Evidence map›Paper›PMID 30071317›Full record

ArticleJournal of biomedical informatics2018

An approach to automatic process deviation detection in a time-critical clinical process.

Sen Yang, Aleksandra Sarcevic, Richard A Farneth, Shuhong Chen, Omar Z Ahmed, Ivan Marsic, Randall S Burd

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare.International journal of environmental research and public health · 2020
    Article
  5. Article
  6. 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.

Sen YangElectrical and Computer Engineering Department, Rutgers University, 94 Brett Road, Piscataway, NJ 08854, USA. Electronic address: sy358@rutgers.edu.
Aleksandra SarcevicCollege of Computing and Informatics, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA. Electronic address: aleksarc@drexel.edu.
Richard A FarnethChildren's National Medical Center, 111 Michigan Ave NW, Washington, DC 20010, USA.
Shuhong ChenElectrical and Computer Engineering Department, Rutgers University, 94 Brett Road, Piscataway, NJ 08854, USA. Electronic address: sc1624@rutgers.edu.
Omar Z AhmedChildren's National Medical Center, 111 Michigan Ave NW, Washington, DC 20010, USA. Electronic address: oahmed3@childrensnational.org.
Ivan MarsicElectrical and Computer Engineering Department, Rutgers University, 94 Brett Road, Piscataway, NJ 08854, USA. Electronic address: marsic@rutgers.edu.
Randall S BurdChildren's National Medical Center, 111 Michigan Ave NW, Washington, DC 20010, USA. Electronic address: rburd@childrensnational.org.

Funding

Intention-aware Recommender System for Improving Trauma Resuscitation OutcomesR01LM011834 · NLM · CHILDREN'S RESEARCH INSTITUTE · PI BURD, RANDALL S., MARSIC, IVAN · 2014 to 2023
$4.7M
NLM NIH HHS R01 LM011834
6 · The paper itself

Abstract

motivationPrior research has shown that minor errors and deviations from recommended guidelines in complex medical processes can accumulate to increase the likelihood that a major error will go uncorrected and lead to an adverse outcome. Real-time automatic and accurate detection of process deviations may help medical teams better prevent or mitigate the effect of errors and improve patient outcomes. Our goal was to develop an approach for automatic detection of errors and process deviations in trauma resuscitation.

methodsUsing video review, we coded activity traces of 95 pediatric trauma resuscitations collected in a Level 1 trauma center over two years (2014-2016). Twenty-four randomly selected activity traces were compared with a knowledge-driven model of trauma resuscitation workflow using a phase-based conformance checking algorithm for detecting true and false deviations (alarms). An analysis of false alarms identified three types of causes: (1) model gaps or discrepancies between the model ("work as imagined") and actual practice ("work as done"), (2) errors in activity traces coding, and (3) algorithm limitations. We repaired the system to remove model gaps, reduce coding errors, and address algorithm limitations. The repaired system was first evaluated with another 20 traces and then applied to the entire dataset of 95 traces.

resultsDuring the training, we detected 573 process deviations in 24 activity traces that include 1099 activities. Among these deviations, only 27% represented true deviations and the remaining 73% were false alarms. This initial deviation detection accuracy was only 66.6%, with a F

conclusionOur approach to automatic deviation detection provides a method for identifying repeated, omitted and out-of-sequence activities that can be included in the design of decision support systems for complex medical processes. Our findings show the importance of assessing detected deviations for repairing a knowledge-driven model that best represents "work as done."

Indexed as

AlgorithmsChildComputational BiologyComputer SystemsDecision Support Systems, ClinicalHumansMedical ErrorsProcess Assessment, Health CareResuscitationTrauma CentersVideo RecordingWorkflowHuman errorProcess deviationsProcess miningTrauma resuscitationWorkflow compliance

Identifiers

PMID30071317
PMCPMC6167602

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Registered trials

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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.