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Background: Clinical coding is an integral part of health information management (HIM) practice which provides
valuable data for healthcare quality evaluation, health resource allocation, health services research, medical billing, public
health programming, Case-Mix/DRG funding. The International Statistical Classification of Diseases and Related H...
Context in source publication
Context 1
... other duties include data entry (55, 27.1%), general HIM duties (50, 24.6%), documentation (43, 21.2%), quality assurance (27, 13.3%) and data analysis (25, 12.3%). Table 2 below shows that type of workplace is associated with clinical coding practices. For instance, private or for- profit healthcare facilities were reported to have good disposition especially towards coding automation (100%) and professionalism (100%) except that they were poor in the area of special coding education (0%). ...
Citations
... In Nigeria, clinical coding is the major source of hospital-based morbidity and mortality data and it serves as an important tool for the assessment of hospital performance and community health status. It is being undertaken in most of the tertiary healthcare facilities in the country 4 . In addition to the use in clinical research, clinical coding also serves in developing healthcare policies, medical billing and funding strategies. ...
... Clinical coding enables hospital episodes to be grouped into meaningful categories, helping healthcare managers to better match patient needs to healthcare resources. In Nigerian tertiary hospitals,clinical coding are done manually this is as a result of various failed attempt to introduce the electronic health records 4 .The International Classification of Diseases (ICD) and Office of the Population, Censuses and Surveys (OPCS) classification of interventions and procedures are used for coding diagnosis and procedures from hospital episodes 5,6 . Accuracy in clinical coding continues to be of great concern both as a result of the application of ICD codes for purposes other than those for which the classifications were originally designed as well as because of the widespread use for making farreaching decisions on funding, clinical, and research 7 . ...
Background: Clinical coding is an important aspect of health information management and the process must be accurate as mistakes in coding can lead to multiplication of errors in patient care and clinical research. Coding accuracy measures the level of agreement between the disease classification systems code(s) and the selected code(s) recorded in the discharged record by the Coder. The integrity of data from clinical coding depends fundamentally on the quality of clinical documentation, availability of discharge summary in the patient record and Coders ability. The study examine accuracy in terms of levels of agreement and including completeness of codes and factors that may contribute to error in coding. Methods: A sample of 2000 discharged patients' health records that had been previously coded was randomly selected and re-coded by an experienced Clinical coder. Data extraction format was used to extract information on coding accuracy and factors that could lead to errors in coding. Data analyses were done using SPSS Version 25 with focus on descriptive statistics. Results: Coders in the clinical coding unit of the hospital are Health Information Management Professionals, with no formal training in coding but, on-the-job training. Discharge summary is not completed in most discharged patients' health records therefore, coders read through records to select diagnoses for coding. Conclusion: Absence of discharge summary could be counterproductive to clinical coding process in the hospital, resulting to time wasting, incomplete coding and coding error. Clinicians are therefore advised to write discharged summary in order to reduce coding error. Clinical Coding should be seen as an area of specialization in HIM hence, coders should be specially trained and encouraged to attend continuing professional development programme related to clinical coding. The Department of Health Records should retain experienced clinical coders and the clinical coding unit should be well-staffed to reduce work-load that could increase error in coding.
... Understanding the clinical coding problems, and analysing their underlying causes, are of special importance to improve the clinical coding quality (Alonso et al., 2020;Doktorchik et al., 2020;Zafirah et al., 2018). In this regard, different studies highlighted the most important factors leading to incorrect clinical coding in different countries such as incomplete documentation of medical records, failure to implement standard documentation and clinical coding, shortage of clinical coders, and inappropriate guidelines (Adeleke et al., 2015;Alonso et al., 2020;Doktorchik et al., 2020;Priyatilake et al., 2019;Tang et al., 2017). Investigating the experiences of clinical coders and health information managers can help identify clinical coding problems. ...
... There are several studies in this respect in Nigeria, Canada, and Portugal (Adeleke et al., 2015;Alonso et al., 2020;Doktorchik et al., 2020;Tang et al., 2017); however, to our best knowledge, the Iranian clinical coders' perspectives on clinical coding problems have not been reported. ...
... In terms of individual factors, the most important root causes of problems were the shortage of clinical coders, lowskilled coders, clinical coders' insufficient communication with physicians and lack of continuing education courses. Adeleke et al. (2015) also indicated that the inadequate number of clinical coders, insufficient trainers for clinical coding, and inexperienced clinical coders were the most important factors that influenced clinical coding errors in Nigeria. A Canadian study similarly highlighted clinical coders' experience, their limited opportunities for training and continuing education, and increased workload as the reasons for decreased clinical coding accuracy (Doktorchik et al., 2020). ...
Background
Improving the quality of coded data requires the identification and evaluation of the root causes of clinical coding problems to inform appropriate solutions.
Objective
The objective of this study was to identify the root causes of clinical coding problems.
Method
Twenty-one clinical coders from three cities in Iran were interviewed. The five formal categories in Ishikawa's cause-and-effect diagram were applied as pre-determined themes for the data analysis.
Results
The study indicated 16 root causes of clinical coding problems in the five main themes: (i) policies, protocols, and processes (lack of clinical documentation guidelines; lack of audit of clinical coding and feedback to clinical coders; the long interval between documentation and clinical coding; and not using coded data for reimbursement; (ii) individual factors (shortage of clinical coders; low-skilled clinical coders; clinical coders' insufficient communication with physicians; and the lack of continuing education; (iii) equipment and materials (incomplete medical records; lack of access to electronic medical records and electronic coding support tools; (iv) working environment (lack of an appropriate, dynamic, and motivational workspace; and (v) management factors (mangers' inattention to the importance of coding and clinical documentation; and to providing the required staff support.
Conclusion
The study identified 16 root causes of clinical coding problems that stand in the way of clinical coding quality improvement.
Implications
The quality of clinical coding could be improved by hospital managers and health policymakers taking these problems into account to develop strategies and implement solutions that target the root causes of clinical coding problems.
... Maps will standardize the translation of coding systems to a certain extent and therefore improve coding accuracy simply and efficiently. Experts review a code resulting from a map that was successful and necessary to ensure accuracy concerning the context of a specific patient encounter and compliance with applicable coding guidelines and reimbursement policies (20,21). ...
Introduction: Medical coding is the transformation of healthcare diagnosis, procedures, medical services, and equipment into universal medical alphanumeric codes. Utilization of international disease classification provides higher-quality information for measuring healthcare service quality, safety, and efficacy. The Ethiopian National classification of disease (NCoD) was developed as part of Health Management information System (HMIS) reform with consideration of accommodating code in International Classification of disease (ICD-10). There is limited resource about the utilization status and related determinants of NCoD by health care professionals at tertiary level hospitals. This study is designed to assess the utilization status of NCoD and improve the quality of clinical coding through mapping of NCoD and ICD-10. Methods: Quasi-experimental study considering "Mapping" as an intervention was employed in this study. Retrospective medical record reviews were carried out to assess the utilization of NCoD and its challenges at Tikur Anebsa Specialized Hospital (TASH) for a period of one year (2018/2019). Qualitative approach used to get expert insight on NCoD implementation challenges and design of mapping exercises as an intervention. Seven thousand five hundred forty-seven (20%) of the medical records from the total of 37,734 medical records were selected randomly for review. A data abstraction checklist was developed to collect relevant information on individual patient charts, patient electronic records specific on a confirmed diagnosis. The reference mapping approach was employed for the mapping output between ICD-10 and NCoD. Both ICD-10 and NCoD were mapped side by side using percentage comparison and absolute difference. Result: Data for document review was taken from the electronic medical record database. Out of the total, 3021 (40%) of records were miss-classified based on the national classification of disease. From the miss-coded record, 1749 (58%) of them used ICD code to classify the diagnosis. Reasons provided for poor utilization of NCoD among physicians include, perception of having a limited list of diagnosis in the NCoD, not being familiarized, inadequate capacity building about NCoD use, and absence of enforcing mechanism on the use of standard diagnostic coding among professionals. Utilization of disease classification coding provides higher-quality information for measuring healthcare service quality, safety, and efficacy. This will in turn provide better data for quality measurement and medical error reduction (patient safety), outcomes measurement, operational planning, and healthcare delivery systems design and reporting. Conclusion: Extended NCoD categories were mapped from ICD-10. Standard ways of coding disease diagnosis and coding of new cases into the existing category was established. This study recommends that due emphasis should be given in monitoring and evaluation of medical coding knowledge and adherence of health professionals, and it should be supported with appropriate technologies to improve the accessibility and quality of health information. [Ethiop. J. Health Dev. 2021; 35(SI-1):59-65]
... The starting point to sustaining the quality of disease coding is establishing its utilization [8]. For instance, in Africa, only South Africa has properly structured standards for guiding the utilization of ICD-10 in clinical coding [9]. Kenya, therefore, falls under the many nations lacking genuine national standards for ICD-10 usage, tailored to the local challenges. ...
... HIM has a central role in the modern healthcare system and refers to the control and management of healthcare data, which includes the coding of diagnoses and procedures, the storage of medical records and other individual patient data, as well as the billing process (Adeleke et al., 2015). Fiorito and Edens (2016, p. 2) proposed the following definition: 'HIM is the practice of acquiring, analysing, and protecting digital and traditional medical information vital to providing quality patient care.' ...
Rapid economic growth resulting from the ascendancy of Saudi Arabia as an international oil producer, and the recognition by the government of the right of all citizens and most expatriate workers to free healthcare facilitated the development of a three-tier health system ranked 26th in the world by the World Health Organisation in 2000. Concurrently, the increasing financial burden of interwoven demographic and socioeconomic factors such as unprecedented population growth, increased life expectancy, and the rise of noncommunicable diseases, necessitated the diversification of health funding in the form of mandatory healthcare insurance. The coding of the clinical documentation of diagnoses and interventions of patient health episodes by clinical coders has become the international standard for submitting health insurance claims and in 2013, a contract was negotiated with the Australian government to adopt the complete ICD-10-AM package.
A mixed methods approach was selected to determine the factors impacting on the ICD-10-AM implementation in seven public hospitals, which had not previously submitted claims or employed clinical coders. Data were obtained from a quantitative Likert scale questionnaire completed by a random sample of 283 respondents and a qualitative semi-structured interview was conducted with seven purposively selected experts while only one physician indicated a desire to be interviewed. Instrument design and content were based on factors drawn from ICD-10 implementation literature representing developed and developing nations. The reviewed Saudi literature covered healthcare management, staffing conditions, inadequate technology and interoperability, and the failure to follow through with previous reform attempts.
Derived factors were categorised as organisational (planning, staffing, training, and technology); Health information (purpose, benefits, practice, and a knowledge of anatomy, pathology, and interventions); National (implementation support, funding, maintenance, upgrading, and the unified system). SPSS computation of the 5-point Likert scale (1 = strongly agree; 5 = strongly disagree) yielded an overall mean of 4.01 for the 23 items, foreshadowed by a strong negative response to three demographic items querying prior clinical coding certification or ICD-10 training, and implementation status. A 9% minority of highly qualified professionals differed from the majority. Three years after the original implementation date, factors deemed essential, particularly organisational awareness, training, and adequate staff specialists were still being ignored.
Most respondents had been excluded from job-specific training, showed little understanding of the relevance of ICD-10 and clinical coding in health information management, or a vision of their hospital as a component of a national system. In the only hospital practicing clinical coding it was tasked to the physicians, continuing a Saudi pattern of mediocre reform attempts symptomatic of a fragmented health system lacking leadership.
... Codes also allow insurance providers to map equivalences across different healthcare providers who may use different terminologies or abbreviations in claim forms. 4,5 The standard of medical coding for diagnosis that is widely used around the world is The International Statistical Classification of Diseases and Related Health Problems (ICD). ICD is published by the World Health Organization (WHO) and is a classification of diseases, signs and symptoms, abnormal fidings and complaints, social circumstances, and external causes of injury or disease. ...
Background: The accuracy of clinical coding is very important in the proper financing of health care centers. During January to February 2019, only 35 out of 60 obstetrical cases that were well coded (58%) in Naili DBS Hospital and this miscoding would led to a big financial loss. The aim of this study is to determine the effect of training on coding accuracy.Methods: This study was conducted during April 2019 in Naili DBS Hospital using quasi experimental method, with one group pretest and post-test design. All 11 participants were given a pretest consisted of 10 long cases (maximum score=38) and the training was conducted based on the identified needs from the preliminary audit. They were then given a post-test to see the effect of the training.Results: The mean score of pretest was 10.7 and the mean score of post-test was 19.7. The individual scores were normalized and then analyzed using SPSS with paired sample T-test. Based on statistical analysis, p<0.005 meaning the traning is statistically significant on improving the coding accuracy in obstetrical diagnosis.Conclusions: The training has significantly improved the score of well coded obstetrical diagnosis, even though the participants have not reached the maximum score. Furthermore, our study suggests that it is important to analyze the coders’ performance months after the training by conducting a coding audit.
... Studies in Sri Lanka [33], Thailand [34], and China [35] have revealed massive misclassification of the cause of death in hospitals. On the contrary, a recent study in Nigeria, reported a high level of nationwide implementation of ICD-10 [36]. Generally, poor quality of data was observed in the majority of hospitals in Tanzania. ...
Background
Accurate and reliable hospital information on the pattern and causes of death is important to monitor and evaluate the effectiveness of health policies and programs. The objective of this study was to assess the availability, accessibility, and quality of hospital mortality data in Tanzania.
Methods
This cross-sectional study involved selected hospitals of Tanzania and was carried out from July to October 2016. Review of hospital death registers and forms was carried out to cover a period of 10 years (2006–2015). Interviews with hospital staff were conducted to seek information as regards to tools used to record mortality data, staff involved in recording and availability of data storage and archiving facilities.
Results
A total of 247,976 death records were reviewed. The death register was the most (92.3%) common source of mortality data. Other sources included the International Classification of Diseases (ICD) report forms, Inpatient registers, and hospital administrative reports. Death registers were available throughout the 10-year period while ICD-10 forms were available for the period of 2013–2015. In the years between 2006 and 2010 and 2011–2015, the use of death register increased from 82 to 94.9%. Three years after the introduction of ICD-10 procedure, the forms were available and used in 28% (11/39) hospitals. The level of acceptable data increased from 69% in 2006 to 97% in 2015. Inconsistency in the language used, use of non-standard nomenclature for causes of death, use of abbreviations, poorly and unreadable handwriting, and missing variables were common data quality challenges. About 6.3% (n = 15,719) of the records had no patient age, 3.5% (n = 8790) had no cause of death and ~ 1% had no sex indicated. The frequency of missing sex variable was most common among under-5 children. Data storage and archiving in most hospitals was generally poor. Registers and forms were stored in several different locations, making accessibility difficult.
Conclusion
Overall, this study demonstrates gaps in hospital mortality data availability, accessibility, and quality, and highlights the need for capacity strengthening in data management and periodic record reviews. Policy guidelines on the data management including archiving are necessary to improve data.
... Clinical coding is an integral part of HIM practice which provides valuable data for health care quality evaluation, health care resource allocation, health services research, medical billing, public health programming and Case-Mix/DRG funding. [1] In sub-Saharan Africa, several countries use their own coding system by (1) setting up their own code system, (2) using an in-house designed code system based on another reference terminology, or (3) by making use of an existing code system. However, to the best of our knowledge, there is little literature about clinical coding in sub-Saharan Africa. ...
Clinical coding is a requirement to provide valuable data for billing, epidemiology and health care resource allocation. In sub-Saharan Africa, we observe a growing awareness of the need for coding of clinical data, not only in health insurances, but also in governments and the hospitals. Presently, coding systems in sub-Saharan Africa are often used for billing purposes. In this paper we consider the use of a nomenclature to also have a clinical impact. Often coding systems are assumed to be complex and too extensive to be used in daily practice. Here, we present a method for constructing a new nomenclature based on existing coding systems by considering a minimal subset in the sub-Saharan region. Evaluation of completeness will be done nationally using the requirements of national registries. The nomenclature requires an extension character for dealing with codes that have to be used for multiple registries. Hospitals will benefit most by using this extension character.
Background Reliable 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 statement To 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.
Methods A 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.
Results Research 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%.
Conclusion This 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.
BACKGROUND: The introduction of a mandatory health insurance system contributing towards the funding of national healthcare in Saudi Arabia necessitates the implementation of clinical coding and a unified health classification system, which has previously not been a feature of Saudi healthcare. As the Ministry of Health (MOH) moves to introduce ICD-10-AM, the Australian modification of the WHO ICD-10, in the Kingdom’s public hospitals, it is important to understand the factors that will influence its successful implementation. OBJECTIVE: The purpose of this article is to develop and evaluate the internal consistency reliability and validity of a questionnaire establishing the factors influencing the the implementation ICD-10-AM and clinical coding in Saudi public hospitals. METHOD: The content validity method was initiated by sending the whole draft questionnaire to a panel of experts to indicate values for each item based on a scale of content validity created by the researchers and, subsequently, using the internal consistency reliability and factorial validity methods to estimate the internal reliability of clusters of items, which were assumed to measure the same factors, grouped in this study into three factorial categories, health information (clinical documentation, classification, and coding requirements), organization (the implementation preparation in individual organizations), and national (institutional support through the national hierarchical structure). RESULTS: The content validity identified all items of the proposed questionnaire to be valid. Based on the content validity test, several items were removed as they did not meet the proposed model and the final questionnaire was created in accord with the pilot study result. The pilot study utilized Cronbach's α and factor analysis to examine the reliability and validity of Part 2 of the questionnaire and the findings indicated high internal consistency reliability and factorial validity.
