Browsing by Author "Southern, Danielle A."
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- ItemOpen AccessAcute coronary syndrome patients admitted to a cardiology vs non-cardiology service: variations in treatment & outcome(2017-05-16) O’Neill, Deirdre E; Southern, Danielle A.; Norris, Colleen M.; O’Neill, Blair J; Curran, Helen J; Graham, Michelle MAbstract Background Specialized cardiology services have contributed to reduced mortality in acute coronary syndromes (ACS). We sought to evaluate the outcomes of ACS patients admitted to non-cardiology services in Southern Alberta. Methods Retrospective chart review performed on all troponin-positive patients in the Calgary Health Region identified those diagnosed with ACS by their attending team. Patients admitted to non-cardiology and cardiology services were compared, using linked data from the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry and the Strategic Clinical Network for Cardiovascular Health and Stroke. Results From January 1, 2007 to December 31, 2008, 2105 ACS patients were identified, with 1636 (77.7%) admitted to cardiology and 469 (22.3%) to non-cardiology services. Patients admitted to non-cardiology services were older, had more comorbidities, and rarely received cardiology consultation (5.1%). Cardiac catheterization was underutilized (5.1% vs 86.4% in cardiology patients (p < 0.0001)), as was evidence-based pharmacotherapy (p < 0.0001). Following adjustment for baseline comorbidities, 30-day through 4-year mortality was significantly higher on non-cardiology vs. cardiology services (49.1% vs. 11.0% respectively at 4-years, p < 0.0001). Conclusion In a large ACS population in the Calgary Health Region, 25% were admitted to non-cardiology services. These patients had worse outcomes, despite adjustment for baseline risk factor differences. Although many patients were appropriately admitted to non-cardiology services, the low use of investigations and secondary prevention medications may contribute to poorer patient outcome. Further research is required to identify process of care strategies to improve outcomes and lessen the burden of illness for patients and the health care system.
- ItemOpen AccessAlcohol and Drug Use Disorders among Patients with Myocardial Infarction: Associations with Disparities in Care and Mortality(Public Library of Science (PLoS), 2013-09-11) Beck, Cynthia A.; Southern, Danielle A.; Saitz, Richard; Knudtson, Merril L.; Ghali, William A.
- ItemOpen AccessCoding mechanisms for diagnosis timing in the International Classification of Diseases, Version 11(2022-09-16) Sundararajan, Vijaya; Le Pogam, Marie-Annick; Southern, Danielle A.; Pincus, Harold A.; Ghali, William A.Abstract Background Diagnoses that arise after admission are of interest because they can represent complications of health care, acute conditions arising de novo, or acute decompensation of a chronic comorbidity occurring during the hospital stay. Three countries in the world have adopted diagnosis timing codes for a number of years. Their experience demonstrates the feasibility and utility of associating an International Classification of Diseases, Version 9 or International Classification of Diseases, Version 10 diagnostic code with information on diagnosis timing, either as part of a diagnostic field or as a separate field. However, diagnosis timing is not an integrated feature of these two classifications as it will be for International Classification of Diseases, Version 11. Methods We examine the different types of diagnosis timing that can be used to describe complex patients and present examples of how the new International Classification of Diseases, Version 11 codes may be used. Results Extension codes are one of the important new features of International Classification of Diseases, Version 11 and allow more specificity in diagnosis timing. Conclusion Imbedded and standardized diagnosis timing information is possible within the International Classification of Diseases, Version 11 classification system.
- ItemOpen AccessDecision algorithm for when to use the ICD-11 3-part model for healthcare harms(2022-06-07) Eastwood, Cathy A.; Khair, Shahreen; Southern, Danielle A.Abstract Accurate data collection of healthcare-related adverse events provides a foundation for quality and health system improvement. The International Classification of Diseases for Mortality and Morbidity Statistics, 11th revision (ICD-11 MMS) includes new codes to identify harm or injury and the events or actions leading to the adverse events. However, it is difficult to choose the correct codes without in-depth understanding of which event may be classified as an injury or harm. A 3-part model will be available in the ICD-11 MMS to cluster the codes for the harm or injury that occurred, the causal factors, and the mode (mechanism) involved. While field testing coding of adverse events, our team developed a decision tree (algorithm), which guides when to use the 3-part model. The decision tree now resides in the ICD-11 Reference Guide. This paper is part of a special ICD-11 paper series and outlines the steps used in the decision-tree (algorithm) and provides examples to help understand the process. While it may take coders some time to gain experience to use the 3-part model and decision-tree, the ICD-11 Reference Guide and this paper can be helpful resources to help clarify the process.
- ItemOpen AccessDevelopment of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study(2023-09-02) Wu, Guosong; Cheligeer, Cheligeer; Southern, Danielle A.; Martin, Elliot A.; Xu, Yuan; Leal, Jenine; Ellison, Jennifer; Bush, Kathryn; Williamson, Tyler; Quan, Hude; Eastwood, Cathy A.Abstract Background Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection. Methods This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administrative data, electronic medical records (EMR) free text data, and both. The performance of machine learning models was assessed by sensitivity, specificity, positive predictive value, negative predictive value, F1 score, the area under the receiver operating characteristic curve (ROC AUC) and the area under the precision–recall curve (PR AUC). In addition, a bootstrap 95% confidence interval (95% CI) was calculated. Results There were 22,059 unique patients with 27,360 hospital admissions resulting in 88,351 days of hospital stay. This included 16,561 (60.5%) TKA and 10,799 (39.5%) THA procedures. There were 235 ascertained SSIs. Of them, 77 (32.8%) were superficial incisional SSIs, 57 (24.3%) were deep incisional SSIs, and 101 (42.9%) were organ space SSIs. The incidence rates were 0.37 for superficial incisional SSIs, 0.21 for deep incisional SSIs, 0.37 for organ space and 0.58 for complex SSIs per 100 surgical procedures, respectively. The optimal XGBoost models using administrative data and text data combined achieved a ROC AUC of 0.906 (95% CI 0.835–0.978), PR AUC of 0.637 (95% CI 0.528–0.746), and F1 score of 0.79 (0.67–0.90). Conclusions Our findings suggest machine learning models derived from administrative data and EMR text data achieved high performance and can be used to automate the detection of complex SSIs.
- ItemOpen AccessExploring data reduction strategies in the analysis of continuous pressure imaging technology(2023-03-01) Peng, Mingkai; Southern, Danielle A.; Ocampo, Wrechelle; Kaufman, Jaime; Hogan, David B.; Conly, John; Baylis, Barry W.; Stelfox, Henry T.; Ho, Chester; Ghali, William A.Abstract Background Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. Objective To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. Methods Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. Results A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. Conclusions Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.
- ItemOpen AccessField testing a new ICD coding system: methods and early experiences with ICD-11 Beta Version 2018(2022-11-08) Eastwood, Cathy A.; Southern, Danielle A.; Khair, Shahreen; Doktorchik, Chelsea; Cullen, Denise; Ghali, William A.; Quan, HudeAbstract Objective A beta version (2018) of International Classification of Diseases, 11th Revision for MMS (ICD-11), needed testing. Field-testing involves real-world application of the new codes to examine usability. We describe creating a dataset and characterizing the usability of ICD-11 code set by coders. We compare ICD-11 against ICD-10-CA (Canadian modification) and a reference standard dataset of diagnoses. Real-world usability encompasses code selection and time to code a complete inpatient chart using ICD-11 compared with ICD-10-CA. Methods and results A random sample of inpatient records previously coded using ICD-10-CA was selected from hospitals in Calgary, Alberta (N = 2896). Nurses examined these charts for conditions and healthcare-related harms. Clinical coders re-coded the same charts using ICD-11 codes. Inter-rater reliability (IRR) and coding time improved with ICD-11 coding experience (23.6 to 9.9 min average per chart). Code structure comparisons and challenges encountered are described. Overall, 86.3% of main condition codes matched. Coder comments regarding duplicate codes, missing codes, code finding issues enabled improvements to the ICD-11 Browser, Coding Tool, and Reference Guide. Training is essential for solid IRR with 17,000 diagnostic categories in the new ICD-11. As countries transition to ICD-11, our coding experiences and methods can inform users for implementation or field testing.
- ItemOpen AccessInterpreting and coding causal relationships for quality and safety using ICD-11(2023-11-16) Januel, Jean-Marie; Southern, Danielle A.; Ghali, William A.Abstract Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of “connecting terms,” key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare.
- ItemOpen AccessThe measurement of progression of diabetic retinopathy(2001) Southern, Danielle A.; Fick, Gordon H.
- ItemOpen AccessThe three-part model for coding causes and mechanisms of healthcare-related adverse events(2022-02-24) Southern, Danielle A.; Harrison, James E.; Romano, Patrick S.; Le Pogam, Marie-Annick; Pincus, Harold A.; Ghali, William A.Abstract ICD-11 provides a promising new way to capture healthcare-related harm or injury. In this paper, we elaborate on the framework for describing healthcare-related events where there is a presumed causal link between an event and underlying healthcare-related factors. The three-part model for describing healthcare-related harm or injury in ICD-11 consists of (1) a healthcare-related activity that is the cause of injury or other harm (selected from Chapter 23 of ICD-11); (2) a mode or mechanism of injury or harm, related to the underlying cause (also from Chapter 23 of ICD-11); and (3) the harmful consequences of the event to the patient, selected from any of Chapters 1 through 22 of ICD-11 (most importantly, the injury or harm experienced by the patient). Concepts from these three elements are linked/clustered through postcoordination to reflect the three-part model in a single coded expression. ICD-11 contains many novel features, and the three-part model described here for healthcare-related adverse events is a notable example.