Capture, Coding, and Reporting of Health Care and Medicine Information in Australian Workers' Compensation Systems.

OBJECTIVE: This study aims to characterize the approaches to collecting, coding, and reporting health care and medicines data within Australian workers' compensation schemes. METHODS: We conducted a cross-sectional survey of data and information professionals in major Australian workers' compensation jurisdictions. Questionnaires were developed with input from key informants and a review of existing documentation. RESULTS: Twenty-five participants representing regulators (40%) and insurers (60%) with representation from all Australian jurisdictions were included. Health care and medicines data sources, depth, coding standards, and reporting practices exhibited significant variability across the Australian workers' compensation schemes. CONCLUSIONS: Substantial variability exists in the capture, coding, and reporting of health care and medicine data in Australian workers' compensation jurisdictions. There are opportunities to advance understanding of medicines and health service delivery in these schemes through greater harmonization of data collection, data coding, and reporting.

Abortion Miscoding-Legal Risks for Clinicians and Hospital Systems.

This Viewpoint discusses the legal risks physicians and health care facilities may incur by miscoding a surgical or chemical abortion as a miscarriage to conceal an abortion procedure.

Comparing reliability of ICD-10-based COVID-19 comorbidity data to manual chart review, a retrospective cross-sectional study.

International Statistical Classification of Disease and Related Health Problems, 10th Revision codes (ICD-10) are used to characterize cohort comorbidities. Recent literature does not demonstrate standardized extraction methods. OBJECTIVE: Compare COVID-19 cohort manual-chart-review and ICD-10-based comorbidity data; characterize the accuracy of different methods of extracting ICD-10-code-based comorbidity, including the temporal accuracy with respect to critical time points such as day of admission. DESIGN: Retrospective cross-sectional study. MEASUREMENTS: ICD-10-based-data performance characteristics relative to manual-chart-review. RESULTS: Discharge billing diagnoses had a sensitivity of 0.82 (95% confidence interval [CI]: 0.79-0.85; comorbidity range: 0.35-0.96). The past medical history table had a sensitivity of 0.72 (95% CI: 0.69-0.76; range: 0.44-0.87). The active problem list had a sensitivity of 0.67 (95% CI: 0.63-0.71; range: 0.47-0.71). On day of admission, the active problem list had a sensitivity of 0.58 (95% CI: 0.54-0.63; range: 0.30-0.68)and past medical history table had a sensitivity of 0.48 (95% CI: 0.43-0.53; range: 0.30-0.56). CONCLUSIONS AND RELEVANCE: ICD-10-based comorbidity data performance varies depending on comorbidity, data source, and time of retrieval; there are notable opportunities for improvement. Future researchers should clearly outline comorbidity data source and validate against manual-chart-review.

Advancements in the National Vital Statistics System to Meet the Real-Time Data Needs of a Pandemic.

The National Center for Health Statistics' (NCHS's) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133-2140. https://doi.org/10.2105/AJPH.2021.306519).

Out-of-Hospital COVID-19 Deaths: Consequences for Quality of Medical Care and Accuracy of Cause of Death Coding.

Objectives. To examine age and temporal trends in the proportion of COVID-19 deaths occurring out of hospital or in the emergency department and the proportion of all noninjury deaths assigned ill-defined causes in 2020. Methods. We analyzed newly released (March 2021) provisional COVID-19 death tabulations for the entire United States. Results. Children (younger than 18 years) were most likely (30.5%) and elders aged 64 to 74 years were least likely (10.4%) to die out of hospital or in the emergency department. In parallel, among all noninjury deaths, younger people had the highest proportions coded to symptoms, signs, and ill-defined conditions, and percentage symptoms, signs, and ill-defined conditions increased from 2019 to 2020 in all age-race/ethnicity groups. The majority of young COVID-19 decedents were racial/ethnic minorities. Conclusions. The high proportions of all noninjury deaths among children, adolescents, and young adults that were coded to ill-defined causes in 2020 suggest that some COVID-19 deaths were missed because of systemic failures in timely access to medical care for vulnerable young people. Public Health Implications. Increasing both availability of and access to the best hospital care for young people severely ill with COVID-19 will save lives and improve case fatality rates.

Improved efficiency of coding systems with health information technology.

This study aimed to investigate the impact of health information technology (IT) on the Case Mix Index (CMI). This study was a retrospective cohort study using hospital financial data from the Office of Statewide Health Planning and Development (OSHPD) in California. A total of 309 unique hospitals were included in the study for 7 years, from 2009 to 2015, resulting in 2,135 hospital observations. The effects of health information technology (IT) on the Case Mix Index (CMI) was evaluated using dynamic panel data analysis to control endogeneity issues. This study found that more health IT adoption could lead to a lower CMI by improving coding systems. Policy makers, researchers, and healthcare providers must be cautious when interpreting the effect of health IT on the CMI. To encourage the adoption of health IT, the cost savings and reimbursement reductions resulting from health IT adoption should be compared. If any profit loss occurs (i.e., the cost savings is less than reimbursement reduction), more incentives should be provided to healthcare providers.

Making the call in the field: Validating emergency medical services identification of anatomic trauma triage criteria.

BACKGROUND: The National Field Triage Guidelines were created to inform triage decisions by emergency medical services (EMS) providers and include eight anatomic injuries that prompt transportation to a Level I/II trauma center. It is unclear how accurately EMS providers recognize these injuries. Our objective was to compare EMS-identified anatomic triage criteria with International Classification of Diseases-10th revision (ICD-10) coding of these criteria, as well as their association with trauma center need (TCN). METHODS: Scene patients 16 years and older in the NTDB during 2017 were included. National Field Triage Guidelines anatomic criteria were classified based on EMS documentation and ICD-10 diagnosis codes. The primary outcome was TCN, a composite of Injury Severity Score greater than 15, intensive care unit admission, urgent surgery, or emergency department death. Prevalence of anatomic criteria and their association with TCN was compared in EMS-identified versus ICD-10-coded criteria. Diagnostic performance to predict TCN was compared. RESULTS: There were 669,795 patients analyzed. The ICD-10 coding demonstrated a greater prevalence of injury detection. Emergency medical service-identified versus ICD-10-coded anatomic criteria were less sensitive (31% vs. 59%), but more specific (91% vs. 73%) and accurate (71% vs. 68%) for predicting TCN. Emergency medical service providers demonstrated a marked reduction in false positives (9% vs. 27%) but higher rates of false negatives (69% vs. 42%) in predicting TCN from anatomic criteria. Odds of TCN were significantly greater for EMS-identified criteria (adjusted odds ratio, 4.5; 95% confidence interval, 4.46-4.58) versus ICD-10 coding (adjusted odds ratio 3.7; 95% confidence interval, 3.71-3.79). Of EMS-identified injuries, penetrating injury, flail chest, and two or more proximal long bone fractures were associated with greater TCN than ICD-10 coding. CONCLUSION: When evaluating the anatomic criteria, EMS demonstrate greater specificity and accuracy in predicting TCN, as well as reduced false positives compared with ICD-10 coding. Emergency medical services identification is less sensitive for anatomic criteria; however, EMS identify the most clinically significant injuries. Further study is warranted to identify the most clinically important anatomic triage criteria to improve our triage protocols. LEVEL OF EVIDENCE: Care management, Level IV; Prognostic, Level III.

Examining the reliability of ICD-10 discharge coding in Red Cross War Memorial Children's Hospital administrative database.

BACKGROUND: Discharge diagnostic data from hospital administrative databases are often used to inform decisions relating to a variety of vital applications. These may include the allocation of resources, quality-of-care assessments, clinical research and formulation of healthcare policy. Accurately coded and reliably captured patient discharge data are of paramount importance for any hospital and health system to function efficiently. OBJECTIVES: To retrospectively examine the reliability of the International Classification of Diseases version 10 (ICD-10) discharge coding in Red Cross War Memorial Children's Hospital (RCWMCH)'s administrative database for primary and secondary discharge diagnoses, and to formulate recommendations for improvement to the current system. METHODS: This study was a retrospective folder review of 450 patient admissions to the short-stay and general paediatric wards at RCWMCH between 1 August 2013 and 1 September 2014. The principal investigator (PI) completed ICD-10 discharge coding for each admission and compared it with the corresponding admission data captured for each patient in the Clinicom (Siemens Medical Solutions, Germany) health information system. Agreement comparison was done to 4- and 3-character ICD-10 code specificity. RESULTS: Of the initial 450 randomly selected folders, 396 (88%) were analysed during the folder review process. The median number of total diagnoses (primary diagnosis plus secondary diagnoses) coded by the PI folder review was 3, with a distribution of 1 - 10 (interquartile range (IQR) 2 - 4). The median number of total diagnoses coded in Clinicom was 1, with a distribution of 1 - 3 (IQR 1 - 1). Agreement of primary diagnosis coding to 4 characters was 26.3%, with slight improvement to 34.3% when assessed to 3 characters. Agreement of secondary diagnoses to 4 characters was 14.9%, and 27.7% when assessed to 3 characters. CONCLUSIONS: Reliability of administrative ICD-10 discharge data from RCWMCH is poor. Inadequacies regarding the employment of dedicated and/or adequately trained coding personnel may significantly contribute to the problem and should be addressed.

Coding of Sexual Assault by Emergency Physicians: A Nationally Representative Study.

INTRODUCTION: Sexual assault is a public health problem that affects many Americans and has multiple long-lasting effects on victims. Medical evaluation after sexual assault frequently occurs in the emergency department, and documentation of the visit plays a significant role in decisions regarding prosecution and outcomes of legal cases against perpetrators. The American College of Emergency Physicians recommends coding such visits as sexual assault rather than adding modifiers such as "alleged." METHODS: This study reviews factors associated with coding of visits as sexual assault compared to suspected sexual assault using the 2016 Nationwide Emergency Department Sample. RESULTS: Younger age, female gender, a larger number of procedure codes, urban hospital location, and lack of concurrent alcohol use are associated with coding for confirmed sexual assault. CONCLUSION: Implications of this coding are discussed.

Identifying options for oncology therapy regimen codification to improve standardization-combined results of an expert panel and a review.

WHAT IS KNOWN AND OBJECTIVE: Chemotherapy drugs are often administered in combinations with predefined interdependent doses and cycle intervals. As yet, there is no global standardization system to describe these complex regimens in a universally comprehensive manner. The aim of this review is to identify which efforts for standardization have been undertaken and which recommendations for databases and nomenclature of chemotherapy regimens are available. METHODS: A literature review was performed to identify all peer-reviewed full-text articles about oncology therapy regimen codification. In addition, the results of this search were evaluated and consensus recommendations from a European expert panel were subsequently added. RESULTS: This review gives an overview of attempts to standardize chemotherapy nomenclature described in the literature, as well as of previously published identified gaps in regimen codification. In addition, we summarized the suggestions for improvement of chemotherapy codification found in the available literature, combining them with the expertise from a European expert panel of oncology pharmacists. WHAT IS NEW AND CONCLUSIONS: We believe that one of the most important error-prevention measures is standardization. However, there is a paucity of data how it may be achieved. Currently available data suggest that standardization has a positive impact on usability for data networks, prescription software, safety and the measurement of the quality of cancer care delivery. Standardization is also a strong pre-requisite for all discussions including oncology pharmacists and oncologists when evaluating chemotherapy regimen in countries in Europe but also all over the world. The recommendations compiled in this review can help to support overdue standardization efforts in this important therapeutic area.

Accuracy and reliability of injury coding in the national Dutch Trauma Registry.

OBJECTIVE: Injury coding is well known for lack of completeness and accuracy. The objective of this study was to perform a nationwide assessment of accuracy and reliability on Abbreviated Injury Scale (AIS) coding by Dutch Trauma Registry (DTR) coders and to determine the effect on Injury Severity Score (ISS). Additionally, the coders' characteristics were surveyed. METHODS: Three fictional trauma cases were presented to all Dutch trauma coders in a nationwide survey (response rate 69%). The coders were asked to extract and code the cases' injuries according to the AIS manual (version 2005, update 2008). Reference standard was set by three highly experienced coders. Summary statistics were used to describe the registered AIS codes and ISS distribution. The primary outcome measures were accuracy of injury coding and inter-rater agreement on AIS codes. Secondary outcome measures were characteristics of coders: profession, work setting, experience in injury coding and training level in injury coding. RESULTS: The total number of different AIS codes used to describe 14 separate injuries in the three cases was 89. Mean accuracy per AIS code was 42.2% (range 2.4-92.7%). Mean accuracy on number of AIS codes was 23%. Overall inter-rater agreement per AIS code was 49.1% (range 2.4-92.7%). The number of assigned AIS codes varied between 0 and 18 per injury. Twenty-seven percentage of injuries were overlooked. ISS was correctly scored in 42.4%. In 31.7%, the AIS coding of the two more complex cases led to incorrect classification of the patient as ISS < 16 or ISS ≥ 16. Half (47%) of the coders had no (para)medical degree, 26% were working in level I trauma centers, 37% had less than 2 years of experience and 40% had no training in AIS coding. CONCLUSIONS: Accuracy of and inter-rater agreement on AIS injury scoring by DTR coders is limited. This may in part be due to the heterogeneous backgrounds and training levels of the coders. As a result of the inconsistent coding, the number of major trauma patients in the DTR may be over- or underestimated. Conclusions based on DTR data should therefore be drawn with caution.

Use of a Clinical Electrocardiographic Database to Enhance Atrial Fibrillation/Atrial Flutter Identification Algorithms Based on Administrative Data.

Background Administrative data have limited sensitivity for case finding of atrial fibrillation/atrial flutter (AF/AFL). Linkage with clinical repositories of interpreted ECGs may enhance diagnostic yield of AF/AFL. Methods and Results We retrieved 369 ECGs from the institutional Marquette Universal System for Electrocardiography (MUSE) repository as validation samples, with rhythm coded as AF (n=49), AFL (n=50), or other competing rhythm diagnoses (n=270). With blinded, duplicate review of ECGs as the reference comparison, we compared multiple MUSE coding definitions for identifying AF/AFL. We tested the agreement between MUSE diagnosis and reference comparison, and calculated the sensitivity and specificity. Using a data set linking clinical registries, administrative data, and the MUSE repository (n=11 662), we assessed the incremental diagnostic yield of AF/AFL by incorporating ECG data to administrative data-based algorithms. The agreement between MUSE diagnosis and reference comparison depended on the coding definitions applied, with the Cohen κ ranging from 0.57 to 0.75. Sensitivity ranged from 60.6% to 79.1%, and specificity ranged from 93.2% to 98.0%. A coding definition with AF/AFL appearing in the first 3 ECG statements had the highest sensitivity (79.1%), with little loss of specificity (94.5%). Compared with the algorithms with only administrative data, incorporating ECG data increased the diagnostic yield of preexisting AF/AFL by 14.5% and incident AF/AFL by 7.5% to 16.1%. Conclusions Routine ECG interpretation using MUSE coding is highly specific and moderately sensitive for AF/AFL detection. Inclusion of MUSE ECG data in AF/AFL case identification algorithms can identify cases missed using administrative data-based algorithms alone.

Administrative Coding in Electronic Health Care Record-Based Research of NAFLD: An Expert Panel Consensus Statement.

BACKGROUND AND AIMS: Electronic health record (EHR)-based research allows the capture of large amounts of data, which is necessary in NAFLD, where the risk of clinical liver outcomes is generally low. The lack of consensus on which International Classification of Diseases (ICD) codes should be used as exposures and outcomes limits comparability and generalizability of results across studies. We aimed to establish consensus among a panel of experts on ICD codes that could become the reference standard and provide guidance around common methodological issues. APPROACH AND RESULTS: Researchers with an interest in EHR-based NAFLD research were invited to collectively define which administrative codes are most appropriate for documenting exposures and outcomes. We used a modified Delphi approach to reach consensus on several commonly encountered methodological challenges in the field. After two rounds of revision, a high level of agreement (>67%) was reached on all items considered. Full consensus was achieved on a comprehensive list of administrative codes to be considered for inclusion and exclusion criteria in defining exposures and outcomes in EHR-based NAFLD research. We also provide suggestions on how to approach commonly encountered methodological issues and identify areas for future research. CONCLUSIONS: This expert panel consensus statement can help harmonize and improve generalizability of EHR-based NAFLD research.

Reporting radiographers' interpretation and use of the British Society of Thoracic Imaging's coding system when reporting COVID-19 chest x-rays.

INTRODUCTION: The United Kingdom (UK) has experienced one of the worst initial waves of the COVID-19 pandemic. Clinical signs help guide initial diagnosis, though definitive diagnosis is made using the laboratory technique reverse transcription polymerase chain reaction (RT-PCR). The chest x-ray (CXR) is used as the primary imaging investigation in the United Kingdom (UK) for patients with suspected COVID-19. In some hospitals these CXRs may be reported by a radiographer. METHODS: Retrospective review of CXR reports by radiographers for suspected COVID-19 patients attending the Emergency Department (ED) of a hospital in the UK. Interpretation and use of the British Society of Thoracic Imaging (BSTI) coding system was assessed. Report description and code use were cross-checked. Report and code usage were checked against the RT-PCR result to determine accuracy. Report availability was checked against the availability of the RT-PCR result. A confusion matrix was utilised to determine performance. The data were analysed manually using Excel. RESULTS: Sample size was 320 patients; 54.1% male patients (n = 173), 45.9% female patients (n = 147). The correct code matched report descriptions in 316 of the 320 cases (98.8%). In 299 of the 320 cases (93.4%), the reports were available before the RT-PCR swab result. CXR sensitivity for detecting COVID-19 was 85% compared to 93% for the initial RT-PCR. CONCLUSION: Reporting radiographers can adequately utilise and apply the BSTI classification system when reporting COVID-19 CXRs. They can recognise the classic CXR appearances of COVID-19 and those with normal appearances. Future best practice includes checking laboratory results when reporting CXRs with ambiguous appearances. IMPLICATIONS FOR PRACTICE: Utilisation of reporting radiographers to report CXRs in any future respiratory pandemic should be considered a service-enabling development.

Development and Testing of Compatible Diagnosis Code Lists for the Functional Comorbidity Index: International Classification of Diseases, Ninth Revision, Clinical Modification and International Classification of Diseases, 10th Revision, Clinical Modification.

BACKGROUND: The Functional Comorbidity Index (FCI) was developed for community-based adult populations, with function as the outcome. The original FCI was a survey tool, but several International Classification of Diseases (ICD) code lists-for calculating the FCI using administrative data-have been published. However, compatible International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and ICD-10-CM versions have not been available. OBJECTIVE: We developed ICD-9-CM and ICD-10-CM diagnosis code lists to optimize FCI concordance across ICD lexicons. RESEARCH DESIGN: We assessed concordance and frequency distributions across ICD lexicons for the FCI and individual comorbidities. We used length of stay and discharge disposition to assess continuity of FCI criterion validity across lexicons. SUBJECTS: State Inpatient Databases from Arizona, Colorado, Michigan, New Jersey, New York, Utah, and Washington State (calendar year 2015) were obtained from the Healthcare Cost and Utilization Project. State Inpatient Databases contained ICD-9-CM diagnoses for the first 3 calendar quarters of 2015 and ICD-10-CM diagnoses for the fourth quarter of 2015. Inpatients under 18 years old were excluded. MEASURES: Length of stay and discharge disposition outcomes were assessed in separate regression models. Covariates included age, sex, state, ICD lexicon, and FCI/lexicon interaction. RESULTS: The FCI demonstrated stability across lexicons, despite small discrepancies in prevalence for individual comorbidities. Under ICD-9-CM, each additional comorbidity was associated with an 8.9% increase in mean length of stay and an 18.5% decrease in the odds of a routine discharge, compared with an 8.4% increase and 17.4% decrease, respectively, under ICD-10-CM. CONCLUSION: This study provides compatible ICD-9-CM and ICD-10-CM diagnosis code lists for the FCI.

Utilization of Social Determinants of Health ICD-10 Z-Codes Among Hospitalized Patients in the United States, 2016-2017.

BACKGROUND: The inclusion of Z-codes for social determinants of health (SDOH) in the 10th revision of the International Classification of Diseases (ICD-10) may offer an opportunity to improve data collection of SDOH, but no characterization of their utilization exists on a national all-payer level. OBJECTIVE: To examine the prevalence of SDOH Z-codes and compare characteristics of patients with and without Z-codes and hospitals that do and do not use Z-codes. RESEARCH DESIGN: Retrospective cohort study using 2016 and 2017 National Inpatient Sample. PARTICIPANTS: Total of 14,289,644 inpatient hospitalizations. MEASURES: Prevalence of SDOH Z-codes (codes Z55-Z65) and descriptive statistics of patients and hospitals. RESULTS: Of admissions, 269,929 (1.9%) included SDOH Z-codes. Average monthly SDOH Z-code use increased across the study period by 0.01% per month (P<0.001). The cumulative number and proportion of hospitals that had ever used an SDOH Z-code also increased, from 1895 hospitals (41%) in January 2016 to 3210 hospitals (70%) in December 2017. Hospitals that coded at least 1 SDOH Z-code were larger, private not-for-profit, and urban teaching hospitals. Compared with admissions without an SDOH Z-code, admissions with them were for patients who were younger, more often male, Medicaid recipients or uninsured. A higher proportion of admissions with SDOH Z-codes were for mental health (44.0% vs. 3.3%, P<0.001) and alcohol and substance use disorders (9.6% vs. 1.1%, P<0.001) compared with those without. CONCLUSIONS: The uptake of SDOH Z-codes has been slow, and current coding is likely poorly reflective of the actual burden of social needs experienced by hospitalized patients.