Bibliografías temáticas / Public health Data processing

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Literatura académica sobre el tema "Public health Data processing"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 19 de febrero de 2022

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Artículos de revistas sobre el tema "Public health Data processing"

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.

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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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Tesis sobre el tema "Public health Data processing"

Chitondo, Pepukayi David Junior. "Data policies for big health data and personal health data." Thesis, Cape Peninsula University of Technology, 2016. http://hdl.handle.net/20.500.11838/2479.

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Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2016.<br>Health information policies are constantly becoming a key feature in directing information usage in healthcare. After the passing of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 and the Affordable Care Act (ACA) passed in 2010, in the United States, there has been an increase in health systems innovations. Coupling this health systems hype is the current buzz concept in Information Technology, „Big data‟. The prospects of big data are full of potential, even more so in the healthcare field where the accuracy of data is life critical. How big health data can be used to achieve improved health is now the goal of the current health informatics practitioner. Even more exciting is the amount of health data being generated by patients via personal handheld devices and other forms of technology that exclude the healthcare practitioner. This patient-generated data is also known as Personal Health Records, PHR. To achieve meaningful use of PHRs and healthcare data in general through big data, a couple of hurdles have to be overcome. First and foremost is the issue of privacy and confidentiality of the patients whose data is in concern. Secondly is the perceived trustworthiness of PHRs by healthcare practitioners. Other issues to take into context are data rights and ownership, data suppression, IP protection, data anonymisation and reidentification, information flow and regulations as well as consent biases. This study sought to understand the role of data policies in the process of data utilisation in the healthcare sector with added interest on PHRs utilisation as part of big health data.

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Indrakanti, Saratchandra. "Computational Methods for Vulnerability Analysis and Resource Allocation in Public Health Emergencies." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804902/.

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POD (Point of Dispensing)-based emergency response plans involving mass prophylaxis may seem feasible when considering the choice of dispensing points within a region, overall population density, and estimated traffic demands. However, the plan may fail to serve particular vulnerable sub-populations, resulting in access disparities during emergency response. Federal authorities emphasize on the need to identify sub-populations that cannot avail regular services during an emergency due to their special needs to ensure effective response. Vulnerable individuals require the targeted allocation of appropriate resources to serve their special needs. Devising schemes to address the needs of vulnerable sub-populations is essential for the effectiveness of response plans. This research focuses on data-driven computational methods to quantify and address vulnerabilities in response plans that require the allocation of targeted resources. Data-driven methods to identify and quantify vulnerabilities in response plans are developed as part of this research. Addressing vulnerabilities requires the targeted allocation of appropriate resources to PODs. The problem of resource allocation to PODs during public health emergencies is introduced and the variants of the resource allocation problem such as the spatial allocation, spatio-temporal allocation and optimal resource subset variants are formulated. Generating optimal resource allocation and scheduling solutions can be computationally hard problems. The application of metaheuristic techniques to find near-optimal solutions to the resource allocation problem in response plans is investigated. A vulnerability analysis and resource allocation framework that facilitates the demographic analysis of population data in the context of response plans, and the optimal allocation of resources with respect to the analysis are described.

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Asiimwe, Sarah. "Use of health information for operational and strategic decision-making by division level managers of Kampala City Council Health Department." Thesis, University of the Western Cape, 2002. http://etd.uwc.ac.za/index.php?module=etd&amp.

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O'Donnell, Melissa. "Towards prevention - a population health approach to child abuse and neglect : health indicators and the identification of antecedent causal pathways." University of Western Australia. School of Paediatrics and Child Health, 2009. http://theses.library.uwa.edu.au/adt-WU2010.0029.

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[Truncated abstract] The primary aims of this thesis were to investigate health indicators of child maltreatment, as well as pathways into the child protection system using routinely collected government databases, enabling a preventative health approach to child abuse and neglect. This thesis aims to improve understanding of the trends in child maltreatment and the factors, at the child and family level, which increase or reduce vulnerability to child maltreatment so more effective prevention policies and practices can be developed. This project uses longitudinal de-identified population data from the Western Australian Government Departments of Child Protection, Health and Disability Services. These data contained information on demographic, clinical, social and child protection outcomes of children and their families. Record linkage of administrative data was undertaken to: investigate health indicators of abuse and neglect using Hospital Morbidity data to enable the monitoring of population trends in abuse and neglect; compare proportion of cases obtained using health indicators with the Department of Child Protection data, and describe the physical, psychological and social characteristics of abused and/or neglected children and families. Statistical techniques utilised include logistic and Cox regression to investigate risk of adverse child outcomes, taking into account potential confounding and time to event. The main findings include: There has been an increase in assault and maltreatment related hospital admissions over the last 25 years. ... There has been a marked increase in the birth prevalence of Neonatal Withdrawal Syndrome (NWS) in Western Australia over the last 25 years, from 1 per 10,000 live births in 1980, to 31 per 10,000 live births in 2005. Specific maternal characteristics associated with having a child with NWS are identified and these children have an increased risk of child protection involvement. A population level analysis of child and parental factors determined the estimated increase in risk of substantiated child maltreatment for child intellectual disability, parental admissions for mental health, substance use, and assault, as well as greater socio-economic disadvantage. Conclusions This is the first body of research which has extensively used longitudinal, population level linked health and child protection data to investigate health indicators of child abuse and neglect and antecedent causal pathways. Monitoring injuries and conditions associated with child abuse and neglect in routinely collected data and using multiple sources of ascertainment are important initiatives in child maltreatment surveillance. Health indicators of child abuse and neglect are not subject to the same definitional and policy issues as child protection data and therefore provide a more valid comparison over time and between jurisdictions. The identification of factors which increase vulnerability for children and families to child maltreatment is essential in the implementation of prevention strategies including universal public health approaches as well as the identification of at-risk families for targeted intervention.

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Chartree, Jedsada. "Monitoring Dengue Outbreaks Using Online Data." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500167/.

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Internet technology has affected humans' lives in many disciplines. The search engine is one of the most important Internet tools in that it allows people to search for what they want. Search queries entered in a web search engine can be used to predict dengue incidence. This vector borne disease causes severe illness and kills a large number of people every year. This dissertation utilizes the capabilities of search queries related to dengue and climate to forecast the number of dengue cases. Several machine learning techniques are applied for data analysis, including Multiple Linear Regression, Artificial Neural Networks, and the Seasonal Autoregressive Integrated Moving Average. Predictive models produced from these machine learning methods are measured for their performance to find which technique generates the best model for dengue prediction. The results of experiments presented in this dissertation indicate that search query data related to dengue and climate can be used to forecast the number of dengue cases. The performance measurement of predictive models shows that Artificial Neural Networks outperform the others. These results will help public health officials in planning to deal with the outbreaks.

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Mchunu, Nokubalela Ntombiyethu. "Adequacy of healthcare information systems to support data quality in the public healthcare sector, in the Western Cape, South Africa." Thesis, Cape Peninsula University of Technology, 2012. http://hdl.handle.net/20.500.11838/1387.

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Thesis submitted in fulfilment of the requirements for the degree Master of Technology (MTech) in Information Technology In the Faculty of Informatics and Design, at the Cape Peninsula University of Technology (CPUT), 2013<br>Healthcare services are vital to all human beings, as our daily lives depend on them. In South Africa approximately eighty per cent of the population uses the public healthcare services. In the current healthcare systems data corruption exists which threatens data quality in the systems. The aim of this study was to understand the existing information handling processes and factors that affect the accuracy and integrity of healthcare data. A qualitative research methodology, under the interpretive paradigm was used for this investigation. Activity theory is used to formulate an analytical framework, the “healthcare information system data quality activity theory framework”. This was very helpful for understanding the healthcare information handling process as an activity system that consists of actors with individual goals. Though the goals are varied, they are joined together by the common objective. The logic of the framework is that a realisation of goals in the activity system depends on a number of factors. At the beginning, there must be a synchronous inter-linkage between the goals of the actors, the mediating factors such as adequate tools, user skills, enabling policies, and the systematic procedures that are diligently enforced. It is assumed that any situation which prevents this inter-linkage will have a negative impact on the realisation of the sought objective. The framework therefore, was very helpful in informing questions, the data collection and ultimately, the analysis processes. The public healthcare sector is the main source of data; other sources were literature, the Internet and books. The analysis of data was done using content analysis to find what themes emerge and the relationship (s) between them in what is being analysed. The findings reveal a lack of adherence to information handling procedures and processes which lead to corrupt data in the systems. In addition, most users have limited skills, which is a hindrance to them in performing their duties as expected by the healthcare sector. In fact, the healthcare sector is also challenged by systems which are constantly slow or down, due to limited network capacity and human errors. The presence of these challenges suggests non-adherence to data handling procedures, which explains the existing corrupt data in the healthcare systems. Therefore the recommendation is that the public healthcare administration must enhance their training programs. The training must be re-designed to cater for the needs of all users, regardless of their background. It needs to improve user skills and boast their confidence in using electronic systems. Obviously, any changes and improvements need to be sustainable, and the sector is unlikely to succeed without enforcement of new procedures. Therefore, adherence to data handling procedures must be strictly enforced, with policies thoroughly communicated to the users. That way, the sector will not only have systems and related policies, but also ensure their full exploitation for improved service delivery in the public healthcare sector in South Africa.

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Wilmot, Peter Nicholas. "Modelling cooling tower risk for Legionnaires' Disease using Bayesian Networks and Geographic Information Systems." Title page, contents and conclusion only, 1999. http://web4.library.adelaide.edu.au/theses/09SIS.M/09sismw744.pdf.

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Includes bibliographical references (leaves 115-120) Establishes a Bayesian Belief Network (BBN) to model uncertainty of aerosols released from cooling towers and Geographic Information Systems (GIS) to create a wind dispersal model and identify potential cooling towers as the source of infection. Demonstrates the use of GIS and BBN in environmental epidemiology and the power of spatial information in the area of health.

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Lin, Dong. "Novel FDBC with creative technology for integrating advantages of distributed and centralized systems." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492977.

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Vuorio, R. (Riikka). "Use of public sector’s open spatial data in commercial applications." Master's thesis, University of Oulu, 2014. http://urn.fi/URN:NBN:fi:oulu-201311201883.

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The objective of this study was to analyse how young Finnish information technology (IT) companies utilize the public sector’s open spatial data. The aim was to find out to what extent companies use public sector’s open spatial data in products and how companies are using it. In addition, defects related to data and its use and companies’ awareness of public sector open data were canvassed. Defects and unawareness might prevent or retard the utilization of public sector’s data. Public sector is collecting vast amount of data from various areas when performing public tasks. The major part of the data is spatial, meaning the data has a location aspect. Public sector is opening the data for everybody to use freely and companies could use this open spatial data for commercial purposes. High expectations have been set for the data opening: along with it, innovations and business — new companies and digital products — will be created. The European Union has promoted greatly the public sector data opening with its legislative actions. First with the PSI directive (directive on re-use of public sector data) and later with the INSPIRE directive (directive on establishing and Infrastructure for Spatial Information in the European Community). The both directives are aiming to facilitate the re-use and dissemination of public sector data, whereas the INSPIRE directive has focused on the use of interoperable spatial data by creating the spatial data infrastructure. Even if the developments are still on going, these undertakings have already created possibilities for companies to use public sector data. This applies especially to the spatial data. This study was quantitative by nature and the empirical data for the study was collected through online survey, which was targeted to randomly selected Finnish IT companies established during the years 2009–2012. Data was analyzed by descriptive statistics. The results can be generalized to the whole target population in Finland. The results of this study shows that the number of companies utilizing public sector’s open spatial data is small and the public sector’s open spatial data has not yet enabled establishing of new companies. However, companies have developed few new products with the contribution of public sector’s open spatial data and the value of the data for the products is not minor. The thesis concludes that there is a need for greater investment in promoting the public sector’s open data amongst companies: the awareness of public sector’s open spatial data could be increased. In addition, coverage of datasets and interface services could be improved. Perhaps by eliminating these defects, the number of utilizers of public sector’s open spatial data would increase. Now there is a quiet sign of awakening of the business to utilize public sector’s data.

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Ponsimaa, P. (Petteri). "Discovering value for health with grocery shopping data." Master's thesis, University of Oulu, 2016. http://urn.fi/URN:NBN:fi:oulu-201605221849.

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Food retailers are taking more active role in the customer value creation process and shifting their attention from the sale of goods to support customer’s value-creation to discover more innovative service-based business models. From customer data consumers may develop more responsible consumption behaviour, make more economical choices, and raise awareness on food healthiness. This exploratory study sets out to answer the question what value if any does the use of grocery shopping data bring to the customers. Using design science research, the thesis makes use of grocery purchase data available to S-Group customers and presents ways of applying the data while making it meaningful for them. The aim was to construct visualization application prototypes for seeking value and benefits of purchase data experienced by the customers. To evaluate the application design, a study group of eight customers were invited to provide purchase data and feedback on the data visualizations. The focus was on building designs of the grocery consumption patterns based on customer interviews and then evaluating the impact on the study group via interviews and usage data. The visualization prototypes allowed the participants to discover something new of their shopping and food consumption behaviour, not known to them before the study and not visible from the mere purchase data. Interviews suggested that the visualizations of health data encourage reflection of consuming habits, and thus may be used as a tool for increasing awareness of one’s shopping behaviour. A number of limitations in the data utilization were met hindering inference-making and reflecting on the data. Lastly, the prototypes led the participants to envision new digital health services, some of which might have commercial value.

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Libros sobre el tema "Public health Data processing"

Kenkyūjo, Tōkyō Toritsu Eisei. Eisei gyōsei jōhō shisutemu no kaihatsu ni kansuru kenkyū. Tōkyō: Tōkyō Toritsu Eisei Kenkyūjo, 2000.

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Montana. Legislature. Legislative Audit Division. Medicaid data review: Department of Public Health and Human Services. Helena, MT: Legislative Audit Division, 2007.

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Québec (Province). Ministère de la santé et des services sociaux. Direction des systèmes d'information. Informatisation du réseau de la santé et des services sociaux: Portrait. [Québec]: Gouvernement du Québec, Ministère de la santé et des services sociaux, Direction des systèmes d'information, 1990.

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Australian Institute of Health and Welfare. National health information model: Version 2. Canberra: Australian Institute of Health and Welfare, 2003.

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1951-, McLafferty Sara, ed. GIS and public health. 2nd ed. New York: The Guilford Press, 2012.

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Introduction to geographic information systems in public health. Gaithersburg, Md: Aspen Publishers, 2002.

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Basile, Kathleen C. Sexual violence surveillance: Uniform definitions and recommended data elements. Atlanta, Ga: Centers for Disase Control and Prevention, National Center for Injury Prevention and Control, 2009.

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Capítulos de libros sobre el tema "Public health Data processing"

Li, Zhenlong, Zhipeng Gui, Barbara Hofer, Yan Li, Simon Scheider, and Shashi Shekhar. "Geospatial Information Processing Technologies." In Manual of Digital Earth, 191–227. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_6.

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Abstract The increasing availability of geospatial data offers great opportunities for advancing scientific discovery and practices in society. Effective and efficient processing of geospatial data is essential for a wide range of Digital Earth applications such as climate change, natural hazard prediction and mitigation, and public health. However, the massive volume, heterogeneous, and distributed nature of global geospatial data pose challenges in geospatial information processing and computing. This chapter introduces three technologies for geospatial data processing: high-performance computing, online geoprocessing, and distributed geoprocessing, with each technology addressing one aspect of the challenges. The fundamental concepts, principles, and key techniques of the three technologies are elaborated in detail, followed by examples of applications and research directions in the context of Digital Earth. Lastly, a Digital Earth reference framework called discrete global grid system (DGGS) is discussed.

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Natsiavas, Pantelis, Nicos Maglaveras, and Vassilis Koutkias. "A Public Health Surveillance Platform Exploiting Free-Text Sources via Natural Language Processing and Linked Data: Application in Adverse Drug Reaction Signal Detection Using PubMed and Twitter." In Knowledge Representation for Health Care, 51–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55014-5_4.

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Nordberg, Ana. "Biobank and Biomedical Research: Responsibilities of Controllers and Processors Under the EU General Data Protection Regulation." In GDPR and Biobanking, 61–89. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49388-2_5.

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AbstractBiobanks are essential infrastructures in current health and biomedical research. Advanced scientific research increasingly relies on processing and correlating large amounts of genetic, clinical and behavioural data. These data are particularly sensitive in nature and the risk of privacy invasion and misuse is high. The EU General Data Protection Regulation (GDPR) developed and increased harmonisation, resulting in a framework in which the specific duties and obligations of entities processing personal data—controllers and processors—were defined. Biobanks, in the exercise of their functions, assume the role of controllers and/or processors and as such need to comply with a number of complex rules. This chapter analyses these rules in the light of Article 89 GDPR, which creates safeguards and derogations relating to ‘processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes’. It identifies key compliance challenges faced by biobanks as data controllers and processors, such as determining whether the GDPR is applicable and its intersection with other regulations; when a biobank should be considered controller and processor; and what are the main duties of biobanks as data controllers and processors and options for compliance.

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Dobrin, Adam. "Public Health Data." In Homicide Data Sources, 19–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-19881-1_3.

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Flowers, Julian, and Katie Johnson. "Data Presentation." In Public Health Intelligence, 203–23. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28326-5_11.

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Chassang, Gauthier, Michael Hisbergues, and Emmanuelle Rial-Sebbag. "Research Biobanking, Personal Data Protection and Implementation of the GDPR in France." In GDPR and Biobanking, 257–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-49388-2_14.

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AbstractSince 1978 and the initial French data protection law (Loi n°78-17 du 6 Janvier 1978), consecutive modifications regarding the protection of personal health data, especially in 2004, 2016 and 2018, set up a strict legal regime for processing sensitive personal data, including for research purposes. In recent years, French law has evolved proactively and in parallel with the work of the European Union (EU) on the preparation of what became the General Data Protection Regulation (GDPR), which has been in force since May 2018. This Chapter performs a state-of-art analysis (as of 1 July 2019) of the French legal framework for research biobanks and data protection rules applying to biobanking, in particular those related to data subjects’ rights and Article 89 of the GDPR. Firstly, it provides updated information about the national landscape of active research biobanks in France (Sect. 1). Secondly, it explores how the French law embodies the developments brought by the GDPR and how it envisages individuals’ rights in the context of research biobanking (Sects. 2 and 3). Thirdly, this Chapter analyses existing and potential national exemptions to individuals’ rights, including with regard to Article 89 GDPR, and how France conceives of processing activities of ‘public interest’ (Sect. 4). Finally, the authors address ongoing debates around bioethics law in France and argue for the creation of a specific Act focused on biobanking as a means of integrating, clarifying and developing not only data protection rules but also other activities related to samples, human or not, in a unique, operational and compact act (Sect. 5).

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Choisy, Marc, Philavanh Sitboulang, Malyvanh Vongpanhya, Chantalay Saiyavong, Bouaphanh Khamphaphongphanh, Bounlay Phommasack, Fabrice Quet, Yves Buisson, Jean-Daniel Zucker, and Wilbert van Pahuis. "Rescuing Public Health Data." In Socio-Ecological Dimensions of Infectious Diseases in Southeast Asia, 171–90. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-527-3_11.

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Füller, Henning. "Biosecuring public health." In Big Data, Surveillance and Crisis Management, 81–97. 1 Edition. | New York : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315638423-5.

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Malley, Brian, Daniele Ramazzotti, and Joy Tzung-yu Wu. "Data Pre-processing." In Secondary Analysis of Electronic Health Records, 115–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43742-2_12.

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Rassia, Stamatina Th. "Research Data Collection." In SpringerBriefs in Public Health, 33–39. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53444-2_6.

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Actas de conferencias sobre el tema "Public health Data processing"

Setyowati, Maryani, and Vilda Ana Viera Setyawati. "Implementation of Maternal Health Data Processing of Computerization for Preventing the Case of Maternal Mortality by Midwives at Puskesmas in Supporting SDG's Achievements." In The 2nd International Symposium of Public Health. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0007511502020208.

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Budiningsari, R. Dwi, and Ika Ratna Palupi. "Knowledge, Attitude and Practice on Food Hygiene and Sanitation, Optimistic Bias of Food Handlers, and their Association with Participation in Food Safety Training at A Hospital in Yogyakarta." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the7thicph.04.13.

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Background: Incompatible sanitation hygiene practice during food processing in hospitals is possible due to optimism bias. This bias occurs when food handlers perceive that they are unlikely to cause foodborne illness. There is a lack of studies into this phenomenon. This study aimed to analyze knowledge, attitude, and practice of sanitation hygiene and the optimistic bias of food handlers and their relationship with participation in food safety training. Subjects and Method: This was a cross-sectional study conducted in April to May, 2019. A sample consisting of all food handlers during the preparation, processing, and serving of food was taken at a hospital in Yogyakarta. Sample data on participation in food safety training, knowledge and attitude toward hygiene and sanitation, and optimistic bias, were collected by face-to-face interview using questionnaire and observation. Knowledge, attitude and practice with a score of more than 70% was categorized as good. The dependent variable was optimistic bias. The independent variable was attending food safety training. The data were tested by Student t. Result: Study subjects had good food safety knowledge, attitude and practices with mean scores of 72.4%, 71.2%, and 97.6%, respectively. Knowledge on sources of contamination was low (25%). More than 50% of food handlers were talking while their worked. The food handlers perceived themselves as less likely to cause a foodborne disease, demonstrating the tendency of an optimistic bias. Food handlers who participated in training (Mean= 6.40; SD= 2.56) perceived themselves at higher risk than the untrained counterparts (Mean= 5.25; SD= 4.42), but this difference was statistically non-significant (p= 0.454). Conclusion: Food handlers have good knowledge, attitude, and practice, but they tend to demonstrate optimistic bias with may cause ignorant of food safety procedure. The optimistic bias must be redressed to improve awareness toward food safety procedure. Keyword: sanitation hygiene, optimism bias, food handlers, food safety training participation Correspondence: Dwi Budiningsari. Department of Health Nutrition, Faculty of Medicine, Public Health, and Nursing. Universitas Gadjah Mada. Email: budiningsari@ugm.ac.id. Mobile: 08211969393 DOI: https://doi.org/10.26911/the7thicph.04.13

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Landa, Beinish. "Public Health as a Social Issue: The Role of Digital Technologies Originating from the Internet & Big Data Era." In The Public/Private in Modern Civilization, the 22nd Russian Scientific-Practical Conference (with international participation) (Yekaterinburg, April 16-17, 2020). Liberal Arts University – University for Humanities, Yekaterinburg, 2020. http://dx.doi.org/10.35853/ufh-public/private-2020-69.

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Digitalisation is gradually penetrating all spheres of society The rationale for this study is the unprecedented measures taken by the President and the Government of the Russian Federation to encourage the population to engage in physical education and sport in order to preserve and improve health and increase the life expectancy of Russians. Federal targeted programmes have made it possible to build and commission thousands of modern sports complexes. The study was primarily aimed at the elaboration and implementation of a technique of digital information and diagnostic support of mass health surveillance. The research methodology developed at our University contains three process phases: measurement, calculations, and appraisal. The implementation of these procedures allows both the individual trajectory of the complex development of each subject and the processing of unlimited amounts of information concerning normative test takers. At each stage, digital technologies are used to generate the database, to store it, to process the results obtained and to pass them on to other organisations upon demand, making the achievements of the methodology transparent and open. The scientific novelty of the research lies in the fact that the methodology, by monitoring the dynamics of individual and collective achievements, handles information on all groups of the population in a prompt, reliable and valid manner and is used by us not only to modernise the process of physical education, but also to assess the health-promoting activities of any organisation. Digitisation originated from the Internet and BigData era, entails raising the level of evidence-based decision-making in physical education and sport to a new, modern level.

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Dewantara, Bayu Putra, Bhisma Murti, and Vitri Widyaningsih. "Factors Affecting the Use of Personal Protective Equipment among Workers at A Plywood Plants, in Lumajang, East Java: Application of Health Belief Model and Social Cognitive Theory." In The 7th International Conference on Public Health 2020. Masters Program in Public Health, Universitas Sebelas Maret, 2020. http://dx.doi.org/10.26911/the6thicph.02.50.

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Background: By products of wood processing such as wood dust and noise are well known with respect to occupational health effects in workers at plywood plants. Previous studies suggested that workers in sawmills, plywood/particle board factories, and veneer plants are at risk of developing allergenic disorders, lung disease, and cancer. Employers have duties concerning the provision and use of personal protective equipment (PPE) at work. PPE is equipment that will protect the user against the risk of accidents or of adverse effects on health. This study aimed to investigate factors affecting the use of personal protective equipment among workers at a plywood plants. Subjects and Method: A cross-sectional study was conducted in Lumajang, East Java, in December 2019. A sample of 200 workers was selected randomly. The dependent variable was the use of PPE. The independent variables were motivation, training, attitude, outcome expectation, perceived benefit, vicarious experience, observational learning, regulatory compliance, and reinforcement. The data were collected by questionnaire and analyzed by a multiple logistic regression. Results: The use PPE increased with high motivation (OR= 7.00; 95% CI= 1.46 to 33.54; p= 0.015), had trained (OR= 22.56; 95% CI= 3.43 to 148.35; p= 0.001), positive attitude (OR= 8.66; 95% CI= 1.71 to 43.84; p= 0.009), high outcome expectation (OR= 5.71; 95% CI= 0.83 to 39.02; p= 0.075), high perceived benefit (OR= 8.60; 95% CI= 1.63 to 45.32; p= 0.011), vicarious experience (OR= 16.89; 95% CI= 3.13 to 91.01; p= 0.001), observational learning (OR= 25.78; 95% CI= 4.36 to 152.54; p<0.001), compliance to regulation (OR= 5.80; 95% CI= 0.93 to 35.83; p= 0.058), and reinforcement (OR= 4.83; 95% CI= 1.14 to 20.47; p= 0.032). Conclusion: The use PPE increases with high motivation, had trained, positive attitude, high outcome expectation, high perceived benefit, vicarious experience, observational learning, compliance to regulation, and reinforcement. Keywords: personal protective equipment, health belief model, social cognitive theory Correspondence: Bayu Putra Dewantara. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java, Indonesia. Email: bayuputradewantara-@gmail.com. Mobile: +6281352347536. DOI: https://doi.org/10.26911/the6thicph.02.50

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Chowdhury, Souma, and Ali Mehmani. "Optimal Metamodeling to Interpret Activity-Based Health Sensor Data." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68385.

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Wearable sensors are revolutionizing the health monitoring and medical diagnostics arena. Algorithms and software platforms that can convert the sensor data streams into useful/actionable knowledge are central to this emerging domain, with machine learning and signal processing tools dominating this space. While serving important ends, these tools are not designed to provide functional relationships between vital signs and measures of physical activity. This paper investigates the application of the metamodeling paradigm to health data to unearth important relationships between vital signs and physical activity. To this end, we leverage neural networks and a recently developed metamodeling framework that automatically selects and trains the metamodel that best represents the data set. A publicly available data set is used that provides the ECG data and the IMU data from three sensors (ankle/arm/chest) for ten volunteers, each performing various activities over one-minute time periods. We consider three activities, namely running, climbing stairs, and the baseline resting activity. For the following three extracted ECG features — heart rate, QRS time, and QR ratio in each heartbeat period — models with median error of <25% are obtained. Fourier amplitude sensitivity testing, facilitated by the metamodels, provides further important insights into the impact of the different physical activity parameters on the ECG features, and the variation across the ten volunteers.

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Han, Zhuoyang, Ang Li, and Yu Sun. "An Automated Data-Driven Prediction of Product Pricing Based on Covid-19 Case Number using Data Mining and Machine Learning." In 9th International Conference on Natural Language Processing (NLP 2020). AIRCC Publishing Corporation, 2020. http://dx.doi.org/10.5121/csit.2020.101420.

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In early 2020, a global outbreak of Corona Disease Virus 2019 (Covid-19) emerged as an acute respiratory infectious Disease with high infectivity and incidence. China imposed a blockade on the worst affected city of Wuhan at the end of January 2020, and over time, covid19 spread rapidly around the world and was designated pandemic by the World Health Organization on March 11. As the epidemic spread, the number of confirmed cases and the number of deaths in countries around the world are changing day by day. Correspondingly, the price of face masks, as important epidemic prevention materials, is also changing with each passing day in international trade. In this project, we used machine learning to solve this problem. The project used python to find algorithms to fit daily confirmed cases in China, daily deaths, daily confirmed cases in the world, and daily deaths in the world, the recorded mask price was used to predict the effect of the number of cases on the mask price. Under such circumstances, the demand for face masks in the international trade market is enormous, and because the epidemic changes from day to day, the prices of face masks fluctuate from day to day and are very unstable. We would like to provide guidance to traders and the general public on the purchase of face masks by forecasting face mask prices.

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Li, Xue, Xin Zhao, and Mingyang Zhong. "Advancing public health genomics." In 2016 International Workshop on Big Data and Information Security (IWBIS). IEEE, 2016. http://dx.doi.org/10.1109/iwbis.2016.7872883.

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Katsis, Yannis, Nikos Koulouris, Yannis Papakonstantinou, and Kevin Patrick. "Assisting Discovery in Public Health." In SIGMOD/PODS'17: International Conference on Management of Data. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3077257.3077269.

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Anisetti, Marco, Valerio Bellandi, Marco Cremonini, Ernesto Damiani, and Jonatan Maggesi. "Big data platform for public health policies." In 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, 2017. http://dx.doi.org/10.1109/uic-atc.2017.8397457.

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Potash, Eric, Joe Brew, Alexander Loewi, Subhabrata Majumdar, Andrew Reece, Joe Walsh, Eric Rozier, Emile Jorgenson, Raed Mansour, and Rayid Ghani. "Predictive Modeling for Public Health." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2788629.

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Informes sobre el tema "Public health Data processing"

McCabe, Ashleigh. Description of the MHS Health Level 7 Pharmacy Intravenous Data for Public Health Surveillance. Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada596897.

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McCabe, Ashleigh. Description of the MHS Health Level 7 Pharmacy Outpatient Data for Public Health Surveillance. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada597305.

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McCabe, Ashleigh. Description of the MHS Health Level 7 Pharmacy Unit-Dose Data for Public Health Surveillance. Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada596834.

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Dague, Laura, Thomas DeLeire, Donna Friedsam, Daphne Kuo, Lindsey Leininger, Sarah Meier, and Kristen Voskuil. Estimates of Crowd-Out from a Public Health Insurance Expansion Using Administrative Data. Cambridge, MA: National Bureau of Economic Research, May 2011. http://dx.doi.org/10.3386/w17009.

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Wetmore, Alan, and Thomas DeFelice. ARL Support and Analysis to the Army Public Health Command Kabul Air Quality Data Collection (Spring 2014). Fort Belvoir, VA: Defense Technical Information Center, May 2016. http://dx.doi.org/10.21236/ad1011920.

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Zinn, Zachary. Surveillance and the ‘New Normal’ of Covid-19: Public Health, Data, and Justice | Social Science Research Council. Social Science Research Council, February 2021. http://dx.doi.org/10.35650/ssrc.2080.d.2021.

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Ama Pokuaa, Fenny, Aba Obrumah Crentsil, Christian Kwaku Osei, and Felix Ankomah Asante. Fiscal and Public Health Impact of a Change in Tobacco Excise Taxes in Ghana. Institute of Development Studies (IDS), November 2020. http://dx.doi.org/10.19088/ictd.2020.003.

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This working paper predicts the fiscal and public health outcomes from a change in the excise tax structure for cigarettes in Ghana. More than 5,000 people are killed by diseases caused by tobacco every year in Ghana (Tobacco Atlas 2018). Currently the country has a unitary tax administration approach, with a uniform ad valorem tax structure on all excisable products, including tobacco. However, the ECOWAS directive on tobacco control, in line with the WHO Framework Convention on Tobacco Control (WHO 2003), recommends a simple tax structure – using a mixed excise system with a minimum specific tax floor to overcome the limitations of an ad valorem system on tobacco products, especially cigarettes. The study therefore simulates mixed tax policy interventions, and assesses their effect on government revenue and public health relative to the current ad valorem tax system. Primary data collection of tobacco prices in three geographical zones of the country was conducted in February 2020, across both rural and urban localities. This was supported with secondary data from national and international databases. Based on the assumption that Ghana adopts a mixed tax structure, the simulation shows that, if the government imposes a specific excise tax of GH₵4.00 (US$0.80) per pack in addition to the current ad valorem rate of 175 per cent of the CIF value, the average retail price of a cigarette pack would increase by 128 per cent, cigarette consumption decrease by 27 per cent, tobacco excise tax revenue increase by 627 per cent, and overall tobacco-related government tax revenue increase by 201 per cent.1 Additionally, there would be significant declines in smoking prevalence (3.3%), smoking intensity (1,448 cigarettes per year), and 3,526 premature smoking-related deaths would be avoided. The paper advocates for a strong tax administration and technical capacity, with continuous commitment by the government to adjust the tax rate in line with the rate of inflation and per capita income growth.

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Wiecha, Jean L., and Mary K. Muth. Agreements Between Public Health Organizations and Food and Beverage Companies: Approaches to Improving Evaluation. RTI Press, January 2021. http://dx.doi.org/10.3768/rtipress.2021.op.0067.2101.

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Efforts in the United States and abroad to address the chronic disease epidemic have led to the emergence of voluntary industry agreements as a substitute for regulatory approaches to improve the healthfulness of foods and beverages. Because of the lack of access to data and limited budgets, evaluations of these agreements have often been limited to process evaluation with less focus on outcomes and impact. Increasing scientific scope and rigor in evaluating voluntary food and beverage industry agreements would improve potential public health benefits and understanding of the effects of these agreements. We describe how evaluators can provide formative, process, and outcome assessment and discuss challenges and opportunities for impact assessment. We explain how logic models, industry profiles, quasi-experimental designs, mixed-methods approaches, and third-party data can improve the effectiveness of agreement design and evaluation. These methods could result in more comprehensive and rigorous evaluation of voluntary industry agreements, thus providing data to bolster the public health impacts of future agreements. However, improved access to data and larger evaluation budgets will be needed to support improvements in evaluation.

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Kowalski, Amanda. What Do Longitudinal Data on Millions of Hospital Visits Tell us About The Value of Public Health Insurance as a Safety Net for the Young and Privately Insured? Cambridge, MA: National Bureau of Economic Research, January 2015. http://dx.doi.org/10.3386/w20887.

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Cuesta, Ana, Lucia Delgado, Sebastián Gallegos, Benjamin Roseth, and Mario Sánchez. Increasing the Take-up of Public Health Services: An Experiment on Nudges and Digital Tools in Uruguay. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003397.

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In this paper, we test whether promoting digital government tools increases the take-up of an important public health prevention service: cervical cancer screening. We implemented an at-scale field experiment in Uruguay, randomly encouraging women to make medical appointments with a digital application or reminding them to do it as usual at their local clinic. Using administrative records, we found that the digital application nearly doubled attendance of a screening appointment compared to reminders and tripled the rate compared to a pure control group (3.2 percentage point increase over a base of 1.9 percent). Survey data suggests that the impacts of the intervention were mostly mediated by reduced transaction costs. Our results highlight the potential of investing in digital government to improve the take-up of public services.

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