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

Abstract

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.

Classification performance of administrative coding data for detection of invasive fungal infection in paediatric cancer patients

“…Although it is a reasonable assumption that clinical coder experience is associated with our misclassification estimates, in the setting of IFI where clinical case definitions are complex, it is conceivable that other factors are at play. This includes the complexity of translating clinical data indicating invasive aspergillosis into ICD-10-AM [ 24 , 26 ], the absence of clear definitions [ 27 , 28 ], subjective interpretation of existing guidelines [ 24 , 25 , 27 ], delays in diagnosis [ 29 ], and the review of multiple data sources to make a confirmatory diagnosis of mould infection [ 1 , 2 , 30 ]. This setting underscores the importance of clear, complete, legible and standardised documentation of IFI to mitigate misclassification in current coding workflows.…”

“…Although the number of invasive aspergillosis cases in the gold-standard data were small (N = 15), our findings are likely indicative of the uncertainty in discriminating between yeast and mould infections at the clinical coding level. A recent qualitative study [25] identified clinical coders' experience and awareness of IFI as a factor associated with discordant coding. Although it is a reasonable assumption that clinical coder experience is associated with our misclassification estimates, in the setting of IFI where clinical case definitions are complex, it is conceivable that other factors are at play.…”

“…First, harmonisation of clinical EORTC/MSG definitions with existing Australian Coding Standards may help safeguard accurate detection of IFI in the coded data by reducing ascertainment of false positive cases (for example, our high number of false positive cases [N = 38] coded as 'candidiasis of other sites' [code B37.88]). In fact, recent qualitative research proposes the use of Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) codes in electronic health records as a more granular tool to standardise terminology and facilitate clinical coding of complex diseases [25,31,32]. Second, ensuring that chart documentation is complete, legible and streamlined will ensure clinical coders have sufficient access to the data required to assign the appropriate IFI code(s) [25,33,34].…”

Findings from the Health Information Management Section of the 2020 International Medical Informatics Association Yearbook

“…Methods to assess the quality differed across studies. One approach to assessing quality involved focus groups and interviews of Health Information Managers and coders [1,2]. This qualitative research found problems in completeness, accuracy, and consistency in coding.…”

Predictors of missing Electronic Medical Record (EMR) discharge summaries: A retrospective study on data from a large Canadian cohort

“…The inpatient database relies heavily on electronic inpatient documents (specifically the discharge summaries). When the discharge summary (DS) is unavailable electronically, trained coders use the paper DS, which often presents challenges in discerning illegible handwriting for the presence of codable diagnoses [4]. As a result, many healthcare facilities are transitioning to a fully electronicbased documentation system [5].…”