Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Sep 12, 2019
Date Accepted: Jan 27, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Global Goods Maturity Model Indicates Surveillance Outbreak Response Management & Analysis System (SORMAS) as a Mature Digital Health “Global Goods”
ABSTRACT
Background:
Funding enterprises, non-governmental organizations, and private-owned health companies repeatedly build vertical, disease-focused tools that are faced with information exchange difficulties because they are not interoperable with each another and run simultaneous duplicate systems within these countries costing a fortune while delivering reduced results. There is a need to bridge the knowledge gap with respect to these stakeholders knowing which tools are out there being used and can solve their problems without re-inventing the wheel to create something that already exists. The maturity of a tool is based on certain clearly defined indicators which guides many countries and stakeholders on how to improve their selection and implementation of such tools. Global goods are free and open source software (FOSS) which a strong community supports, having a clear governance structure, and funded by multiple sources. Global goods are deployed at a significant scale, used across multiple countries with demonstrated effectiveness, interoperable and standard application. The following key concepts are prevalent throughout the paper in determining the maturity of SORMAS according to the global goods maturity model (GGMM): global utility, community support and software maturity.
Objective:
The objective of this paper is to measure the maturity level of SORMAS under the public health disease surveillance category of the digital square software global maturity model.
Methods:
We used the GGMM version 1.0 form to guide the development and determine the growth level of SORMAS over time. GGMM contains 15 sub-indicators grouped into the following core indicators: 1) global utility; 2) community support and 3) software maturity.
Results:
The model showed that SORMAS had a 10 point-score each in global utility, community support and software maturity. In 2019, SORMAS has currently reached a combined total score of 30/30 (100%) of the full score of the global good maturity model for digital health software tools.
Conclusions:
SORMAS as a mature system has proven to be an effective digital health intervention tool in West Africa in any epidemiological situation. SORMAS appears to be the first software tool within the disease surveillance category to reach 100% in the global goods maturity model. We strategically plan to expand SORMAS to other countries in the western African region and indeed Africa. The public health implication of this study to stakeholders and epidemiologists is that SORMAS can be used for any public health intervention in the aspect of disease surveillance and outbreak response. We have broaden the community engagement with a clear software road map, security and scalability and have developed measures to sustain the full score of global goods maturity model for digital health software tools.
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