Evidence map›Paper›PMID 24607863›Full record

ReviewJournal of biomedical informatics2014

Time motion studies in healthcare: what are we talking about?

Marcelo Lopetegui, Po-Yin Yen, Albert Lai, Joseph Jeffries, Peter Embi, Philip Payne

Abstract readReview
In one paragraph

Review in Journal of biomedical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
145citing papers in PubMed, 2 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

145 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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85 more citing papers are in PubMed but not listed here.

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.

Marcelo LopeteguiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USA. Electronic address: marcelo.lopetegui@osumc.edu.
Po-Yin YenDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Albert LaiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Joseph JeffriesDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Peter EmbiDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
Philip PayneDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.

Funding

University of Iowa Clinical and Translational Science Program (TL1)UL1RR025755 · NCRR · OHIO STATE UNIVERSITY · PI JACKSON, REBECCA D · 2008 to 2011
$26.3M
The Ohio State University Center for clinical and Translational ScienceUL1TR001070 · NCATS · OHIO STATE UNIVERSITY · PI JACKSON, REBECCA D · 2013 to 2017
$22.6M
NCATS NIH HHS UL1 TR001070NCRR NIH HHS UL1 RR025755NCRR NIH HHS UL1-RR025755-04
6 · The paper itself

Abstract

Time motion studies were first described in the early 20th century in industrial engineering, referring to a quantitative data collection method where an external observer captured detailed data on the duration and movements required to accomplish a specific task, coupled with an analysis focused on improving efficiency. Since then, they have been broadly adopted by biomedical researchers and have become a focus of attention due to the current interest in clinical workflow related factors. However, attempts to aggregate results from these studies have been difficult, resulting from a significant variability in the implementation and reporting of methods. While efforts have been made to standardize the reporting of such data and findings, a lack of common understanding on what "time motion studies" are remains, which not only hinders reviews, but could also partially explain the methodological variability in the domain literature (duration of the observations, number of tasks, multitasking, training rigor and reliability assessments) caused by an attempt to cluster dissimilar sub-techniques. A crucial milestone towards the standardization and validation of time motion studies corresponds to a common understanding, accompanied by a proper recognition of the distinct techniques it encompasses. Towards this goal, we conducted a review of the literature aiming at identifying what is being referred to as "time motion studies". We provide a detailed description of the distinct methods used in articles referenced or classified as "time motion studies", and conclude that currently it is used not only to define the original technique, but also to describe a broad spectrum of studies whose only common factor is the capture and/or analysis of the duration of one or more events. To maintain alignment with the existing broad scope of the term, we propose a disambiguation approach by preserving the expanded conception, while recommending the use of a specific qualifier "continuous observation time motion studies" to refer to variations of the original method (the use of an external observer recording data continuously). In addition, we present a more granular naming for sub-techniques within continuous observation time motion studies, expecting to reduce the methodological variability within each sub-technique and facilitate future results aggregation.

Indexed as

Delivery of Health CareTime and Motion StudiesDatabase Management SystemsMedical Subject HeadingsMethodsStandardizationTime and Motion Studies

Identifiers

PMID24607863
PMCPMC4058370

What OpenQuestion holds

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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.