniversity of Minnesota, Minneapolis, MN, USA.
Hongfang LiuDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Hua XuThe University of Texas School of Biomedical Informatics at Houston, Houston, TX, USA. Electronic address: hua.xu@uth.tmc.edu.

Funding

Informatics for Integrating Biology/ the Bedside (RMI)U54LM008748 · NLM · BRIGHAM AND WOMEN'S HOSPITAL · PI KOHANE, ISAAC S. · 2004 to 2013
$39.0M
Secondary use of EMRs for surgical complication surveillanceR01EB019403 · NIBIB · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, STORLIE, CURTIS · 2015 to 2022
$3.8M
Michigan Institute for Clinical and Health Research (MICHR) (KL2)KL2RR024987 · NCRR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI SHANLEY, THOMAS P. · 2007 to 2011
$2.8M
Natural language processing for clinical and translational researchR01GM102282 · NIGMS · MAYO CLINIC ROCHESTER · PI LIU, HONGFANG, PAKHOMOV, SERGUEI VS · 2013 to 2016
$2.5M
Michigan Institute for Clinical and Health Research (MCHR)KL2TR000434 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI MASHOUR, GEORGE ALEXANDER · 2012 to 2016
$1.7M
Challenges in Natural Language Processing in Clinical TextR13LM011411 · NLM · STATE UNIVERSITY OF NEW YORK AT ALBANY · PI UZUNER, OZLEM · 2012 to 2016
$98k
NCATS NIH HHS KL2 TR000434NCRR NIH HHS KL2 RR024987NIBIB NIH HHS R01 EB019403NIGMS NIH HHS R01 GM102282NLM NIH HHS R13 LM011411NLM NIH HHS U54 LM008748
6 · The paper itself

Abstract

objectiveIn recognition of potential barriers that may inhibit the widespread adoption of biomedical software, the 2014 i2b2 Challenge introduced a special track, Track 3 - Software Usability Assessment, in order to develop a better understanding of the adoption issues that might be associated with the state-of-the-art clinical NLP systems. This paper reports the ease of adoption assessment methods we developed for this track, and the results of evaluating five clinical NLP system submissions. MATERIALS AND

methodsA team of human evaluators performed a series of scripted adoptability test tasks with each of the participating systems. The evaluation team consisted of four "expert evaluators" with training in computer science, and eight "end user evaluators" with mixed backgrounds in medicine, nursing, pharmacy, and health informatics. We assessed how easy it is to adopt the submitted systems along the following three dimensions: communication effectiveness (i.e., how effective a system is in communicating its designed objectives to intended audience), effort required to install, and effort required to use. We used a formal software usability testing tool, TURF, to record the evaluators' interactions with the systems and 'think-aloud' data revealing their thought processes when installing and using the systems and when resolving unexpected issues.

resultsOverall, the ease of adoption ratings that the five systems received are unsatisfactory. Installation of some of the systems proved to be rather difficult, and some systems failed to adequately communicate their designed objectives to intended adopters. Further, the average ratings provided by the end user evaluators on ease of use and ease of interpreting output are -0.35 and -0.53, respectively, indicating that this group of users generally deemed the systems extremely difficult to work with. While the ratings provided by the expert evaluators are higher, 0.6 and 0.45, respectively, these ratings are still low indicating that they also experienced considerable struggles. DISCUSSION: The results of the Track 3 evaluation show that the adoptability of the five participating clinical NLP systems has a great margin for improvement. Remedy strategies suggested by the evaluators included (1) more detailed and operation system specific use instructions; (2) provision of more pertinent onscreen feedback for easier diagnosis of problems; (3) including screen walk-throughs in use instructions so users know what to expect and what might have gone wrong; (4) avoiding jargon and acronyms in materials intended for end users; and (5) packaging prerequisites required within software distributions so that prospective adopters of the software do not have to obtain each of the third-party components on their own.

Indexed as

Attitude to ComputersNatural Language ProcessingSoftwareData MiningElectronic Health RecordsHumansMiddle AgedPattern Recognition, AutomatedUser-Computer InterfaceHuman–computer interactionNatural language processing [L01.224.065.580]Software design [L01.224.900.820]Software validation [L01.224.900.868]UsabilityUser-computer interface [L01.224.900.910]

Identifiers

PMID26210361
PMCPMC4974203

What OpenQuestion holds

Textmetadata
LicenceTDM
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.