Information design to support growth, quality, and equity of the US transplant system.

The Organ Procurement and Transplantation Network, an arm of the Health Resources and Services Administration, has a contract with the United Network for Organ Sharing since 1986 to provide central oversight of organ donation and transplants in the United States. The United Network for Organ Sharing has recently come under scrutiny, prompting a review by the National Academies of Sciences, Engineering, and Medicine as summarized in its recent report and also by the US Senate Finance Committee. The national news services have opined about organ donation ethics, access to transplantation particularly for medically underserved populations, and management of organ transplantation data. These critiques raise important concerns that deserve our best response as a transplant community. Broadly, we suggest that the data management approach of the Organ Procurement and Transplantation Network be replaced with a patient-centric omnichannel network in which all donor and recipient data exist in a single longitudinal record that can be used by all applications. A more comprehensive and standardized approach to donor data collection would drive quality improvement across organ procurement organizations and help address inequities in transplantation. Finally, a substantial increase in organ donation would be prompted by considering organ donors as a public health resource, meriting transparent publicly available data collection with respect to organ donor referral, screening, and management.

Social Media is Addictive and Influences Behavior: Should it Be Regulated as a Digital Therapeutic?

Recently, we were deeply saddened by the findings of the coroner investigating the death of 14-year-old Molly Russell. Deeply saddened and angry but not surprised. This case should be seen as a sentinel event, given that this is the first time social media was directly implicated as a cause of death. We should use this opportunity to advance proposals for the regulations of the health effects of social media.

Health Care and Cybersecurity: Bibliometric Analysis of the Literature.

BACKGROUND: Over the past decade, clinical care has become globally dependent on information technology. The cybersecurity of health care information systems is now an essential component of safe, reliable, and effective health care delivery. OBJECTIVE: The objective of this study was to provide an overview of the literature at the intersection of cybersecurity and health care delivery. METHODS: A comprehensive search was conducted using PubMed and Web of Science for English-language peer-reviewed articles. We carried out chronological analysis, domain clustering analysis, and text analysis of the included articles to generate a high-level concept map composed of specific words and the connections between them. RESULTS: Our final sample included 472 English-language journal articles. Our review results revealed that majority of the articles were focused on technology: Technology-focused articles made up more than half of all the clusters, whereas managerial articles accounted for only 32% of all clusters. This finding suggests that nontechnological variables (human-based and organizational aspects, strategy, and management) may be understudied. In addition, Software Development Security, Business Continuity, and Disaster Recovery Planning each accounted for 3% of the studied articles. Our results also showed that publications on Physical Security account for only 1% of the literature, and research in this area is lacking. Cyber vulnerabilities are not all digital; many physical threats contribute to breaches and potentially affect the physical safety of patients. CONCLUSIONS: Our results revealed an overall increase in research on cybersecurity and identified major gaps and opportunities for future work.

Reimagining the United States organ procurement and transplant network.

The United States system of solid organ transplantation is overseen by the Organ Procurement Transplantation Network (OPTN). Recent announcements from the Health Resources and Services Administration (HRSA) indicate their clear intention to reform the system. We suggest that the original intention of the National Organ Transplant Act (NOTA) to require one entity to oversee transplantation is critical to integrate policy with the complex realities of organ procurement and transplantation practice. We suggest that a contemporary business platform model best captures the appropriate structure for coordinating organ transplantation, as the seamless exchange of organs between related groups is the essential function to facilitate. A business platform framework that includes public and private, academic and industry partners can best accomplish the important goal of equitable and efficient organ transplantation.

Doing it right: Caring for and protecting patient information for US organ donors and transplant recipients.

In the current US organ transplantation system, there are no regulations defining how organ procurement organizations must manage personal data and protect the privacy of donors and recipients. In response to the recent announcement of a major overhaul of the US transplantation system, we describe a practical approach to improving transplant data quality and protecting the autonomy of patients interacting with the system.

Does clinical research account for diversity in deploying digital health technologies?

Digital health technologies (DHTs) should expand access to clinical research to represent the social determinants of health (SDoH) across the population. The frequency of reporting participant SDoH data in clinical publications is low and is not known for studies that utilize DHTs. We evaluated representation of 11 SDoH domains in 126 DHT-enabled clinical research publications and proposed a framework under which these domains could be captured and subsequently reported in future studies. Sex, Race, and Education were most frequently reported (in 94.4%, 27.8%, and 20.6% of publications, respectively). The remaining 8 domains were reported in fewer than 10% of publications. Medical codes were identified that map to each of the proposed SDoH domains and the resulting resource is suggested to highlight that existing infrastructure could be used to capture SDoH data. An opportunity exists to increase reporting on the representation of SDoH among participants to encourage equitable and inclusive research progress through DHT-enabled clinical studies.

Artificial intelligence and heart failure: A state-of-the-art review.

Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.

Assessing the utility of molecular diagnostic classification for cancers of unknown primary.

BACKGROUND: Roughly 5% of metastatic cancers present with uncertain origin, for which molecular classification could influence subsequent management; however, prior studies of molecular diagnostic classifiers have reported mixed results with regard to clinical impact. In this retrospective study, we evaluated the utility of a novel molecular diagnostic classifier by assessing theoretical changes in treatment and additional testing recommendations from oncologists before and after the review of classifier predictions. METHODS: We retrospectively analyzed de-identified records from 289 patients with a consensus diagnosis of cancer of uncertain/unknown primary (CUP). Two (or three, if adjudication was required) independent oncologists separately reviewed patient clinical information to determine the course of treatment before they reviewed results from the molecular diagnostic classifier and subsequently evaluated whether the predicted diagnosis would alter their treatment plan. RESULTS: Results from the molecular diagnostic classifier changed the consensus oncologist-reported treatment recommendations for 235 out of 289 patients (81.3%). At the level of individual oncologist reviews (n = 414), 64.7% (n = 268) of treatment recommendations were based on CUP guidelines prior to review of results from the molecular diagnostic classifier. After seeing classifier results, 98.1% (n = 207) of the reviews, where treatment was specified (n = 211), were guided by the tissue of origin-specific guidelines. Overall, 89.9% of the 414 total reviews either expressed strong agreement (n = 242) or agreement (n = 130) that the molecular diagnostic classifier result increased confidence in selecting the most appropriate treatment regimen. CONCLUSIONS: A retrospective review of CUP cases demonstrates that a novel molecular diagnostic classifier could affect treatment in the majority of patients, supporting its clinical utility. Further studies are needed to prospectively evaluate whether the use of molecular diagnostic classifiers improves clinical outcomes in CUP patients.

The global need for education on cancer health.

Non-communicable diseases, such as cancer, diabetes, and chronic cardiovascular and respiratory diseases, remain a major cause of mortality and morbidity worldwide resulting in more than 36 million deaths annually. Of primary importance in the reduction of this pain and suffering is the local use of education to eliminate misconception, enable prevention, reduce the associated social stigmas, and prepare a workforce that can care for its own people as well as feed an economic engine helping to combat the poverty that so often determines the availability and quality of care. The need to develop these local capabilities is especially acute for children, as 80% of pediatric cancer cases occur in low- and middle-income countries, places where the survival rates are lowest.

Beyond privacy: A deeper understanding of the internet is required to protect digitized trial participants.

Understanding privacy in the digital age requires a deep understanding of the forces driving the internet and the specific resulting harms. To protect patients in digitized clinical trials, we must look beyond the traditional and historical concept of medical privacy.

Health advertising on Facebook: Privacy and policy considerations.

In this study, we analyzed health-advertising tactics of digital medicine companies (n = 5) to evaluate varying types of cross-site-tracking middleware (n = 32) used to extract health information from users. More specifically, we examine how browsing data can be exchanged between digital medicine companies and Facebook for advertising and lead generation and advertising purposes. Our analysis focused on companies offering services to patient advocates in the cancer community who frequently engage on social media. We co-produced this study with public cancer advocates leading or participating in breast cancer groups on Facebook. Following our analysis, we raise policy questions about what constitutes a health privacy breach based on existing federal laws such as the Health Breach Notification Rule and The HIPAA Privacy Rule. We discuss how these common marketing practices enable surveillance and targeting of medical ads to vulnerable patient populations without consent.

Audit of Data Sharing by Pharmaceutical Companies for Anticancer Medicines Approved by the US Food and Drug Administration.

Importance: Emerging policies drafted by the pharmaceutical industry indicate that they will transparently share clinical trial data. These data offer an unparalleled opportunity to advance evidence-based medicine and support decision-making. Objective: To evaluate the eligibility of independent, qualified researchers to access individual participant data (IPD) from oncology trials that supported US Food and Drug Administration (FDA) approval of new anticancer medicines within the past 10 years. Design, Setting, and Participants: In this quality improvement study, a cross-sectional analysis was performed of pivotal clinical trials whose results supported FDA-approved anticancer medicines between January 1, 2011, and June 30, 2021. These trials' results were identified from product labels. Exposures: Eligibility for IPD sharing was confirmed by identification of a public listing of the trial as eligible for sharing or by receipt of a positive response from the sponsor to a standardized inquiry. Main Outcomes and Measures: The main outcome was frequency of IPD sharing eligibility. Reasons for data sharing ineligibility were requested and collated, and company-, drug-, and trial-level subgroups were evaluated and presented using χ2 tests and forest plots. Results: During the 10-year period examined, 115 anticancer medicines were approved by the FDA on the basis of evidence from 304 pharmaceutical industry-sponsored trials. Of these trials, 136 (45%) were eligible for IPD sharing and 168 (55%) were not. Data sharing rates differed substantially among industry sponsors, with the most common reason for not sharing trial IPD being that the collection of long-term follow-up data was still ongoing (89 of 168 trials [53%]). Of the top 10 anticancer medicines by global sales, nivolumab, pembrolizumab, and pomalidomide had the lowest eligibility rates for data sharing (<10% of trials). Conclusions and Relevance: There has been a substantial increase in IPD sharing for industry-sponsored oncology trials over the past 5 years. However, this quality improvement study found that more than 50% of queried trials for FDA-approved anticancer medicines were ineligible for IPD sharing. Data accessibility would be substantially improved if, at the time of FDA registration of a medicine, all data that support the registration were made available.

Effective knowledge management in translational medicine.

BACKGROUND: The growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health. METHODS: The pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern. RESULTS: The system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface. CONCLUSIONS: The implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs.

A Primer on Biodefense Data Science for Pandemic Preparedness.

The coronavirus outbreak is sweeping the globe with outbreaks reported on every continent except Antarctica as of March 2020. Data scientists are uniquely and diversely skilled in ways that can be highly effective in minimizing, combatting, and recovering from the impacts of the COVID-19 outbreak. In this Opinion, the basics of biodefense as well as specific opportunities for the data science community to contribute are discussed.