ICD-data collection features: an international survey

Background:  The International Classification of Diseases (ICD) is globally used for coding morbidity and mortality statistics, however, its use, as well as the data collection features vary greatly across countries. Objective: To characterize hospital ICD-coded data collection worldwide. Methods: After an in-depth grey and academic literature review, an online survey was created to poll the 194 World Health Organization (WHO) member countries. Questions focused on hospital data collection systems and ICD-coded data features. The survey was distributed, using different methods, to potential participants that met the specific criteria, as well as organizations specialized in the topic, such as WHO Collaborating Centers (WHO-CC) or International Federation of Health Information Management Association (IFHIMA), to be forwarded to their representatives. Answers were analyzed using descriptive statistics. Results: Data from 48 respondents from 26 different countries has been collected. Results reveal worldwide use of ICD, with variations in the maximum allowable coding fields for diagnoses and interventions. For instance, in some countries there is an unlimited number of coding fields (Netherlands, Thailand and Iran), as opposed to others with only 1-6 available (Guatemala or Mauritius). Disparities also exist in the definition of a main condition, as 60% of the countries use “reason for admission” and 40% utilize “resource use”. Additionally, the mandatory type of data fields in the hospital morbidity database (e.g. patient demographics, admission type, discharge disposition, diagnoses, …) differ among countries, with diagnosis timing and physician information being the least frequently required. Conclusion: These survey data will establish the current state of ICD use internationally, which will ultimately be valuable to the WHO for the promotion of ICD and the rollout of ICD-11. Additionally, it will improve international comparisons of health data, and encourage further research on how to improve ICD coding

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

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

Canadian Approaches to Optimizing Quality of Administrative Data for Health System Use, Research, and Linkage

Theme: Data and Linkage Quality Objectives: • To define health data quality from clinical, data science, and health system perspectives • To describe some of the international best practices related to quality and how they are being applied to Canada’s administrative health data. • To compare methods for health data quality assessment and improvement in Canada (automated logical checks, chart quality indicators, reabstraction studies, coding manager perspectives) • To highlight how data linkage can be used to provide new insights into the quality of original data sources • To highlight current international initiatives for improving coded data quality including results from current ICD-11 field trials Dr. Keith Denny: Director of Clinical Data Standards and Quality, Canadian Insititute for Health Information (CIHI), Adjunct Research Professor, Carleton University, Ottawa, ON. He provides leadership for CIHI’s information quality initiatives and for the development and application of clinical classifications and terminology standards. Maureen Kelly: Manager of Information Quality at CIHI, Ottawa, ON. She leads CIHI’s corporate quality program that is focused on enhancing the quality of CIHI’s data sources and information products and to fostering CIHI’s quality culture. Dr. Cathy Eastwood: Scientific Manager, Associate Director of Alberta SPOR Methods & Development Platform, Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB. She has expertise in clinical data collection, evaluation of local and systemic data quality issues, disease classification coding with ICD-10 and ICD-11. Dr. Hude Quan: Professor, Community Health Sciences, Cumming School of Medicine, University of Calgary, Director Alberta SPOR Methods Platform; Co-Chair of Hypertension Canada, Co-Chair of Person to Population Health Collaborative of the Libin Cardiovascular Institute in Calgary, AB. He has expertise in assessing, validating, and linking administrative data sources for conducting data science research including artificial intelligence methods for evaluating and improving data quality. Intended Outcomes: “What is quality health data?” The panel of experts will address this common question by discussing how to define high quality health data, and measures being taken to ensure that they are available in Canada. Optimizing the quality of clinical-administrative data, and their use-value, first requires an understanding of the processes used to create the data. Subsequently, we can address the limitations in data collection and use these data for diverse applications. Current advances in digital data collection are providing more solutions to improve health data quality at lower cost. This panel will describe a number of quality assessment and improvement initiatives aimed at ensuring that health data are fit for a range of secondary uses including data linkage. It will also discuss how the need for the linkage and integration of data sources can influence the views of the data source’s fitness for use. CIHI content will include: • Methods for optimizing the value of clinical-administrative data • CIHI Information Quality Framework • Reabstraction studies (e.g. physician documentation/coders’ experiences) • Linkage analytics for data quality University of Calgary content will include: • Defining/measuring health data quality • Automated methods for quality assessment and improvement • ICD-11 features and coding practices • Electronic health record initiative

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

The Economic Impacts of ICD-9 to ICD-10 Health Indicator Coding System Transition in the Calgary Region

Introduction Coded data serves a critical part in the process of identifying the resource allocation required for each department in a hospital and for research purposes. This paper attempts a cost-benefit analysis of the transition from ICD-9 health indicator coding system to ICD-10 coding system and quantify the economic impacts. Objectives and Approach The hypothesis adopted by this paper is that the transition from ICD-9 to ICD-10 has been beneficial for the health system due better disease management, resulting in cost savings and facilitation of high quality health research. Analyzing the inflation-adjusted costs compared with the benefits accrued from implementing the new coding system would enable informed decision making for the stakeholders at government and other levels of health provision. The methodology involves constructing ‘benefit scenarios’ via analysis of existing literature and interviewing coding managers; costs are evaluated using data collected on re-training coders and productivity losses during the transition phase. Results An example of a benefit scenario would take the form of cost savings associated with correctly identifying people with diabetes (due to coded charts), hence resulting in a decline in blood sugar (HbA1c) levels via better disease management. This in turn may cause reductions in other high blood-sugar related diseases and thus increase efficiency for government funding in the health care sector. Improved data quality in ICD-10 is expected to have resulted in gains from specificity due to increased sensitivity of data classification and grouping. Actual cost of re-training of coders and ICD-10 software provider fees are expected to be higher than the costs anticipated before ICD-10 implementation. Productivity losses in the transition phase are expected to have declined as coders became more adept at coding. Conclusion/Implications An economic evaluation proves to be a vital part of eliciting whether the transition to the newer method of coding, ICD-10, has been beneficial to the end users of the data. It is important to understand the efficiency of resource allocation to healthcare and the financial implications such investments entail

Strengths and Barriers to Coding Hospital Chart Information from Health Information Manager Perspectives: A Qualitative Study

Introduction It is essential that clinical documentation and data coding be of high quality for the production of healthcare data for research or administrative purposes. However, there is a limited understanding of the facilitators and barriers of coded data quality and strategies to improve it. Objectives and Approach Our objective was to qualitatively assess what influences coded data quality from the perspective of health information managers who are responsible for the work of coding specialists. Nine health information managers and/or coding quality coordinators who oversee coding specialists were identified and recruited from nine provinces across Canada to participate in this study. Semi-structured interviews were conducted which asked questions on participant demographics, responsibilities, data quality, costs and budget of coding, continuing education for Health Information Management (HIM), suggestions for quality improvement, and barriers to quality improvement. Interviews were recorded and transcribed, and analyzed using Directed Content Analysis methodology. Results Interviewees were primarily responsible for managing staff, quality assurance, audits, reporting, budget, data collection, and transcription. Managers reported that the experienced coders under their employ strengthened coding quality. Common barriers to coding quality included incomplete and unorganized chart documentation, which led to undercoding, and lack of communication and access to physicians for clarification when needed. Further, coding quality suffered as a result of limited resources (e.g. staffing and budget) being available to HIM departments for an ever-expanding workload, that was commonly due to increasingly complex charts and additional project data. Managers unanimously reported that coding quality improvements can be made by 1) making interactive training programs available to coding specialists, and 2) streamlining sources of information from charts (i.e., transitioning to standardized electronic charting). Conclusion/Implications Although coding quality is generally regarded as high across Canada, quality can be hampered by incomplete and inconsistent chart documentation, lack of resources (e.g. financial support, staff, education), and inconsistent coding standards across hospitals and provinces. This study presents novel evidence for coding quality improvement across Canada

Massive Stars in the Field and Associations of the Magellanic Clouds: the Upper Mass Limit, the Initial Mass Function, and a Critical Test of Main-Sequence Stellar Evolutionary Theory

We investigate the massive star population of the Magellanic Clouds with an emphasis on the field population, which we define as stars located further from any OB association than massive stars are likely to travel during their short lifetimes. The field stars must have been born as part of more modest star-forming events than those that have populated the large OB associations found throughout the Clouds. We use new and existing data to answer the following questions: Does the field produce stars as massive as those found in associations? Is the initial mass function (IMF) of these field massive stars the same as those of large OB complexes? How well do the Geneva low-metallicity evolutionary models reproduce what is seen in the field population, with its mixed ages? To address these issues we begin by updating existing catalogs of LMC and SMC members with our own new spectral types and derive H-R diagrams (HRDs) of 1584 LMC and 512 SMC stars. We use new photometry and spectroscopy of selected regions in order to determine the incompleteness corrections of the catalogs as a function of mass and find that we can reliably correct the number of stars in our HRDs down to 25 M.. Using these data, we derive distance moduli for the Clouds via spectroscopic parallax, finding values of 18.4 +/- 0.1 and 19.1 +/- 0.3 for the LMC and SMC. The average reddening of the field stars is small: E(B - V) = 0.13 (LMC) and 0.09 (SMC), with little spread. We find that the field does produce stars as massive as any found in associations, with stars as massive as 85 M. present in the HRD even when safeguards against the inclusion of runaway stars are included. However, such massive stars are much less likely to be produced in the field (relative to lower mass stars) than in large OB complexes: the slope of the IMF of the field stars is very steep, GAMMA = -4.1 +/- 0.2 (LMC) and GAMMA = -3.7 +/- 0.5 (SMC). These may be compared with GAMMA = -1.3 +/- 0.3, which we rederive for the Magellanic Cloud associations. (We compare our association IMFs with the somewhat different results recently derived by Hill et al. and demonstrate that the latter suffer from systematic effects due to the lack of spectroscopy.) Our reanalysis of the Garmany et al. data reveals that the Galactic field population has a similarly steep slope, with GAMMA = -3.4 +/- 1.3, compared to GAMMA = -1.5 +/- 0.2 for the entire Galactic sample. We do not see any difference in the IMFs of associations in the Milky Way, LMC, and SMC. We find that the low metallicity evolutionary tracks and isochrones do an excellent job of reproducing the distribution of stars in the HRD at higher masses, and in particular match the width of the main-sequence well. There may or may not be an absence of massive stars with ages less than 2 Myr in the Magellanic Clouds, as others have found for Galactic stars; our reddening data renders unlikely the suggestion that such an absence (if real) would be due to the length of time it takes for a massive star to emerge. There is an increasing discrepancy between the theoretical ZAMS and the blue edge of the main-sequence at lower luminosities; this may reflect a metallicity dependence for the intrinsic colors of stars of early B and later beyond that predicted by model atmospheres, or it may be that the low metallicity ZAMS is misplaced to higher temperatures. Finally, we use the relative number of field main-sequence and Wolf-Rayet stars to provide a selection-free determination of what mass progenitors become WR stars in the Magellanic Clouds. Our data suggest that stars with initial masses > 30 M. evolve to a WR phase in the LMC; while the statistics are considerably less certain for the SMC, they are consistent with this limit being modestly higher there, possibly 50 M., in qualitative agreement with modern evolutionary calculations

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

Further explorations of illness uncertainty: Carer’s experiences of Parkinson’s disease

Objective: Dominant models of illness uncertainty define uncertainty as ‘an inability to determine the meaning of illness-related events’. Recent research has shown patient uncertainty to be multidimensional encompassing personal issues indirectly affected by illness. The nature of carer uncertainty has yet to be fully explored. The present study aimed to investigate the nature of illness uncertainty in the carers of patients with Parkinson’s disease (PD). Design: Eighteen carers of a spouse with PD participated in semi-structured interviews. Transcripts were thematically analysed, statements were coded as uncertain if they reflected ‘a lack of certainty, or a state of limited knowledge, understanding or worry regarding an existing or future outcome’. Results: The domains of uncertainty expressed by carers closely fitted the five domain framework of patient uncertainty: symptoms and prognosis, medical management, self-management, social functioning and impact. An additional ‘carer-role’ domain was identified. Conclusions: Carer uncertainty about PD went beyond issues directly related to the illness. The findings have implications for research into uncertainty suggesting that widely used measures may not be accurately capturing the nature of carer uncertainty about chronic illness. The breadth of uncertainty reported has implications for the provision of appropriate support to improve caregiver well-being