Evidence map›Paper›PMID 34785532›Full record

ArticleCMAJ open

Analyzing supply and demand on a general internal medicine ward: a cross-sectional study.

Michael Fralick, Neal Kaw, Mingkun Wang, Muhammad Mamdani, Ophyr Mourad

Open access · diamondAbstract read
In one paragraph

Article in CMAJ open. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 32% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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 at 1 institution in 1 country.

Michael FralickSinai Health System and the Department of Medicine (Fralick), University of Toronto; Li Ka Shing Centre for Healthcare Analytics Research and Training (Fralick, Kaw, Wang, Mamdani), St. Michael's Hospital, Toronto, Ont.; Operations Research Center (Kaw), Massachusetts Institute of Technology, Cambridge, Mass.; Unity Health (Mourad), Department of Medicine, University of Toronto, Toronto, Ont. mike.fralick@mail.utoronto.ca.
Neal KawSinai Health System and the Department of Medicine (Fralick), University of Toronto; Li Ka Shing Centre for Healthcare Analytics Research and Training (Fralick, Kaw, Wang, Mamdani), St. Michael's Hospital, Toronto, Ont.; Operations Research Center (Kaw), Massachusetts Institute of Technology, Cambridge, Mass.; Unity Health (Mourad), Department of Medicine, University of Toronto, Toronto, Ont.
Mingkun WangSinai Health System and the Department of Medicine (Fralick), University of Toronto; Li Ka Shing Centre for Healthcare Analytics Research and Training (Fralick, Kaw, Wang, Mamdani), St. Michael's Hospital, Toronto, Ont.; Operations Research Center (Kaw), Massachusetts Institute of Technology, Cambridge, Mass.; Unity Health (Mourad), Department of Medicine, University of Toronto, Toronto, Ont.
Muhammad MamdaniSinai Health System and the Department of Medicine (Fralick), University of Toronto; Li Ka Shing Centre for Healthcare Analytics Research and Training (Fralick, Kaw, Wang, Mamdani), St. Michael's Hospital, Toronto, Ont.; Operations Research Center (Kaw), Massachusetts Institute of Technology, Cambridge, Mass.; Unity Health (Mourad), Department of Medicine, University of Toronto, Toronto, Ont.
Ophyr MouradSinai Health System and the Department of Medicine (Fralick), University of Toronto; Li Ka Shing Centre for Healthcare Analytics Research and Training (Fralick, Kaw, Wang, Mamdani), St. Michael's Hospital, Toronto, Ont.; Operations Research Center (Kaw), Massachusetts Institute of Technology, Cambridge, Mass.; Unity Health (Mourad), Department of Medicine, University of Toronto, Toronto, Ont.
University of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe capacity of general internal medicine (GIM) clinical teaching units has been strained by decreasing resident supply and increasing patient demand. The objective of our study was to compare the number of residents (supply) with the volume and duration of patient care activities (demand) to identify inefficiency.

methodsUsing the most recently available data from an academic teaching hospital in Toronto, Ontario, we identified each occurrence of a set of patient care activities that took place on the clinical teaching unit from 2015 to 2019. We completed a descriptive analysis of the frequencies of these activities and how the frequencies varied by hour, day, week, month and year. Patient care activities included admissions, rounds, responding to pages, meeting with patients and their families, patient transfers, discharges and responding to cardiac arrests. The estimated time to complete each task was based on the available data in our electronic medical record system and interviews with GIM physicians and trainees. To calculate resident utilization, the person-hours of patient care tasks was divided by the person-hours of resident supply. Resident utilization was computed for 3 scenarios corresponding to varying levels of resident absenteeism.

resultsDuring the study period, there were 14 581 consultations to GIM from the emergency department. Patient volumes tended to be highest during January and lowest during May and June, and highest on Monday morning and lowest on Friday night. Daily admissions to hospital from the emergency department were higher on weekdays than on weekends, and hourly admissions peaked at 8 am and between 3 pm and 1 am. Weekday resident utilization was generally highest between 8 am and 2 pm, and lowest between 1 am and 8 am. In a scenario in which all residents were present apart from those who were post-call, resident utilization generally never exceeded 100%; in scenarios in which at least 1 resident was absent owing to illness or vacation, it was common for resident utilization to approach or exceed 100%, particularly during daytime working hours.

interpretationAnalyzing supply and demand on a GIM ward has allowed us to identify periods when supply and demand are not aligned and to demonstrate empirically the vulnerability of current staffing models. These data have the potential to inform and optimize scheduling on an internal medicine ward.

Indexed as

Health Services Needs and DemandInternship and ResidencyPersonnel Staffing and SchedulingCross-Sectional StudiesHospitals, TeachingHumansInternal MedicineModels, TheoreticalOntarioPatient AdmissionPatients' RoomsSeasons

Identifiers

PMID34785532
PMCPMC8598238
OpenAlexW3213263703

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.