PRISM :: Browsing by Author "Quan, Hude"

Browsing by Author "Quan, Hude"

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Open Access
A Comprehensive Case Study of an Orthopaedic Surgery Central Intake Service in the Winnipeg Regional Health Authority: A Single-Entry Model to Manage Waiting Times for Total Joint Replacement Surgery of the Hip and Knee
(2018-04-09) Damani, Zaheed; Marshall, Deborah A.; Noseworthy, Thomas Wm.; Quan, Hude; Bohm, Éric Richard; MacKean, Gail; Yelin, Edward H.; Hildebrand, Kevin A.
Background: Single-entry is an increasingly prominent waiting time management strategy in healthcare but its impact on quality of care is poorly understood. We evaluated the Winnipeg Central Intake Service (WCIS) for total joint replacement (TJR) surgery of the hip or knee, a single-entry model (SEM) to manage patients referred for TJR surgery of the hip or knee. Methods: A pre/post mixed-methods case study approach was used to measure the WCIS' influence on six dimensions of quality of care: acceptability, accessibility, appropriateness, effectiveness, efficiency, safety. Qualitative interviews were used to assess experiences of patients, family physicians, orthopaedic surgeons, surgical office assistants and WCIS project team members. A pre/post intervention cross-sectional design was used to quantitatively assess changes in the six dimensions by comparing historical and prospective cohorts. Results: Our qualitative inquiry revealed that benefits of the WCIS included streamlined processes, greater patient access, improved measurement and monitoring of outcomes. Challenges included low awareness, change readiness, and initial participation among stakeholders. Unanticipated consequences included workload increases, confusion around stakeholder expectations, and under-reporting of data by surgeons' offices. Stakeholder acceptability was conditional, not universal. Assessment of capacity and readiness to change, and efforts to increase awareness, preparedness and uptake are critical. Factors for successful implementation include clear communication, robust data collection, physician leadership, and patience by all (especially implementation teams) allowing for an effective top-down, and bottom up approach. Our quantitative analysis revealed that the WCIS reduced variability across surgeon waiting times, with modest reductions in overall waits for surgery. There was improvement in some, but not all, dimensions of quality. Waiting time was significantly improved (WT) for consult for TJR of the hip (WT1) and all WTs for TJR of the knee. Total knee replacement surgeries performed within the nationally-recommended 26-week benchmark increased by 5.9% post-WCIS. Post-surgical complication rates (safety) were lower post-WCIS. Accessibility and safety were the only quality dimensions that changed (post-WCIS for TJR of the hip and knee). Conclusion: Overall, WCIS implementation contributed to improvement in some, but not all dimensions of quality of care. This is the first study to comprehensively assess the influence of SEMs on the delivery of TJRs across all dimensions of quality. Findings of this research are generally consistent with existing literature related to SEMs and change management in healthcare. SEMs show an ability to improve accessibility without adversely affecting other dimensions of quality, albeit with conditional, not universal stakeholder acceptability. Limitations of this study include non-longitudinal cohorts, and availability and quality of data. Findings from this research can help strengthen existing SEMs and inform development of new ones for improved patient experience and outcomes and system performance.
Open Access
A data quality assessment to inform hypertension surveillance using primary care electronic medical record data from Alberta, Canada
(2021-02-02) Garies, Stephanie; McBrien, Kerry; Quan, Hude; Manca, Donna; Drummond, Neil; Williamson, Tyler
Abstract Background Hypertension is a common chronic condition affecting nearly a quarter of Canadians. Hypertension surveillance in Canada typically relies on administrative data and/or national surveys. Routinely-captured data from primary care electronic medical records (EMRs) are a complementary source for chronic disease surveillance, with longitudinal patient-level details such as sociodemographics, blood pressure, weight, prescribed medications, and behavioural risk factors. As EMR data are generated from patient care and administrative tasks, assessing data quality is essential before using for secondary purposes. This study evaluated the quality of primary care EMR data from one province in Canada within the context of hypertension surveillance. Methods We conducted a cross-sectional, descriptive study using primary care EMR data collected by two practice-based research networks in Alberta, Canada. There were 48,377 adults identified with hypertension from 53 clinics as of June 2018. Summary statistics were used to examine the quality of data elements considered relevant for hypertension surveillance. Results Patient year of birth and sex were complete, but other sociodemographic information (ethnicity, occupation, education) was largely incomplete and highly variable. Height, weight, body mass index and blood pressure were complete for most patients (over 90%), but a small proportion of outlying values indicate data inaccuracies were present. Most patients had a relevant laboratory test present (e.g. blood glucose/glycated hemoglobin, lipid profile), though a very small proportion of values were outside a biologically plausible range. Details of prescribed antihypertensive medication, such as start date, strength, dose, frequency, were mostly complete. Nearly 80% of patients had a smoking status recorded, though only 66% had useful information (i.e. categorized as current, past, or never), and less than half had their alcohol use described; information related to amount, frequency or duration was not available. Conclusions Blood pressure and prescribed medications in primary care EMR data demonstrated good completeness and plausibility, and contribute valuable information for hypertension epidemiology and surveillance. The use of other clinical, laboratory, and sociodemographic variables should be used carefully due to variable completeness and suspected data errors. Additional strategies to improve these data at the point of entry and after data extraction (e.g. statistical methods) are required.
Open Access
ACSC Indicator: testing reliability for hypertension
(2017-06-26) Walker, Robin L; Ghali, William A; Chen, Guanmin; Khalsa, Tej K; Mangat, Birinder K; Campbell, Norm R C; Dixon, Elijah; Rabi, Doreen; Jette, Nathalie; Dhanoa, Robyn; Quan, Hude
Abstract Background With high-quality community-based primary care, hospitalizations for ambulatory care sensitive conditions (ACSC) are considered avoidable. The purpose of this study was to test the inter-physician reliability of judgments of avoidable hospitalizations for one ACSC, uncomplicated hypertension, derived from medical chart review. Methods We applied the Canadian Institute for Health Information’s case definition to obtain a random sample of patients who had an ACSC hospitalization for uncomplicated hypertension in Calgary, Alberta. Medical chart review was conducted by three experienced internal medicine specialists. Implicit methods were used to judge avoidability of hospitalization using a validated 5-point scale. Results There was poor agreement among three physicians raters when judging the avoidability of 82 ACSC hospitalizations for uncomplicated hypertension (κ = 0.092). The κ also remained low when assessing agreement between raters 1 and 3 (κ = 0.092), but the κ was lower (less than chance agreement) for raters 1 and 2 (κ = -0.119) and raters 2 and 3 (κ = -0.008). When the 5-point scale was dichotomized, there was fair agreement among three raters (κ = 0.217). The proportion of ACSC hospitalizations for uncomplicated hypertension that were rated as avoidable was 32.9%, 6.1% and 26.8% for raters 1, 2, and 3, respectively. Conclusions This study found a low proportion of ACSC hospitalization were rated as avoidable, with poor to fair agreement of judgment between physician raters. This suggests that the validity and utility of this health indicator is questionable. It points to a need to abandon the use of ACSC entirely; or alternatively to work on the development of explicit criteria for judging avoidability of hospitalization for ACSC such as hypertension.
Open Access
Administrative health data in Canada: lessons from history
(BioMed Central, 2015-08-19) Lucyk, Kelsey; Lu, Mingshan; Sajobi, Tolulope; Quan, Hude
BACKGROUND: Health decision-making requires evidence from high-quality data. As one example, the Discharge Abstract Database (DAD) compiles data from the majority of Canadian hospitals to form one of the most comprehensive and highly regarded administrative databases available for health research, internationally. However, despite the success of this and other administrative health data resources, little is known about their history or the factors that have led to their success. The purpose of this paper is to provide an historical overview of administrative data for health research in Canada to contribute to the institutional memory of this field. METHODS: We conducted a qualitative content analysis of approximately 20 key sources to construct an historical narrative of administrative health data in Canada. Specifically, we searched for content related to key events, individuals, challenges, and successes in this field over time. RESULTS AND DISCUSSION: In Canada, administrative data for health research has developed in tangent with provincial research centres. Interestingly, the lessons learned from this history align with the original recommendations of the 1964 Royal Commission on Health Services: (1) standardization, and (2) centralization of data resources, that is (3) facilitated through governmental financial support. CONCLUSIONS: The overview history provided here illustrates the need for longstanding partnerships between government and academia, for classification and standardization are time-consuming and ever-evolving processes. This paper will be of interest to those who work with administrative health data, and also for countries that are looking to build or improve upon their use of administrative health data for decision-making.
Open Access
Applications of Data Science to Electronic Health Data in Health Services Research
(2022-09-06) Lee, Seungwon; Quan, Hude; Lee, Joon; Naugler, Chris Terrance; Shaheen, Abdel-Aziz; Samuel, Susan Matthew; Kaul, Padma
The application of data science to medical big data is an essential for achieving precision medicine and building a learning health system. There are many electronic health databases that contain big data in medicine. Largely, these electronic health databases are divided into administrative data, electronic medical records (EMR) data, and other types such as clinical registries. These databases were designed for different purposes and have informed the health system and stakeholders. Bringing together these datasets for data-driven research is an essential step. This manuscript-based thesis focuses on applying data science to electronic health data. The first part of this thesis explores the Allscripts Sunrise Clinical Manager (SCM) EMR data for research purposes, including its advantages and challenges. The work then proceeds to establish a linkage process of this database with other databases for establishing a disease cohort. The second part presents a systematic scoping review that explores how data science has been applied to similarly linked data to define conditions and comorbidities. Capturing comorbidities and outcomes is fundamental for studying treatment effects and tailoring medical decisions. The third and last part narrows the focus disease to non-alcoholic fatty liver disease and applies data science methodologies to answer specific disease-context related health services research questions. The completion of this work demonstrates the successful application of data science to electronic health data for health services research. Specifically, the first part paves the way for routinely using SCM EMR data for research in Alberta. Organizational procedures on data storage and transfer are also mapped out. These activities may not be of direct scientific value but are crucial for building the infrastructure capable of supporting scientific works. Second part informs the current data science applications on how to identify comorbidities and outcomes. This part sheds light on the potential directions of currently ongoing and future research. The third part successfully combines data analytics and existing health services research methods (i.e., epidemiology), and demonstrates that data tools can be developed to reduce the burden on care providers and the health system. Multidisciplinary collaboration and inputs from diverse perspectives are vital for achieving precision medicine.
Open Access
Authors’ opinions on publication in relation to annual performance assessment
(BioMed Central, 2010-03-09) Walker, Robin L.; Sykes, Lindsay; Hemmelgarn, Brenda; Quan, Hude
Open Access
Barriers to data quality resulting from the process of coding health information to administrative data: a qualitative study
(2017-11-22) Lucyk, Kelsey; Tang, Karen; Quan, Hude
Abstract Background Administrative health data are increasingly used for research and surveillance to inform decision-making because of its large sample sizes, geographic coverage, comprehensivity, and possibility for longitudinal follow-up. Within Canadian provinces, individuals are assigned unique personal health numbers that allow for linkage of administrative health records in that jurisdiction. It is therefore necessary to ensure that these data are of high quality, and that chart information is accurately coded to meet this end. Our objective is to explore the potential barriers that exist for high quality data coding through qualitative inquiry into the roles and responsibilities of medical chart coders. Methods We conducted semi-structured interviews with 28 medical chart coders from Alberta, Canada. We used thematic analysis and open-coded each transcript to understand the process of administrative health data generation and identify barriers to its quality. Results The process of generating administrative health data is highly complex and involves a diverse workforce. As such, there are multiple points in this process that introduce challenges for high quality data. For coders, the main barriers to data quality occurred around chart documentation, variability in the interpretation of chart information, and high quota expectations. Conclusions This study illustrates the complex nature of barriers to high quality coding, in the context of administrative data generation. The findings from this study may be of use to data users, researchers, and decision-makers who wish to better understand the limitations of their data or pursue interventions to improve data quality.
Open Access
Blood pressure at age 60–65 versus age 70–75 and vascular dementia: a population based observational study
(2017-10-27) Peng, Mingkai; Chen, Guanmin; Tang, Karen L; Quan, Hude; Smith, Eric E; Faris, Peter; Hachinski, Vladimir; Campbell, Norm R C
Abstract Background Vascular dementia (VaD) is the second most common form of dementia. However, there were mixed evidences about the association between blood pressure (BP) and risk of VaD in midlife and late life and limited evidence on the association between pulse pressure and VaD. Methods This is a population-based observational study. 265,897 individuals with at least one BP measurement between the ages of 60 to 65 years and 211,116 individuals with at least one BP measurement between the ages of 70 to 75 years were extracted from The Health Improvement Network in United Kingdom. Blood pressures were categorized into four groups: normal, prehypertension, stage 1 hypertension, and stage 2 hypertension. Cases of VaD were identified from the recorded clinical diagnoses. Multivariable survival analysis was used to adjust other confounders and competing risk of death. All the analysis were stratified based on antihypertensive drug use status. Multiple imputation was used to fill in missing values. Results After accounting for the competing risk of death and adjustment for potential confounders, there was an association between higher BP levels in the age 60–65 cohort with the risk of developing VaD (hazard ratio [HR] 1.53 (95% confidence interval: 1.04, 2.25) for prehypertension, 1.90 (1.30, 2.78) for stage 1 hypertension, and 2.19 (1.48, 3.26) for stage 2 hypertension) in the untreated group. There was no statistically significant association between BP levels and VaD in the treated group in the age 60–65 cohort and age 70–75 cohort. Analysis on Pulse Pressure (PP) stratified by blood pressure level showed that PP was not independently associated with VaD. Conclusion High BP between the ages of 60 to 65 years is a significant risk for VaD in late midlife. Greater efforts should be placed on early diagnosis of hypertension and tight control of BP for hypertensive patients for the prevention of VaD.
Open Access
Building Knowledge about Health Services Utilization by Refugees
(2010) Kiss, Valerie Eva; Quan, Hude
Open Access
Canadian in-hospital mortality for patients with emergency-sensitive conditions: a retrospective cohort study
(2019-10-22) Berthelot, Simon; Lang, Eddy S; Quan, Hude; Stelfox, Henry T
Abstract Background The emergency department (ED) sensitive hospital standardized mortality ratio (ED-HSMR) measures risk-adjusted mortality for patients admitted to hospital with conditions for which ED care may improve health outcomes. This study aimed to describe in-hospital mortality across Canadian provinces using the ED-HSMR. Methods Hospital discharge data were analyzed from April 2009 to March 2012. The ED-HSMR was calculated as the ratio of observed deaths among patients with emergency-sensitive conditions in a hospital during a year (2010–11 or 2011–12) to the expected deaths for the same patients during the reference year (2009–10), multiplied by 100. The expected deaths were estimated using predictive models fitted from the reference year. Aggregated provincial ED-HSMR values were calculated. A HSMR value above or below 100 respectively means that more or fewer deaths than expected occurred within a province. Results During the study period, 1,335,379 patients were admitted to hospital in Canada with an emergency-sensitive condition as the most responsible diagnosis. More in-hospital deaths (95% confidence interval) than expected were respectively observed for the years 2010–11 and 2011–12 in Newfoundland [124.3 (116.3–132.6); & 117.6 (110.1–125.5)] and Nova Scotia [116.4 (110.7–122.5) & 108.7 (103.0–114.5)], while mortality was as expected in Prince Edward Island [99.9 (86.5–114.8) & 100.7 (87.5–115.3)] and Manitoba [99.2 (94.5–104.1) & 98.3 (93.5–103.3)], and less than expected in all other provinces and territories. Conclusions Our study revealed important variation in risk-adjusted mortality for patients admitted to hospital with emergency-sensitive conditions among Canadian provinces. The ED-HSMR may be a useful outcome indicator to complement existing process indicators in measuring ED performance. Trial registration N/A – Retrospective cohort study.
Open Access
Canadian Pregnancy Outcomes in Rheumatoid Arthritis and Systemic Lupus Erythematosus
(Hindawi Publishing Corporation, 2011-08-15) Barnabe, Cheryl; Faris, Peter D.; Quan, Hude
Open Access
Canadian Pregnancy Outcomes in Rheumatoid Arthritis and Systemic Lupus Erythematosus
(2011-10-19) Barnabe, Cheryl; Faris, Peter D.; Quan, Hude
Objective. To describe obstetrical and neonataloutcomes in Canadian women with rheumatoid arthritis (RA) orsystemic lupus erythematosus (SLE). Methods. Anadministrative database of hospitalizations for neonatal delivery(1998–2009) from Calgary, Alberta was searched to identifywomen with RA (38 pregnancies) or SLE (95 pregnancies), and womenfrom the general population matched on maternal age and year ofdelivery (150 and 375 pregnancies, resp.). Conditionallogistic regression was used to calculate odds ratios (OR) formaternal and neonatal outcomes, adjusting for parity. Results. Women with SLE had increased odds forpreeclampsia or eclampsia (SLE OR 2.16 (95% CI 1.10–4.21;); RA OR 2.33 (95% CI 0.76–7.14; )). Women with SLEhad increased odds for cesarean section after adjustment fordysfunctional labour, instrumentation and previous cesareansection (OR 3.47 (95% CI 1.67–7.22; )). Neonates born towomen with SLE had increased odds of prematurity (SLE OR 6.17(95% CI 3.28–11.58; ); RA OR 2.66 (95% CI 0.90–7.84;)) and of SGA (SLE OR 2.54 (95% CI 1.42–4.55; ); RAOR 2.18 (95% CI 0.84–5.66; )) after adjusting for maternalhypertension. There was no excess risk of congenital defects inneonates. Conclusions. There is increased obstetrical and neonatal morbidityin Canadian women with RA or SLE.
Open Access
Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing
(2023-09-02) Pan, Jie; Zhang, Zilong; Peters, Steven R.; Vatanpour, Shabnam; Walker, Robin L.; Lee, Seungwon; Martin, Elliot A.; Quan, Hude
Abstract Background Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders’ abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes. Methods CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients’ chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervised machine learning (ML) methods: random forest and XGBoost. Using chart review as the reference standard, we compared the model performances with those of the commonly applied International Classification of Diseases (ICD-10-CA) codes, on the metrics of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Result Of the study sample (n = 3036), the prevalence of CeVD was 11.8% (n = 360); the median patient age was 63; and females accounted for 50.3% (n = 1528) based on chart data. Among 49 extracted clinical documents from the EMR, four document types were identified as the most influential text sources for identifying CeVD disease (“nursing transfer report,” “discharge summary,” “nursing notes,” and “inpatient consultation.”). The best performing NLP model was XGBoost, combining the Unified Medical Language System concepts extracted by cTAKES (e.g., top-ranked concepts, “Cerebrovascular accident” and “Transient ischemic attack”), and the term frequency-inverse document frequency vectorizer. Compared with ICD codes, the model achieved higher validity overall, such as sensitivity (25.0% vs 70.0%), specificity (99.3% vs 99.1%), PPV (82.6 vs. 87.8%), and NPV (90.8% vs 97.1%). Conclusion The NLP algorithm developed in this study performed better than the ICD code algorithm in detecting CeVD. The NLP models could result in an automated EMR tool for identifying CeVD cases and be applied for future studies such as surveillance, and longitudinal studies.
Open Access
Child health insurance coverage: a survey among temporary and permanent residents in Shanghai
(BioMed Central, 2008-11-17) Lu, Mingshan; Zhang, Jing; Ma, Jin; Li, Bing; Quan, Hude
Open Access
Color Coded Health Data: Factors related to willingness to share health information in South Asians
(2020-09-22) Naeem, Iffat; Chowdhury, Tanvir T; Quan, Hude; Saini, Vineet
Background: Canada is becoming an increasing multicultural society welcoming individuals of various ethnicities. Ethnicity has become an established modifier of health in Canada, where ethnocultural communities face health disparities for multiple health outcomes. To understand these health disparities further, a call for high quality health data for ethnocultural communities has been made. Since health information availability is controlled by the participant, it is important to understand the willingness to share health information by an ethnic population to increase data availability within ethnocultural communities. Objectives: The objectives of this study aimed to explore and synthesize factors associated with willingness to share health information via a rapid review of literature and qualitative interviews with (South Asian) SA participants, the largest ethnic group in Canada. Findings: Triangulating results from both the rapid review of literature and the qualitative interviews, revealed that factors associated with sharing health information operated at 3 different levels: 1) community level, 2) individual level, and 3) process level. These factors also operated through a lens that considered the cultural and sociodemographic aspect of ethnocultural communities. Conclusions: The results of this study reveal important factors associated with sharing health information for ethnocultural communities, and support the need for culturally sensitive and respectful engagement with the community, ethically sound research practices that make participants feel comfortable to share their information, and an easy and incentivised process to share their information feasibly. Future study should aim to understand and measure data-sharing partnerships between researchers and ethnocultural communities to maximize data availability for ethnic populations.
Open Access
Comparing public and private hospitals in China: Evidence from Guangdong
(BioMed Central, 2010-03-23) Eggleston, Karen; Lu, Mingshan; Li, Congdong; Wang, Jian; Yang, Zhe; Zhang, Jing; Quan, Hude
Open Access
A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients
(BioMed Central, 2009-07-30) Quach, Susan; Hennessy, Deirdre A.; Faris, Peter; Fong, Andrew; Quan, Hude; Doig, Christopher
Open Access
Comparison of risk adjustment methods in patients with liver disease using electronic medical record data
(2017-01-07) Xu, Yuan; Li, Ning; Lu, Mingshan; Dixon, Elijah; Myers, Robert P; Jolley, Rachel J; Quan, Hude
Abstract Background Risk adjustment is essential for valid comparison of patients’ health outcomes or performances of health care providers. Several risk adjustment methods for liver diseases are commonly used but the optimal approach is unknown. This study aimed to compare the common risk adjustment methods for predicting in-hospital mortality in cirrhosis patients using electronic medical record (EMR) data. Methods The sample was derived from Beijing YouAn hospital between 2010 and 2014. Previously validated EMR extraction methods were applied to define liver disease conditions, Charlson comorbidity index (CCI), Elixhauser comorbidity index (ECI), Child-Turcotte-Pugh (CTP), model for end-stage liver disease (MELD), MELD sodium (MELDNa), and five-variable MELD (5vMELD). The performance of the common risk adjustment models as well as models combining disease severity and comorbidity indexes for predicting in-hospital mortality was compared using c-statistic. Results Of 11,121 cirrhotic patients, 69.9% were males and 15.8% age 65 or older. The c-statistics across compared models ranged from 0.785 to 0.887. All models significantly outperformed the baseline model with age, sex, and admission status (c-statistic: 0.628). The c-statistics for the CCI, ECI, MELDNa, and CTP were 0.808, 0.825, 0.849, and 0.851, respectively. The c-statistic was 0.887 for combination of CTP and ECI, and 0.882 for combination of MELDNa score and ECI. Conclusions The liver disease severity indexes (i.e., CTP and MELDNa score) outperformed the CCI and ECI for predicting in-hospital mortality among cirrhosis patients using Chinese EMRs. Combining liver disease severity and comorbidities indexes could improve the discrimination power of predicting in-hospital mortality.
Open Access
Correction to: Methods for identifying 30 chronic conditions: application to administrative data
(2019-09-04) Tonelli, Marcello; Wiebe, Natasha; Fortin, Martin; Guthrie, Bruce; Hemmelgarn, Brenda R; James, Matthew T; Klarenbach, Scott W; Lewanczuk, Richard; Manns, Braden J; Ronksley, Paul; Sargious, Peter; Straus, Sharon; Quan, Hude
Following publication of the original manuscript [1], the authors noted several errors in Table 1. Details of the requested corrections are shown below:
Open Access
Data enhancement for co-morbidity measurement among patients referred for sleep diagnostic testing: an observational study
(BioMed Central, 2009-07-15) Ronksley, Paul E; Tsai, Willis H; Quan, Hude; Faris, Peter; Hemmelgarn, Brenda