Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example.

OBJECTIVES: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data. SETTING: University hospital ICU. SUBJECTS: Data from ICU patients admitted between 2003 and 2016. INTERVENTIONS: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database. MEASUREMENTS AND MAIN RESULTS: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous. CONCLUSIONS: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets.

Limitations for health research with restricted data collection from UK primary care.

PURPOSE: UK primary care provides a rich data source for research. The impact of proposed data collection restrictions is unknown. This study aimed to assess the impact of restricting the scope of electronic health record (EHR) data collection on the ability to conduct research. The study estimated the consequences of restricted data collection on published Clinical Practice Research Datalink studies from high impact journals or referenced in clinical guidelines. METHODS: A structured form was used to systematically analyse the extent to which individual studies would have been possible using a database with data collection restrictions in place: (1) retrospective collection of specified diseases only; (2) retrospective collection restricted to a 6- or 12-year period; (3) prospective and retrospective collection restricted to non-sensitive data. Outcomes were categorised as unfeasible (not reproducible without major bias); compromised (feasible with design modification); or unaffected. RESULTS: Overall, 91% studies were compromised with all restrictions in place; 56% studies were unfeasible even with design modification. With restrictions on diseases alone, 74% studies were compromised; 51% were unfeasible. Restricting collection to 6/12 years had a major impact, with 67 and 22% of studies compromised, respectively. Restricting collection of sensitive data had a lesser but marked impact with 10% studies compromised. CONCLUSION: EHR data collection restrictions can profoundly reduce the capacity for public health research that underpins evidence-based medicine and clinical guidance. National initiatives seeking to collect EHRs should consider the implications of restricting data collection on the ability to address vital public health questions.

Fourth Amendment Protections of Health Information After Carpenter v. United States: The Devil's In The Database.

Every day, companies collect health information from customers and analyze it for commercial purposes. This poses a significant threat to privacy, particularly as the Fourth Amendment protection of this deeply personal information is limited. Generally, law enforcement officers do not need probable cause and a warrant to access these private health information databases; only a subpoena is required, and sometimes nothing at all. The Fourth Amendment protections for health information may, however, have changed after the Supreme Court's 2018 decision in Carpenter v. United States, which held that the Fourth Amendment protects people from warrantless searches of historical cell-site location information possessed by their cell phone providers. The Court explained that, because of the nature of historical cell-site location information, individuals retain a reasonable expectation of privacy despite the information being in the possession of a third party. In reaching its holding, the Supreme Court considered the type of data, the uniqueness of cell-site location information, the impact of technological advancement on privacy, the voluntariness of the disclosure, and the property rights associated with the records. Many of these factors could support heightened Fourth Amendment protection for health information. This Article argues that Carpenter v. United States provides additional protections for future searches of health information in private databases.

The impact of the General Data Protection Regulation on health research.

Background: On the May 25, 2018 the General Data Protection Regulation (hereafter the GDPR or the Regulation) came into force, replacing the Data Protection Directive 95/46/EC (upon which the Data Protection Act 1998 is based), and imposing new responsibilities on organizations which process the data of European Union citizens. Sources of data: This piece examines the impact of the Regulation on health research. Areas of agreement: The Regulation seeks to harmonize data privacy laws across Europe, to protect and empower all EU citizen's data privacy and to reshape the way that organizations approach data privacy (See the GDPR portal at: https://www.eugdpr.org/ (accessed 8 May 2018). As a Regulation the GDPR is directly applicable in all member states as opposed to a directive which requires national implementing measures (In the UK the Data Protection Act 1998 was the implementing legislation for the Data Protection Directive 95/46/EC.). Areas of controversy: The Regulation is sector wide, but its impact on organizations us sector specific. In some sectors, the Regulation inhibits the processing of personal data, whilst in others it enables that processing. The Regulation takes the position that the 'processing of data should be designed to serve mankind' (Recital 4). Whilst it does not spell out what exactly is meant by this, it indicates that a proportionate approach will be taken to the protection of personal data, where that data can be processed for common goods such as healthcare. Thus, the protection of personal data is not absolute, but considered in relation to its function in society and balance with other fundamental rights in accordance with the principle of proportionality (Recital 4). Differing interpretations of proportionality can detract from the harmonization objective of the Regulation. Growing points: Reflecting the commitment to proportionality, scientific research holds a privileged position in the Regulation. Throughout the Regulation provision is made for organizations that process personal data for scientific research purposes to avoid restrictive measures which might impede the increase of knowledge. However, the application of the Regulation differs across health research sectors and across jurisdictions. Transparency and engagement across the health research sector is required to promote alignment. Areas timely for developing research: Research which focuses on the particular problems which arise in the context of the regulation's application to health research would be welcome. Particularly in the context of the operation of the Regulation alongside the duty of confidentiality and the variation in approaches across Member States.

Sharing Research Data and Intellectual Property Law: A Primer.

Sharing research data by depositing it in connection with a published article or otherwise making data publicly available sometimes raises intellectual property questions in the minds of depositing researchers, their employers, their funders, and other researchers who seek to reuse research data. In this context or in the drafting of data management plans, common questions are (1) what are the legal rights in data; (2) who has these rights; and (3) how does one with these rights use them to share data in a way that permits or encourages productive downstream uses? Leaving to the side privacy and national security laws that regulate sharing certain types of data, this Perspective explains how to work through the general intellectual property and contractual issues for all research data.

State Tobacco Policies as Predictors of Evidence-Based Cessation Method Usage: Results From a Large, Nationally Representative Dataset.

Introduction: Evidence-based cessation methods including nicotine replacement therapy (NRT), non-NRT medications, quitlines, and behavioral treatments are underutilized by smokers attempting to quit. Although a number of studies have demonstrated a relationship between state-level tobacco policies (eg, taxation, appropriations) and cessation, whether such state-level factors influence likelihood of using an evidence-based treatment is unclear. Accordingly, the aims of the present study were: (1) to describe evidence-based cessation method utilization by state and (2) to examine the effect of state-level factors on cessation method utilization above and beyond individual-level predictors. Methods: Data were utilized from the 2010-2011 Tobacco Use Supplement to the Current Population Survey (TUS-CPS). Participants included 9232 smokers who reported a past-year quit attempt. Data on 11 state-level predictors were collated from national datasets. Analyses were based on: (1) descriptive characterization of quit method usage, (2) logistic regression models to determine state-level factors as predictors of quit method utilization, controlling for individual-level predictors, (3) cluster analyses grouping states with similar state-level factors, and (4) examination of cluster as a predictor of cessation method. Results: Tobacco control appropriations significantly predicted NRT, quitline, and behavioral treatment utilization. Additional state-level factors that demonstrated significant relationships included Medicaid coverage of non-NRT medications and behavioral treatment, tobacco tax rate, smoking prevalence, and percentage of population uninsured. State clustering significantly predicted quit method across all four methods. Conclusions: State-level factors influence the likelihood of residents utilizing evidence-based quit methods. Results are discussed in terms of implications for tobacco policy at the state level. Implications: Results from the present study highlight state tobacco control appropriations as a robust predictor of evidence-based cessation method utilization. Other significant state-level predictors of evidence-based cessation method utilization included Medicaid coverage of non-NRT medications and behavioral treatment, tobacco tax rate, smoking prevalence, and percentage of population uninsured. Moreover, state-level predictors clustered together to significantly predict evidence-based cessation method utilization. Thus, increasing tobacco control appropriations, extending health insurance coverage, maximizing revenue from tobacco taxation and tobacco settlements, and ultimately decreasing smoking prevalence are important targets for individual states to promote utilization of evidence-based cessation methods.

Public Regulatory Databases as a Source of Insight for Neuromodulation Devices Stimulation Parameters.

OBJECTIVE: The Shannon model is often used to define an expected boundary between non-damaging and damaging modes of electrical neurostimulation. Numerous preclinical studies have been performed by manufacturers of neuromodulation devices using different animal models and a broad range of stimulation parameters while developing devices for clinical use. These studies are mostly absent from peer-reviewed literature, which may lead to this information being overlooked by the scientific community. We aimed to locate summaries of these studies accessible via public regulatory databases and to add them to a body of knowledge available to a broad scientific community. METHODS: We employed web search terms describing device type, intended use, neural target, therapeutic application, company name, and submission number to identify summaries for premarket approval (PMA) devices and 510(k) devices. We filtered these records to a subset of entries that have sufficient technical information relevant to safety of neurostimulation. RESULTS: We identified 13 product codes for 8 types of neuromodulation devices. These led us to devices that have 22 PMAs and 154 510(k)s and six transcripts of public panel meetings. We found one PMA for a brain, peripheral nerve, and spinal cord stimulator and five 510(k) spinal cord stimulators with enough information to plot in Shannon coordinates of charge and charge density per phase. CONCLUSIONS: Analysis of relevant entries from public regulatory databases reveals use of pig, sheep, monkey, dog, and goat animal models with deep brain, peripheral nerve, muscle and spinal cord electrode placement with a variety of stimulation durations (hours to years); frequencies (10-10,000 Hz) and magnitudes (Shannon k from below zero to 4.47). Data from located entries indicate that a feline cortical model that employs acute stimulation might have limitations for assessing tissue damage in diverse anatomical locations, particularly for peripheral nerve and spinal cord simulation.

The European Union Policy in the Field of Rare Diseases.

Rare diseases, are defined by the European Union as life-threatening or chronically debilitating diseases with low prevalence (less than 5 per 10,000). The specificities of rare diseases - limited number of patients and scarcity of relevant knowledge and expertise - single them out as a unique domain of very high European added-value.The legal instruments at the disposal of the European Union, in terms of the Article 168 of the Treaties, are very limited. However a combination of instruments using the research and the pharmaceutical legal basis and an intensive and creative use of funding from the Health Programmes has permitted to create a solid basis that Member States have considered enough to put rare diseases in a privileged position in the health agenda.The adoption of the Commission Communication, in November 2008, and of the Council Recommendation, in June 2009, and in 2011 the adoption of the Directive on Cross-border healthcare., have created an operational framework to act in the field of rare disease with European coordination in several areas (classification and codification, European Reference Networks, orphan medicinal products, the Commission expert group on rare diseases, etc.).Rare diseases is an area with high and practical potential for the European cooperation.

[Required Framework for the Collection of Real-life Data: An Example from University Eye Hospital Munich]. / Rahmenbedingungen zur Sammlung von "Real-Life"-Daten am Beispiel der Augenklinik der Universität München.

Background The importance of evaluating real-life data is constantly increasing. Currently available computer systems better allow for analyses of data, as more and more data is available in a digital form. Before a project for real-life data analyses is started, technical considerations and staff, legal, and data protection procedures need to be addressed. In this manuscript, experiences made at the University Eye Hospital in Munich will be shared. Materials and Methods Legal requirements, as found in laws and guidelines governing documentation and data privacy, are highlighted. Technical requirements for information technology infrastructure and software are defined. A survey conducted by the German Ophthalmological Society, among German eye hospitals investigating the current state of digitalization, was conducted. Also, staff requirements are outlined. Results A database comprising results of 330,801 patients was set up. It includes all diagnoses, procedures, clinical findings and results from diagnostic devices. This database was approved by the local data protection officer. In less than half of German eye hospitals (n = 21) that participated in the survey (n = 54), a complete electronic documentation is done. Fourteen institutions are completely paper-based, and the remainder of the hospitals used a mixed system. Conclusion In this work, we examined the framework that is required to develop a comprehensive database containing real-life data from clinics. In future, these databases will become increasingly important as more and more innovation are made in decision support systems. The base for this is comprehensive and well-curated databases.

Deciphering the Sunshine Act: Transparency Regulation and Financial Conflicts in Health Care.

The Physician Payments Sunshine Act ("Sunshine Act"), enacted to address financial conflicts in health care, is the first comprehensive federal legislation mandating public reporting of payments between drug companies, device manufacturers, and medicine. This article analyzes the Sunshine Act's uneven record, exploring how the law serves as an intriguing example of the uncertain case for transparency regulation in health care. The Sunshine Act's bumpy rollout demonstrates that commanding transparency through legislation can be arduous because of considerable implementation challenges. Capturing all the relevant information about financial relationships and reporting it with sufficient contextual and comparative data has proven disappointingly difficult. In addition, the law suffers from uncertainty and poor design as to the intended audience. Indeed, there is strong reason to believe that it will not significantly impact decision-making of primary recipients like patients. Yet the Sunshine Act nonetheless retains important and perhaps underappreciated value. From the almost four years of information generated, we have learned that industry-medicine financial ties vary significantly by physician specialty, and somewhat by physician gender. In many medical fields the distribution of top dollar payments tends to be heavily skewed to a few recipients, all of which have important implications for optimal management of financial conflicts and for health policy more generally. Accordingly, the Sunshine Act's greatest potential is not guiding decisions of individual patients or physicians, but its downstream effects. This Article traces how secondary audiences, such as regulators, watchdogs, and counsel are already starting to make productive use of Sunshine Act information. Public reporting has, for example, made more feasible linking industry payment information with Medicare reimbursement data. As a result, policymakers can more closely examine correlations between industry spending directed at individual physicians and their prescribing and referral decisions. Moreover, savvy counsel are recognizing that Sunshine Act information provides explosive evidence in private civil litigation and this Article explores the first wave of cases.

The U.S. Culture Collection Network Lays the Foundation for Progress in Preservation of Valuable Microbial Resources.

The U.S. Culture Collection Network was formed in 2012 by a group of culture collection scientists and stakeholders in order to continue the progress established previously through efforts of an ad hoc group. The network is supported by a Research Coordination Network grant from the U.S. National Science Foundation (NSF) and has the goals of promoting interaction among collections, encouraging the adoption of best practices, and protecting endangered or orphaned collections. After prior meetings to discuss best practices, shared data, and synergy with genome programs, the network held a meeting at the U.S. Department of Agriculture (USDA)-Agricultural Research Service (ARS) National Center for Genetic Resources Preservation (NCGRP) in Fort Collins, Colorado in October 2015 specifically to discuss collections that are vulnerable because of changes in funding programs, or are at risk of loss because of retirement or lack of funding. The meeting allowed collection curators who had already backed up their resources at the USDA NCGRP to visit the site, and brought collection owners, managers, and stakeholders together. Eight formal collections have established off-site backups with the USDA-ARS, ensuring that key material will be preserved for future research. All of the collections with backup at the NCGRP are public distributing collections including U.S. NSF-supported genetic stock centers, USDA-ARS collections, and university-supported collections. Facing the retirement of several pioneering researchers, the community discussed the value of preserving personal research collections and agreed that a mechanism to preserve these valuable collections was essential to any future national culture collection system. Additional input from curators of plant and animal collections emphasized that collections of every kind face similar challenges in developing long-range plans for sustainability.

The United States Culture Collection Network (USCCN): Enhancing Microbial Genomics Research through Living Microbe Culture Collections.

The mission of the United States Culture Collection Network (USCCN; http://usccn.org) is "to facilitate the safe and responsible utilization of microbial resources for research, education, industry, medicine, and agriculture for the betterment of human kind." Microbial culture collections are a key component of life science research, biotechnology, and emerging global biobased economies. Representatives and users of several microbial culture collections from the United States and Europe gathered at the University of California, Davis, to discuss how collections of microorganisms can better serve users and stakeholders and to showcase existing resources available in public culture collections.

Heart failure is a major contributor to hospital readmission penalties.

BACKGROUND: The Hospital Readmissions Reduction Program provides incentives to hospitals to reduce early readmissions for heart failure (HF), acute myocardial infarction (AMI), and pneumonia (PNE). METHODS AND RESULTS: To examine the contribution of each diagnosis to readmissions penalty size, data were obtained from the Center for Medicare and Medicaid Services, American Hospital Association, and United States Census Bureau including number of cases; readmissions payment adjustment factor (values <1 indicate a penalty for excess readmissions), excess readmission ratio (ERR, or ratio of adjusted predicted readmission based on comorbidities, frailty, and individual patient demographics to expected probability of readmission at an average hospital) for each diagnosis, hospital teaching status, bed number, and zip code socioeconomic status. Of 2,228 hospitals with ≥25 cases per diagnosis, 1,636 received a penalty. Univariate correlation coefficients between penalty and ERR were -0.66, -0.61, and -0.43 for HF, PNE, and AMI, respectively (all P < .001). Correlation between ERRs was greatest for PNE and HF (0.30; P < .001) and weakest for PNE and AMI (0.12; P < .001). In regression analyses, the HF ERR explained the most variance in the penalty (R(2) range 0.21-0.44). CONCLUSION: HF ERR, not the number of cases, was related to penalty magnitude. These findings have implications for the design of hospital-based quality initiatives regarding readmissions.

Comparison between bioconcentration factor (BCF) data provided by industry to the European Chemicals Agency (ECHA) and data derived from QSAR models.

The bioconcentration factor (BCF) is the ratio of the concentration of a chemical in an organism to the concentration in the surrounding environment at steady state. It is a valuable indicator of the bioaccumulation potential of a substance. BCF is an essential environmental property required for regulatory purposes within the Registration, Evaluation, Authorization and restriction of Chemicals (REACH) and Globally Harmonized System (GHS) regulations. In silico models for predicting BCF can facilitate the risk assessment for aquatic toxicology and reduce the cost and number of animals used. The aim of the present study was to examine the correlation of BCF data derived from the dossiers of registered chemicals submitted to the European Chemical Agency (ECHA) with the results of a battery of Quantitative Structure-Activity Relationship (QSAR). After data pruning, statistical analysis was performed using the predictions of the selected models. Results in terms of R(2) had low rating around 0.5 for the pruned dataset. The use of the model applicability domain index (ADI) led to an improvement of the performance for compounds falling within it. The variability of the experimental data and the use of different parameters to define the applicability domain can influence the performance of each model. All available information should be adapted to the requirements of the regulation to obtain a safe decision.

(Q)SAR assessments of potentially mutagenic impurities: a regulatory perspective on the utility of expert knowledge and data submission.

(Quantitative) structure activity relationship [(Q)SAR] modeling is the primary tool used to evaluate the mutagenic potential associated with drug impurities. General recommendations regarding the use of (Q)SAR in regulatory decision making have recently been provided in the ICH M7 guideline. Although (Q)SAR alone is capable of achieving reasonable sensitivity and specificity, reliance on a simple positive or negative prediction can be problematic. The key to improving (Q)SAR performance is to integrate supporting information, also referred to as expert knowledge, into the final conclusion. In the regulatory context, expert knowledge is intended to (1) maximize confidence in a (Q)SAR prediction, (2) provide rationale to supersede a positive or negative (Q)SAR prediction, or (3) provide a basis for assessing mutagenicity in absence of a (Q)SAR prediction. Expert knowledge is subjective and is associated with great variability in regards to content and quality. However, it is still a critical component of impurity evaluations and its utility is acknowledged in the ICH M7 guideline. The current paper discusses the use of expert knowledge to support regulatory decision making, describes case studies, and provides recommendations for reporting data from (Q)SAR evaluations.

Population biobanking and international collaboration.

Population-based biobanks promise to be important resources for genetic research. However, the study of normal genomic variation across populations requires the collection of data and biological samples from individuals on a large scale. While international collaboration has become both a scientific and an ethical imperative, international sharing of data and samples poses many challenges. Significant variation persists among the legal and ethical norms governing population biobanks in different jurisdictions. Many of these norms do not clearly provide for international access. To illustrate these problems, we collected and compared applicable legislative instruments, as well as ethical guidelines issued by national, regional, and international bodies. In addition, harmonization is faced with important limitations and may not be sufficient to ensure effective international sharing. Population biobanks are therefore looking for new ways to promote sharing and improve interoperability. The formation of biobank networks and the development of common governance tools are two approaches that are setting the groundwork for international collaboration in genetic research.

[The UN-Convention on the Rights of People with Disabilities and the transition from school to vocational training and career in Germany: public data sources under close scrutiny]. / Die UN-Behindertenrechtskonvention und der Übergang Schule-Ausbildung-Beruf in Deutschland: Öffentliche Datenquellen auf dem Prüfstand.

This paper analyzes public data sources and their requirements for the transition from school to vocational training and career of people with disabilities in the context of Article 31 of the UN-Convention on Rights of People with Disabilities. Different focuses of the public data sources within the involved systems and challenges in data analysis will be presented. These manifest themselves as cross-system interface problems when it comes to the identification and whereabouts of young people with disabilities at the transition from school to vocational training and employment. With these challenges public data sources on the transition from school to vocational training and employment are especially under scrutiny when it comes to developing and implementing policies in respect to the Convention on Rights for People with disabilities and the provision of adequate planning data.

[Use of routine data from statutory health insurances for federal health monitoring purposes]. / Nutzungsmöglichkeiten von Routinedaten der Gesetzlichen Krankenversicherung in der Gesundheitsberichterstattung des Bundes.

Federal health monitoring deals with the state of health and the health-related behavior of populations and is used to inform politics. To date, the routine data from statutory health insurances (SHI) have rarely been used for federal health monitoring purposes. SHI routine data enable analyses of disease frequency, risk factors, the course of the disease, the utilization of medical services, and mortality rates. The advantages offered by SHI routine data regarding federal health monitoring are the intersectoral perspective and the nearly complete absence of recall and selection bias in the respective population. Further, the large sample sizes and the continuous collection of the data allow reliable descriptions of the state of health of the insurants, even in cases of multiple stratification. These advantages have to be weighed against disadvantages linked to the claims nature of the data and the high administrative hurdles when requesting the use of SHI routine data. Particularly in view of the improved availability of data from all SHI insurants for research institutions in the context of the "health-care structure law", SHI routine data are an interesting data source for federal health monitoring purposes.