Validity of ICD-10-based algorithms to identify patients with influenza in inpatient and outpatient settings.

PURPOSE: To evaluate the validity of ICD-10-CM code-based algorithms as proxies for influenza in inpatient and outpatient settings in the USA. METHODS: Administrative claims data (2015-2018) from the largest commercial insurer in New Jersey (NJ), USA, were probabilistically linked to outpatient and inpatient electronic health record (EHR) data containing influenza test results from a large NJ health system. The primary claims-based algorithms defined influenza as presence of an ICD-10-CM code for influenza, stratified by setting (inpatient/outpatient) and code position for inpatient encounters. Test characteristics and 95% confidence intervals (CIs) were calculated using test-positive influenza as a reference standard. Test characteristics of alternative outpatient algorithms incorporating CPT/HCPCS testing codes and anti-influenza medication pharmacy claims were also calculated. RESULTS: There were 430 documented influenza test results within the study period (295 inpatient, 135 outpatient). The claims-based influenza definition had a sensitivity of 84.9% (95% CI 72.9%-92.1%), specificity of 96.3% (95% CI 93.1%-98.0%), and PPV of 83.3% (95% CI 71.3%-91.0%) in the inpatient setting, and a sensitivity of 76.7% (95% CI 59.1%-88.2%), specificity of 96.2% (95% CI 90.6%-98.5%), PPV of 85.2% (95% CI 67.5%-94.1%) in the outpatient setting. Primary inpatient discharge diagnoses had a sensitivity of 54.7% (95% CI 41.5%-67.3%), specificity of 99.6% (95% CI 97.7%-99.9%), and PPV of 96.7% (95% CI 83.3%-99.4%). CPT/HCPCS codes and anti-influenza medication claims were present for few outpatient encounters (sensitivity 3%-10%). CONCLUSIONS: In a large US healthcare system, inpatient ICD-10-CM codes for influenza, particularly primary inpatient diagnoses, had high predictive value for test-positive influenza. Outpatient ICD-10-CM codes were moderately predictive of test-positive influenza.

A compressed large language model embedding dataset of ICD 10 CM descriptions.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements, download and use without any other setup. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

ICD-10-CM codes infrequently used to document human trafficking in 2019 Nationwide Emergency Department Sample.

INTRODUCTION: People who experience human trafficking (HT) visit emergency departments (ED). The International Classification of Diseases, Clinical Modification (ICD-10-CM) introduced codes to document HT in June 2018. The aim of this study is to identify characteristics of ED patients who experienced forced labor or sexual exploitation as a documented external cause of morbidity in US visits. METHODS: Nationally representative surveillance based on patient visits to 989 hospital-owned EDs in the Nationwide Emergency Department Sample in 2019 became available in 2021. Eight ICD-10-CM codes to classify HT as an external cause of morbidity were combined into one HT variable for analysis in 2021-2022. RESULTS: A weighted count of 517 of 33.1 million ED visits (0.0016%) documented HT as an external cause of morbidity. Of them, sexual exploitation (71.6%) was documented more frequently than labor exploitation (28.4%). Most HT-related codes were visits by females (87.3%) from large metropolitan areas, and identified as white. Approximately 40% of visits were from ZIP codes with a median household income less than $48,000 annually. Relative to all other ED visits, patients with HT as an external cause of morbidity had higher odds of being female (OR = 6.54, 95% CI:3.59, 11.92) and being a minor (OR = 1.76, 95% CI:1.02, 3.04). CONCLUSION: HT was rarely documented as an external cause of morbidity in 989 hospitals' ED visits from a nationally representative sample in 2019. Documentation of recently added HT ICD-10-CM codes does not appear to have been implemented sufficiently to yield an unbiased representation of those who experienced HT and presented in the ED. Efforts to enhance the utility of ICD-10-CM HT codes for surveillance and documentation must first address ED personnel training on identification and response to HT. In doing so, ED personnel also need to address ethical concerns (e.g. stigma, confidentiality, risk of patient harm) and allow for informed consent among trafficked patients in order to be scaled up responsibly.

Positive Predictive Values of Anaphylaxis Diagnosis in Claims Data: A Multi-Institutional Study in Taiwan.

Real-world data sources can facilitate essential understanding of the epidemiological features of anaphylaxis. However, the accuracy of case-identifying definitions based on diagnosis codes for anaphylaxis in healthcare databases remains understudied. We conducted a cross-sectional study analyzing claims data from the largest multi-institutional healthcare system in Taiwan during 2017-2021. We included patients with incident anaphylaxis identified by either ICD-10-CM codes for anaphylaxis (Group 1) or ICD-10-CM codes for severe allergic or drug adverse events and additional modifier codes for acute allergy events (Group 2). We randomly selected 20% of the cases to determine the positive predictive value (PPV) of anaphylaxis case-identifying definitions in Groups 1 and 2 after review of electronic medical records by two physicians. From the original cohort (n = 2,176), we randomly selected 433 patients with either a diagnosis of anaphylaxis (Group 1), or a diagnosis of severe allergic and drug adverse events with additional modifier codes for acute allergy events (Group 2). In Group 1, we judged 135 / 170 patients as true anaphylaxis cases, giving a PPV of 79.4% (95% CI: 73.3-85.5). In Group 2, we judged 47 / 263 patients as true anaphylaxis cases, giving a PPV of 17.9% (95% CI: 13.3-22.5). In conclusion, acceptable PPVs were observed when anaphylaxis cases were identified by ICD-10-CM codes for anaphylaxis, but not by ICD-10-CM codes for severe allergic or drug adverse event with additional modifier codes for acute allergy events. Our multi-institutional findings could serve as a fundamental reference for further studies of anaphylaxis based on real-world healthcare databases.

One year outcomes of atrial fibrillation ablation: Contemporary analysis of the United States Nationwide Readmission Database.

BACKGROUND: Data on long-term outcomes of catheter ablation (CA) for atrial fibrillation (AF) in outside of clinical trials settings are sparse. OBJECTIVE: We aimed to assess outcomes and readmissions at 1 year following admission for CA for AF. METHODS: Utilizing the Nationwide Readmissions Database (2016-2018), we identified patients with CA among all patients with a primary admission diagnosis of AF, and a control group by propensity score match adjusted for age, sex, comorbidities, CHA2DS2-VASc scores, and the hospital characteristics. The primary outcome was a composite of unplanned heart failure (HF), AF and stroke-related readmissions, and death at 1 year, and secondary outcomes were hospital outcomes and all-cause readmission rates. RESULTS: The study cohort consisted of 29,771 patients undergoing CA and 63,988 controls. Patients undergoing CA were younger with lower CHA2DS2-VASc scores and less comorbidities. Over a follow-up of 170 ±1.1 days, the primary outcome occurred in 5.2% in CA group and 6.0% of controls (hazard ratio [HR] and 95% confidence interval [CI]: 0.86 [0.76-0.94], p = .002). CA affected AF and stroke related readmission, but showed no effect on HF and mortality outcome. Male sex (HR: 0.83 [0.74-0.94], p = .03), younger age (HR: 0.71 [0.61-0.83], p < .001], and lower CHA2DS2-VASc scores (HR: 0.68 [0.55-0.84], p < .001) were associated with lower risk of primary outcome with CA. CONCLUSION: In this study, CA for AF was associated with significantly lower AF and stroke-related admissions, but not to HF or all-cause readmission. Better outcomes were seen among males, younger patients, and in patients with less comorbidities and low CHA2DS2-VASc scores.

Validation of diagnosis codes to identify hospitalized COVID-19 patients in health care claims data.

PURPOSE: Health plan claims may provide complete longitudinal data for timely, real-world population-level COVID-19 assessment. However, these data often lack laboratory results, the standard for COVID-19 diagnosis. METHODS: We assessed the validity of ICD-10-CM diagnosis codes for identifying patients hospitalized with COVID-19 in U.S. claims databases, compared to linked laboratory results, among six Food and Drug Administration Sentinel System data partners (two large national insurers, four integrated delivery systems) from February 20-October 17, 2020. We identified patients hospitalized with COVID-19 according to five ICD-10-CM diagnosis code-based algorithms, which included combinations of codes U07.1, B97.29, general coronavirus codes, and diagnosis codes for severe symptoms. We calculated the positive predictive value (PPV) and sensitivity of each algorithm relative to laboratory test results. We stratified results by data source type and across three time periods: February 20-March 31 (Time A), April 1-30 (Time B), May 1-October 17 (Time C). RESULTS: The five algorithms identified between 34 806 and 47 293 patients across the study periods; 23% with known laboratory results contributed to PPV calculations. PPVs were high and similar across algorithms. PPV of U07.1 alone was stable around 93% for integrated delivery systems, but declined over time from 93% to 70% among national insurers. Overall PPV of U07.1 across all data partners was 94.1% (95% CI, 92.3%-95.5%) in Time A and 81.2% (95% CI, 80.1%-82.2%) in Time C. Sensitivity was consistent across algorithms and over time, at 94.9% (95% CI, 94.2%-95.5%). CONCLUSION: Our results support the use of code U07.1 to identify hospitalized COVID-19 patients in U.S. claims data.

Safety and complications of catheter ablation for atrial fibrillation: Predictors of complications from an updated analysis the National Inpatient Database.

BACKGROUND: Catheter ablation is increasingly employed in the management of atrial fibrillation (AF). Data regarding safety of ablation of AF is largely derived from controlled clinical trials. OBJECTIVES: The aim of this study was to analyze safety and complications of AF ablation performed in a "real world" setting outside of clinical trials, and obtain insights on predictors of complications. METHODS: We utilized the National Inpatient Sample database, to identify all patients who underwent AF ablations between 2015 and 2017 using International Classification of Disease-Tenth revision codes. Complications were defined as per the Agency for Health Care Research and Quality Guidelines. Statistical tests including multivariate logistic regression were performed to determine predictors of complications. RESULTS: Among 14,875 cases of AF ablation between 2015 and 2017, a total of 1884 complications were identified among 1080 (7.2%) patients. Patients with complications were likely to be older and female with a higher burden of comorbidities. A 27% increase in complications was observed from 2015 to 2017, driven by an increase in pericardial complications. Multivariate regression analysis revealed that pulmonary hypertension (adjusted odds ratio [aOR]: 1.99, p = .041) and chronic kidney disease (CKD; aOR: 1.67, p = .024), were independent predictors of complications. Centers with higher procedural volumes were associated with lower complication rates. CONCLUSIONS: Complication rates related to AF ablations remain substantially high. Presence of pulmonary hypertension and CKD are predictive of higher procedural complications. Furthermore, hospital procedure volume is an important factor that correlates with complication rates.

Validity of ICD-10-CM diagnoses to identify hospitalizations for serious infections among patients treated with biologic therapies.

PURPOSE: Identifying hospitalizations for serious infections among patients dispensed biologic therapies within healthcare databases is important for post-marketing surveillance of these drugs. We determined the positive predictive value (PPV) of an ICD-10-CM-based diagnostic coding algorithm to identify hospitalization for serious infection among patients dispensed biologic therapy within the FDA's Sentinel Distributed Database. METHODS: We identified health plan members who met the following algorithm criteria: (1) hospital ICD-10-CM discharge diagnosis of serious infection between July 1, 2016 and August 31, 2018; (2) either outpatient/emergency department infection diagnosis or outpatient antimicrobial treatment within 7 days prior to hospitalization; (3) inflammatory bowel disease, psoriasis, or rheumatological diagnosis within 1 year prior to hospitalization, and (4) were dispensed outpatient biologic therapy within 90 days prior to admission. Medical records were reviewed by infectious disease clinicians to adjudicate hospitalizations for serious infection. The PPV (95% confidence interval [CI]) for confirmed events was determined after further weighting by the prevalence of the type of serious infection in the database. RESULTS: Among 223 selected health plan members who met the algorithm, 209 (93.7% [95% CI, 90.1%-96.9%]) were confirmed to have a hospitalization for serious infection. After weighting by the prevalence of the type of serious infection, the PPV of the ICD-10-CM algorithm identifying a hospitalization for serious infection was 80.2% (95% CI, 75.3%-84.7%). CONCLUSIONS: The ICD-10-CM-based algorithm for hospitalization for serious infection among patients dispensed biologic therapies within the Sentinel Distributed Database had 80% PPV for confirmed events and could be considered for use within pharmacoepidemiologic studies.

Effect of ICD-9-CM to ICD-10-CM coding system transition on identification of common conditions: An interrupted time series analysis.

PURPOSE: To evaluate the effect of diagnostic coding system transition on the identification of common conditions recorded in Taiwan's national claims database. METHODS: Using the National Health Insurance Research Database, we estimated the 3-month prevalence of recorded diagnosis of 32 conditions based on the ICD-9-CM codes in 2014-2015 and the ICD-10-CM codes in 2016-2017. Two algorithms were assessed for ICD-10-CM: validated ICD-10 codes in the literature and codes translated from ICD-9-CM using an established mapping algorithm. We used segmented regression analysis on time-series data to examine changes in the 3-month prevalence (both level and trend) before and after the ICD-10-CM implementation. RESULTS: Significant changes in the level were found in 19 and 11 conditions when using the ICD-10 codes from the literature and mapping algorithm, respectively. The conditions with inconsistent levels by both of the algorithms were valvular heart disease, peripheral vascular disease, mild liver disease, moderate to severe liver disease, metastatic cancer, rheumatoid arthritis and collagen vascular diseases, coagulopathy, blood loss anemia, deficiency anemia, alcohol abuse, and psychosis. Nine conditions had significant changes in the trend when using the ICD-10 codes from the literature or mapping algorithm. CONCLUSIONS: Less than half of the 32 conditions studied had a smooth transition between the ICD-9-CM and ICD-10-CM coding systems. Researchers should pay attention to the conditions where the coding definitions result in inconsistent time series estimates.

Use of ICD-10-CM T codes in hospital claims data to identify adverse drug events in Taiwan.

WHAT IS KNOWN AND OBJECTIVE: Adverse drug events (ADEs) are a major public health concern worldwide and may prolong hospital stays, causing a burden on the healthcare system and increasing the associated costs. Therefore, optimizing medication use and reducing ADEs are priorities for public health. Medication safety can be monitored and improved by identifying ADEs. The utilization of diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems (ICD) system for the identification of ADEs has been firmly established. In Taiwan, however, the validity of recording ADEs on the basis of inpatient ICD-10-CM T codes has not been evaluated. Therefore, this study investigated the potential usefulness of ICD-10-CM T codes in routine hospital data for the identification of ADEs and for increasing the rate of reporting. METHODS: We use hospital claims data of hospitalized patients from one medical centre in northern Taiwan between 1 July 2016 and 30 June 2018. We defined an ADE to have taken place if an ICD-10-CM T code was present among the primary or secondary diagnosis codes. The inpatients who were discharged with T codes in a primary or secondary diagnosis were identified by the computerized T code information platform, and the retrospective review of the medical charts was performed by pharmacists to confirm the ADEs. RESULTS AND DISCUSSION: 1384 inpatients who were discharged with the relevant T codes in a primary or secondary diagnosis were identified during the study period. Code T36 (poisoning by, adverse effect of or underdosing of systemic antibiotics) was the most common code, accounting for 56.6%, followed by T42 (17.7%; poisoning by, adverse effect of or underdosing of antiepileptic, sedative-hypnotic or antiparkinsonism drug). Overall, 789 clinically significant ADEs were identified after medical chart review. The dermatologic system was the most commonly involved. The overall positive predictive value for a flagged code representing an ADE was 57%. Furthermore, the use of T codes to confirm the number of ADE cases increased the ADE reporting rate by 9.17%. WHAT IS NEW AND CONCLUSION: The PPV of ICD-10-CM T codes analysed in our study was insufficient for identifying ADEs during hospitalization. The sensitivity and specificity of this were inadequate. However, the T code system can be used as an auxiliary resource for pharmacists to identify potential ADEs and report the information as prompts on the physician order entry system. When a physician prescribes a drug that may cause an ADE in a patient, an alert is issued to ensure medication safety. In conclusion, the T codes did not perform well in our study and caution should be exercised in their use to identify ADEs on their own.

Translating claims-based CHA2 DS2 -VaSc and HAS-BLED to ICD-10-CM: Impacts of mapping strategies.

PURPOSE: The CHA2 DS2 -VaSc and HAS-BLED risk scores are commonly used in the studies of oral anticoagulants (OACs). The best ways to map these scores to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes is unclear, as is how they perform in various types of OAC users. We aimed to assess the distributions of CHA2 DS2 -VaSc and HAS-BLED scores and C-statistics for outcome prediction in the ICD-10-CM era using different mapping strategies. METHODS: We compared the distributions of CHA2 DS2 -VaSc and HAS-BLED scores from various mapping strategies in atrial fibrillation patients before, during, and after ICD-10-CM transition. We estimated the C-statistics predicting the 90-day risk of hospitalized stroke (for CHA2 DS2 -VaSc) or hospitalized bleeding (for HAS-BLED) in patients identified at least 6 months after the ICD-10-CM transition, overall and by anticoagulant type. RESULTS: Forward-backward mapping produced higher CHA2 DS2 -VaSc and HAS-BLED scores in the ICD-10-CM era compared to the ICD-9-CM era: the mean difference was 0.074 (95% confidence interval 0.064-0.085) for CHA2 DS2 -VaSc and 0.055 (0.048-0.062) for HAS-BLED. Both scores had higher C-statistics in patients taking no OACs (0.697 [0.677-0.717] for CHA2 DS2 -VaSc; 0.719 [0.702-0.737] for HAS-BLED) or direct OACs (0.695 [0.654-0.735] for CHA2 DS2 -VaSc; 0.700 [0.673-0.728] for HAS-BLED) than those taking warfarin (0.655 [0.613-0.697] for CHA2 DS2 -VaSc; 0.663 [0.6320.695] for HAS-BLED). CONCLUSIONS: Existing mapping strategies generally preserved the distributions of CHA2 DS2 -VaSc and HAS-BLED scores after ICD-10-CM transition. Both scores performed better in patients on no OACs or direct OACs than patients on warfarin.

Validation of ICD-10-CM surveillance codes for traumatic brain injury inpatient hospitalizations.

Objective: Using inpatient data from a 1,160-bed health system, we assessed the positive predictive value (PPV) of ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) codes included in a traumatic brain injury (TBI) surveillance definition proposed by the Centers for Disease Control and Prevention (CDC) in 2016. Methods: A random sample of 196 records with ICD-10-CM TBI codes was reviewed. The PPVs for the ICD-10-CM codes' ability to capture true TBI cases were calculated as the percentage of records with confirmed clinical provider-documented TBI and reported with 95% confidence intervals [95%CIs]. Results: The estimated overall PPV was 74% [67.9%, 80.1%] when the codes were listed in any diagnostic field, but 91.5% [86.2%, 96.8%] when listed as the principal diagnosis. S06 codes (intracranial injury) had an overall PPV of 80.2% [74.3%, 86.1%] and 96.9% [93.3%, 100%] when listed as the principal diagnosis. S02.0-.1 codes (vault/base skull fractures) in any position without co-existing S06 codes had a PPV of 15.8% [0%, 33.2%]. Conclusions: Intracranial injury codes (S06) in any diagnostic position had a very high estimated PPV. Further research is needed to determine the utility of other codes included in the CDC proposed definition for TBI surveillance.

Assessing the impact of the new ICD-10-CM coding system on pharmacoepidemiologic studies-An application to the known association between angiotensin-converting enzyme inhibitors and angioedema.

PURPOSE: To replicate the well-established association between angiotensin-converting enzyme inhibitors versus beta blockers and angioedema in the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) era. METHODS: We conducted a retrospective, inception cohort study in a large insurance database formatted to the Sentinel Common Data Model. We defined study periods spanning the ICD-9-CM era only, ICD-10-CM era only, and ICD-9-CM and ICD-10-CM era and conducted simple-forward mapping (SFM), simple-backward mapping (SBM), and forward-backward mapping (FBM) referencing the General Equivalence Mappings to translate the outcome (angioedema) and covariates from ICD-9-CM to ICD-10-CM. We performed propensity score (PS)-matched and PS-stratified Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: In the ICD-9-CM and ICD-10-CM eras spanning April 1 to September 30 of 2015 and 2016, there were 152 017 and 145 232 angiotensin-converting enzyme inhibitor initiators and 115 073 and 116 652 beta-blocker initiators, respectively. The PS-matched HR was 4.19 (95% CI, 2.82-6.23) in the ICD-9-CM era, 4.37 (2.92-6.52) in the ICD-10-CM era using SFM, and 4.64 (3.05-7.07) in the ICD-10-CM era using SBM and FBM. The PS-matched HRs from the mixed ICD-9-CM and ICD-10-CM eras ranged from 3.91 (2.69-5.68) to 4.35 (3.33-5.70). CONCLUSION: The adjusted HRs across different diagnostic coding eras and the use of SFM versus SBM and FBM produced numerically different but clinically similar results. Additional investigations as ICD-10-CM data accumulate are warranted.

Early impact of the ICD-10-CM transition on selected health outcomes in 13 electronic health care databases in the United States.

PURPOSE: To describe the consistency in the frequency of 5 health outcomes across the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) eras in the US. METHODS: We examined the incidence of 3 acute conditions (acute myocardial infarction [AMI], angioedema, ischemic stroke) and the prevalence of 2 chronic conditions (diabetes, hypertension) during the final 5 years of the ICD-9-CM era (January 2010-September 2015) and the first 15 months of the ICD-10-CM era (October 2015-December 2016) in 13 electronic health care databases in the Sentinel System. For each health outcome reviewed during the ICD-10-CM era, we evaluated 4 definitions, including published algorithms derived from other countries, as well as simple-forward, simple-backward, and forward-backward mapping using the General Equivalence Mappings. For acute conditions, we also compared the incidence between April to December 2014 and April to December 2016. RESULTS: The analyses included data from approximately 172 million health plan members. While the incidence or prevalence of AMI and hypertension performed similarly across the 2 eras, the other 3 outcomes did not demonstrate consistent trends for some or all the ICD-10-CM definitions assessed. CONCLUSIONS: When using data from both the ICD-9-CM and ICD-10-CM eras, or when using results from ICD-10-CM data to compare to results from ICD-9-CM data, researchers should test multiple ICD-10-CM outcome definitions as part of sensitivity analysis. Ongoing assessment of the impact of ICD-10-CM transition on identification of health outcomes in US electronic health care databases should occur as more data accrue.

Transition to international classification of disease version 10, clinical modification: the impact on internal medicine and internal medicine subspecialties.

BACKGROUND: The US health care system uses diagnostic codes for billing and reimbursement as well as quality assessment and measuring clinical outcomes. The US transitioned to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) on October, 2015. Little is known about the impact of ICD-10-CM on internal medicine and medicine subspecialists. METHODS: We used a state-wide data set from Illinois Medicaid specified for Internal Medicine providers and subspecialists. A total of 3191 ICD-9-CM codes were used for 51,078 patient encounters, for a total cost of US $26,022,022 for all internal medicine. We categorized all of the ICD-9-CM codes based on the complexity of mapping to ICD-10-CM as codes with complex mapping could result in billing or administrative errors during the transition. Codes found to have complex mapping and frequently used codes (n = 295) were analyzed for clinical accuracy of mapping to ICD-10-CM. Each subspecialty was analyzed for complexity of codes used and proportion of reimbursement associated with complex codes. RESULTS: Twenty-five percent of internal medicine codes have convoluted mapping to ICD-10-CM, which represent 22% of Illinois Medicaid patients, and 30% of reimbursements. Rheumatology and Endocrinology had the greatest proportion of visits and reimbursement associated with complex codes. We found 14.5% of ICD-9-CM codes used by internists, when mapped to ICD-10-CM, resulted in potential clinical inaccuracies. CONCLUSIONS: We identified that 43% of diagnostic codes evaluated and used by internists and that account for 14% of internal medicine reimbursements are associated with codes which could result in administrative errors.

Clinician's Primer to ICD-10-CM Coding for Cleft Lip/Palate Care.

On October 1, 2015, the United States required use of the Clinical Modification of the International Classification of Diseases, 10th Revision (ICD-10-CM) for diagnostic coding. This primer was written to assist the cleft care community with understanding and use of ICD-10-CM for diagnostic coding related to cleft lip and/or palate (CL/P).

ICD-10-based expanded code set for use in cleft lip/palate research and surveillance.

BACKGROUND: On October 1, 2015, the United States required use of the Clinical Modification of the International Classification of Diseases, 10th Revision (ICD-10-CM) for diagnostic coding. The ICD-10-CM code set is limited to gross categories for cleft lip and/or cleft palate (using only four of a possible seven characters). METHODS: Herein, a clinically useful expansion of the ICD-10-CM code set is proposed to improve the diagnostic accuracy necessary for individual clinical, research, and statistical projects that require it. (This is similar to how the Centers for Disease Control and Prevention/British Paediatric Association Code served to extend the ICD-9 code base.) RESULTS: Our proposed expansion does not replace the required use of ICD-10-CM for clinical, administrative, or financial transactions. Rather, it is offered as an optional set of cleft codes that could be used in parallel to document true classification-level data with phenotypic accuracy. CONCLUSION: The expanded set is "collapsible" into the official ICD-10-CM codes; this improves compatibility of the expanded codes that would be contained in research and epidemiologic databases with the standard codes from hospital electronic medical record systems and administrative billing data. Birth Defects Research (Part A) 106:905-914, 2016. © 2016 Wiley Periodicals, Inc.

Diagnosis coding of neurocognitive disorders.

Objective: Diagnosis coding is a core clinical competency. A basic understanding of the structure of the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), the conventions and rules for diagnosis coding, and what constitutes accurate coding, is fundamental to the clinician's knowledge base. This commentary seeks to provide a practical framework for clinicians to perform accurate diagnosis coding of neurocognitive disorders. Method: This paper: (1) summarizes the structure of the ICD-10-CM, (2) describes the rules and conventions of diagnosis coding for diagnostic categories relevant to neurocognitive disorders, (3) presents clinical examples and pragmatic recommendations to help readers improve their day-to-day use of diagnosis codes, and (4) describes limitations and discrepancies in the diagnosis coding advice for neurocognitive disorders presented within the Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) and the DSM-5-TR Neurocognitive Disorders Supplement. Its content originates from the ICD-10-CM itself and its companion document, the ICD-10-CM Official Guidelines for Coding and Reporting. Conclusion: The ICD-10-CM classification scheme is logically organized and easy to navigate for users who understand its structure and rules. Many neuropsychologists rely on the DSM-5-TR diagnosis coding advice, however that advice is limited with respect to the range of diagnosis codes relevant to neurocognitive disorders and their underlying causes. Relying on the ICD-10-CM directly for diagnosis coding of neurocognitive disorders, rather than the DSM-5-TR or other secondary sources, is therefore preferable and aids clinicians in accurate diagnosis coding.

Opportunities for Nurses to Decrease the Stigma Associated with Housing Instability and Homelessness.

The number of people experiencing homelessness (PEH) in the United States has increased in the past 5 years. PEH have a higher disease burden and early mortality compared to people who are housed. Stigma adds to the burden of disease and disease management for PEH. In this article the authors review stigma, define housing and homelessness, describe the health and health care disparities PEH experience, and using the socio-ecological model as a framework, offer opportunities for nurses to intervene in efforts to decrease the stigma that PEH and housing instability encounter to improve health outcomes.

Validation of Diagnostic Codes to Identify Glaucoma in Taiwan's Claims Data: A Multi-Institutional Study.

Background: Healthcare databases play a crucial role in improving our understanding of glaucoma epidemiology, which is the leading cause of irreversible blindness globally. However, the accuracy of diagnostic codes used in these databases to detect glaucoma is still uncertain. Aim: To assess the accuracy of ICD-9-CM and ICD-10-CM codes in identifying patients with glaucoma, including two distinct subtypes, primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG). Methods: We analyzed electronic medical records data from a 2% random sample of patients who newly underwent visual field examination in Taiwan's largest multi-institutional healthcare system from 2011 to 2020. The diagnosis of glaucoma was confirmed by two ophthalmologists, based on the glaucoma diagnostic criteria. The positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity for ICD-9-CM codes 365.1X and 365.2X, and ICD-10-CM codes H4010X, H4011X, H4012X, H4020X, H4021X, H4022X, H4023X and H4024X for glaucoma were calculated. Results: We randomly selected 821 patients (mean age: 56.9 years old; female: 50.5%) from the original cohort of 41,050 newly receiving visual field examination in the study. Among 464 cases with an ICD-9-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 86.5, 96.5, 91.9, and 90.9%, respectively. Among 357 cases with an ICD-10-CM glaucoma code, the sensitivity, specificity, PPV and NPV for glaucoma were 87.0, 92.8, 92.2 and 87.9%, respectively. The accuracy of diagnostic codes to identify POAG and PACG remained consistent. Conclusion: The diagnostic codes were highly reliable for identifying cases of glaucoma in Taiwan's routine healthcare practice. These results provide confidence when using ICD-9-CM and ICD-10-CM codes to define glaucoma cases in healthcare database research in Taiwan.