scholarly journals The Surveillance Outbreak Response Management and Analysis System (SORMAS): Digital Health Global Goods Maturity Assessment

10.2196/15860 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e15860 ◽  
Author(s):  
Daniel Tom-Aba ◽  
Bernard Chawo Silenou ◽  
Juliane Doerrbecker ◽  
Carl Fourie ◽  
Carl Leitner ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Web Application ◽  
Health Workers ◽  
Digital Health ◽  
Community Support ◽  
Management Tool ◽  
Maturity Model ◽  
Outbreak Response ◽  
Health Community ◽  
Analysis System

Background Digital health is a dynamic field that has been generating a large number of tools; many of these tools do not have the level of maturity required to function in a sustainable model. It is in this context that the concept of global goods maturity is gaining importance. Digital Square developed a global good maturity model (GGMM) for digital health tools, which engages the digital health community to identify areas of investment for global goods. The Surveillance Outbreak Response Management and Analysis System (SORMAS) is an open-source mobile and web application software that we developed to enable health workers to notify health departments about new cases of epidemic-prone diseases, detect outbreaks, and simultaneously manage outbreak response. Objective The objective of this study was to evaluate the maturity of SORMAS using Digital Square’s GGMM and to describe the applicability of the GGMM on the use case of SORMAS and identify opportunities for system improvements. Methods We evaluated SORMAS using the GGMM version 1.0 indicators to measure its development. SORMAS was scored based on all the GGMM indicator scores. We described how we used the GGMM to guide the development of SORMAS during the study period. GGMM contains 15 subindicators grouped into the following core indicators: (1) global utility, (2) community support, and (3) software maturity. Results The assessment of SORMAS through the GGMM from November 2017 to October 2019 resulted in full completion of all subscores (10/30, (33%) in 2017; 21/30, (70%) in 2018; and 30/30, (100%) in 2019). SORMAS reached the full score of the GGMM for digital health software tools by accomplishing all 10 points for each of the 3 indicators on global utility, community support, and software maturity. Conclusions To our knowledge, SORMAS is the first electronic health tool for disease surveillance, and also the first outbreak response management tool, that has achieved a 100% score. Although some conceptual changes would allow for further improvements to the system, the GGMM already has a robust supportive effect on developing software toward global goods maturity.

scholarly journals The Surveillance Outbreak Response Management and Analysis System (SORMAS): Digital Health Global Goods Maturity Assessment (Preprint)

2019 ◽  
Author(s):  
Daniel Tom-Aba ◽  
Bernard Chawo Silenou ◽  
Juliane Doerrbecker ◽  
Carl Fourie ◽  
Carl Leitner ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Web Application ◽  
Health Workers ◽  
Digital Health ◽  
Community Support ◽  
Management Tool ◽  
Maturity Model ◽  
Outbreak Response ◽  
Health Community ◽  
Analysis System

BACKGROUND Digital health is a dynamic field that has been generating a large number of tools; many of these tools do not have the level of maturity required to function in a sustainable model. It is in this context that the concept of global goods maturity is gaining importance. Digital Square developed a global good maturity model (GGMM) for digital health tools, which engages the digital health community to identify areas of investment for global goods. The Surveillance Outbreak Response Management and Analysis System (SORMAS) is an open-source mobile and web application software that we developed to enable health workers to notify health departments about new cases of epidemic-prone diseases, detect outbreaks, and simultaneously manage outbreak response. OBJECTIVE The objective of this study was to evaluate the maturity of SORMAS using Digital Square’s GGMM and to describe the applicability of the GGMM on the use case of SORMAS and identify opportunities for system improvements. METHODS We evaluated SORMAS using the GGMM version 1.0 indicators to measure its development. SORMAS was scored based on all the GGMM indicator scores. We described how we used the GGMM to guide the development of SORMAS during the study period. GGMM contains 15 subindicators grouped into the following core indicators: (1) global utility, (2) community support, and (3) software maturity. RESULTS The assessment of SORMAS through the GGMM from November 2017 to October 2019 resulted in full completion of all subscores (10/30, (33%) in 2017; 21/30, (70%) in 2018; and 30/30, (100%) in 2019). SORMAS reached the full score of the GGMM for digital health software tools by accomplishing all 10 points for each of the 3 indicators on global utility, community support, and software maturity. CONCLUSIONS To our knowledge, SORMAS is the first electronic health tool for disease surveillance, and also the first outbreak response management tool, that has achieved a 100% score. Although some conceptual changes would allow for further improvements to the system, the GGMM already has a robust supportive effect on developing software toward global goods maturity.

AVADAR Application: a learning model for designing a mobile-based disease surveillance system using participatory community reporting in Africa (Preprint)

2019 ◽  
Author(s):  
Godwin Akpan ◽  
Johnson Muluh Ticha ◽  
Lara M.F. Paige ◽  
Daniel Rasheed Oyaole ◽  
Patrick Briand ◽  
...  
Keyword(s):  
Community Participation ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Health Workers ◽  
Digital Health ◽  
Black Box ◽  
Project Planning ◽  
Acceptance Testing ◽  
Surveillance Strategy ◽  
Design And Implementation

BACKGROUND Acute Flaccid Paralysis (AFP) surveillance is the bedrock of polio case detection. The Auto Visual AFP Detection and Reporting (AVADAR) is a digital health intervention designed as a supplemental community surveillance system. OBJECTIVE This paper describes the design and implementation process that made AVADAR a successful disease surveillance strategy at the community level. METHODS This paper outlines the methods for the design and implementation of the AVADAR application. It explains the co-design of the application, the implementation of a helpdesk support structure, the process involved in trouble shooting the application, the benefits of utilizing a closed user group for telecommunication requirements, and the use of a consented video. We also describe how these features combined led to user acceptance testing using black box methodology. RESULTS A total of 198 community informants across two provinces, four districts and 32 settlements were interviewed about application performance, usability, security, load, stress and functionality testing black box components. The responses showed most community participants giving positive reviews. Data from the Blackbox testing yielded optimum acceptance ratings from over 90% of the users involved in the testing. A total of 22380 AFP Alerts were sent out by community informants and 21589 (95%) were investigated by health workers or WHO AVADAR coordinators. Overall there was 93% assimilation at regional level. About 83% of investigations were done in the vicinity of the alerts in 2018 compared to 77% in 2017. CONCLUSIONS AVADAR implementation model offers a simplistic step by step model that includes community participation as an integral tool for the successful deployment of a mobile based surveillance reporting tool. AVADAR can be a veritable source of project planning data and a mobile application for other interventions that target using community participation to influence health outcomes.

scholarly journals Public health reporting and outbreak response: synergies with evolving clinical standards for interoperability

2020 ◽  
Vol 27 (7) ◽  
pp. 1136-1138 ◽  
Author(s):  
Ninad K Mishra ◽  
Jon Duke ◽  
Leslie Lenert ◽  
Saugat Karki
Keyword(s):  
Public Health ◽  
Fast Moving ◽  
Disease Surveillance ◽  
Health Workers ◽  
Clinical Information ◽  
Patient Privacy ◽  
Outbreak Response ◽  
Healthcare Resources ◽  
Public Health Reporting ◽  
Application Programming

Abstract Public health needs up-to-date information for surveillance and response. As healthcare application programming interfaces become widely available, a novel data gathering mechanism could provide public health with critical information in a timely fashion to respond to a fast-moving epidemic. In this article, we extrapolate from our experiences using a Fast Healthcare Interoperability Resource-based architecture for infectious disease surveillance for sexually transmitted diseases to its application to gather case information for an outbreak. One of the challenges with a fast-moving outbreak is to accurately assess its demand on healthcare resources, since information specific to comorbidities is often not available. These comorbidities are often associated with poor prognosis and higher resource utilization. If the comorbidity data and other clinical information were readily available to public health workers, they could better address community disruption and manage healthcare resources. The use of FHIR resources available through application programming and filtered through tools such as described herein will give public health the flexibility needed to investigate rapidly emerging disease while protecting patient privacy.

User Training Evaluation of Surveillance Outbreak Response Management & Analysis System (SORMAS) among Disease Surveillance Notification Officers (DSNOs) in Nigeria 2018 (Preprint)

2019 ◽  
Author(s):  
Daniel Tom-Aba ◽  
Bernard Chawo Silenou ◽  
Chinedu Chukwujekwu Arinze ◽  
Ferdinand Oyiri ◽  
Olawunmi Adeoye ◽  
...  
Keyword(s):  
Data Quality ◽  
Disease Surveillance ◽  
Sub Saharan Africa ◽  
Contact Tracing ◽  
P Value ◽  
Surveillance Systems ◽  
Outbreak Response ◽  
Before And After ◽  
The Mean ◽  
Analysis System

BACKGROUND Electronic health (eHealth) systems increase the efficiency of disease surveillance by reducing delays in the availability of data, usability, improve processing of data and detect outbreaks. Mobile health (mHealth) technology plays a strong role in containing any disease outbreak and eHealth interventions are being used in many of the countries in sub-Saharan Africa to track global progress towards health related outcomes and to help guide clinical decision making and management. The Center for Disease Control and Prevention (CDC) guideline recommends that in evaluating surveillance systems, effectiveness and efficiency of surveillance systems are to be improved by continuous monitoring and evaluation and this cannot be obtained without effective training of health workers. OBJECTIVE The basis of this study is evaluate the knowledge gained before and after Surveillance Outbreak Response Management and Analysis System (SORMAS) training by measuring the following attributes: usefulness, acceptability, data quality and work load time of SORMAS when used by public health officers for their daily tasks. METHODS Our study is a pre/post observational study design which accesses two types of evaluation (pre-evaluation and post-evaluation questionnaires) administered during the very first SORMAS training of the district level officers. We asked the participants to select correct responses out of a 9-multiple choice option what they thought were the functionalities of SORMAS before and after the training. We provided 6/9 correct responses (67%) and 3 incorrect responses (33%). Users were scored based on the correct responses and a proportion score assigned to each user for the pre-training score and the post training score. The outcome of the measurement which was the post training score (percentage) was used to generate a pass/fail score within a 75% dichotomized threshold per user. RESULTS We rejected the null hypothesis that there is no difference between the scores obtained before and after the training by the SORMAS users. The mean score of those who passed was 83% after the training compared to the mean score of 68% before the training. For contact tracing experience, effect was 0.681 (p-value=0.03, OR=1.98, 95%CI [0.069, 1.293]). For participants who stated that they would need same time per case record, effect was 1.771 (p-value=0.001, OR=5.88, 95%CI [0.425, 3.118]). For participants who stated that data quality will improve, the effect was 2.963 (p-value=<0.001, OR=19.34, 95%CI [1.301, 4.624]). For participants who stated that they would recommend SORMAS to their colleagues, the effect was 0.332 (p-value=0,692, OR=1.39, 95%CI [-1.314, 1.979]). CONCLUSIONS Contact tracing experience, data quality, workload and acceptability predictor variables were observed to have a direct effect on the outcome (pass score). The model generated fitted the data and we are 82% accurate that there was indeed knowledge gain comparing before and after the training

scholarly journals Reliability and Security in the Implementation of Digital Health Service Application with the Application of "Reliability Certification or Electronic Certification" based on Indonesian Law

2021 ◽  
Vol 8 (3) ◽  
pp. 417-438
Author(s):  
Charles Hanif
Keyword(s):  
Health Service ◽  
Health Services ◽  
Web Application ◽  
Health Workers ◽  
Digital Health ◽  
Rapid Change ◽  
Secondary Data ◽  
Legal Responsibility ◽  
Potential Problems ◽  
The Government

Societies are always changing rapidly. Initially, societies recognize ordinary health services. Now, people acknowledge web application based digital health services. This rapid change raises potential problems such as the sale of illegal drugs, user data theft, and illegal health workers. Unfortunately, the government has not provided actions to respond the issues quickly. There is no law that underlies the implementation of web application. It increases the possibility of other potential problems. Therefore, it is necessary to question the reliability and security of the implementation of the application and the form of legal responsibility of the organizer. This study used a normative juridical method, which is carried out by examining secondary data as the main study material. The study reveals that the reliability and security in the implementation of applications both as an electronic system and as a health service facility can still be optimized. There are two forms of legal responsibility for the application operator, namely liability in the form of obligations and in the form of sanctions.

scholarly journals Apply IOT technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for COVID-19

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Wei-Ling Lin ◽  
Chun-Hung Hsieh ◽  
Tung-Shou Chen ◽  
Jeanne Chen ◽  
Jian-Le Lee ◽  
...  
Keyword(s):  
Body Temperature ◽  
Temperature Measurement ◽  
Measurement System ◽  
Disease Surveillance ◽  
Disease Surveillance System ◽  
Temperature Detection ◽  
Wide Range ◽  
Body Temperature Measurement ◽  
Analysis System ◽  
Temperature Measurement System

Today, the most serious threat to global health is the continuous outbreak of respiratory diseases, which is called Coronavirus Disease 2019 (COVID-19). The outbreak of COVID-19 has brought severe challenges to public health and has attracted great attention from the research and medical communities. Most patients infected with COVID-19 will have fever. Therefore, the monitoring of body temperature has become one of the most important basis for pandemic prevention and testing. Among them, the measurement of body temperature is the most direct through the Forehead Thermometer, but the measurement speed is relatively slow. The cost of fast-checking body temperature measurement equipment, such as infrared body temperature detection and face recognition temperature machine, is too high, and it is difficult to build Disease Surveillance System (DSS). To solve the above-mentioned problems, the Intelligent pandemic prevention Temperature Measurement System (ITMS) and Pandemic Prevention situation Analysis System (PPAS) are proposed in this study. ITMS is used to detect body temperature. However, PPAS uses big data analysis techniques to prevent pandemics. In this study, the campus field is used as an example, in which ITMS and PPAS are used. In the research, Proof of Concept (PoC), Proof of Service (PoS), and Proof of Business (PoB) were carried out for the use of ITMS and PPAS in the campus area. From the verification, it can be seen that ITMS and PPAS can be successfully used in campus fields and are widely recognized by users. Through the verification of this research, it can be determined that ITMS and PPAS are indeed feasible and capable of dissemination. The ITMS and PPAS are expected to give full play to their functions during the spread of pandemics. All in all, the results of this research will provide a wide range of applied thinking for people who are committed to the development of science and technology.

scholarly journals Mobile health messaging service and helpdesk for South African mothers (MomConnect): history, successes and challenges

2018 ◽  
Vol 3 (Suppl 2) ◽  
pp. e000559 ◽  
Author(s):  
Peter Barron ◽  
Joanne Peter ◽  
Amnesty E LeFevre ◽  
Jane Sebidi ◽  
Marcha Bekker ◽  
...  
Keyword(s):  
Pregnant Women ◽  
South African ◽  
Mobile Health ◽  
Health Workers ◽  
Digital Health ◽  
Universal Coverage ◽  
Text Messages ◽  
Time Data ◽  
Department Of Health ◽  
Early Decision

MomConnect is a flagship programme of the South African National Department of Health that has reached over 1.5 million pregnant women. Using mobile technology, MomConnect provides pregnant and postpartum women with twice-weekly health information text messages as well as access to a helpdesk for patient queries and feedback. In just 3 years, MomConnect has been taken to scale to reach over 95% of public health facilities and has reached 63% of all pregnant women attending their first antenatal appointment. The helpdesk has received over 300 000 queries at an average of 250 per day from 6% of MomConnect users. The service is entirely free to its users. The rapid deployment of MomConnect has been facilitated by strong government leadership, and an ecosystem of mobile health implementers who had experience of much of the content and technology required. An early decision to design MomConnect for universal coverage has required the use of text-based technologies (short messaging service and Unstructured Supplementary Service Data) that are accessible via even the most basic mobile phones, but cumbersome to use and costly at scale. Unlike previous mobile messaging services in South Africa, MomConnect collects the user’s identification number and facility code during registration, enabling future linkages with other health and population databases and geolocated feedback. MomConnect has catalysed additional efforts to strengthen South Africa’s digital health architecture. The rapid growth in smartphone penetration presents new opportunities to reduce costs, increase real-time data collection and expand the reach and scope of MomConnect to serve health workers and other patient groups.

scholarly journals Digital Health-Enabled Community-Centered Care (D-CCC): A Scalable Model to Empower Future Community Health Workers utilizing Human-in-the-Loop AI

2021 ◽  
Author(s):  
Sarah M. Rodrigues ◽  
Anil Kanduri ◽  
Adeline M. Nyamathi ◽  
Nikil Dutt ◽  
Pramod P. Khargonekar ◽  
...  
Keyword(s):  
Community Health ◽  
Community Health Workers ◽  
Closed Loop ◽  
Health Workers ◽  
Digital Health ◽  
Human In The Loop ◽  
Collaborative Community ◽  
Health And Social Care ◽  
The Future ◽  
Centered Care

AbstractDigital Health-Enabled Community-Centered Care (D-CCC) represents a pioneering vision for the future of community-centered care. Utilizing an artificial intelligence-enabled closed-loop digital health platform designed for, and with, community health workers, D-CCC enables timely and individualized delivery of interventions by community health workers to the communities they serve. D-CCC has the potential to transform the current landscape of manual, episodic and restricted community health worker-delivered care and services into an expanded, digitally interconnected and collaborative community-centered health and social care ecosystem which centers around a digitally empowered community health workforce of the future.

scholarly journals Remote Gait Type Classification System Using Markerless 2D Video

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1824
Author(s):  
Pedro Albuquerque ◽  
João Pedro Machado ◽  
Tanmay Tulsidas Verlekar ◽  
Paulo Lobato Correia ◽  
Luís Ducla Soares
Keyword(s):  
Gait Analysis ◽  
Learning Strategies ◽  
Web Application ◽  
State Of The Art ◽  
The State ◽  
Remote Healthcare ◽  
Visual Artifacts ◽  
Analysis System ◽  
Private Network ◽  
User Friendly

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.

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