Evidence map›Paper›PMID 28871456›Full record

ArticleHealth care management science2019

Queueing theoretic analysis of labor and delivery : Understanding management styles and C-section rates.

Matthew Gombolay, Toni Golen, Neel Shah, Julie Shah

Abstract read
PubMed Publisher
In one paragraph

Article in Health care management science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.4field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 16 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

4 authors at 3 institutions in 1 country.

Matthew GombolayGeorgia Institute of Technology, North Ave NW, Atlanta, GA, 30332, USA. gombolay@mit.edu.ORCID http://orcid.org/0000-0002-5321-6038
Toni GolenBeth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA.
Neel ShahBeth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA, 02215, USA.
Julie ShahMassachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, 02139, USA.
Beth Israel Deaconess Medical Center · USGeorgia Institute of Technology · USMassachusetts Institute of Technology · US

Funding

National Science Foundation 2388357
6 · The paper itself

Abstract

Childbirth is a complex clinical service requiring the coordinated support of highly trained healthcare professionals as well as management of a finite set of critical resources (such as staff and beds) to provide safe care. The mode of delivery (vaginal delivery or cesarean section) has a significant effect on labor and delivery resource needs. Further, resource management decisions may impact the amount of time a physician or nurse is able to spend with any given patient. In this work, we employ queueing theory to model one year of transactional patient information at a tertiary care center in Boston, Massachusetts. First, we observe that the M/G/∞ model effectively predicts patient flow in an obstetrics department. This model captures the dynamics of labor and delivery where patients arrive randomly during the day, the duration of their stay is based on their individual acuity, and their labor progresses at some rate irrespective of whether they are given a bed. Second, using our queueing theoretic model, we show that reducing the rate of cesarean section - a current quality improvement goal in American obstetrics - may have important consequences with regard to the resource needs of a hospital. We also estimate the potential financial impact of these resource needs from the hospital perspective. Third, we report that application of our model to an analysis of potential patient coverage strategies supports the adoption of team-based care, in which attending physicians share responsibilities for patients.

Indexed as

Labor, ObstetricBed OccupancyCesarean SectionDelivery, ObstetricFemaleHumansModels, StatisticalObstetrics and Gynecology Department, HospitalPersonnel Staffing and SchedulingPregnancyC-section rateHealthcare costHospital managementHypercube modelLabor and deliveryObstetricsQueueing theory

Identifiers

PMID28871456
OpenAlexW2752482664

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.