Clinical coding quality is increasingly becoming an important arm in health and statistics. The objective of this research was to establish whether training could improve the quality of clinical coding in Nairobi City County Hospitals. A beforeand-after interventional design was used for the study. The study was conducted at Mbagathi County Referral Hospital and Mama Lucy Kibaki Hospital, with the latter acting as the control group. The study took the form of a baseline and two followup studies. The intervention was training on ICD-10. A sample of 612 subjects with 306 cases from each hospital was audited. Pretesting was conducted at Mama Lucy Kibaki Hospital. Data analysis was done using Statistical Package for Social Science (SPSS) Version 25. Fisher’s Exact and Paired T- test were conducted to establish the significance of differences between the two groups. The study revealed a low proportional (52%) of files were coded in MCRH than in MLKH (62%) therefore, biasing the intervention to MCRH. The mean for MLKH was 3.63 ± 0.916 compared to 3.56 ± 726 for MCRH. The mean difference of on how to use of ICU-10 was 0.25. The mean speed of coding was better in MCRH (4.00 ± 1.000) than in MLKH (3.13 ± 1.458). Coding of cause of death was wanting in MCRH (4.00 ± 1.453) than in MLKH (4.13 ± 0.35). Completeness also varied across. The difference in coding of external injury files between MLKH and MCRH prior to and after intervention was explicit. Coding of external injury files in the intervention arm improved to 100% from 97.3%. While that of control arm enhanced from 50% to 83.3%. The fisher exact p value was <0.001 before intervention but reduced 0.018 post intervention. Coding for medical procedure files was much less complete before training at 33.3% in MLKH and 93.3% in MCRH. However, coding changed to 83.3% and 100% correspondingly after the training. The Fisher Exact p-value for coding of medical procedures was <0.001 prior to training and 0.001 after training. Accuracy in assigning the appropriate code for diseases and injuries significantly varied after training (p=<0.001) contrary to indifferent (p=0.665) before training. However, the difference before (p<0.001) and after the intervention (p<0.001) in assigning the appropriate code for medical procedure was evident. Accuracy in assigning the appropriate code for death certification also varied significantly before (p=0.009) and after the intervention (p<0.001). The study revealed mean difference after the training. T-Test was statistically significant in death certification (t=-12.283; d.f=38; p=0.000), assigning the appropriate code for medical procedure (t=6.969; d.f=42; p=0.000) and assigning the appropriate code for external causes of injuries (t=-4.953; d.f=73; p=0.000). Appropriate code for comorbidities was (t=7.473; d.f=78), p=0.000), correct code for diseases and injuries (t=-5.015; d.f=226; p=0.000). The study findings support the hypothesis that training of health records and information officers significantly improved the quality of clinical coding. The International Classification of Diseases (ICD) is the customary instrument used in diagnostics for epidemiology, clinical purposes and health management . It includes an examination of specific cohorts and their overall well-being. The ICD tool is important in monitoring incidence or prevalence of specified diseases and other health related problems. Therefore, ICD provides an overall picture of the health status of people and countries. ICD is used widely in the health sector by health care providers, policy-makers and facilities. ICD is applicable in classification of diseases and other health related problems recorded in the different forms of health and vital records like health records and death certificates. The annals enable easy loading and retrieval of information multiple reasons, one being compilation of national statistics on mortality and morbidity by the WHO Member States . Disease classification is defined as a system used in the categorization of morbid entities in accordance with an established criteria . The axis of the classification depends on the intended use of the compiled statistics. The tenth revision of the International Statistical Classification of Disease and Related Health problems famously known as ICD-10 is the latest in the series. The contents of ICD-10 have been divided into three major volumes. Volume 1 is a tabular list that contains reports of the 10th revision international conference, the classification at three and four character levels and classifications of neoplasm morphologies, a special tabulated list of morbidity and mortality, nomenclature regulations and definitions. Volume 2 is an instructional manual that brings together the write-ups on the classification and certification in volume 1. It has the background, instruction and guidance manual on the use of volume 1 and the historical background of the ICD. Volume 3 contains an index with an introduction and more expanded instructions for its use. Each volume of the ICD has two sections. The first section has an alphabetical order of the components with their codes while the second section has a numerical tabular list of the codes of the same contents . The clinical coding in Kenya is based on the WHO  guidelines on International Statistical Classification of Diseases and Related Health Problems published in 2006. However, these guidelines were designed having in mind
the developed world; thus there is need for relevant authorities to initiate a training manual for the coders so as to reflect applicability within the local context (WHO) . Work on the 10th revision of the ICD started in September 1983 when a Preparatory Meeting on ICD-10 was convened in Geneva . International Classification of Diseases-10 contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases . World Health Organization (WHO) brought out the 10th version of ICD-10 in 1993 for methodical coding of illness and death causes in the medical records of medical organizations to be used for reporting by the member states. In global health estimates technical paper  the countries that have adapted accuracy and completeness in reporting using coding method were included in their 2010–2012 report . The inclusion criteria included level of completeness of recorded mortality data. The pace of implementation and adoption of ICD-10 in many countries, Kenya included, is diverse and does not live up to the reported standards put forth by WHO . Understanding and acceptance of ICD as reporting tool is major concern . The use of ICD in developed countries such as Kenya is unique and challenging due to its clinical nature . Failure to follow some basic rules of coding as well as misreporting by clinicians is also a major hindrance to ensuring high quality clinical coding . Clarity of abbreviations is a major concern and source of error in clinical coding . Also touted as a major source of error are incomplete or inaccurate code descriptions which vary from coder to coder or from one health professional to another. Training and awareness have been advanced as a remedy, yet trials of their effect are limited. Materials and Methods The study was carried out at Mama Lucy Kibaki County Hospital and Mbagathi County and Referral Hospital both public hospitals in Nairobi City County. Mama Lucy Kibaki County Hospital was selected as the control site while Mbagathi County and Referral Hospital was the intervention site based on the results of the baseline study. This was an interventional trial that used a before-and-after on the results, coding was influenced by both coder awareness level, keenness in documentation and interpretation. The study revealed the importance of adequate training, planning and awareness as key ingredients to effective implementation of ICD-10. Enhanced training improves documentation, which in turn enables providers to analyze patient details, thereby leading to better care coordination and health outcomes. The study recommends greater investment in staff through ICD-10 training and recruitment as well as IT systems across all hospitals within the county.
Kiongo JG, Yitambe A and Otieno GO