Canadian Pregnancy Outcomes in Rheumatoid Arthritis and Systemic Lupus Erythematosus

Objective. To describe obstetrical and neonatal outcomes in Canadian women with rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE). Methods. An administrative database of hospitalizations for neonatal delivery (1998–2009) from Calgary, Alberta was searched to identify women with RA (38 pregnancies) or SLE (95 pregnancies), and women from the general population matched on maternal age and year of delivery (150 and 375 pregnancies, resp.). Conditional logistic regression was used to calculate odds ratios (OR) for maternal and neonatal outcomes, adjusting for parity. Results. Women with SLE had increased odds for preeclampsia or eclampsia (SLE OR 2.16 (95% CI 1.10–4.21; P = 0.024); RA OR 2.33 (95% CI 0.76–7.14; P = 0.138)). Women with SLE had increased odds for cesarean section after adjustment for dysfunctional labour, instrumentation and previous cesarean section (OR 3.47 (95% CI 1.67–7.22; P < 0.001)). Neonates born to women with SLE had increased odds of prematurity (SLE OR 6.17 (95% CI 3.28–11.58; P < 0.001); RA OR 2.66 (95% CI 0.90–7.84; P = 0.076)) and of SGA (SLE OR 2.54 (95% CI 1.42–4.55; P = 0.002); RA OR 2.18 (95% CI 0.84–5.66; P = 0.108)) after adjusting for maternal hypertension. There was no excess risk of congenital defects in neonates. Conclusions. There is increased obstetrical and neonatal morbidity in Canadian women with RA or SLE

Ethnic differences in the use of prescription drugs: a cross-sectional analysis of linked survey and administrative data

Background Evidence from the United States and Europe suggests that the use of prescription drugs may vary by ethnicity. In Canada, ethnic disparities in prescription drug use have not been as well documented as disparities in the use of medical and hospital care. We conducted a cross-sectional analysis of survey and administrative data to examine needs-adjusted rates of prescription drug use by people of different ethnic groups. Methods For 19 370 non-Aboriginal people living in urban areas of British Columbia, we linked data on self-identified ethnicity from the Canadian Community Health Survey with administrative data describing all filled prescriptions and use of medical services in 2005. We used sex-stratified multivariable logistic regression analysis to measure differences in the likelihood of filling prescriptions by drug class (antihypertensives, oral antibiotics, antidepressants, statins, respiratory drugs and nonsteroidal anti-inflammatory drugs [NSAIDs]). Models were adjusted for age, general health status, treatment-specific health status, socio-economic factors and recent immigration (within 10 years). Results We found evidence of significant needs-adjusted variation in prescription drug use by ethnicity. Compared with women and men who identified themselves as white, those who were South Asian or of mixed ethnicity were almost as likely to fill prescriptions for most types of medicines studied; moreover, South Asian men were more likely than white men to fill prescriptions for antibiotics and NSAIDs. The clearest pattern of use emerged among Chinese participants: Chinese women were significantly less likely to fill prescriptions for antihypertensives, antibiotics, antidepressants and respiratory drugs, and Chinese men for antidepressant drugs and statins. Interpretation We found some disparities in prescription drug use in the study population according to ethnic group. The nature of some of these variations suggest that ethnic differences in beliefs about pharmaceuticals may generate differences in prescription drug use; other variations suggest that there may be clinically important disparities in treatment use

Lack of patient involvement in care decisions and not receiving written discharge instructions are associated with unplanned readmissions up to one year

This retrospective, cross-sectional study examined the relationship between aspects of inpatient communication and discharge instructions and unplanned, all-cause readmissions using individual-level data up to one-year post-discharge. Patients completed the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) telephone survey within 6 weeks of hospital discharge in Alberta, Canada. Survey data were linked to corresponding inpatient records. Independent variables included selected demographic characteristics, clinical variables, and five survey questions: a) patient involvement in care decisions, b) receiving written information at discharge, c) understanding the purpose of taking medications, d) understanding responsibility for one’s health, and e) discussing help needed when returning home. From April 2011 to March 2014, 24,869 patients with a mean age of 52.8±19.8 years (range=18-100) were included. 18.6% of patients (n=4,821) experienced an unplanned hospital readmission within 43 to 365 days post-discharge. In adjusted, logistic regression models, patients who felt they were not involved in care decisions were more likely to be readmitted (OR=1.34; 95%CI: 1.17-1.53), as were patients who reported not receiving written information about signs and symptoms to watch out for post-discharge (OR=1.24; 95%CI: 1.15-1.35). Odds of readmission did not differ according to understanding of medications, understanding responsibility for one’s health, or discussion of help needed when returning home. This study provides objective data, showing that specific hospital actions are associated with unplanned readmissions. It is an example of how patient-reported measures may be linked to administrative data to drive quality improvement initiatives

Authors' opinions on publication in relation to annual performance assessment

<p>Abstract</p> <p>Background</p> <p>In the past 50 years there has been a substantial increase in the volume of published research and in the number of authors per scientific publication. There is also significant pressure exerted on researchers to produce publications. Thus, the purpose of this study was to survey corresponding authors in published medical journals to determine their opinion on publication impact in relation to performance review and promotion.</p> <p>Methods</p> <p>Cross-sectional survey of corresponding authors of original research articles published in June 2007 among 72 medical journals. Measurement outcomes included the number of publications, number of authors, authorship order and journal impact factor in relation to performance review and promotion.</p> <p>Results</p> <p>Of 687 surveys, 478 were analyzed (response rate 69.6%). Corresponding authors self-reported that number of publications (78.7%), journal impact factor (67.8%) and being the first author (75.9%) were most influential for their annual performance review and assessment. Only 17.6% of authors reported that the number of authors on a manuscript was important criteria for performance review and assessment. A higher percentage of Asian authors reported that the number of authors was key to performance review and promotion (41.4% versus 7.8 to 22.2%). compared to authors from other countries.</p> <p>Conclusions</p> <p>The number of publications, authorship order and journal impact factor were important factors for performance reviews and promotion at academic and non-academic institutes. The number of authors was not identified as important criteria. These factors may be contributing to the increase in the number of authors per publication.</p

Interactive Data Visualization of Patient Experience and Inpatient Datasets using Tableau Desktop

Introduction Data visualization is a valuable means for reporting and interpreting large datasets. It allows users to present key messages from the data in a simple way. Although many industries have adopted and embedded data visualization within their analytic teams, healthcare has only recently begun to realize this potential. Objectives and Approach The objective of this interactive presentation is to introduce Tableau (visualization software) and to provide quick, impactful examples of its use. Visualizations will be created using a free version of Tableau Desktop; available to students and academics. Two blinded datasets encompassing hospital discharges in Alberta from April 2014 to March 2016 will be used. The first dataset will contain approximately 50,000 patient experience surveys, as completed by patients within 6 weeks of their hospital discharge. The second dataset will contain inpatient records from the Discharge Abstract Database (DAD). Visualizations will be created using the individual and combined datasets. Results Following a brief description of each dataset and its respective elements, a variety of interactive visualizations will be created in real-time. From the patient experience dataset, we will be able to quickly determine which hospitals have the highest overall rating from their patients. We will then display the results from all survey questions from a single hospital; allowing for a determination of areas where care is delivered well, and to provide opportunities for improvements. From the DAD, we will highlight hospital length of stay, and its relation with gender, age group, geography, and clinical condition. In the final portion of the presentation, both datasets will be linked to examine the relationships between survey responses, patient demographics, and clinical characteristics. Conclusion/Implications Data visualization has great potential in healthcare. From this presentation, attendees will receive an introduction to its use using practical, real-world examples. The dynamic visualizations in this presentation will be created in mere minutes; a small fraction of the time typically spent by analysts to create static, paper-based reports

ICD-data collection features: an international survey

Background:&nbsp; The International Classification of Diseases (ICD) is globally used for coding morbidity and mortality statistics, however, its use, as well as the data collection features vary greatly across countries. Objective: To characterize hospital ICD-coded data collection worldwide. Methods: After an in-depth grey and academic literature review, an online survey was created to poll the 194 World Health Organization (WHO) member countries. Questions focused on hospital data collection systems and ICD-coded data features. The survey was distributed, using different methods, to potential participants that met the specific criteria, as well as organizations specialized in the topic, such as WHO Collaborating Centers (WHO-CC) or International Federation of Health Information Management Association (IFHIMA), to be forwarded to their representatives. Answers were analyzed using descriptive statistics. Results: Data from 48 respondents from 26 different countries has been collected. Results reveal worldwide use of ICD, with variations in the maximum allowable coding fields for diagnoses and interventions. For instance, in some countries there is an unlimited number of coding fields (Netherlands, Thailand and Iran), as opposed to others with only 1-6 available (Guatemala or Mauritius). Disparities also exist in the definition of a main condition, as 60% of the countries use “reason for admission” and 40% utilize “resource use”. Additionally, the mandatory type of data fields in the hospital morbidity database (e.g. patient demographics, admission type, discharge disposition, diagnoses, …) differ among countries, with diagnosis timing and physician information being the least frequently required. Conclusion: These survey data will establish the current state of ICD use internationally, which will ultimately be valuable to the WHO for the promotion of ICD and the rollout of ICD-11. Additionally, it will improve international comparisons of health data, and encourage further research on how to improve ICD coding

Canadian Approaches to Optimizing Quality of Administrative Data for Health System Use, Research, and Linkage

Theme: Data and Linkage Quality Objectives: • 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 initiative

Determining Potentially Avoidable Emergency Medical Services (EMS) Transports: A Population Level Study Using Linked Administrative Data in Alberta Health Services (AHS)

Introduction Traditionally Emergency Medical Services (EMS) transports patients to Emergency Departments (EDs). However, some patients might be appropriately managed in alternative settings outside the ED. A number of non-traditional EMS programs have evolved in Alberta, in an attempt to provide quality care through a community-based care model. Objectives and Approach The project aimed to identify and quantify potentially avoidable EMS transports to EDs in Alberta. We identified 911 responses by ground ambulance in Alberta between September 1, 2017 and December 31, 2017. Patients 18 years and over transported to EDs were linked to Alberta Provincial Registry for more accurate demographic Information, and linked to Long Term Care (LTC) and ED data to capture patient characteristics and frequency of potentially avoidable EMS transports to EDs, defined as the Canadian Triage and Acuity Scale (CTAS) Level IV and Level V in EDs not requiring inpatient admission. Results We identified 72,182 transports to EDs, of which 1 in 4 patients were rural residents. After excluding individuals<18 years and non-Alberta residents, we were able to match 58,137 of the 60,020 EMS transports to EDs (96.8%). Overall, 7,697 (13%) were triaged as less urgent with no hospital admission. Patients 65 years and over accounted for almost half (49%) of the transports in this cohort, 6% of which were for LTC clients. Percentage of potentially avoidable transports in LTC clients were similar to seniors living in the community (12%). Geographic visualization at the provincial level indicated variation across the province. In general, rural residents were more likely than urban residents to be transported to EDs with less urgent conditions (18% vs 12%). Conclusion/Implications This is the first analysis exploring potentially avoidable EMS transports to EDs in Alberta, Canada, where a comprehensive, single source of EMS system data is currently available. The project suggests opportunities for future EMS research and policies focusing on enhancing community–based care

Enhancing description of hospital-conditions with ICD-11 cluster coding: Better codes for monitoring and prevention

Introduction Exposure to health care events sometimes has unintended and undesired consequences. Health care and complications arising in the course of care are diverse and complex. Representing them comprehensively in information systems is challenging, and presently beyond the bounds of practicality for routine administrative information systems that include ICD coded data. Objectives and Approach The ICD-11 conceptual model for hospital-acquired conditions has 3 components: 1) harm to patient 2) cause or source of harm and 3) mode or mechanism. A key feature of the Quality and Safety (Q\&S) code-set in ICD-11 is that a cluster of codes is required to represent an event or injury. Use of the term ‘cluster’ is novel in ICD-11 and so is the extent and the requirement for post-coordination. The cluster required to code a Q\&S case has three codes, one for each of the three components of the model given above. Results The first component, ‘harm’, is represented by an ICD–11 diagnosis code, from any chapter of the classification. Q\&S causes or sources of harm fall into 4 types that capture events caused by substances (drugs and medicaments, etc.), procedures, devices, and a mix of other types of causes (e.g. problems associated with transfusions, incorrect diagnosis, etc.). Q\&S ‘mode or mechanism’ refers to the main way in which the ‘cause’ leads to the ‘harm’ and are specific to the type of ‘cause’ (Table 1). Table 1 - Examples of corresponding Q\&S Mode or Mechanism Cause or Source of Harm Mode or Mechanism Substance Overdose, under-dose, wrong substance. Procedure Accidental perforation of an organ during a procedure. Device Dislodgement. Malfunction. Other cause Mismatched blood. Patient dropped during transfer from OR table. Conclusion/Implications This new conceptual model for coding healthcare-related harm, dependent on the clustering of codes, has great potential to improve the clinical detail of adverse event descriptions, and the overall quality of coded health data, for better monitoring and strategies for prevention