Browsing by Author "Otero Varela, Lucia"

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    Developing a Data Integrated COVID-19 Tracking System for Decision-Making and Public Use
    (International Journal of Popular Data Science, 2020-09-28) Krusina, Alexander; Chen, Oscar; Otero Varela, Lucia; Doktorchik, Chelsea; Avati, Vince; Knudsen, Søren; Southern, Danielle; Eastwood, Cathy; Sharma, Nishan; Williamson, Tyler
    Introduction The unprecedented COVID-19 pandemic unveiled a strong need for advanced and informative surveillance tools. The Centre for Health Informatics (CHI) at the University of Calgary took action to develop a surveillance dashboard, which would facilitate the education of the public, and answer critical questions posed by local and national government. Objectives The objective of this study was to create an interactive method of surveillance, or a “COVID-19 Tracker” for Canadian use. The Tracker offers user-friendly graphics characterizing various aspects of the current pandemic (e.g. case count, testing, hospitalizations, and policy interventions). Methods Six publicly available data sources were used, and were selected based on the frequency of updates, accuracy and types of data, and data presentation. The datasets have different levels of granularity for different provinces, which limits the information that we are able to show. Additionally, some datasets have missing entries, for which the “last observation carried forward” method was used. The website was created and hosted online, with a backend server, which is updated on a daily basis. The Tracker development followed an iterative process, as new figures were added to meet the changing needs of policy-makers. Results The resulting Tracker is a dashboard that visualizes real-time data, along with policy interventions from various countries, via user-friendly graphs with a hover option that reveals detailed information. The interactive features allow the user to customize the figures by jurisdiction, country/region, and the type of data shown. Data is displayed at the national and provincial level, as well as by health regions. Conclusions The COVID-19 Tracker offers real-time, detailed, and interactive visualizations that have the potential to shape crucial decision-making and inform Albertans and Canadians of the current pandemic.
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    Development of International Indicators for Assessing the Quality of ICD-coded Administrative Health Data
    (2020-12-22) Otero Varela, Lucia; Quan, Hude; Eastwood, Cathy A.; Walker, Robin L.; Leal, Jenine R.
    Introduction: Health data are generated at each patient encounter with the healthcare system worldwide, then collected and stored as administrative health data. As an example, inpatient data are coded in the hospital morbidity database using the International Classification of Diseases (ICD), which is a reference standard for reporting diseases and health conditions globally. The quality of ICD-coded data is affected by multiple factors, such as worldwide variations in ICD use and its meta-features across countries, which can hinder meaningful comparisons of morbidity data. Assessing data quality is therefore essential for the ultimate goal of improving it. Given the current lack of an international approach for, we aimed to develop a standardized method for assessing hospital morbidity data quality. Methods: First, we conducted an international online questionnaire to better understand the differences in coding practices and hospital data collection systems across countries. Second, through the combination of a comprehensive environmental scan and a Delphi consensus process, we developed a set of global data quality indicators (DQIs) for the hospital morbidity database. Results: The international questionnaire revealed variances in all aspects of ICD data collection features, including: the maximum number of coding fields allowed for diagnosis and interventions, the definition of main condition, as well as the data fields that are mandatory to capture in the hospital morbidity database. The Delphi exercise resulted in 24 DQIs, encompassing five dimensions of data quality (e.g., Relevance, Accuracy and reliability, Comparability and coherence, Timeliness, and Accessibility and clarity), and can be used to assess data quality using the same standard across countries and to highlight areas in need of improvement. Conclusion: Emphasis should be placed on standardizing ICD data collection systems and enhancing the quality of ICD-coded data. These findings could facilitate international comparisons of health data and data quality, and could serve as a guidance for policy- and decision-makers worldwide.
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    Evaluation of interventions to improve electronic health record documentation within the inpatient setting: a protocol for a systematic review
    (2019-02-13) Otero Varela, Lucia; Wiebe, Natalie; Niven, Daniel J; Ronksley, Paul E; Iragorri, Nicolas; Robertson, Helen L; Quan, Hude
    Abstract Background Electronic health records (EHRs) are increasing in popularity across national and international healthcare systems. Despite their augmented availability and use, the quality of electronic health records is problematic. There are various reasons for poor documentation quality within the EHR, and efforts have been made to address these areas. Previous systematic reviews have assessed intervention effectiveness within the outpatient setting or within paper documentation. This systematic review aims to assess the effectiveness of different interventions seeking to improve EHR documentation within an inpatient setting. Methods We will employ a comprehensive search strategy that encompasses four distinct themes: EHR, documentation, interventions, and study design. Four databases (MEDLINE, EMBASE, CENTRAL, and CINAHL) will be searched along with an in-depth examination of the grey literature and reference lists of relevant articles. A customized hybrid study quality assessment tool has been designed, integrating components of the Downs and Black and Newcastle-Ottawa Scales, into a REDCap data capture form to facilitate data extraction and analysis. Given the predicted high heterogeneity between studies, it may not be possible to standardize data for a quantitative comparison and meta-analysis. Thus, data will be synthesized in a narrative, semi-quantitative manner. Discussion This review will summarize the current level of evidence on the effectiveness of interventions implemented to improve inpatient EHR documentation, which could ultimately enhance data quality in administrative health databases. Systematic review registration PROSPERO CRD42017083494
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    Exploring the differences in ICD and hospital morbidity data collection features across countries: an international survey
    (2021-04-07) Otero Varela, Lucia; Doktorchik, Chelsea; Wiebe, Natalie; Quan, Hude; Eastwood, Catherine
    Abstract Background The International Classification of Diseases (ICD) is the reference standard for reporting diseases and health conditions globally. Variations in ICD use and data collection across countries can hinder meaningful comparisons of morbidity data. Thus, we aimed to characterize ICD and hospital morbidity data collection features worldwide. Methods An online questionnaire was created to poll the World Health Organization (WHO) member countries that were using ICD. The survey included questions focused on ICD meta-features and hospital data collection systems, and was distributed via SurveyMonkey using purposive and snowball sampling. Accordingly, senior representatives from organizations specialized in the topic, such as WHO Collaborating Centers, and other experts in ICD coding were invited to fill out the survey and forward the questionnaire to their peers. Answers were collated by country, analyzed, and presented in a narrative form with descriptive analysis. Results Responses from 47 participants were collected, representing 26 different countries using ICD. Results indicated worldwide disparities in the ICD meta-features regarding the maximum allowable coding fields for diagnosis, the definition of main condition, and the mandatory type of data fields in the hospital morbidity database. Accordingly, the most frequently reported answers were “reason for admission” as main condition definition (n = 14), having 31 or more diagnostic fields available (n = 12), and “Diagnoses” (n = 26) and “Patient demographics” (n = 25) for mandatory data fields. Discrepancies in data collection systems occurred between but also within countries, thereby revealing a lack of standardization both at the international and national level. Additionally, some countries reported specific data collection features, including the use or misuse of ICD coding, the national standards for coding or lack thereof, and the electronic abstracting systems utilized in hospitals. Conclusions Harmonizing ICD coding standards/guidelines should be a common goal to enhance international comparisons of health data. The current international status of ICD data collection highlights the need for the promotion of ICD and the adoption of the newest version, ICD-11. Furthermore, it will encourage further research on how to improve and standardize ICD coding.