Factors affecting clinicians’ adherence to principles of diagnosis documentation: A concept mapping approach for improved decision-making

Abstract

Background: The quality of data in electronic health records (EHRs) depends on adherence of clinicians to principles of diagnosis documentation. Objective: A concept mapping (CM) approach was used to extract factors related to quality of clinicians’ documentation that govern EHR data quality. Method: Influential factors extracted from brainstorming sessions were sorted by individual participants, followed by a quantitative analysis using multidimensional scaling and cluster analysis to categorise sorted factors. Finally, a questionnaire was used to elicit the importance-feasibility of the extracted factors. Results were visualised by cluster maps and Go-Zone plots. Result: Factors were classified into seven clusters: “knowledge about International Classification of Diseases and clinical coding,” “need for facilitators and guidelines,” “explaining the importance of the issue and defining responsibilities,” “cooperation of other personnel,” “codify legal requirements,” “workload” and “clinical obstacles,” as ranked by importance. Conclusion: To enhance the quality of EHR data, a collaboration between physicians, nurses, managers and EHR developers is required. CM is an acceptable approach to meet this objective. Our findings highlight the significance of clinical coding knowledge, awareness about its importance and applicability and use of well-structured information systems. In combination, these three factors can have a strong positive impact on the quality of EHR data. Implications: A list of solutions is provided for policymakers, and two interventions suggested, based on the findings of this study, including the adoption of EHRs that incorporate documentation guidelines. We further propose updated clinical training programs and a monitoring and feedback mechanism to facilitate the EHR documentation process.

Digital Health Data Quality Issues: Systematic Review

“…Some studies have proposed techniques for improving completeness, including developing fit-for-purpose user interfaces [ 68 , 96 , 97 ], standardizing documentation practices, [ 98 , 99 ], automating documentation [ 100 ], and performing quality control [ 99 ].…”

“…For data to be considered complete, it needs to be captured at sufficient breadth and depth over time [12,18]. Some studies have proposed techniques for improving completeness, including developing fit-for-purpose user interfaces [68,96,97], standardizing documentation practices, [98,99], automating documentation [100], and performing quality control [99].…”

Factors affecting the quality of diagnosis coding data with a triangulation view: A qualitative study

“…The importance of clinical documentation improvement was reported in other studies 18,39 . As such, we conducted a study using a concept mapping approach to extract factors affecting clinicians’ adherence to principles of diagnosis documentation from the viewpoint of physicians 40 . The extracted factors were classified into 7 clusters including: ‘knowledge about ICD and clinical coding,’ ‘need for facilitators and guidelines,’ ‘explain the importance of the issue and define responsibilities,’ ‘cooperation of other personnel,’ ‘codify legal requirements,’ ‘workload,’ and ‘clinical obstacles,’ as ranked by their importance.…”

“…18,39 As such, we conducted a study using a concept mapping approach to extract factors affecting clinicians' adherence to principles of diagnosis documentation from the viewpoint of physicians. 40 The extracted factors were classified into 7 clusters including:…”

Clinical documentation integrity: Its role in health data integrity, patient safety and quality outcomes and its impact on clinical coding and health information management

“…Jebraeily et al (2023) found causes of clinical coding problems standing in the way of quality improvement fell into five groupings: policies, protocols and processes; individual factors; equipment and materials; working environment and management factors; and quality of clinical coding could be improved by hospital managers and health policymakers focusing on strategies and solutions that target these root causes. Hosseini et al (2022) argued that data quality enhancement requires collaboration between physicians, nurses, managers and developers of software, suggesting a concept mapping approach to meet this objective. Kilkenny et al (2023) recommended ongoing education for CCs to achieve high-quality coding and more accurate data.…”