Use of open-text responses to recode categorical survey data on postpartum contraception use among women in the United States: A mixed-methods inquiry of Pregnancy Risk Assessment Monitoring System data.

BACKGROUND: Postpartum contraception prevents unintended pregnancies and short interpregnancy intervals. The Pregnancy Risk Assessment Monitoring System (PRAMS) collects population-based data on postpartum contraception nonuse and reasons for not using postpartum contraception. In addition to quantitative questions, PRAMS collects open-text responses that are typically left unused by secondary quantitative analyses. However, abundant preexisting open-text data can serve as a resource for improving quantitative measurement accuracy and qualitatively uncovering unexpected responses. We used PRAMS survey questions to explore unprompted reasons for not using postpartum contraception and offer insight into the validity of categorical responses. METHODS AND FINDINGS: We used 31,208 categorical 2012 PRAMS survey responses from postpartum women in the US to calculate original prevalences of postpartum contraception use and nonuse and reasons for contraception nonuse. A content analysis of open-text responses systematically recoded data to mitigate survey bias and ensure consistency, resulting in adjusted prevalence calculations and identification of other nonuse themes. Recoded contraception nonuse slightly differed from original reports (21.5% versus 19.4%). Both calculations showed that many respondents reporting nonuse may be at a low risk for pregnancy due to factors like tubal ligation or abstinence. Most frequent nonuse reasons were not wanting to use birth control (27.1%) and side effect concerns (25.0%). Other open-text responses showed common themes of infertility, and breastfeeding as contraception. Comparing quantitative and qualitative responses revealed contradicting information, suggesting respondent misinterpretation and confusion surrounding the term "pregnancy prevention." Though this analysis may be limited by manual coding error and researcher biases, we avoided coding exhaustion via 1-hour coding periods and validated reliability through intercoder kappa scores. CONCLUSIONS: In this study, we observed that respondents reporting contraception nonuse often described other methods of pregnancy prevention and contraception barriers that were not included in categorical response options. Open-text responses shed light on a more comprehensive list of pregnancy prevention methods and nonuse options. Our findings contribute to survey questions that can lead to more accurate depiction of postpartum contraceptive behavior. Additionally, future use of these qualitative methods may be used to improve other health behavior survey development and resulting data.

Applying resolved and remission codes reduced prevalence of multimorbidity in an urban multi-ethnic population.

OBJECTIVE: To estimate the prevalence and determinants of multimorbidity in an urban, multi-ethnic area over 15-years and investigate the effect of applying resolved/remission codes on prevalence estimates. STUDY DESIGN AND SETTING: This is a population-based retrospective cross-sectional study using electronic health records of adults registered between 2005 -2020 in general practices in one inner London borough (n = 826,936). Classification of resolved/remission was based on clinical coding defined by the patient's general practitioner. RESULTS: The crude and age-adjusted prevalence of multimorbidity over the study period were 21.2% (95% CI: 21.1 -21.3) and 30.8% (30.6 -31.0), respectively. Applying resolved/remission codes decreased the crude and age-adjusted prevalence estimates to 18.0% (95% CI: 17.9 -18.1) and 27.5% (27.4 -27.7). Asthma (53.2%) and depression (20.2%) were responsible for most resolved and remission codes. Substance use (Adjusted Odds Ratio 10.62 [95% CI: 10.30 -10.95]), high cholesterol (2.48 [2.44 -2.53]), and moderate obesity (2.19 [2.15 -2.23]) were the strongest risk factor determinants of multimorbidity outside of advanced age. CONCLUSION: Our study highlights the importance of applying resolved/remission codes to obtain an accurate prevalence and the increased burden of multimorbidity in a young, urban, and multi-ethnic population. Understanding modifiable risk factors for multimorbidity can assist policymakers in designing effective interventions to reduce progression to multimorbidity.

Gender Disparities in Stroke Code Activation in Patients with Intracerebral Hemorrhage.

OBJECTIVES: Routine implementation of protocol-driven stroke "codes" results in timelier and more effective acute stroke management. However, it is unclear if patient demographics contribute to disparities in stroke code activation. We aimed to explore these demographic factors in a retrospective cohort study of patients with intracerebral hemorrhage (ICH). MATERIALS AND METHODS: We identified consecutive patients with non-traumatic ICH who presented directly to our Comprehensive Stroke Center over 2 years and collected data on demographics, clinical features, and stroke code activation. We used multivariable logistic regression to examine differences in stroke code activation based on patient demographics while adjusting for initial clinical features (NIH Stroke Scale, FAST [facial drooping, arm weakness, speech difficulties] vs. non-FAST symptoms, time from last-known-well [LKW], and systolic blood pressure [SBP]). RESULTS: Among 265 patients, 68% (n=179) had a stroke code activation. Stroke codes occurred less frequently in women (62%) than men (72%) and in non-white (57%) vs. white patients (70%). Non-stroke code patients were less likely to have FAST symptoms (37% vs. 87%) and had lower initial SBP (mean±SD 159.3±34.2 vs. 176.0±31.9 mmHg) than stroke code patients. In our primary multivariable models, neither age nor race were associated with stroke code activation. However, women were significantly less likely to have stroke codes than men (OR 0.49 [95% CI 0.24-0.98]), as were non-FAST symptoms (OR 0.11 [95% CI 0.05-0.22]). CONCLUSIONS: Our data suggest gender disparities in emergency stroke care that should prompt further investigations into potential systemic biases. Increased awareness of atypical stroke symptoms is also warranted.

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.

Explainable automated coding of clinical notes using hierarchical label-wise attention networks and label embedding initialisation.

BACKGROUND: Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to improve the efficiency and accuracy of medical coding. Recent studies on deep learning for automated medical coding achieved promising performances. However, the explainability of these models is usually poor, preventing them to be used confidently in supporting clinical practice. Another limitation is that these models mostly assume independence among labels, ignoring the complex correlations among medical codes which can potentially be exploited to improve the performance. METHODS: To address the issues of model explainability and label correlations, we propose a Hierarchical Label-wise Attention Network (HLAN), which aimed to interpret the model by quantifying importance (as attention weights) of words and sentences related to each of the labels. Secondly, we propose to enhance the major deep learning models with a label embedding (LE) initialisation approach, which learns a dense, continuous vector representation and then injects the representation into the final layers and the label-wise attention layers in the models. We evaluated the methods using three settings on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS (National Health Service) COVID-19 (Coronavirus disease 2019) shielding codes. Experiments were conducted to compare the HLAN model and label embedding initialisation to the state-of-the-art neural network based methods, including variants of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). RESULTS: HLAN achieved the best Micro-level AUC and F1 on the top-50 code prediction, 91.9% and 64.1%, respectively; and comparable results on the NHS COVID-19 shielding code prediction to other models: around 97% Micro-level AUC. More importantly, in the analysis of model explanations, by highlighting the most salient words and sentences for each label, HLAN showed more meaningful and comprehensive model interpretation compared to the CNN-based models and its downgraded baselines, HAN and HA-GRU. Label embedding (LE) initialisation significantly boosted the previous state-of-the-art model, CNN with attention mechanisms, on the full code prediction to 52.5% Micro-level F1. The analysis of the layers initialised with label embeddings further explains the effect of this initialisation approach. The source code of the implementation and the results are openly available at https://github.com/acadTags/Explainable-Automated-Medical-Coding. CONCLUSION: We draw the conclusion from the evaluation results and analyses. First, with hierarchical label-wise attention mechanisms, HLAN can provide better or comparable results for automated coding to the state-of-the-art, CNN-based models. Second, HLAN can provide more comprehensive explanations for each label by highlighting key words and sentences in the discharge summaries, compared to the n-grams in the CNN-based models and the downgraded baselines, HAN and HA-GRU. Third, the performance of deep learning based multi-label classification for automated coding can be consistently boosted by initialising label embeddings that captures the correlations among labels. We further discuss the advantages and drawbacks of the overall method regarding its potential to be deployed to a hospital and suggest areas for future studies.

Visualization and application of disease diagnosis codes for population health management using porcine diseases as a model.

Accurate and timely results of diagnostic investigations and laboratory testing guide clinical interventions for the continuous improvement of animal health and welfare. Infectious diseases can severely limit the health, welfare, and productivity of populations of animals. Livestock veterinarians submit thousands of samples daily to veterinary diagnostic laboratories (VDLs) for disease diagnosis, pathogen monitoring, and surveillance. Individual diagnostic laboratory reports are immediately useful; however, aggregated historical laboratory data are increasingly valued by clinicians and decision-makers to identify changes in the health status of various animal populations over time and geographical space. The value of this historical information is enhanced by visualization of trends of agent detection, disease diagnosis, or both, which helps focus time and resources on the most significant pathogens and fosters more effective communication between livestock producers, veterinarians, and VDL professionals. Advances in data visualization tools allow quick, efficient, and often real-time scanning and analysis of databases to inform, guide, and modify animal health intervention algorithms. Value is derived at the farm, production system, or regional level. Visualization tools allow client-specific analyses, benchmarking, formulation of research questions, and monitoring the effects of disease management and precision farming practices. We present here the approach taken to visualize trends of disease occurrence using porcine disease diagnostic code data for the period 2010 to 2019. Our semi-automatic standardized creation of a visualization platform allowed the transformation of diagnostic report data into aggregated information to visualize and monitor disease diagnosis.

Disease diagnostic coding to facilitate evidence-based medicine: current and future perspectives.

Technologic advances in information management have rapidly changed laboratory testing and the practice of veterinary medicine. Timely and strategic sampling, same-day assays, and 24-h access to laboratory results allow for rapid implementation of intervention and treatment protocols. Although agent detection and monitoring systems have progressed, and wider tracking of diseases across veterinary diagnostic laboratories exists, such as by the National Animal Health Laboratory Network (NAHLN), the distinction between detection of agent and manifestation of disease is critical to improved disease management. The implementation of a consistent, intuitive, and useful disease diagnosis coding system, specific for veterinary medicine and applicable to multiple animal species within and between veterinary diagnostic laboratories, is the first phase of disease data aggregation. Feedback loops for continuous improvement that could aggregate existing clinical and laboratory databases to improve the value and applications of diagnostic processes and clinical interventions, with interactive capabilities between clinicians and diagnosticians, and that differentiate disease causation from mere agent detection, remain incomplete. Creating an interface that allows aggregation of existing data from clinicians, including final diagnosis, interventions, or treatments applied, and measures of outcomes, is the second phase. Prototypes for stakeholder cooperation, collaboration, and beta testing of this vision are in development and becoming a reality. We focus here on how such a system is being developed and utilized at the Iowa State University Veterinary Diagnostic Laboratory to facilitate evidence-based medicine and utilize diagnostic coding for continuous improvement of animal health and welfare.

The effects of coding intensity in Medicare Advantage on plan benefits and finances.

OBJECTIVE: To assess how beneficiary premiums, expected out-of-pocket costs, and plan finances in the Medicare Advantage (MA) market are related to coding intensity. DATA SOURCES/STUDY SETTING: MA plan characteristics and administrative records from the Centers for Medicare and Medicaid Services (CMS) for the sample of beneficiaries enrolled in both MA and Part D between 2008 and 2015. Medicare claims and drug utilization data for Traditional Medicare (TM) beneficiaries were used to calibrate an independent measure of health risk. STUDY DESIGN: Coding intensity was measured by comparing the CMS risk score for each MA contract with a contract level risk score developed using prescription drug data. We conducted regressions of plan outcomes, estimating the relationship between outcomes and coding intensity. To develop prescription drug scores, we assigned therapeutic classes to beneficiaries based on their prescription drug utilization. We then regressed nondrug spending for TM beneficiaries in 2015 on demographic and therapeutic class identifiers for 2014 and used the coefficients to predict relative risk. PRINCIPAL FINDINGS: We found that, for each $1 increase in potential revenue resulting from coding intensity, MA plan bid submissions declined by $0.10 to $0.19, and another $0.21 to $0.45 went toward reducing plans' medical loss ratios, an indication of higher profitability. We found only a small impact on beneficiary's projected out-of-pocket costs in a plan, which serves as a measure of the generosity of plan benefits, and a $0.11 to $0.16 reduction in premiums. As expected, coding intensity's effect on bids was substantially larger in counties with higher levels of MA competition than in less competitive counties. CONCLUSIONS: While coding intensity increases taxpayers' costs of the MA program, enrollees and plans both benefit but with larger gains for plans. The adoption of policies to more completely adjust for coding intensity would likely affect both beneficiaries and plan profits.

Coding agricultural injury: Factors affecting coder agreement.

OBJECTIVES: To determine coders' agreement level for the Occupational Injury and Illness Classification System (OIICS) source of injury and injury event codes, and the Farm and Agricultural Injury Classification (FAIC) code in the AgInjuryNews.org and to determine the effects of supplemental information and follow-up discussion in final code assignments. METHODS: Two independent researchers initially coded 1304 injury cases from AgInjurynews.org using the OIICS and the FAIC coding schemes. Code agreement levels for injury source, event, and FAIC and the effect of supplemental information and follow-up discussions on final coding was assessed. RESULTS: Coders' agreement levels were almost perfect for OIICS source and event categories at the 3-digit level, with lower agreement at the 4-digit level. By using supplemental information and follow-up discussion, coders improved the coding accuracy by an average 20% for FAIC. Supplemental information and follow-up discussions had helped finalize the disagreed codes 55% of the time for OIICS source coding assignments and 40% of time for OIICS event coding assignments for most detailed 4-digit levels. Five key themes emerged regarding accurate and consistent coding of the agricultural injuries: inclusion/exclusion based on industry classification system; inconsistent/discrepant reports; incomplete/nonspecific reports; effects of supplemental information on coding; and differing interpretations of code selection rules. Practical applications: Quantifying the level of agreement for agricultural injuries will lead to a better understanding of coding discrepancies and may uncover areas for improvement to coding scheme itself. High level of initial and final agreement with FAIC and OIICS codes suggest that these coding schemes are user-friendly and amenable to widespread use.

Association of Diagnostic Coding-Based Frailty and Outcomes in Patients With Heart Failure: A Report From the Veterans Affairs Health System.

Background The aim of this study was to determine whether frailty is associated with increased admission and mortality risk in the setting of heart failure. Methods and Results This retrospective cohort analysis included patients treated within the Veterans Affairs Health System who had International Classification of Diseases, Ninth Revision (ICD-9) codes for heart failure on 2 or more dates over a 2-year period. The clinical variables identifiable in claims data, such as demographic variables and markers of physical and cognitive dysfunction, were used to identify patients meeting the frailty phenotype. Of 388 785 extracted patients with coding of heart failure between 2015 and 2018, 163 085 patients (41.9%) with ejection fraction (EF) measurement were included in the present analysis (38.3% with reduced EF and 61.7% with preserved EF). There were 16 660 patients (10.2%) who were identified as frail (9.1% in heart failure with reduced EF and 10.9% in heart failure with preserved EF). Frail patients were older, more often depressed, and were likely to have been admitted in the previous year. One-year all-cause mortality rate was 9.7% and 28.1%, and admission rate was 58.1% and 79.5% for nonfrail and frail patients, respectively. Frailty was associated with mortality and admission risk compared with the nonfrail group (adjusted odds ratio [OR], 1.71; 95% CI, 1.65-1.77 for mortality; adjusted OR, 1.29; 95% CI, 1.24-1.34 for admission) independent of EF. Conclusions Frailty based on diagnostic coding was associated with particularly higher risk of mortality despite adjustment for known clinical variables. Our findings underscore the importance of nontraditional parameters in the prognostic assessment.

Patient Coded Severity and Payment Penalties Under the Hospital Readmissions Reduction Program: A Machine Learning Approach.

OBJECTIVE: The objective of this study was to examine variation in hospital responses to the Centers for Medicare and Medicaid's expansion of allowable secondary diagnoses in January 2011 and its association with financial penalties under the Hospital Readmission Reduction Program (HRRP). DATA SOURCES/STUDY SETTING: Medicare administrative claims for discharges between July 2008 and June 2011 (N=3102 hospitals). RESEARCH DESIGN: We examined hospital variation in response to the expansion of secondary diagnoses by describing changes in comorbidity coding before and after the policy change. We used random forest machine learning regression to examine hospital characteristics associated with coded severity. We then used a 2-part model to assess whether variation in coded severity was associated with readmission penalties. RESULTS: Changes in severity coding varied considerably across hospitals. Random forest models indicated that greater baseline levels of condition categories, case-mix index, and hospital size were associated with larger changes in condition categories. Hospital coding of an additional condition category was associated with a nonsignificant 3.8 percentage point increase in the probability for penalties under the HRRP (SE=2.2) and a nonsignificant 0.016 percentage point increase in penalty amount (SE=0.016). CONCLUSION: Changes in patient coded severity did not affect readmission penalties.

Administrative data identify sickle cell disease: A critical review of approaches in U.S. health services research.

To identify people living with sickle cell disease (SCD) and study their healthcare utilization, researchers can either use clinical records linked to administrative data or use billing diagnosis codes in stand-alone administrative databases. Correct identification of individuals clinically managed for SCD using diagnosis codes in claims databases is limited by the accuracy of billing codes in outpatient encounters. In this critical review, we assess the strengths and limitations of claims-based SCD case-finding algorithms in stand-alone administrative databases that contain both inpatient and outpatient records. Validation studies conducted using clinical records and newborn screening for confirmation of SCD case status have found that algorithms that require three or more nonpharmacy claims or one inpatient claim plus two or more outpatient claims with SCD codes show acceptable accuracy (positive predictive value and sensitivity) in children and adolescents. Future studies might seek to assess the accuracy of case-finding algorithms over the lifespan.

National Ambulance Surveillance System: A novel method using coded Australian ambulance clinical records to monitor self-harm and mental health-related morbidity.

Self-harm and mental health are inter-related issues that substantially contribute to the global burden of disease. However, measurement of these issues at the population level is problematic. Statistics on suicide can be captured in national cause of death data collected as part of the coroner's review process, however, there is a significant time-lag in the availability of such data, and by definition, these sources do not include non-fatal incidents. Although survey, emergency department, and hospitalisation data present alternative information sources to measure self-harm, such data do not include the richness of information available at the point of incident. This paper describes the mental health and self-harm modules within the National Ambulance Surveillance System (NASS), a unique Australian system for monitoring and mapping mental health and self-harm. Data are sourced from paramedic electronic patient care records provided by Australian state and territory-based ambulance services. A team of specialised research assistants use a purpose-built system to manually scrutinise and code these records. Specific details of each incident are coded, including mental health symptoms and relevant risk indicators, as well as the type, intent, and method of self-harm. NASS provides almost 90 output variables related to self-harm (i.e., type of behaviour, self-injurious intent, and method) and mental health (e.g., mental health symptoms) in the 24 hours preceding each attendance, as well as demographics, temporal and geospatial characteristics, clinical outcomes, co-occurring substance use, and self-reported medical and psychiatric history. NASS provides internationally unique data on self-harm and mental health, with direct implications for translational research, public policy, and clinical practice. This methodology could be replicated in other countries with universal ambulance service provision to inform health policy and service planning.

Validation study of bullous pemphigoid and pemphigus vulgaris recording in routinely collected electronic primary healthcare records in England.

OBJECTIVES: The validity of bullous pemphigoid and pemphigus vulgaris recording in routinely collected healthcare data in the UK is unknown. We assessed the positive predictive value (PPV) for bullous pemphigoid and pemphigus vulgaris primary care Read codes in the Clinical Practice Research Datalink (CPRD) using linked inpatient data (Hospital Episode Statistics (HES)) as the diagnostic benchmark. SETTING: Adult participants with bullous pemphigoid or pemphigus vulgaris registered with HES-linked general practices in England between January 1998 and December 2017. Code-based algorithms were used to identify patients from the CPRD and extract their benchmark blistering disease diagnosis from HES. PRIMARY OUTCOME MEASURE: The PPVs of Read codes for bullous pemphigoid and pemphigus vulgaris. RESULTS: Of 2468 incident cases of bullous pemphigoid and 431 of pemphigus vulgaris, 797 (32.3%) and 85 (19.7%) patients, respectively, had a hospitalisation record for a blistering disease. The PPV for bullous pemphigoid Read codes was 93.2% (95% CI 91.3% to 94.8%). Of the bullous pemphigoid cases, 3.0% had an HES diagnosis of pemphigus vulgaris and 3.8% of another blistering disease. The PPV for pemphigus vulgaris Read codes was 58.5% (95% CI 48.0% to 68.9%). Of the pemphigus vulgaris cases, 24.7% had an HES diagnosis of bullous pemphigoid and 16.5% of another blistering disease. CONCLUSIONS: The CPRD can be used to study bullous pemphigoid, but recording of pemphigus vulgaris needs to improve in primary care.

Characteristics of Decedents in Home Settings Using Medicare Place of Hospice Service Codes.

Very little is known about the characteristics of the Medicare beneficiaries receiving hospice at home, defined using the Medicare Healthcare Common Procedure Coding System codes, as a traditional home, an assisted living facility, or a nursing home. A secondary analysis of 2015 Medicare data using regression to describe the characteristics of decedents (n = 675 782) in hospice residing in a traditional home, an assisted living facility, and a nursing home was completed. Results suggest that the proportion of Medicare decedents in hospice with more than 180 lifetime days in hospice was highest among those who resided in an assisted living facility (25.03%) compared with those who resided in a nursing home (18.87%) or in a traditional home (13.04%). Regression findings suggest that, compared with decedents in hospice without dementia who resided in a traditional home, decedents in hospice with a primary diagnosis of dementia were more likely to reside in an assisted living facility (adjusted odds ratio, 2.29; P < .0001) when controlling for other factors. In summary, decedents in hospice who resided in a traditional home have different characteristics than decedents who resided in an assisted living facility or a nursing home. Interdisciplinary providers should consider these differences when managing hospice interventions.

Managing mental health on a prolonged deployment: UK military exercise SAIF SAREEA 3.

INTRODUCTION: This paper presents the burden of mental health cases throughout UK military exercise SAIF SAREEA 3 (SS3), a low-tempo armoured brigade exercise in Oman from June to November 2018, and aims to discuss ways that mental health may be better managed on future large exercises. METHODS: A retrospective review of all attendances at army medical facilities and relevant computerised medical records was undertaken. RESULTS: 14 mental health cases were identified, which required 51 follow-up presentations throughout the duration of SS3. This represented 1.2% of all first patient presentations, and 6.3% of all follow-up work. 64% had diagnoses which predated deployment and could all be classified within 10th revision of International Statistical Classification of Diseases and Related Health Problems as either F30-F39 mood (affective) disorders, or F40-F48 neurotic, stress-related and somatoform disorders; all new diagnoses made while deployed were adjustment disorders. The medical officer spent an average of 147 min total clinical care time per patient. Six patients were aeromedically evacuated (AE), which represented 26% of all AE cases from SS3. CONCLUSIONS: Presentations were low, but time consuming and with poor disposal outcomes. Most conditions predated the exercise, and could have been predicted to worsen through the deployment. Given the disproportionate burden that mental health cases afforded during SS3, future brigade-sized deployments should include deployed mental health professionals in order to offer evidence-based therapy which should lead to improved disposal outcomes and a reduced AE burden.

Diagnostic Category Prevalence in 3 Classification Systems Across the Transition to the International Classification of Diseases, Tenth Revision, Clinical Modification.

Importance: On October 1, 2015, the US transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for recording diagnoses, symptoms, and procedures. It is unknown whether this transition was associated with changes in diagnostic category prevalence based on diagnosis classification systems commonly used for payment and quality reporting. Objective: To assess changes in diagnostic category prevalence associated with the ICD-10-CM transition. Design, Setting, and Participants: This interrupted time series analysis and cross-sectional study examined level and trend changes in diagnostic category prevalence associated with the ICD-10-CM transition and clinically reviewed a subset of diagnostic categories with changes of 20% or more. Data included insurance claim diagnoses from the IBM MarketScan Commercial Database from January 1, 2010, to December 31, 2017, for more than 18 million people aged 0 to 64 years with private insurance. Diagnoses were mapped using 3 common diagnostic classification systems: World Health Organization (WHO) disease chapters, Department of Health and Human Services Hierarchical Condition Categories (HHS-HCCs), and Agency for Healthcare Research and Quality Clinical Classification System (AHRQ-CCS). Data were analyzed from December 1, 2018, to January 21, 2020. Exposures: US implementation of ICD-10-CM. Main Outcomes and Measures: Monthly rates of individuals with at least 1 diagnosis in a diagnostic classification category per 10 000 eligible members. Results: The analytic sample contained information on 2.1 billion enrollee person-months with 3.4 billion clinically assigned diagnoses; the mean (range) monthly sample size was 22.1 (18.4 to 27.1 ) million individuals. While diagnostic category prevalence changed minimally for WHO disease chapters, the ICD-10-CM transition was associated with level changes of 20% or more among 20 of 127 HHS-HCCs (15.7%) and 46 of 282 AHRQ-CCS categories (16.3%) and with trend changes of 20% or more among 12 of 127 of HHS-HCCs (9.4%) and 27 of 282 of AHRQ-CCS categories (9.6%). For HHS-HCCs, monthly rates of individuals with any acute myocardial infarction diagnosis increased 131.5% (95% CI, 124.1% to 138.8%), primarily because HHS added non-ST-segment-elevation myocardial infarction diagnoses to this category. The HHS-HCC for diabetes with chronic complications increased by 92.4% (95% CI, 84.2% to 100.5%), primarily from including new diabetes-related hypoglycemia and hyperglycemia codes, and the rate for completed pregnancy with complications decreased by 54.5% (95% CI, -58.7% to -50.2%) partly due to removing vaginal birth after cesarean delivery as a complication. Conclusions and Relevance: These findings suggest that the ICD-10-CM transition was associated with large prevalence changes for many diagnostic categories. Diagnostic classification systems developed using ICD-9-CM may need to be refined using ICD-10-CM data to avoid unintended consequences for disease surveillance, performance assessment, and risk-adjusted payments.

Physician code creep after the initiation of outpatient volume control program and implications for appropriate ICD-10-CM coding.

BACKGROUND: Most studies on the physician code creep (i.e., changes in case mix record-keeping practices to improve reimbursement) have focused on episodes (inpatient hospitalizations or outpatient procedures). Little is known regarding changes in diagnostic coding practices for better reimbursement among a fixed cohort of patients with chronic diseases. METHODS: To examine whether physicians in tertiary medical centers changed their coding practices after the initiation of the Outpatient Volume Control Program (OVCP) in Taiwan, we conducted a retrospective observational study of four patient cohorts (two interventions and two controls) from January 2016 to September 2017 in Taiwan. The main outcomes were the number of outpatient visits with four coding practices: 1) OVCP monitoring code recorded as primary diagnosis; 2) OVCP monitoring code recorded as secondary diagnosis; 3) non-OVCP monitoring code recorded as primary diagnosis; 4) non-OVCP monitoring code recorded as secondary diagnosis. RESULTS: The percentage change of the number of visits with coding practice 1 between 2016Q1 and 2017Q3 was - 74% for patients with hypertension and - 73% with diabetes in tertiary medical centers and - 23% and - 17% in clinics, respectively. By contrast, the percentage changes of coding practice 3 were + 73% for patients with hypertension and + 46% for patients with diabetes in tertiary medical centers and - 19% and - 2% in clinics, respectively. CONCLUSIONS: Physician code creep occurred after the initiation of the OVCP. Education regarding appropriate outpatient coding for physicians will be relatively effective when proper coding is related to reimbursement.