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

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

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

Sehnsucht and alienation in Schubert's Mignon settings / Acacia M. Doktorchik

iv, 46 leaves ; 29 cmSehnsucht (longing) and alienation were two central themes of 19th century German Romanticism in literature, music and art. Franz Schubert was one of the great masters of the Romantic era to understand and express these intense emotions through his compositions. This paper discusses Sehnsucht and alienation in Schubert’s settings of the Mignon songs from Johann Wolfgang von Goethe’s novel Wilhelm Meisters Lehjahre (Master William’s Apprenticeship). Mignon, a secondary character in this novel, is a prime example of one who experiences these emotions and whose principal medium of expressing herself is through her five songs. My thesis focuses on how Schubert portrays Mignon’s longing through use of dissonance, harmonic progressions, melodic contour and shifts in vocal register

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

Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data.

OBJECTIVE: Data quality assessment is a challenging facet for research using coded administrative health data. Current assessment approaches are time and resource intensive. We explored whether association rule mining (ARM) can be used to develop rules for assessing data quality. MATERIALS AND METHODS: We extracted 2013 and 2014 records from the hospital discharge abstract database (DAD) for patients between the ages of 55 and 65 from five acute care hospitals in Alberta, Canada. The ARM was conducted using the 2013 DAD to extract rules with support ≥0.0019 and confidence ≥0.5 using the bootstrap technique, and tested in the 2014 DAD. The rules were compared against the method of coding frequency and assessed for their ability to detect error introduced by two kinds of data manipulation: random permutation and random deletion. RESULTS: The association rules generally had clear clinical meanings. Comparing 2014 data to 2013 data (both original), there were 3 rules with a confidence difference >0.1, while coding frequency difference of codes in the right hand of rules was less than 0.004. After random permutation of 50% of codes in the 2014 data, average rule confidence dropped from 0.72 to 0.27 while coding frequency remained unchanged. Rule confidence decreased with the increase of coding deletion, as expected. Rule confidence was more sensitive to code deletion compared to coding frequency, with slope of change ranging from 1.7 to 184.9 with a median of 9.1. CONCLUSION: The ARM is a promising technique to assess data quality. It offers a systematic way to derive coding association rules hidden in data, and potentially provides a sensitive and efficient method of assessing data quality compared to standard methods

Data on coding association rules from an inpatient administrative health data coded by International classification of disease - 10th revision (ICD-10) codes

Data presented in this article relates to the research article entitled “Exploration of association rule mining for coding consistency and completeness assessment in inpatient administrative health data” (Peng et al. [1]) in preparation). We provided a set of ICD-10 coding association rules in the age group of 55 to 65. The rules were extracted from an inpatient administrative health data at five acute care hospitals in Alberta, Canada, using association rule mining. Thresholds of support and confidence for the association rules mining process were set at 0.19% and 50% respectively. The data set contains 426 rules, in which 86 rules are not nested. Data are provided in the supplementary material. The presented coding association rules provide a reference for future researches on the use of association rule mining for data quality assessment

Preterm Birth: Understanding Temporal Changes in Anxiety and Depression Measures

Background: This study aimed to understand whether there is a pattern of change in levels of anxiety and depression between the second and third trimesters of pregnancy that are associated with a risk of PTB. Chronic stress was assessed as a potential modifier of the relationship. Methods: This study conducted a secondary data analysis on the All Our Babies prospective cohort. Logistic regression modeling was used to analyze the data. Results: A worsening of anxiety during pregnancy increased the odds of preterm delivery (OR 2.70, 95% CI 1.28, 5.69; p=0.009). An improvement in anxiety reduced the odds of PTB (OR 0.96, 95% CI 0.94, 0.98; p=<0.001). Consistently low depression decreased the odds of PTB (OR 0.65, 95% CI 0.45, 0.96; p=0.029). Chronic stress did not modify any of these relationships. Conclusions: Efforts should be made to replicate these results in a cohort with a larger sample size

Exploring the differences in ICD and hospital morbidity data collection features across countries: an international survey

Abstract Background The International Classification of Diseases (ICD) is the reference standard for reporting diseases and health conditions globally. Variations in ICD use and data collection across countries can hinder meaningful comparisons of morbidity data. Thus, we aimed to characterize ICD and hospital morbidity data collection features worldwide. Methods An online questionnaire was created to poll the World Health Organization (WHO) member countries that were using ICD. The survey included questions focused on ICD meta-features and hospital data collection systems, and was distributed via SurveyMonkey using purposive and snowball sampling. Accordingly, senior representatives from organizations specialized in the topic, such as WHO Collaborating Centers, and other experts in ICD coding were invited to fill out the survey and forward the questionnaire to their peers. Answers were collated by country, analyzed, and presented in a narrative form with descriptive analysis. Results Responses from 47 participants were collected, representing 26 different countries using ICD. Results indicated worldwide disparities in the ICD meta-features regarding the maximum allowable coding fields for diagnosis, the definition of main condition, and the mandatory type of data fields in the hospital morbidity database. Accordingly, the most frequently reported answers were “reason for admission” as main condition definition (n = 14), having 31 or more diagnostic fields available (n = 12), and “Diagnoses” (n = 26) and “Patient demographics” (n = 25) for mandatory data fields. Discrepancies in data collection systems occurred between but also within countries, thereby revealing a lack of standardization both at the international and national level. Additionally, some countries reported specific data collection features, including the use or misuse of ICD coding, the national standards for coding or lack thereof, and the electronic abstracting systems utilized in hospitals. Conclusions Harmonizing ICD coding standards/guidelines should be a common goal to enhance international comparisons of health data. The current international status of ICD data collection highlights the need for the promotion of ICD and the adoption of the newest version, ICD-11. Furthermore, it will encourage further research on how to improve and standardize ICD coding