Promotion of training course on ICD-10 Poisoning coding : necessity to adopt preventive strategies

BACKGROUND

Poisoning is considered the most common cause of referral to emergency departments and hospitalization in the intensive care unit (ICU). Training or retraining of coders and ensuring the positive impact of these trainings in assigning accurate codes to poisoning cases is necessary to adopt practical health measures for optimal management of this disease. The present study aimed to evaluate the impact of holding a training course on poisoning coding rules based on ICD-10 in clinical coders.

METHODS

This study is descriptive and analytical. With the target population included the coders of hospitals affiliated with Shahid Beheshti University of Medical Sciences (N = 45). In order to evaluate the training course on poisoning coding rules, the Conex Input Process Product (CIPP) evaluation model was used. This model was the first goal-oriented approach evaluation model. According to the CIPP model, evaluation of the training course held in four components, including Context factors (course objectives and priority of objectives), Input factors (instructor, curriculum, facilities, equipment, and training location), Process factors (teaching process, learning, management, and support), and Product factors (feedback, knowledge, and skills). A researcher-made questionnaire containing 39 questions with a 5-point Likert scale was used to collect data. The validity of the questionnaire was calculated through content validity, and its reliability was calculated using Cronbach's alpha coefficient (alpha = 90% in all components). In order to analyze the data, descriptive statistics (frequency percentage distribution) and inferential statistics (one-sample t-test) were used.

RESULTS

The findings of this study were presented in four components of context, input, process, and product evaluation. The average criterion for all questions in the questionnaire was considered 3. As a result, the significance level obtained from the one sample t-test was equal to P = 0. 0001.The training course had a favorable effect in terms of context, input, process and products.

CONCLUSION

The knowledge and skills of clinical coders can be enhanced by updating medical knowledge, holding training courses, workshops, seminars, and conducting clinical coder accreditation. Extensive and continuous training for clinical coders is essential due to the impact of code quality on financial forecasting, electronic health records, and conducting research.

Validation of ICD-10-AM Coding for Myocardial Infarction Subtype in Hospitalisation Data

BACKGROUND

International Classification of Disease (ICD) codes are central for identifying myocardial infarction (MI) in administrative hospitalisation data, however validation of MI subtype codes is limited. We measured the sensitivity and specificity of ICD-10-AM (Australian Modification) codes for ST-elevation MI (STEMI) and non-STEMI (NSTEMI).

METHODS

A sample of MI admissions was obtained from a dataset containing all MI hospitalisations in Western Australia (WA) for 2003, 2008 and 2013. Clinical data were collected from hospital medical records (n=799 patients). Cases were classified by ICD-10-AM codes for STEMI, NSTEMI and unspecified MI, and compared to clinical classification from review of available electrocardiographs (ECGs) and cardiac biomarkers (n=660). Sensitivity and specificity for ICD-10-AM coding versus clinical classification was measured, stratified by calendar year of discharge.

RESULTS

The majority of classifiable cases had MI recorded in the principal diagnosis field (STEMI n=293, 84.2%; NSTEMI n=202, 74.3%; unspecified MI n=20, 50.0%). Overall sensitivity of the ICD-10-AM STEMI code was 86.3% (95% CI 81.7-90.0%) and was higher when restricted to MI as a principal versus secondary diagnosis (88.8% vs 66.7%). Comparable values for NSTEMI were 66.7% (95% CI 61.5-71.6%), and 68.8% vs 61.4% respectively. Between 2003 and 2013, sensitivity for both MI subtypes increased: 80.2-89.5% for STEMI, and 51.2-73.8% for NSTEMI. Specificity was high for NSTEMI throughout (88.2% 95% CI 84.1-91.6%), although improving over time for STEMI (68.1-76.4%).

CONCLUSIONS

The sensitivity and specificity of ICD-10-AM codes for MI subtypes in hospitalisation data are generally high, particularly for principal diagnosis cases. However, the temporal improvement in sensitivity in coding of MI subtypes, particularly NSTEMI, may necessitate modification to trend studies using administrative hospitalisation data.

ICD-10 codes do not accurately reflect ankle fracture injury patterns

OBJECTIVE

To determine the accuracy of International Classification of Disease Version 10 (ICD-10) coding for ankle fracture injury patterns.

DESIGN

Retrospective cohort study PATIENTS: 97 adult patients with fractures about the ankle (rotational ankle fracture or distal tibia fracture) from 2016 to 2020, selected by stratified random sampling.

INTERVENTION

Assignment of an ICD-10 code representative of a rotational ankle fracture, pilon fracture, or unspecified fracture of the lower leg.

OUTCOME MEASUREMENTS

Injury radiographs were reviewed by three authors to determine the correct code. Agreement between the correct code and the electronic medical record (EMR) assigned code was determined using kappa's statistic in the aggregate as well as percent agreement, sensitivity, specificity, and positive predictive value (PPV) between individual codes.

RESULTS

59 of 97 cases (60.8%) demonstrated discordance between the existing EMR and surgeon-assigned codes. Aggregate agreement between all codes was fair (K = 0.26). Lateral malleolus fracture codes demonstrated the highest PPV (0.91, 95% CI 0.72-0.99), while the lowest PPV was found for "other fractures of the lower leg" (0.05, 95% CI 0.0-0.24) and "other fracture of the fibula" (0.0, 95% CI 0.0-0.15). Generalized "other fracture" codes comprised 45% of EMR codes compared to only 6% of assigned codes (p < 0.001). EMR codes were specific but not sensitive.

CONCLUSION

There is substantial discordance between existing EMR and surgeon-assigned ICD-10 codes for ankle fractures. Database research that relies on ICD-10 coding as a surrogate for primary clinical data should be interpreted with caution and institutions should make efforts to increase the accuracy of their coding.

Hospitalizations and Outcomes of T1MI Observed Before and After the Introduction of MI Subtype Codes

BACKGROUND

International Classification of Disease (ICD)-10 coding of type 1 myocardial infarction (MI) is used for reimbursement, value-based programs, and clinical research.

OBJECTIVES

This study sought to determine whether the introduction of ICD-10 codes for type 2 and types 3-5 MI was associated with changes in hospitalizations for ICD-10 codes now attributed to type 1 MI.

METHODS

Using the Nationwide Readmissions Database, we identified patients with ICD-10 codes now attributed to type 1 MI between January 2016 and December 2018. Patients were stratified according to the timing of their event in relation to the introduction of the type 2 and types 3-5 MI codes on October 1, 2017.

RESULTS

There were 2,680,323 hospitalizations for ICD-10 codes now attributed to type 1 MI; after adjustment for seasonality, there was a 13.7% decline in hospitalizations after the introduction of the new subtype codes. Patients with ICD-10 codes now attributed to type 1 MI after the coding change were less likely to be female, had lower prevalence of several cardiovascular and noncardiovascular comorbidities, and had higher rates of coronary angiography and revascularization. After introduction of the new codes, there was a positive deflection in the slope of risk-adjusted in-hospital mortality (0.007%; P <0.001) and a negative deflection in risk-adjusted 30-day readmission (-0.002%; P = 0.05) for patients with ICD-10 codes now attributed to type 1 MI.

CONCLUSIONS

The introduction of ICD-10 codes for type 2 and types 3-5 MI was associated with a decrease in hospitalizations for ICD-10 codes now attributed to type 1 MI and changes in the observed characteristics and treatment patterns of these patients.

Supervised Learning for the ICD-10 Coding of French Clinical Narratives

Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we present a corpus of French clinical narratives annotated with the ICD-10 codes. Then, we propose automatic methods based on neural network approaches for the automatic detection of the ICD-10 codes. The results show that we need 1) more examples per class given the number of classes to assign, and 2) a better word/concept vector representation of documents in order to accurately assign codes.

Coding and prescription rates of osteoporosis are low among distal radius fracture patients in Japan

INTRODUCTION

This study aimed to clarify the coding and prescription rates for osteoporosis in distal radius fracture patients and to investigate the associated factors to help prevent subsequent osteoporotic fracture.

MATERIALS AND METHODS

Between 2014-2015, among 294,374 eligible individuals (42% female) aged 50-75 years in a health insurance claims database, we identified 192 individuals (mean age: 59.8 years, 74% female), counted the coding of distal radius fracture (International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) code: S525, S526), and determined if the patient had been assigned the code for osteoporosis and been prescribed osteoporosis medications. Logistic regression was performed to identify factors related to each rate.

RESULTS

The osteoporosis coding rate and osteoporosis medication prescription rate were 17.2% (n = 33) and 10.9% (n = 21), respectively. Most codes were assigned ≤ 3 months after injury (88%) at the distal radius fracture treatment facilities (84.8%). Patients who were assigned the code for osteoporosis or treated with osteoporosis medications were older (p = 0.08, p = 0.02, respectively), female (p = 0.05, p = 0.06, respectively) and having comorbidity (p = 0.02, p = 0.07, respectively). After adjustment, being female and having comorbidity remained the independent factors for the assignment of the code for osteoporosis (OR: 3.30, 95%, CI: 1.08-10.07, OR: 2.77, 95% CI: 1.24-6.12, respectively). No factor remained significant for the osteoporosis prescription. Active vitamin D analogues were most frequently prescribed medication (67%) followed by bisphosphonates (48%).

CONCLUSION

The overall coding and prescription rates for osteoporosis after distal radius fracture were low, which suggested that physician adherence to the osteoporosis guideline was low.

Construction of a semi-automatic ICD-10 coding system

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.

Assessing the accuracy of ICD-10 coding for measuring rates of and mortality from acute kidney injury and the impact of electronic alerts: an observational cohort study

Background

The application of a uniform definition for acute kidney injury (AKI) is vital to advance understanding and management of AKI. International Classification of Diseases (Tenth Revision) (ICD-10) coding is frequently used to define AKI, but its accuracy is unclear. The aim of this study was to determine whether ICD-10 coding is a reliable method of monitoring rates and outcomes of AKI in inpatients compared with biochemically defined AKI, and whether electronic alerts (e-alerts) for AKI affect ICD-10 AKI coding.

Methods

An observational cohort study of all 505 662 adult admissions to acute hospitals in two Scottish Health Boards [National Health Service (NHS) Tayside and NHS Fife] from January 2013 to April 2017 was performed. AKI e-alerts were implemented in NHS Tayside in April 2015. Sensitivity, specificity, positive and negative predictive values of ICD-10 coding for AKI compared with biochemically defined AKI using the Kidney Disease: Improving Global Outcomes definition and relative risk of 30-day mortality in people with ICD-10 and biochemically defined AKI before and after AKI e-alert implementation were performed.

Results

Sensitivity of ICD-10 coding for identifying biochemically defined AKI was very poor in both health boards for all AKI (Tayside 25.7% and Fife 35.8%) and for Stages 2 and 3 AKI (Tayside 43.8% and Fife 53.8%). Positive predictive value was poor both for all AKI (Tayside 76.1% and Fife 45.5%) and for Stages 2 and 3 AKI (Tayside 45.5% and Fife 36.8%). Measured mortality fell following implementation of AKI e-alerts in the ICD-10-coded population but not in the biochemically defined AKI population, reflecting an increase in the proportion of Stage 1 AKI in ICD-10-coded AKI. There was no evidence that the introduction of AKI e-alerts in Tayside improved ICD-10 coding of AKI.

Conclusion

ICD-10 coding should not be used for monitoring of rates and outcomes of AKI for either research or improvement programmes.

Improving the Metrics and Data Reporting for Maternal Mortality: A Challenge to Public Health Surveillance and Effective Prevention

BACKGROUND

The current measuring metric and reporting methods for assessing maternal mortality are seriously flawed. Evidence-based prevention strategies require consistently reported surveillance data and validated measurement metrics. Main Body: The denominator of live births used in the maternal mortality ratio reinforces the mistaken notion that all maternal deaths are consequent to a live birth and, at the same time, inappropriately inflates the value of the ratio for subpopulations of women with the highest percentage of pregnancies ending in outcomes other than a live birth. Inadequate methods for identifying induced or spontaneous abortion complications assure that most maternal deaths associated with those pregnancy outcomes are unlikely to be attributed. Absent the ability to identify all maternal deaths, and without the ability to differentiate those deaths by specific pregnancy outcomes, existing variations in pregnancy outcome-specific maternal deaths are masked by the use of an aggregated (all outcome) numerator. Under these circumstances, clear and accurate data is not available to inform evidence-based preventive strategies. As the result, algorithms applied for analyzing maternal mortality data may return distorted results Conclusion: Improvement in the effectiveness of maternal mortality surveillance will require: mandatory certification of all fetal losses; linkage of death, birth and all fetal loss (induced and natural) certificates; modification of the structure of the overall maternal mortality ratio to enable pregnancy outcome-specific ratio calculations; development of the appropriate ICD codes which are specific to induced and spontaneous abortions; education for providers on identifying and reporting early pregnancy losses; and, flexible information systems and methods which integrate these capabilities and inform users.

The influence of a specific ophthalmological electronic health record on ICD-10 coding

BACKGROUND

A specific Electronic Health Record (EHR) for ophthalmology was introduced in an academic center in Germany. As diagnoses coding corresponding to the International Classification of Diseases Version 10 (ICD-10) is mandatory for billing reasons in Germany, we analyzed whether a change occurred in the diversity and number of diagnoses after the EHR introduction. The number of patients was also analyzed. Proper diagnoses coding is of the utmost importance for further data analysis or billing.

METHODS

Graphical User Interfaces (GUIs) were created by using Advanced Business Application Programming language in EHR "i.s.h.med." Development of an EHR was conducted in close collaboration between physicians and software engineers. ICD-10 coding was implemented by using a "hit list" and a search engine for diagnoses. An observational analysis of a 6-month period prior to and after the introduction of an ophthalmological specific EHR was conducted by investigating the diversity and number of diagnoses in various ophthalmological disease categories and the number of patient consultations.

RESULTS

During the introduction of a specific ophthalmological EHR, we observed a significant increase in the emergency department cases (323.9 vs. 359.9 cases per week), possibly related to documentation requirements. The number of scheduled outpatients didn't change significantly (355.12 vs. 360.24 cases per week). The variety of diagnoses also changed: on average, 156.2 different diagnoses were made per week throughout our hospital before the EHR launch, compared to 186.8 different diagnoses per week thereafter (p < 0.05). Additionally, a significantly higher number of diagnoses per case and per week were observed in both emergency and subspecialty outpatient clinics (1.15 vs. 1.22 and 1.10 vs. 1.47, respectively).

CONCLUSIONS

An optimized EHR was created for ophthalmological needs and for simplified ICD-10 coding. The implementation of digital patient recording increased the diversity of the diagnoses used per case as well as the number of diagnoses coded per case. A general limitation to date is the suboptimal precision of ICD-10 coding in ophthalmology. Correct coding is of utmost importance for future data analysis.

Positive predictive value of diagnosis coding for hemolytic anemias in the Danish National Patient Register

Purpose

The nationwide public health registers in Denmark provide a unique opportunity for evaluation of disease-associated morbidity if the positive predictive values (PPVs) of the primary diagnosis are known. The aim of this study was to evaluate the predictive values of hemolytic anemias registered in the Danish National Patient Register.

Patients and methods

All patients with a first-ever diagnosis of hemolytic anemia from either specialist outpatient clinic contact or inpatient admission at Odense University Hospital from January 1994 through December 2011 were considered for inclusion. Patients with mechanical reason for hemolysis such as an artificial heart valve, and patients with vitamin-B12 or folic acid deficiency were excluded.

Results

We identified 412 eligible patients: 249 with a congenital hemolytic anemia diagnosis and 163 with acquired hemolytic anemia diagnosis. In all, hemolysis was confirmed in 359 patients, yielding an overall PPV of 87.1% (95% confidence interval [CI]: 83.5%–90.2%). A diagnosis could be established in 392 patients of whom 355 patients had a hemolytic diagnosis. Diagnosis was confirmed in 197 of the 249 patients with congenital hemolytic anemia, yielding a PPV of 79.1% (95% CI: 73.5%–84.0%). Diagnosis of acquired hemolytic anemia could be confirmed in 136 of the 163 patients, resulting in a PPV of 83.4% (95% CI: 76.8%–88.8%). For hemoglobinopathy PPV was 84.1% (95% CI: 77.4%–89.4%), for hereditary spherocytosis PPV was 80.6% (95% CI: 69.5%–88.9%), and for autoimmune hemolytic anemia PPV was 78.4% (95% CI: 70.4%–85.0%).

Conclusion

The PPV of hemolytic anemias was moderately high. The PPVs were comparable in the three main categories of overall hemolysis, and congenital and acquired hemolytic anemia.