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Ouvrage récompensé par l' Académie des sciences morales et politiques. ; Mode of access: Internet.

Sixty juvenile offenders placed on probation in three provincial centers surrounding the city of Melbourne, Australia were studied over a 15 month period. A comparison of the recidivist half of this group with those who committed no further offenses revealed some striking differences in the social functioning of their respective families as well as in the latter's attitude toward their child, their acceptance or rejection of a resocialization role, and the degree of their cooperation with the probation agency. The implications for correctional policy lead to a suggestion for an intervention strategy which, depending on a family's level of functioning, utilizes degrees of family-agency partnership.

IntroductionICES is an entity in Ontario, Canada that collects and uses the personal health information (PHI) of individuals for evaluation, planning and monitoring of the provincial health system. It currently does not have legal authority to collect PHI from, or disclose PHI to, municipalities for the purpose of supporting evidence-based policymaking and enabling "Smarter Cities".
Objectives and ApproachTo assess how ICES could allow municipalities to access PHI, while maintaining strong privacy and security data protection, we first: (i) explored the legal data trust model as a vehicle for broader collection and use of municipal data, and (ii) analyzed the regulatory changes and type of framework that would enable broader access and use of PHI by municipalities. Following this and to demonstrate the value of access to ICES data for municipal planning, we identified a case project involving a municipal health stakeholder. Leveraging ICES' remote access model, two local public health analysts performed analytics on de-sensitized, individual-level data in a secure analytic environment.
ResultsWe determined that a legal data trust is not the appropriate model for the type of data sharing envisioned, but rather, a data governance and ethical use framework complimentary to a new legal regime for Smart Cities would be optimal. In Phase II the local municipal partner was able to identify several use cases for the ICES data that would support local policy making; access to these data was considered a critical enabler to improved evidence-based decision making.
Conclusion / ImplicationsAllowing municipal policy makers to use data under a complimentary framework to a new legal regime, may improve policy and produce direct economic impact for municipalities where evidence needed for decision-making is lacking; representing a practical step forward towards Smart Cities.

ABSTRACTObjectivesPrior to the launch of ICES Data & Analytic Services (DAS) in March 2014, only ICES scientists and analysts could access ICES data, and data could only be accessed at physical ICES locations. The DAS infrastructure, which allows public sector researchers to work with coded record level data remotely through a secure virtual environment, together with broader trends including high profile reports that call for increased access to data and the Ontario government's Open Data initiative, prompted ICES to launch a pilot project to explore potential DAS work with the private sector.
ApproachThree mandatory principles were established for all work with the private sector: (i) alignment with ICES' mission, vision and values; (ii) transparency; (iii) private sector work must not detract from ICES' research institute work. The pilot included: a jurisdictional scan; informal conversations with private sector organizations to determine potential services/studies of interest; extensive discussions with data partners; the selection and conduct of two pilot studies; focus groups with members of the general public and scientists; external advice on business model options; and an external evaluation of the pilot. No changes to data sharing agreements or ICES processes were required as work with the private sector and public sector are equally allowed under Ontario law.
ResultsThe two pilot studies were successfully completed. The first study "The disease burden of gout in Ontario: A real world data retrospective study" was performed by researchers at IMS Brogan (a healthcare analytic services provider) who were provided with access to coded record-level data using the DAS iDAVE environment and performed their own analyses. In the second pilot study, "The impact of adherence to biologics on healthcare resource utilization in rheumatoid arthritis", Janssen researchers established the research question and study design, and DAS staff and scientists provided advice about data holdings, performed the analyses, and provided Janssen and three government-funded decision making bodies with results tables. Research Ethics Board approval was required for both studies, and both private sector organizations are in the process of publishing findings.
ConclusionsICES was able to work with private sector organizations without compromising the three principles. Based on the evaluation of the private sector pilot, and the findings from the focus groups, ICES will begin offering limited analytic services to private sector researchers beginning June 2016 under ICES' existing corporate structure, and bring recommendations regarding ongoing operations to the ICES Board in June 2017.

ApproachAs part of a learning period to optimize the use of RWE for decision-making for drugs for rare diseases, the [organization name removed to allow for blind review] conducted an environmental scan to map real-world data in patient registries. Over 400 patient registries were identified, signaling the potential wealth of untapped information to support decision-making by linking registry data with health services data. To better understand the challenges faced by registry holders hoping to link registry data with health services data sources available in Canada, a series of interviews were conducted with several Canadian rare disease registries. In addition, a literature review was completed, and Canadian experts in epidemiology, privacy, record linkage, registry science, and health services research were consulted to inform the development of a roadmap to meet various stakeholder needs.
ResultsThe resulting roadmap consists of 8 specific steps covering topics related to registry purpose, informed consent, ethical approval, participant privacy, governance, data linkability, participant identifiability and jurisdictional requirements.
ConclusionThe roadmap is currently undergoing pilot testing by a pan-Canadian rare disease registry. A final [organization name] report and an accompanying roadmap in a checklist format to facilitate implementation will be finalized, disseminated across key stakeholders, and made publicly available.
ImplicationsWhile developed for registries, the roadmap applies to the linkage of clinical trial or cohort study data, or other systematically gathered patient-level data to health services data.

ABSTRACTObjectivesThere is a growing need to broaden access to administrative health data in order to support decision making and planning by health system stakeholders. An initiative funded by the Ontario Ministry of Health and Long-Term Care, the Applied Health Research Question (AHRQ) portfolio leverages the linked administrative health data holdings and the scientific and clinical expertise at ICES to answer questions generated by stakeholders that will have a direct impact on health care policy, planning or practice.
ApproachEligible requesters include government ministries, health care providers and planners. Requests detail the purpose of the research question, the related scientific literature, and the planned use and intended impact of the research findings. An internal review team meets monthly to adjudicate; requests demonstrably needing research findings rapidly are adjudicated on an ad hoc basis. Eligible requests are those that aim to inform evidence-based decision making, do not advocate for a particular answer and are feasible in terms of data availability. All projects are reviewed by the internal privacy office to ensure that use of the administrative health data is in accordance with both data sharing agreements and legislation governing use of personal health information. At no cost to the requesting organization, ICES scientists and research staff formulate the analysis plan, conduct the analysis and prepare the research product (data tables, a slide deck and/or a written report); and, may opt to publish noteworthy findings. All research products must be cleared for risk of re-identification prior to being shared externally.
ResultsRequests have steadily increased from 43 submissions in fiscal year 2012/13, to 59 in 2014/15 and 74 to date in 2015/16. In fiscal year 2014/15, provincial government and government agencies were the most frequent requesters (39%), followed by hospitals and other health care providers (19%), disease advocacy groups (12%) and professional associations (10%). Requests include assessment of health care utilization; health system performance and evaluation; and chronic disease prevalence and treatment. Time to complete reports varies from 5 days to 24 months, depending on project complexity and requirements. Requesters report that AHRQ research findings have influenced decision-making, policy development and health care practice; and have inspired future research.
ConclusionThis initiative demonstrates the value and feasibility of using the linked administrative health data to answer questions to meet the unique needs of health planners and policymakers, and presents an opportunity for collaboration beyond the academic research community.

ObjectivesPatient Support Programs (PSPs) provide real-world evidence on short-term outcomes and drug adherence for specialty care. These data are collected directly from patients by pharmaceutical companies. Data linkage is facilitated by a publicly-funded health research institute which houses population-based, individual-level health administrative data for 14 million Ontario residents in Canada since 1992. Linkage provides a more comprehensive and transparent view of patients' pathways and care.
ApproachIndustry-funded research conducted through the data & analytic service must align with the institute's mission, vision and values, and demonstrate a clear public benefit. For transparency, and to support broader public benefit and research access, final deliverables and analytic plans are posted on the institute's website. Privacy impact assessments are conducted to ensure ethics approvals, consent and data sharing agreements are established, prior to data importation. To safeguard privacy, designated data covenantors encrypt and link the PSP data with in-house data holdings. Analyses are performed by senior analytic staff, providing researchers with summary-level reports.
ResultsSince 2016, the institute has worked with organizations to link data for research, including PSP data on thousands of patients, enabling crucial insight into treatment patterns, drug adherence, costs and long-term outcomes.
ConclusionLinkage of privately-owned PSP data with administrative health data at the institute, provides opportunities to identify gaps in care and improve quality of research. Data challenges around bias, transparency, completeness, and comprehensiveness are minimized.
ImplicationsResults provide decision-makers and healthcare professionals with a trusted and comprehensive understanding of patient care pathways to improve health outcomes.

BackgroundIn 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.
ObjectivesCreate 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.
MethodsOur 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.
ConclusionProvincial 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.

Background and rationale There is widespread enthusiasm to improve health through the application of artificial intelligence and machine learning (AI/ML) methods to large population-level health datasets. Achieving this may require successful collaboration between institutions as well as between computer scientists (CS), machine learning researchers (MLR) and health service researchers (HSR).
Main Aim Describe lessons learned in creating the Health Artificial Data and Analysis Platform (HAIDAP) in Ontario, Canada.
Methods/Approach A partnership between a HSR institute (ICES), an AI/ML institute (Vector) and a high-performance computing center (HPC4H) was initiated in 2017 to enable the application of AI/ML methods to population-level health data for the province of Ontario (population 14M). We describe lessons learned (and being learned) following the HAIDAP's launch.
Results The HAIDAP was launched in 2019. Major learnings include: 1) importance of institutional partnerships and alignment with institutional strategies; 2) potential of joint institutional risk-sharing models; 3) need for scientific collaborations bridging disciplines around joint research projects; 4) sensitivity to different scientific cultures (e.g., academic prestige of conference proceedings for MLR vs journal publications for HSR; traditional statistical vs. ML model assumptions); 5) differences in research timeline expectations; 6) different experience with and expectations for access to de-identified routinely collected data (e.g., need for research ethics committee project approvals and privacy impact assessments); 7) developing data access models that enable greater flexibility (e.g., importing code or using open source tools); 8) broadening data access models to allow modern high-dimensional exploratory data analysis; 9) obtaining support of information/privacy regulator; 10) the hardware is the (relatively) easy part compared to other success factors.
Conclusion The HAIDAP has enabled multi-disciplinary collaborations and novel AI/ML research of Ontario's population-level health data. Collectively we have learned that additional effort is required to develop systems and processes enabling more efficient access to data and analytic tools for the analysis of administrative health data.

ICES upholds a strong reputation for generating high-quality evidence to inform policy and practice through its collaborations with a broad range of health system stakeholders including government policymakers and healthcare providers including clinicians. Supported by the Ontario Ministry of Health and Ministry of Long-Term Care, the ICES Applied Health Research Question (AHRQ) Program leverages the data holdings and, scientific and clinical expertise to generate evidence tailored to the information needs of requestors. This paper outlines the approach, process, strengths, challenges and the resulting influence and impact to the healthcare landscape in Ontario.

ObjectiveTo provide an update on the key characteristics, refinements to project flow, and future opportunities for a program providing customized research evidence to health system policymakers and providers.
ApproachWith a goal to inform health system decision making, the program answers research questions posed by health system requestors that can be answered with linked, population-based administrative data. Now in its 10th year of operation, the program has been refined over time to improve efficiency, usefulness of research products, and requestor satisfaction. Satisfaction surveys and individual-level engagements with requestors are used to collect feedback on the needs of an increasingly diverse group of requests.
ResultsWith 508 requests, 82 Data Sharing Agreements, and 257 unique requestors since its inception, the program informs governmental decision making, evaluates intervention effectiveness, and aids grassroots organizations' planning for services. Notable updates to the program include, inserting multiple opportunities for connection between the program and requestors through the project life cycle to understand goals and needs; assigning staff scientists to shepherd projects and promote efficiency; training coordinators to review privacy impact assessments; and (soon) accepting a broader range of projects to include data beyond the health sector e.g. education.
ConclusionThis program continues to improve and is an exemplar of sustainable approaches to supporting health system requestors with evidence from administrative data in program evaluation, planning and policy change.
ImplicationsBeing responsive to requestor needs has allowed the program to attract an increasingly diverse range of requesters who can obtain impactful and timely research evidence.