ArticleHealth services research2013
Practice variation, bias, and experiential learning in cesarean delivery: a data-based system dynamics approach.
Article in Health services research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled 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.
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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models.BMC health services research · 2019Pooled it
- Tools for Supporting the MCH Workforce in Addressing Complex Challenges: A Scoping Review of System Dynamics Modeling in Maternal and Child Health.Maternal and child health journal · 2022Article
- Article
- Capacity and patient flow planning in post-term pregnancy outpatient clinics: a computer simulation modelling study.BMC health services research · 2020Article
- From behavioural simulation to computer models: how simulation can be used to improve healthcare management and policy.BMJ simulation & technology enhanced learning · 2020Review
- Simulation model of the relationship between cesarean section rates and labor duration.Health care management science · 2019Article
- A discrete event simulation model to evaluate the use of community services in the treatment of patients with Parkinson's disease in the United Kingdom.BMC health services research · 2017Article
- Modeling the hypothalamus-pituitary-adrenal axis: A review and extension.Mathematical biosciences · 2015Review
- Statewide Hospital Discharge Data: Collection, Use, Limitations, and Improvements.Health services research · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo simulate physician-driven dynamics of delivery mode decisions (scheduled cesarean delivery [CD] vs. vaginal delivery [VD] vs. unplanned CD after labor), and to evaluate a behavioral theory of how experiential learning leads to emerging bias toward more CD and practice variation across obstetricians. DATA SOURCES/STUDY
settingHospital discharge data on deliveries performed by 300 randomly selected obstetricians in Florida who finished obstetrics residency and started practice after 1991. STUDY
designWe develop a system dynamics simulation model of obstetricians' delivery mode decision based on the literature of experiential learning. We calibrate the model and investigate the extent to which the model replicates the data. PRINCIPAL
findingsOur learning-based simulation model replicates the empirical data, showing that physicians are more likely to schedule CD as they practice longer. Variation in CD rates is related to the way that physicians learn from outcomes of past decisions and accumulate experience.
conclusionsThe repetitive nature of medical decision making, learning from past practice, and accumulating experience can account for increases in CD decisions and practice variation across physicians. Policies aimed at improving medical decision making should account for providers' feedback-based learning mechanisms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.