Coding Agreement on Identification of Main Resource Use Using ICD-10 and ICD-11

Introduction Main condition coding definitions in the International Classification of Disease (ICD) 10th and 11th versions are broadly defined in the current and upcoming versions of ICD, and coding health data can involve subjective coding specialist interpretation. Inconsistent coding can lead to inaccurate reporting, and lower quality data for research use. Objectives and Approach Main condition coding agreement was compared between ICD-10 and ICD-11. 730 hospital charts were randomly selected from Foothills Medical Centre in Calgary, Alberta. These charts were previously coded using ICD-10, and six professional coding specialists recoded them using ICD-11. To compare frequencies of ICD-10 to ICD-11, we used current WHO crosswalk tables to match codes. For any missing codes, manual comparison by done by a qualified reviewer. In Canada, the “main condition” is the clinically significant reason for the hospital visit. If multiple problems were present, the diagnosis using the greatest amount of resources is coded, “main resource use”. Results Overall, 730 ICD-10 coded charts were analyzed. Of these charts, 79% (577) had matching resource coding between ICD-10 and ICD-11, and 21% (153) had mismatching coding. Matching coding was either considered an exact match between definitions (23.2%, 134), or similar but not identical (often one code has greater detail, 76.8%, 443). Mismatching codes were either due to different codes for similar conditions (13.1%, 20), different codes for not similar but related conditions (43.8%, 67), or completely different codes for unrelated conditions (43.1%, 66). Conclusion/Implications ICD-10 and ICD-11 main resource codes had a high match frequency indicating consistency between coding practices and ICD definitions (577/730, 79%). Future research will aim to understand underlying causes of mismatched main resource use codes. This research will help us understand issues in coding and contribute to future ICD-11 revisions

Enhancing description of hospital-conditions with ICD-11 cluster coding: Better codes for monitoring and prevention

Introduction Exposure to health care events sometimes has unintended and undesired consequences. Health care and complications arising in the course of care are diverse and complex. Representing them comprehensively in information systems is challenging, and presently beyond the bounds of practicality for routine administrative information systems that include ICD coded data. Objectives and Approach The ICD-11 conceptual model for hospital-acquired conditions has 3 components: 1) harm to patient 2) cause or source of harm and 3) mode or mechanism. A key feature of the Quality and Safety (Q\&S) code-set in ICD-11 is that a cluster of codes is required to represent an event or injury. Use of the term ‘cluster’ is novel in ICD-11 and so is the extent and the requirement for post-coordination. The cluster required to code a Q\&S case has three codes, one for each of the three components of the model given above. Results The first component, ‘harm’, is represented by an ICD–11 diagnosis code, from any chapter of the classification. Q\&S causes or sources of harm fall into 4 types that capture events caused by substances (drugs and medicaments, etc.), procedures, devices, and a mix of other types of causes (e.g. problems associated with transfusions, incorrect diagnosis, etc.). Q\&S ‘mode or mechanism’ refers to the main way in which the ‘cause’ leads to the ‘harm’ and are specific to the type of ‘cause’ (Table 1). Table 1 - Examples of corresponding Q\&S Mode or Mechanism Cause or Source of Harm Mode or Mechanism Substance Overdose, under-dose, wrong substance. Procedure Accidental perforation of an organ during a procedure. Device Dislodgement. Malfunction. Other cause Mismatched blood. Patient dropped during transfer from OR table. Conclusion/Implications This new conceptual model for coding healthcare-related harm, dependent on the clustering of codes, has great potential to improve the clinical detail of adverse event descriptions, and the overall quality of coded health data, for better monitoring and strategies for prevention

Interpreting and coding causal relationships for quality and safety using ICD-11

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

Advancing data collection of hospital-related harms: Validity of the new ICD-11 Quality & Safety Use Case

Introduction The beta version of the new ICD-11 includes a 3-part model for coding hospital acquired conditions (harms) to enhance adverse event descriptions. This method includes code clusters for detail each condition/event (e.g. bleed), cause (e.g. anticoagulant drug), and mode (over-dose). Objectives and Approach To compare the proportion of adverse events captured in ICD-11 to clinical chart review. A large field trial of 3000 inpatient charts are being coded with ICD-11 and chart review. Hospital admissions were randomly selected between January- June 2015 for adults at 3 Calgary hospitals. Chart reviewers were nurses trained to identify 11 categories of harms. Six coding specialists were trained to code with the ICD-11 3-part model for harm description. Coding decision trees and case examples of hospital-related harms were reviewed extensively by both teams. Coding training focused on new codes, code clustering, and extension codes for cause and mode of the harm. Results Of the 1,009 records reviewed and coded using ICD-11 to date, chart reviewers and coding specialists accurately identified 49 (37%) of the same charts with documented hospital harms. Both correctly identified 797 (91\%) of cases with no harm. Detailed analysis will follow. Study case examples will demonstrate advanced features of ICD-11 and the coding rules being collaboratively developed by our team, CIHI, and and WHO representatives. Conclusion/Implications Identification of hospital-related harms was consistent between coding specialists using ICD-11 principles and clinical chart reviewers. Variation existed in determining the cause and the mode of the harm. Case examples exemplify the new 3-part model for ICD-11 description of hospital-related harms

Advancing data collection of hospital-related harms: Results from hospital discharges dually coded with ICD-10 and ICD-11

Introduction Hospital safety performance is difficult to monitor when under-coding of hospital harms is common. The beta version of ICD-11 includes a 3-part model for coding harms to enhance adverse event descriptions. This method includes code clusters to detail each condition/event (e.g. bleed), cause (e.g. anticoagulant drug), and mode (over-dose). Objectives and Approach The study objective was to compare the proportion of adverse events captured using different classification systems. A large field trial of inpatient charts, previously coded in ICD-10 were coded with ICD-11. Coding training for the new ICD-11 focused on new codes, code clustering, and extension codes for cause and mode of the harm. Sensitivity, Specificity, NPV and PPV were reported for ICD-10 compared to ICD-11. Results Of the 1,009 records reviewed and coded using ICD-11 to date, 128 cases were coded as a harm in ICD-10 using our previously published PSI work. Coders identified 88 cases with the new ICD-11. Sensitivity and specificity were as follows: 31.3% and 94.6%. ICD-11 had NPV and PPV of 45.5% and 90.5% respectively compared to ICD-10. Detailed clinical comparison of mismatched codes will be completed. Study case examples will demonstrate advanced features of ICD-11, the coding rules being collaboratively developed by our team, CIHI, and WHO representatives, and potential analytic challenges. Conclusion/Implications The new ICD-11 found 8% of hospital admission were associated with a harm. Although the sensitivity was modest, specificity is quite high and correctly Identifies those cases without a harm. Clinical review of mismatched codes will provide further detailed code comparisons

A Metadata Manifesto: The Need for Global Health Metadata

Administrative health data recorded for individual health episodes (such as births, deaths, physician visits, and hospital stays) are being widely used to study policy-relevant scientific questions about population health, health services, and quality of care. Furthermore, an increasing number of international health comparisons are being undertaken with these data. An essential pre-requisite to such international comparative work is a detailed characterization of existing international health data resources, so that they can be more readily used in comparison studies across counties. A major challenge to such international comparative work is the variability across countries in the extent, content, and validity of existing administrative data holdings. Recognizing this, we have undertaken an international pilot process of compiling detailed data about data – i.e., a “meta-data catalogue” – for existing international administrative health data holdings. The methodological process for collecting these meta-data is described here, along with some general descriptive results for selected countries included in the pilot

An administrative data merging solution for dealing with missing data in a clinical registry: adaptation from ICD-9 to ICD-10

<p>Abstract</p> <p>Background</p> <p>We have previously described a method for dealing with missing data in a prospective cardiac registry initiative. The method involves merging registry data to corresponding ICD-9-CM administrative data to fill in missing data 'holes'. Here, we describe the process of translating our data merging solution to ICD-10, and then validating its performance.</p> <p>Methods</p> <p>A multi-step translation process was undertaken to produce an ICD-10 algorithm, and merging was then implemented to produce complete datasets for 1995–2001 based on the ICD-9-CM coding algorithm, and for 2002–2005 based on the ICD-10 algorithm. We used cardiac registry data for patients undergoing cardiac catheterization in fiscal years 1995–2005. The corresponding administrative data records were coded in ICD-9-CM for 1995–2001 and in ICD-10 for 2002–2005. The resulting datasets were then evaluated for their ability to predict death at one year.</p> <p>Results</p> <p>The prevalence of the individual clinical risk factors increased gradually across years. There was, however, no evidence of either an abrupt drop or rise in prevalence of any of the risk factors. The performance of the new data merging model was comparable to that of our previously reported methodology: c-statistic = 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 (95% CI 0.780, 0.790) for the ICD-9-CM model. The two models also exhibited similar goodness-of-fit.</p> <p>Conclusion</p> <p>The ICD-10 implementation of our data merging method performs as well as the previously-validated ICD-9-CM method. Such methodological research is an essential prerequisite for research with administrative data now that most health systems are transitioning to ICD-10.</p

Training Coding Specialists for the Future: Methods and Materials for the Beta Version of ICD-11

Introduction In June 2018, the World Health Organization (WHO) will release the 11th Version of International Classification of Diseases (ICD-11). New training methods and materials are required. As a WHO Collaborating Center, with Canadian Institute for Health Information (CIHI) members, we trained 6 coding professionals for testing ICD-11 coding processes. Objectives and Approach The objective was to achieve a high level of inter-rater reliability using ICD-11 for acute care chart coding. We used Adult Learning principles with CIHI members and 6 certified coding specialists to co-create presentations, practice materials, and decision trees to teach knowledge and skill with ICD-11 tooling and content. Training involved 14 hours of interactive learning plus additional practice hours. A bank of questions and coding scenarios tested knowledge and application of ICD-11 terminology and principles. Coding was undertaken on a set of 3000 randomly selected inpatient Calgary hospital discharges as part of a large CIHR funded ICD-11 field trial. Results The coding team achieved an average score of 84% on the ICD-11 coding quiz and 0.65 (0.33 -1.0) agreement on parent code of main condition for the coding quiz scenarios.  60 inpatient charts were coded by more than one coder to test inter-rater reliability.  Agreement was ≧ 0.80 for the majority of parent codes for main condition. Coding differences may be due to unfamiliar code choices or training gaps. New code descriptions in ICD-11 enhance code selection. Challenges included training while codes were being built in the ICD-11 browser, and minimal coding rules or standards. Conclusion/Implications Recommendations include more code descriptions in the browser and rules in a reference guide, teaching from simple to complex conditions, and multiple scenarios with ‘gold standard’ codes for practice. Reference Guide, Coding Tool, and Browser recommendations have been shared with members of the WHO Morbidity and Quality & Safety Advisory groups