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Objectives Administrative data are widely used in research, health policy, and the evaluation of health service delivery. We undertook a qualitative study to explore the barriers to high quality coding of chart information to administrative data, at the level of coders in Canada. Approach Our study design is qualitative. We recruited professional medical chart coders and data users working across Alberta, Canada, using a multimodal recruitment strategy. We conducted an in-depth, semi-structured interview with each participant. All interviews were audio-recorded and transcribed. We conducted thematic analysis (e.g., line-by-line open coding) of interview transcripts. Codes were then collated into themes and compared across our dataset to ensure accurate interpretations of the data. The study team met to discuss, modify, and interpret emergent themes in the context of the barriers to coding administrative data. Results We recruited 28 coding specialists. In general, coders had high job satisfaction and sense of collegiality, as well as sufficient resources to address their coding questions. They believed themselves to be adequately trained and consistently put in the extra effort when searching charts to find additional information that accurately reflected the patient journey. Barriers to high quality coding from the coder perspective included: 1) Incomplete and inaccurate information in physician progress notes and discharge summaries; 2) Difficulty navigating a complex hybrid of paper and electronic medical records; 3) Focus on productivity rather than quality by the employer, which at times resulted in inconsistent instructions for coding secondary diagnoses and discordant expectations between the employer and the coders’ professional standards. Conclusion Future interventions to improve the quality of administrative data should focus on physician education of necessary components in charting, evaluation of electronic medical records from the perspectives of those who play a key role in abstracting data, and evaluation of productivity guidelines for coders and their effects on data quality.

Objective A thorough understanding of the health status of Canadians must take into account their ethnicity, given the genetic and social effects of race and ethnicity on health. Our objective is to describe Canadian data sources that collect ethnicity data and the degree of granularity that exists for ethnic classifications within these sources. We contextualize changes to the collection of ethnic data by considering their historical, social, and political circumstances. Approach Our methods are informed by environmental scan and history methodology. We searched publicly available government documents, peer-reviewed literature, and contacted key informants to gain a comprehensive understanding of the Canadian sources available for collecting nationally representative ethnicity data. Two investigators, using qualitative content analysis, analyzed these sources independently. We extracted information form sources relating to the ethnic classifications (e.g., race, ethnic origins, colour, ancestry) and constructed a historical timeline of key changes. We mapped these to Canada’s changing social and political landscape and drew on contemporary literature to consider the implications of these changes for population health. The study team met to discuss findings, interpretations, and themes that emerged from these sources. Results There are four main sources of ethnicity data in Canada used for health research: 1) Provincial health insurance registries, 2) Canadian Health Measures Survey, 3) Canadian Community Health Survey, 4) Census. Of these, ethnicity data are most limited in the provincial health insurance registries, flagging only Aboriginal status. The other three data sources are nationally administered, with all asking individuals to select, out of 11 categories, self-identified racial or ethnic groups. Historically, Canada’s changing policies on multiculturalism and immigration have influenced the collection of ethnic data to become more inclusive and granular. Important periods include early attempts at nation-building during the late 19th century, social changes post-WWII and the introduction of multiculturalism into federal policy, and present-day efforts in ethnic classifications for research purposes and preserving cultural diversity. Conclusions There is a need for greater granularity in ethnic classifications to reflect the diversity of the Canadian population. Consideration should be made to capture ethnicity as a social, cultural, and historical concept concept, especially in large data sources that influence health decision-making, such as the census. For example, questions on ethnic classification may consider incorporating questions about sense of belonging with the identified ethnic ancestry, rather than relying solely on reported ethnic origin and race.

Objectives Unplanned hospital readmissions are an indicator of quality of care, and are associated with significant costs to healthcare systems. Previous research has shown that poor communication and discharge experiences are associated with higher readmission rates. This, however, has only been examined in the short-term, and in many instances, at the hospital-level. The purpose of the study was to examine the relationship between aspects of inpatient communication and discharge instructions and unplanned readmissions at the individual-level up to one-year post-discharge. Approach The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) telephone survey was completed by patients within 6 weeks of hospital discharge in Alberta, Canada. Survey data were linked to corresponding inpatient records using personal health number, discharge date, and facility codes. Unplanned readmissions (yes vs. no; all causes) from 43 to 365 days post-hospital discharge comprised the outcome variable. Independent variables included selected demographic characteristics, clinical variables, and five survey questions: a) patient involvement in their care decisions, b) receiving written information at discharge, c) understanding the purpose of taking medications, d) understanding responsibility for one’s health, and e) discussing help needed when returning home. Multivariate logistic regression was used to examine each question in the presence of the other predictors. Odds ratios and 95% confidence intervals were calculated. Results From April 2011 to March 2014, 24,868 patients completed a survey which was successfully linked to the corresponding inpatient record. The cohort had a mean age of 52.8±19.8 years of age (range=18-100), and was predominantly female (65.4%). 18.6% of patients (n=4,620) experienced an unplanned hospital readmission within 43 to 365 days post-discharge. Patients who felt that they were not involved in their care decisions were more likely to be readmitted (OR=1.79; 95%CI: 1.59-2.01), as were patients who did not receive written information at discharge (OR=1.96; 95%CI: 1.83-2.11). Odds of unplanned readmissions did not differ according to understanding of medications (OR=1.08; 95%CI: 0.90-1.30), understanding responsibility for one’s health (OR=1.02; 95%CI: 0.86-1.20), or discussion of help needed when returning home (OR=1.03; 95%CI: 0.93-1.14). Conclusion Our results demonstrate that a lack of patient involvement in their care and not receiving written information at discharge is associated with increased unplanned readmission rate up to one-year post-discharge. This present study provides an example of how patient-reported measures may be linked to individual-level administrative data to drive healthcare improvements. Future research examining patient-reported hospital experience and other health system measures is warranted.

Objectives An increasing number of physicians are remunerated by alternative forms of payment, instead of conventional fee-for-service (FFS) payments. Changes in physician remuneration methods can to influence the completeness of physician billing claims databases, because physicians on alternative payments may not consistently complete billing records. However, there is no established technique to estimate the magnitude of data loss. This proof-of-concept study estimated completeness of physician claims by comparing them with prescription drug records. We applied the method to estimate completeness of non-fee-for-service (NFFS) and FFS physician claims data over time in Manitoba, Canada. Approach Our method uses information on the date of patient initiation of a new prescription medication, payment method of the prescribing physician, and presence/absence of a physician billing claim prior to the medication initiation date. A billing claim within 7 days of the medication initiation date was defined as a captured claim; if there was no claim in this observation window, it was classified as missed. Our method was applied to annual patient cohorts who initiated a common prescription medication (i.e., anti-hypertensives) between fiscal years 1998/99 and 2012/13. A sensitivity analysis used a 21-day observation window to identify captured/missing claims. Multivariable hierarchical logistic regression models tested patient and prescriber characteristics associated with missing claims. Results The cohort consisted of 274, 462 individuals with a new anti-hypertensive prescription medication. A total of 9.2% of the cohort had a NFFS prescribing physician in 1998/99; this increased to 20.2% in 2012/13 (linear trend p-value < .0001). The percentage of NFFS prescribers almost doubled, from 10.0% to 17.8%. The percentage of the annual cohorts with a FFS prescribing physician and a missing claim remained close to 13.0%. However, the percentage of the annual cohorts with a NFFS prescribing physician and a missing claim increased from 15.6% to 23.3% (linear trend p-value < .0001), and was always higher than the FFS percentage. Patient age, sex, and comorbidity and physician specialty and practice location were associated with the odds of a missing claim. Conclusion The percentage of missing claims was higher for patients with NFFS than FFS prescribing physicians, demonstrating the impact of physician remuneration on database completeness. The trend of greater data loss in later than earlier years suggests that completeness of physician billing claims data may be decreasing. Our method can be applied across jurisdictions to compare the impact of physician payment methods on data quality.