Training and support to improve ICD coding quality: A controlled before-and-after impact evaluation. Dyers Robin,Ward Grant,Du Plooy Shane,Fourie Stephanus,Evans Juliet,Mahomed Hassan South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde BACKGROUND:The proposed National Health Insurance policy for South Africa (SA) requires hospitals to maintain high-quality International Statistical Classification of Diseases (ICD) codes for patient records. While considerable strides had been made to improve ICD coding coverage by digitising the discharge process in the Western Cape Province, further intervention was required to improve data quality. The aim of this controlled before-and-after study was to evaluate the impact of a clinician training and support initiative to improve ICD coding quality. OBJECTIVE:To compare ICD coding quality between two central hospitals in the Western Cape before and after the implementation of a training and support initiative for clinicians at one of the sites. METHODS:The difference in differences in data quality between the intervention site and the control site was calculated. Multiple logistic regression was also used to determine the odds of data quality improvement after the intervention and to adjust for potential differences between the groups. RESULTS:The intervention had a positive impact of 38.0% on ICD coding completeness over and above changes that occurred at the control site. Relative to the baseline, patient records at the intervention site had a 6.6 (95% confidence interval 3.5 - 16.2) adjusted odds ratio of having a complete set of ICD codes for an admission episode after the introduction of the training and support package. The findings on impact on ICD coding accuracy were not significant. CONCLUSION:There is sufficient pragmatic evidence that a training and support package will have a considerable positive impact on ICD coding completeness in the SA setting. 10.7196/SAMJ.2017.v107i6.12075

Accuracy and Completeness of Clinical Coding Using ICD-10 for Ambulatory Visits. Horsky Jan,Drucker Elizabeth A,Ramelson Harley Z AMIA ... Annual Symposium proceedings. AMIA Symposium This study describes a simulation of diagnostic coding using an EHR. Twenty-three ambulatory clinicians were asked to enter appropriate codes for six standardized scenarios with two different EHRs. Their interactions with the query interface were analyzed for patterns and variations in search strategies and the resulting sets of entered codes for accuracy and completeness. Just over a half of entered codes were appropriate for a given scenario and about a quarter were omitted. Crohn's disease and diabetes scenarios had the highest rate of inappropriate coding and code variation. The omission rate was higher for secondary than for primary visit diagnoses. Codes for immunization, dialysis dependence and nicotine dependence were the most often omitted. We also found a high rate of variation in the search terms used to query the EHR for the same diagnoses. Changes to the training of clinicians and improved design of EHR query modules may lower the rate of inappropriate and omitted codes.

Construction of a semi-automatic ICD-10 coding system. Zhou Lingling,Cheng Cheng,Ou Dong,Huang Hao BMC medical informatics and decision making BACKGROUND:The International Classification of Diseases, 10th Revision (ICD-10) has been widely used to describe the diagnosis information of patients. Automatic ICD-10 coding is important because manually assigning codes is expensive, time consuming and error prone. Although numerous approaches have been developed to explore automatic coding, few of them have been applied in practice. Our aim is to construct a practical, automatic ICD-10 coding machine to improve coding efficiency and quality in daily work. METHODS:In this study, we propose the use of regular expressions (regexps) to establish a correspondence between diagnosis codes and diagnosis descriptions in outpatient settings and at admission and discharge. The description models of the regexps were embedded in our upgraded coding system, which queries a diagnosis description and assigns a unique diagnosis code. Like most studies, the precision (P), recall (R), F-measure (F) and overall accuracy (A) were used to evaluate the system performance. Our study had two stages. The datasets were obtained from the diagnosis information on the homepage of the discharge medical record. The testing sets were from October 1, 2017 to April 30, 2018 and from July 1, 2018 to January 31, 2019. RESULTS:The values of P were 89.27 and 88.38% in the first testing phase and the second testing phase, respectively, which demonstrate high precision. The automatic ICD-10 coding system completed more than 160,000 codes in 16 months, which reduced the workload of the coders. In addition, a comparison between the amount of time needed for manual coding and automatic coding indicated the effectiveness of the system-the time needed for automatic coding takes nearly 100 times less than manual coding. CONCLUSIONS:Our automatic coding system is well suited for the coding task. Further studies are warranted to perfect the description models of the regexps and to develop synthetic approaches to improve system performance. 10.1186/s12911-020-1085-4

Improving accuracy of clinical coding in surgery: collaboration is key. Heywood Nick A,Gill Michael D,Charlwood Natasha,Brindle Rachel,Kirwan Cliona C, The Journal of surgical research BACKGROUND:Clinical coding data provide the basis for Hospital Episode Statistics and Healthcare Resource Group codes. High accuracy of this information is required for payment by results, allocation of health and research resources, and public health data and planning. We sought to identify the level of accuracy of clinical coding in general surgical admissions across hospitals in the Northwest of England. METHOD:Clinical coding departments identified a total of 208 emergency general surgical patients discharged between 1st March and 15th August 2013 from seven hospital trusts (median = 20, range = 16-60). Blinded re-coding was performed by a senior clinical coder and clinician, with results compared with the original coding outcome. Recorded codes were generated from OPCS-4 & ICD-10. RESULTS:Of all cases, 194 of 208 (93.3%) had at least one coding error and 9 of 208 (4.3%) had errors in both primary diagnosis and primary procedure. Errors were found in 64 of 208 (30.8%) of primary diagnoses and 30 of 137 (21.9%) of primary procedure codes. Median tariff using original codes was £1411.50 (range, £409-9138). Re-calculation using updated clinical codes showed a median tariff of £1387.50, P = 0.997 (range, £406-10,102). The most frequent reasons for incorrect coding were "coder error" and a requirement for "clinical interpretation of notes". CONCLUSIONS:Errors in clinical coding are multifactorial and have significant impact on primary diagnosis, potentially affecting the accuracy of Hospital Episode Statistics data and in turn the allocation of health care resources and public health planning. As we move toward surgeon specific outcomes, surgeons should increase collaboration with coding departments to ensure the system is robust. 10.1016/j.jss.2016.05.023