Evidence map›Paper›PMID 35675075›Full record

ArticleJAMA network open2022

Use of a Real-Time Locating System to Assess Internal Medicine Resident Location and Movement in the Hospital.

Michael A Rosen, Amanda K Bertram, Monica Tung, Sanjay V Desai, Brian T Garibaldi

Abstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. A Systems Approach to Improving Well-Being in Graduate Medical Education.Academic medicine : journal of the Association of American Medical Colleges · 2025
    Article
  5. Article
  6. Article
  7. Machine Intelligence in Cerebrovascular and Endovascular Neurosurgery.Advances in experimental medicine and biology · 2024
    Review
  8. Three Missed Critical Nursing Care Processes on Labor and Delivery Units During the COVID-19 Pandemic.Journal of obstetric, gynecologic, and neonatal nursing : JOGNN · 2023
    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

5 authors.

Michael A RosenArmstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Amanda K BertramDivision of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Monica Tungmedical student at Johns Hopkins University School of Medicine, Baltimore, Maryland.
Sanjay V DesaiDivision of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Brian T GaribaldiDivision of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The patient-physician clinical encounter is the cornerstone of medical training, yet residents spend as little as 12% of their time in direct patient contact. Objectives: To use a real-time locating system (RTLS) to characterize intern work experiences in the hospital, understand factors associated with time spent at patients' bedsides, and inform future interventions to increase time spent with patients. Design, Setting, and Participants: This cross-sectional study was conducted from July 1, 2018, to June 30, 2019 (ie, the academic year 2018-2019). Internal medicine residents from postgraduate year 1 (interns) at an academic medical center wore an infrared badge that recorded location and duration (eg, patient room, ward hall, physician workroom). Data were analyzed from September 1, 2020, to August 30, 2021. Main Outcomes and Measures: Main outcome was time (in minutes) at the bedside; the unit of analysis was a 24-hour intern day or interval of time within the day (eg, rounding period). Descriptive statistics are reported overall, by intern, and for 5 clinical service categories. Multilevel modeling assessed the association of intern, service, and calendar time with time spent at the bedside. Results: Data from 43 of 52 interns (82.7%) encompassing 95 275 hours of observations were included for analyses. Twenty-six interns (60.5%) were women. Interns were detected for a mean (SD) of 722.8 (194.4) minutes per 24-hour period; 13.4% of this time was spent in patient rooms (mean [SD] time, 96.8 [57.2] minutes) and 33.3% in physician workrooms (mean [SD] time, 240.9 [228.8] minutes). Mean percentage of time at the bedside during a 24-hour period varied among interns from 8.8% to 18.3%. Mean (SD) percentage of time at the bedside varied by service for the 24-hour period from 11.7% (6.6%) for nononcology subspecialties to 15.4% (6.0%) for oncology, and during rounds from 8.0% (12.4%) for nononcology subspecialties to 26.5% (12.1%) for oncology. In multilevel modeling, the individual intern accounted for 8.1% of overall variance in time spent at the bedside during a 24-hour period, and service accounted for 18.0% of variance during rounds. Conclusions and Relevance: The findings of this cross-sectional study support previous evidence suggesting that interns spend only a small proportion of time with hospitalized patients. The differences in time spent in patients' rooms among interns and during rounds constitute an opportunity to design interventions that bring trainees back to the bedside.

Indexed as

Internship and ResidencyPhysiciansCross-Sectional StudiesFemaleHospitalsHumansMaleTime and Motion Studies

Identifiers

PMID35675075
PMCPMC9178434

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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.