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Monitoring Mental Healthcare Services Using Business Analytics
OBJECTIVES: Monitoring healthcare activities is the first step for health stakeholders and health professionals to improve the quality and performance of healthcare services. However, monitoring remains a challenge for healthcare facilities, especially in developing countries. Fortunately, advances...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Medical Informatics
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278510/ https://www.ncbi.nlm.nih.gov/pubmed/32547811 http://dx.doi.org/10.4258/hir.2020.26.2.146 |
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author | Housbane, Samy Khoubila, Adil Ajbal, Khaoula Serhier, Zineb Agoub, Mohamed Battas, Omar Othmani, Mohamed Bennani |
author_facet | Housbane, Samy Khoubila, Adil Ajbal, Khaoula Serhier, Zineb Agoub, Mohamed Battas, Omar Othmani, Mohamed Bennani |
author_sort | Housbane, Samy |
collection | PubMed |
description | OBJECTIVES: Monitoring healthcare activities is the first step for health stakeholders and health professionals to improve the quality and performance of healthcare services. However, monitoring remains a challenge for healthcare facilities, especially in developing countries. Fortunately, advances in business analytics address this need. This paper aims to describe the experience of a low-income healthcare facility in a developing country in using business analytics descriptive techniques and to discuss business analytics implementation challenges and opportunities in such an environment. METHODS: Business analytics descriptive techniques were applied on 3 years’ electronic medical records of outpatient consultation of the University Psychiatric Centre (CPU) of Casablanca. Statistical analysis was conducted to compare results over years. RESULTS: Over the 3 monitored years, the monthly number of computerized physician order entries increased significantly (p < 0.001). Physicians improved their personal recording over years. Schizophrenia as well as depressive and bipolar disorders were noted at the top of outpatient mental disorders. Antipsychotics are the most prescribed drugs, and a significant annual decrease in outpatient care wait time was noted (p < 0.001). CONCLUSIONS: Business analytics allowed CPU to monitor mental healthcare outpatient activity and to adopt its business processes according to outcomes. However, challenges mainly in the organizational dimension of the decision-making process and the definition of strategic key metrics, data structuration, and the quality of data entry had to be considered for the optimal use of business analytics. |
format | Online Article Text |
id | pubmed-7278510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Korean Society of Medical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-72785102020-06-15 Monitoring Mental Healthcare Services Using Business Analytics Housbane, Samy Khoubila, Adil Ajbal, Khaoula Serhier, Zineb Agoub, Mohamed Battas, Omar Othmani, Mohamed Bennani Healthc Inform Res Case Report OBJECTIVES: Monitoring healthcare activities is the first step for health stakeholders and health professionals to improve the quality and performance of healthcare services. However, monitoring remains a challenge for healthcare facilities, especially in developing countries. Fortunately, advances in business analytics address this need. This paper aims to describe the experience of a low-income healthcare facility in a developing country in using business analytics descriptive techniques and to discuss business analytics implementation challenges and opportunities in such an environment. METHODS: Business analytics descriptive techniques were applied on 3 years’ electronic medical records of outpatient consultation of the University Psychiatric Centre (CPU) of Casablanca. Statistical analysis was conducted to compare results over years. RESULTS: Over the 3 monitored years, the monthly number of computerized physician order entries increased significantly (p < 0.001). Physicians improved their personal recording over years. Schizophrenia as well as depressive and bipolar disorders were noted at the top of outpatient mental disorders. Antipsychotics are the most prescribed drugs, and a significant annual decrease in outpatient care wait time was noted (p < 0.001). CONCLUSIONS: Business analytics allowed CPU to monitor mental healthcare outpatient activity and to adopt its business processes according to outcomes. However, challenges mainly in the organizational dimension of the decision-making process and the definition of strategic key metrics, data structuration, and the quality of data entry had to be considered for the optimal use of business analytics. Korean Society of Medical Informatics 2020-04 2020-04-30 /pmc/articles/PMC7278510/ /pubmed/32547811 http://dx.doi.org/10.4258/hir.2020.26.2.146 Text en © 2020 The Korean Society of Medical Informatics This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Case Report Housbane, Samy Khoubila, Adil Ajbal, Khaoula Serhier, Zineb Agoub, Mohamed Battas, Omar Othmani, Mohamed Bennani Monitoring Mental Healthcare Services Using Business Analytics |
title | Monitoring Mental Healthcare Services Using Business Analytics |
title_full | Monitoring Mental Healthcare Services Using Business Analytics |
title_fullStr | Monitoring Mental Healthcare Services Using Business Analytics |
title_full_unstemmed | Monitoring Mental Healthcare Services Using Business Analytics |
title_short | Monitoring Mental Healthcare Services Using Business Analytics |
title_sort | monitoring mental healthcare services using business analytics |
topic | Case Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278510/ https://www.ncbi.nlm.nih.gov/pubmed/32547811 http://dx.doi.org/10.4258/hir.2020.26.2.146 |
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