Incorrect Code Research Articles

AI-Powered Coding Tools: A Study of Advancements, Challenges, and Future Directions

This review examines the effects of AI-assisted programming in contemporary software development, paying particular attention to tools driven by Large Language Models (LLMs), such as GPT-4o and GitHub Copilot. Starting with data processing and ending with model deployment, the review describes the standard workflow for training machine learning models and how programmers use these models to improve their coding processes. GitHub Copilot, an AI-powered code generator, and GPT-4o, a general-purpose LLM, are compared in terms of accuracy, usability, and efficiency when assisting with programming tasks. The results show that although both tools greatly facilitate coding, they each have particular advantages and disadvantages. GitHub Copilot is excellent at integrating with IDEs, providing contextual code recommendations and streamlining processes. In contrast, GPT-4o shows better accuracy when creating code from scratch, but it does not have Copilot’s seamless IDE integration. The review also identifies some of the current drawbacks of AI-powered coding tools, including the possibility of producing faulty or vulnerable code as a result of training on unreliable datasets and the inability to comprehend context, which can occasionally result in functionally correct but practically incorrect code. In order to filter and fix problematic code before training, the review recommends using advanced algorithms for data pre-processing. It also suggests improving the interpretability of code generated by AI to help developers better comprehend and trust the results.

Using H‐Convergence to Calculate the Numerical Errors for 1D Unsaturated Seepage Under Steady‐State Conditions

ABSTRACTUnsaturated zones are important for geotechnical design, geochemical reactions, and microbial reactions. The numerical analysis of unsaturated seepage is complex because it involves highly nonlinear partial differential equations. The permeability can vary by orders of magnitude over short vertical distances. This article defines and uses H‐convergence tests to quantify numerical errors made by uniform meshes with element size (ES) for 1D steady‐state conditions. The quantitative H‐convergence should not be confused with a qualitative mesh sensitivity study. The difference between numerical and mathematical convergences is stated. A detailed affordable method for an H‐convergence test is presented. The true but unknown solution is defined as the asymptote of the numerical solutions for all solution components when ES decreases to zero. The numerical errors versus ES are then assessed with respect to the true solution, and using a log–log plot, which indicates whether a code is correct or incorrect. If a code is correct, its results follow the rules of mathematical convergence in a mathematical convergence domain (MCD) which is smaller than the numerical convergence domain (NCD). If a code is incorrect, it has an NCD but no MCD. Incorrect algorithms of incorrect codes need to be modified and repaired. Existing codes are shown to converge numerically within large NCDs but generate large errors, up to 500%, in the NCDs, a dangerous situation for designers.

Tinjauan Ketepatan Pengkodean Penyakit pada Rekam Medis Pasien di Rumah Sakit Tk. III Dr. R. Soeharsono Banjarmasin

Medical recorders in assigning disease codes appropriately according to the classification applied in Indonesia use the International Statistical Classification of Diseases and Related Health Problems 10 (ICD-10) guidelines on diseases and medical actions in health services and management so that there are no errors in determining disease diagnoses and actions to be taken to patients. Determination of the diagnosis of the disease and the actions that will be taken to the patient. Based on preliminary studies conducted at the Tk. III Dr. R. Soeharsono Banjarmasin Hospital in January 2023, it was found that there were still incompletely written disease diagnoses and incorrect disease diagnosis codes with ICD-10. The purpose of this study was to determine the completeness of the writing of disease diagnoses and the accuracy of disease coding in patient medical records. This type of research is descriptive quantitative. The research design used a cross sectional approach. Sampling was carried out using purposive sampling method based on the number of patient medical records on January 2 - January 28, 2023 and obtained 96 patient medical records. Data collection methods using observation and questionnaires. The conclusion is that in 96 patient medical record files at the Tk. III Dr. R. Soeharsono Banjarmasin Hospital there are 12 incomplete diagnoses filled in and there are 3 incorrect disease diagnosis codes, the cause is because the diagnosis coder has never attended training related to coding competence.

Impact of Inaccurate Clinical Coding on Financial Outcome: A Study in a local hospital in Najran, Saudi Arabia.

Coding in medical procedures is crucial for patients, and errors made by hospital administration during the coding process can have an impact on both the financial results and the course of therapy. The present study aims to assess the accuracy of diagnostic and procedural codes as recorded by the hospital's coders and to also evaluate their impact on the hospital's revenue. In a local hospital in Najran, Saudi Arabia, a cross-sectional observational analysis was conducted on patients with a clinical coder. The percentage of precision and error following the re-coding of cases was calculated using a statistical analysis. Primary diagnosis was incorrectly coded in 57 (26 per cent) records, and secondary diagnosis was incorrectly coded in 21 (9.9 per cent) records. Inaccurate medical labelling has been seen in emergency rooms, operating rooms, and gynaecology facilities. The percentage of records with the most incorrect coding was found to be 16 (7.5 per cent) in the emergency room, 10 (4.7 per cent) in the surgical clinic, and 5 (2.3 per cent) in the gynaecology/OBS clinic. Six (2.8 per cent) records in the private clinic had inaccurate secondary diagnoses, followed by four (1.9 per cent) and two (1 per cent) records in nephrology. The percentage of inaccurate clinical codes in primary diagnoses reached (26.8 per cent) and the percentage of incorrect clinical codes in secondary diagnoses reached (9.9 per cent).

Verifikasi Usulan Anggaran Untuk Meningkatkan Daya Serap Dana BLU Universitas Pendidikan Ganesha

Verifikasi Usulan Anggaran Untuk Meningkatkan Daya Serap Dana BLU Universitas Pendidikan Ganesha

“No Abnormality Detected”: A Mixed-Methods Examination of Emergency Department Coding Practices for People in Suicidal Crisis

Background Accurate identification of suicidal crisis presentations to emergency departments (EDs) can lead to timely mental health support, improve patient experience, and support evaluations of suicide prevention initiatives. Poor coding practices within EDs are preventing appropriate patient care. Aims of the study are (1) examine the current suicide-related coding practices, (2) identify the factors that contribute to staff decision-making and patients receiving the incorrect code or no code. Method A mixed-methods study was conducted. Quantitative data were collated from six EDs across Merseyside and Cheshire, United Kingdom from 2019 to 2021. Attendances were analyzed if they had a presenting complaint, chief complaint, or primary diagnosis code related to suicidal crisis, suicidal ideation, self-harm or suicide attempt. Semi-structured interviews were conducted with staff holding various ED positions (n = 23). Results A total of 15,411 suicidal crisis and self-harm presentations were analyzed. Of these, 21.8% were coded as ‘depressive disorder’ and 3.8% as ‘anxiety disorder’. Absence of an appropriate suicidal crisis code resulted in staff coding presentations as ‘no abnormality detected’ (23.6%) or leaving the code blank (18.4%). The use of other physical injury codes such as ‘wound forearm’, ‘head injury’ were common. Qualitative analyses elucidated potential causes of inappropriate coding, such as resource constraints and problems with the recording process. Conclusion People attending EDs in suicidal crisis were not given a code that represented the chief presentation. Improved ED coding practices related to suicidal crisis could result in considerable benefits for patients and more effective targeting of resources and interventions.

Validity of ICD-10 diagnosis codes for placenta accreta spectrum disorders.

The 10th revision of the International Classification of Diseases, Clinical Modification (ICD-10) includes diagnosis codes for placenta accreta spectrum for the first time. These codes could enable valuable research and surveillance of placenta accreta spectrum, a life-threatening pregnancy complication that is increasing in incidence. We sought to evaluate the validity of placenta accreta spectrum diagnosis codes that were introduced in ICD-10 and assess contributing factors to incorrect code assignments. We calculated sensitivity, specificity, positive predictive value and negative predictive value of the ICD-10 placenta accreta spectrum code assignments after reviewing medical records from October 2015 to March 2020 at a quaternary obstetric centre. Histopathologic diagnosis was considered the gold standard. Among 22,345 patients, 104 (0.46%) had an ICD-10 code for placenta accreta spectrum and 51 (0.23%) had a histopathologic diagnosis. ICD-10 codes had a sensitivity of 0.71 (95% CI 0.56, 0.83), specificity of 0.98 (95% CI 0.93, 1.00), positive predictive value of 0.61 (95% CI 0.48, 0.72) and negative predictive value of 1.00 (95% CI 0.96, 1.00). The sensitivities of the ICD-10 codes for placenta accreta spectrum subtypes- accreta, increta and percreta-were 0.55 (95% CI 0.31, 0.78), 0.33 (95% CI 0.12, 0.62) and 0.56 (95% CI 0.31, 0.78), respectively. Cases with incorrect code assignment were less morbid than cases with correct code assignment, with a lower incidence of hysterectomy at delivery (17% vs 100%), blood transfusion (26% vs 75%) and admission to the intensive care unit (0% vs 53%). Primary reasons for code misassignment included code assigned to cases of occult placenta accreta (35%) or to cases with clinical evidence of placental adherence without histopatholic diagnostic (35%) features. These findings from a quaternary obstetric centre suggest that ICD-10 codes may be useful for research and surveillance of placenta accreta spectrum, but researchers should be aware of likely substantial false positive cases.

Intelligent Program Correction and Evaluation System

The growing popularity of computing applications has sparked the interest of students in computer programming languages. Minor mistakes are prevalent while writing small code blocks due to the coder’s lack of knowledge and carelessness. Instead of merely providing syntax warnings, it would be better to offer developers with an Integrated Development Environment (IDE) that can automatically correct short code blocks containing mistakes. This makes code composition easier and more time-efficient, thus improving the efficacy of large-scale development environments. Because Python is becoming more popular, the goal of our study is to enhance the efficiency of writing Python code by offering an automatic code-correcting approach. Furthermore, automatic program evaluation has been performed to assist in the debugging of small code blocks, which will ultimately be employed in the creation of real-time computer applications. The proposed technique would be useful for new learners who wish to create small Python code blocks for ease of writing and debugging on online platforms (like edX, Coursera, and Udacity). One of the major contributions of the project is to create an erroneous dataset of Python coding that contains all potential forms of probable syntax errors. The dataset induces variety through the use of multiple coding templates and is used to train deep learning models. We used the state-of-the-art text-to-text T5 transformer network model to automatically repair and evaluate the incorrect code. The outcomes of auto-correction are examined using the ROUGE and BLEU scores, as well as accuracy. The model corrects Python code with single, double, and the multiple number of errors with greater than 80% accuracy. Similarly, the performance of the basic T5 transformer network for program auto-evaluation with and without mistakes has been examined, and the model achieves greater than 65% accuracy in both cases. The proposed T5 base transformer outperforms the SOTA auto-correction models in terms of accuracy, according to a comparison study of the proposed method with the earlier techniques for auto-correcting codes.

ТОЧНІСТЬ СЛОВОВЖИВАННЯ В МЕДІАТЕКСТІ ЯК ОДИН ІЗ ЧИННИКІВ ЕФЕКТИВНОЇ КОМУНІКАЦІЇ

Media text is a type of text directly connected with reality. The method of verbal representation of reality depends on the journalist’s language competence, the ability to choose the appropriate word to denote a subject, process or phenomenon, the ability to combine words into phrases and sentences based on semantic and logical compatibility. At the level of the text, the corresponding feature is the preciseness of word usage. Texts which do not comply with the principle of preciseness of word usage, contain incorrect code and cannot be correctly decoded, therefore they are ineffective, as they are not able to inform the recipient and to influence him. The analysis of media texts revealed that non-compliance with the requirement of preciseness of word usage causes various communicative barriers. An incorrectly chosen word can create an emotional load of the text that does not correspond to the content of the message. On the other hand, the flaw is emotionlessness, neutral expressive tone, indifference, lack of empathy contrary to the facts reflected in the media text. In both cases, the media text will dissonance with the emotions caused by its content, because a false code of pragmatic information is embedded here. Comprehension of the media text can be complicated due to incorrect coding of subject-logical information: using a word or a idioms in improper context; confusion of the word meanings and not only linguistic and logical, but also factual errors as a result; logical deviations due to incorrectly selected or redundant conjunctions, excessive or insufficient word usage; not taking into account the meanings that arise at the word combination level; errors of automatic translation. Non-observance of the principle of preciseness of word usage can lead to non-textual communicative barriers: the recipient may lose confidence in the journalist’s language and even professional competence, lose confidence in the publication or the media in general.

Factors Causing Pending Claims at Nyi Ageng Serang Regional General Hospital

Nyi Ageng Serang Regional General Hospital submits monthly collective claims for the Social Health Insurance Administration Body patients. Based on the observations made at the Nyi Ageng Serang Regional General Hospital, the Social Health Insurance Administration Body claim file was pending by the Social Health Insurance Administration Body verifier because it needed to be confirmed. The pending claims will cause the payment of claims from the Social Health Insurance Administration Body to the hospital to be delayed, and it might affect service activities and financing for the hospital. Pending claims must be explored more deeply to the causal factors so they can be handled appropriately. This study aims to describe the factors causing pending claims for the Social Health Insurance Administration Body at the Nyi Ageng Serang Regional General Hospital. Data collection techniques used observation techniques, documentation studies, and interviews. Data analysis was performed using descriptive analysis. External factors that have caused pending claims include discrepancies in claim rates with the Minister of Health Regulations, incorrect codes, and incomplete codes. Meanwhile, the internal factors causing pending claims, according to the 5M management element, were the Man, Material, and Method factors.

Designing NLP applications to support ICD coding: an impact analysis and guidelines to enhance baseline performance when processing patient discharge notes

Financial costs are a major concern in the healthcare system, with medical billing and coding playing a key role in facilitating transactions and financing procedures. Billing involves filing claims with insurance companies and requires scrutiny of clinical summaries and electronic health records to correctly match diagnoses, prescriptions, and procedures to standardized codes. Accuracy in assigning International Classification of Diseases (ICD) codes is critical to proper reimbursement of care. Incorrect codes waste time and resources, and cause administrative and financial problems for hospitals, insurance companies and patients. Manual medical coding is a labor-intensive and error-prone process that creates additional administrative burden and inconvenience for hospitals, insurance companies, and patients. To simplify the process, clinical records are often processed to automatically identify and extract clinical concepts and corresponding ICD codes. Deep learning and natural language processing techniques have shown promise in a variety of tasks but applying them to medical coding has been challenging. Accurate coding requires a deep understanding of medical terminology, context, and guidelines that may be difficult to capture with traditional deep learning methods. Although deep learning shows promise in healthcare, its specific impact on ICD coding is not fully understood, and translating scalable deep learning methods into practical improvements in ICD coding remains a challenge. Evaluating deep learning models under the scenarios of real-world coding and comparing them to established practice is critical to determining their true effectiveness. In this work, we address the automation of ICD coding by highlighting pitfalls and contrasting different perspectives. We investigated automatic ICD coding using baseline machine learning models, with a focus on identifying ICD-9 codes in discharge notes from Medical Information Mart for Intensive Care (MIMIC) database. A thorough evaluation of different models and approaches is crucial to avoid over-reliance on any method. Our findings show that simpler methods can achieve comparable results to deep learning models while still requiring fewer computational resources.

Hubungan Ketepatan Kode Diagnosis Terhadap Status Klaim JKN Rawat Inap di Rumah Sakit Pelabuhan Jakarta

The accuracy of determining the diagnosis code is necessary for the benefit of patient health information in carrying out health services. The accuracy of the diagnosis code is also required for the benefit of claiming National Health Insurance (JKN). The research aims to determine the correlation between the accuracy of the diagnosis code and the status of inpatient National Health Insurance (JKN) claims. The research method was an inferential analysis with a cross-sectional research design using the chi-square statistical test. The samples of the research were 125 medical records. Samples were taken by way of proportional stratified random sampling. Based on the research results, the accuracy of determining the diagnosis code was 114 (91,2%) while the inaccuracy of determining the diagnosis code was 11 (8,8%). JKN claims received were 104 (83,2%) while JKN claims that were postponed were 21 (16,8%). The results of the chi-square statistical test obtained odd ratio value of 20,7, meaning that the correct diagnosis codes has 20,7 times chance of JKN claims approval than the incorrect diagnosis codes. The significance value obtained of 0.000. The p-value shows <0.05, so Ho is rejected and Ha is accepted or there is a correlation between the accuracy of the diagnosis code and the status of inpatient JKN claims at the Pelabuhan Jakarta Hospital. This research concludes that there is a correlation between the accuracy of the diagnosis code and the status of inpatient JKN claims.

REVIEW OF THE ACCURACY OF THE DIAGNOSIS CODE OF DENGUE HAEMORRHAGIC FEVER IN MEDICAL RECORDS IN RSUD dr. RASIDIN PADANG

ABSTRACT
 
 The accuracy of the code is needed so that the information resulting from the diagnosis and action is accurate. However, the authors found that at RSUD dr.Rasidin Padang there were still incorrect codes. This is because the diagnosis of Dengue Haemorrhagic Fever is not correct in giving the code according to the ICD-10. The purpose of this study was to determine the accuracy of the diagnostic code for dengue haemorrhagic fever in medical records at dr.racidin. This research method used is quantitative with a descriptive approach. This research was conducted from February to May 2022. The number of samples was 59 medical records using the total sampling method, and the tool used was a checklist table with univariate analysis. The results of this study were obtained from 59 medical records, there were 37 (62.7%) medical records with inappropriate codes, and a small proportion of 10 (16.9%) medical records that were not clearly written, and there were 39 incomplete diagnosis writings. (66.1%) this was due to errors in determining the diagnosis code, doctors' writings that were difficult to read, incomplete completeness, non-specific written diagnoses based on ICD-10, and the use of abbreviations that were not standardized in hospitals. Therefore, researchers suggest that coders should pay attention to the rules and procedures for coding a diagnosis based on ICD-10.
 Keywords: Coding Accuracy, Clarity, Completeness

TINJAUAN KETIDAKTEPATAN KODE DIAGNOSIS PENYAKIT DAN TINDAKAN MEDIS KASUS GIGI DAN MULUT RAWAT JALAN DI RUMAH SAKIT X JAKARTA

ABSTRACT
 This research is to identify the Standard Operating Procedure (SPO) coding, calculate the number of inaccuracies in the disease diagnosis code and medical treatment in outpatient dental and oral cases and the factors causing the code inaccuracy. The research method used is descriptive analysis research method with data collection techniques by observation and interviews. The population of this study amounted to 2141 medical records, especially in the clinical summary in November 2021-April 2022, samples taken as many as 96 clinical summaries. The results of the study: the SOP has been carried out but is in the process of being revised and not yet perfect, the inaccuracy rate of the incorrect disease diagnosis code is 29 (30.20%) and the correct one is 67 (69.80%), while the inaccurate medical code of action is 18 (18.75%) and the correct one is 78 (81.25%), Causing factors: SOP steps are not perfect, the competence of coding officers is still lacking, identification of medical diagnoses and actions is less detailed, and lack of evaluation, especially periodic coding audits.
 Bibliography : 17 (2006 – 2020)
 Keywords : Code, Diagnosis, Medical Action, Clinical Summary, Dental, Oral

Mobile Learning in Medical Coding Course

Medical coding comprises the assignment of appropriate ICD-10 codes to patient diagnoses through coding exercises. However, due to their lack of knowledge, students often commit coding errors, resulting in incorrect code assignments. The purpose of developing Medical Coding Simulation (MedCoS) is therefore to help students improve their motor and technical skills in challenging environments. This research aims to predict how likely students are to use MedCoS by analysing their attitudes (AT), subjective norms (SN), and perceived behavioural control (PBC). In order to achieve this objective, descriptive, reliability, and multiple regression analyses were conducted using SPSS. The study involves students from the fifth and sixth semesters who took both courses. The majority of respondents (90.2%, n = 102) were between the ages of 23 and 24. The findings suggest that the attitudes and perceived behavioral control of students can predict their intend to use MedCoS. This significant result enables MedCoS to plan the next phase of application development in order to effectively enhance course performance.

Analisis Ketepatan Kode Diagnosis pada Kasus Persalinan Pasien Rawat Inap di Rumah Sakit Patria Ikkt Tahun 2022

Diagnostic coding must comply with ICD-10 rules. According to WHO, the coding of delivery cases consists of the code for the mother's condition (O00-O75), the method of delivery (O80-084), and the Outcome of delivery Z37.-., while the code is Z37.-. used as an additional code to determine the outcome of labor. So that the coder officer must have knowledge in setting the diagnosis code. Coding accuracy is very necessary because it is used as a reporting material. To determine the Standard Operating Procedure (SOP), the percentage of accuracy of the diagnosis code for inpatient labor cases based on 3M (complications, delivery method, and outcome of delivery) and the cause of the inaccuracy of the diagnosis code for labor cases. Descriptive research type with a quantitative approach. The population is medical records of inpatients in labor cases with a sample of 100 medical records. The sampling technique is by systematic random sampling. Collecting data by means of observation, interviews, and documentation. Research results: SOPs already exist, but not yet complete. The percentage of accuracy of the correct diagnosis code for labor cases is 22.33% while the incorrect diagnosis code is 77.67%. The cause of the inaccuracy of the ICD-10 code for delivery cases is that the diagnosis does not include the method of delivery and the outcome of delivery, and has never been done. evaluation or audit coding. The accuracy of the diagnosis of labor cases is still incomplete. Improve the SOP entry and complete the delivery method and outcome of delivery in medical records and registers.

Tinjauan Ketepatan Kode Cedera Multiple Pada Kasus External Cause di RSUP Dr. M. Djamil Padang

The accuracy of the code is very necessary so that the information generated from the diagnosis and medical treatment is accurate. However, researchers found in Dr. M. Djamil Padang there are still 29 incorrect codes (76.3%). This is because the determination of multiple injury codes is written separately and fracture diagnosis is not equipped with a fifth character code. The purpose of this study was to determine the accuracy of multiple injury codes in external cause cases at RSUP Dr. M. Djamil Padang. This research was conducted from May to June 2022. The type of research conducted was quantitative with a descriptive approach. The number of samples was 38 using the purposive sampling method, the data collection instrument used a checklist table with univariate analysis. The results of the research that has been carried out found that the frequency of diagnostic accuracy is 27 (71.1%) incorrect diagnoses, the frequency of conformity of primary and secondary diagnoses is 19 (50.0%) incorrect diagnoses, and the frequency of accuracy of diagnostic codes is 29 (76, 3%) code is not correct. So it can be concluded that there are still causes of inaccuracy in coding, which can be seen from the 3 components of the analysis carried out that affects the accuracy of the code. Therefore, researchers suggest that coders should pay attention to the rules and procedures for coding diagnoses based on ICD-10.

Hospital transfer and delayed reduction of traumatic hip dislocations.

Native hip dislocations are high energy injuries that cause substantial patient morbidity. Expedient reduction has been demonstrated to improve patient outcomes. The objective of our study was to compare complication rates in patients with native hip dislocations who presented directly to a level-one trauma center with those transferred from an outside hospital (OSH). Our hypothesis was that those transferred from an OSH would experience a delay in reduction and subsequently would experience higher rates of avascular necrosis (AVN), post-traumatic arthritis (PTA), and need for secondary surgery. We conducted a retrospective chart review of all native hip dislocations from our level-one trauma center between January 2007 and December 2020. The initial query resulted 628 patients which was refined to 90 patients after excluding patients for incorrect diagnosis code or less than 6months of follow-up. Our primary outcome was the development of AVN, PTA, and need for secondary surgery. Time from injury to reduction was recorded for all patients included. For every one hour of delay in time to reduction, there was a 3.4% increase in the risk of developing AVN (p = 0.004) and a 4.3% increase in risk for developing PTA (p = 0.01). The risk of requiring a secondary surgery increased 4.6% for each hour of delay in reduction (p = 0.03). The average time to reduction of transferred patients was higher compared to those who presented directly to our center (13.8h vs 5.7h); however, transfer status was not found to be an independent risk factor for the measured outcomes. Transfer status is not an independent risk factor for the development of AVN, PTA, or the need for a secondary surgery. However, transferred patients did experience an average delay of 8h in time to reduction compared to those who presented directly to a trauma center. Of the 27 patients with a reduction delay greater than 12h, 26 (96%) were transferred.

Tinjauan Ketepatan Kode Diagnosis Pada Kasus Bedah Pasien Rawat Inap di RSKD Duren Sawit

Based on Kenmenkes RI in 2014, it explains about coding which has the meaning as an activity of providing main diagnosis codes and secondary diagnoses in accordance with ICD-10 and providing procedure codes in accordance with ICD-9CM. Coding inaccuracies can affect the financing of health services, this study was conducted to see the accuracy of the main and secondary diagnosis codes of surgical cases of inpatients at Duren Sawit Hospital using descriptive research methods with a quantitative approach, namely writing aims to describe the results obtained on the accuracy of diagnosis codification. Informants in this study were inpatient coders at RSKD Duren Sawit, data collection in this study using interviews and observation methods. The results of this study indicate that the coding SPO uses the latest procedures based on an electronic system, the educational background of the coder at RSKD Duren Sawit has an important role in the quality of the correct code. The competence of the coder at RSKD Duren Sawit still has to undergo deeper learning, in the results of coding research on surgical cases of inpatients, it was found that the average dignosis code that had accuracy was 58 (63.74%) and 33 (36.26%) were inappropriate, and it was also found that the results of the accuracy of the secondary diagnosis were 84 (92.30%) and 7 (7.70%) were inappropriate. Based on the 4 characters, the inaccuracy occurred in the main diagnosis of the majority in the 4th character as many as 31 (34.7%). There are factors that become obstacles to the identification of 5M, namely the man factor, the lack of accuracy of doctors in inputting diagnoses and the lack of accuracy of officers in re-examining incorrect diagnosis codes and having to undergo learning related to coding more deeply for diagnosis coding officers who are not from academic graduates of medical records.

Ketepatan Kode Penyakit Bronkopneumonia dan Kelengkapan Rekam Medis di Rumah Sakit St. Elisabeth Medan Tahun 2022

The accuracy of the bronchopneumonia case code is that all diagnoses are filled out correctly based on the completeness of filling out the medical record with code J18.0 as the code in the ICD 10 book. Completeness of filling out the medical record is a medical record that has been completely filled out by a doctor within 1x24 hours, after completing outpatient services. or after an inpatient is decided to go home which includes a patient identity sheet, medical resume, and supporting diagnostic examination sheet. Researchers take specific cases of bronchopneumonia because during the practice service at St. Hospital. Elisabeth in 2021 there are 10 files of bronchopneumonia patients, there are 3 medical record files with incorrect codes. Based on this background, researchers are interested in conducting research with the title of code accuracy and completeness of medical records of bronchopneumonia patients at St. Hospital. Elisabeth Medan 2022. The type of research design used was a descriptive design with a sampling method of 82 respondents in total at St.Elisabeth Hospital Medan 2022. The instrument used in data collection was a checklist study documentation. The results of the study obtain that the correct category code was 70 medical records (85.3%) and the incorrect category are 12 medical records (14.7%) medical record files. While the completeness of the patient identity sheet is 85.3%, the medical resume sheet is 85.3%, and the laboratory support results sheet is 81.7% complete.