Quelques erreurs courantes d'analyse d'un système d'information sanitaire (RD Congo)

Mapatano, Mala Ali; Piripiri, L.

doi:10.3917/spub.054.0551

Cited by 12 publications

(5 citation statements)

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“…Similarly, use of information for planning and decision-making was found to be weak in Brazil (Da Silva & Laprega 2005). Many factors contributed to underperforming information systems, such as difficulty in calculating indicators because of poor choices for denominators in the Democratic Republic of Congo (Mapatano & Piripiri 2005). Also observed were errors in HIS reports; inadequacies in computerisation, human, and capital resources; and low management support in Kenya (Odhiambo-Otieno 2005b).…”

Section: Previous Literaturesmentioning

confidence: 99%

Level of data quality from Health Management Information Systems in a resources limited setting and its associated factors, eastern Ethiopia

Teklegiorgis

Tadesse

Mirutse

et al. 2016

S. Afr. j. inf. manag.

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Background: A Health Information System (HIS) is a system that integrates data collection, processing, reporting, and use of the information necessary for improving health service effectiveness and efficiency through better management at all levels of health services. Despite the credible use of HIS for evidence-based decision-making, countries with the highest burden of ill health and the most in need of accurate and timely data have the weakest HIS in the vast majority of world’s poorest countries. Although a Health Management Information System (HMIS) forms a backbone for strong health systems, most developing countries still face a challenge in strengthening routine HIS. The main focus of this study was to assess the current HIS performance and identify factors affecting data quality in a resource-limited setting, such as Ethiopian health facilities.Methods: A cross-sectional study was conducted by using structured questionnaires in Dire Dawa Administration health facilities. All unit and/or department heads from all government health facilities were selected. The data was analysed using STATA version 11. Frequency and percentages were computed to present the descriptive findings. Association between variables was computed using binary logistic regression.Results: Over all data quality was found to be 75.3% in unit and/or departments. Trained staff to fill format, decision based on supervisor directives and department heads seek feedback were significantly associated with data quality and their magnitudes were (AOR = 2.253, 95% CI [1.082, 4.692]), (AOR = 2.131, 95% CI [1.073, 4.233]) and (AOR = 2.481, 95% CI [1.262, 4.876]), respectively.Conclusion: Overall data quality was found to be below the national expectation level. Low data quality was found at health posts compared to health centres and hospitals. There was also a shortage of assigned HIS personnel, separate HIS offices, and assigned budgets for HIS across all units and/or departments.

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Section: Previous Literaturesmentioning

confidence: 99%

Level of data quality from Health Management Information Systems in a resources limited setting and its associated factors, eastern Ethiopia

Teklegiorgis

Tadesse

Mirutse

et al. 2016

S. Afr. j. inf. manag.

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“…Even if some tools like Microsoft Excel are used, this system is mainly manually managed. Given the limit of the manual management [13,62], the NTCP expressed the need for an electronic system. Thus, the purpose of the pilot project was to describe the methodologies, methods and tools for the development of epidemiological surveillance of TB and to investigate factors considered as important for its adoption.…”

Section: Research Contextmentioning

confidence: 99%

“…Choice of MDA tool: to choose the MDA tool that is more suitable to our problem, an overview of existing tools used in epidemiological surveillance were made. Either these tools were develop using code-centric approach (DHIS 11 , OpenRMS 12 ), either they were specialized in the generation of mobile-based data collection tools (ODK 13 , Magpi 14 ). Only Imogene 15 were found to cover most of our requirements.…”

Section: Interventionmentioning

confidence: 99%

Combining Scrum and Model Driven Architecture for the development of an epidemiological surveillance software

Jiomekong

Tapamo

Camara

2023

Revue Africaine De Recherche en Informatique Et Mathématiques Appliquées

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Epidemiological surveillance systems evolve with time, depending on the context and the data already collected. Then, the software used must evolve in order to meet requirements. However, introducing new requirements in order to update the software takes time, is expensive and may lead to the problem of software regression. The problem of failed software developed for epidemiological surveillance are often the result of an unsystematic transfer of business requirements to the implementation. This problem can be avoided if the system is established using a well-defined framework/architecture permitting the rapid development/update of the surveillance software. Empirical research shows on the one hand that Model Driven Techniques such as Model Driven Architecture (MDA) are more effective than code-centric approaches for the development and the maintenance of software. On the other hand, Agile Processes such as Scrum are more effective than Structured Processes when requirements are subject to frequent change. Researchers demonstrated that developers of medical software such as epidemiological surveillance software are experiencing difficulties when following Structured Processes and code-centric approaches. The main goal of this empirical study was to apply the combination of Scrum and Model Driven Architecture for the development of epidemiological surveillance of tuberculosis. During this research, we found the approach ease of use and very useful when the MDA tool can generate the complete source code. It has had positive effects on programmer productivity and satisfaction, cost-effectiveness, timelines and customer satisfaction. In addition, we learned that to involve non-informatic experts in the development/update, the modeling user interface must be as simple as possible. Les systèmes de surveillance épidémiologiques évoluent avec le temps, en tenant compte du contexte et des données collectées. Ainsi, les logiciels utilisés doivent aussi évoluer pour pouvoir répondre aux besoins. Cependant, de nouvelles exigences des clients peuvent prendre du temps pour être implémentées, coûter plus cher (dans le cas où il faut recruter des programmeurs) et causer des problèmes comme le problème de régression logiciel. Ainsi, le logiciel peut devenir défaillant. Ceci est dû à un mauvais transfert des exigences des clients vers la mise en œuvre. Ce problème peut être évité si le système est développé en utilisant un framework et une architecture permettant le développement/la mise à jour rapide des logiciels de surveillance. La recherche empirique d'une part montre que l'Ingénierie Dirigée par les Modèles (par exemple, Architecture Dirigée par les Modèles ou Model-Driven Architecture - MDA) est plus efficace que les approches basées sur le code pour le développement et la maintenance de certains logiciels. D'autre part, les processus agiles tels que Scrum sont plus efficaces que les processus structurés lorsque les exigences sont sujettes aux changements fréquents. Il a été démontré que le développement des logiciels de santé comme les logiciels de surveillance épidémiologique en utilisant les processus structurés ou les approches de développement centrées sur le code n'est pas évident car ces logiciels sont sujettes aux changements fréquents. Dans ce travail, nous étudions la combinaison de Scrum avec MDA pour le développement du système de surveillance épidémiologique de la tuberculose. A son terme, nous avons trouvé la combinaison de Scrum et MDA simple, très utile lorsque l'outil sous-jacent peut générer tout le code source. La combinaison de Scrum et MDA a eu des effets positifs sur la productivité et la satisfaction des programmeurs et des clients. En plus, nous avons constaté que pour impliquer les non-informaticiens dans le développement ou la mise à jour, l'interface de modélisation doit être aussi simple que possible.

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“…In Sub-Saharan African countries, some health systems health systems struggle to access quality data, where the various criteria related to health data management processes remain unstructured [3]. This situation undermines the quality of the choices of healthcare interventions and also makes it diffi cult to assess the impact of these interventions [4].…”

Section: Challengementioning

confidence: 99%

Assessment of Data Quality in a Fragmented Health Information System: A Case Study in Two Health Regions in Gabon

Koumamba¹,

Diallo²,

Ngoungou³

2022

J Biomed Res Environ Sci

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Background: Quality data is a key element for every organization including the organization of the healthcare system. In fact, quality data allows any healthcare system to follow and evaluate the defined objectives in order to meet the needs of their customers. Therefore, it is necessary that the processes of producing these data are well structured and efficient. Unfortunately, in so many sub-Saharan African countries those processes have limits causing a qualitative issue in the collected data in the healthcare sector (for example, the low levels completeness, low levels of accuracy, low levels of promptitude) this is more and more frequent and it undermines the rightness of the intervention choices in the management of any healthcare system. Objective: To assess the level of performance of Gabon's health information system, we undertook to carry out an evaluation study of the processes used to collect quality data. Methods: This qualitative study was implemented using a five-step approach (identification of indicators in the data collection process, specification of data allowing the calculation of indicators, design of the data collection questionnaire, collection of data and health information system performance evaluation). For this assessment, we have chosen to rely on the characteristics (criteria) of three determinants that help ensure data quality (data recording and storage, verification of data accuracy and data processing and analysis). Results: It appears that 58% of the structures do not meet all the criteria that favor the recording and storage of data, 53% do not meet all the criteria that favor the verification of the accuracy of the data and 56% do not meet all the criteria that promote good data processing and analysis. Discussion: Although the dimensions used to assess the quality of the data in our study are not exhaustive, they nevertheless have the merit of describing and giving an idea of the level of performance of the healthcare information system in Gabon. Conclusion: We note from this study that Gabon's healthcare information system has limitations that do not allow it to produce reliable data to support strategic decisions and the results of this study may serve as material for reflection on the improvement of the current health information system.

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Quelques erreurs courantes d'analyse d'un système d'information sanitaire (RD Congo)

Cited by 12 publications

References 2 publications

Level of data quality from Health Management Information Systems in a resources limited setting and its associated factors, eastern Ethiopia

Level of data quality from Health Management Information Systems in a resources limited setting and its associated factors, eastern Ethiopia

Combining Scrum and Model Driven Architecture for the development of an epidemiological surveillance software

Assessment of Data Quality in a Fragmented Health Information System: A Case Study in Two Health Regions in Gabon

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