Evidence map›Paper›PMID 38615340›Full record

ArticleJournal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing2024

Characterizing nursing time with patients using computer vision.

Carolyn Sun, Caroline Fu, Kenrick Cato

Abstract read
In one paragraph

Article in Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Carolyn SunHunter College and Columbia University, New York, New York, USA.ORCID 0000-0001-9628-6901
Caroline FuNYC Administration for Children's Services, New York, New York, USA.
Kenrick CatoChildren's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID 0000-0002-0704-3826

Funding

AHRQ HHS R03 HS027006AHRQ HHS R03 HS27006-01A1
6 · The paper itself

Abstract

backgroundCompared to other providers, nurses spend more time with patients, but the exact quantity and nature of those interactions remain largely unknown. The purpose of this study was to characterize the interactions of nurses at the bedside using continuous surveillance over a year long period.

methodsNurses' time and activity at the bedside were characterized using a device that integrates the use of obfuscated computer vision in combination with a Bluetooth beacon on the nurses' identification badge to track nurses' activities at the bedside. The surveillance device (AUGi) was installed over 37 patient beds in two medical/surgical units in a major urban hospital. Forty-nine nurse users were tracked using the beacon. Data were collected 4/15/19-3/15/20. Statistics were performed to describe nurses' time and activity at the bedside.

resultsA total of n = 408,588 interactions were analyzed over 670 shifts, with >1.5 times more interactions during day shifts (n = 247,273) compared to night shifts (n = 161,315); the mean interaction time was 3.34 s longer during nights than days (p < 0.0001). Each nurse had an average of 7.86 (standard deviation [SD] = 10.13) interactions per bed each shift and a mean total interaction time per bed of 9.39 min (SD = 14.16). On average, nurses covered 7.43 beds (SD = 4.03) per shift (day: mean = 7.80 beds/nurse/shift, SD = 3.87; night: mean = 7.07/nurse/shift, SD = 4.17). The mean time per hourly rounding (HR) was 69.5 s (SD = 98.07) and 50.1 s (SD = 56.58) for bedside shift report. DISCUSSION: As far as we are aware, this is the first study to provide continuous surveillance of nurse activities at the bedside over a year long period, 24 h/day, 7 days/week. We detected that nurses spend less than 1 min giving report at the bedside, and this is only completed 20.7% of the time. Additionally, hourly rounding was completed only 52.9% of the time and nurses spent only 9 min total with each patient per shift. Further study is needed to detect whether there is an optimal timing or duration of interactions to improve patient outcomes. CLINICAL RELEVANCE: Nursing time with the patient has been shown to improve patient outcomes but precise information about how much time nurses spend with patients has been heretofore unknown. By understanding minute-by-minute activities at the bedside over a full year, we provide a full picture of nursing activity; this can be used in the future to determine how these activities affect patient outcomes.

Indexed as

Nursing Staff, HospitalHumansNurse-Patient RelationsTime Factorsbiometrycomputerscomputer visiondecision supporthospitalmedical‐surgical nursingnursing staffpatientstechnology

Identifiers

PMID38615340
PMCPMC12879746

What OpenQuestion holds

Textmetadata
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.