Promotion of training course on ICD-10 Poisoning coding : necessity to adopt preventive strategies.

BACKGROUND: Poisoning is considered the most common cause of referral to emergency departments and hospitalization in the intensive care unit (ICU). Training or retraining of coders and ensuring the positive impact of these trainings in assigning accurate codes to poisoning cases is necessary to adopt practical health measures for optimal management of this disease. The present study aimed to evaluate the impact of holding a training course on poisoning coding rules based on ICD-10 in clinical coders. METHODS: This study is descriptive and analytical. With the target population included the coders of hospitals affiliated with Shahid Beheshti University of Medical Sciences (N = 45). In order to evaluate the training course on poisoning coding rules, the Conex Input Process Product (CIPP) evaluation model was used. This model was the first goal-oriented approach evaluation model. According to the CIPP model, evaluation of the training course held in four components, including Context factors (course objectives and priority of objectives), Input factors (instructor, curriculum, facilities, equipment, and training location), Process factors (teaching process, learning, management, and support), and Product factors (feedback, knowledge, and skills). A researcher-made questionnaire containing 39 questions with a 5-point Likert scale was used to collect data. The validity of the questionnaire was calculated through content validity, and its reliability was calculated using Cronbach's alpha coefficient (alpha = 90% in all components). In order to analyze the data, descriptive statistics (frequency percentage distribution) and inferential statistics (one-sample t-test) were used. RESULTS: The findings of this study were presented in four components of context, input, process, and product evaluation. The average criterion for all questions in the questionnaire was considered 3. As a result, the significance level obtained from the one sample t-test was equal to P = 0. 0001.The training course had a favorable effect in terms of context, input, process and products. CONCLUSION: The knowledge and skills of clinical coders can be enhanced by updating medical knowledge, holding training courses, workshops, seminars, and conducting clinical coder accreditation. Extensive and continuous training for clinical coders is essential due to the impact of code quality on financial forecasting, electronic health records, and conducting research.

Effect of change in coding rules on recording diabetes in hospital administrative datasets.

AIM: During 2008-2011 Australian Coding Standards mandated a causal relationship between diabetes and inpatient care as a criterion for recording diabetes as a comorbidity in hospital administrative datasets. We aim to measure the effect of the causality mandate on recorded diabetes and associated inter-hospital variations. METHOD: For patients with diabetes, all admissions between 2004 and 2013 to all New South Wales acute public hospitals were investigated. Poisson mixed models were employed to derive adjusted rates and variations. RESULTS: The non-recorded diabetes incidence rate was 20.7%. The causality mandate increased the incidence rate four fold during the change period, 2008-2011, compared to the pre- or post-change periods (32.5% vs 8.4% and 6.9%). The inter-hospital variation was also higher, with twice the difference in the non-recorded rate between hospitals with the highest and lowest rates (50% vs 24% and 27% risk gap). The variation decreased during the change period (29%), while the rate continued to rise (53%). Admission characteristics accounted for over 44% of the variation compared with at most two per cent attributable to patient or hospital characteristics. Contributing characteristics explained less of the variation within the change period compared to pre- or post-change (46% vs 58% and 53%). Hospital relative performance was not constant over time. CONCLUSION: The causality mandate substantially increased the non-recorded diabetes rate and associated inter-hospital variation. Longitudinal accumulation of clinical information at the patient level, and the development of appropriate adoption protocols to achieve comprehensive and timely implementation of coding changes are essential to supporting the integrity of hospital administrative datasets.