scholarly journals Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Alan Katz ◽  
Jennifer Enns ◽  
Sabrina T Wong ◽  
Tyler Williamson ◽  
Alexander Singer ◽  
...  
Keyword(s):  
British Columbia ◽  
Social Services ◽  
Administrative Data ◽  
Data Access ◽  
Health Data ◽  
Quality Data ◽  
Data Governance ◽  
Quality Health Care ◽  
Data Repositories ◽  
Administrative Health Data

Over the last 30 years, public investments in Canada and many other countries have created clinical and administrative health data repositories to support research on health and social services, population health and health policy. However, there is limited capacity to share and use data across jurisdictional boundaries, in part because of inefficient and cumbersome procedures to access these data and gain approval for their use in research. A lack of harmonization among variables and indicators makes it difficult to compare research among jurisdictions. These challenges affect the quality, scope, and impact of work that could be done. The purpose of this paper is to compare and contrast the data access procedures in three Canadian jurisdictions (Manitoba, Alberta and British Columbia), and to describe how we addressed the challenges presented by differences in data governance and architecture in a Canadian cross-jurisdictional research study. We characterize common stages in gaining access to administrative data among jurisdictions, including obtaining ethics approval, applying for data access from data custodians, and ensuring the extracted data is released to accredited individuals in secure data environments. We identify advantages of Manitoba’s flexible ‘stewardship’ model over the more restrictive ‘custodianship’ model in British Columbia, and highlight the importance of communication between analysts in each jurisdiction to compensate for differences in coding variables and poor quality data. Researchers and system planners must have access to and be able to make effective use of administrative health data to ensure that Canadians continue to have access to high-quality health care and benefit from effective health policies. The considerable benefits of collaborative population-based research that spans jurisdictional borders have been recognized by the Canadian Institutes for Health Research in their recent call for the creation of a National Data Platform to resolve many of the issues in harmonization and validation of administrative data elements.

scholarly journals Factors Affecting Access to Administrative Health Data for Research in Canada: A Study Protocol

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Cynthia Kendell ◽  
Adrian Levy ◽  
Geoff Porter ◽  
Elaine Gibson ◽  
Robin Urquhart
Keyword(s):  
Study Protocol ◽  
Case Study Research ◽  
Case Analysis ◽  
Data Access ◽  
Health Data ◽  
Interview Data ◽  
Data Repositories ◽  
Factors Affecting ◽  
Administrative Health Data ◽  
Group 2

IntroductionIn Canada, most provinces have established administrative health data repositories to facilitate access to these data for research. Anecdotally, researchers have described delays and substantial inter-provincial variations in the timeliness of data access approvals and receipt of data. Currently, the reasons for these delays and variations in timeliness are not well understood. This paper provides a study protocol for (1) identifying the factors affecting access to administrative health data for research within select Canadian provinces, and (2) comparing factors across provinces to assess whether and how they contribute to inter-provincial variations in access to administrative health data for research. MethodsA qualitative, multiple-case study research design will be used. Three cases will be included, representing three different provinces. For each case, data will be collected from documents and interviews. Specifically, interviews will be carried out with (1) research stakeholders, and (2) regulatory stakeholders (10 individuals/group*,2 groups/province * 3 provinces =$ 60). During within-case analysis, interview data for each stakeholder group will be analyzed separately using constant comparative analysis. Document analysis will occur iteratively, and will inform interview guide adaptation, and supplement interview data. Cross-case analysis will involve systematic comparison of findings across cases. DiscussionThis study represents the first in-depth examination of access to administrative health data in Canada. The main outcome will be an overarching mid-range theory explaining inter-provincial variations in access to administrative health data in Canada. This theory will be strengthened by the inclusion of the perspectives of both researchers and those involved in the regulation of data access. The findings from this study may be used to improve equitable and timely access to administrative health data across provinces, and may be transferable to other jurisdictions where barriers to access to administrative health data have been reported.

scholarly journals Multi-province epidemiological research using administrative data in Canada: Challenges and opportunities

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Amanda Butler ◽  
Mark Smith ◽  
Wayne Jones ◽  
Carol Adair ◽  
Simone Vigod ◽  
...  
Keyword(s):  
Health Care ◽  
Administrative Data ◽  
Data Access ◽  
Health Data ◽  
National Standards ◽  
Epidemiological Research ◽  
Data Infrastructure ◽  
Administrative Health Data ◽  
Challenges And Opportunities ◽  
Time Required

IntroductionCanada has a publicly-funded universal health care system with information systems managed by 13 provinces and territories. This context creates inconsistencies in data collection and challenges for epidemiological research conducted at the national or multi-jurisdictional level. Objectives and ApproachUsing a recent five-province research project as a case study (BC, AB, MB, ON, QC), we will discuss the strengths and challenges of using Canadian administrative health data in a multi-jurisdictional context. Our goal is to contribute to a better understanding of these challenges and the development of a more integrated and harmonized approach to conducting multi-jurisdictional research. ResultsMulti-jurisdictional data work is feasible but requires detailed coordination and extensive cooperation from all involved. There were noteable variations across provinces in this multi-province study. For example, time required to access the data varied greatly across the five provinces (from 4 to 9 months), and thus there were sequencing challenges, with some provinces being well into the analysis stage while others were still waiting for data. Access to human resources varied across provinces and in some cases led to delays in data abstraction. Cost of data (or analytic support) also varied across provinces, from $12,000 – $15,000. Critical to the success of the project was a coordinating group with expertise in both administrative health data and cross-provincial project coordination. Conclusion/ImplicationsThis project demonstrated the value of comparable data infrastructure with equitable access policies. Many of the disadvantages to multi-province projects using health care administrative data, such as potential coding errors and inconsistencies, can be managed by developing national standards and protocols, and tools that are shared for data cleaning and validation.

scholarly journals Detection of adverse drug events in e-prescribing and administrative health data: a validation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bettina Habib ◽  
Robyn Tamblyn ◽  
Nadyne Girard ◽  
Tewodros Eguale ◽  
Allen Huang
Keyword(s):  
Administrative Data ◽  
Gold Standard ◽  
Adverse Drug Events ◽  
Study Drug ◽  
Health Data ◽  
Diagnostic Code ◽  
Administrative Health Data ◽  
Potential Ades ◽  
Patient Reported

Abstract Background Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection. Methods We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons. Results Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician. Conclusion Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

scholarly journals Multi-province epidemiological research using linked administrative data: a case study from Canada.

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Amanda Leanne Butler ◽  
Mark Smith ◽  
Wayne Jones ◽  
Carol E Adair ◽  
Simone Vigod ◽  
...  
Keyword(s):  
Information Systems ◽  
Administrative Data ◽  
Research Process ◽  
Health Data ◽  
Provincial Government ◽  
Data Systems ◽  
Administrative Health Data ◽  
Healthcare Data ◽  
Research Activities

BackgroundCanada has a publicly-funded universal healthcare system with information systems managed by 13 different provinces and territories. This context creates inconsistencies in data collection and challenges for research or surveillance conducted at the national or multi-jurisdictional level. ObjectiveUsing a recent Canadian research project as a case study, we document the strengths and challenges of using administrative health data in a multi-jurisdictional context. We discuss the implications of using different health information systems and the solutions we adopted to deal with variations. Our goal is to contribute to better understanding of these challenges and the development of a more integrated and harmonized approach to conducting multi-jurisdictional research using administrative data. Context and ModelUsing data from five separate provincial healthcare data systems, we sought to create and report on a set of provincially-comparable mental health and addiction services performance indicators. In this paper, we document the research process, challenges, and solutions. Finally, we conclude by making recommendations for investment in national infrastructure that could help cut costs, broaden scope, and increase use of administrative health data that exists in Canada. ConclusionCanada has an incredible wealth of administrative data that resides in 13 territorial and provincial government systems. Navigating access and improving comparability across these systems has been an ongoing challenge for the past 20 years, but progress is being made. We believe that with some investment, a more harmonized and integrated information network could be developed that supports a broad range of surveillance and research activities with strong policy and program implications.

The creation and use of big administrative data

2019 ◽  
pp. 23-34
Author(s):  
Harvey Goldstein ◽  
Ruth Gilbert
Keyword(s):  
Administrative Data ◽  
Poor Quality ◽  
Quality Data ◽  
Data Repositories ◽  
Public And Private ◽  
The Public ◽  
Public Benefit ◽  
Public Benefits ◽  
Poor Quality Data ◽  
Few Data

his chapter addresses data linkage which is key to using big administrative datasets to improve efficient and equitable services and policies. These benefits need to weigh against potential harms, which have mainly focussed on privacy. In this chapter we argue for the public and researchers to be alert also to other kinds of harms. These include misuses of big administrative data through poor quality data, misleading analyses, misinterpretation or misuse of findings, and restrictions limiting what questions can be asked and by whom, resulting in research not achieved and advances not made for the public benefit. Ensuring that big administrative data are validly used for public benefit requires increased transparency about who has access and whose access is denied, how data are processed, linked and analysed, and how analyses or algorithms are used in public and private services. Public benefits and especially trust require replicable analyses by many researchers not just a few data controllers. Wider use of big data will be helped by establishing a number of safe data repositories, fully accessible to researchers and their tools, and independent of the current monopolies on data processing, linkage, enhancement and uses of data.

scholarly journals P083: Demographic characteristics of people experiencing homelessness presenting to emergency departments

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S94-S94
Author(s):  
I. Burcul ◽  
J. Dai ◽  
Z. Ma ◽  
S. Jamani ◽  
R. Hossain ◽  
...  
Keyword(s):  
Social Services ◽  
Emergency Departments ◽  
Health Data ◽  
Demographic Characteristics ◽  
Homeless Population ◽  
Postal Code ◽  
Administrative Health Data ◽  
Number Of Patients ◽  
Male Patients ◽  
Age Category

Introduction: Despite the visibility of the homeless population, there is limited data on the information of this patient population. Point-in-time counts and survey data from selected samples (such as those admitted to emergency shelter) have primarily been used. This literature suggests that this hard-to-reach population has high rates of presentation at emergency departments (EDs), and as such, EDs often become their main point of contact for health and social services. Leveraging this fact and administrative data we construct a crude census of homeless persons within Ontario. We further examine demographic characteristics of patients experiencing homelessness, and compare this data to findings from previous literature. Methods: All routinely collected administrative health data from EDs located within Ontario, Canada from 2010-2017 were analyzed to examine patient characteristics. Individuals experiencing homelessness were identified by a marker that was adopted in 2009 replacing their recorded postal code with an XX designation. s. Aggregating by LHIN, date and week of year, we examine the overall number of patients experiencing homelessness and number by LHIN location and seasonality. Demographic outcomes examined include age and sex. Results: 640,897 visits to the ED over 7 years were made by 39,525 unique individuals experiencing homelessness. Number of ED visits has steadily increased over 10 years in all of Ontario, despite decline in shelter use for individuals. Presentations were concentrated in large urban centres like Toronto, Ottawa and Hamilton. Fewer presentations occur in the spring and summer months and rise in the winter. Male patients presented older and in greater numbers than female patients. The modal female age of presentation is in the 20-24 age category. The modal male age of presentation is in the 25-29 age category. Older male patients were more likely to have multiple presentations. Conclusion: The utilization of administrative health data offers a novel, cost-effective method to measure demographic characteristics of people experiencing homelessness. Identifying characteristics of homeless patients through this method allows for a more complete understanding of the characteristics of a hard-to-reach population, which will allow policy makers to develop appropriate services for this sub-group. Furthermore, through analysis of trends of demographics over time, changes in the homeless population can be tracked in real-time to allow for coordination and implementation of services in a time-sensitive manner.

scholarly journals Secure data analysis environments: can we agree on criteria for “Appropriate secure access” to linked health data?

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Louisa R Jorm ◽  
Kim McGrail ◽  
J. Charles Victor ◽  
Kerina Jones ◽  
David Ford ◽  
...  
Keyword(s):  
United Kingdom ◽  
Data Analysis ◽  
Medical School ◽  
Perceived Risk ◽  
Health Data ◽  
Data Governance ◽  
Data Repositories ◽  
Secure Access ◽  
Secure Data ◽  
Sydney Australia

Overall objectives or goalMany health data linkage ecosystems across the world have designed and implemented secure data analysis environments as one of their controls to protect patient privacy and confidentiality. These have been shaped by local legislation and data governance policies, available IT infrastructure and resources, and the skills and imagination of their architects. However, at present their various features and functionalities have not been reviewed, synthesised or contrasted. Burton et al [1] have proposed 12 criteria for Data Safe Havens in health and healthcare, which they conceptualise broadly as encompassing data governance and ethics, quality and curation of data repositories, and data security. Under this definition, secure analysis environments, which may or may not be integrated with data repositories, are a component of a Data Safe Haven, addressing the criterion “Appropriate secure access to individually identifying data”. To guide those building and operating these environments, and data custodians and stewards who need to assess their fitness-for-purpose, it would be of great value to discuss and agree an aggregate term (e.g. “Secure Data Lab”) that describes them, and to develop a more detailed set of criteria for what entails “Appropriate secure access” to linked health data. The goal of this session is to describe and document the approaches that have been taken by flagship secure data analysis environments internationally, including their approaches to authentication, assigning permissions, managing the ingress and egress of files and auditing transactions, and their responses to emerging opportunities, including cloud computing and national and international data sharing. We will explore how the interplay of physical, technical and procedural controls have been combined to create existing models, and the extent to which these can balance each other and be applied with flexibility depending on perceived risk and regimes. Session structurePrior to the session, we will develop a draft set of criteria for “Appropriate secure access” to linked health data. The session will comprise presentations describing existing secure analysis environments against the draft criteria, followed by a facilitated discussion. The secure data analysis environments that will be presented include: UNSW Sydney E-Research Institutional Cloud Architecture (ERICA) PopData BC Secure Research Environment (SRE) Institute for Clinical Evaluative Sciences (ICES) Data and Analytic Virtual Environment (IDAVE) Secure Anonymised Information Linkage (SAIL) Gateway Intended output or outcomeWe will write up the outcomes of the session as a scientific paper that proposes an aggregate term for secure data analysis environments for linked health data and a set of criteria for what entails “Appropriate secure access” to linked health data. Presenters and Facilitators Professor Louisa Jorm, Centre for Big Data Research in Health, UNSW Sydney, Australia Dr Tim Churches, South Western Sydney Clinical School, UNSW Sydney, Australia Professor Kim McGrail, Population Data BC, The University of British Columbia, Vancouver, Canada J. Charles Victor, Institute for Clinical Evaluative Sciences, Toronto, Canada Dr Kerina Jones, Swansea University Medical School, Wales, United Kingdom Professor David Ford, Swansea University Medical School, Wales, United Kingdom 1. Burton PR, Murtagh MJ, Boyd A, et al. Data Safe Havens in health research and healthcare. Bioinformatics 2015; 31(20): 3241–3248

scholarly journals Prevalence and risk factors for anxiety and depression disorders in workers with work-related musculoskeletal strain or sprain in British Columbia, Canada: a comparison of men and women using administrative health data

2021 ◽  
pp. oemed-2020-106661
Author(s):  
Andrea Marie Jones ◽  
Mieke Koehoorn ◽  
Ute Bültmann ◽  
Christopher B McLeod
Keyword(s):  
Risk Factors ◽  
British Columbia ◽  
Health Data ◽  
Anxiety And Depression ◽  
Multinomial Regression ◽  
Men And Women ◽  
Administrative Health Data ◽  
Work Related ◽  
Depression Disorders ◽  
New Onset

ObjectiveTo examine the prevalence and risk factors for medically treated anxiety and depression disorders among men and women with musculoskeletal strain or sprain work injury in British Columbia, Canada.MethodsA retrospective population-based cohort of accepted workers’ compensation lost-time claims from 2000 to 2013 was constructed using linked administrative health data. Anxiety and depression disorders were identified using diagnoses from physician, hospital and pharmaceutical records. The 1-year period prevalence was estimated for the year before and the year after injury. Sociodemographic, clinical and work-related risk factors for prevalent and new onset anxiety and depression disorders were examined using multinomial regression.Results13.2% of men and 29.8% of women had medically treated anxiety, depression or both in the year before injury. Only a slight increase (~2%) in the prevalence of these disorders was observed in the year after injury. Somatic and mental comorbidities were both strong risk factors for pre-existing and new onset anxiety and depression for both men and women, but these relationships were stronger for men.ConclusionAnxiety and depression disorders including those from prior to injury are common in workers with musculoskeletal strain or sprain and are associated with a complicated clinical profile. Gender-sensitive and sex-sensitive mental healthcare is an important consideration for work disability management.

scholarly journals Linkage of whole genome sequencing with administrative health, and electronic medical record data for the study of autism spectrum disorder: Feasibility, Opportunities and Challenges

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Jennifer Brooks ◽  
Evdokia Anagnostou ◽  
Farah Rahman ◽  
Karen Tu ◽  
Lavnaya Uruthiramoorthy ◽  
...  
Keyword(s):  
Health System ◽  
Whole Genome Sequencing ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Administrative Data ◽  
Genome Sequencing ◽  
Health Data ◽  
Spectrum Disorder ◽  
Whole Genome ◽  
Administrative Health Data

IntroductionAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) that presents with a high degree of heterogeneity (e.g., co-occurrence of other NDDs and other co-morbid conditions), contributing to differential health system needs. Genetics are known to play an important role in ASD and may be associated with different disease trajectories. Objectives and ApproachIn this proof of principle project, our objective is to link >2,200 children with a confirmed diagnosis of a NDD from the Province of Ontario Neurodevelopmental (POND) Study to administrative health data and electronic medical record (EMR) data in order to identify subgroups of ASD with unique health system trajectories. POND includes detailed phenotype and whole genome sequencing (WGS) data. Identified subgroups will be characterized based on clinical phenotype and genetics. To meet this goal, consideration of WGS-specific privacy and data issues is needed to implement processes which are above and beyond traditional requirements for analyzing individual-level administrative health data. ResultsLinkage of WGS data with administrative health data is an emerging area of research. As such it has presented a number of initial challenges for our study of ASD. Privacy concerns surrounding the use of WGS data and rare-variant analysis are of particular importance. Practical issues required the need for analysts with expertise in administrative data, EMR data and genetic analyses, and specialized software and sufficient processing power to analyze WGS data. Transdisciplinary discussions of the scope and significance of research questions addressed through this linkage were crucial. The identification of genetic determinants of phenotypes and trajectories in ASD could support targeted early interventions; EMR linkage may inform algorithms to identify ASD in broader populations. These approaches could improve both patient outcome and family experience. Conclusion/ImplicationsAs the cost of genetic sequencing decreases, WGS data will become part of the routine clinical management of patients. Linkage of WGS, EMR and administrative data has tremendous potential that has largely not been realized; including population-level ASD research to improve our ability to predict long-term outcomes associated with ASD.

scholarly journals Towards Big data Governance in Cybersecurity

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Longzhi Yang ◽  
Jie Li ◽  
Noe Elisa ◽  
Tom Prickett ◽  
Fei Chao
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Access ◽  
Quality Data ◽  
Data Sets ◽  
Data Governance ◽  
Business Decisions ◽  
Regulatory Frameworks ◽  
Big Data Technologies ◽  
Cooperative Decision Making

AbstractBig data refers to large complex structured or unstructured data sets. Big data technologies enable organisations to generate, collect, manage, analyse, and visualise big data sets, and provide insights to inform diagnosis, prediction, or other decision-making tasks. One of the critical concerns in handling big data is the adoption of appropriate big data governance frameworks to (1) curate big data in a required manner to support quality data access for effective machine learning and (2) ensure the framework regulates the storage and processing of the data from providers and users in a trustworthy way within the related regulatory frameworks (both legally and ethically). This paper proposes a framework of big data governance that guides organisations to make better data-informed business decisions within the related regularity framework, with close attention paid to data security, privacy, and accessibility. In order to demonstrate this process, the work also presents an example implementation of the framework based on the case study of big data governance in cybersecurity. This framework has the potential to guide the management of big data in different organisations for information sharing and cooperative decision-making.

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