How can you use health IT to improve public health surveillance?
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Public health surveillance is the systematic collection, analysis, and dissemination of data on health-related events and outcomes. It helps to monitor trends, identify risks, evaluate interventions, and inform policies and programs. Health IT, or health information technology, is the use of electronic systems and tools to capture, store, share, and use health data. Health IT can enhance public health surveillance by improving data quality, timeliness, accessibility, and usability. In this article, you will learn how you can use health IT to improve public health surveillance in four ways: electronic reporting, data integration, data visualization, and data analytics.
Electronic reporting is the transmission of health data from various sources, such as laboratories, hospitals, clinics, pharmacies, or registries, to public health agencies or authorities. Electronic reporting can improve public health surveillance by reducing errors, delays, and costs associated with manual or paper-based reporting. It can also increase the completeness, accuracy, and consistency of data. Electronic reporting can facilitate the detection and response to outbreaks, epidemics, or emerging threats, as well as the monitoring and evaluation of public health programs and interventions. To use electronic reporting effectively, you need to ensure that the data sources are compatible, standardized, and secure, and that the data recipients are authorized, trained, and responsive.
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Calvin S.
Biochemist at Siemens Healthineers
IT has significantly improved public health in a number of ways. Here are some of the notable contributions: Electronic Health Records (EHRs) provide quick access to patient history. Real-time disease tracking aids rapid responses. Telemedicine offers remote healthcare, especially crucial during emergencies like COVID-19. Health Information Exchanges (HIEs) facilitate seamless information sharing among providers. Data analytics identifies trends for targeted interventions. IT systems streamline vaccine distribution. Remote monitoring through wearables empowers patients. These advancements lead to more effective healthcare delivery, highlighting the importance of leveraging technology to address global healthcare disparities.
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Abhishek Parikh
Project Manager | PhD Scholar Neuroscience
Goverment of India has done commendable job in moving ABHA here, more it gets recognition people will more adopt it. Saving healthcare data and use it on the go is the need of current medical IT industry
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Data integration is the process of combining data from different sources and formats into a unified and coherent view. Data integration can improve public health surveillance by enabling a comprehensive and holistic understanding of the health status, needs, and determinants of a population or a subpopulation. It can also support the comparison and benchmarking of health indicators and outcomes across regions, countries, or groups. Data integration can enhance the collaboration and coordination among different stakeholders and sectors involved in public health surveillance, such as health care providers, researchers, policymakers, or community members. To use data integration effectively, you need to ensure that the data are reliable, valid, and comparable, and that the data integration methods are transparent, ethical, and respectful of privacy and confidentiality.
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James F Fleck
Healthcare Consulting | Keynote Speaker | Professor of Medicine | Design and Analysis of Clinical Trials
The data registry must cover 100% of the target population and must include preliminary health data. This database would be very important for defining the population at risk and monitoring risk factors for chronic diseases. The best example is the Framingham Heart Study, which turns 75 years old. During this period, the initiative contributed to a significant reduction in the mortality rate from cardiovascular diseases.
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Mendel Erlenwein
#1 Podcast Host. Leading the AI Revolution for VBC. Author.
One thing I've found helpful is embracing data integration to gain a more nuanced understanding of public health. It fosters a complete picture of health trends and disparities, aiding in more informed decision-making and resource allocation. Ensuring data integrity and ethical handling is paramount to uphold trust and privacy in this process.
Data visualization is the presentation of data in graphical or pictorial forms, such as charts, maps, dashboards, or infographics. Data visualization can improve public health surveillance by making data more accessible, understandable, and engaging. It can also highlight patterns, trends, gaps, or anomalies in data that might otherwise be overlooked or obscured. Data visualization can communicate complex and diverse data to different audiences and purposes, such as raising awareness, informing decisions, or advocating for actions. To use data visualization effectively, you need to ensure that the data are relevant, accurate, and clear, and that the data visualization tools are appropriate, user-friendly, and attractive.
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James F Fleck
Healthcare Consulting | Keynote Speaker | Professor of Medicine | Design and Analysis of Clinical Trials
The patient must actively participate in longitudinal epidemiological studies. The inclusion of patients is possible through the progressive acceptance of Personal Health Records (PHR). Global e-PHR (www.ephr.org) is an initiative that attempts to build collective intelligence from all stakeholders.
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Mendel Erlenwein
#1 Podcast Host. Leading the AI Revolution for VBC. Author.
In my experience, data visualization is a powerful tool that can transform public health surveillance by conveying complex information simply and effectively. It's essential in identifying and illustrating health trends and translating data into actionable insights for a broad audience. The key is to prioritize relevance and clarity in the visual representations we choose to employ.
Data analytics is the application of statistical or computational techniques to analyze and interpret data. Data analytics can improve public health surveillance by generating insights, knowledge, and evidence from data. It can also support the prediction, prevention, and intervention of health-related events and outcomes. Data analytics can enable the exploration and discovery of new or hidden relationships, associations, or factors in data that might influence or explain health phenomena. It can also test and validate hypotheses, assumptions, or models in data that might inform or improve public health policies and programs. To use data analytics effectively, you need to ensure that the data are sufficient, relevant, and quality-controlled, and that the data analytics methods are rigorous, robust, and reproducible.
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Hesham Mousli
Healthcare Quality & Patient safety consultant, Health economics professional, PhD. Biomedical informatics & Medical statistics.
Healthcare date analytics represents a paramount aid to help policy makers develop public health strategies and resource allocation through its different approaches; descriptive, diagnostic, predictive, and prescriptive techniques. It's crucial to realize which approach(es) shall be utilized to capture the maximum effectiveness and efficiency.
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James F Fleck
Healthcare Consulting | Keynote Speaker | Professor of Medicine | Design and Analysis of Clinical Trials
In the healthcare system, qualified data analysis could be provided by artificial intelligence (AI). Currently, most efforts have focused on AI-driven monitoring tasks. The most important scenario is AI's ability to recognize disease patterns, which is unfortunately underestimated.
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Dr Akash N Rajpal
EVP at Medikabazaar | IIM Calcutta Alumnus | Ex COO Jaslok Hospital | Winner of AIIMS Health Innovation Award | ESG
Secured ABHA ID use in India can be a game changer where patients care-journey in OPD and IPD & retail pharmacy, including relevant family history for connected family members can all be integrated seamlessly with portability providing quick insights to the treating doctor for making informed decisions. Imagine a scenario where a patient walks into a clinic (public or private). Doctor enters ABHA ID & OTP shared by patient in a integrated health system. Doctor is able to see past visits, medicine, diagnostics, past hospitalisation, allergies, etc. based on age or past history there could be some algorithms built in which can recommend some past history or age related tests or leading questions for better compliance, like vaccination etc
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Mendel Erlenwein
#1 Podcast Host. Leading the AI Revolution for VBC. Author.
One time at work, I saw firsthand how data analytics not only informed our public health strategies but actually shaped our intervention methods, leading to more personalized patient care and better health outcomes overall. It highlighted the significance of having a solid foundation of quality data paired with sophisticated analytical techniques.
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