Evidence For A Continuum Model Of Diffusion In Lipid Bilayer Membranes: Synthesis And Studies Of Macrocyclic Polyamides In Monolayer And Lipid Bilayer Systems

This research project is directed towards establishing the relationship between the surface areas of intermediately sized molecules (50-300A{dollar}\sp2{dollar}) and their lateral diffusion coefficients in lipid bilayer membranes. Synthetic methods were developed to allow for the preparation of two series of macrocyclic polyamide amphiphiles, with and without fluorescent nitrobenzoxadiazole (NBD) labels, using macrocyclic polyamine aza-crown ethers as starting materials.;The geometry of the macrocyclic polyamides were determined using variable temperature NMR, which has indicated that both the C(O)-N bond and NBD-N bond in the smallest, labelled macrocyclic polyamide exhibit partial double bond character. As a consequence of this restricted bond rotation, both the amide groups and the NBD-N groups of macrocyclic polyamides can be considered as rigid planar moieties.;The surface areas occupied by the macrocyclic polyamides at the air-water interface were determined using a Langmuir film balance, and were found to be in agreement with surface area measurements of computer molecular models based on the assumption that the amide and NBD-N groups are rigid and planar. Both monolayer and computer experiments have shown that there is a systematic trend of increasing surface area throughout the two series of macrocyclic polyamides.;Finally, the lateral diffusion coefficients of labelled macrocyclic polyamides were determined using Fluorescence Photobleaching Recovery (FPR), and the lateral diffusion coefficients of the amphiphiles were correlated with their surface areas. This correlation has shown that there is a significant dependence of lateral diffusion on the size of amphiphiles with surface areas between 30-250A{dollar}\sp2{dollar}, and is the first demonstrated dependence of lateral diffusion on surface area for amphiphiles in this size regime.;Analysis of the dependence of lateral diffusion on surface area has indicated that the lateral diffusion of macrocyclic polyamides which have surface areas smaller than or equal to that of the phospholipids which comprise the bilayer are best modelled using Free Area theory, which states that amphiphiles which are smaller than phospholipids will diffuse at the rate of lipid self diffusion. In contrast, the lateral diffusion of amphiphiles which have surface areas which are larger than that of phospholipids are best modelled using Sackman-Evans Continuum theory applied to a diffusant which does not span the bilayer membrane. This novel finding suggests that Continuum theory can be applied to the diffusion of molecules which are only slightly larger than the molecules which form the continuum

Notches on the dial: a call to action to develop plain language communication with the public about users and uses of health data

Population data science [1] researchers are not alone in recognizing the value of health and health-related data. In the era of big data, and with advent of machine learning and other artificial intelligence methods, organizations around the world are actively working to turn data into knowledge, and, in some cases, profit. The media and members of the public have taken notice, with high profile news stories about data breaches and privacy concerns [2-4] alongside some stories that call for increased use of data [5,6]. In response, public and private sector data-holding organizations and jurisdictions are turning their attention to policies, processes and regulations intended to ensure that personal data are used in ways that that the public supports. In some cases, these efforts include involving “publics” in decisions about data, such as using patient and lay person advice and other inputs to help shape policies [7-10]

Approaches to Capacity Building for Machine Learning and Artificial Intelligence Applications in Health

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

The Ontario Data Safe Haven: Bringing High Performance Computing to Population-wide Data Assets

Introduction Canadian provincial health systems have a data advantage – longitudinal population-wide data for publicly funded health services, in many cases going back 20 years or more. With the addition of high performance computing (HPC), these data can serve as the foundation for leading-edge research using machine learning and artificial intelligence. Objectives and Approach The Institute for Clinical Evaluative Sciences (ICES) and HPC4Health are creating the Ontario Data Safe Haven (ODSH) – a secure HPC cloud located within the HPC4Health physical environment at the Hospital for Sick Children in Toronto. The ODSH will allow research teams to post, access and analyze individual datasets over which they have authority, and enable linkage to Ontario administrative and other data. To start, the ODSH is focused on creating a private cloud meeting ICES’ legislated privacy and security requirements to support HPC-intensive analyses of ICES data. The first ODSH projects are partnerships between ICES scientists and machine learning. Results As of March 2018, the technological build of the ODSH was tested and completed and the privacy and security policy framework and documentation were completed. We will present the structure of the ODSH, including the architectural choices made when designing the environment, and planned functionality in the future. We will describe the experience to-date for the very first analysis done using the ODSH: the automatic mining of clinical terminology in primary care electronic medical records using deep neural networks. We will also present the plans for a high-cost user Risk Dashboard program of research, co-designed by ICES scientists and health faculty from the Vector Institute for artificial intelligence, that will make use of the ODSH beginning May 2018. Conclusion/Implications Through a partnership of ICES, HPC4Health and the Vector Institute, a secure private cloud ODSH has been created as is starting to be used in leading edge machine learning research studies that make use of Ontario’s population-wide data assets

Building a Pan-Canadian Real World Health Data Network

Background In December 2017 the Canadian Institutes of Health Research (CIHR) issued a request for proposals to develop a pan-Canadian health data platform. This platform will enable cross-jurisdictional research by facilitating the use of rich provincial and national data and ensure engagement with patients and specific populations including Indigenous partners. Academics and policy makers from across Canada operating under the banner of the Pan-Canadian Real-World Health Data Network (PRHDN) have joined forces to address this call. Objectives Create national infrastructure that is built once then made available for research, benchmarking, performance monitoring, multi-jurisdictional evaluations and inter-jurisdictional comparisons to address pressing health and social policy problems in Canada. Methods Our approach will address several issues including creating significant efficiencies in data access, streamlining cross provincial/ territorial ethics and access approvals, establishing standards for data and methods harmonization and providing innovative and privacy-conscious solutions to data access and use. The presentation will focus on the plan to create harmonized common data, algorithms and analytic protocols, and link administrative data to electronic medical records and clinical trials to create an integrated and documented infrastructure for pan-Canadian studies. Comparisons to PopMedNet and the Sentinel Initiative in the US will be made. Conclusion Provincial centres across Canada hold rich sources of health and social data that are linkable at the person-level. With the exception of standardized data managed by the Canadian Institute for Health Information (CIHI), these data are often not comparable from one province to another, thereby limiting use to single-province studies. There is growing interest in Canada in creating an environment that would enable cross-jurisdictional data sharing and analysis’ and in sharing experiences to make effective use of linkable administrative data

Integrating population-wide laboratory testing data with physician audit-and-feedback reports to improve glycemic and cholesterol control among Ontarians with diabetes

Introduction Improving the care and management of patients with diabetes, particularly those with extreme blood glucose and/or cholesterol levels, has been identified as a key priority area for healthcare in Ontario. A multi-organizational collaboration produces audit-and-feedback reports distributed to consenting primary care physicians across the province for quality improvement purposes. Objectives and Approach We examined the feasibility of linking the Ontario Laboratory Information System (OLIS), a large and nearly population-wide database of laboratory test results in Ontario, with the existing provincial audit-and-feedback reporting structure to integrate aggregated, physician-level measures of glycemic and cholesterol control among patients with diabetes. All Ontario residents alive on March 31, 2014, attached to a primary care physician, and diagnosed with diabetes for at least two years were included. These patients were linked to OLIS to extract laboratory test orders and results for glycated hemoglobin (HbA1C) and low-density lipoproteins (LDL) between April 1, 2013 and March 31, 2014. Results There were 1,108,530 diabetes patients included who were assigned to 10,085 primary care physicians. During fiscal year (FY) 2013, 70%, 64%, and 59% of diabetes patients were tested for HbA1C, LDL, and both measures, respectively. Among the 648,238 diabetes patients with at least one of each test in FY2013, 13% had a HbA1C test exceeding a threshold of 9%, 4% had a LDL test exceeding a threshold of 4 mmol/L, and 0.8% exceeded both thresholds. At the physician-level, the median (Interquartile Range) proportions of diabetes patients exceeding the testing thresholds were 12% (9%-16%) for HbA1c and 4% (2%-6%) for LDL. In a multilevel logistic regression model, there was significant between-physician variability in the proportions of diabetes patients exceeding the HbA1C (p Conclusion/Implications We developed a mechanism for integrating population-wide, clinical laboratory test results into physician audit-and-feedback reports to improve diabetes care in Ontario. Significant variation observed in the aggregated, physician-level proportions of diabetes patients testing above clinical thresholds for HbA1C and LDL highlights the importance of reporting such information to physicians

Consensus Statement on Public Involvement and Engagement with Data-Intensive Health Research.

This consensus statement reflects the deliberations of an international group of stakeholders with a range of expertise in public involvement and engagement (PI&E) relating to data-intensive health research. It sets out eight key principles to establish a secure role for PI&E in and with the research community internationally and ensure best practice in its execution. Our aim is to promote culture change and societal benefits through ensuring a socially responsible trajectory for innovations in this field.Peer reviewe

General Public Views on Uses and Users of Administrative Health Data

ABSTRACT Objectives High profile initiatives and reports highlight the potential benefits that could be realized by increasing access to health data, but do members of the general public share this view? The objective was to gain insight into the general public’s attitudes toward users and uses of administrative health data. Approach In fall 2015, four professionally-moderated focus groups with a total of 31 Ontario participants were conducted; two in Thunder Bay, two in Toronto. Participants were asked to review and comment on: general information about research based on linked administrative health data, a case study and models through which various users might use administrative health data. Results Support for research based on linked administrative health data was strongest when people agreed with the purposes for which studies were conducted. The main concerns related to the security of personal data generally (e.g., Canada Revenue Agency hacking incidents were noted) and potentially inappropriate uses of health data, particularly by the private sector (e.g., strong reservations about studies done solely or primarily with a profit motive). Participants were reassured when provided with information about the process for removing or coding identifying information from health data, and about the oversight provided by the Information and Privacy Commissioner of Ontario. However, even when fully informed of privacy and security safeguards, participants still felt that risks unavoidably increase when there are more people and organizations accessing data. Conclusions Members of general public were generally supportive of research based on linked administrative health data but with conditions, particularly when the possibility of private sector research was discussed. Notably, and citing security concerns, focus group participants preferred models that had a limited number of individuals or organizations accessing data