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

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

The three-part model for coding causes and mechanisms of healthcare-related adverse events

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

Area Median Income and Metropolitan Versus Nonmetropolitan Location of Care for Acute Coronary Syndromes: A Complex Interaction of Social Determinants

Background: Metropolitan versus nonmetropolitan status and area median income may independently affect care for and outcomes of acute coronary syndromes. We sought to determine whether location of care modifies the association among area income, receipt of cardiac catheterization, and mortality following an acute coronary syndrome in a universal health care system. Methods and Results: We studied a cohort of 14 012 acute coronary syndrome patients admitted to cardiology services between April 18, 2004, and December 31, 2011, in southern Alberta, Canada. We used multivariable logistic regression to determine the odds of cardiac catheterization within 1 day and 7 days of admission and the odds of 30‐day and 1‐year mortality according to area median household income quintile for patients presenting at metropolitan and nonmetropolitan hospitals. In models adjusting for area income, patients who presented at nonmetropolitan facilities had lower adjusted odds of receiving cardiac catheterization within 1 day of admission (odds ratio 0.22, 95% CI 0.11–0.46, P<0.001). Among nonmetropolitan patients, when examined by socioeconomic status, each incremental decrease in income quintile was associated with 10% lower adjusted odds of receiving cardiac catheterization within 7 days (P<0.001) and 24% higher adjusted odds of 30‐day mortality (P=0.008) but no significant difference for 1‐year mortality (P=0.12). There were no differences in adjusted mortality among metropolitan patients. Conclusion: Within a universal health care system, the association among area income and receipt of cardiac catheterization and 30‐day mortality differed depending on the location of initial medical care for acute coronary syndromes. Care protocols are required to improve access to care and outcomes in patients from low‐income nonmetropolitan communities

ICD-11 for quality and safety: overview of the who quality and safety topic advisory group

This paper outlines the approach that the WHO's Family of International Classifications (WHO-FIC) network is undertaking to create ICD-11. We also outline the more focused work of the Quality and Safety Topic Advisory Group, whose activities include the following: (i) cataloguing existing ICD-9 and ICD-10 quality and safety indicators; (ii) reviewing ICD morbidity coding rules for main condition, diagnosis timing, numbers of diagnosis fields and diagnosis clustering; (iii) substantial restructuring of the health-care related injury concepts coded in the ICD-10 chapters 19/20, (iv) mapping of ICD-11 quality and safety concepts to the information model of the WHO's International Classification for Patient Safety and the AHRQ Common Formats; (v) the review of vertical chapter content in all chapters of the ICD-11 beta version and (vi) downstream field testing of ICD-11 prior to its official 2015 release. The transition from ICD-10 to ICD-11 promises to produce an enhanced classification that will have better potential to capture important concepts relevant to measuring health system safety and quality—an important use case for the classificatio