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1
Taylor, Mark J., and Tess Whitton. "Public Interest, Health Research and Data Protection Law: Establishing a Legitimate Trade-Off between Individual Control and Research Access to Health Data." Laws 9, no. 1 (February 14, 2020): 6. http://dx.doi.org/10.3390/laws9010006.
Повний текст джерелаАнотація:
The United Kingdom’s Data Protection Act 2018 introduces a new public interest test applicable to the research processing of personal health data. The need for interpretation and application of this new safeguard creates a further opportunity to craft a health data governance landscape deserving of public trust and confidence. At the minimum, to constitute a positive contribution, the new test must be capable of distinguishing between instances of health research that are in the public interest, from those that are not, in a meaningful, predictable and reproducible manner. In this article, we derive from the literature on theories of public interest a concept of public interest capable of supporting such a test. Its application can defend the position under data protection law that allows a legal route through to processing personal health data for research purposes that does not require individual consent. However, its adoption would also entail that the public interest test in the 2018 Act could only be met if all practicable steps are taken to maximise preservation of individual control over the use of personal health data for research purposes. This would require that consent is sought where practicable and objection respected in almost all circumstances. Importantly, we suggest that an advantage of relying upon this concept of the public interest, to ground the test introduced by the 2018 Act, is that it may work to promote the social legitimacy of data protection legislation and the research processing that it authorises without individual consent (and occasionally in the face of explicit objection).
2
Siriwardena, N., and M. Dharmawardhana. "Real time data collection and processing using mobile technology: A public health perspective." Sri Lanka Journal of Bio-Medical Informatics 1 (October 24, 2011): 7. http://dx.doi.org/10.4038/sljbmi.v1i0.3539.
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Rodriguez Ayuso, Juan Francisco. "Processing of personal data relating to the health of the data subject in a pandemic situation." Glimpse 22, no. 1 (2021): 95–99. http://dx.doi.org/10.5840/glimpse202122115.
Повний текст джерелаАнотація:
This study offers a systematic, exhaustive and updated investigation of the declaration of the state of alarm and the processing of personal data relating to the health of citizens affected and/or potentially affected by the exceptional situation resulting from COVID-19. Specifically, it analyses the distinction between the state of alarm and the states of exception and siege and the possible effect on the fundamental right to the protection of personal data in exceptional health crisis situations and the effects that this declaration may have on the applicable regulations, issued, at a Community level. Next, and taking into consideration all the general and sectorial regulations applicable to data protection and health, we proceed to the analysis of the legitimate bases and the exceptions that, applicable to situations of health emergency such as the present one, enable the processing, taking into account the nature of the person who intervenes as the controller, making special emphasis on the public interest pursued by the Public Administrations and on the vital interest of the interested party.
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Jones, Julie Miller. "Food processing: criteria for dietary guidance and public health?" Proceedings of the Nutrition Society 78, no. 1 (September 25, 2018): 4–18. http://dx.doi.org/10.1017/s0029665118002513.
Повний текст джерелаАнотація:
The NOVA food categorisation recommends ‘avoiding processed foods (PF), especially ultra-processed foods (UPF)’ and selecting minimally PF to address obesity and chronic disease. However, NOVA categories are drawn using non-traditional views of food processing with additional criteria including a number of ingredients, added sugars, and additives. Comparison of NOVA's definition and categorisation of PF with codified and published ones shows limited congruence with respect to either definition or food placement into categories. While NOVA studies associate PF with decreased nutrient density, other classifications find nutrient-dense foods at all levels of processing. Analyses of food intake data using NOVA show UPF provide much added sugars. Since added sugars are one criterion for designation as UPF, such a proof demonstrates a tautology. Avoidance of foods deemed as UPF, such as wholegrain/enriched bread and cereals or flavoured milk, may not address obesity but could decrease intakes of folate, calcium and dietary fibre. Consumer understanding and implementation of NOVA have not been tested. Neither have outcomes been compared with vetted patterns, such as Dietary Approaches to Stop Hypertension, which base food selection on food groups and nutrient contribution. NOVA fails to demonstrate the criteria required for dietary guidance: understandability, affordability, workability and practicality. Consumers’ confusion about definitions and food categorisations, inadequate cooking and meal planning skills and scarcity of resources (time, money), may impede adoption and success of NOVA. Research documenting that NOVA can be implemented by consumers and has nutrition and health outcomes equal to vetted patterns is needed.
5
Becker, Regina, Adrian Thorogood, Johan Ordish, and Michael J. S. Beauvais. "COVID-19 Research: Navigating the European General Data Protection Regulation." Journal of Medical Internet Research 22, no. 8 (August 27, 2020): e19799. http://dx.doi.org/10.2196/19799.
Повний текст джерелаАнотація:
Researchers must collaborate globally to rapidly respond to the COVID-19 pandemic. In Europe, the General Data Protection Regulation (GDPR) regulates the processing of personal data, including health data of value to researchers. Even during a pandemic, research still requires a legal basis for the processing of sensitive data, additional justification for its processing, and a basis for any transfer of data outside Europe. The GDPR does provide legal grounds and derogations that can support research addressing a pandemic, if the data processing activities are proportionate to the aim pursued and accompanied by suitable safeguards. During a pandemic, a public interest basis may be more promising for research than a consent basis, given the high standards set out in the GDPR. However, the GDPR leaves many aspects of the public interest basis to be determined by individual Member States, which have not fully or uniformly made use of all options. The consequence is an inconsistent legal patchwork that displays insufficient clarity and impedes joint approaches. The COVID-19 experience provides lessons for national legislatures. Responsiveness to pandemics requires clear and harmonized laws that consider the related practical challenges and support collaborative global research in the public interest.
6
Cummings, Stuart W. "Distributed Databases for Clinical Data Processing." Drug Information Journal 27, no. 4 (October 1993): 949–56. http://dx.doi.org/10.1177/009286159302700403.
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Pimazzoni, Monica. "Global Data Management: A Winning Approach to Clinical Data Processing." Drug Information Journal 32, no. 2 (April 1998): 569–71. http://dx.doi.org/10.1177/009286159803200230.
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Woods, Valerie. "Musculoskeletal disorders and visual strain in intensive data processing workers." Occupational Medicine 55, no. 2 (March 1, 2005): 121–27. http://dx.doi.org/10.1093/occmed/kqi029.
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Determann, Lothar. "Healthy Data Protection." Michigan Technology Law Review, no. 26.2 (2020): 229. http://dx.doi.org/10.36645/mtlr.26.2.healthy.
Повний текст джерелаАнотація:
Modern medicine is evolving at a tremendous speed. On a daily basis, we learn about new treatments, drugs, medical devices, and diagnoses. Both established technology companies and start-ups focus on health-related products and services in competition with traditional healthcare businesses. Telemedicine and electronic health records have the potential to improve the effectiveness of treatments significantly. Progress in the medical field depends above all on data, specifically health information. Physicians, researchers, and developers need health information to help patients by improving diagnoses, customizing treatments and finding new cures. Yet law and policymakers are currently more focused on the fact that health information can also be used to harm individuals. Even after the outbreak of the COVID-19 pandemic (which occurred after the manuscript for this article was largely finalized), the California Attorney General Becera made a point of announcing that he will not delay enforcement of the California Consumer Privacy Act (“CCPA”), which his office estimated imposes a $55 billion cost (approximately 1.8% of California Gross State Product) for initial compliance, not including costs of ongoing compliance, responses to data subject requests, and litigation. Risks resulting from health information processing are very real. Contact tracing and quarantines in response to SARS, MERS, and COVID-19 outbreaks curb civil liberties with similar effects to law enforcement investigations, arrests, and imprisonment. Even outside the unusual circumstances of a global pandemic, employers or insurance companies may disfavor individuals with pre-existing health conditions in connections with job offers and promotions as well as coverage and eligibility decisions. Some diseases carry a negative stigma in social circumstances. To reduce the risks of such harms and protect individual dignity, governments around the world regulate the collection, use, and sharing of health information with ever-stricter laws. European countries have generally prohibited the processing of personal data, subject to limited exceptions, for which companies have to identify and then document or apply. The General Data Protection Regulation (“GDPR”) that took effect in 2018 confirms and amplifies a rigid regulatory regime that was first introduced in the German State Hessen in 1970 and demands that organizations minimize the amount of data they collect, use, share, and retain. Healthcare and healthtech organizations have struggled to comply with this regime and have found EU data protection laws fundamentally hostile to data-driven progress in medicine. The United States, on the other hand, has traditionally relied on sector- and harm-specific laws to protect privacy, including data privacy and security rules under the federal Health Insurance Portability and Accountability Act (“HIPAA”) and numerous state laws including the Confidentiality of Medical Information Act (“CMIA”) in California, which specifically address the collection and use of health information. So long as organizations observe the specific restrictions and prohibitions in sector-specific privacy laws, they may collect, use, and share health information. As a default rule in the United States, businesses are generally permitted to process personal information, including health information. Yet, recently, extremely broad and complex privacy laws have been proposed or enacted in some states, including the California Consumer Privacy Act of 2018 (“CCPA”), which have a potential to render compliance with data privacy laws impractical for most businesses, including those in the healthcare and healthtech sectors. Meanwhile, the People’s Republic of China is encouraging and incentivizing data-driven research and development by Chinese companies, including in the healthcare sector. Data-related legislation is focused on cybersecurity and securing access to data for Chinese government agencies and much less on individual privacy interests. In Europe and the United States, the political pendulum has swung too far in the direction of ever more rigid data regulation and privacy laws, at the expense of potential benefits through medical progress. This is literally unhealthy. Governments, businesses, and other organizations need to collect, use and share more personal health information, not less. The potential benefits of health data processing far outweigh privacy risks, which can be better tackled by harm-specific laws. If discrimination by employers and insurance companies is a concern, then lawmakers and law enforcement agencies need to focus on anti-discrimination rules for employers and insurance companies - not prohibit or restrict the processing of personal data, which does not per se harm anyone. The notion of only allowing data processing under specific conditions leads to a significant hindrance of medical progress by slowing down treatments, referrals, research, and development. It also prevents the use of medical data as a tool for averting dangers for the public good. Data “anonymization” and requirements for specific consent based on overly detailed privacy notices do not protect patient privacy effectively and unnecessarily complicate the processing of health data for medical purposes. Property rights to personal data offer no solutions. Even if individuals - not companies creating databases - were granted property rights to their own data originally, this would not ultimately benefit individuals. Given that transfer and exclusion rights are at the core of property regimes, data property rights would threaten information freedom and privacy alike: after an individual sells her data, the buyer and new owner could exercise his data property rights to enjoin her and her friends and family from continued use of her personal data. Physicians, researchers, and developers would not benefit either; they would have to deal with property rights in addition to privacy and medical confidentiality requirements. Instead of overregulating data processing or creating new property rights in data, lawmakers should require and incentivize organizations to earn and maintain the trust of patients and other data subjects and penalize organizations that use data in specifically prohibited ways to harm individuals. Electronic health records, improved notice and consent mechanisms, and clear legal frameworks will promote medical progress, reduce risks of human error, lower costs, and make data processing and sharing more reliable. We need fewer laws like the GDPR or the CCPA that discourage organizations from collecting, using, retaining, and sharing personal information. Physicians, researchers, developers, drug companies, medical device manufacturers and governments urgently need better and increased access to personal health information. The future of medicine offers enormous opportunities. It depends on trust and healthy data protection. Some degree of data regulation is necessary, but the dose makes the poison. Laws that require or intend to promote the minimization of data collection, use, and sharing may end up killing more patients than hospital germs. In this article, I promote a view that is decidedly different from that supported by the vast majority of privacy scholars, politicians, the media, and the broader zeitgeist in Europe and the United States. I am arguing for a healthier balance between data access and data protection needs in the interest of patients’ health and privacy. I strive to identify ways to protect health data privacy without excessively hindering healthcare and medical progress. After an introduction (I), I examine current approaches to data protection regulation, privacy law, and the protection of patient confidentiality (II), risks associated with the processing of health data (III), needs to protect patient confidence (IV), risks for healthcare and medical progress (V), and possible solutions (VI). I conclude with an outlook and call for healthier approaches to data protection (VII).
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Wu, Hong Jiang, Xiang Yang Liu, Hai Yan Zhao, and Xiao Ting Li. "Research on Public Health Service Systems Based on Cloud Computing." Applied Mechanics and Materials 687-691 (November 2014): 2849–52. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.2849.
Повний текст джерелаАнотація:
This research is aimed at building a real time public fitness service system, with massive data storage and processing ability, to meet the public fitness service demand. In this research, we focus on the system positioning, cloud delegation model, service model, service content and operation mechanism of the fitness service system. Method used was system analysis method.
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Bertalya, Bertalya, Prihandoko Prihandoko, Lilis Setyowati, Febrian Iftikhar Irawan, and Syahifa Rahmita Irlianti. "Formulation of city health development index using data mining." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 1 (July 1, 2021): 362. http://dx.doi.org/10.11591/ijeecs.v23.i1.pp362-369.
Повний текст джерелаАнотація:
Every five years Public Health Research publishes a Public Health Development Index that describes public health in Indonesia. The Public Health Development Index is measured using data from the Public Health Research and the National Socio-Economic Survey, and the Village Potential Survey which is obtained by surveying from sampling data. In fact, the Provincial and City Health Offices have health profile data reports every year. For this reason, this study analyzes existing health profile data using data mining techniques to obtain indicator data that are very influential in formulating the City Health Development Index. This City Health Development Index was successfully formulated by adopting the Model of Public Health Development Index in 2013 and using indicators from annual health profile data which obtained from the data mining process, i.e., Random Forest algorithm. The proposed model can be used as the annual report of a city to describe the health condition of that city. For the future research, the model can be adopted to measure some specific aspects of city health condition.
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Sukumar, Sreenivas R., Ramachandran Natarajan, and Regina K. Ferrell. "Quality of Big Data in health care." International Journal of Health Care Quality Assurance 28, no. 6 (July 13, 2015): 621–34. http://dx.doi.org/10.1108/ijhcqa-07-2014-0080.
Повний текст джерелаАнотація:
Purpose – The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. Design/methodology/approach – The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines. Findings – Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance. Practical implications – The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines. Originality/value – Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.
13
Unwin, Elizabeth, James Codde, Louise Gill, Suzanne Stevens, and Timothy Nelson. "The WA Hospital Morbidity Data System: An Evaluation of its Performance and the Impact of Electronic Data Transfer." Health Information Management 26, no. 4 (December 1996): 189–92. http://dx.doi.org/10.1177/183335839702600407.
Повний текст джерелаАнотація:
This paper evaluates the performance of the Hospital Morbidity Data System, maintained by the Health Statistics Branch (HSB) of the Health Department of Western Australia (WA). The time taken to process discharge summaries was compared in the first and second halves of 1995, using the number of weeks taken to process 90% of all discharges and the percentage of records processed within four weeks as indicators of throughput. Both the hospitals and the HSB showed improvements in timeliness during the second half of the year. The paper also examines the impact of a recently introduced electronic data transfer system for WA country public hospitals on the timeliness of morbidity data. The processing time of country hospital records by the HSB was reduced to a similar time as for metropolitan hospitals, but the processing time in the hospitals increased, resulting in little improvement in total processing time.
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Leeson, William, Adam Resnick, Daniel Alexander, and John Rovers. "Natural Language Processing (NLP) in Qualitative Public Health Research: A Proof of Concept Study." International Journal of Qualitative Methods 18 (January 1, 2019): 160940691988702. http://dx.doi.org/10.1177/1609406919887021.
Повний текст джерелаАнотація:
Qualitative data-analysis methods provide thick, rich descriptions of subjects’ thoughts, feelings, and lived experiences but may be time-consuming, labor-intensive, or prone to bias. Natural language processing (NLP) is a machine learning technique from computer science that uses algorithms to analyze textual data. NLP allows processing of large amounts of data almost instantaneously. As researchers become conversant with NLP, it is becoming more frequently employed outside of computer science and shows promise as a tool to analyze qualitative data in public health. This is a proof of concept paper to evaluate the potential of NLP to analyze qualitative data. Specifically, we ask if NLP can support conventional qualitative analysis, and if so, what its role is. We compared a qualitative method of open coding with two forms of NLP, Topic Modeling, and Word2Vec to analyze transcripts from interviews conducted in rural Belize querying men about their health needs. All three methods returned a series of terms that captured ideas and concepts in subjects’ responses to interview questions. Open coding returned 5–10 words or short phrases for each question. Topic Modeling returned a series of word-probability pairs that quantified how well a word captured the topic of a response. Word2Vec returned a list of words for each interview question ordered by which words were predicted to best capture the meaning of the passage. For most interview questions, all three methods returned conceptually similar results. NLP may be a useful adjunct to qualitative analysis. NLP may be performed after data have undergone open coding as a check on the accuracy of the codes. Alternatively, researchers can perform NLP prior to open coding and use the results to guide their creation of their codebook.
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Arisetty, Murty. "A Team-Based Approach to Clinical Data Processing." Drug Information Journal 19, no. 1 (January 1985): 81–84. http://dx.doi.org/10.1177/009286158501900113.
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Leighton, Charles C. "Clinical Data Processing in Retrospect and in Prospect." Drug Information Journal 20, no. 1 (January 1986): 7–15. http://dx.doi.org/10.1177/009286158602000103.
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Gillum, Terry L., Robert H. George, and Jack E. Leitmeyer. "An Autoencoder for Clinical and Regulatory Data Processing." Drug Information Journal 29, no. 1 (January 1995): 107–13. http://dx.doi.org/10.1177/009286159502900115.
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McDowell, Ian, Margaret Stewart, Betsy Kristjansson, Elizabeth Sykes, Gerry Hill, and Joan Lindsay. "Data Collected in the Canadian Study of Health and Aging." International Psychogeriatrics 13, S1 (February 2001): 29–39. http://dx.doi.org/10.1017/s1041610202007962.
Повний текст джерелаАнотація:
The Canadian Study of Health and Aging collected data focusing on the epidemiology of dementia, using interviews and questionnaires, clinical and neuropsychological examinations, physical measurements and blood collection, and access to public records such as death certificates, from people 65 and over in community (N = 9,008) institutional settings (N = 1,255). The study produced 12 data sets, including community health interviews, clinical and neuropsychological assessements, risk factor questionnaires, and caregiver interviews. This report describes the data collection and processing procedures, summarizes the content of each data set, and outlines the information collected in sufficient detail to permit its suitability for secondary analyses to be scrutinized.
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Carpenter, Joseph E., Arthur S. Chang, Alvin C. Bronstein, Richard G. Thomas, and Royal K. Law. "Identifying Incidents of Public Health Significance Using the National Poison Data System, 2013–2018." American Journal of Public Health 110, no. 10 (October 2020): 1528–31. http://dx.doi.org/10.2105/ajph.2020.305842.
Повний текст джерелаАнотація:
Data System. The American Association of Poison Control Centers (AAPCC) and the Centers for Disease Control and Prevention (CDC) jointly monitor the National Poison Data System (NPDS) for incidents of public health significance (IPHSs). Data Collection/Processing. NPDS is the data repository for US poison centers, which together cover all 50 states, the District of Columbia, and multiple territories. Information from calls to poison centers is uploaded to NPDS in near real time and continuously monitored for specific exposures and anomalies relative to historic data. Data Analysis/Dissemination. AAPCC and CDC toxicologists analyze NPDS-generated anomalies for evidence of public health significance. Presumptive results are confirmed with the receiving poison center to correctly identify IPHSs. Once verified, CDC notifies the state public health department. Implications. During 2013 to 2018, 3.7% of all NPDS-generated anomalies represented IPHSs. NPDS surveillance findings may be the first alert to state epidemiologists of IPHSs. Data are used locally and nationally to enhance situational awareness during a suspected or known public health threat. NPDS improves CDC’s national surveillance capacity by identifying early markers of IPHSs.
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Hrzic, Rok, Timo Clemens, Daan Westra, and Helmut Brand. "Comparability in Cross-National Health Research Using Insurance Claims Data: The Cases of Germany and The Netherlands." Das Gesundheitswesen 82, S 01 (November 19, 2019): S83—S90. http://dx.doi.org/10.1055/a-1005-6792.
Повний текст джерелаАнотація:
Abstract Objective Comparison is a key method in learning about what works in health and healthcare. We discuss the importance of comparability in cross-national health research using health insurance claims data, develop a framework to systematically asses these threats and apply it to the German (DaTraV) and Dutch (Vektis) national-level insurance claims datasets. Methods We propose a framework of threats to the comparability of health insurance claims databases, which includes three domains: (1) representation of populations compared, (2) data sources and data processing and (3) database contents and availability for research purposes. We apply the framework to analyze the comparability of DaTraV and Vektis databases using publicly available information (organization’s websites, scientific publications) and our experiences from an interregional project on rare diseases (EMRaDi). Results Both databases were created for the same purpose (morbidity-based risk adjustment) and use the same underlying sources of data. Differences in population representation and uncertainty about data processing procedures represent potential sources of incomparability. Access for research purposes is feasible in both databases but may be subject to long processing time. Conclusions We find important threats to the comparability of the Dutch and German national insurance claims databases and by extension to validity of any comparative health studies that rely on them. Standard adjustment techniques, making more information available about data collection and processing procedures and adding more diagnosis-related descriptors offer ways to overcome the identified threats to comparability.
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Lee, Edmund W. J., and Kasisomayajula Viswanath. "Big Data in Context: Addressing the Twin Perils of Data Absenteeism and Chauvinism in the Context of Health Disparities Research." Journal of Medical Internet Research 22, no. 1 (January 7, 2020): e16377. http://dx.doi.org/10.2196/16377.
Повний текст джерелаАнотація:
Recent advances in the collection and processing of health data from multiple sources at scale—known as big data—have become appealing across public health domains. However, present discussions often do not thoroughly consider the implications of big data or health informatics in the context of continuing health disparities. The 2 key objectives of this paper were as follows: first, it introduced 2 main problems of health big data in the context of health disparities—data absenteeism (lack of representation from underprivileged groups) and data chauvinism (faith in the size of data without considerations for quality and contexts). Second, this paper suggested that health organizations should strive to go beyond the current fad and seek to understand and coordinate efforts across the surrounding societal-, organizational-, individual-, and data-level contexts in a realistic manner to leverage big data to address health disparities.
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Waterhouse, Andrew L. "Consumer Labels can Convey Polyphenolic Content: Implications for Public Health." Clinical and Developmental Immunology 12, no. 1 (2005): 43–46. http://dx.doi.org/10.1080/10446670410001722249.
Повний текст джерелаАнотація:
Polyphenolics are a large group of related substances. Many of these, in fact much of that found in food, is composed of processing-derived substances too complex for complete identification. Recent studies have suggested likely benefits for diets high in polyphenols, particular in reducing heart disease mortality, but other benefits have also been suggested. A consumer label based on the major polyphenolic classes is both manageable and fairly informative as most foods do not contain all possible classes. Differences between class member can be significant, but data on individual substances is impractical and no data is certainly less informative. Equivalency scales may be useful but may skew content of many foods towards the high-equivalency substances, even while the full beneficial effects of each individual substance is poorly described.
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Conway, Mike, Mengke Hu, and Wendy W. Chapman. "Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data." Yearbook of Medical Informatics 28, no. 01 (August 2019): 208–17. http://dx.doi.org/10.1055/s-0039-1677918.
Повний текст джерелаАнотація:
Objective: We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications. Methods: We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook. Results: In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review “modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than “classical" machine learning methods.
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Miningwa, Alex. "INFLUENCE OF SOURCE OF DATA, INFORMATION FLOWS AND EXCHANGE PLATFORMS ON LEVEL OF HIS FEEDBACK IN PUBLIC HEALTH FACILITIES." American Journal of Data, Information and Knowledge Management 2, no. 1 (August 5, 2021): 43–53. http://dx.doi.org/10.47672/ajdikm.763.
Повний текст джерелаАнотація:
Purpose: Data collection is the first step of the information process within the health information system, so health information systems are often classified according to data collection method. The general objective of the study was to evaluate influence of source of data, information flows and exchange platforms on level of HIS feedback in public health facilities
Methodology: The paper used a desk study review methodology where relevant empirical literature was reviewed to identify main themes and to extract knowledge gaps.
Findings: The study concludes that there was feedback at all levels in the HIS. The feedback was on referrals, disease prevalence rates and policy implementation. The feedback was beneficial in terms of helping the health facilities improve data collection, information processing and general implementation of the Health policies. Feedback provided was relevant especially from the Ministry of Health.
Recommendations: There is need for Ministry of Health should to increase interaction (feedback) with the lower level health facilities. Ministry of Health should give priority to all health facilities in terms of processing information obtained and feedback given on timely basis. This will improve decision making in all facilities that share information through HIS. Moreover ministry of Health and Administrators of Health facilities should strengthen HIS for the benefit of improving service delivery in the Health Sector. This can be through increasing capacity of the HIS to satisfy needs of all stakeholders
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Wolfe, Karen. "Data Base or Word Processing: Knowing the Difference Can Make the Difference." AAOHN Journal 40, no. 4 (April 1992): 194–95. http://dx.doi.org/10.1177/216507999204000407.
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Jackson, Heide, and Edward R. Berchick. "Improvements in Uninsurance Estimates for Fully Imputed Cases in the Current Population Survey Annual Social and Economic Supplement." INQUIRY: The Journal of Health Care Organization, Provision, and Financing 57 (January 2020): 004695802092355. http://dx.doi.org/10.1177/0046958020923554.
Повний текст джерелаАнотація:
In 2019, the Current Population Survey Annual Social and Economic Supplement introduced updates to data processing, including to the imputation of health insurance for cases with no reported health insurance information. This article examines the impact on health insurance estimates of modernized imputation procedures that were part of a redesign of the Current Population Survey Annual Social and Economic Supplement. We use descriptive analysis and multinomial logistic regression to examine whether imputation biases estimates of health insurance coverage using data from the 2017 Current Population Survey Annual Social and Economic Supplement, which used legacy methods, and the 2017 Current Population Survey Annual Social and Economic Supplement Research File, which debuted the processing redesign. We find that cases with all of their health insurance information imputed using legacy methods were more likely to be uninsured or to be covered by multiple insurance types after adjusting for factors associated with having missing data. With the processing updates, fully imputed cases do not differ from other cases in their likelihood of being uninsured, having private coverage, having public coverage, or in having private and public coverage. Processing updates in the Current Population Survey Annual Social and Economic Supplement improved data quality by increasing the percent of people with any health insurance coverage and decreasing the percent of people with multiple types of coverage, especially among fully imputed cases.
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Lyons, Ronan A. "How much does quality matter: the value of data." Injury Prevention 26, no. 4 (July 21, 2020): 397–99. http://dx.doi.org/10.1136/injuryprev-2019-043369.
Повний текст джерелаАнотація:
In a world of competing priorities, accurate production of information on the scale of the injury burden and the effectiveness of prevention-orientated interventions and policies is important; hence, data quality matters. This article surveys the literature about what is known about data quality in the injury field and developments to improve the quality and usability of information, particularly through triangulation of data sources, data linkage and unlocking the potential for more deeply phenotyped data through natural language processing.
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Martínez-Castaño, Rodrigo, Juan C. Pichel, and David E. Losada . "A Big Data Platform for Real Time Analysis of Signs of Depression in Social Media." International Journal of Environmental Research and Public Health 17, no. 13 (July 1, 2020): 4752. http://dx.doi.org/10.3390/ijerph17134752.
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In this paper we propose a scalable platform for real-time processing of Social Media data. The platform ingests huge amounts of contents, such as Social Media posts or comments, and can support Public Health surveillance tasks. The processing and analytical needs of multiple screening tasks can easily be handled by incorporating user-defined execution graphs. The design is modular and supports different processing elements, such as crawlers to extract relevant contents or classifiers to categorise Social Media. We describe here an implementation of a use case built on the platform that monitors Social Media users and detects early signs of depression.
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Salsburg, David. "Deming Principles Applied to Processing Data from Case Report Forms." Drug Information Journal 36, no. 1 (January 2002): 135–41. http://dx.doi.org/10.1177/009286150203600117.
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Black, Dennis, Kjeld Molvig, Anna Bagniewska, Stan Edlavitch, Cary Fox, Stephen Hulley, and W. McFate Smith. "A Distributed Data Processing System for a Multicenter Clinical Trial." Drug Information Journal 20, no. 1 (January 1986): 83–92. http://dx.doi.org/10.1177/009286158602000113.
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Alwitt, Josh, and John Kinney. "The Impact of Document Image Management on Clinical Data Processing." Drug Information Journal 27, no. 4 (October 1993): 995–1000. http://dx.doi.org/10.1177/009286159302700409.
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English, Ned, Andrew Anesetti-Rothermel, Chang Zhao, Andrew Latterner, Adam F. Benson, Peter Herman, Sherry Emery, et al. "Image Processing for Public Health Surveillance of Tobacco Point-of-Sale Advertising: Machine Learning–Based Methodology." Journal of Medical Internet Research 23, no. 8 (August 27, 2021): e24408. http://dx.doi.org/10.2196/24408.
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Background With a rapidly evolving tobacco retail environment, it is increasingly necessary to understand the point-of-sale (POS) advertising environment as part of tobacco surveillance and control. Advances in machine learning and image processing suggest the ability for more efficient and nuanced data capture than previously available. Objective The study aims to use machine learning algorithms to discover the presence of tobacco advertising in photographs of tobacco POS advertising and their location in the photograph. Methods We first collected images of the interiors of tobacco retailers in West Virginia and the District of Columbia during 2016 and 2018. The clearest photographs were selected and used to create a training and test data set. We then used a pretrained image classification network model, Inception V3, to discover the presence of tobacco logos and a unified object detection system, You Only Look Once V3, to identify logo locations. Results Our model was successful in identifying the presence of advertising within images, with a classification accuracy of over 75% for 8 of the 42 brands. Discovering the location of logos within a given photograph was more challenging because of the relatively small training data set, resulting in a mean average precision score of 0.72 and an intersection over union score of 0.62. Conclusions Our research provides preliminary evidence for a novel methodological approach that tobacco researchers and other public health practitioners can apply in the collection and processing of data for tobacco or other POS surveillance efforts. The resulting surveillance information can inform policy adoption, implementation, and enforcement. Limitations notwithstanding, our analysis shows the promise of using machine learning as part of a suite of tools to understand the tobacco retail environment, make policy recommendations, and design public health interventions at the municipal or other jurisdictional scale.
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Budhiningtias Winanti, Marliana, and Meylan Lesnusa. "Sistem Informasi Pelayanan Data Pasien pada Laboratorium UPTD Balai Kesehatan Paru Masyarakat (BKPM) Provinsi Maluka." Jurnal Manajemen Informatika (JAMIKA) 9, no. 1 (May 13, 2019): 1–8. http://dx.doi.org/10.34010/jamika.v9i1.1533.
Повний текст джерелаАнотація:
The development of globalization brings with significant impact for every layer of society, especially the development of many technologies needed by every human being, not least in the areas of employment such as health, and others. In this case the Public Lung Health Center (BKPM) Maluku province is the center of the health inspection service laboratory. Where every day a lot of people who come from different places to check their condition to obtain the required health outcomes, but the increase in performance of health services is still not properly fit most people's expectations, because of patient data recording system is still done manually, a long time patient data storage system still manually which takes in the search for patient data it is considered not effective, patient examination data processing is still considered a long time because the process is done. Therefore created an information system to assist agencies in addressing the problem and help some of the difficulties that exist. The inspection data processing system designed to help process patient data input, data storage, and so forth process is computerized. The method used a structured and methods of development of information systems data processing desktop-based health care checks are made using the method Prototype, with tools such as system development flowmap, context diagram, DFD and database design tool.
Keywords: Information Systems, Health Services, data processing.
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Chorianopoulos, Konstantinos, and Karolos Talvis. "Flutrack.org: Open-source and linked data for epidemiology." Health Informatics Journal 22, no. 4 (July 26, 2016): 962–74. http://dx.doi.org/10.1177/1460458215599822.
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Epidemiology has made advances, thanks to the availability of real-time surveillance data and by leveraging the geographic analysis of incidents. There are many health information systems that visualize the symptoms of influenza-like illness on a digital map, which is suitable for end-users, but it does not afford further processing and analysis. Existing systems have emphasized the collection, analysis, and visualization of surveillance data, but they have neglected a modular and interoperable design that integrates high-resolution geo-location with real-time data. As a remedy, we have built an open-source project and we have been operating an open service that detects flu-related symptoms and shares the data in real-time with anyone who wants to built upon this system. An analysis of a small number of precisely geo-located status updates (e.g. Twitter) correlates closely with the Google Flu Trends and the Centers for Disease Control and Prevention flu-positive reports. We suggest that public health information systems should embrace an open-source approach and offer linked data, in order to facilitate the development of an ecosystem of applications and services, and in order to be transparent to the general public interest.
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Bhat, Mohammad Hanan. "A Comprehensive Multi-Modal Framework for Plant Health Monitoring." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 20, 2021): 1793–95. http://dx.doi.org/10.22214/ijraset.2021.36739.
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: Plant health monitoring has been a significant field of research since a very long time. The scope of this research work conducted lies in the vast domain of plant pathology with its applications extending in the field of agriculture production monitoring to forest health monitoring. It deals with the data collection techniques based on IOT, pre-processing and post-processing of Image dataset and identification of disease using deep learning model. Therefore, providing a multi-modal end-to-end approach for plant health monitoring. This paper reviews the various methods used for monitoring plant health remotely in a non-invasive manner. An end-to-end low cost framework has been proposed for monitoring plant health by using IOT based data collection methods and cloud computing for a single-point-of-contact for the data storage and processing. The cloud agent gateway connects the devices and collects the data from sensors to ensure a single source of truth. Further, the deep learning computational infrastructure provided by the public cloud infrastructure is exploited to train the image dataset and derive the plant health status
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Kassekert, R., M. Easwar, M. Glaser, R. Ventham, and A. Bate. "PNS271 Automation in Routine Use for Data Collection and Processing for Scalable Faster RWE Generation." Value in Health 23 (December 2020): S686. http://dx.doi.org/10.1016/j.jval.2020.08.1715.
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Blumenthal, Wendy, Temitope O. Alimi, Sandra F. Jones, David E. Jones, Joseph D. Rogers, Vicki B. Benard, and Lisa C. Richardson. "Using informatics to improve cancer surveillance." Journal of the American Medical Informatics Association 27, no. 9 (September 1, 2020): 1488–95. http://dx.doi.org/10.1093/jamia/ocaa149.
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Abstract Objectives This review summarizes past and current informatics activities at the Centers for Disease Control and Prevention National Program of Cancer Registries to inform readers about efforts to improve, standardize, and automate reporting to public health cancer registries. Target audience The target audience includes cancer registry experts, informaticians, public health professionals, database specialists, computer scientists, programmers, and system developers who are interested in methods to improve public health surveillance through informatics approaches. Scope This review provides background on central cancer registries and describes the efforts to standardize and automate reporting to these registries. Specific topics include standardized data exchange activities for physician and pathology reporting, software tools for cancer reporting, development of a natural language processing tool for processing unstructured clinical text, and future directions of cancer surveillance informatics.
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Buchan, I., and J. Ainsworth. "Combining Health Data Uses to Ignite Health System Learning." Methods of Information in Medicine 54, no. 06 (2015): 479–87. http://dx.doi.org/10.3414/me15-01-0064.
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SummaryObjectives: In this paper we aim to characterise the critical mass of linked data, methods and expertise required for health systems to adapt to the needs of the populations they serve – more recently known as learning health systems. The objectives are to: 1) identify opportunities to combine separate uses of common data sources in order to reduce duplication of data processing and improve information quality; 2) identify challenges in scaling-up the reuse of health data sufficiently to support health system learning.Methods: The challenges and opportunities were identified through a series of e-health stakeholder consultations and workshops in Northern England from 2011 to 2014. From 2013 the concepts presented here have been refined through feedback to collaborators, including patient/citizen representatives, in a regional health informatics research network (www.herc.ac.uk).Results: Health systems typically have separate information pipelines for: 1) commissioning services; 2) auditing service performance; 3) managing finances; 4) monitoring public health; and 5) research. These pipelines share common data sources but usually duplicate data extraction, aggregation, cleaning/preparation and analytics. Suboptimal analyses may be performed due to a lack of expertise, which may exist elsewhere in the health system but is fully committed to a different pipeline. Contextual knowledge that is essential for proper data analysis and interpretation may be needed in one pipeline but accessible only in another. The lack of capable health and care intelligence systems for populations can be attributed to a legacy of three flawed assumptions: 1) universality: the generalizability of evidence across populations; 2) time-invariance: the stability of evidence over time; and 3) reducibility: the reduction of evidence into specialised subsystems that may be recombined.Conclusions: We conceptualize a population health and care intelligence system capable of supporting health system learning and we put forward a set of maturity tests of progress toward such a system. A factor common to each test is data-action latency; a mature system spawns timely actions proportionate to the information that can be derived from the data, and in doing so creates meaningful measurement about system learning. We illustrate, using future scenarios, some major opportunities to improve health systems by exchanging conventional intelligence pipelines for networked critical masses of data, methods and expertise that minimise data-action latency and ignite system-learning.
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Lys, Candice, Dionne Gesink, Carol Strike, and June Larkin. "Body Mapping as a Youth Sexual Health Intervention and Data Collection Tool." Qualitative Health Research 28, no. 7 (January 5, 2018): 1185–98. http://dx.doi.org/10.1177/1049732317750862.
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In this article, we describe and evaluate body mapping as (a) an arts-based activity within Fostering Open eXpression Among Youth (FOXY), an educational intervention targeting Northwest Territories (NWT) youth, and (b) a research data collection tool. Data included individual interviews with 41 female participants (aged 13–17 years) who attended FOXY body mapping workshops in six communities in 2013, field notes taken by the researcher during the workshops and interviews, and written reflections from seven FOXY facilitators on the body mapping process (from 2013 to 2016). Thematic analysis explored the utility of body mapping using a developmental evaluation methodology. The results show body mapping is an intervention tool that supports and encourages participant self-reflection, introspection, personal connectedness, and processing difficult emotions. Body mapping is also a data collection catalyst that enables trust and youth voice in research, reduces verbal communication barriers, and facilitates the collection of rich data regarding personal experiences.
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Martin-Sanchez, F., and V. Maojo. "Bioinformatics: Towards New Directions for Public Health." Methods of Information in Medicine 43, no. 03 (2004): 208–14. http://dx.doi.org/10.1055/s-0038-1633861.
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Summary Objectives: Epidemiologists are reformulating their classical approaches to diseases by considering various issues associated to “omics” areas and technologies. Traditional differences between epidemiology and genetics include background, training, terminologies, study designs and others. Public health and epidemiology are increasingly looking forward to using methodologies and informatics tools, facilitated by the Bioinformatics community, for managing genomic information. Our aim is to describe which are the most important implications related with the increasing use of genomic information for public health practice, research and education. To review the contribution of bioinformatics to these issues, in terms of providing the methods and tools needed for processing genetic information from pathogens and patients. To analyze the research challenges in biomedical informatics related with the need of integration of clinical, environmental and genetic data and the new scenarios arisen in public health. Methods: Review of the literature, Internet resources and material and reports generated by internal and external research projects. Results: New developments are needed to advance in the study of the interactions between environmental agents and genetic factors involved in the development of diseases. The use of biomarkers, biobanks, and integrated genomic/clinical databases poses serious challenges for informaticians in order to extract useful information and knowledge for public health, biomedical research and healthcare. Conclusions: From an informatics perspective, integrated medical/biological ontologies and new semantic-based models for managing information provide new challenges for research in areas such as genetic epidemiology and the “omics” disciplines, among others. In this regard, there are various ethical, privacy, informed consent and social implications, that should be carefully addressed by researchers, practitioners and policy makers.
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Perpoil, Antoine, Gael Grimandi, Stéphane Birklé, Jean-François Simonet, Anne Chiffoleau, and François Bocquet. "Public Health Impact of Using Biosimilars, Is Automated Follow up Relevant?" International Journal of Environmental Research and Public Health 18, no. 1 (December 29, 2020): 186. http://dx.doi.org/10.3390/ijerph18010186.
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Biologic reference drugs and their copies, biosimilars, have a complex structure. Biosimilars need to demonstrate their biosimilarity during development but unpredictable variations can remain, such as micro-heterogeneity. The healthcare community may raise questions regarding the clinical outcomes induced by this micro-heterogeneity. Indeed, unwanted immune reactions may be induced for numerous reasons, including product variations. However, it is challenging to assess these unwanted immune reactions because of the multiplicity of causes and potential delays before any reaction. Moreover, safety assessments as part of preclinical studies and clinical trials may be of limited value with respect to immunogenicity assessments because they are performed on a standardised population during a limited period. Real-life data could therefore supplement the assessments of clinical trials by including data on the real-life use of biosimilars, such as switches. Furthermore, real-life data also include any economic incentives to prescribe or use biosimilars. This article raises the question of relevance of automating real life data processing regarding Biosimilars. The objective is to initiate a discussion about different approaches involving Machine Learning. So, the discussion is established regarding implementation of Neural Network model to ensure safety of biosimilars subject to economic incentives. Nevertheless, the application of Machine Learning in the healthcare field raises ethical, legal and technical issues that require further discussion.
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Pynzaru, Iury V. "HEALTH ASSESSMENT OF WORKERS OF MEAT PROCESSING PLANTS." Hygiene and sanitation 98, no. 3 (April 29, 2019): 280–87. http://dx.doi.org/10.18821/0016-9900-2019-98-3-280-287.
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Health assessment of workers of four meat processing plants in the Republic of Moldova in the 2011-2015 was carried out. The analysis of temporary disability showed the incidence the respiratory diseases (13.9±1.3 cases for 134.0±17.1 days per 100 workers) to prevail in the structure of disability), followed by the diseases of circulatory system (5.90±0.52 cases and 85.0±9.0 days per 100 workers) as well as the diseases of bone and muscular system (3.54±0.67 cases and 55.2±12.9 days per 100 workers), and diseases of digestive system (3.11±0.44 cases and 45.9±6.2 days of 100 workers) and injuries, poisonings and some other consequences of the influence of the external factors (3.02±0.40 cases and 48.8±10.3 days per 100 workers). Indices of the frequency and severity showed a tendency to fall. The index of diseases prevalence showed on average annually decreasing by 6.7 cases per 100 workers (R2 = 0.95), and an index of the duration of diseases for 77.0 days per 100 workers (R2 = 0,95). The meat processing plants suffered from economic losses because of the temporary disability of workers in the amount of 1892434.25 lei/107009.6 dollars. The obtained data indicate the need for the development and implementation of preventive measures.
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Mchenry, Daniel James, and Nigel Mckelvey. "The Ethical Issues Surrounding Sections 175-178 of the UK's Data Protection Bill." International Journal of Innovation in the Digital Economy 10, no. 1 (January 2019): 53–60. http://dx.doi.org/10.4018/ijide.2019010105.
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There is growing concern that a new data ethics regime that has been introduced into the current draft of the data protection bill may be ethically problematic. The changes are designed to be a framework for data processing but health data privacy advocacy group medconfidential has voiced their concerns at the development. The provider has claimed that the government is trying to push through the regime without first giving the public a chance to engage in discussions about how public-sector data should be stored and processed. This article will discuss ethical issues surrounding the proposed bill as well as big data analytics and will discuss the role played by government in data processing in the UK. It will discuss the arguments for and against the government's proposals. It will be important for the UK government along with all data processors and data controllers to ensure that large amounts of data from all UK citizens across all sectors is handled correctly.
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Geukes, Cornelia, and Horst M. Müller. "Physiological Correlates of Processing Health-Related Information: An Idea for the Adoption of a Foreign Field." Nursing Reports 11, no. 1 (March 17, 2021): 175–86. http://dx.doi.org/10.3390/nursrep11010017.
Повний текст джерелаАнотація:
Measuring health may refer to the measurement of general health status through measures of physical function, pain, social health, psychological aspects, and specific disease. Almost no evidence is available on the possible interaction of physiological measures and correlating emotional–affective states that are triggered by dealing with individual health-relevant issues and their specific processing modes. Public health research has long been concerned with the processing of health-related information. However, it is not yet clear which factors influence access and the handling of health-related information in detail. One way to close this research gap could be adopting methods from neurocognitive experiments to add psychophysiological data to existing approaches in health-related research. In this article, we present some of these methods and give a narrative overview and description of their usefulness for enlarged research in public health.
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Liu, Jingjing, Guangyuan Shi, Jing Zhou, and Qiumei Yao. "Prediction of College Students’ Psychological Crisis Based on Data Mining." Mobile Information Systems 2021 (May 17, 2021): 1–7. http://dx.doi.org/10.1155/2021/9979770.
Повний текст джерелаАнотація:
The development of a college students’ psychological management system has become an essential indicator to monitor and prevent the psychological crisis. University student management databases accumulate massive data, but the conventional data processing tasks are restricted to simple statistical analysis, storage, and query management. This paper discusses the application of big data technology for the current psychological management system by investigating psychological crisis screening indicators. Data mining techniques are used to realize the dynamic management of psychological early warning data, real-time monitoring of high-risk groups’ psychology, and improvement of the accuracy and effectiveness of early identification and warning of students’ psychological crisis. Based on a combination of qualitative and quantitative analysis, we conduct a series of studies on three typical types of network public opinions, i.e., Internet rumors, online public opinions of college students, and emergent public health incidents in terms of the transmission mechanism, early warning, and decision-making mechanism, as well as the evolution mechanism of the network public opinion.
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Brown, Adrian Paul, and Sean M. Randall. "Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model." JMIR Medical Informatics 8, no. 9 (September 23, 2020): e18920. http://dx.doi.org/10.2196/18920.
Повний текст джерелаАнотація:
Background The linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians. Objective This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local. Methods A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data. Results The cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time). Conclusions The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.
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Botes, Marietjie, Melodie Nöthling Slabbert, and Antonel Olckers. "Data Commercialisation in the South African Health Care Context." Potchefstroom Electronic Law Journal 24 (August 12, 2021): 1–35. http://dx.doi.org/10.17159/1727-3781/2021/v24i0a8577.
Повний текст джерелаАнотація:
Realisation of the value and the commercialisation potential of data is gaining exponential momentum. The combination of historical data exploitations and the use of technologies that allow for the triangulation of data results in the collection, storage, and processing of massive amounts of data require diligent data management, including adherence to privacy and other laws, both nationally and internationally. The intrinsic value of scientific data, especially in genomics, becomes apparent when data are shared, often in collaboration with international partners, and compiled into big data sets that are subsequently used for benefit, including commercial benefit. The purpose of this article is to explore the commercialisation of data in South Africa against the backdrop of the legal framework governing the protection of personal information, confidentiality and privacy, with a specific focus on genetic and genomic information. Related issues, such as the collection and sharing of data, ownership of data and challenges about informed consent are also considered. After a brief evaluation of the African regulatory landscape relating to the protection of personal information, the article concludes with a few recommendations aimed at improving the status quo and sensitising the South African public as to the value of their data and personal information, as well as the potential uses and abuses to which their personal information may be subjected
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Lach, Daniel Eryk. "Przetwarzanie i ochrona danych dotyczących zdrowia przez organizatora systemu opieki zdrowotnej." Studia Prawa Publicznego, no. 3 (31) (October 15, 2020): 53–72. http://dx.doi.org/10.14746/spp.2020.3.31.3.
Повний текст джерелаАнотація:
The protection of individuals regarding to the processing of personal data is one of the fundamental rights. The General Data Protection Regulation (GDPR) lays down rules relating to the protection of natural persons with regard to the processing of personal data and rules relating to the free movement of personal data. Data concerning health is one of the areas the GDPR defines as special personal data, the so-called sensitive data. With regard to these data, the GDPR allows their processing only on an exceptional basis, in certain situations. According to Art. 6 sec. 1 let. e GDPR and art. 9 sec. 2 let. b GDPR, data processing is allowed, inter alia, when such processing is necessary for the purposes of meeting the obligations and exercising specific rights of the controller or of the data subject in the field of employment and social security and social protection law. In turn, Art. 9 sec. 2 let. h GDPR permits the processing of health data that is necessary for the purposes of providing health or social care or treatment, or for managing health or social care systems and services on the basis of European Union or Member State law. The article discusses the national legal regulations regarding the collection and processing of personal data concerning health in the light of the organization of the health care system and the tasks of the National Health Fund (NFZ) as a placeholder, whose task is only to manage financial resources and conclude health care contracts on its own behalf with independent healthcare providers and their accounting. Against the background of the GDPR, the author discusses the provisions of the acts on health care services financed from public funds and on the information system in health care. Finally, specific regulation regarding the COVID-19 pandemic are presented.
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Sheikh, Asim. "The Data Protection (Amendment) Act, 2003: The Data Protection Directive and its Implications for Medical Research in Ireland." European Journal of Health Law 12, no. 4 (2005): 357–72. http://dx.doi.org/10.1163/157180905775088568.
Повний текст джерелаАнотація:
AbstractDirective 95/46/EC on the Protection of Individuals with regard to the Processing of Personal Data and on the Free Movement of Such Data has been transposed into national law and is now the Data Protection (Amendment) Act, 2003.The Directive and the transposing Act provide for new obligations to those processing data. The new obligation of primary concern is the necessity to obtain consent prior to the processing of data (Article 7, Directive 95/46/EC). This has caused much concern especially in relation to 'secondary data' or 'archived data'.There exist, what seem to be in the minds of the medical research community, two competing interests: (i) that of the need to obtain consent prior to processing data and (ii) the need to protect and foster medical research. At the same time as the introduction of the Act, other prior legislation, i.e. the Freedom of Information Act, 1997-2003, has encouraged candour within the doctor-patient relationship and the High Court in Ireland, in the case of Geoghegan v. Harris, has promulgated the 'reasonable-patient test' as being the correct law in relation to the disclosure of risks to patients. The court stated that doctors have a duty to disclose all material risks to patients. The case demonstrates an example of a move toward a more open medical relationship. An example of this rationale was also recently seen in the United Kingdom in the House of Lords decision in Chester v. Afshar. Within the medical research community in Ireland, the need to respect the autonomy of patients and research participants by providing information to such parties has also been observed (Sheikh A. A., 2000 and Irish Council for Bioethics, 2005).Disquiet has been expressed in Ireland and other jurisdictions by the medical research communities in relation to the exact working and meaning of the Directive and therefore the transposing Acts (Strobl et al). This may be due to the fact that, as observed by Beyleveld "The Directive makes no specific mention of medical research and, consequently, it contains no provisions for medical research as an explicitly delineated category." (Beyleveld D., 2004) This paper examines the Irish Act and discusses whether the concerns expressed are well-founded and if the Act is open to interpretation such that it would not hamper medical research and public health work.
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Sujin, J. S., N. Gandhiraj, D. Selvakumar, and Satheesh S. Kumar. "Public E-Health Network System Using Arduino Controller." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 544–49. http://dx.doi.org/10.1166/jctn.2019.7766.
Повний текст джерелаАнотація:
An E-health Multifunctional system is proposed for people in all age category in this work. Simply, the different sensors are fixed on a coat. When the coatis worn by people, the sensors situated in the coat will be get activated. Then it will automatically examine various parameters about health and the same information send to monitor or website or mobile. This is used for personal and social use. An example is presented with different sensor signals and continuous Real time monitoring the health changes from the home. Sensors are embedded in to the atmosphere, it will give various changes which is differs from behavior of human biological signals and activity with patterns. Changes in signals are detected to each potential point of changing health. The information from the sensors investigated and it will store and uses this for making a comparison of last 180 days status. A 1-D alert algorithm integrated to the system where the health alerts to nurse or doctor which is already fixed or pre decided. Research work is based on analyze of each alert signals and monitoring the comparative signal on the hospital relevance. This result is used for a training classes and best medical solutions. The information about health is managed for future purpose using database. A methodology is introduced in this work with 10 sensors that are capable of multisensory signals and data Management of Real-time signals. Information is processed at various sensor nodes and transfers to the main hub for further processing.