Cumming School of Medicine

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https://prism.ucalgary.ca/handle/1880/100023

The University of Calgary Faculty of Medicine was established in 1967 and renamed the Cumming School of Medicine in 2014. The Cumming School of Medicine is a national research leader in brain and mental health, chronic diseases and cardiovascular sciences.

Cumming School of Medicine

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Browsing Cumming School of Medicine by Subject "Administrative data"

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    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.
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    Clinical factors contributing to high cost hospitalizations in a Canadian tertiary care centre
    (BioMed Central, 2017-11-25) Rashidi, Babak; Kobewka, Daniel M; Campbell, David J T; Forster, Alan J; Ronksley, Paul E
    Background Like much of the developed world, healthcare costs in Canada are rising. A small proportion of patients account for a large proportion of healthcare spending and much of this spending occurs in acute care settings. The purpose of our study was to determine potentially modifiable factors related to care processes that contribute to high-cost admissions. Methods Using a mixed-methods study design, factors contributing to high-cost admissions were identified from literature and case review. We defined pre- and post-admission factors contributing to high-cost admissions. Pre-admission factors included reason for admission (e.g. complex medical, elective surgery, trauma, etc.). Post-admission factors included medical complications, disposition delays, clinical services delays, and inefficient clinical decision-making. We selected a random sample of admissions in the top decile of inpatient cost from the Ottawa Hospital between January 1 and December 31, 2010. A single reviewer classified cases based on the pre- and post-admission factors. We combined this information with data derived from the Ottawa Hospital Data Warehouse to describe patient-level clinical and demographic characteristics and costs incurred. Results We reviewed 200 charts which represents ~5% of all high cost admissions within the Ottawa Hospital in 2010. Post-admission factors contributing to high-cost admissions were: complications (60%), disposition delays (53%), clinical service delays (39%), and inefficient clinical decision-making (13%). Further, these factors varied substantially across service delivery lines. The mean (standard deviation (SD)) cost per admission was $49,923 CDN ($45,773). The most common reason for admission was “complex medical” (49%) and the overall median (IQR) length of stay was 27 (18–48) days. Approximately 1 in 3 high cost admissions (29%) included time in the intensive care unit (ICU). Conclusions While high cost admissions often include time in ICU and have long lengths of stay, a substantial proportion of costs were attributable to complications and potentially preventable delays in care processes. These findings suggest opportunities exist to improve outcomes and reduce costs for this diverse patient population.
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    Systematic review and assessment of validated case definitions for depression in administrative data
    (BMC, 2014) Fiest, Kirsten M.; Jette, Nathalie; Quan, Hude; St Germaine-Smith, Christine; Metcalfe, Amy; Patten, Scott B.; Beck, Cynthia A.
    Background: Administrative data are increasingly used to conduct research on depression and inform health services and health policy. Depression surveillance using administrative data is an alternative to surveys, which can be more resource-intensive. The objectives of this study were to: (1) systematically review the literature on validated case definitions to identify depression using International Classification of Disease and Related Health Problems (ICD) codes in administrative data and (2) identify individuals with and without depression in administrative data and develop an enhanced case definition to identify persons with depression in ICD-coded hospital data. Methods: (1) Systematic review: We identified validation studies using ICD codes to indicate depression in administrative data up to January 2013. (2) Validation: All depression case definitions from the literature and an additional three ICD-9-CM and three ICD-10 enhanced definitions were tested in an inpatient database. The diagnostic accuracy of all case definitions was calculated [sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV)]. Results: (1) Systematic review: Of 2,014 abstracts identified, 36 underwent full-text review and three met eligibility criteria. These depression studies used ICD-9 and ICD-10 case definitions. (2) Validation: 4,008 randomly selected medical charts were reviewed to assess the performance of new and previously published depression-related ICD case definitions. All newly tested case definitions resulted in Sp >99%, PPV >89% and NPV >91%. Sensitivities were low (28-35%), but higher than for case definitions identified in the literature (1.1-29.6%). Conclusions: Validating ICD-coded data for depression is important due to variation in coding practices across jurisdictions. The most suitable case definitions for detecting depression in administrative data vary depending on the context. For surveillance purposes, the most inclusive ICD-9 & ICD-10 case definitions resulted in PPVs of 89.7% and 89.5%, respectively. In cases where diagnostic certainty is required, the least inclusive ICD-9 and -10 case definitions are recommended, resulting in PPVs of 92.0% and 91.1%. All proposed case definitions resulted in suboptimal levels of sensitivity (ranging from 28.9%-35.6%). The addition of outpatient data (such as pharmacy records) for depression surveillance is recommended and should result in improved measures of validity.