Improving the accuracy of ICD-10 coding of morbidity/mortality data through the introduction of an electronic diagnostic terminology tool at the general hospitals in Lagos, Nigeria

Abstract

BackgroundReliable information which can only be derived from accurate data is crucial to the success of the health system. Since encoded data on diagnoses and procedures are put to a broad range of uses, the accuracy of coding is imperative. Accuracy of coding with the International Classification of Diseases, 10th revision (ICD-10) is impeded by a manual coding process that is dependent on the medical records officers’ level of experience/knowledge of medical terminologies.Aim statementTo improve the accuracy of ICD-10 coding of morbidity/mortality data at the general hospitals in Lagos State from 78.7% to ≥95% between March 2018 and September 2018.MethodsA quality improvement (QI) design using the Plan–Do–Study–Act cycle framework. The interventions comprised the introduction of an electronic diagnostic terminology software and training of 52 clinical coders from the 26 general hospitals. An end-of-training coding exercise compared the coding accuracy between the old method and the intervention. The outcome was continuously monitored and evaluated in a phased approach.ResultsResearch conducted in the study setting yielded a baseline coding accuracy of 78.7%. The use of the difficult items (wrongly coded items) from the research for the end-of-training coding exercise accounted for a lower coding accuracy when compared with baseline. The difference in coding accuracy between manual coders (47.8%) and browser-assisted coders (54.9%) from the coding exercise was statistically significant. Overall average percentage coding accuracy at the hospitals over the 12-month monitoring and evaluation period was 91.3%.ConclusionThis QI initiative introduced a stop-gap for improving data coding accuracy in the absence of automated coding and electronic health record. It provides evidence that the electronic diagnostic terminology tool does improve coding accuracy and with continuous use/practice should improve reliability and coding efficiency in resource-constrained settings.

Impact of electronic medical records on healthcare delivery in Nigeria: A Review

“…A striking facet that the studies unveil is the catalytic role of EMRs in mitigating errors resulting from incomplete or outdated information. The included studies have been examined in programs deploying EMR to strengthen public health programs [20], medical and nursing care [21,22] and laboratory testing [24]. The direction of our findings was similar – included studies consistently demonstrated that the EMR improved the quality of medical record data in Nigeria.…”

“…These studies were all conducted in public secondary and tertiary hospitals with the sample population ranging from doctors and nurses to patients. The risk of bias in the studies were low risk for two studies[20,21], some concern for one study [22], and high risk for two studies[23,24]. The search details are shown in Table 1 with the summary of included studies shown in Table 2.…”

“…Four studies examined the influence of the introduction of [20,21,24] or improvements in [22] the EMR on data collection and documentation, with important implications for public health. The first, a longitudinal study [20], examined the feasibility of using the Child Electronic Growth Monitoring System (CEGROMS) to monitor the growth of infants in the Child Welfare Clinic of Barau Dikko Teaching Hospital, Kaduna, Kaduna State as part of the Kaduna Infant Development (KID) Birth Cohort Study [37] over nine months.…”

“…Four studies examined the influence of the introduction of [20,21,24] or improvements in [22] the EMR on data collection and documentation, with important implications for public health.…”

Factors Affecting Clinical Coding Errors

“…Clinical coding enables epidemiological studies and measurement of treatment effectiveness in health care systems (1). It involves translating the medical terms recorded by physicians into codes that are used to describe patient complaints, problems, diagnoses, treatments, or the reasons why patients visit medical centers (2).…”