PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 25, 20260 citationsOpen Access

Time-Series Forecasting Model for Evaluating District Hospitals Systems in South Africa,

View Full Paper
NMNkosi Maselekoa

Key Points

  • The research aims to evaluate the performance of district hospitals in South Africa using a time-series forecasting model.
  • Employs a longitudinal study design focusing on district hospitals in South Africa.
  • Utilizes an autoregressive integrated moving average (ARIMA) model for analysis.
  • Incorporates robust standard errors to quantify uncertainty in forecasts.
  • Observes a consistent decline in patient waiting times by approximately 10% over five years.
  • Demonstrates the effectiveness of ARIMA models in forecasting hospital system reliability.

Abstract

In South Africa, district hospitals play a crucial role in healthcare delivery, especially for underserved populations. A longitudinal study employing a time-series forecasting model to evaluate hospital systems' performance. The model includes an autoregressive integrated moving average (ARIMA) equation with robust standard errors for uncertainty quantification. The ARIMA model identified a consistent decline in patient waiting times by approximately 10% over the five-year period, indicating system improvements despite challenges. This study validates the effectiveness of ARIMA models in forecasting hospital system reliability and suggests further refinement for broader application. District health authorities should consider implementing similar forecasting methods to enhance resource allocation and patient care. district hospitals, time-series analysis, forecasting, South Africa, healthcare systems Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Nkosi Maselekoa (2002) studied this question.

synapsesocial.com/papers/699e91d7f5123be5ed04fae1https://doi.org/10.5281/zenodo.18742412
Ask AI
Helpful
Bookmark
Share
View Full Paper