ReviewHealthcare policy = Politiques de sante2016
A Review of Discharge-Prediction Processes in Acute Care Hospitals.
Review in Healthcare policy = Politiques de sante, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Evaluating the Machine Learning Models in Predicting Intensive Care Unit Discharge for Neurosurgical Patients Undergoing Craniotomy: A Big Data Analysis.Neurocritical care · 2025Article
- Concurrent prediction of in-hospital mortality and length of stay using single-task, multiclass and multitask machine learning.BMJ digital health & AI · 2025Article
- Early Expected Discharge Date Accuracy During Hospitalization: A Multivariable Analysis.Journal of medical systems · 2023Article
- Planning with care complexity: Factors related to discharge delays of hospitalised people with disability.Health & social care in the community · 2022Article
- Early prediction of patient discharge disposition in acute neurological care using machine learning.BMC health services research · 2022Article
- Predicting next-day discharge via electronic health record access logs.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Understanding the Accuracy of Clinician Provided Estimated Discharge Dates.Journal of medical systems · 2021Observational
- Application of Predictive Modelling to Improve the Discharge Process in Hospitals.Healthcare informatics research · 2020Article
- Development and Validation of a Machine Learning Model to Aid Discharge Processes for Inpatient Surgical Care.JAMA network open · 2019Observational
- Video intervention to improve incident reporting among medical trainees.BMJ open quality · 2019Article
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
Abstract
AIMS AND
objectivesDischarge prediction is designed to streamline inpatient flow and reduce hospital overcrowding without adding capacity. This study's objective was to describe the literature on discharge prediction and assess its usefulness in evaluating the implementation and outcomes of discharge prediction projects.
methodsThe authors reviewed the current peer-reviewed and grey literature on discharge prediction projects in acute care hospitals. Project descriptions were analyzed using Donabedian's structure-process-outcome model for evaluating complex healthcare innovations.
resultsThe review revealed a paucity of literature on the use and effectiveness of discharge prediction. There is high variation in its use and generally poor reporting of both implementation and outcomes.
conclusionsThe literature on discharge prediction generally lacks the descriptive detail that would be useful to parties considering or planning a discharge prediction initiative. Further study is required to determine how best to integrate these prediction tools into acute care hospitals.
Indexed as
Identifiers
28032828PMC5221715What 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.