scholarly journals DERIVATION OF A MODEL THAT ACCURATELY PREDICTS CARDIOVASCULAR FROM NON-CARDIOVASCULAR CAUSE OF DEATH USING ADMINISTRATIVE HEALTH DATA SOURCES

2019 ◽  
Vol 35 (10) ◽  
pp. S17
Author(s):  
S. Patel ◽  
A. Khan ◽  
A. Sivaswamy ◽  
L. Ferreira-Legere ◽  
P. Austin ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Health Data ◽  
Data Sources ◽  
Administrative Health Data

Administrative health databases for addressing emerging issues in adults with CHD: a systematic review

2018 ◽  
Vol 28 (6) ◽  
pp. 844-853 ◽  
Author(s):  
Sarah Cohen ◽  
Harel Gilutz ◽  
Ariane J. Marelli ◽  
Laurence Iserin ◽  
Arriel Benis ◽  
...  
Keyword(s):  
Temporal Trends ◽  
The United States ◽  
Health Data ◽  
Data Sources ◽  
Resource Utilisation ◽  
Cross Sectional ◽  
Administrative Health Data ◽  
Patient Level ◽  
Administrative Health Databases ◽  
The Impact

AbstractThe need for population-based studies of adults with CHD has motivated the growing use of secondary analyses of administrative health data in a variety of jurisdictions worldwide. We aimed at systematically reviewing all studies using administrative health data sources for adult CHD research from 2006 to 2016. Using PubMed and Embase (1 January, 2006 to 1 January, 2016), we identified 2217 abstracts, from which 59 studies were included in this review. These comprised 12 different data sources from six countries. Of these, 55% originated in the United States of America, 28% in Canada, and 17% in Europe and Asia. No study was published before 2007, after which the number of publications grew exponentially. In all, 41% of the studies were cross-sectional and 25% were retrospective cohort studies with a wide variation in the availability of patient-level compared with hospitalisation-level episodes of care; 58% of studies from eight different data sources linked administrative data at a patient level; and 37% of studies reported validation procedures. Assessing resource utilisation and temporal trends of relevant epidemiological and outcome end points were the most reported objectives. The median impact factor of publication journals was 4.04, with an interquartile range of 3.15, 7.44. Although not designed for research purposes, administrative health databases have become powerful data sources for studying adult CHD populations because of their large sample sizes, comprehensive records, and long observation periods, providing a useful tool to further develop quality of care improvement programmes. Data linkage with electronic records will become important in obtaining more granular life-long adult CHD data. The health services nature of the data optimises the impact on policy and public health.

scholarly journals A big data analytics platform to support simulation modeling for osteoarthritis care pathways

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Shelly Vik ◽  
Behnam Sharif ◽  
Judy Seidel ◽  
Deborah A Marshall
Keyword(s):  
Big Data ◽  
Simulation Models ◽  
Health Data ◽  
Data Cube ◽  
Data Sources ◽  
Multidimensional Data ◽  
Technical Solution ◽  
Data Sets ◽  
Administrative Health Data ◽  
Systems Research

IntroductionTechnical solutions have been used in industry settings for many years to facilitate efficient management and analyses of big data sources. An initiative to apply a business solution to support development of simulation models for health systems research using nearly two decades of provincial administrative health data is described. Objectives and ApproachAdministrative data including practitioner claims, hospitalizations and ambulatory care visits for patients with a diagnosis of osteoarthritis were obtained from Alberta Health for the period 1994/95 to 2012/13. These data were incorporated into a multidimensional data cube using Microsoft SQL Server Analysis Services. Initial steps required dimensional modeling to restructure the data into a star schema format. This involved appending several data sets and defining additional reference tables to contain stratification variables and denominator data for rate calculations. The modeling expert worked closely with the information technology team throughout the process and assessed validity of the output. ResultsDevelopment and validation of the multidimensional cube occurred in iterations over approximately 12 months. The final solution resulted in an analytics platform that compiled data from approximately 400 million records obtained from four different administrative data sources. Ten dimension tables containing 102 variables provided enhanced flexibility to conduct ad hoc stratified analyses in a fraction of the time that would be required using conventional methods. For example, some analyses that previously required a day of analyst time could be performed in less than 15 minutes. The efficiencies in analytic time were achieved by the pre-aggregated measures and slice and dice capability of the data cube, which negated many intermediary steps for data extraction and time consuming iterative analyses required for development of the simulation models. Conclusion/ImplicationsThis project demonstrated how a technical solution applied in industry can be utilized to address challenges encountered by researchers related to managing and analyzing large administrative health data sets. The methods could be applied in many other research settings to facilitate access to and analyses of information using big data.

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 Imagining the postantibiotic future: the visual culture of a global health threat

2020 ◽  
pp. medhum-2020-011884
Author(s):  
Rachel Irwin
Keyword(s):  
Global Health ◽  
Visual Culture ◽  
Bacterial Infections ◽  
Health Data ◽  
Data Sources ◽  
Story Telling ◽  
Specific Data ◽  
Public Health Data ◽  
Collective Imagination ◽  
Global Data

This article is concerned with the visual culture of global health data using antimicrobial resistance (AMR) as an example. I explore how public health data and knowledge are repackaged into visualisations and presented in four contemporary genres: the animation, the TED Talk, the documentary and the satire programme. I focus on how different actors describe a world in which there are no or few antibiotics that are effective against bacterial infections. I examine the form, content and style of the visual cultural of AMR, examining how these genres tell a story of impending apocalypse while also trying to advert it. This is a form of story-telling based around the if/then structure: we are told that if we do not take certain actions today, then we will face a postantibiotic future with certain, often catastrophic, consequences. Within this if/then structure, there are various aims and objectives: the goal may be preventing further spread of AMR, building awareness or pushing for certain policy or funding decisions. These stories also serve to place or deflect blame, on animals, occupations, patients, industries and others and to highlight risks and consequences. These examples share similarities in the forms of story-telling and narrative, and in the use of specific data sources and other images. By using several Swedish examples, I demonstrate how global data are reinterpreted for a national audience. Overall, I argue that while the convergence of a dominant narrative indicates scientific consensus, this consensus also stifles our collective imagination in finding new solutions to the problem. Finally, I also use the example of AMR to discuss the need for a broader social science and humanities engagement with the visual culture of global health data.

Patient-generated health data analytics on ambulatory behaviours of people living with T2D: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Meghan Shyama Nagpal ◽  
Antonia Barbaric ◽  
Diana Sherifali ◽  
Plinio P Morita ◽  
Joseph A Cafazzo
Keyword(s):  
Scoping Review ◽  
Health Behaviours ◽  
Health Data ◽  
Clinical Findings ◽  
Machine Learning Algorithms ◽  
Data Sources ◽  
Critical Examination ◽  
Online Forums ◽  
Behavioural Patterns ◽  
Health Data Analytics

BACKGROUND Complications due to Type 2 Diabetes (T2D) can be mitigated through proper self-management which can positively change health behaviours. Technological tools are available to help people living with T2D manage their condition and such tools provide a large repository for patient-generated health data (PGHD). Analytics can provide insights about the ambulatory behaviours of people living with T2D. OBJECTIVE The objective of this review was to investigate analytical insights can be derived through PGHD with respect to ambulatory behaviours of people living with T2D. METHODS A scoping review using the Arksey & O’Malley framework was conducted in which a comprehensive search of the literature was conducted by two reviewers. Three electronic databases (PubMed, IEEE, ACM) were searched using keywords associated with diabetes, behaviours, and analytics. Several rounds of screening using predetermined inclusion and exclusion criteria were conducted and studies were selected. Critical examination took place through a descriptive-analytical narrative method and data extracted from the studies was classified into thematic categories. These categories reflect the findings of this study as per our objective. RESULTS We identified 43 studies that met the inclusion criteria for this review. While 70% of the studies examined PGHD independently, 30% of the studies combined PGHD with other data sources. The majority of these studies used machine learning algorithms to perform their analysis. Themes identified through this review include 1) predicting diabetes / obesity, 2) factors that contribute to diabetes / obesity, 3) insights from social media & online forums, 4) predicting glycemia, 5) improved adherence / outcomes, 6) analysis of sedentary behaviours, 7) deriving behavioural patterns, 8) discovering clinical findings, and 9) developing design principles. CONCLUSIONS The increased volume and availability of PGHD has the potential to derive analytical insights regarding the ambulatory behaviours of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavioural patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, that would not be available through other data sources.

Administrative health data: guilty until proven innocent

2017 ◽  
Vol 29 (1) ◽  
pp. 253-254 ◽  
Author(s):  
A. R. Levy ◽  
B. Sobolev
Keyword(s):  
Health Data ◽  
Administrative Health Data

scholarly journals General Public Views on Uses and Users of Administrative Health Data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
P. Alison Paprica ◽  
Michael Schull
Keyword(s):  
Private Sector ◽  
General Public ◽  
Personal Data ◽  
Health Data ◽  
General Information ◽  
Privacy And Security ◽  
Administrative Health Data ◽  
Potential Benefits ◽  
Public Views ◽  
Linked Administrative Health Data

ABSTRACTObjectivesHigh 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. ApproachIn 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. ResultsSupport 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. ConclusionsMembers 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.

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.

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