Suchergebnisse

"Installed in Salt Lake County, state of Utah, January 1st, 1916." ; Mode of access: Internet.

"Between 1908 and 1920, Roger C. Sullivan and his political allies consolidated their control of the Chicago and Illinois Democratic parties, creating the 'Chicago Democratic machine.' Based upon new research, this first study of Sullivan and the early days of the 'machine' focuses on the daily realities of the city's power politics and the personalities who shaped them"--

Many health systems and research institutes are interested in supplementing their traditional analyses of linked data with machine learning (ML) and other artificial intelligence (AI) methods and tools. However, the availability of individuals who have the required skills to develop and/or implement ML/AI is a constraint, as there is high demand for ML/AI talent in many sectors. The three organizations presenting are all actively involved in training and capacity building for ML/AI broadly, and each has a focus on, and/or discrete initiatives for, particular trainees.
P. Alison Paprica, Vector Institute for artificial intelligence, Institute for Clinical Evaluative Sciences, University of Toronto, Canada. Alison is VP, Health Strategy and Partnerships at Vector, responsible for health strategy and also playing a lead role in "1000AIMs" – a Vector-led initiative in support of the Province of Ontario's \$30 million investment to increase the number of AI-related master's program graduates to 1,000 per year within five years.
Frank Sullivan, University of St Andrews Scotland. Frank is a family physician and an associate director of HDRUK@Scotland. Health Data Research UK \url{https://hdruk.ac.uk/} has recently provided funding to six sites across the UK to address challenging healthcare issues through use of data science. A 50 PhD student Doctoral Training Scheme in AI has also been announced. Each site works in close partnership with National Health Service bodies and the public to translate research findings into benefits for patients and populations.
Yin Aphinyanaphongs – INTREPID NYU clinical training program for incoming clinical fellows. Yin is the Director of the Clinical Informatics Training Program at NYU Langone Health. He is deeply interested in the intersection of computer science and health care and as a physician and a scientist, he has a unique perspective on how to train medical professionals for a data drive world. One version of this teaching process is demonstrated in the INTREPID clinical training program. Yin teaches clinicians to work with large scale data within the R environment and generate hypothesis and insights.
The session will begin with three brief presentations followed by a facilitated session where all participants share their insights about the essential skills and competencies required for different kinds of ML/AI application and contributions. Live polling and voting will be used at the end of the session to capture participants' view on the key learnings and take away points.
The intended outputs and outcomes of the session are:

Participants will have a better understanding of the skills and competencies required for individuals to contribute to AI applications in health in various ways
Participants will gain knowledge about different options for capacity building from targeted enhancement of the skills of clinical fellows, to producing large number of applied master's graduates, to doctoral-level training

After the session, the co-leads will work together to create a resource that summarizes the learnings from the session and make them public (though publication in a peer-reviewed journal and/or through the IPDLN website)

BackgroundAir pollution is associated with poor health and higher mortality. However, studies that link high spatial resolution air pollution data for several pollutants to individual-level data over prolonged period (>10 years) and assess multiple health outcomes are limited. In this study, we investigated the association between 16-years exposure to air pollution and all-cause and cause-specific (cardiovascular, respiratory, cancer, infectious, and mental/behavioural disorders) mortality and hospital admissions in Scotland.
MethodsIndividual-level data from the "Scottish Longitudinal Study" for 202,237 individuals (2002-2017) were linked to yearly concentrations of NO2, SO2, PM10, and PM2.5 pollutants at 1-Km2 spatial resolution using the individual's residential postcode. The association between air pollution and mortality and hospital admissions was examined using Cox Proportional-Hazards and multilevel mixed-effects negative binomial models, respectively.
ResultsIncreasing concentrations of PM2.5, PM10, and NO2 pollutants were associated with higher rates of all-cause, cardiovascular, respiratory, cancer, and infectious mortality and hospital admissions. Mortality from respiratory diseases increased by 6.2% (95%CI=2.8%-9.6%), 2.5% (95%CI=0.5%-4.5%), and 1.2% (95%CI=0.5%-1.9%) per 1 µg/m3 increase in PM2.5, PM10 and NO2 pollutants, respectively. Exposure to SO2 was mainly linked to mental/behavioural disorders mortality (HR=1.05; 95%CI=1.02-1.07) and respiratory hospital admissions (IRR=1.02; 95%CI=1.01-1.03).
ConclusionsThis study revealed a positive association between air pollution and mortality and hospital admissions in Scotland. Interventions on air pollution through stricter environmental regulations could help ease the mortality and hospital admission burden, for both physical and mental illness.

The linkage of Electronic Medical Records, Administrative and other data sources is highly valuable for research and health system monitoring. Once linked, combined resources can be analyzed to provide the answers to a variety of health questions that otherwise could not be answered. However, legislative and administrative barriers, including lengthy processes for data sharing agreements, may preclude timely linkage which is a key requirement during pandemics.
ObjectiveTo develop a method using a patient's health trajectory to probabilistically link primary care Electronic Medical Record (EMR) data with administrative and other data, without the need to transfer large datasets or identifiable information. To determine the legislative feasibility, accuracy and validity of this linkage process.
Study DesignIdentify data strings that do not directly identify patients and could be used as unique linkage variables. The data strings, which we are calling dataprints, are sufficiently similar over time in different databases. One example in Ontario, Canada, is the pattern of submitted health claims. For every patient seen by a family physician, there exists a unique pattern of dates/billing codes/diagnoses over time. These unique patterns are reasonably similar in EMR and administrative datasets. We will apply an algorithm which turns the string in the selected dataprints to an irreversibly hashed code for each person. The hashed code and no additional information will be provided by both data controllers to a trusted-third party who will determine which records match and send a mapping table to both. This enables analyses to be run in parallel, without divulging any direct person identifiers.
DatasetIndividuals contained in the University of Toronto Practice Based Research Network (UTOPIAN).
Outcome MeasuresLinkage quality will be assessed by the number of true matches and represented by sensitivity, specificity and positive and negative predictive values.
ResultsThe method will be evaluated against a validated, deterministically linked reference standard at North York General Hospital using de-identified EMR and hospital data. Results will inform processes to enable analyses across datasets while adhering to privacy legislation.

Introduction Diabetes Action Canada is developing a data repository and registry of potential research participants to support research, QI, and service to improve diabetes care. Central to the repository are pseudonymised linkable electronic medical records (EMRs) from family practices that are participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).
Objectives and Approach We sought to develop an information governance process that would engender the trust of patients and the health care professionals (HCPs) that their EMR data were being managed responsibly in the best interests of patients living with diabetes. Following an extensive literature review, we developed a principles-based governance framework and operational model, with a strong focus on patient participation in the governance process. We recruited patients through our pre-existing patient advisory circles and physicians through our partners in CPCSSN. In January 2018, we held a training workshop for Research Governing Committee (RGC) members.
Results We identified eight values-based principles to guide our governance process: transparency; accountability; following the rule of law; integrity of purpose, science and ethics; participation and inclusiveness; impartiality and independence; effectiveness; efficiency and responsiveness; and reflexivity and continuous quality improvement of process. Patients represent 50% of RGC members and HCPs 20%. Patient members provide their perspectives on: goals and outcomes of the research; the benefits and burdens among people living with diabetes; and the communication preferences of patients around recruitment. HCPs provide a deep understanding of the settings and systems in which care is provided to ensure contextual integrity of the research. Two researchers and one person with bioethics expertise provide technical and ethics perspectives on data requests.
Conclusion/Implications Governance must go beyond legal compliance to ensure a 'social licence' for the use of the data. In part, we address this through our guiding principles, our emphasis on patient and healthcare provider perspectives, and focus on research that is scientifically sound, ethically robust and in the public interest.

This work was supported by the Academy of Medical Sciences, the Wellcome Trust, the Government Department of Business, Energy and Industrial Strategy, the British Heart Foundation Diabetes UK, and the Global Challenges Research Fund (Grant number SBF004\1093 awarded to KK). ; Objectives Multimorbidity—the co-occurrence of at least two chronic diseases in an individual—is an important public health challenge in ageing societies. The vast majority of multimorbidity research takes a cross-sectional approach, but longitudinal approaches to understanding multimorbidity are an emerging research area, being encouraged by multiple funders. To support development in this research area, the aim of this study is to scope the methodological approaches and substantive findings of studies that have investigated longitudinal multimorbidity trajectories. Design We conducted a systematic search for relevant studies in four online databases (Medline, Scopus, Web of Science and Embase) in May 2020 using predefined search terms and inclusion and exclusion criteria. The search was complemented by searching reference lists of relevant papers. From the selected studies, we systematically extracted data on study methodology and findings and summarised them in a narrative synthesis. Results We identified 35 studies investigating multimorbidity longitudinally, all published in the last decade, and predominantly in high-income countries from the Global North. Longitudinal approaches employed included constructing change variables, multilevel regression analysis (eg, growth curve modelling), longitudinal group-based methodologies (eg, latent class modelling), analysing disease transitions and visualisation techniques. Commonly identified risk factors for multimorbidity onset and progression were older age, higher socioeconomic and area-level deprivation, overweight and poorer health behaviours. Conclusion The nascent research area employs a diverse range of longitudinal approaches that characterise accumulation and disease combinations and to a lesser extent disease sequencing and progression. Gaps include understanding the long-term, life course determinants of different multimorbidity trajectories, and doing so across diverse populations, including those from low-income and middle-income countries. This can provide a detailed picture of morbidity development, with important implications from a clinical and intervention perspective. ; Publisher PDF ; Peer reviewed