Browsing by Author "Eastwood, Cathy A."
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- 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 International Indicators for Assessing the Quality of ICD-coded Administrative Health Data(2020-12-22) Otero Varela, Lucia; Quan, Hude; Eastwood, Cathy A.; Walker, Robin L.; Leal, Jenine R.Introduction: Health data are generated at each patient encounter with the healthcare system worldwide, then collected and stored as administrative health data. As an example, inpatient data are coded in the hospital morbidity database using the International Classification of Diseases (ICD), which is a reference standard for reporting diseases and health conditions globally. The quality of ICD-coded data is affected by multiple factors, such as worldwide variations in ICD use and its meta-features across countries, which can hinder meaningful comparisons of morbidity data. Assessing data quality is therefore essential for the ultimate goal of improving it. Given the current lack of an international approach for, we aimed to develop a standardized method for assessing hospital morbidity data quality. Methods: First, we conducted an international online questionnaire to better understand the differences in coding practices and hospital data collection systems across countries. Second, through the combination of a comprehensive environmental scan and a Delphi consensus process, we developed a set of global data quality indicators (DQIs) for the hospital morbidity database. Results: The international questionnaire revealed variances in all aspects of ICD data collection features, including: the maximum number of coding fields allowed for diagnosis and interventions, the definition of main condition, as well as the data fields that are mandatory to capture in the hospital morbidity database. The Delphi exercise resulted in 24 DQIs, encompassing five dimensions of data quality (e.g., Relevance, Accuracy and reliability, Comparability and coherence, Timeliness, and Accessibility and clarity), and can be used to assess data quality using the same standard across countries and to highlight areas in need of improvement. Conclusion: Emphasis should be placed on standardizing ICD data collection systems and enhancing the quality of ICD-coded data. These findings could facilitate international comparisons of health data and data quality, and could serve as a guidance for policy- and decision-makers worldwide.
- 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 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 AccessSupervised consumption site enables cost savings by avoiding emergency services: a cost analysis study(2022-03-28) Khair, Shahreen; Eastwood, Cathy A.; Lu, Mingshan; Jackson, JenniferAbstract Background and aims We report on a cost analysis study, using population level data to determine the emergency service costs avoided from emergency overdose management at supervised consumption services (SCS). Design We completed a cost analysis from a payer’s perspective. In this setting, there is a single-payer model of service delivery. Setting In Calgary, Canada, ‘Safeworks Harm Reduction Program’ was established in late 2017 and offers 24/7 access to SCS. The facility is a nurse-led service, available for client drop-in. We conducted a cost analysis for the entire duration of the program from November 2017 to January 2020, a period of 2 years and 3 months. Methods We assessed costs using the following factors from government health databases: monthly operational costs of providing services for drug consumption, cost of providing ambulance pre-hospital care for clients with overdoses who could not be revived at the facility, cost of initial treatment in an emergency department, and benefit of costs averted from overdoses that were successfully managed at the SCS. Results The proportion of clients who have overdosed at the SCS has decreased steadily for the duration of the program. The number of overdoses that can be managed on site at the SCS has trended upward, currently 98%. Each overdose that is managed at the SCS produces approximately $1600 CAD in cost savings, with a savings of over $2.3 million for the lifetime of the program. Conclusion Overdose management at an SCS creates cost savings by offsetting costs required for managing overdoses using emergency department and pre-hospital ambulance services.