Hypertension identification using inpatient clinical notes from electronic medical records: an explainable, data-driven algorithm study.

BACKGROUND: Case identification is important for health services research, measuring health system performance and risk adjustment, but existing methods based on manual chart review or diagnosis codes can be expensive, time consuming or of limited validity. We aimed to develop a hypertension case definition in electronic medical records (EMRs) for inpatient clinical notes using machine learning. METHODS: A cohort of patients 18 years of age or older who were discharged from 1 of 3 Calgary acute care facilities (1 academic hospital and 2 community hospitals) between Jan. 1 and June 30, 2015, were randomly selected, and we compared the performance of EMR phenotype algorithms developed using machine learning with an algorithm based on the Canadian version of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD), in identifying patients with hypertension. Hypertension status was determined by chart review, the machine-learning algorithms used EMR notes and the ICD algorithm used the Discharge Abstract Database (Canadian Institute for Health Information). RESULTS: Of our study sample (n = 3040), 1475 (48.5%) patients had hypertension. The group with hypertension was older (median age of 71.0 yr v. 52.5 yr for those patients without hypertension) and had fewer females (710 [48.2%] v. 764 [52.3%]). Our final EMR-based models had higher sensitivity than the ICD algorithm (> 90% v. 47%), while maintaining high positive predictive values (> 90% v. 97%). INTERPRETATION: We found that hypertension tends to have clear documentation in EMRs and is well classified by concept search on free text. Machine learning can provide insights into how and where conditions are documented in EMRs and suggest nonmachine-learning phenotypes to implement.

Training and experience of coding with the World Health Organization's International Classification of Diseases, Eleventh Revision.

BACKGROUND: The new International Classification of Diseases, Eleventh Revision for Mortality and Morbidity Statistics (ICD-11) was developed and released by the World Health Organization (WHO) in June 2018. Because ICD-11 incorporates new codes and features, training materials for coding with ICD-11 are urgently needed prior to its implementation. OBJECTIVE: This study outlines the development of ICD-11 training materials, training processes and experiences of clinical coders while learning to code using ICD-11. METHOD: Six certified clinical coders were recruited to code inpatient charts using ICD-11. Training materials were developed with input from experts from the Canadian Institute for Health Information and the WHO, and the clinical coders were trained to use the new classification. Monthly team meetings were conducted to enable discussions on coding issues and to select the correct ICD-11 codes. The training experience was evaluated using qualitative interviews, a questionnaire and a coding quiz. RESULTS: total of 3011 charts were coded using ICD-11. In general, clinical coders provided positive feedback regarding the training program. The average score for the coding quiz (multiple choice, True/False) was 84%, suggesting that the training program was effective. Feedback from the coders enabled the ICD-11 code content, electronic tooling and terminologies to be updated. CONCLUSION: This study provides a detailed account of the processes involved with training clinical coders to use ICD-11. Important findings from the interviews were reported at the annual WHO conferences, and these findings helped improve the ICD-11 browser and reference guide.

Automating data collection methods in electronic health record systems: a Social Determinant of Health (SDOH) viewpoint.

Social Determinant of Health (SDOH) data are important targets for research and innovation in Health Information Systems (HIS). The ways we envision SDOH in "smart" information systems will play a considerable role in shaping future population health landscapes. Current methods for data collection can capture wide ranges of SDOH factors, in standardised and non-standardised formats, from both primary and secondary sources. Advances in automating data linkage and text classification show particular promise for enhancing SDOH in HIS. One challenge is that social communication processes embedded in data collection are directly related to the inequalities that HIS attempt to measure and redress. To advance equity, it is imperative thatcare-providers, researchers, technicians, and administrators attend to power dynamics in HIS standards and practices. We recommend: 1. Investing in interdisciplinary and intersectoral knowledge generation and translation. 2. Developing novel methods for data discovery, linkage and analysis through participatory research. 3. Channelling information into upstream evidence-informed policy.

Field testing a new ICD coding system: methods and early experiences with ICD-11 Beta Version 2018.

OBJECTIVE: A beta version (2018) of International Classification of Diseases, 11th Revision for MMS (ICD-11), needed testing. Field-testing involves real-world application of the new codes to examine usability. We describe creating a dataset and characterizing the usability of ICD-11 code set by coders. We compare ICD-11 against ICD-10-CA (Canadian modification) and a reference standard dataset of diagnoses. Real-world usability encompasses code selection and time to code a complete inpatient chart using ICD-11 compared with ICD-10-CA. METHODS AND RESULTS: A random sample of inpatient records previously coded using ICD-10-CA was selected from hospitals in Calgary, Alberta (N = 2896). Nurses examined these charts for conditions and healthcare-related harms. Clinical coders re-coded the same charts using ICD-11 codes. Inter-rater reliability (IRR) and coding time improved with ICD-11 coding experience (23.6 to 9.9 min average per chart). Code structure comparisons and challenges encountered are described. Overall, 86.3% of main condition codes matched. Coder comments regarding duplicate codes, missing codes, code finding issues enabled improvements to the ICD-11 Browser, Coding Tool, and Reference Guide. Training is essential for solid IRR with 17,000 diagnostic categories in the new ICD-11. As countries transition to ICD-11, our coding experiences and methods can inform users for implementation or field testing.

International Classification of Diseases clinical coding training: An international survey.

BACKGROUND: The International Classification of Diseases (ICD) is widely used by clinical coders worldwide for clinical coding morbidity data into administrative health databases. Accordingly, hospital data quality largely depends on the coders' skills acquired during ICD training, which varies greatly across countries. OBJECTIVE: To characterise the current landscape of international ICD clinical coding training. METHOD: An online questionnaire was created to survey the 194 World Health Organization (WHO) member countries. Questions focused on the training provided to clinical coding professionals. The survey was distributed to potential participants who met specific criteria, and to organisations specialised in the topic, such as WHO Collaborating Centres, to be forwarded to their representatives. Responses were analysed using descriptive statistics. RESULTS: Data from 47 respondents from 26 countries revealed disparities in all inquired topics. However, most participants reported clinical coders as the primary person assigning ICD codes. Although training was available in all countries, some did not mandate training qualifications, and those that did differed in type and duration of training, with college or university degree being most common. Clinical coding certificates most frequently entailed passing a certification exam. Most countries offered continuing training opportunities, and provided a range of support resources for clinical coders. CONCLUSION: Variability in clinical coder training could affect data collection worldwide, thus potentially hindering international comparability of health data. IMPLICATIONS: These findings could encourage countries to improve their resources and training programs available for clinical coders and will ultimately be valuable to the WHO for the standardisation of ICD training.

Impact of the COVID-19 Pandemic on Hospitalizations for Alcoholic Hepatitis or Cirrhosis in Alberta, Canada.

BACKGROUND & AIMS: Coronavirus disease 2019 (COVID-19) pandemic lockdown and restrictions had significant disruption to patient care. We aimed to evaluate the impact of COVID-19 restrictions on hospitalizations of patients with alcoholic and nonalcoholic cirrhosis as well as alcoholic hepatitis (AH) in Alberta, Canada. METHODS: We used validated International Classification of Diseases (ICD-9 and ICD-10) coding algorithms to identify liver-related hospitalizations for nonalcoholic cirrhosis, alcoholic cirrhosis, and AH in the province of Alberta between March 2018 and September 2020. We used the provincial inpatient discharge and laboratory databases to identify our cohorts. We used elevated alanine aminotransferase or aspartate aminotransferase, elevated international normalized ratio, or bilirubin to identify AH patients. We compared COVID-19 restrictions (April-September 2020) with prior study periods. Joinpoint regression was used to evaluate the temporal trends among the 3 cohorts. RESULTS: We identified 2916 hospitalizations for nonalcoholic cirrhosis, 2318 hospitalizations for alcoholic cirrhosis, and 1408 AH hospitalizations during our study time. The in-hospital mortality rate was stable in relation to the pandemic for alcoholic cirrhosis and AH. However, nonalcoholic cirrhosis patients had lower in-hospital mortality rate after March 2020 (8.5% vs 11.5%; P = .033). There was a significant increase in average monthly admissions in the AH cohort (22.1/10,000 admissions during the pandemic vs 11.6/10,000 admissions before March 2020; P < .001). CONCLUSIONS: Before and during COVID-19 monthly admission rates were stable for nonalcoholic and alcoholic cirrhosis; however, there was a significant increase in AH admissions. Because alcohol sales surged during the pandemic, future impact on alcoholic liver disease could be detrimental.

Describing agreement in the main condition coding field using Canadian ICD-11 inpatient data.

INTRODUCTION: Countries use varying coding standards, which impact international coded data comparability. The 'main condition' (MC) field is coded within the Discharge Abstract Database as "reason for admission" or "largest resource use". OBJECTIVE: We offer a preliminary analysis on the frequency of and contributing factors to MC definition agreements within an inpatient Canadian dataset. METHODS: Six professional coders performed a chart review between August 2016 and June 2017 on 3,000 randomly selected inpatient charts from three acute care hospitals in Calgary, Alberta. Coders classified the MC as "reason for admission", "largest resource use" or "both". Patients were admitted between 1st January and 30th June 2015 and met the inclusion criteria if they were ≥18 years, had an Alberta personal health care number, and had an inpatient visit for any service outside of obstetrics. Agreement between the two MC definitions was stratified by length of stay (LOS), emergency department admission, hospital of origin, discharge location, age, sex, procedures, and comorbidities. Chi-square analysis and frequency of inconsistencies were reported. RESULTS: Only 34 (1.51%) of the 2,250 patient charts had disagreeing MC definitions. Age, emergency visit on admit, LOS, hospital, and discharge location were associated with MC agreement. Chronic conditions were seen more often in MC definition agreements, and acute conditions seen within those disagreeing. CONCLUSION: There was a small proportion of cases in which the condition bringing the patient to hospital was not also the condition occupying the largest resources. Within disagreements, further research using a larger sample size is needed to explore the presence of MC in a secondary/tertiary condition, the association between patient complexity and disagreeing MC definitions, and the nature of the conditions seen in the inconsistent MC definitions.

CREATE: A New Data Resource to Support Cardiac Precision Health.

BACKGROUND: The initiatives of precision medicine and learning health systems require databases with rich and accurately captured data on patient characteristics. We introduce the Clinical Registry, AdminisTrative Data and Electronic Medical Records (CREATE) database, which includes linked data from 4 population databases: Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH; a national clinical registry), Sunrise Clinical Manager (SCM) electronic medical record (city-wide), the Discharge Abstract Database (DAD), and the National Ambulatory Care Reporting System (NACRS). The intent of this work is to introduce a cardiovascular-specific database for pursuing precision health activities using big data analytics. METHODS: We used deterministic data linkage to link SCM electronic medical record data to APPROACH clinical registry data using patient identifier variables. The APPROACH-SCM data set was subsequently linked to DAD and NACRS to obtain inpatient and outpatient cohort data. We further validated the quality of the linkage, where applicable, in these databases by comparing against the Alberta Health Insurance Care Plan registry database. RESULTS: We achieved 99.96% linkage across these 4 databases. Currently, there are 30,984 patients with 35,753 catheterizations in the CREATE database. The inpatient cohort contained 65.75% (20,373/30,984) of the patient sample, whereas the outpatient cohort contained 29.78% (9226/30,984). The infrastructure and the process to update and expand the database has been established. CONCLUSIONS: CREATE is intended to serve as a database for supporting big data analytics activities surrounding cardiac precision health. The CREATE database will be managed by the Centre for Health Informatics at the University of Calgary, and housed in a secure high-performance computing environment.

CONTEXTE: Les initiatives en matière de médecine de précision et les systèmes de santé apprenants ont besoin de bases de données riches et exactes sur les caractéristiques des patients. Nous présentons ici la base de données CREATE ( C linical Re gistry, A dminis t rative Data and E lectronic Medical Records), qui regroupe les données couplées de quatre bases de données populationnelles : le registre clinique national APPROACH ( A lberta P rovincial Pr oject for O utcome A ssessment in C oronary H eart Disease), le système de gestion des dossiers médicaux électroniques SCM (Sunrise Clinical Manager, utilisé à l'échelle municipale), la Base de données sur les congés des patients (BDCP), et le Système national d'information sur les soins ambulatoires (SNISA). Notre objectif est d'offrir une base de données portant précisément sur les maladies cardiovasculaires, afin de soutenir les activités en santé de précision nécessitant l'analyse de mégadonnées. MÉTHODOLOGIE: Nous avons utilisé une méthode de couplage déterministe pour apparier les données du système SCM à celles du registre APPROACH à l'aide de variables d'identification des patients. L'ensemble de données SCM-APPROACH a ensuite été couplé aux données de la BDCP et du SNISA, afin d'obtenir les données des cohortes des patients hospitalisés et des patients ambulatoires. Lorsque c'était possible, nous avons en outre validé la qualité du couplage en comparant les données à celles de la base de données du Régime d'assurance maladie de l'Alberta. RÉSULTATS: Nous avons obtenu un taux de couplage de 99,96 % pour les quatre bases de données. À l'heure actuelle, la base de données CREATE compte 30 984 patients ayant subi 35 753 cathétérismes. La cohorte des patients hospitalisés représente 65,75 % (20 373/30 984) de l'échantillon, tandis que la cohorte des patients ambulatoires représente 29,78 % (9226/30 984). L'infrastructure et le processus de mise à jour et d'expansion de la base de données ont été définis. CONCLUSIONS: La base de données CREATE est destinée à soutenir les activités d'analyse de mégadonnées nécessaires à la santé cardiaque de précision. Elle sera gérée par le Centre for Health Informatics de l'Université de Calgary et hébergée dans un environnement informatique à haut rendement sécurisé.

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

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.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review.

BACKGROUND: Electronic 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. OBJECTIVE: This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. METHODS: A 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. RESULTS: A 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. CONCLUSIONS: Recognizing 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.

Quality assessment of RNA in long-term storage: The All Our Families biorepository.

BACKGROUND: The All Our Families (AOF) cohort study is a longitudinal population-based study which collected biological samples from 1948 pregnant women between May 2008 and December 2010. As the quality of samples can decline over time, the objective of the current study was to assess the association between storage time and RNA (ribonucleic acid) yield and purity, and confirm the quality of these samples after 7-10 years in long-term storage. METHODS: Maternal whole blood samples were previously collected by trained phlebotomists and stored in four separate PAXgene Blood RNA Tubes (PreAnalytiX) between 2008 and 2011. RNA was isolated in 2011 and 2018 using PAXgene Blood RNA Kits (PreAnalytiX) as per the manufacturer's instruction. RNA purity (260/280), as well as RNA yield, were measured using a Nanodrop. The RNA integrity number (RIN) was also assessed from 5-25 and 111-130 months of storage using RNA 6000 Nano Kit and Agilent 2100 BioAnalyzer. Descriptive statistics, paired t-test, and response feature analysis using linear regression were used to assess the association between various predictor variables and quality of the RNA isolated. RESULTS: Overall, RNA purity and yield of the samples did not decline over time. RNA purity of samples isolated in 2011 (2.08, 95% CI: 2.08-2.09) were statistically lower (p<0.000) than samples isolated in 2018 (2.101, 95% CI: 2.097, 2.104), and there was no statistical difference between the 2011 (13.08 µg /tube, 95% CI: 12.27-13.89) and 2018 (12.64 µg /tube, 95% CI: 11.83-13.46) RNA yield (p = 0.2964). For every month of storage, the change in RNA purity is -0.01(260/280), and the change in RNA yield between 2011 and 2018 is -0.90 µ g / tube. The mean RIN was 8.49 (95% CI:8.44-8.54), and it ranged from 7.2 to 9.5. The rate of change in expected RIN per month of storage is 0.003 (95% CI 0.002-0.004), so while statistically significant, these results are not relevant. CONCLUSIONS: RNA quality does not decrease over time, and the methods used to collect and store samples, within a population-based study are robust to inherent operational factors which may degrade sample quality over time.

Enhancing ICD-Code-Based Case Definition for Heart Failure Using Electronic Medical Record Data.

BACKGROUND: Surveillance and outcome studies for heart failure (HF) require accurate identification of patients with HF. Algorithms based on International Classification of Diseases (ICD) codes to identify HF from administrative data are inadequate owing to their relatively low sensitivity. Detailed clinical information from electronic medical records (EMRs) is potentially useful for improving ICD algorithms. This study aimed to enhance the ICD algorithm for HF definition by incorporating comprehensive information from EMRs. METHODS: The study included 2106 inpatients in Calgary, Alberta, Canada. Medical chart review was used as the reference gold standard for evaluating developed algorithms. The commonly used ICD codes for defining HF were used (namely, the ICD algorithm). The performance of different algorithms using the free text discharge summaries from a population-based EMR were compared with the ICD algorithm. These algorithms included a keyword search algorithm looking for HF-specific terms, a machine learning-based HF concept (HFC) algorithm, an EMR structured data based algorithm, and combined algorithms (the ICD and HFC combined algorithm). RESULTS: Of 2106 patients, 296 (14.1%) were patients with HF as determined by chart review. The ICD algorithm had 92.4% positive predictive value (PPV) but low sensitivity (57.4%). The EMR keyword search algorithm achieved a higher sensitivity (65.5%) than the ICD algorithm, but with a lower PPV (77.6%). The HFC algorithm achieved a better sensitivity (80.0%) and maintained a reasonable PPV (88.9%) compared with the ICD algorithm and the keyword algorithm. An even higher sensitivity (83.3%) was reached by combining the HFC and ICD algorithms, with a lower PPV (83.3%). The structured EMR data algorithm reached a sensitivity of 78% and a PPV of 54.2%. The combined EMR structured data and ICD algorithm had a higher sensitivity (82.4%), but the PPV remained low at 54.8%. All algorithms had a specificity ranging from 87.5% to 99.2%. CONCLUSIONS: Applying natural language processing and machine learning on the discharge summaries of inpatient EMR data can improve the capture of cases of HF compared with the widely used ICD algorithm. The utility of the HFC algorithm is straightforward, making it easily applied for HF case identification.

Development and validation of data quality rules in administrative health data using association rule mining.

BACKGROUND: Data quality assessment presents a challenge for research using coded administrative health data. The objective of this study is to develop and validate a set of coding association rules for coded diagnostic data. METHODS: We used the Canadian re-abstracted hospital discharge abstract data coded in International Classification of Disease, 10th revision (ICD-10) codes. Association rule mining was conducted on the re-abstracted data in four age groups (0-4, 20-44, 45-64; ≥ 65) to extract ICD-10 coding association rules at the three-digit (category of diagnosis) and four-digit levels (category of diagnosis with etiology, anatomy, or severity). The rules were reviewed by a panel of 5 physicians and 2 classification specialists using a modified Delphi rating process. We proposed and defined the variance and bias to assess data quality using the rules. RESULTS: After the rule mining process and the panel review, 388 rules at the three-digit level and 275 rules at the four-digit level were developed. Half of the rules were from the age group of ≥65. Rules captured meaningful age-specific clinical associations, with rules at the age group of ≥65 being more complex and comprehensive than other age groups. The variance and bias can identify rules with high bias and variance in Alberta data and provides directions for quality improvement. CONCLUSIONS: A set of ICD-10 data quality rules were developed and validated by a clinical and classification expert panel. The rules can be used as a tool to assess ICD-coded data, enabling the monitoring and comparison of data quality across institutions, provinces, and countries.

Patients, clinicians and researchers working together to improve cardiovascular health: a qualitative study of barriers and priorities for patient-oriented research.

OBJECTIVES: The overall goal of this study is to identify priorities for cardiovascular (CV) health research that are important to patients and clinician-researchers. We brought together a group of CV patients and clinician-researchers new to patient-oriented research (POR), to build a multidisciplinary POR team and form an advisory committee for the Libin Cardiovascular Institute of Alberta. DESIGN: This qualitative POR used a participatory health research paradigm to work with participants in eliciting their priorities. Therefore, participants were involved in priority setting, and analysis of findings. Participants also developed a plan for continued engagement to support POR in CV health research. SETTING: Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Canada. PARTICIPANTS: A total of 23 participants, including patients and family caregivers (n=12) and clinician-researchers (n=11). RESULTS: Participants identified barriers and facilitators to POR in CV health (lack of awareness of POR and poor understanding on the role of patients) and 10 research priorities for improving CV health. The CV health research priorities include: (1) CV disease prediction and prevention, (2) access to CV care, (3) communication with providers, (4) use of eHealth technology, (5) patient experiences in healthcare, (6) patient engagement, (7) transitions and continuity of CV care, (8) integrated CV care, (9) development of structures for patient-to-patient support and (10) research on rare heart diseases. CONCLUSIONS: In this study, research priorities were identified by patients and clinician-researchers working together to improve CV health. Future research programme and projects will be developed to address these priorities. A key output of this study is the creation of the patient advisory council that will provide support and will work with clinician-researchers to improve CV health.

A qualitative evaluation of clinically coded data quality from health information manager perspectives.

BACKGROUND: It is essential that clinical documentation and clinical coding be of high quality for the production of healthcare data. OBJECTIVE: This study assessed qualitatively the strengths and barriers regarding clinical coding quality from the perspective of health information managers. METHOD: Ten health information managers and clinical coding quality coordinators who oversee clinical coders (CCs) were identified and recruited from nine provinces across Canada. Semi-structured interviews were conducted, which included questions on data quality, costs of clinical coding, education for health information management, suggestions for quality improvement and barriers to quality improvement. Interviews were recorded, transcribed and analysed using directed content analysis and informed by institutional ethnography. RESULTS: Common barriers to clinical coding quality included incomplete and unorganised chart documentation, and lack of communication with physicians for clarification. Further, clinical coding quality suffered as a result of limited resources (e.g. staffing and budget) being available to health information management departments. Managers unanimously reported that clinical coding quality improvements can be made by (i) offering interactive training programmes to CCs and (ii) streamlining sources of information from charts. CONCLUSION: Although clinical coding quality is generally regarded as high across Canada, clinical coding managers perceived quality to be limited by incomplete and inconsistent chart documentation, and increasing expectations for data collection without equal resources allocated to clinical coding professionals. IMPLICATIONS: This study presents novel evidence for clinical coding quality improvement across Canada.

Validation of a case definition for depression in administrative data against primary chart data as a reference standard.

BACKGROUND: Because the collection of mental health information through interviews is expensive and time consuming, interest in using population-based administrative health data to conduct research on depression has increased. However, there is concern that misclassification of disease diagnosis in the underlying data might bias the results. Our objective was to determine the validity of International Classification of Disease (ICD)-9 and ICD-10 administrative health data case definitions for depression using review of family physician (FP) charts as the reference standard. METHODS: Trained chart reviewers reviewed 3362 randomly selected charts from years 2001 and 2004 at 64 FP clinics in Alberta (AB) and British Columbia (BC), Canada. Depression was defined as presence of either: 1) documentation of major depressive episode, or 2) documentation of specific antidepressant medication prescription plus recorded depressed mood. The charts were linked to administrative data (hospital discharge abstracts and physician claims data) using personal health numbers. Validity indices were estimated for six administrative data definitions of depression using three years of administrative data. RESULTS: Depression prevalence by chart review was 15.9-19.2% depending on year, region, and province. An ICD administrative data definition of '2 depression claims with depression ICD codes within a one-year window OR 1 discharge abstract data (DAD) depression diagnosis' had the highest overall validity, with estimates being 61.4% for sensitivity, 94.3% for specificity, 69.7% for positive predictive value, and 92.0% for negative predictive value. Stratification of the validity parameters for this case definition showed that sensitivity was fairly consistent across groups, however the positive predictive value was significantly higher in 2004 data compared to 2001 data (78.8 and 59.6%, respectively), and in AB data compared to BC data (79.8 and 61.7%, respectively). CONCLUSIONS: Sensitivity of the case definition is often moderate, and specificity is often high, possibly due to undercoding of depression. Limitations to this study include the use of FP charts data as the reference standard, given the potential for missed or incorrect depression diagnoses. These results suggest that that administrative data can be used as a source of information for both research and surveillance purposes, while remaining aware of these limitations.

Identifying Canadian patient-centred care measurement practices and quality indicators: a survey.

BACKGROUND: Patient-centred quality indicators allow health care systems to monitor and evaluate patient-centred care practices and identify gaps in health care quality. Our objective was to determine whether Canadian provinces and territories measure patient-centred care, identify patient-centred quality indicators currently being used and compare patient-centred care practices and measurement in Canada to those of health care systems in other countries. METHODS: An online survey was developed to collect data on demographic characteristics, patient-centred care practices, and indicators used at quality improvement organizations and health care authorities. The survey was conducted with quality improvement leads in Canada and 4 other countries. Content analysis methods were used to analyze and report the data. Patient-centred quality indicators were identified and categorized according to the Donabedian framework (structure, process, outcome). RESULTS: The survey had a response rate of 47/67 (70%) and a completion rate of 58/60 (97%). We obtained completed surveys from 12 of the 13 provinces and territories in Canada. Respondents from most provinces indicated their organization used patient-centred care measures to inform practices. Respondents in only 4 provinces/territories reported using patient-centred quality indicators, for a total of 61 unique indicators. Most indicators used across Canada assessed aspects of care related to the Donabedian components of process and outcome. Findings for Canada were comparable to those for Sweden, England, Australia and New Zealand, where many measures are still in development. INTERPRETATION: This study provided greater insight into patient-centred care measurement across Canada, Sweden, England, Australia and New Zealand and helped us to identify patient-centred quality indicators currently in use. These results will inform the development of a standard set of patient-centred quality indicators for implementation by health care organizations to improve the quality of health care.

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.

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.