Data Management Research Articles

Social Robot Privacy Concern (SRPC): Rethinking privacy concerns within the hospitality domain

Understanding privacy needs has become paramount in the evolving landscape of social robots in the hospitality industry. Traditional privacy concerns, dominated by transactional interactions, fail to encapsulate the complexities of human-social robot interaction on service frontlines. Drawing from the communication management theory and the media equation theory, this study conceptualized "Social Robot Privacy Concerns" (SRPC) and discovered its multidimensionality. The SRPC construct is characterized by three key dimensions: (1) Information Privacy Concern, addressing data management and collection; (2) Interpersonal Privacy Concern, focusing on the robot's physical presence; and (3) Environmental Privacy Concern, encompassing broader contexts of government regulations, media influence, and surveillance mechanisms. The findings unveil that these dimensions are pivotal in navigating the challenges posed by service robots implemented in the hospitality industry, necessitating a nuanced approach to understanding and addressing emergent privacy needs. This study also empirically validated the scale to measure the SRPC construct, providing a foundational tool for future research.

Factors Influencing the Adoption of Distributed Ledger Technology in Developing Countries: An Examination of Nigerian Construction Industry

ABSTRACT The construction industry is known for its intricate and fragmented nature, often plagued by inefficient processes and data management challenges. Distributed Ledger Technology (DLT) presents a promising solution, potentially revolutionizing data sharing, transparency, and security. However, the successful adoption of DLT necessitates a comprehensive understanding of its implications, effective organizational changes, and strategic implementation strategies. This study examines the critical factors in adopting DLT into construction processes and workflows. A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews with key stakeholders in the Nigerian construction industry. The findings emphasize the importance of educating staff about DLT’s benefits, strategic planning aligned with business goals, collaboration with DLT experts, universities, and industry partners, careful planning and robust security measures, and organizational changes such as dedicated DLT teams and engaging suppliers. Recommendations include prioritizing continuous improvement, investing in training programs, and exploring integration with emerging technologies. The study provides empirical insights into DLT adoption challenges and strategies, synthesizing these findings with existing literature, and offers a roadmap for construction firms, policymakers, and researchers seeking to leverage DLT for enhanced efficiency and innovation in the construction industry.

What could improve surgical data system at health facilities with high surgical volume in Ethiopia?

BackgroundThe effective management of surgical and anesthesia care relies on quality data and its readily availability for both patient-centered decision-making and facility-level improvement efforts. Recognizing this critical need, the Strengthening Systems for Improved Surgical Outcomes (SSISO) project addressed surgical care data management and information use practices across 23 health facilities from October 2019 to September 2022. This study aimed to evaluate the effectiveness of SSISO interventions in enhancing practices related to surgical data capture, reporting, analysis, and visualization.MethodsThis study employed a mixed method, pre- post intervention evaluation design to assess changes in data management and utilization practices at intervention facilities. The intervention packages included capacity building trainings, monthly mentorship visits facilitated by a hub-and-spoke approach, provision of data capture tools, and reinforcement of performance review teams. Data collection occurred at baseline (February – April 2020) and endline (April – June 2022). The evaluation focused on the availability and appropriate use of data capture tools, as well as changes in performance review practices. Appropriate use of registers was defined as filling all the necessary data onto the registers, and this was verified by completeness of selected key data elements in the registers.ResultsThe proportion of health facilities with Operation Room (OR) scheduling, referral, and surgical site infection registers significantly increased by 34.8%, 56.5% and 87%, respectively, at project endline compared to baseline. Availability of OR and Anesthesia registers remained high throughout the project, at 91.3% and 95.6%, respectively. Furthermore, the appropriate use of these registers improved, with statistically significant increases observed for OR scheduling registers (34.8% increase). Increases were also noted for OR register (9.5% increase) and anesthesia register (4.5% increase), although not statistically significant. Assessing the prior three months reports, the report submissions to the Ministry of Health/Regional Health Bureau (MOH/RHB) rose from 85 to 100%, reflecting complete reporting at endline period. Additionally, the proportion of surgical teams analyzing and displaying data for informed decision-making significantly increased from 30.4% at baseline to 60.8% at endline period.ConclusionThe implemented interventions positively impacted surgical data management and utilization practice at intervention facilities. These positive changes were likely attributable to capacity building trainings and regular mentorship visits via hub-and-spoke approach. Hence, we recommend further investigation into the effectiveness of similar intervention packages in improving surgical data management, data analysis and visualization practices in low- and middle-income country settings.

The experiences of daily smartphone use among older adults in Brazil: A grounded theory analysis

BackgroundDigital inclusion of older people is an important agenda for the future and well-being of the older population, as it represents a form of social inclusion with significant gains for healthy aging. The present study aimed to understand the experiences of Brazilian older adults who use smartphones and the implications in their daily lives. MethodA qualitative study was developed using a constructivist grounded approach. Older people (≥60 years) with prior access to a smartphone were invited to participate in the study. Participation took place through semi-structured, audio-recorded interviews. Data collection and analysis occurred simultaneously using a constant comparative approach. Data management and analysis used the Atlas.ti® software. ResultsOverall, 37 older people participated, with an average age of 70.60 ± 6.95 years, predominantly female (n = 33), white (n = 25), married (n = 26) and retired (n = 27). The first sample group included 27 older people with family support and the second group included 10 older people without family support (who lived alone in their homes). A core category emerged from the data analysis—“Expanding the personal and social development of older adults through smartphone communication”—around which three main categories emerged: 1) The smartphone as “a window to the world”; 2) The problem of misinformation and fake news on virtual networks; 3) Technological paradoxes in the use of smartphones. ConclusionsThe digital transition associated with smartphone use brings unimaginable benefits to older adults who adopt positive habits to improve their well-being and expand their social connections, following efforts proposed by the Decade of Healthy Aging. However, older adults still face disparities and experience a significant digital divide, which represents a major challenge. Barriers to access are further aggravated in developing countries. We hope this study contributes to public health policies on digital inclusion and healthy aging.

Assessing external exposome by implementing an Environmental Data Management System using Open Data

Due to the increasing importance of exposome in environmental epidemiology, feasibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evaluated. The EDMS includes data from 10 European cities (Celje (Slovenia), Łódź (Poland), Manchester (UK), Palermo (Italy), Paris (France), Porto (Portugal), Regensburg (Germany), Reus (Spain), Rijeka (Croatia), Thessaloniki (Greece)) about external non-specific and specific exposome factors at the city or country level (2017–2020). Findings showed that the highest values of life expectancy were in Reus females (86 years) and Palermo males (81 years). UK had the highest obesity rate (28%), Croatia the highest prescribed drug consumption (62%), Greece and Portugal the highest smoking rates (37%, 42%) and daily alcohol consumption (21%), respectively. The most polluted cities were Thessaloniki for PM10 (38 µg/m3), Łódź for PM2.5 (25 µg/m3), Porto for NO2 (62 µg/m3) and Rijeka for O3 (92 µg/m3). Thessaloniki had the highest grey space (98%) and Łódź the highest cumulative amount of pollen (39,041 p/m3). The highest daily noise levels ≥ 55 dB was in Reus (81% to traffic) and Regensburg (21% to railway). In drinking water, arsenic had the highest value in Thessaloniki (6.4 µg/L), boron in Celje (24 mg/L) and lead in Paris (46.7 µg/L). Portugal and Greece showed the highest pesticide residues in food (7%). In conclusion, utilizing open-access databases enables the translation of research findings into actionable strategies for public health interventions.

Impact of Virtual Design and Construction (VDC) on the Construction Industry: Changes and Challenges for Project Managers

The study focuses on the adoption and implementation of Virtual Design and Construction (VDC) in the construction industry and its impact on the project manager’s job. By utilizing VDC, project managers are now required to focus on collaboration, data management, technical expertise, and adaptability to effectively use the technology in their projects. These changes have expanded the skill sets required of project managers, while providing opportunities for improved project delivery and outcomes, but have not dramatically changed the role and responsibilities of a project manager. This study aimed to answer two questions: the changes in the digitalization of project manager work over the last five years, and the challenges that construction project managers face when using the VDC framework by conducting a literature review, interviews, and document study. Three direct challenges were identified: the absence of VDC mention in contracts, lack of mutual understanding between the team and stakeholders, and inadequate leadership support. Further analysis revealed four additional challenges: integration with traditional processes, data management and analysis, technical expertise, and change management.

Digital Transformation of Smart Village: A Systematic Literature Review

Digital Transformation (DT) has occurred in various sectors, even in the context of villages. This study conducts a Systematic Literature Review (SLR) to identify characteristics, stages, and impacts of DT on Smart Villages (SV). Relevant articles are selected following a search in four databases, applying inclusion and exclusion criteria, and assessing the relevancy of the articles. DT progress through stages of technology adoption and strategic change that impact activities. The characteristic of DT in SV is the ease of access, digital literacy, online services, innovation, relevant infrastructure, availability of information, data management, appropriate tools in a platform, and mature use of relevant modern technology. The stages of DT discuss the preparation, implementation, and monitoring stages of concepts and digital platforms. The impact of DT is oriented toward the benefits received through activities and performance in the Village.

Exploring the perceptions and experiences of mobile teams on COVID-19 vaccine uptake at the community level: evidence from Benue and Niger states

BackgroundAs part of the fight against coronavirus disease, Nigeria received nearly 4 million doses of the COVID-19 vaccine via the COVID-19 Vaccines Global Access (COVAX) Facility, marking a historic step towards equitable global distribution of COVID-19 vaccines. Although evidence exists on COVID-19 hesitancy in Nigeria, yet, we are unaware of any study on the optimization of COVID-19 vaccination from the lenses of the mobile teams.ObjectiveThis study seeks to explore the perceptions and experiences of mobile teams in selected communities during the implementation of COVID-19 vaccination in Benue and Niger.MethodsAn exploratory approach was adopted, and the study was conducted in Niger and Benue states based on poor performance in COVID-19 vaccination. Focus Group Discussions (FDGs) were conducted among 12 mobile vaccination teams from 12 LGAs. The recorded discussions were transcribed and coded (inductively and deductively) using Dedoose software (v9.0). Four themes and seven sub-themes were generated from the participants’ responses.ResultsSeventy-two (72) health workers including vaccinators, validators, Electronic Management of Immunization Data (EMID) recorders, social mobilizers, and paper recorders participated in this study. Health workers’ perceptions and experiences were thematized using the health building blocks. The mobilization teams in Benue and Niger states perceived that their mobilization efforts contributed to improved coverage, increased accessibility, and reduced hesitancy among the community dwellers. Challenges reported by the teams were vaccine misconceptions, requests for incentives in exchange for vaccine uptake, poor network services, distance to communities, and vaccine stockout.ConclusionThis study concluded that social mobilizers play key roles in vaccine uptake, especially at the community level. Their roles in creating awareness, sensitization, and bringing the vaccine closer to hard-to-reach communities contributed to the success attained in the fight against COVID-19 in both states.

GUANIN: An all-in-one GUi-driven Analyzer for NanoString Interactive Normalization.

Most tools for normalizing NanoString gene expression data, apart from the default NanoString nCounter software, are R packages that focus on technical normalization and lack configurable parameters. However, content normalization is the most sensitive, experiment-specific, and relevant step to preprocess NanoString data. Currently this step requires the use of multiple tools and a deep understanding of data management by the researcher. We present GUANIN, a comprehensive normalization tool that integrates both new and well-established methods, offering a wide variety of options to introduce, filter, choose, and evaluate reference genes for content normalization. GUANIN allows the introduction of genes from and endogenous subset as reference genes, addressing housekeeping-related selection problems. It performs a specific and straightforward normalization approach for each experiment, using a wide variety of parameters with suggested default values. GUANIN provides a large number of informative output files that enable the iterative refinement of the normalization process. In terms of normalization, GUANIN matches or outperforms other available methods. Importantly, it allows researchers to interact comprehensively with the preprocessing process without programming knowledge, thanks to its easy-to-use Graphical User Interface (GUI). GUANIN can be installed with pip install guanin and it is available at https://pypi.org/project/guanin/. Source code, documentation and case studies are available at https://github.com/julimontoto/guanin under the GPLv3 license. Supplementary data are available at Bioinformatics online.

Privacy vs convenience: Understanding intention-behavior divergence post-GDPR

The privacy paradox describes a scenario in which individuals express privacy concerns but still share private data online. We explore how the paradox can be understood following the introduction of the European Union's landmark GDPR (General Data Protection Regulation) legislation. Through qualitative interviews with online platform users, we find that individuals are concerned about personal data but remain constrained in their privacy self-management. In this context of limited perceived control, users' privacy attitudes are guided by anticipated value from using the platform and the convenience of privacy protection measures. Our study also highlights the role of peer influence on users' privacy choices, specifically through micro- and macro-network effects. We identify that (1) users move to privacy-protecting platforms to align with their social network, or because of information disseminated within their networks; and (2) users remain on platforms offering minimal privacy protection despite privacy concerns due to presence of their entire peer network. These findings provide a unified view on Privacy Paradox post-GDPR, bringing together a more comprehensive range of influences on individual-level privacy dynamics. Our research underscores the need for policymakers to streamline and standardize data protection measures lest the intentions of GDPR be undermined. We also highlight the need to go beyond a reliance on privacy self-management by better regulating the architecture of data management and enforcing principles of privacy by design and default.

Business readiness for dual transformation: an analysis of business capabilities for digital and sustainable transformation

In a dynamic business environment, the convergence of Digital Transformation and Sustainable Transformation has emerged as a comprehensive strategy for companies seeking to thrive in the modern era. For this transformation to successfully develop, the appropriate conditions must be present to carry out a process of digitisation and sustainability. Therefore, this research aimed to identify the readiness level of companies established in the city of Tijuana, Mexico, to adopt a dual transformation process. An analysis of the different digital and sustainable transformation models was carried out, and a self-diagnosis model was designed. Then, a quantitative instrument was applied to a sample of 148 small, medium and large companies in Tijuana between 2022 and mid-2023. The analysis consisted of a descriptive analysis of the data and a correlation analysis. It concludes that the elements that most explain the dual transformation are strategy, culture, organisational and technical capabilities. Likewise, the related factors are definition and communication, alignment, and scope of the objectives within the strategy; change management, knowledge and collaboration in the culture; within the organisation: leadership, processes and organisational structure; and finally in the technical capabilities data management, training and systems integration. The results of this research allowed us to identify and propose strategies to implement the dual transformation and increase the scalability and competitiveness of the adopting companies.

Addressing the Evidence Gap in Aneurysmal Subarachnoid Hemorrhage: The Need for a Pragmatic Randomized Trial Platform.

Aneurysmal subarachnoid hemorrhage (aSAH) occurs less often than other stroke types but affects younger patients, imposing a disproportionately high burden of long-term disability. Although management advances have improved outcomes over time, relatively few aSAH treatments have been tested in randomized clinical trials (RCTs). One lesson learned from COVID-19 is that trial platforms can facilitate the efficient execution of multicenter RCTs even in complex diseases during challenging conditions. An aSAH trial platform with standardized eligibility criteria, randomization procedures, and end point definitions would enable the study of multiple targeted interventions in a perpetual manner, with treatments entering and leaving the platform based on predefined decision algorithms. An umbrella institutional review board protocol and clinical trial agreement would allow individual arms to be efficiently added as amendments rather than stand-alone protocols. Standardized case report forms using the National Institutes of Health/National Institute of Neurological Disorders and Stroke common data elements and general protocol standardization across arms would create synergies for data management and monitoring. A Bayesian analysis framework would emphasize frequent interim looks to enable early termination of trial arms for futility, common controls, borrowing of information across arms, and adaptive designs. A protocol development committee would assist investigators and encourage pragmatic designs to maximize generalizability, reduce site burden, and execute trials efficiently and cost-effectively. Despite decades of steady clinical progress in the management of aSAH, poor patient outcomes remain common, and despite the increasing availability of RCT data in other fields, it remains difficult to perform RCTs to guide more effective care for aSAH. The development of a platform for pragmatic RCTs in aSAH would help close the evidence gap between aSAH and other stroke types and improve outcomes for this important disease with its disproportionate public health burden.

Redefining Enterprise Data Management with AI-Powered Automation

In today's rapidly evolving digital landscape, the volume of enterprise data has surged exponentially, posing significant challenges in effective data management. Traditional data management techniques are becoming increasingly inadequate to handle the complexity and scale of modern enterprise data. This paper presents an innovative approach to revolutionize enterprise data management through AI-powered automation, a solution that enhances accuracy, efficiency, and decision-making processes within organizations. By leveraging advanced artificial intelligence technologies, such as machine learning, natural language processing, and predictive analytics, our proposed system aims to streamline data processing, ensure data quality, and provide real-time insights. This paper will discuss the limitations of existing data management systems, illustrate the novel methodologies integrated within our AI-driven framework, and demonstrate the system's efficacy through empirical results. The transformative potential of AI in automating data management processes not only addresses current challenges but also sets a foundation for future advancements in the field. As enterprises strive to maintain a competitive edge, the adoption of AI-powered automation for data management is not merely an option but a necessity for sustaining growth and innovation.

Researchers' perceptions of the trustworthiness, for reuse purposes, of government health data in Victoria, Australia: Implications for policy and practice.

In 2022 the Australian Data Availability and Transparency Act (DATA) commenced, enabling accredited "data users" to access data from "accredited data service providers." However, the DATA Scheme lacks guidance on "trustworthiness" of the data to be utilised for reuse purposes. Objectives: To determine: (i) Do researchers using government health datasets trust the data? (ii) What factors influence their perceptions of data trustworthiness? and (iii) What are the implications for government and data custodians? Method: Authors of published studies (2008-2020) that utilised Victorian government health datasets were surveyed via a case study approach. Twenty-eight trust constructs (identified via literature review) were grouped into data factors, management properties and provider factors. Results: Fifty experienced health researchers responded. Most (88%) believed that Victorian government health data were trustworthy. When grouped, data factors and management properties were more important than data provider factors in building trust. The most important individual trust constructs were: "compliant with ethical regulation" (100%) and "monitoring privacy and confidentiality" (98%). Constructs of least importance were knowledge of "participant consent" (56%) and "major focus of the data provider was research" (50%). Conclusion: Overall, the researchers trusted government health data, but data factors and data management properties were more important than data provider factors in building trust. Implications: Government should ensure the DATA Scheme incorporates mechanisms to validate those data utilised by accredited data users and data providers have sufficient quality (intrinsic and extrinsic) to meet the requirements of "trustworthiness," and that evidentiary documentation is provided to support these "accredited data."

Women's Needs and Experiences of Using Digital Technologies for Gestational Diabetes: An Integrative Systematic Review.

An integrative review methodology including quantitative, qualitative and mixed-method studies from electronic databases, grey literature, and government websites. Women's experiences and needs of using GDM digital technologies were synthesised into five overarching themes: educational information, coaching components, data management, healthcare professional remote services, peer support features, and functionality and features. This systematic review gives information on the variation of available features across technologies and specific features that were preferred/disliked by the women.

Wheel Wear Modeling and Re-Profiling Strategy Optimization Based on the Maintenance Data of a Three-Axle Bogie Locomotive

Wear caused by wheel–rail contact forces is inevitable during vehicle operation, which has an important impact on the security and stability of train operation. Therefore, it is of great significance to study wheel wear patterns and optimize re-profiling strategies to extend service life. Based on the wheel wear data of three-axle bogie locomotives, this paper proposes a data-driven hybrid wheel wear model and optimization schemes of the re-profiling strategy. The wear model consists of a wheel flange thickness wear model, a wheel diameter wear model, and a re-profiling ratio coefficient model. Then, utilizing the above models, the optimization of the re-profiling strategy for different axle position wheels is raised, and the optimization of the complete vehicle re-profiling strategy is presented by considering the wheel diameter difference. Finally, a wheel data-driven analysis platform was developed to enable the management and utilization of wheel maintenance data. Analysis of extensive maintenance data indicates that the guide wheels wear the fastest, approximately 22.2% higher than the middle wheels, which wear the slowest. The re-profiling ratio coefficient model indicates that the ratio coefficient increases as the wheel flange thickness before re-profiling increases. Simulations demonstrate a longer expected wheel life with a flange thickness between 28 and 32.5 mm. Compared to measured values, the optimization strategy reduces complete vehicle re-profiling by 21.5%. Through the initial implementation at CRRC Dalian Locomotive Ltd, it has become evident that this methodology offers a viable solution to enhance the service longevity of locomotive wheels.

Penerapan Metode Prototype pada Sistem Pemesanan Jasa dan Produk Berbasis Mobile Sebagai Upaya Untuk Meningkatkan dan Memperluas Jangkauan Layanan

The rapid development of science and technology has had a significant impact on various aspects of life, including the way people carry out their daily activities. Beauty salons are now not only the domain of women but also in demand by men so that customer satisfaction is increasingly prioritized. To improve service and operational efficiency, an Android-based booking application was designed that allows customers to make reservations without having to come directly to the salon. The use of this application aims to minimize time and facilitate the management of transactions and customer data by the salon. This booking application was built using a prototype method that allows iterative application development based on user feedback. This method was chosen to ensure that the application can better meet user needs and be adjusted based on input received during the development process. The result of this research is a booking application that makes it easy for users to order services and products online anytime and anywhere, and helps Ellma Salon manage customer and order data more efficiently. This application is expected to be an effective solution to the problems faced by Ellma Salon, increase customer satisfaction, and expand the range of salon services

Advancing architectural frameworks for vibration signature classification in rotating machinery

Advancements in data-driven predictive maintenance have significantly improved digital twin applications for rotating machinery, offering robust solutions for smart manufacturing challenges. These improvements are crucial since equipment failures can cause extensive and costly disruptions to both maintenance schedules and operations. As precision and reliability are critical in production processes, undetected fluctuations in operating frequencies can swiftly escalate to complete part failure, leading to prolonged repairs and productivity loss. This study explores an integrated dataflow pipeline, specifically through Siemens’ MindSphere, to enable continuous predictive maintenance and enhance data acquisition and management. Particularly, conditions such as normal operation, mass balance, rotating imbalance, and mechanical looseness are classified using support vector machine (SVM), neural network (NN), and K-Nearest Neighbor (KNN) methods for the purpose of comparing results. Our results highlight the efficacy of ensemble techniques in collecting and diagnosing vibration signatures, thereby enabling proactive maintenance. To classify various failure signatures, we have proposed a framework to interpret time-series and frequency-dependent data for determining failure types. This research exemplifies how merging data-driven methods with digital twin can improve the accuracy and reliability of condition monitoring. Additionally, we introduce a cloud-based architecture for the diagnosis of rotating machinery, utilizing Application Programming Interface (API) configurations, and develop a real-time dashboard for streaming and visualizing classified data, fostering immediate and informed decision-making.

Real-time path planning for autonomous vehicle off-road driving

Background Autonomous driving is a growing research area that brings benefits in science, economy, and society. Although there are several studies in this area, currently there is no a fully autonomous vehicle, particularly, for off-road navigation. Autonomous vehicle (AV) navigation is a complex process based on application of multiple technologies and algorithms for data acquisition, management and understanding. Particularly, a self-driving assistance system supports key functionalities such as sensing and terrain perception, real time vehicle mapping and localization, path prediction and actuation, communication and safety measures, among others. Methods In this work, an original approach for vehicle autonomous driving in off-road environments that combines semantic segmentation of video frames and subsequent real-time route planning is proposed. To check the relevance of the proposal, a modular framework for assistive driving in off-road scenarios oriented to resource-constrained devices has been designed. In the scene perception module, a deep neural network is used to segment Red-Green-Blue (RGB) images obtained from camera. The second traversability module fuses Light Detection And Ranging (LiDAR) point clouds with the results of segmentation to create a binary occupancy grid map to provide scene understanding during autonomous navigation. Finally, the last module, based on the Rapidly-exploring Random Tree (RRT) algorithm, predicts a path. The Freiburg Forest Dataset (FFD) and RELLIS-3D dataset were used to assess the performance of the proposed approach. The theoretical contributions of this article consist of the original approach for image semantic segmentation fitted to off-road driving scenarios, as well as adapting the shortest route searching A* and RRT algorithms to AV path planning. Results The reported results are very promising and show several advantages compared to previously reported solutions. The segmentation precision achieves 85.9% for FFD and 79.5% for RELLIS-3D including the most frequent semantic classes. While compared to other approaches, the proposed approach is faster regarding computational time for path planning.

Enhancing Community Health Programs: Roles, Operations and Data-Driven Innovations

Aims: To examine how data-driven strategies can be integrated into community health programs as innovative approaches for effective operation. Problem Statement: Numerous government engagements in different crucial sectors have limited her concerns about health provision and distribution to many citizens living in remote communities. Health provision characterized with easy accessibility and low cost should not be marginalized and should be the right of every citizen. However, many lives have been lost due to lack of this in some communities. Significance of Study: To curb this, non-profit sectors are now engaging in community health programs to ameliorate the difficulties faced by distanced citizens living in remote localities. With the advent of this program, it is imperative to incorporate data-driven strategies as innovative approaches to improve and enhance the current state of operations in community health programs. Discussion: A medical practice that addresses the well-being of people within a particular geographical location is referred to as community health. This technical review paper discusses the concepts, roles and mode of operations of community health workers and the programs that are embedded. The attributes of a robust well-functioning health system as stated by World Health Organisation (WHO) were discussed alongside program components that are often executed by village workers. Value-based care, patient-centered care and P4 medicine were identified as the major ideologies behind data-driven strategies and the characteristic features of each were adequately discussed. Conclusion: A data-driven healthcare innovative approach must put into consideration the real definition of data management policies, organize training for health care workers who are handling health data and also support the implementation in order to secure the designed information systems.