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BackgroundElectronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. ObjectiveThis review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. MethodsA scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. ResultsA total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. ConclusionsRecognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

BackgroundExisting administrative data patient safety indicators (PSIs) have been limited by uncertainty around the timing of onset of included diagnoses.ObjectiveWe undertook de novo PSI development through a data-driven approach that drew upon "diagnosis timing" information available in some countries' administrative hospital data.Research designAdministrative database analysis and modified Delphi rating process.SubjectsAll hospitalized adults in Canada in 2009.MeasuresWe queried all hospitalizations for ICD-10-CA diagnosis codes arising during hospital stay. We then undertook a modified Delphi panel process to rate the extent to which each of the identified diagnoses has a potential link to suboptimal quality of care. We grouped the identified quality/safety-related diagnoses into relevant clinical categories. Lastly, we queried Alberta hospital discharge data to assess the frequency of the newly defined PSI events.ResultsAmong 2,416,413 national hospitalizations, we found 2590 unique ICD-10-CA codes flagged as having arisen after admission. Seven panelists evaluated these in a 2-round review process, and identified a listing of 640 ICD-10-CA diagnosis codes judged to be linked to suboptimal quality of care and thus appropriate for inclusion in PSIs. These were then grouped by patient safety experts into 18 clinically relevant PSI categories. We then analyzed data on 2,381,652 Alberta hospital discharges from 2005 through 2012, and found that 134,299 (5.2%) hospitalizations had at least 1 PSI diagnosis.ConclusionThe resulting work creates a foundation for a new set of PSIs for routine large-scale surveillance of hospital and health system performance.

BackgroundGuidelines recommend that surgery be considered in patients with drug-resistant epilepsy, yet delays to epilepsy surgery still exist. A Web-based, evidence-informed clinical decision tool (www.toolsforepilepsy.com) was developed to help physicians determine which patients are appropriate for an epilepsy surgery evaluation. We evaluated the usability and feasibility of the tool with the intended end users in order to improve implementation into practice.MethodsUsability testing was conducted with relevant end users. After the tool was modified based on usability results, another group of end users trialed the tool in their clinical practice. This latter group of end users then participated in focus groups and semi-structured interviews to address barriers and facilitators to tool implementation. Finally, a stakeholder meeting was held with domain experts and end users to discuss further changes to the tool and implementation strategies.ResultsSix overall themes were identified through usability testing, and an additional 11 themes were identified through the focus groups and interviews. The tool was modified based on these findings, which were then presented at the stakeholder meeting of experts and end users for further refinement. The findings were also used to guide discussions of potential implementation strategies at the meeting.ConclusionThis study provides guidance on how to improve the usability of clinical decision tools by engaging end users, experts, and other key stakeholders. The modifications to the tool should facilitate its implementation in clinical practice and ultimately enhance the quality of care persons with epilepsy receive.

The World Health Organization (WHO) plans to submit the 11th revision of the International Classification of Diseases (ICD) to the World Health Assembly in 2018. The WHO is working toward a revised classification system that has an enhanced ability to capture health concepts in a manner that reflects current scientific evidence and that is compatible with contemporary information systems. In this paper, we present recommendations made to the WHO by the ICD revision's Quality and Safety Topic Advisory Group (Q&S TAG) for a new conceptual approach to capturing healthcare-related harms and injuries in ICD-coded data. The Q&S TAG has grouped causes of healthcare-related harm and injuries into four categories that relate to the source of the event: (a) medications and substances, (b) procedures, (c) devices and (d) other aspects of care. Under the proposed multiple coding approach, one of these sources of harm must be coded as part of a cluster of three codes to depict, respectively, a healthcare activity as a 'source' of harm, a 'mode or mechanism' of harm and a consequence of the event summarized by these codes (i.e. injury or harm). Use of this framework depends on the implementation of a new and potentially powerful code-clustering mechanism in ICD-11. This new framework for coding healthcare-related harm has great potential to improve the clinical detail of adverse event descriptions, and the overall quality of coded health data.