JHIAPSMCON 2024 ; Presentation on Need for big data in tribal health
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The document discusses the critical need for big data in improving tribal health in India, highlighting that tribal communities face significant healthcare challenges and disparities. By leveraging big data analytics, insights can be gained into demographics, disease patterns, and social determinants to enhance healthcare access and interventions. It also emphasizes the technological and infrastructure requirements, along with the constraints faced in implementing big data solutions in these regions.
JHIAPSMCON 2024 ; Presentation on Need for big data in tribal health
- 1. The Need for Big Data in Tribal Health for its better understanding and improvement . Dr Rishabh Kumar Rana MBBS. , MD. , CCIP. Fellow Public Health Research ( FPHS India ) Assistant Professor , SNMMCH , Dhanbad. India
- 2. Tribal Health in India: The Big Data Opportunity India's tribal communities face unique healthcare challenges. Harnessing the power of big data can unlock transformative insights to improve tribal health outcomes and bridge the gap in access to quality care.
- 3. Defining Big Data: Understanding the Concept Volume Big data refers to the massive and ever- growing datasets that cannot be managed or analyzed using traditional data processing techniques. Velocity Big data is characterized by the rapid speed at which data is generated, collected, and processed, requiring real-time or near-real- time analysis. Variety Big data encompasses a wide range of data types, including structured, unstructured, and semi- structured data from diverse sources, such as social media, sensors, and transactions. Veracity The quality and reliability of big data, ensuring the data is accurate, relevant, and trustworthy, is a critical component of effective data-driven decision-making. Favaretto M, De Clercq E, Schneble CO, Elger BS. What is your definition of Big Data? Researchers' understanding of the phenomenon of the decade. PLoS One. 2020;15(2):e0228987. Published 2020 Feb 25. doi:10.1371/journal.pone.0228987
- 4. Why Tribal Health Needs Big Data in India India's tribal communities face significant health disparities compared to the general population. Big data analytics can help bridge this gap by providing insights into the unique challenges and needs of tribal populations, enabling targeted interventions and improved healthcare access. Leveraging large-scale data on demographics, disease patterns, social determinants, and access to care can empower policymakers and healthcare providers to make data-driven decisions that address the root causes of health inequities in tribal regions.
- 5. The Census: The Origin of Big Data 1 Decennial Surveys The census, a decennial survey conducted in countries around the world, has been the foundation for gathering large-scale population data for centuries. 2 Technological Advancements The increasing use of tabulating machines and later, computers, revolutionized the way census data was collected, stored, and analyzed, laying the groundwork for modern big data practices. 3 Data Explosion As the scope and detail of census questionnaires expanded, the volume of data generated skyrocketed, highlighting the need for more advanced data management and processing capabilities. https://www.ibm.com/history/punched-card-tabulator
- 6. Census Data and the Rise of IBM and Computers In the late 19th century, the U.S. government's decennial census data posed a major data processing challenge. This led to the development of punch card technology by Herman Hollerith, which laid the foundation for the modern computer industry. Hollerith's company eventually became International Business Machines (IBM), a pioneer in data processing solutions. The rapid growth of census data drove the innovation and adoption of early computing technologies that transformed business and society.
- 7. Examples of Big Data Applications in Healthcare Predictive Analytics Big data can be used to develop predictive models that anticipate healthcare needs, identify high- risk patients, and optimize resource allocation. Personalized Medicine By analyzing vast genomic and clinical data, big data can enable the development of tailored treatments and therapies for individual patients. Remote Monitoring Big data from connected health devices can provide continuous, real-time monitoring of patient health, leading to earlier interventions and improved outcomes. Disease Surveillance Big data analytics can identify patterns and trends in disease spread, enabling public health officials to respond more effectively to outbreaks.
- 8. Components of Big Data: Variety, Velocity, Volume, and Veracity 1 Variety Big data encompasses a wide range of data types, from structured numerical data to unstructured text, images, and multimedia content, requiring diverse data management strategies. 2 Velocity The speed at which data is generated, collected, and processed is a crucial component, as real-time or near-real-time analysis is often required for timely decision-making. 3 Volume The sheer amount of data being generated, often in the terabytes or petabytes range, necessitates advanced storage and processing capabilities to effectively manage and extract insights. 4 Veracity The quality and reliability of data are essential, as big data initiatives must contend with the challenges of incomplete, inconsistent, or inaccurate information to ensure the trustworthiness of insights.
- 9. SOME of the EXAMPLES of BIG DATA from Health and challenges 1GB & …….. 100MB 150kb India US UK GBD CDC NIH HMIS NANHES Census Data MS EXCEL
- 10. An example of NANHES data base in .CSV format An example of NANHES data and its size 541 MB https://www.kaggle.com/datasets?search=NANHES
- 11. An example of explaining the data set https://www.kaggle.com/datasets?search=NANHES
- 12. Right Software needed to open right format . .JSON format opened using SPSS . Not labeled . 46k cases 300 variables . Without labelling
- 13. Right format to store the data . NANHES from CDC and explaining all codes https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overviewlab.aspx?BeginYea r=2019
- 15. NFHS 5 Data
- 16. Big Data and ABDM: The Way Forward for Tribal Health Unlocking Tribal Health Insights Big data analytics can uncover valuable insights into the unique health challenges and needs of tribal communities in India. By leveraging diverse data sources, policymakers can develop targeted ABDM Integration The Ayushman Bharat Digital Mission (ABDM) aims to create a centralized digital health infrastructure. Integrating big data capabilities with ABDM can enhance data-driven decision making and improve access to quality Predictive Analytics Big data-powered predictive models can help anticipate disease outbreaks, identify high-risk individuals, and allocate resources more efficiently in tribal regions. This can lead to proactive, personalized Telemedicine Expansion Big data insights can guide the expansion of telemedicine services to remote tribal areas, bridging the urban-rural healthcare gap and ensuring equitable access to medical expertise.
- 17. Resources Needed for Working with Big Data Powerful Hardware Robust servers, high- capacity storage, and advanced computing power are essential to handle the massive volumes of data associated with big data initiatives. Robust Data Infrastructure A scalable and flexible data architecture, including data lakes, data warehouses, and NoSQL databases, is crucial for managing diverse data sources and formats. Advanced Analytics Tools Specialized software and tools, such as machine learning algorithms, data visualization platforms, and predictive modeling frameworks, are needed to extract Skilled Data Professionals A multidisciplinary team of data engineers, data scientists, and domain experts is required to effectively manage, analyze, and interpret big data for informed decision-
- 18. Constraints and Challenges in Big Data Handling Implementing big data solutions for tribal health in India faces significant constraints, such as limited infrastructure, funding, and skilled personnel. Integrating diverse data sources and ensuring data quality and privacy are major challenges. Inadequate internet connectivity and electricity supply in remote tribal areas hinder real-time data collection and analysis. Budgetary constraints limit investments in big data technologies and training programs for healthcare workers.