Suchergebnisse

National statistical institutes, research institutes, and commercial marketing research organizations are making an increased use of computer-assisted interviewing (CAI) systems for collecting survey data. The growing possibilities of computer hardware and software have made it possible to develop very large and complex electronic questionnaires. In consequence, it has become more and more difficult for developers, interviewers, supervisors, and managers to keep control of the content and structure of CAI instruments. The TADEQ project proposes to develop a flexible tool for documenting and analyzing electronic questionnaires. As a documentation tool, it must be able to produce a human-readable presentation of the electronic questionnaire. This article will describe the first stage of the project: the user consultation and translation of the results into a user requirement to serve as a basis for the remainder of the project.

IntroductionThree Canadian clinical-administrative hospital databases were linked to the Canadian Vital Statistics Death Database (CVSD) to provide information about patients who died following discharge from hospital as well as supplementary information about patients that died in-hospital. Quality was assessed using a guided approach and through feedback from initial users.
Objectives and ApproachThe linked datasets were created to develop and validate health care indicators and performance measures and perform outcome analyses. It is therefore imperative to evaluate the data's fitness for use. Quality was assessed by calculating coverage of deaths for all linked contributors, creating a profile of the linked dataset and analyzing issues that were identified by users. These analyses were guided by an existing Data Source Assessment Tool, which provides a set of criteria that allow for assessment across five dimensions of quality, thus allowing for appropriate determination of a given set of data's fitness for use.
ResultsDeterministic linkage of the datasets resulted in linkage rates that ranged from 66.9% to 90.9% depending on the dataset or data year. Linkage rates also varied by Canadian jurisdictions and patient cohort. Variables had good data availability with rates of 95% or higher. Initial users identified a significant number of duplicate records that were flagged to and corrected by the data supplier. 1.4\% of acute hospital deaths had discrepancies in the death date captured in the two linked sources; the vast majority had a difference of only one day. A user group and issue tracking process were created to share information about the linked data and guarantee that issues are triaged to the appropriate party and allow for timely follow up with the data supplier.
Conclusion/ImplicationsDocumentation provided by the data supplier was vital to understanding the linkage methodology and its impact on linkage rates. A guided data assessment ensured that strengths and limitations were identified and shared to support appropriate use. Feedback to the data supplier is supporting ongoing improvements to the linkage methodology.

Theme: Data and Linkage QualityObjectives:

To define health data quality from clinical, data science, and health system perspectives
To describe some of the international best practices related to quality and how they are being applied to Canada's administrative health data.
To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives)
To highlight how data linkage can be used to provide new insights into the quality of original data sources
To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials

Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI's information quality initiatives and for the development and application of clinical classifications and terminology standards.
Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI's corporate quality program that is focused on enhancing the quality of CIHI's data sources and information products and to fostering CIHI's quality culture.
Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11.
Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality.
Intended Outcomes:"What is quality health data?" The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data.
Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost.
This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source's fitness for use.
CIHI content will include:

Methods for optimizing the value of clinical-administrative data
CIHI Information Quality Framework
Reabstraction studies (e.g. physician documentation/coders' experiences)
Linkage analytics for data quality

University of Calgary content will include:

Defining/measuring health data quality
Automated methods for quality assessment and improvement
ICD-11 features and coding practices
Electronic health record initiatives

IntroductionMany Canadians prefer to remain in the community at end-of-life, and to die at home. To do so, early and integrated palliative care (PC) is needed to provide individuals with care and support services across care settings. Measuring access to PC can help to evaluate progress over time.
Objectives and ApproachThis presentation will show findings from our study on whether Canadian decedents had access to PC in the last year of life. Data from physician billings, drug claims, home care, long-term care and acute care were linked to identify decedents and PC service use. These data were also used to examine how PC may affect, or be affected by other interactions with the health system, including inpatient alternate level care days, admissions from long-term care, emergency department visits and stays in intensive care units. Gaps in data availability and quality will also be highlighted.
ResultsAbout 70% of decedents were identified using administrative health data, although there were variations across jurisdictions due to differences in data availability and quality (9%-81%). For decedents identified across care settings, few received PC in the community in their last year of life, ranging from 4% in long-term care to 12% in home care. More decedents were identified as palliative in acute care (44%) but only one-third received PC in another setting despite multiple interactions with the health system in the last year of life. Those who had integrated, community-based PC were less likely to have a stay in an intensive care unit, and more likely to die in the community. However, few Canadian decedents had access to this type of care.
Conclusion/ImplicationsData linkage identified opportunities for earlier integration of PC and improved care transitions. However, lack of common definitions of PC across sectors and jurisdictions, limitations in data availability and issues in PC coding were identified. Improvements in PC data are required to evaluate progress for the future.

IntroductionData quality assessment is a challenging facet for researches using coded administrative health data. Our previous study had demonstrated the potentials of association rule mining to assess data quality. The objective of this study is to develop and validate a set of coding association rules for data quality assessment.
Objectives and ApproachWe used the Canadian reabstracted hospital discharge abstract data (DAD) with clinical diagnosis coded in International Classification of Disease – 10th revision, Canada (ICD-10-CA) codes for rule development. The DAD data were divided into 5 age groups. Association rule mining were conducted on reabstracted DAD in each age group to extract ICD-10 coding association rules at the three and four digits levels. The rule strength was assessed using support and confidence. The rules will be reviewed by a panel of 5 physicians and 2 coding specialists to assess their appropriateness from clinical and coding perspectives using a modified Delphi rating
ResultsIn total, 975 rules at the three digits level and 822 rules at the four digits level were learned from the data. Half of the rules were in the age group of ≥65 and no rules were found in the age group of 5 to 19. The interquartile range of rule confidences were 0.112 to 0.425 in the three digits level and 0.073 to 0.222 in the four digits level. Two-thirds of rules had the diagnosis codes related to endocrine and metabolic disorders and diseases of circulatory, respiratory and genitourinary systems. The panel review will be conducted in early April and will have the final set of rules available before the conference.
Conclusion/ImplicationsThis study developed a set of validated ICD-10 coding association rules and creates a useful tool to cost-effectively assess data quality in routinely collected administrative health data.