Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning

Achieving a Quintuple Aim for Telehealth in Pediatrics

Pediatric practice increasingly involves providing care for children with medical complexity. Telehealth offers a strategy for providers and health care systems to improve care for these patients and their families. However, lack of awareness related to the unintended negative consequences of telehealth on vulnerable populations--coupled with failure to intentional design best practices for telehealth initiatives--implies that these novel technologies may worsen health disparities in the long run. This article reviews the positive and negative implications of telehealth. In addition, to achieve optimal implementation of telehealth, it discusses 10 considerations to promote optimal care of children using these technologies.

Review of Clinical Research Informatics

OBJECTIVES

Clinical Research Informatics (CRI) declares its scope in its name, but its content, both in terms of the clinical research it supports-and sometimes initiates-and the methods it has developed over time, reach much further than the name suggests. The goal of this review is to celebrate the extraordinary diversity of activity and of results, not as a prize-giving pageant, but in recognition of the field, the community that both serves and is sustained by it, and of its interdisciplinarity and its international dimension.

METHODS

Beyond personal awareness of a range of work commensurate with the author's own research, it is clear that, even with a thorough literature search, a comprehensive review is impossible. Moreover, the field has grown and subdivided to an extent that makes it very hard for one individual to be familiar with every branch or with more than a few branches in any depth. A literature survey was conducted that focused on informatics-related terms in the general biomedical and healthcare literature, and specific concerns ("artificial intelligence", "data models", "analytics", etc.) in the biomedical informatics (BMI) literature. In addition to a selection from the results from these searches, suggestive references within them were also considered.

RESULTS

The substantive sections of the paper-Artificial Intelligence, Machine Learning, and "Big Data" Analytics; Common Data Models, Data Quality, and Standards; Phenotyping and Cohort Discovery; Privacy: Deidentification, Distributed Computation, Blockchain; Causal Inference and Real-World Evidence-provide broad coverage of these active research areas, with, no doubt, a bias towards this reviewer's interests and preferences, landing on a number of papers that stood out in one way or another, or, alternatively, exemplified a particular line of work.

CONCLUSIONS

CRI is thriving, not only in the familiar major centers of research, but more widely, throughout the world. This is not to pretend that the distribution is uniform, but to highlight the potential for this domain to play a prominent role in supporting progress in medicine, healthcare, and wellbeing everywhere. We conclude with the observation that CRI and its practitioners would make apt stewards of the new medical knowledge that their methods will bring forward.

Health Policy and Privacy Challenges Associated With Digital Technology

Importance

Digital technology is part of everyday life. Digital interactions generate large amounts of data that can reveal information about the health of individual consumers (the digital health footprint).

Objective

Τo describe health privacy challenges associated with digital technology.

Design, Setting, and Participants

For this qualitative study, In-depth, semistructured, qualitative interviews were conducted with 26 key experts from diverse fields in the US between January 1 and July 31, 2018. Open-ended questions and hypothetical scenarios were used to identify sources of digital information that contribute to consumers' health-relevant digital footprints and challenges for health privacy. Participants also completed a survey instrument on which they rated the health relatedness of digital data sources.

Main Outcomes and Measures

Health policy challenges associated with digital technology based on qualitative responses to expert interviews.

Results

Although experts' ratings of digital data sources suggested a possible distinction between health and nonhealth data, qualitative interviews uniformly indicated that all data can be health data, particularly when aggregated across sources and time. Five key characteristics of the digital health footprint were associated with health privacy policy challenges: invisibility (people are unaware of how their data are tracked), inaccuracy (data in the digital health footprint can be inaccurate), immortality (data have no expiration date and are aggregated over time), marketability (data have immense commercial value and are frequently bought and sold), and identifiability (individuals can be readily reidentified and anonymity is nearly impossible to achieve). There are virtually no regulatory structures in the US to protect health privacy in the context of the digital health footprint.

Conclusions and Relevance

The findings suggest that a sector-specific approach to digital technology privacy in the US may be associated with inadequate health privacy protections.

Privacy challenges and research opportunities for genomic data sharing

The sharing of genomic data holds great promise in advancing precision medicine and providing personalized treatments and other types of interventions. However, these opportunities come with privacy concerns, and data misuse could potentially lead to privacy infringement for individuals and their blood relatives. With the rapid growth and increased availability of genomic datasets, understanding the current genome privacy landscape and identifying the challenges in developing effective privacy-protecting solutions are imperative. In this work, we provide an overview of major privacy threats identified by the research community and examine the privacy challenges in the context of emerging direct-to-consumer genetic-testing applications. We additionally present general privacy-protection techniques for genomic data sharing and their potential applications in direct-to-consumer genomic testing and forensic analyses. Finally, we discuss limitations in current privacy-protection methods, highlight possible mitigation strategies and suggest future research opportunities for advancing genomic data sharing.

Reasonable Persons, Autonomous Persons, and Lady Hale: Determining a Standard for Risk Disclosure

Among various kinds of disclosures typically required in research as well as in clinical scenarios, risk information figures prominently. A key question is, what kinds of risk information would the reasonable person want to know? I will argue, however, that the reasonable person construct is and always has been incapable of settling this very question. After parsing the nebulous if not "contentless" character of the reasonable person, I will explain how Western courts have actually adjudicated cases of "negligent nondisclosure," that is, cases in which patient-plaintiffs allege that their informed consent rights were violated by the failure of their health providers to inform them of reasonably foreseeable risks that subsequently materialized. To support my argument, I will scrutinize the landmark decision by the United Kingdom's Supreme Court in Montgomery v. Lanarkshire Health Board and, in particular, Justice Brenda Hale's concurrence.

Research, Digital Health Information and Promises of Privacy: Revisiting the Issue of Consent

The obligation to maintain the privacy of patients and research participants is foundational to biomedical research. But there is growing concern about the challenges of keeping participant information private and confidential. A number of recent studies have highlighted how emerging computational strategies can be used to identify or reidentify individuals in health data repositories managed by public or private institutions. Some commentators have suggested the entire concept of privacy and anonymity is “dead”, and this raises legal and ethical questions about the consent process and safeguards relating to health privacy. Members of the public and research participants value privacy highly, and inability to ensure it could affect participation. Canadian common law and legislation require a full and comprehensive disclosure of risks during informed consent, including anything a reasonable person in the participant or patient’s position would want to know. Research ethics policies require similar disclosures, as well as full descriptions of privacy related risks and mitigation strategies at the time of consent. In addition, the right to withdraw from research gives rise to a need for ongoing consent, and material information about changes in privacy risk must be disclosed. Given the research ethics concept of “non-identifiability” is increasingly questionable, policies based around it may be rendered untenable. Indeed, the potential inability to ensure anonymity could have significant ramifications for the research enterprise.

Artificial Intelligence in Radiology-Ethical Considerations

Artificial intelligence (AI) is poised to change much about the way we practice radiology in the near future. The power of AI tools has the potential to offer substantial benefit to patients. Conversely, there are dangers inherent in the deployment of AI in radiology, if this is done without regard to possible ethical risks. Some ethical issues are obvious; others are less easily discerned, and less easily avoided. This paper explains some of the ethical difficulties of which we are presently aware, and some of the measures we may take to protect against misuse of AI.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists' Intelligence?

BACKGROUND

Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the explosion of the amount of data; medicine is not an exception. Rather than replacing clinicians, AI is augmenting the intelligence of clinicians in diagnosis, prognosis, and treatment decisions.

SUMMARY

Kidney disease is a substantial medical and public health burden globally, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality as well as a huge economic burden. Even though the existing research and applied works have made certain contributions to more accurate prediction and better understanding of histologic pathology, there is a lot more work to be done and problems to solve.

KEY MESSAGES

AI applications of diagnostics and prognostics for high-prevalence and high-morbidity types of nephropathy in medical-resource-inadequate areas need special attention; high-volume and high-quality data need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built.

Regulating the Secondary Use of Data for Research: Arguments Against Genetic Exceptionalism

As accessing, collecting, and storing personal information become increasingly easier, the secondary use of data has the potential to make healthcare research more cost and time effective. The widespread reuse of data, however, raises important ethical and policy issues, especially because of the sensitive nature of genetic and health-related information. Regulation is thus crucial to determine the conditions upon which data can be reused. In this respect, the question emerges whether it is appropriate to endorse genetic exceptionalism and grant genetic data an exceptional status with respect to secondary use requirements. Using Swiss law as a case study, it is argued that genetic exceptionalism in secondary use regulation is not justified for three reasons. First, although genetic data have particular features, also other non-genetic data can be extremely sensitive. Second, having different regulatory requirements depending on the nature of data hinders the creation of comprehensible consent forms. Third, empirical evidence about public preferences concerning data reuse suggests that exceptional protection for genetic data alone is not justified. In this sense, it is claimed that regulation concerning data reuse should treat genetic data as important, but not exceptional.

Detecting the impact of subject characteristics on machine learning-based diagnostic applications

Collection of high-dimensional, longitudinal digital health data has the potential to support a wide-variety of research and clinical applications including diagnostics and longitudinal health tracking. Algorithms that process these data and inform digital diagnostics are typically developed using training and test sets generated from multiple repeated measures collected across a set of individuals. However, the inclusion of repeated measurements is not always appropriately taken into account in the analytical evaluations of predictive performance. The assignment of repeated measurements from each individual to both the training and the test sets ("record-wise" data split) is a common practice and can lead to massive underestimation of the prediction error due to the presence of "identity confounding." In essence, these models learn to identify subjects, in addition to diagnostic signal. Here, we present a method that can be used to effectively calculate the amount of identity confounding learned by classifiers developed using a record-wise data split. By applying this method to several real datasets, we demonstrate that identity confounding is a serious issue in digital health studies and that record-wise data splits for machine learning- based applications need to be avoided.

The impact of artificial intelligence in medicine on the future role of the physician

The practice of medicine is changing with the development of new Artificial Intelligence (AI) methods of machine learning. Coupled with rapid improvements in computer processing, these AI-based systems are already improving the accuracy and efficiency of diagnosis and treatment across various specializations. The increasing focus of AI in radiology has led to some experts suggesting that someday AI may even replace radiologists. These suggestions raise the question of whether AI-based systems will eventually replace physicians in some specializations or will augment the role of physicians without actually replacing them. To assess the impact on physicians this research seeks to better understand this technology and how it is transforming medicine. To that end this paper researches the role of AI-based systems in performing medical work in specializations including radiology, pathology, ophthalmology, and cardiology. It concludes that AI-based systems will augment physicians and are unlikely to replace the traditional physician-patient relationship.

Putting the data before the algorithm in big data addressing personalized healthcare

Technologies leveraging big data, including predictive algorithms and machine learning, are playing an increasingly important role in the delivery of healthcare. However, evidence indicates that such algorithms have the potential to worsen disparities currently intrinsic to the contemporary healthcare system, including racial biases. Blame for these deficiencies has often been placed on the algorithm-but the underlying training data bears greater responsibility for these errors, as biased outputs are inexorably produced by biased inputs. The utility, equity, and generalizability of predictive models depend on population-representative training data with robust feature sets. So while the conventional paradigm of big data is deductive in nature-clinical decision support-a future model harnesses the potential of big data for inductive reasoning. This may be conceptualized as clinical decision questioning, intended to liberate the human predictive process from preconceived lenses in data solicitation and/or interpretation. Efficacy, representativeness and generalizability are all heightened in this schema. Thus, the possible risks of biased big data arising from the inputs themselves must be acknowledged and addressed. Awareness of data deficiencies, structures for data inclusiveness, strategies for data sanitation, and mechanisms for data correction can help realize the potential of big data for a personalized medicine era. Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data.

Hypocrisy Around Medical Patient Data: Issues of Access for Biomedical Research, Data Quality, Usefulness for the Purpose and Omics Data as Game Changer

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data’s analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today’s level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive.