Precision Medicine Informatics: Principles, Prospects, and Challenges

Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.Comment: 22 pages, 8 figures, 5 tables, journal pape

Patients' and professionals' views related to ethical issues in precision medicine: a mixed research synthesis

Results Many patients and professionals expect high benefits from precision medicine and have a positive attitude towards it. However, patients and professionals also perceive some risks. Commonly perceived risks include: lack of evidence for accuracy of tests and efficacy of treatments; limited knowledge of patients, which makes informed consent more difficult; possible unavailability of access to precision medicine for underprivileged people and ethnic minorities; misuse of data by insurance companies and employers, potential of racial stigmatization due to genetic information; unwanted communication of incidental findings; changes in doctor-patient-relationship through focusing on data; and the problem that patients could feel under pressure to optimize their health. Conclusions National legislation and guidelines already minimize many risks associated with precision medicine. However, from our perspective some problems require more attention. Should hopes for precision medicine's benefits be fulfilled, then the ethical principle of justice would require an unlimited access to precision medicine for all people. The potential for autonomous patients' decisions must be greatly enhanced by improvements in patient education. Harm from test results must be avoided in any case by the highest possible data security level and communication guidelines. Changes in the doctor-patient relationship and the impact of precision medicine on the quality of life should be further investigated. Additionally, the cost-effectiveness of precision medicine should be further examined, in order to avoid malinvestment

Patients' perspective on emergency treatment of ophthalmologic diseases during the first phase of SARS-CoV2 pandemic in a tertiary referral center in Germany - the COVID-DETOUR questionnaire study

Background During the first wave of the COVID-19 pandemic, the need of treatment of urgent ophthalmological diseases and the possible risk of a SARS-CoV-2 infection had to be weighed against each other. In this questionnaire study, we aimed to analyze potential barriers and patients' health beliefs during and after the lockdown early 2020 in a tertiary referral center in Kiel, Germany. Results Ninety-three patients were included, 43 in subgroup A (before April 20th) and 50 in subgroup B (April 20th or later). Retinal disorders were the most common causes for admission (approximately 60%).. Only 8 patients (8.6%) experienced a delay between their decision to visit a doctor until the actual examination. Every fourth patient was afraid of a COVID-19 infection, and expected a higher likelihood for an infection at the hospital. Patients with comorbidities tended to be more likely to be afraid of an infection (correlation coefficient 0.183, p = 0.0785) and were significantly more likely to be concerned about problems with organizing follow-up care (corr. Coefficient 0.222, p = 0.0328). Higher age was negatively correlated with fear of infection (corr. Coefficient - 0.218, p-value 0.034). Conclusion In this questionnaire study, only a minority of patients indicated a delay in treatment, regardless of whether symptoms occurred before or after the lockdown before April 20th, 2020. While patients with comorbidities were more concerned about infection and problems during follow-up care, patients of higher age - who have a higher mortality - were less afraid. Protection of high-risk groups should be prioritized during the SARS-CoV-2 pandemic. Trial registration The study was registered as DRKS00021630 at the DRKS (Deutsches Register Klinischer Studien) before the conduction of the study on May 5th, 2020

History and development of personalized medicine

The development of personalized medicine is associated not so much with the idea of personalization presentedby Hippocrates more than 2400 years ago but with the development and increase of the precision in diagnosisand treatment (Gordon & Koslow, 2011; Sykiotis, Kalliolias, Papavassiliou, 2005).Even then, the „father of medicine“ – Hippocrates discussed how much more important is to be known what kind of aperson has a disease than to know what sort of disease a person has. In addition, Hippocrates also noted that differentmedicines should be given to different patients (Pray, 2008).Today, the term „personalized medicine“ is defined as „providing the right patient with the right drug at the right timewith the right dose“ (FDA).According to the National Institutes of Health (NIH), precision medicine is a „new approach to the prevention and treatmentof the disease and it is based on the individual variability of the genes, the environment and the lifestyle of eachperson.“ This approach enables doctors and scientists – to accurately predict which therapeutic and preventative strategies for a particular disease would have an effect in which groups of people.This is an opposition to the universal approach in which therapeutic and preventive strategies are developed for the„average-statistical“ person, without taking into account the differences between each individual.Personalized medicine is also defined as a „medical model based on the identification of the individual’s phenotype andgenotype (e.g. molecular profiling, medical imaging, lifestyle data, etc.). In order to be created an appropriate therapeuticstrategy for the right person at the right time and/or to identify the predisposition to an illness for timely and focusedprevention „(The European Alliance for Personalized Medicine (EAPM).The aim of the article is to be made a historical review of personalized medicine by examining and defining the mainstages of development – from its emergence to the present.The study is based on a literature review of scientific sources (printed and electronic) related to the article topic.As a result of the study, the following 5 stages of personalized medicine development are defined:• Preparatory stage (1931-1984) – from the idea of individual of treatment to mapping of the human genome. In thisstage, the topic of individualized treatment has begun to be discussed. In addition, the foundations of the creation ofpersonalized medicine are placed in order to be a revolution in medicine today.• The first stage (1984 – 2002) – human genome mapping, approval of first target therapy and first companion diagnosisco-developed with the drug. The term „personalized medicine“ emerged.• Second Stage (2003-2012) – Establishment of companion diagnosis by which medical professionals determine whichtarget therapy is appropriate for patients and at what dosage.• Stage Three (2012-2016) – the final results of the Human Genome project are presented. The essence and the importanceof personalized medicine and companion diagnosis are appreciated. The European Alliance for PersonalizedMedicine (EAPM, 2012), The Bulgarian Association for Personalized Medicine – (BAPEMED,2014) and BulgarianAlliance for Precision and Personalized Medicine (BAPPM, 2016).• Fourth stage (from 2017) – The target therapies and drugs become common and necessary practice. There is tremendousgrowth in the development of personalized medicine. By 2018, the Food and Drug Administration (FDA)approved 115 target cancer therapies (National Cancer Institute, 2018), and 41 target therapies and drugs wereregistered by the (Bulgarian Drug Agency, BDA) in Bulgaria until 2018.In conclusion, today’s personalized medicine is changing the medicine and patient care by providing personalized treatmentbased on the individual genetic characteristics of each patient and his illness

Challenges and solutions to system-wide use of precision oncology as the standard of care paradigm

The personalised oncology paradigm remains challenging to deliver despite technological advances in genomics-based identification of actionable variants combined with the increasing focus of drug development on these specific targets. To ensure we continue to build concerted momentum to improve outcomes across all cancer types, financial, technological and operational barriers need to be addressed. For example, complete integration and certification of the ‘molecular tumour board’ into ‘standard of care’ ensures a unified clinical decision pathway that both counteracts fragmentation and is the cornerstone of evidence-based delivery inside and outside of a research setting. Generally, integrated delivery has been restricted to specific (common) cancer types either within major cancer centres or small regional networks. Here, we focus on solutions in real-world integration of genomics, pathology, surgery, oncological treatments, data from clinical source systems and analysis of whole-body imaging as digital data that can facilitate cost-effectiveness analysis, clinical trial recruitment, and outcome assessment. This urgent imperative for cancer also extends across the early diagnosis and adjuvant treatment interventions, individualised cancer vaccines, immune cell therapies, personalised synthetic lethal therapeutics and cancer screening and prevention. Oncology care systems worldwide require proactive step-changes in solutions that include inter-operative digital working that can solve patient centred challenges to ensure inclusive, quality, sustainable, fair and cost-effective adoption and efficient delivery. Here we highlight workforce, technical, clinical, regulatory and economic challenges that prevent the implementation of precision oncology at scale, and offer a systematic roadmap of integrated solutions for standard of care based on minimal essential digital tools. These include unified decision support tools, quality control, data flows within an ethical and legal data framework, training and certification, monitoring and feedback. Bridging the technical, operational, regulatory and economic gaps demands the joint actions from public and industry stakeholders across national and global boundaries

Process mining for healthcare: Characteristics and challenges

Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Dirección de Investigación de la Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile - PUENTE [Grant No. 026/ 2021]; and Agencia Nacional de Investigación y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019–21190116, ANID-PFCHA/ Doctorado Nacional/2020–21201411]. With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-accessplan).Peer ReviewedArticle signat per 55 autors/es: Jorge Munoz-Gama (a)* , Niels Martin (b,c)* , Carlos Fernandez-Llatas (d,g)* , Owen A. Johnson (e)* , Marcos Sepúlveda (a)* , Emmanuel Helm (f)* , Victor Galvez-Yanjari (a)* , Eric Rojas (a) , Antonio Martinez-Millana (d) , Davide Aloini (k) , Ilaria Angela Amantea (l,q,r) , Robert Andrews (ab), Michael Arias (z) , Iris Beerepoot (o) , Elisabetta Benevento (k) , Andrea Burattin (ai), Daniel Capurro (j) , Josep Carmona (s) , Marco Comuzzi (w), Benjamin Dalmas (aj,ak), Rene de la Fuente (a) , Chiara Di Francescomarino (h) , Claudio Di Ciccio (i) , Roberto Gatta (ad,ae), Chiara Ghidini (h) , Fernanda Gonzalez-Lopez (a) , Gema Ibanez-Sanchez (d) , Hilda B. Klasky (p) , Angelina Prima Kurniati (al), Xixi Lu (o) , Felix Mannhardt (m), Ronny Mans (af), Mar Marcos (v) , Renata Medeiros de Carvalho (m), Marco Pegoraro (x) , Simon K. Poon (ag), Luise Pufahl (u) , Hajo A. Reijers (m,o) , Simon Remy (y) , Stefanie Rinderle-Ma (ah), Lucia Sacchi (t) , Fernando Seoane (g,am,an), Minseok Song (aa), Alessandro Stefanini (k) , Emilio Sulis (l) , Arthur H. M. ter Hofstede (ab), Pieter J. Toussaint (ac), Vicente Traver (d) , Zoe Valero-Ramon (d) , Inge van de Weerd (o) , Wil M.P. van der Aalst (x) , Rob Vanwersch (m), Mathias Weske (y) , Moe Thandar Wynn (ab), Francesca Zerbato (n) // (a) Pontificia Universidad Catolica de Chile, Chile; (b) Hasselt University, Belgium; (c) Research Foundation Flanders (FWO), Belgium; (d) Universitat Politècnica de València, Spain; (e) University of Leeds, United Kingdom; (f) University of Applied Sciences Upper Austria, Austria; (g) Karolinska Institutet, Sweden; (h) Fondazione Bruno Kessler, Italy; (i) Sapienza University of Rome, Italy; (j) University of Melbourne, Australia; (k) University of Pisa, Italy; (l) University of Turin, Italy; (m) Eindhoven University of Technology, The Netherlands; (n) University of St. Gallen, Switzerland; (o) Utrecht University, The Netherlands; (p) Oak Ridge National Laboratory, United States; (q) University of Bologna, Italy; (r) University of Luxembourg, Luxembourg; (s) Universitat Politècnica de Catalunya, Spain; (t) University of Pavia, Italy; (u) Technische Universitaet Berlin, Germany; (v) Universitat Jaume I, Spain; (w) Ulsan National Institute of Science and Technology (UNIST), Republic of Korea; (x) RWTH Aachen University, Germany; (y) University of Potsdam, Germany; (z) Universidad de Costa Rica, Costa Rica; (aa) Pohang University of Science and Technology, Republic of Korea; (ab) Queensland University of Technology, Australia; (ac) Norwegian University of Science and Technology, Norway; (ad) Universita degli Studi di Brescia, Italy; (ae) Lausanne University Hospital (CHUV), Switzerland; (af) Philips Research, the Netherlands; (ag) The University of Sydney, Australia; (ah) Technical University of Munich, Germany; (ai) Technical University of Denmark, Denmark; (aj) Mines Saint-Etienne, France; (ak) Université Clermont Auvergne, France; (al) Telkom University, Indonesia; (am) Karolinska University Hospital, Sweden; (an) University of Borås, SwedenPostprint (published version

Process mining for healthcare: Characteristics and challenges

[EN] Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.This work is partially supported by ANID FONDECYT 1220202, Direccion de Investigacion de la Vicerrectoria de Investigacion de la Pontificia Universidad Catolica de Chile-PUENTE [Grant No. 026/2021] ; and Agencia Nacional de Investigacion y Desarrollo [Grant Nos. ANID-PFCHA/Doctorado Nacional/2019-21190116, ANID-PFCHA/Doctorado Nacional/2020-21201411] . With regard to the co-author Hilda Klasky, this manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE) . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan)Munoz Gama, J.; Martin, N.; Fernández Llatas, C.; Johnson, OA.; Sepúlveda, M.; Helm, E.; Galvez-Yanjari, V.... (2022). Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 127:1-15. https://doi.org/10.1016/j.jbi.2022.10399411512

On designing an algorithmically enhanced NHS: towards a conceptual model for the successful implementation of algorithmic clinical decision support software in the National Health Service

Established in 1948, the National Health Service (NHS) has lasted 75 years. It is, however, under considerable strain: facing chronic staff shortages; record numbers of emergency attendances; an ambulance wait-time crisis; and more. Increasingly, policymakers are of the view that the solution to these problems is to rely more heavily on one of the NHS’s greatest resources: its data. It is hoped that by combining the NHS’s data riches with the latest techniques in artificial intelligence (AI), that the means to make the NHS more effective, more efficient, and more consistent, can be identified and acted upon via the implementation of Algorithmic Clinical Decision Support Software (ACDSS). Yet, getting this implementation right will be both technically and ethically difficult. It will require a careful re-design of the NHS’s information infrastructure to ensure the implementation of ACDSS results in intended positive emergence (benefits), and not unintended negative emergence (harms and risks). This then is the purpose of my thesis. I seek to help policymakers with this re-design process by answering the research question ‘What are the information infrastructure requirements for the successful implementation of ACDSS in the NHS?’. I adopt a mixed-methods, theory-informed, and interpretive approach, and weave the results into a narrative policy synthesis. I start with an analysis of why current attempts to implement ACDSS into the NHS’s information infrastructure are failing and what needs to change to increase the chances of success; anticipate what might happen if these changes are not made; identify the exact requirements for bringing forth the changes; explain why the likelihood of these requirements being met by current policy is limited; and conclude by explaining how the likelihood of policy meeting the identified requirements can be increased by designing the ACDSS’s supporting information infrastructure around the core concepts of ‘utility, usability, efficacy, and trustworthiness’

Integrative methods for analyzing big data in precision medicine

We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of “Big Data” in biology and medicine. These data offer many opportunities to advance precision medicine. We outline key challenges in precision medicine and present recent advances in data integration-based methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, biomarkers discovery, and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face

P5 eHealth: An Agenda for the Health Technologies of the Future

This open access volume focuses on the development of a P5 eHealth, or better, a methodological resource for developing the health technologies of the future, based on patients’ personal characteristics and needs as the fundamental guidelines for design. It provides practical guidelines and evidence based examples on how to design, implement, use and elevate new technologies for healthcare to support the management of incurable, chronic conditions. The volume further discusses the criticalities of eHealth, why it is difficult to employ eHealth from an organizational point of view or why patients do not always accept the technology, and how eHealth interventions can be improved in the future. By dealing with the state-of-the-art in eHealth technologies, this volume is of great interest to researchers in the field of physical and mental healthcare, psychologists, stakeholders and policymakers as well as technology developers working in the healthcare sector