ArticleJournal of medical systems2022
A Mobile App for the Precise Measurement of Healthcare Provider Activity Times to Support Time-Driven Activity Based Costing Studies.
Article in Journal of medical systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Cost of physiotherapy non-attendance at a metropolitan hospital in Australia: A time-driven activity-based costing study.BMJ open · 2025Article
- Time-Driven Activity-Based Costing (TDABC) applied in a chemotherapy department of a public reference oncology hospital.Einstein (Sao Paulo, Brazil) · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The duration of activities performed by healthcare providers are pivotal to Time-Driven Activity-Based Costing (TDABC) models. This study examines the use of a smartphone mobile application technology to record activity times. This study validates the accuracy of activity times recorded on a smartphone mobile application, dTool, compared to observed length of time recordings in the operating room. For analysis, we performed two one-sided tests for the measurements "Case Start" and "Case End". Equivalence bounds were specified in terms of raw mean difference of 1 min (upper) and -1 min (lower). The total number of comparisons in the observer protocol was 72 (32 "case start" patient comparisons and 40 "case end" patient comparisons measured over 45 individual OR cases). Given equivalence bounds of -1.000 and 1.000 (on a raw scale) and an alpha of 0.05, both equivalence tests were significant: provider and third-party observer protocol presented t(40) = 3.228 and p = < 0.001; observer timing protocol presented t(68.68) = 56.762, p = < 0.001. Conclusions: With this novel smartphone technology, a healthcare provider can reliably self-record activity LoT using dTool while providing patient care. Future TDABC studies incorporating this technology will reduce the potential operational barriers to implementation.
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Registered trials
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