A critical anthropology essay on eHealth and precision medicine’s discourses

UIDB/04038/2020 UIDP/04038/2020“eHealth” and “Precision Medicine” are two major concepts in the new medical discourse. There are several signs of the implementation of a public policy generated around them. In this paper I present some of these signs, starting from the experience of attending a course on digital health and precision medicine directed to train leaders in this area. The reflection on the effects of these signals on populations and individuals’ lives suggests the presence of an ambivalence in the motivations that underpin the public policy discourse on eHealth and precision medicine which results as an overvaluation of health systems’ management economic aspects and an undervaluation of flexibility in healthcare providing resulting in a misadjustement to the necessarily ecological nature of individuals’ and populations’ lives.publishersversionpublishe

The Value Proposition for Pathologists: A Population Health Approach

© The Author(s) 2020. The transition to a value-based payment system offers pathologists the opportunity to play an increased role in population health by improving outcomes and safety as well as reducing costs. Although laboratory testing itself accounts for a small portion of health-care spending, laboratory data have significant downstream effects in patient management as well as diagnosis. Pathologists currently are heavily engaged in precision medicine, use of laboratory and pathology test results (including autopsy data) to reduce diagnostic errors, and play leading roles in diagnostic management teams. Additionally, pathologists can use aggregate laboratory data to monitor the health of populations and improve health-care outcomes for both individual patients and populations. For the profession to thrive, pathologists will need to focus on extending their roles outside the laboratory beyond the traditional role in the analytic phase of testing. This should include leadership in ensuring correct ordering and interpretation of laboratory testing and leadership in population health programs. Pathologists in training will need to learn key concepts in informatics and data analytics, health-care economics, public health, implementation science, and health systems science. While these changes may reduce reimbursement for the traditional activities of pathologists, new opportunities arise for value creation and new compensation models. This report reviews these opportunities for pathologist leadership in utilization management, precision medicine, reducing diagnostic errors, and improving health-care outcomes

Enhancing Precision Medicine: An Automatic Pipeline Approach for Exploring Genetic Variant-Disease Literature

[EN] Advancements in genomics have generated vast amounts of data, requiring efficient methods for exploring the relationships between genetic variants and diseases. This paper presents a pipeline approach that automatically integrates diverse biomedical databases, including NCBI Gene, MeSH, LitVar2, PubTator, and SynVar, for retrieving comprehensive information about genes, variants, diseases, and associated literature. The pipeline consists of multiple stages: querying and searching across the different databases, extracting relevant data, and applying filters to refine the results. Its goal is to bridge the gap in information retrieval related to genetic variants and diseases by providing a systematic framework for discovering relevant literature. The pipeline uses open-access sources to uncover additional articles not referenced in expert reports that mention the genetic variants of interest. In this paper, we present the methodology of the pipeline, discuss its limitations and highlight its potential for advancing information systems, data management, and interoperability in the domains of genomics and precision medicine.This work is partially supported by MCIN/AEI/10.13039/501100011033, by the 'European Union' and 'NextGenerationEU/MRR', and by 'ERDF A way of making Europe' under grants PDC2021-120846-C44 and PID2021-126061OB-C41. It is also partially supported by the Generalitat Valenciana underproject CIPROM/2021/023. We would like to thank the authors of LitVar2 for their valuable assistance.Contreras-Ochando, L.; Marco-García, P.; León-Palacio, A.; Hurtado Oliver, LF.; Pla Santamaría, F.; Segarra Soriano, E. (2023). Enhancing Precision Medicine: An Automatic Pipeline Approach for Exploring Genetic Variant-Disease Literature. Springer Cham. 35-43. https://doi.org/10.1007/978-3-031-47112-4_43543Allot, A., Peng, Y., Wei, C.H., Lee, K., Phan, L., Lu, Z.: LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC. Nucleic Acids Res. 46(W1), W530–W536 (2018)Allot, A., et al.: Tracking genetic variants in the biomedical literature using LitVar 2.0. Nat. Genet. 55, 901–903 (2023)Cano-Gamez, E., Trynka, G.: From GWAS to function: using functional genomics to identify the mechanisms underlying complex diseases. Front. Genet. 11, 424 (2020)Chunn, L.M., et al.: Mastermind: a comprehensive genomic association search engine for empirical evidence curation and genetic variant interpretation. Front. Genet. 11, 577152 (2020)Den Dunnen, J.T., et al.: HGVS recommendations for the description of sequence variants: 2016 update. Hum. Mutat. 37(6), 564–569 (2016)den Dunnen, J.T.: Sequence variant descriptions: HGVS nomenclature and mutalyzer. Curr. Protoc. Hum. Genet. 90(1), 7–13 (2016)NHS England: Accelerating genomic medicine in the NHS. NHS England Website (2022). www.england.nhs.uk/long-read/accelerating-genomic-medicine-in-the-nhs. Accessed 23 Nov 2022Ginsburg, G.S., Phillips, K.A.: Precision medicine: from science to value. Health Aff. 37(5), 694–701 (2018)Goetz, L.H., Schork, N.J.: Personalized medicine: motivation, challenges, and progress. Fertil. Steril. 109(6), 952–963 (2018)Hassan, M., et al.: Innovations in genomics and big data analytics for personalized medicine and health care: a review. Int. J. Mol. Sci. 23(9), 4645 (2022)Krainc, T., Fuentes, A.: Genetic ancestry in precision medicine is reshaping the race debate. Proc. Natl. Acad. Sci. 119(12), e2203033119 (2022)Landrum, M.J., et al.: ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42(D1), D980–D985 (2014)Luo, J., Wu, M., Gopukumar, D., Zhao, Y.: Big data application in biomedical research and health care: a literature review. Biomed. Inform. Insights 8, BII-S31559 (2016)Pasche, E., Mottaz, A., Caucheteur, D., Gobeill, J., Michel, P.A., Ruch, P.: Variomes: a high recall search engine to support the curation of genomic variants. Bioinformatics 38(9), 2595–2601 (2022)Povey, S., Lovering, R., Bruford, E., Wright, M., Lush, M., Wain, H.: The HUGO gene nomenclature committee (HGNC). Hum. Genet. 109, 678–680 (2001)Saberian, N.: Text Mining of Variant-Genotype-Phenotype Associations from Biomedical Literature. Wayne State University (2020)Smigielski, E.M., Sirotkin, K., Ward, M., Sherry, S.T.: dbSNP: a database of single nucleotide polymorphisms. Nucleic Acids Res. 28(1), 352–355 (2000)Strianese, O., et al.: Precision and personalized medicine: how genomic approach improves the management of cardiovascular and neurodegenerative disease. Genes 11(7), 747 (2020)Uffelmann, E., et al.: Genome-wide association studies. Nat. Rev. Methods Primers 1(1), 59 (2021

An ethnography of communication at a conference on biomedical applications of new biotechnologies

UIDB/04038/2020 UIDP/04038/2020The discursiveness of digital health and precision medicine taking place at the confluence of the positions of the developers of the biotechnologies, the medical applications of these technologies and the reactions of the public establishes and stabilizes the final form of precision medicine’s socio-technological alignment matrix. Hence, the analysis of this discursive alignment process, which is largely designed by the debate at conferences, is of pivotal importance for the understanding of current biomedicine. Realizing the importance of this discursiveness construction for the final expression of future healthcare practices, I conducted an analysis of the discourses produced through the various communications that were presented at an international congress on the applications of biotechnologies in biomedicine. This paper aims to present some outcomes of such analysis.publishersversionpublishe

Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

[EN] Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients¿ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients¿ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.This research was partially funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 727560.Valero Ramon, Z.; Fernández Llatas, C.; Valdivieso, B.; Traver Salcedo, V. (2020). Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. Sensors. 20(18):1-25. https://doi.org/10.3390/s20185330S1252018Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi:10.1007/s11036-013-0489-0Brennan, P., Perola, M., van Ommen, G.-J., & Riboli, E. (2017). Chronic disease research in Europe and the need for integrated population cohorts. European Journal of Epidemiology, 32(9), 741-749. doi:10.1007/s10654-017-0315-2Raghupathi, W., & Raghupathi, V. (2018). An Empirical Study of Chronic Diseases in the United States: A Visual Analytics Approach to Public Health. International Journal of Environmental Research and Public Health, 15(3), 431. doi:10.3390/ijerph15030431Forouzanfar, M. H., Afshin, A., Alexander, L. T., Anderson, H. R., Bhutta, Z. A., Biryukov, S., … Charlson, F. J. (2016). Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1659-1724. doi:10.1016/s0140-6736(16)31679-8Gómez, J., Oviedo, B., & Zhuma, E. (2016). Patient Monitoring System Based on Internet of Things. Procedia Computer Science, 83, 90-97. doi:10.1016/j.procs.2016.04.103Harvey, A., Brand, A., Holgate, S. T., Kristiansen, L. V., Lehrach, H., Palotie, A., & Prainsack, B. (2012). The future of technologies for personalised medicine. New Biotechnology, 29(6), 625-633. doi:10.1016/j.nbt.2012.03.009Larry Jameson, J., & Longo, D. L. (2015). Precision Medicine—Personalized, Problematic, and Promising. Obstetrical & Gynecological Survey, 70(10), 612-614. doi:10.1097/01.ogx.0000472121.21647.38Collins, F. S., & Varmus, H. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793-795. doi:10.1056/nejmp1500523Glasgow, R. E., Kwan, B. M., & Matlock, D. D. (2018). Realizing the full potential of precision health: The need to include patient-reported health behavior, mental health, social determinants, and patient preferences data. Journal of Clinical and Translational Science, 2(3), 183-185. doi:10.1017/cts.2018.31Whittemore, A. S. (2010). Evaluating health risk models. Statistics in Medicine, 29(23), 2438-2452. doi:10.1002/sim.3991Reynolds, B. C., Roem, J. L., Ng, D. K. S., Matsuda-Abedini, M., Flynn, J. T., Furth, S. L., … Parekh, R. S. (2020). Association of Time-Varying Blood Pressure With Chronic Kidney Disease Progression in Children. JAMA Network Open, 3(2), e1921213. doi:10.1001/jamanetworkopen.2019.21213Campbell, H., Hotchkiss, R., Bradshaw, N., & Porteous, M. (1998). Integrated care pathways. BMJ, 316(7125), 133-137. doi:10.1136/bmj.316.7125.133Schienkiewitz, A., Mensink, G. B. M., & Scheidt-Nave, C. (2012). Comorbidity of overweight and obesity in a nationally representative sample of German adults aged 18-79 years. BMC Public Health, 12(1). doi:10.1186/1471-2458-12-658Must, A. (1999). The Disease Burden Associated With Overweight and Obesity. JAMA, 282(16), 1523. doi:10.1001/jama.282.16.1523Audureau, E., Pouchot, J., & Coste, J. (2016). Gender-Related Differential Effects of Obesity on Health-Related Quality of Life via Obesity-Related Comorbidities. Circulation: Cardiovascular Quality and Outcomes, 9(3), 246-256. doi:10.1161/circoutcomes.115.002127Everhart, J. E., Pettitt, D. J., Bennett, P. H., & Knowler, W. C. (1992). Duration of Obesity Increases the Incidence of NIDDM. Diabetes, 41(2), 235-240. doi:10.2337/diab.41.2.235Wannamethee, S. G. (2005). Overweight and obesity and weight change in middle aged men: impact on cardiovascular disease and diabetes. Journal of Epidemiology & Community Health, 59(2), 134-139. doi:10.1136/jech.2003.015651Ziegelstein, R. C. (2018). Perspectives in Primary Care: Knowing the Patient as a Person in the Precision Medicine Era. The Annals of Family Medicine, 16(1), 4-5. doi:10.1370/afm.2169Tricoli, A., Nasiri, N., & De, S. (2017). Wearable and Miniaturized Sensor Technologies for Personalized and Preventive Medicine. Advanced Functional Materials, 27(15), 1605271. doi:10.1002/adfm.201605271Saponara, S., Donati, M., Fanucci, L., & Celli, A. (2016). An Embedded Sensing and Communication Platform, and a Healthcare Model for Remote Monitoring of Chronic Diseases. Electronics, 5(4), 47. doi:10.3390/electronics5030047Alvarez, C., Rojas, E., Arias, M., Munoz-Gama, J., Sepúlveda, M., Herskovic, V., & Capurro, D. (2018). Discovering role interaction models in the Emergency Room using Process Mining. Journal of Biomedical Informatics, 78, 60-77. doi:10.1016/j.jbi.2017.12.015Fernández-Llatas, C., Benedi, J.-M., García-Gómez, J., & Traver, V. (2013). Process Mining for Individualized Behavior Modeling Using Wireless Tracking in Nursing Homes. Sensors, 13(11), 15434-15451. doi:10.3390/s131115434Shahar, Y. (1997). A framework for knowledge-based temporal abstraction. Artificial Intelligence, 90(1-2), 79-133. doi:10.1016/s0004-3702(96)00025-2Orphanou, K., Stassopoulou, A., & Keravnou, E. (2016). DBN-Extended: A Dynamic Bayesian Network Model Extended With Temporal Abstractions for Coronary Heart Disease Prognosis. IEEE Journal of Biomedical and Health Informatics, 20(3), 944-952. doi:10.1109/jbhi.2015.2420534Spruijt-Metz, D., Hekler, E., Saranummi, N., Intille, S., Korhonen, I., Nilsen, W., … Pavel, M. (2015). Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Translational Behavioral Medicine, 5(3), 335-346. doi:10.1007/s13142-015-0324-1Rojas, E., Munoz-Gama, J., Sepúlveda, M., & Capurro, D. (2016). Process mining in healthcare: A literature review. Journal of Biomedical Informatics, 61, 224-236. doi:10.1016/j.jbi.2016.04.007Yoo, S., Cho, M., Kim, E., Kim, S., Sim, Y., Yoo, D., … Song, M. (2016). Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital. International Journal of Medical Informatics, 88, 34-43. doi:10.1016/j.ijmedinf.2015.12.018Ibanez-Sanchez, G., Fernandez-Llatas, C., Martinez-Millana, A., Celda, A., Mandingorra, J., Aparici-Tortajada, L., … Traver, V. (2019). Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case. International Journal of Environmental Research and Public Health, 16(10), 1783. doi:10.3390/ijerph16101783Chambers, D. A., Feero, W. G., & Khoury, M. J. (2016). Convergence of Implementation Science, Precision Medicine, and the Learning Health Care System. JAMA, 315(18), 1941. doi:10.1001/jama.2016.3867Cameranesi, M., Diamantini, C., Mircoli, A., Potena, D., & Storti, E. (2020). Extraction of User Daily Behavior From Home Sensors Through Process Discovery. 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Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities.This work is written on behalf of the Women’s Brain Project (WBP) (www.womensbrainproject.com/), an international organization advocating for women’s brain and mental health through scientific research, debate and public engagement. The authors would like to gratefully acknowledge Maria Teresa Ferretti and Nicoletta Iacobacci (WBP) for the scientific advice and insightful discussions; Roberto Confalonieri (Alpha Health) for reviewing the manuscript; the Bioinfo4Women programme of Barcelona Supercomputing Center (BSC) for the support. This work has been supported by the Spanish Government (SEV 2015–0493) and grant PT17/0009/0001, of the Acción Estratégica en Salud 2013–2016 of the Programa Estatal de Investigación Orientada a los Retos de la Sociedad, funded by the Instituto de Salud Carlos III (ISCIII) and European Regional Development Fund (ERDF). EG has received funding from the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement No 116030 (TransQST), which is supported by the European Union’s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA).Peer ReviewedPostprint (published version

Position guidelines and evidence base concerning determinants of childhood obesity with a European perspective

Childhood obesity is one of the most pressing global public health issues, with rates increasing fastest in countries at low levels of income. Obesity occurring during childhood is likely to persist throughout the life course, and it is a cause of increased disease risk from the early years of life. This supplement is the result of collaborations involving a large and multidisciplinary group of researchers that were established in the context of the ongoing European Horizon 2020 project Science and Technology in childhood Obesity Policy (STOP). The aim, as in the entire STOP project, is to generate evidence that can support better policies to tackle the problem of childhood obesity in Europe and elsewhere. Quality of life and health well-being concerning children needs to consider personalized, population, and planetary facets to tackle childhood obesity at early stages of life, for in-deep phenotyping, integrating personalized medicine and precision public health interventions at global levels. This supplement contributes to this aim. © 2021 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation

Longitudinal Monitoring of SARS-CoV-2 IgM and IgG Seropositivity to Detect COVID-19.

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a novel beta-coronavirus that has recently emerged as the cause of the 2019 coronavirus pandemic (COVID-19). Polymerase chain reaction (PCR) based tests are optimal and recommended for the diagnosis of an acute SARS-CoV-2 infection. Serology tests for viral antibodies provide an important tool to diagnose previous exposure to the virus. Here we evaluate the analytical performance parameters of the Diazyme SARS-CoV-2 IgM/IgG serology assays and describe the kinetics of IgM and IgG seroconversion observed in patients with PCR-confirmed COVID-19 who were admitted to our hospital.MethodsWe validated the performance of the Diazyme assay in 235 presumed SARS-CoV-2 negative subjects to determine specificity. Subsequently, we evaluated the SARS-CoV-2 IgM and IgG seroconversion of 54 PCR-confirmed COVID-19 patients and determined sensitivity of the assay at three different timeframes.ResultSensitivity and specificity for detecting seropositivity at ≥15 days following a positive SARS-CoV-2 PCR result, was 100.0% and 98.7% when assaying for the panel of IgM and IgG. The median time to seropositivity observed for a reactive IgM and IgG result from the date of a positive PCR was 5 days (IQR: 2.75-9 days) and 4 days (IQR: 2.75-6.75 days), respectively.ConclusionsOur data demonstrate that the Diazyme IgM/IgG assays are suited for the purpose of detecting SARS-CoV-2 IgG and IgM in patients with suspected SARS-CoV-2 infections. For the first time, we report longitudinal data showing the evolution of seroconversion for both IgG and IgM in a cohort of acutely ill patients in the United States. We also demonstrate a low false positive rate in patients who were presumed to be disease free

Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes.

There is a great and growing need to ascertain what exactly is the state of a patient, in terms of disease progression, actual care practices, pathology, adverse events, and much more, beyond the paucity of data available in structured medical record data. Ascertaining these harder-to-reach data elements is now critical for the accurate phenotyping of complex traits, detection of adverse outcomes, efficacy of off-label drug use, and longitudinal patient surveillance. Clinical notes often contain the most detailed and relevant digital information about individual patients, the nuances of their diseases, the treatment strategies selected by physicians, and the resulting outcomes. However, notes remain largely unused for research because they contain Protected Health Information (PHI), which is synonymous with individually identifying data. Previous clinical note de-identification approaches have been rigid and still too inaccurate to see any substantial real-world use, primarily because they have been trained with too small medical text corpora. To build a new de-identification tool, we created the largest manually annotated clinical note corpus for PHI and develop a customizable open-source de-identification software called Philter ("Protected Health Information filter"). Here we describe the design and evaluation of Philter, and show how it offers substantial real-world improvements over prior methods

Healthy People 2020 Structured Evidence Queries for PubMed: Practice Informed by Research

Objective: Healthy People 2020 is a set of objectives with 10-year targets to guide national health promotion and disease prevention efforts. Public health professionals may have limited time to identify relevant research articles on public health strategies. NLM recognized the need to reduce the time and increase the precision of finding research to support evidence-based actions to achieve HP2020 objectives. Methods: NLM collaborated with the HHS Office of Disease Prevention and Health Promotion to develop pre-formulated search strategies ─ structured evidence queries (SEQs) ─ of PubMed to make research evidence related to HP2020 objectives easier to find. The queries were developed by librarians, working in consultation with subject matter experts in public health. The PubMed search strategies are organized by HP2020 topic areas on the HP2020 SEQ website that is supported by NLM with assistance of the collaboration, Partners in Information Access for the Public Health Workforce. The website also provides search queries for the Healthy People 2020 Leading Health Indicators, a subset of high-priority health issues that represent significant threats to the public’s health. Information to help users learn more about PubMed, obtain full-text copies of articles, and find additional resources for public health practice are provided on the site. Results: The Healthy People 2020 Structured Evidence Queries website, http://phpartners.org/hp2020, launched in June 2011. As of the end of 2012, structured evidence queries were formulated for 268 health objectives in 24 Healthy People 2020 topic areas, with the expectation of full coverage by May 2013. The SEQs are also integrated with the HHS HealthyPeople.gov website. The PubMed search strategies were designed to return a manageable number of relevant citations for busy public health professionals to review. Users retrieve the most recent research articles indexed for MEDLINE on Healthy People objectives each time a SEQ is selected and run in PubMed. The search strategies can be modified to address particular practice and research needs. The website includes FAQs on how to modify and save searches, obtain copies of articles, and receive assistance from the National Network of Libraries of Medicine. Additional resources on public health topics are available from the Partners in Information Access for the Public Health Workforce website, http://PHPartners.org. Conclusions: The HP2020 SEQs provide peer-reviewed research evidence to support national objectives for improving the health of all Americans. The resource is the outcome of an effective partnership between librarians, public health professionals, and subject experts