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Introduction I. The data sharing examples 1. Private financial data and national statistics: The case of StatCan 2. Mobility data and public health: The case of PHAC II. Discussion 1. [...], Governments increasingly seek to use personal data sourced from the private sector for purposes that range from the generation of statistics to municipal planning. The data collected by companies is often high volume and rich in detail. Location and mobility data--which have many applications--are collected by multiple private sector actors, from cellular service providers to app developers and data brokers. Financial sector organizations amass rich data about the spending and borrowing habits of consumers. Even genetic data is collected by private sector companies. The range of available data is constantly growing as more and more data is harvested, and as companies seek secondary markets for their data. This paper explores issues raised by public sector access to and use of personal data held by the private sector. The analysis is framed around two examples from Canada that involved actual or attempted access by government agencies to private sector personal data for public purposes. The first involved Statistics Canada's attempt to collect and use data held by credit monitoring companies and financial institutions to generate economic statistics. The second was the use, during the COVID-19 pandemic, of mobility data by the Public Health Agency of Canada (PHAC) to assess the effectiveness of public health policies in reducing the transmission of COVID-19 during lockdowns. Each of these instances led to public outcry and to investigation or inquiry. The paper examines the legal bases for the use of private sector data by government, the safeguards available to protect privacy, and the framing of concerns over the use of these data by different interested parties. Given that legal frameworks for data sharing may not keep pace with data needs and methods, this paper also takes a normative approach which examines whether and in what circumstances such data sharing should take place. Les gouvernements cherchent de plus en plus utiliser les donn es personnelles provenant du secteur priv des fins qui vont de la production de statistiques la planification municipale. Les donn es collect es par les entreprises sont souvent volumineuses et tr s d taill es. Les donn es de localisation et de mobilit --qui ont de nombreuses applications--sont collect es par de multiples acteurs du secteur priv , des fournisseurs de services cellulaires aux d veloppeurs d'applications et aux courtiers en donn es. Les organisations du secteur financier accumulent de riches donn es sur les habitudes de d pense et d'emprunt des consommateurs. M me les donn es g n tiques sont collect es par des entreprises du secteur priv . L' ventail des donn es disponibles ne cesse de s' largir, car de plus en plus de donn es sont collect es et les entreprises cherchent des march s secondaires pour leurs donn es. Dans le pr sent article, nous explorons les questions soulev es par l'acc s du secteur public aux donn es personnelles d tenues par le secteur priv et par leur utilisation. L'analyse s'articule autour de deux exemples canadiens qui impliquent l'acc s r el ou la tentative d'acc s par des agences gouvernementales des donn es personnelles du secteur priv des fins publiques. Le premier concerne la tentative de Statistique Canada de collecter et d'utiliser des donn es d tenues par des soci t s de surveillance du cr dit et des institutions financi res pour produire des statistiques conomiques. La seconde est l'utilisation, pendant la pand mie de COVID-19, de donn es sur la mobilit par l'Agence de sant publique du Canada (ASPC) pour valuer l'efficacit des politiques de sant publique dans la r duction de la transmission de la COVID-19 pendant les fermetures d' tablissements. Chacun de ces cas a suscit des protestations publiques et a donn lieu une enqu te ou une investigation. Dans l'article, nous examinons les bases juridiques de l'utilisation des donn es du secteur priv par le gouvernement, les garanties disponibles pour prot ger la vie priv e et la formulation des pr occupations relatives l'utilisation de ces donn es par les diff rentes parties int ress es. tant donn que les cadres juridiques pour le partage des donn es peuvent ne pas suivre l' volution des besoins et des m thodes en mati re de donn es, nous adoptons galement une approche normative qui examine si et dans quelles circonstances un tel partage de donn es devrait avoir lieu.

In a transformational journey from replacement to reinvention, the New Mexico Medicaid Management Information System Replacement (MMISR) program is reimagining its Medicaid Management Information System (MMIS) with a modular solution, [...]

A major pre-award administrative challenge research universities face is turnaround time for generation of high-quality NIH Data Training Tables for NIH training grants (e.g., T32, K12, TL1, KL2, R25s) which are required for training grant submission proposals to the National Institutes of Health (NIH). Universities with dedicated training grant submission offices generally require data preparation following a structured timeline of several months in advance of the grant submission due date, while other universities with less or no dedicated support for training grant submissions use an ad hoc approach. In these cases, department or program administrators may collect the data manually, in Excel or REDCap, or similar manually maintained methods for those tables requested by the specific NIH grant announcement for the relevant participating graduate predoctoral and/or postdoctoral (including clinical) training programs across the university, depending on the training focus and the "participating faculty" provided by the proposed program director (PD/PI) for the grant. We describe an efficient "federated" method of data collection and construction for NIH Tables (2, 4, 5A/B, 6A/B & -8A part III/8C part III) for new and renewal applications by combining the use of REDCap and NIH xTRACT, leveraging the strengths of each. Keywords: REDCap; NIH xTRACT; NIH Data Training Tables; NIH Training Grants, INTRODUCTION The NIH funds an extensive portfolio of training grants, both individual and institutional. These include formal training at various levels spanning high school to senior career faculty. While individual [...]

INTRODUCTION Today's students have never known a world without technology. In particular, computers and the internet permeate all aspects of students' lives. (1) The pervasiveness of this technology extends to [...], In recent years, student surveillance has rapidly grown. As schools have experimented with new technologies, transitioned to remote and hybrid instruction, and faced pressure to protect student safety, they have increased surveillance of school accounts and school-issued devices. School surveillance extends beyond school premises to monitor student activities that occur off-campus. It reaches students' most intimate data and spaces, including things students likely believe are private: internet searches, emails, and messages. This Comment focuses on the problems associated with off-campus surveillance of school accounts and school-issued devices, including chilling effects that fundamentally alter student behavior, reinforcement of the school-to-prison pipeline, and disproportionate impacts on certain groups--including low-income students, LGBTQIA2S+ students, and students with disabilities. This Comment argues that the current legal landscape--federal and state laws, the Fourth Amendment, and the First Amendment--inadequately protects student privacy. Drawing on aspects of existing privacy frameworks, this Comment proposes solutions that could be implemented by the Supreme Court, federal and state legislators, and school districts. All of these solutions aim to increase student privacy protections. First, the Supreme Court should clarify whether speech on school-issued devices is protected by adopting clear categories of "school speech" that are subject to discipline. Second, new federal and state protections could bolster student privacy. Such laws should limit data sharing when collected from school-issued devices used at home, mandate data minimization and further limitation of data collected, and implement mandatory tracking of the impacts of student surveillance. Third, school districts should conduct audits and increase transparency to demonstrate their commitment to protect student privacy.

Introduction Microbiome research holds the promise of elucidating new mechanisms of disease development and developing innovative approaches for disease prevention and treatment. However, a substantial challenge that stymies this potential [...]

PURPOSE The purpose of this study is to evaluate recent trends in primary care physician (PCP) electronic health record (EHR) workload. METHODS This longitudinal study observed the EHR use of 141 academic PCPs over 4 years (May 2019 to March 2023). Ambulatory full-time equivalency (aFTE), visit volume, and panel size were evaluated. Electronic health record time and inbox message volume were measured per 8 hours of scheduled clinic appointments. RESULTS From the pre-COVID-19 pandemic year (May 2019 to February 2020) to the most recent study year (April 2022 to March 2023), the average time PCPs spent in the EHR per 8 hours of scheduled clinic appointments increased (+ 28.4 minutes, 7.8%), as did time in orders (+ 23.1 minutes, 58.9%), inbox (+14.0 minutes, 24.4%), chart review (+ 7.2 minutes, 13.0%), notes (+2.9 minutes, 2.3%), outside scheduled hours on days with scheduled appointments (+6.4 minutes, 8.2%), and on unscheduled days (+13.6 minutes, 19.9%). Primary care physicians received more patient medical advice requests (+5.4 messages, 55.5%) and prescription messages (+2.3, 19.5%) per 8 hours of scheduled clinic appointments, but fewer patient calls (-2.8, -10.5%) and results messages (-0.3, -2.7%). While total time in the EHR continued to increase in the final study year (+ 7.7 minutes, 2.0%), inbox time decreased slightly from the year prior (-2.2 minutes, -3.0%). Primary care physicians' average aFTE decreased 5.2% from 0.66 to 0.63 over 4 years. CONCLUSIONS Primary care physicians' time in the EHR continues to grow. While PCPs' inbox time may be stabilizing, it is still substantially higher than pre- pandemic levels. It is imperative health systems develop strategies to change the EHR workload trajectory to minimize PCPs' occupational stress and mitigate unnecessary reductions in effective physician workforce resulting from the increased EHR burden. Key words: electronic health records (EHR); workload; burnout; practice of primary care; patient portal, INTRODUCTION There are not enough hours in the day for primary care physicians (PCPs) to do all that is expected of them. It would take nearly 27 hours per day [...]

I INTRODUCTION 101 A. Literature Review 102 B. Scope And Methodology 104 II DISCUSSION 104 A. Communicative Action And Strategic Action 104 B. Application To Administrative Law 111 III CONCLUSION [...]

The Coordinating SNAP and Nutrition Supports (CSNS) grant program aims to align the Supplemental Nutrition Assistance Program (SNAP) with other federal, state, and local nutrition supports to reduce hunger among [...]

Background Degenerative cervical myelopathy (DCM) is a progressive chronic spinal cord injury estimated to affect 1 in 50 adults. Without standardised guidance, clinical research studies have selected outcomes at their discretion, often underrepresenting the disease and limiting comparability between studies. Utilising a standard minimum data set formed via multi-stakeholder consensus can address these issues. This combines processes to define a core outcome set (COS)-a list of key outcomes-and core data elements (CDEs), a list of key sampling characteristics required to interpret the outcomes. Further 'how' these outcomes should be measured and/or reported is then defined in a core measurement set (CMS). This can include a recommendation of a standardised time point at which outcome data should be reported. This study defines a COS, CDE, and CMS for DCM research. Methods and findings A minimum data set was developed using a series of modified Delphi processes. Phase 1 involved the setup of an international DCM stakeholder group. Phase 2 involved the development of a longlist of outcomes, data elements, and formation into domains. Phase 3 prioritised the outcomes and CDEs using a two-stage Delphi process. Phase 4 determined the final DCM minimal data set using a consensus meeting. Using the COS, Phase 5 finalised definitions of the measurement construct for each outcome. In Phase 6, a systematic review of the literature was performed, to scope and define the psychometric properties of measurement tools. Phase 7 used a modified Delphi process to inform the short-listing of candidate measurement tools. The final measurement set was then formed through a consensus meeting (Phase 8). To support implementation, the data set was then integrated into template clinical research forms (CRFs) for use in future clinical trials (Phase 9). In total, 28 outcomes and 6 domains (Pain, Neurological Function, Life Impact, Radiology, Economic Impact, and Adverse Events) were entered into the final COS. Thirty two outcomes and 4 domains (Individual, Disease, Investigation, and Intervention) were entered into the final CDE. Finally, 4 outcome instruments (mJOA, NDI, SF-36v2, and SAVES2) were identified for the CMS, with a recommendation for trials evaluating outcomes after surgery, to include baseline measurement and at 6 months from surgery. Conclusions The AO Spine RECODE-DCM has produced a minimum data set for use in DCM clinical trials today. These are available at https://myelopathy.org/minimum-dataset/. While it is anticipated the CDE and COS have strong and durable relevance, it is acknowledged that new measurement tools, alongside an increasing transition to study patients not undergoing surgery, may necessitate updates and adaptation, particularly with respect to the CMS., Author(s): Benjamin M. Davies 1,2,*, Xiaoyu Yang 1, Danyal Z. Khan 3,4, Oliver D. Mowforth 1,2, Alvaro Y. Touzet 5, Aria Nouri 1,6, James S. Harrop 7, Bizhan Aarabi 8, [...]