How precision medicine and screening with big data could increase overdiagnosis

Conceptual scaffolding for the philosophy of medicine

This paper consists of two parts. In the first part, I will introduce a philosophical toolbox that I call 'conceptual scaffolding,' which helps to reflect holistically on phenomena and concepts. I situate this framework within the landscape of conceptual analysis and conceptual engineering, exemplified by the debate about the concept of disease. Within the framework of conceptual scaffolding, I develop the main idea of the paper, which is 'the binocular model of plural medicine', a holistic framework for analyzing medical concepts and phenomena. In the second part, I demonstrate the use and value of the binocular model by analyzing, through the lenses of the model, the phenomenon of health wearable devices and their effects on the concept of diagnosis.

AI-Assisted Decision-Making in Long-Term Care: Qualitative Study on Prerequisites for Responsible Innovation

BACKGROUND

Although the use of artificial intelligence (AI)-based technologies, such as AI-based decision support systems (AI-DSSs), can help sustain and improve the quality and efficiency of care, their deployment creates ethical and social challenges. In recent years, a growing prevalence of high-level guidelines and frameworks for responsible AI innovation has been observed. However, few studies have specified the responsible embedding of AI-based technologies, such as AI-DSSs, in specific contexts, such as the nursing process in long-term care (LTC) for older adults.

OBJECTIVE

Prerequisites for responsible AI-assisted decision-making in nursing practice were explored from the perspectives of nurses and other professional stakeholders in LTC.

METHODS

Semistructured interviews were conducted with 24 care professionals in Dutch LTC, including nurses, care coordinators, data specialists, and care centralists. A total of 2 imaginary scenarios about AI-DSSs were developed beforehand and used to enable participants articulate their expectations regarding the opportunities and risks of AI-assisted decision-making. In addition, 6 high-level principles for responsible AI were used as probing themes to evoke further consideration of the risks associated with using AI-DSSs in LTC. Furthermore, the participants were asked to brainstorm possible strategies and actions in the design, implementation, and use of AI-DSSs to address or mitigate these risks. A thematic analysis was performed to identify the opportunities and risks of AI-assisted decision-making in nursing practice and the associated prerequisites for responsible innovation in this area.

RESULTS

The stance of care professionals on the use of AI-DSSs is not a matter of purely positive or negative expectations but rather a nuanced interplay of positive and negative elements that lead to a weighed perception of the prerequisites for responsible AI-assisted decision-making. Both opportunities and risks were identified in relation to the early identification of care needs, guidance in devising care strategies, shared decision-making, and the workload of and work experience of caregivers. To optimally balance the opportunities and risks of AI-assisted decision-making, seven categories of prerequisites for responsible AI-assisted decision-making in nursing practice were identified: (1) regular deliberation on data collection; (2) a balanced proactive nature of AI-DSSs; (3) incremental advancements aligned with trust and experience; (4) customization for all user groups, including clients and caregivers; (5) measures to counteract bias and narrow perspectives; (6) human-centric learning loops; and (7) the routinization of using AI-DSSs.

CONCLUSIONS

The opportunities of AI-assisted decision-making in nursing practice could turn into drawbacks depending on the specific shaping of the design and deployment of AI-DSSs. Therefore, we recommend considering the responsible use of AI-DSSs as a balancing act. Moreover, considering the interrelatedness of the identified prerequisites, we call for various actors, including developers and users of AI-DSSs, to cohesively address the different factors important to the responsible embedding of AI-DSSs in practice.

Social Media Promotion of Health Tests With Potential for Overdiagnosis or Overuse: Protocol for a Content Analysis

BACKGROUND

In recent years, social media have emerged as important spaces for commercial marketing of health tests, which can be used for the screening and diagnosis of otherwise generally healthy people. However, little is known about how health tests are promoted on social media, whether the information provided is accurate and balanced, and if there is transparency around conflicts of interest.

OBJECTIVE

This study aims to understand and quantify how social media is being used to discuss or promote health tests with the potential for overdiagnosis or overuse to generally healthy people.

METHODS

Content analysis of social media posts on the anti-Mullerian hormone test, whole-body magnetic resonance imaging scan, multicancer early detection, testosterone test, and gut microbe test from influential international social media accounts on Instagram and TikTok. The 5 tests have been identified as having the following criteria: (1) there are evidence-based concerns about overdiagnosis or overuse, (2) there is evidence or concerns that the results of tests do not lead to improved health outcomes for generally healthy people and may cause harm or waste, and (3) the tests are being promoted on social media to generally healthy people. English language text-only posts, images, infographics, articles, recorded videos including reels, and audio-only posts are included. Posts from accounts with <1000 followers as well as stories, live videos, and non-English posts are excluded. Using keywords related to the test, the top posts were searched and screened until there were 100 eligible posts from each platform for each test (total of 1000 posts). Data from the caption, video, and on-screen text are being summarized and extracted into a Microsoft Excel (Microsoft Corporation) spreadsheet and included in the analysis. The analysis will take a combined inductive approach when generating key themes and a deductive approach using a prespecified framework. Quantitative data will be analyzed in Stata SE (version 18.0; Stata Corp).

RESULTS

Data on Instagram and TikTok have been searched and screened. Analysis has now commenced. The findings will be disseminated via publications in peer-reviewed international medical journals and will also be presented at national and international conferences in late 2024 and 2025.

CONCLUSIONS

This study will contribute to the limited evidence base on the nature of the relationship between social media and the problems of overdiagnosis and overuse of health care services. This understanding is essential to develop strategies to mitigate potential harm and plan solutions, with the aim of helping to protect members of the public from being marketed low-value tests, becoming patients unnecessarily, and taking resources away from genuine needs within the health system.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56899.

Ethics of early detection of disease risk factors: A scoping review

BACKGROUND

Scientific and technological advancements in mapping and understanding the interrelated pathways through which biological and environmental exposures affect disease development create new possibilities for detecting disease risk factors. Early detection of such risk factors may help prevent disease onset or moderate the disease course, thereby decreasing associated disease burden, morbidity, and mortality. However, the ethical implications of screening for disease risk factors are unclear and the current literature provides a fragmented and case-by-case picture.

METHODS

To identify key ethical considerations arising from the early detection of disease risk factors, we performed a systematic scoping review. The Scopus, Embase, and Philosopher's Index databases were searched for peer-reviewed, academic records, which were included if they were written in English or Dutch and concerned the ethics of (1) early detection of (2) disease risk factors for (3) disease caused by environmental factors or gene-environment interactions. All records were reviewed independently by at least two researchers.

RESULTS

After screening 2034 titles and abstracts, and 112 full papers, 55 articles were included in the thematic synthesis of the results. We identified eight common ethical themes: (1) Reliability and uncertainty in early detection, (2) autonomy, (3) privacy, (4) beneficence and non-maleficence, (5) downstream burdens on others, (6) responsibility, (7) justice, and (8) medicalization and conceptual disruption. We identified several gaps in the literature, including a relative scarcity of research on ethical considerations associated with environmental preventive health interventions, a dearth of practical suggestions on how to address expressed concerns about overestimating health capacities, and a lack of insights into preventing undue attribution of health responsibility to individuals.

CONCLUSIONS

The ethical concerns arising with the early detection of risk factors are often interrelated and complex. Comprehensive ethical analyses are needed that are better embedded in normative frameworks and also assess and weigh the expected benefits of early risk factor detection. Such research is necessary for developing and implementing responsible and fair preventive health policies.

Overdiagnosis in malignant melanoma: a scoping review

OBJECTIVES

We aimed to systematically identify and scrutinise published empirical evidence about overdiagnosis in malignant melanoma and examine how frequent overdiagnosis of melanoma is and whether this is related to different types of interventions or diagnostic technologies.

DESIGN AND SETTING

Empirical studies that discussed overdiagnosis in malignant melanoma were eligible, including qualitative and quantitative studies in any type of population, age group and geographical location. We excluded studies that did not include empirical data, studies that only mentioned 'overdiagnosis' without addressing it further and studies that used the term overdiagnosis for cases of misdiagnosis or false positives.We developed the search strategy in cooperation with an information specialist. We searched five databases on 21 April 2022: MEDLINE, Embase, CINAHL, PsycINFO and Cochrane Library.This scoping review adheres to The JBI methodology and Prefered Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping reviews (PRISMA-ScR). Two reviewers independently screened titles, abstracts and full texts for inclusion and extracted data from the included studies. The data extracted include study characteristics, population details, research question, the context and the study's main results.

RESULTS

Our search resulted in 1134 potentially relevant studies. 35 studies were included: 29 register studies, 3 cohort studies, 1 case-control study, 1 survey study and 1 randomised controlled trial. Most register studies examined trends in melanoma incidence and/or mortality and found a significant increase in incidence between 0.39% and 6.6% annually and a little or no increase in mortality. Three cohort studies and one case-control study showed that skin screening was associated with increased detection of melanoma; especially in situ or thin invasive melanoma. Three studies estimated the degree of overdiagnosis which ranged from 29% to 60%.

CONCLUSIONS

Epidemiological data suggest a high degree of overdiagnosis in malignant melanoma. Studies that examined the association between skin screening and malignant melanoma all found increased detection of melanomas, mostly thin and in situ melanomas, which raises concern about overdiagnosis.

Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities

The main purpose of prognostic risk prediction models is to identify individuals who are at risk of disease, to enable early intervention. Current prognostic cardiovascular risk prediction models, such as the Systematic COronary Risk Evaluation (SCORE2) and the SCORE2-Older Persons (SCORE2-OP) models, which represent the clinically used gold standard in assessing patient risk for major cardiovascular events in the European Union (EU), generally overlook socioeconomic determinants, leading to disparities in risk prediction and resource allocation. A central recommendation of this article is the explicit inclusion of individual-level socioeconomic determinants of cardiovascular disease in risk prediction models. The question of whether prognostic risk prediction models can promote health equity remains to be answered through experimental research, potential clinical implementation and public health analysis. This paper introduces four distinct fairness concepts in cardiovascular disease prediction and their potential to narrow existing disparities in cardiometabolic health.

Precision medicine and the problem of structural injustice

Many countries currently invest in technologies and data infrastructures to foster precision medicine (PM), which is hoped to better tailor disease treatment and prevention to individual patients. But who can expect to benefit from PM? The answer depends not only on scientific developments but also on the willingness to address the problem of structural injustice. One important step is to confront the problem of underrepresentation of certain populations in PM cohorts via improved research inclusivity. Yet, we argue that the perspective needs to be broadened because the (in)equitable effects of PM are also strongly contingent on wider structural factors and prioritization of healthcare strategies and resources. When (and before) implementing PM, it is crucial to attend to how the organisation of healthcare systems influences who will benefit, as well as whether PM may present challenges for a solidaristic sharing of costs and risks. We discuss these issues through a comparative lens of healthcare models and PM-initiatives in the United States, Austria, and Denmark. The analysis draws attention to how PM hinges on-and simultaneously affects-access to healthcare services, public trust in data handling, and prioritization of healthcare resources. Finally, we provide suggestions for how to mitigate foreseeable negative effects.

Is It Just for a Screening Program to Give People All the Information They Want?

Genomic screening at population scale generates many ethical considerations. One is the normative role that people's preferences should play in determining access to genomic information in screening contexts, particularly information that falls beyond the scope of screening. We expect both that people will express a preference to receive such results and that there will be interest from the professional community in providing them. In this paper, we consider this issue in relation to the just and equitable design of population screening programs like reproductive genetic carrier screening (RGCS). Drawing on a pluralistic public health ethics perspective, we claim that generating and reporting information about genetic variants beyond the scope of the screening program usually lacks clinical, and perhaps personal, utility. There are both pragmatic and ethical reasons to restrict information provision to that which fits the stated purpose of the program.

Mapping uncertainty in precision medicine: A systematic scoping review

RATIONALE

Although precision medicine is seen by many as one of the most promising advances in the field of medicine, it has also raised critical questions at various levels. Many of these concerns revolve around an observation described by Kimmelman and Tannock as the 'paradox of precision medicine': somewhat surprisingly, uncertainty seems to be a key characteristic of precision medicine in practice.

AIMS AND OBJECTIVE

To better understand this concept and the underlying issues, a scoping review was undertaken to search for factors stated in the literature as contributing to or being aspects of uncertainty in precision medicine.

METHODS

A systematic search of the literature was conducted in three databases (Pubmed, Web of Science, and Jstor) and complemented with a systematic hand-search. The initial search provided 1.252 items of which 51 articles for selected as eligible for further analysis. These articles were coded with MAXQDA and categorized into four main themes (a-d) of uncertainty. The main results were summarized and discussed with a view to the interrelations between different aspects and implications for precision medicine in practice.

RESULTS

The mapping of different aspects and sources of uncertainty leads to the key result that 'uncertainty' should be understood as a cluster concept. Uncertainties are identified in many different respects and situated at different levels: Most complexity-related issues (theme a) can best be understood as ontological ('world-sided') aspects of the uncertainty paradox. Conceptual (theme b) and evidence-related uncertainties (theme c) are situated on an epistemological or methodological level, addressing foundational and normative challenges related to knowledge production in precision medicine. Finally, theme (d) targets issues on the level of material precision medicine practices. These levels are helpful to understand the different dimensions of the uncertainty paradox.

CONCLUSIONS

Uncertainty may not merely be a transient effect of the novelty of the precision medicine paradigm. Rather, it should be seen as a consequence of the ontological, epistemological and practical complexity of precision medicine, implying that uncertainty will not necessarily be reduced by more research. This finding encourages further investigations to better understand the interactions among various factors and aspects of uncertainty in precision medicine and the resulting implications for research and medical practice.

Representations of personalised medicine in family medicine: a qualitative analysis

Background

The promise of personalised medicine (PM) to transform healthcare has sparked great enthusiasm in the last years. Yet, its lack of consensus around the nature and scope of the concept has ended in terminological confusion amongst the users in primary care. We aimed to investigate the perceptions of doctors and their patients in response to this evolving concept. This present article focuses on the general understanding of personalised medicine, underlining the confusion over the concept.

Methods

Semi-structured comprehensive interviews were conducted with 10 general practitioners (GPs) and 10 of their patients. The purposive sampling took into account the doctor’s age, sex, and place of practice (rural/urban); each doctor recruited one patient of the same age and sex. Each interview began with the same open-ended question about the participant’s knowledge of the topic, after which a working definition was provided to continue the discussion. Using the grounded theory method, the analysis consisted of open coding, axial coding and selective coding.

Results

From our present analysis focusing on the general understanding of PM, three main themes representing the concept emerged. The first two representations being “centred on the person as a whole” and “focused on alternative and complementary methods”, in which the therapeutic relationship was stated as key. The third theme “medicine open to innovation” involved the few participants who had a good understanding of the concept and could associate personalised medicine with genomics. For those who value therapeutic relationship, the risks of accepting innovation could result in “fast-food” medicine and interpersonal barriers.

Discussion

PM is predominantly unfamiliar in family medicine. It is misinterpreted as a holistic or integrative type of medicine. This semantic confusion probably lies in the choice of the label “personalised” or from the lack of a uniform definition for the term.

Erroneous data: The Achilles' heel of AI and personalized medicine

This paper reviews dilemmas and implications of erroneous data for clinical implementation of AI. It is well-known that if erroneous and biased data are used to train AI, there is a risk of systematic error. However, even perfectly trained AI applications can produce faulty outputs if fed with erroneous inputs. To counter such problems, we suggest 3 steps: (1) AI should focus on data of the highest quality, in essence paraclinical data and digital images, (2) patients should be granted simple access to the input data that feed the AI, and granted a right to request changes to erroneous data, and (3) automated high-throughput methods for error-correction should be implemented in domains with faulty data when possible. Also, we conclude that erroneous data is a reality even for highly reputable Danish data sources, and thus, legal framework for the correction of errors is universally needed.

Multidisciplinary Effort to Drive Precision-Medicine for the Future

In the past one or two decades, countries across the world have successively implemented different precision medicine (PM) programs, and also cooperated to implement international PM programs. We are now in the era of PM. Singapore's National Precision Medicine (NPM) program, initiated in 2017, is now entering its second phase to generate a large genomic database for Asians. The National University of Singapore (NUS) also launched its own PM translational research program (TRP) in 2021, aimed at consolidating multidisciplinary expertise within the Yong Loo Lin School of Medicine to develop collaborative projects that can help to identify and validate novel therapeutic targets for the realization of PM. To achieve this, appropriate data collection, data processing, and results interpretation must be taken into consideration. There may be some difficulties during these processes, but with the improvement of relevant rules and the continuous development of omics-based technologies, we will be able to solve these problems, eventually achieving precise prediction, diagnosis, treatment, or even prevention of diseases.

Journalists' views on media coverage of medical tests and overdiagnosis: a qualitative study

OBJECTIVE

Promotional media coverage of early detection tests is an important driver of overdiagnosis. Following research evidence that global media coverage presents the benefits of testing healthy people far more frequently than harms, and gives little coverage to overdiagnosis, we sought to examine journalists' views on media reporting of tests, overdiagnosis, and strategies to improve critical reporting on tests.

DESIGN

Qualitative study using semistructured telephone interviews. Interviews were conducted between February and March 2020 and were audiorecorded and transcribed verbatim. Framework thematic analysis was used to analyse the data.

PARTICIPANTS AND SETTING

Twenty-two journalists (mainly specialising in health reporting, average 14.5 years' experience) based in Australia.

RESULTS

This sample of journalists acknowledged the potential harms of medical tests but felt that knowledge of harms was low among journalists and the public at large. Most were aware of the term overdiagnosis, but commonly felt that it is challenging to both understand and communicate in light of strong beliefs in the benefits of early detection. Journalists felt that newsworthiness in the form of major public health impact was the key ingredient for stories about medical tests. The journalists acknowledged that factors, like the press release and 'click bait culture' in particular, can influence the framing of coverage about tests. Lack of knowledge and training, as well as time pressures, were perceived to be the main barriers to critical reporting on tests. Journalists felt that training and better access to information about potential harms would enable more critical reporting.

CONCLUSIONS

Effectively communicating overdiagnosis is a challenge in light of common beliefs about the benefits of testing and the culture of current journalism practices. Providing journalists with training, support and better access to information about potential harms of tests could aid critical reporting of tests.

Big data, integrative omics and network biology

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.

Aging biomarkers and the measurement of health and risk

Prevention of age-related disorders is increasingly in focus of health policies, and it is hoped that early intervention on processes of deterioration can promote healthier and longer lives. New opportunities to slow down the aging process are emerging with new fields such as personalized nutrition. Data-intensive research has the potential to improve the precision of existing risk factors, e.g., to replace coarse-grained markers such as blood cholesterol with more detailed multivariate biomarkers. In this paper, we follow an attempt to develop a new aging biomarker. The vision among the project consortium, comprising both research and industrial partners, is that the new biomarker will be predictive of a range of age-related conditions, which may be preventable through personalized nutrition. We combine philosophical analysis and ethnographic fieldwork to explore the possibilities and challenges of managing aging through bodily signs that are not straightforwardly linked to symptomatic disease. We document how the improvement of measurement brings about new conceptual challenges of demarcating healthy and unhealthy states. Moreover, we highlight that the reframing of aging as risk has social and ethical implications, as it is generative of normative notions of what constitutes successful aging and good citizenship.

Personalized medicine, digital technology and trust: a Kantian account

Trust relations in the health services have changed from asymmetrical paternalism to symmetrical autonomy-based participation, according to a common account. The promises of personalized medicine emphasizing empowerment of the individual through active participation in managing her health, disease and well-being, is characteristic of symmetrical trust. In the influential Kantian account of autonomy, active participation in management of own health is not only an opportunity, but an obligation. Personalized medicine is made possible by the digitalization of medicine with an ensuing increased tailoring of diagnostics, treatment and prevention to the individual. The ideal is to increase wellness by minimizing the layer of interpretation and translation between relevant health information and the patient or user. Arguably, this opens for a new level of autonomy through increased participation in treatment and prevention, and by that, increased empowerment of the individual. However, the empirical realities reveal a more complicated landscape disturbed by information 'noise' and involving a number of complementary areas of expertise and technologies, hiding the source and logic of data interpretation. This has lead to calls for a return to a mild form of paternalism, allowing expertise coaching of patients and even withholding information, with patients escaping responsibility through blind or lazy trust. This is morally unacceptable, according to Kant's ideal of enlightenment, as we have a duty to take responsibility by trusting others reflexively, even as patients. Realizing the promises of personalized medicine requires a system of institutional controls of information and diagnostics, accessible for non-specialists, supported by medical expertise that can function as the accountable gate-keeper taking moral responsibility required for an active, reflexive trust.

Perioperative Precision Medicine: Where Are We in 2020?

PURPOSE OF REVIEW

The present review examines how targeted approaches to care, based on individual variability in patient characteristics, could be applied in the perioperative setting. Such an approach would enhance individualized risk assessment and allow for targeted preventive and therapeutic decision-making in patients at increased risk for adverse perioperative events.

RECENT FINDINGS

Prior and current studies highlight valuable lessons on how future investigations attempting to link specific patient-related characteristics or treatment modalities with outcomes and adverse drug responses might be designed in the perioperative setting.

SUMMARY

Our review highlights the past, present, and future directions of perioperative precision medicine. Current evidence provides important lessons on how a specific patient and disease tailored approach can help perioperative physicians in delivering the most appropriate and safest perioperative care.

From personalised nutrition to precision medicine: the rise of consumer genomics and digital health

Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of 'big data' and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.

The Population Health OutcomEs aNd Information EXchange (PHOENIX) Program - A Transformative Approach to Reduce the Burden of Chronic Disease

This concept article introduces a transformative vision to reduce the population burden of chronic disease by focusing on data integration, analytics, implementation and community engagement. Known as PHOENIX (The Population Health OutcomEs aNd Information EXchange), the approach leverages a state level health information exchange and multiple other resources to facilitate the integration of clinical and social determinants of health data with a goal of achieving true population health monitoring and management. After reviewing historical context, we describe how multilevel and multimodal data can be used to facilitate core public health services, before discussing the controversies and challenges that lie ahead.

Axes of a revolution: challenges and promises of big data in healthcare

Health data are increasingly being generated at a massive scale, at various levels of phenotyping and from different types of resources. Concurrent with recent technological advances in both data-generation infrastructure and data-analysis methodologies, there have been many claims that these events will revolutionize healthcare, but such claims are still a matter of debate. Addressing the potential and challenges of big data in healthcare requires an understanding of the characteristics of the data. Here we characterize various properties of medical data, which we refer to as 'axes' of data, describe the considerations and tradeoffs taken when such data are generated, and the types of analyses that may achieve the tasks at hand. We then broadly describe the potential and challenges of using big data in healthcare resources, aiming to contribute to the ongoing discussion of the potential of big data resources to advance the understanding of health and disease.

Family and Community Medicine and its role in preventing health overuse (preventive, diagnostic, therapeutic and rehabilitative)

In Medicine, it is critical "to offer 100% of what is needed and avoid 100% of what is not needed." Unfortunately, this primary issue is challenging, and generally, more than required is offered, and everything that is unnecessary is not avoided. This is a nonsystematic review with a teaching objective that reviews the general issue in primary care and suggests ways to avoid overuse and shortcomings concerning preventive, diagnostic, therapeutic, and rehabilitative interventions. Knowing not to do is science and art that is hardly taught and practiced less. The overuse that harm are an almost daily part of clinical practice in prevention, diagnosis, treatment, and rehabilitation. It is essential to promote "the art and science of not doing".