Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Ghanaian Clinics Systems,

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Bibliographic Details
Main Author: Adarkwa, Kofi
Format: Recurso digital
Language:English
Published: Zenodo 2004
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author Adarkwa, Kofi
author_facet Adarkwa, Kofi
contents <p>The study aims to evaluate clinical outcomes in rural Ghanaian clinics by implementing a time-series forecasting model. A time-series forecasting model was developed using historical data from rural Ghanaian clinics, with a focus on patient outcomes over a one-year period. The model incorporates statistical techniques to predict future trends based on past performance. The model demonstrated an accuracy rate of 85% in predicting clinical outcomes, indicating its potential for improving healthcare delivery and resource allocation. The time-series forecasting model proved effective in evaluating clinical outcomes in rural Ghanaian clinics, offering a robust tool for monitoring and enhancing healthcare systems. Further research should be conducted to validate the model across different geographical regions and clinic settings. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_18780806
institution Zenodo
language eng
publishDate 2004
publisher Zenodo
record_format zenodo
spellingShingle Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Ghanaian Clinics Systems,
Adarkwa, Kofi
Rural
Ghana
Time-series
Forecasting
Evaluation
Methodology
Analytics
<p>The study aims to evaluate clinical outcomes in rural Ghanaian clinics by implementing a time-series forecasting model. A time-series forecasting model was developed using historical data from rural Ghanaian clinics, with a focus on patient outcomes over a one-year period. The model incorporates statistical techniques to predict future trends based on past performance. The model demonstrated an accuracy rate of 85% in predicting clinical outcomes, indicating its potential for improving healthcare delivery and resource allocation. The time-series forecasting model proved effective in evaluating clinical outcomes in rural Ghanaian clinics, offering a robust tool for monitoring and enhancing healthcare systems. Further research should be conducted to validate the model across different geographical regions and clinic settings. Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.</p>
title Time-Series Forecasting Model for Evaluating Clinical Outcomes in Rural Ghanaian Clinics Systems,
topic Rural
Ghana
Time-series
Forecasting
Evaluation
Methodology
Analytics
url https://doi.org/10.5281/zenodo.18780806