Abstract

The quality of clinical coding influences not only hospital revenue but also the quality and efficiency of healthcare services. Assessing the coders' satisfaction is essential to optimizing the quality of clinical coding. This mixed-method study used a qualitative approach to propose the study model while testing the model through a quantitative approach. The relevant variables of the satisfaction model were assessed through a survey targeting clinical coders across the country on a timely basis. Fourteen experts participated in establishing the model with three dimensions: professional, organizational, and clinical. Each dimension has its relevant variables. One hundred eighty-four clinical coders participated in phase two. 34.5% were male, 61% held a diploma, 38% had a bachelor's and above, and 49.7% worked in hospitals having fully electronic health records. We found that organizational and clinical dimensions strongly correlate with coders' satisfaction. Noticeably, the most influencing variables were the availability of coding policies and the computer-assisted coding (CAC) system. The results show that the model explains the satisfaction of the clinical coders, and organizational and clinical-related variables are crucial. Although gender-based differences exist, training (regardless of the training mode), coding policies, and the CAC system substantially influence coders' satisfaction. A significant stream of the literature supports these findings. However, attempting a holistic approach to assess coders' satisfaction and affecting coding quality is the added value of this study. Optimizing clinical coding practice requires organization-wide initiatives and policies to regulate coding practices and standards to promote the quality and timeliness of clinical documentation. Training is indispensable not only for clinical coders, but physicians also need to understand the rationale and value of clinical coding. Better utilization of the outcomes of the coding process and adopting the CAC system are significant drivers to enhance coders' satisfaction.

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