Digital Data Research Articles

Seeding innovation: the role of internal and external digital data in agri-food product innovation

PurposeSince previous literature provides fragmented and conflicting results about the use of digital data for product innovation, the article aims to comprehensively explore and shed light on how agri-food firms utilise external and internal digital data sources when dealing with different product innovations, such as incremental, architecture and radical innovation.Design/methodology/approachThis paper adopts an exploratory multiple-case study and a theory-building process, focussing on the agri-food industry. We collected primary and secondary data from eight manufacturing companies.FindingsThe findings of this research show an empirical framework of six agri-food firms’ digital data utilisation behaviours: the supervisor, the passive supervisor, the developer, the passive developer, the pathfinder and the conjunction behaviour. These digital data utilisation behaviours vary according to a combination of data sources, such as internal data related to inside phenomenon measures (e.g. data generated by sensors installed in the production plan) or external data (e.g., market trends, overall sector sales), and innovation purposes.Practical implicationsThis article offers guiding principles that assist agri-food companies when utilising internal and external digital data sources for specific product innovation outcomes such as incremental, architectural and radical innovation.Originality/valueThe significance of external and internal data sources in stimulating product innovation has garnered substantial attention within academic discussions, highlighting the critical importance of analysing digital data for driving such innovation. Nonetheless, the predominant approach is to study a single innovation outcome through the lens of digital technology. In contrast, our study stands out by adopting a fundamental perspective on data sources, enabling a more nuanced explanation of the overall product innovation outcomes within the agri-food sector.

Study on Seismic Source Parameter Characteristics of Baihetan Reservoir Area in the Lower Reaches of the Jinsha River

The source parameters of earthquakes (stress drop, corner frequency, seismic moment, source size, radiant energy, etc.) provide important information about the source features, the state of stress, and the mechanism of earthquake rupture dynamics. Using the digital observation data obtained from a high-density seismic monitoring network deployed in the Baihetan reservoir area of the lower Jinsha River, we obtained Brune source parameters of the 459 earthquakes ranging in magnitude ML 1.50~4.70. The results revealed seismic moments M0 within the range of 2.03 × 1012~1.45 × 1016 N·m, corner frequencies between 2.00 and 10.00 Hz, and source dimensions varying from 130.00 to 480.00 m, with stress drops spanning from 0.12 to 61.24 MPa. It is noteworthy that the majority of the earthquakes had stress drops less than 10.00 MPa, with as much as 73.30% of these events having stress drops within the range of 0.10 to 2.00 MPa. We found that stress drop, corner frequency, and source size in the study area exhibited positive correlations with earthquake magnitude. Earthquakes occurring at shallower depths for the same magnitude tended to have smaller stress drops and corner frequencies, but larger rupture scales. During the first 2 years of impoundment with significant water level fluctuation, earthquakes beneath or near the reservoir released higher stress drops relative to pre-reservoir conditions, with average stress drops significantly elevated from 5.52 to 13.562 Mpa for events above ML3 since the impoundment. The radiated energy released by earthquakes with magnitudes below ML3.0 are significantly more than before impoundment, indicating that earthquakes of similar magnitudes in the reservoir area may produce greater intensity and perceptibility following the impoundment. According to our result, the triggered seismicity will continue to be active under annual regulation changes in the water level of the Baihetan Dam at high elevations in future years.

Orthorectification of Bridges from High Resolution Satellite Images

Abstract. High-resolution satellite imagery requires high-resolution height reference data to create orthorectified images. High-precision digital surface data and LiDAR data are expensive to acquire quickly and accurately. Facility databases, on the other hand, are very efficient because once established, the information can be utilized persistently. The temporal resolution between the structure in the database and the captured image affects the ortho resolution created. Landmarks such as bridges are rarely subject to changes after construction. This makes facility database a suitable material to generate orthophotos of bridge structures. In this paper, we used a bridge database to perform orthorectification for structures with height variations. We removed relief displacement from satellite imagery at different angles and verified orthorectification feasibility for bridges of different heights and lengths. The orthoimage was overlaid with a topographic map to verify the orthorectification visually. As a result, we confirmed that the facility database was able to generate orthophotos of the bridges. The length and slope of bridge structures indicated the need for precise database updates in the future research.

Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.

Multimedia Data Mining and Processing for News Source Attribution

The desire for unbiased journalism that effectively counters disinformation is widely recognised. News consumers are not only interested in news, but they also want unbiased journalism that cuts through disinformation, and they want it from trusted news sources. Consequently, media researchers need to explore ways to facilitate news-source identification, irrespective of the platform used. However, the availability of multimedia data sources has seen a remarkable surge in recent years, encompassing demographic data, social media data, geodata, and pervasive digital trace data. Multimedia data mining is a procedure of discovering stimulating trends via media data using video, text, and audio that are not generally available by simple enquiries and related outputs. Researchers face the challenge of integrating these diverse sources to enhance news source attribution in multimedia data including platforms like Facebook, WhatsApp and Instagram. The paper presents a review of multimedia data approaches and their application to news source attribution research. Also, the examination of the benefits and limitations of these techniques and discussion on future directions were mentioned. Consideration was on machine learning and statistical approaches to multimedia data, which include deep learning, and probabilistic modelling. Similarly, a discussion on the importance of data privacy and ethics in news source attribution research was stated. The contribution of this study is highly relevant for news media research groups striving to improve their capability to attribute sources in multimedia data, thereby combatting disinformation and amplifying trusted media brands. Keywords: Data mining, Multimedia, Data process, News source attribution, Unbiased journalism

How does digital technology administrative penalty affect big data technology innovation: evidence from China

Administrative regulation is an essential institutional arrangement for governing innovation in big data technology. Administrative penalties are one of its main methods. It is worth investigating whether administrative penalties can serve as an institutional safeguard to promote technological innovation. This study collects data on digital technology administrative penalty (DTAP) and big data technology innovation (BDTI) from 2008 to 2020 in 281 cities in China and empirically evaluates the impact and mechanism of DTAP on BDTI. The findings suggest that the normative impact of the DTAP system can foster a conducive business ecosystem for big data innovation. The incentive mechanism motivates firms to increase their long-term investment in technological innovation, while the deterrence mechanism ensures the existence of a regulated competitive market. These mechanisms play a crucial role in facilitating BDTI. Mechanism tests show that the DTAP has the potential to promote novel business models in the digital economy and accelerate progress in industrial digitalisation, which in turn promotes innovation in big data technologies. The impact of the DTAP on promoting BDTI is diverse, with a significantly greater impact in the first-tier cities. DTAP plays a more important role in fostering BDTI in places where the digital factor-driven industry is still in its early stages of development.

Re-use of research data in the social sciences. Use and users of digital data archive.

The aim of this paper is to investigate the re-use of research data deposited in digital data archive in the social sciences. The study examines the quantity, type, and purpose of data downloads by analyzing enriched user log data collected from Swiss data archive. The findings show that quantitative datasets are downloaded increasingly from the digital archive and that downloads focus heavily on a small share of the datasets. The most frequently downloaded datasets are survey datasets collected by research organizations offering possibilities for longitudinal studies. Users typically download only one dataset, but a group of heavy downloaders form a remarkable share of all downloads. The main user group downloading data from the archive are students who use the data in their studies. Furthermore, datasets downloaded for research purposes often, but not always, serve to be used in scholarly publications. Enriched log data from data archives offer an interesting macro level perspective on the use and users of the services and help understanding the increasing role of repositories in the social sciences. The study provides insights into the potential of collecting and using log data for studying and evaluating data archive use.

Strategies for digital entrepreneurship success: the role of digital implementation and dynamic capabilities

PurposeTo augment sales revenue, B2B digital start-ups aim to create and sustain commercial relationships with industry incumbents. However, since these incumbents have traditionally struggled with implementing disruptive digital artifacts, most studies have almost exclusively concentrated on their challenges, leaving the digital start-ups' side underexplored. Therefore, this study seeks to understand how digital start-ups navigate digital implementation (DI) hardships to ultimately achieve digital entrepreneurship success.Design/methodology/approachAn abductive explanatory multi-case study of four industries that pose a variety of implementation challenges for B2B digital start-ups (agriculture, insurance, real estate and construction, and healthcare) was conducted using data collected from 40 interviews with Israeli experts and relevant digital data observations.FindingsThis study articulates two main observations. (1) Throughout their journeys, digital start-ups have utilized newly created and/or refined dynamic capabilities (DC) to successfully implement their digital artifacts. Simultaneously, successful DI has enabled digital start-ups to create new DC or sustain and evolve current DC. (2) We provide empirical evidence outlining how digital start-ups using continuous learning have combined causation and effectuation logic throughout their DI journeys.Originality/valueThis study answers a call to explore more explicit digital-related drivers (i.e. DI) for digital entrepreneurship success by studying a highly-ranked country on the Global Entrepreneurship Index (GEI) to achieve this. Moreover, it illustrates how digital start-ups evolve throughout their commercial relationships with industry incumbents, thereby enabling an effective approach for successful DI. Such an approach can be considered very valuable for both practitioners and policymakers. Consequently, it advances digital entrepreneurship as an independent research topic.

Where have Ukrainian refugees gone? Identifying potential settlement areas across European regions integrating digital and traditional geographic data

AbstractThe escalation of conflict in Ukraine has triggered the largest refugee crisis in Europe since WWII. As of early April 2024, over 5.9 million people have fled Ukraine. Large‐scale efforts have been made to identify the major receiving countries. However, less is known about the subnational areas within host countries where refugees have migrated. Identifying these areas is key for the appropriate allocation of humanitarian aid. By combining digital Facebook API data and traditional data from Eurostat, this paper aims to identify and characterise potential settlement areas of Ukrainians across the main destination countries in Europe. We identify high concentrations of Ukrainians in urban areas with a preexisting diaspora and tight labour market conditions across southern, northern‐west and central Poland and the city of Prague in the Czech Republic. We also find potential settlements in key urban agglomerations with a moderate diaspora and high levels of unemployment in Spain. Only in Romania, refugees seem to have settled in rural areas which show a moderate diaspora but low levels of unemployment. Potential settlement areas in Germany, Italy and the United Kingdom are spread across the country. Surprisingly, we do not identify potential settlement areas in bordering regions with Ukraine within neighbouring countries, suggesting that refugees may have used them as transit points. Our findings point out that different packages of humanitarian assistance may be needed according to the number of refugees and the characteristics of settlement areas.

Supplemental Material for Digital Data and Personality: A Systematic Review and Meta-Analysis of Human Perception and Computer Prediction

Supplemental Material for Digital Data and Personality: A Systematic Review and Meta-Analysis of Human Perception and Computer Prediction

Review and Assessment of Existing and Future Techniques for Traceability with Particular Focus on Applicability to ABS Plastics

It is generally recognized that the use of physical and digital information-based solutions for tracking plastic materials along a value chain can favour the transition to a circular economy and help to overcome obstacles. In the near future, traceability and information exchange between all actors in the value chain of the plastics industry will be crucial to establishing more effective recycling systems. Recycling plastics is a complex process that is particularly complicated in the case of acrylonitrile butadiene styrene (ABS) plastic because of its versatility and use in many applications. This literature study is part of a larger EU-funded project with the acronym ABSolEU (Paving the way for an ABS recycling revolution in the EU). One of its goals is to propose a suitable traceability system for ABS products through physical marking with a digital connection to a suitable data-management system to facilitate the circular use of ABS. The aim of this paper is therefore to review and assess the current and future techniques for traceability with a particular focus on their use for ABS plastics as a basis for this proposal. The scientific literature and initiatives are discussed within three technological areas, viz., labelling and traceability systems currently in use, digital data sharing systems and physical marking. The first section includes some examples of systems used commonly today. For data sharing, three digital technologies are discussed, viz., Digital Product Passports, blockchain solutions and certification systems, which identify a product through information that is attached to it and store, share and analyse data throughout the product’s life cycle. Finally, several different methods for physical marking are described and evaluated, including different labels on a product’s surface and the addition of a specific material to a polymer matrix that can be identified at any point in time with the use of a special light source or device. The conclusion from this study is that the most promising data management technology for the near future is blockchain technology, which could be shared by all ABS products. Regarding physical marking, producers must evaluate different options for individual products, using the most appropriate and economical technology for each specific product. It is also important to evaluate what information should be attached to a specific product to meet the needs of all actors in the value chain.

FDI impact: catalyzing digital capabilities in host nations

Purpose This study aims to investigate the impact of foreign direct investment (FDI) on national digital capability, specifically differentiating the impact between FDI greenfield and mergers and acquisitions (M&A). The research also investigates factors shaping digital capabilities, encompassing government transparency and absorptive capability, while exploring the mediating influence of absorptive capability in the FDI–digital capability relationship. Design/methodology/approach An econometric model has been developed to examine the interrelationship between national digital capability, FDI inflows, national absorptive capability and government transparency. The data set encompasses 55 countries over a period of nine years (2013–2021). National digital capability data is derived from the well-established index published by the World Competitive Centre (WCC). The sources of the explanatory variables align with standard practices, drawing from reputable institutions (UNCTAD and the World Bank, among others). Findings The findings reveal a significant positive impact of FDI, particularly in greenfield investments, on national digital capability. Government transparency and research and development (R&D) investment are crucial factors contributing to digital capabilities. Additionally, the absorptive capacity, reflected by R&D investment, also emerges as a potential moderating factor, influencing the impact of FDI inflows on digital capabilities. Practical implications The results recommend that policymakers and stakeholders should carefully consider the role of FDI, especially in greenfield investments, as a catalyst for enhancing national digital capability. The findings also underscore the significance of promoting government transparency and directing investments towards R&D to nurture digital capabilities. Moreover, understanding the mediating role of absorptive capability can inform strategies aimed at optimizing the impact of FDI on digital capabilities. Originality/value This study contributes uniquely to the existing literature by being the first to systematically explore the influence of FDI on national digital capability. Furthermore, it presents innovative empirical findings on the role of absorptive capability in enhancing the FDI impact on national digital capability, an area that remains relatively uncharted in current literature.

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries

BackgroundIn low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data.MethodsWe implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software.ResultsThe model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%.ConclusionsWe demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Prioritization of soil erosion-prone sub-watersheds using geomorphometric and statistical-based weighted sum priority approach in the middle Omo-Gibe River basin, Southern Ethiopia

ABSTRACT Soil erosion, a major factor contributing to land degradation, poses significant environmental challenges across sub-Saharan Africa, including Ethiopia. This study identifies erosion-prone sub-watersheds in southern Ethiopia's middle Omo-Gibe River basin using digital elevation model data, analyzed using a geomorphometric and statistically weighted sum priority approach in a Geographic Information System environment. Additionally, hypsometric analysis, lithology, land use/cover analysis, and field observations were integrated to enhance methodological reliability in assessing soil erosion patterns and subsequent land degradation in the basin. Results highlight sub-watersheds with varying degrees of erosion sensitivity, emphasizing morphometric parameters and landscape characteristics. Furthermore, hypsometric analysis confirms a moderate to high susceptibility of sub-watersheds to soil erosion. Moreover, half of the river basin consists of shrubland, encompassing scrubs, range lands, and small wooded areas. These are less efficient in mitigating erosion compared to forests, especially in the face of significant summer rainfall and ensuing runoff. Statistical prioritization identified 19 sub-watersheds, covering 43% of the basin's total area, as highly prone to soil erosion and land degradation. The study's findings are valuable for Ethiopian watershed management decision-makers, enabling informed resource allocation and targeted interventions to control soil erosion and protect water resources by utilizing the outputs of this study.

Innova4Health: an integrated approach for prevention of recurrence and personalized treatment of Major Depressive Disorder

BackgroundMajor Depressive Disorder (MDD) is a prevalent mental health condition characterized by persistent low mood, cognitive and physical symptoms, anhedonia (loss of interest in activities), and suicidal ideation. The World Health Organization (WHO) predicts depression will become the leading cause of disability by 2030. While biological markers remain essential for understanding MDD's pathophysiology, recent advancements in social signal processing and environmental monitoring hold promise. Wearable technologies, including smartwatches and air purifiers with environmental sensors, can generate valuable digital biomarkers for depression assessment in real-world settings. Integrating these with existing physical, psychopathological, and other indices (autoimmune, inflammatory, neuroradiological) has the potential to improve MDD recurrence prevention strategies.MethodsThis prospective, randomized, interventional, and non-pharmacological integrated study aims to evaluate digital and environmental biomarkers in adolescents and young adults diagnosed with MDD who are currently taking medication. The study implements a sensor-integrated platform built around an open-source “Pothos” air purifier system. This platform is designed for scalability and integration with third-party devices. It accomplishes this through software interfaces, a dedicated app, sensor signal pre-processing, and an embedded deep learning AI system. The study will enroll two experimental groups (10 adolescents and 30 young adults each). Within each group, participants will be randomly allocated to Group A or Group B. Only Group B will receive the technological equipment (Pothos system and smartwatch) for collecting digital biomarkers. Blood and saliva samples will be collected at baseline (T0) and endpoint (T1) to assess inflammatory markers and cortisol levels.ResultsFollowing initial age-based stratification, the sample will undergo detailed classification at the 6-month follow-up based on remission status. Digital and environmental biomarker data will be analyzed to explore intricate relationships between these markers, depression symptoms, disease progression, and early signs of illness.ConclusionThis study seeks to validate an AI tool for enhancing early MDD clinical management, implement an AI solution for continuous data processing, and establish an AI infrastructure for managing healthcare Big Data. Integrating innovative psychophysical assessment tools into clinical practice holds significant promise for improving diagnostic accuracy and developing more specific digital devices for comprehensive mental health evaluation.

Blockchain end user adoption and societal challenges: Exploring privacy, rights, and security dimensions

AbstractThe focus of this review article is on the societal problems and end user acceptance of blockchain technology. The paper begins by outlining the importance of blockchain in modernizing trust and data management systems and highlighting its rapid spread across numerous industries. In‐depth analysis of the adoption‐influencing aspects is done, which also lists the advantages and typical end‐user problems. It examines the privacy implications, restrictions on pseudonymity, and function of technologies that improve privacy, such as zero‐knowledge proofs, while also exploring the legal and regulatory environment around blockchain, putting a focus on digital identity, intellectual property, and data ownership. It also evaluates blockchain security features, such as flaws and risks associated with smart contracts, discusses best practices for boosting security, discusses the societal effects of blockchain, and makes suggestions for legislators, companies, and scholars. The use of blockchain technology and its effects on privacy, rights, and security are discussed in real‐world case studies as well.

Vaccine effectiveness against emerging COVID-19 variants using digital health data

BackgroundParticipatory surveillance of self-reported symptoms and vaccination status can be used to supplement traditional public health surveillance and provide insights into vaccine effectiveness and changes in the symptoms produced by an infectious disease. The University of Maryland COVID Trends and Impact Survey provides an example of participatory surveillance that leveraged Facebook’s active user base to provide self-reported symptom and vaccination data in near real-time.MethodsHere, we develop a methodology for identifying changes in vaccine effectiveness and COVID-19 symptomatology using the University of Maryland COVID Trends and Impact Survey data from three middle-income countries (Guatemala, Mexico, and South Africa). We implement conditional logistic regression to develop estimates of vaccine effectiveness conditioned on the prevalence of various definitions of self-reported COVID-like illness in lieu of confirmed diagnostic test results.ResultsWe highlight a reduction in vaccine effectiveness during Omicron-dominated waves of infections when compared to periods dominated by the Delta variant (median change across COVID-like illness definitions: −0.40, IQR[−0.45, −0.35]. Further, we identify a shift in COVID-19 symptomatology towards upper respiratory type symptoms (i.e., cough and sore throat) during Omicron periods of infections. Stratifying COVID-like illness by the National Institutes of Health’s (NIH) description of mild and severe COVID-19 symptoms reveals a similar level of vaccine protection across different levels of COVID-19 severity during the Omicron period.ConclusionsParticipatory surveillance data alongside methodologies described in this study are particularly useful for resource-constrained settings where diagnostic testing results may be delayed or limited.

Semantic Web Practices: Infrastructural Politics and the Future of the Web

Semantic Web Practices: Infrastructural Politics and the Future of the Web

Securing File Storage by Hybrid Cryptography

The proliferation of digital data in modern times necessitates robust security measures to safeguard confidentiality and integrity. This paper introduces a "File Storage System using Hybrid Cryptography," a pivotal solution addressing the pressing need for secure file storage and retrieval. Hybrid cryptography merges symmetric and asymmetric encryption techniques, ensuring a resilient and reliable data protection mechanism. This system leverages the strengths of symmetric encryption's speed and efficiency while integrating the key management advantages of asymmetric encryption. Through an exploration of historical cryptographic foundations, the emergence of hybrid cryptography, and contemporary file storage challenges, this paper highlights the importance of such an approach. The proposed system's phases, from encryption to decryption, elucidate its implementation and efficacy. Future research directions are outlined, focusing on enhanced key management, performance optimization, multi-platform integration, and user-centric solutions. Challenges such as quantum computing threats, scalability, and integration with emerging technologies are discussed. The conclusion emphasizes the significance of hybrid cryptography in addressing data security concerns amidst escalating digital data volumes, offering an efficient and secure framework for data storage and sharing. The research gaps identified prompt further exploration into quantum-resistant systems, scalability optimization, user-friendly interfaces, cross-cultural legal considerations, ethical implications, sustainability, and integration with emerging technologies.

Online Medicine Application with Personalized Blockchain Wallet and AI-Based Doctor Consultation System

The "Online Medicine Application with Personalized Blockchain Wallet and AI-based Doctor Consultancy System" presents a visionary approach to address the evolving healthcare landscape by integrating cutting-edge technologies. This initiative encompasses an intuitive online medical application facilitating seamless healthcare service access, alongside a secure and personalized blockchain wallet for confidential health record management. Additionally, it features an AI-driven doctor consultancy system for intelligent diagnosis and treatment recommendations. Emphasizing digital transformation, accessibility, and data security, the project aims to enhance healthcare convenience and patient-centricity. Successful implementation promises to revolutionize healthcare delivery, empowering individuals while addressing regulatory challenges. This report delineates the project methodology, utilized technologies, system architecture, data privacy measures, implementation phases, and testing processes. Moreover, it discusses user training, support strategies, deployment plans, and future directions. By amalgamating these components, the project endeavours to redefine healthcare accessibility and quality, transforming the delivery and experience of healthcare services. Keywords: Blockchain Healthcare, Medical Records Security, Medication Catalog, Personalized Blockchain Wallet, AI Chatbot, Smart Contracts, Patient Privacy