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1.
Ann Intern Med ; 175(4): OC1, 2022 04.
Article in English | MEDLINE | ID: covidwho-1811215
2.
Am J Public Health ; 111(12): 2133-2140, 2021 12.
Article in English | MEDLINE | ID: covidwho-1562412

ABSTRACT

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).


Subject(s)
COVID-19/mortality , Data Collection/standards , Public Health Surveillance/methods , Vital Statistics , Cause of Death , Clinical Coding/standards , Guidelines as Topic , Health Status Disparities , Humans , SARS-CoV-2 , Time Factors , United States/epidemiology
3.
J Med Virol ; 94(4): 1550-1557, 2022 Apr.
Article in English | MEDLINE | ID: covidwho-1540145

ABSTRACT

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.


Subject(s)
COVID-19/diagnosis , Clinical Coding/standards , International Classification of Diseases/standards , COVID-19/epidemiology , COVID-19/virology , Clinical Coding/methods , Comorbidity , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Philadelphia , Reproducibility of Results , Retrospective Studies , SARS-CoV-2
7.
PLoS One ; 16(7): e0252509, 2021.
Article in English | MEDLINE | ID: covidwho-1388919

ABSTRACT

The current global pandemic due to the SARS-CoV-2 has pushed the limits of global health systems across all aspects of clinical care, including laboratory diagnostics. Supply chain disruptions and rapidly-shifting markets have resulted in flash-scarcity of commercial laboratory reagents; this has motivated health care providers to search for alternative workflows to cope with the international increase in demand for SARS-CoV-2 testing. The aim of this study is to present a reproducible workflow for real time RT-PCR SARS-CoV-2 testing using OT-2 open-source liquid-handling robots (Opentrons, NY). We have developed a framework that includes a code template which is helpful for building different stand-alone robotic stations, capable of performing specific protocols. Such stations can be combined together to create a complex multi-stage workflow, from sample setup to real time RT-PCR. Using our open-source code, it is easy to create new stations or workflows from scratch, adapt existing templates to update the experimental protocols, or to fine-tune the code to fit specific needs. Using this framework, we developed the code for two different workflows and evaluated them using external quality assessment (EQA) samples from the European Molecular Genetics Quality Network (EMQN). The affordability of this platform makes automated SARS-CoV-2 PCR testing accessible for most laboratories and hospitals with qualified bioinformatics personnel. This platform also allows for flexibility, as it is not dependent on any specific commercial kit, and thus it can be quickly adapted to protocol changes, reagent, consumable shortages, or any other temporary material constraints.


Subject(s)
COVID-19 Nucleic Acid Testing/instrumentation , SARS-CoV-2/isolation & purification , Clinical Coding , Early Diagnosis , Humans , RNA, Viral/genetics , Real-Time Polymerase Chain Reaction/instrumentation , Reverse Transcriptase Polymerase Chain Reaction/instrumentation , Robotics , SARS-CoV-2/genetics , Workflow
8.
Br J Gen Pract ; 71(712): e806-e814, 2021 11.
Article in English | MEDLINE | ID: covidwho-1339630

ABSTRACT

BACKGROUND: Long COVID describes new or persistent symptoms at least 4 weeks after onset of acute COVID-19. Clinical codes to describe this phenomenon were recently created. AIM: To describe the use of long-COVID codes, and variation of use by general practice, demographic variables, and over time. DESIGN AND SETTING: Population-based cohort study in English primary care. METHOD: Working on behalf of NHS England, OpenSAFELY data were used encompassing 96% of the English population between 1 February 2020 and 25 May 2021. The proportion of people with a recorded code for long COVID was measured overall and by demographic factors, electronic health record software system (EMIS or TPP), and week. RESULTS: Long COVID was recorded for 23 273 people. Coding was unevenly distributed among practices, with 26.7% of practices having never used the codes. Regional variation ranged between 20.3 per 100 000 people for East of England (95% confidence interval [CI] = 19.3 to 21.4) and 55.6 per 100 000 people in London (95% CI = 54.1 to 57.1). Coding was higher among females (52.1, 95% CI = 51.3 to 52.9) than males (28.1, 95% CI = 27.5 to 28.7), and higher among practices using EMIS (53.7, 95% CI = 52.9 to 54.4) than those using TPP (20.9, 95% CI = 20.3 to 21.4). CONCLUSION: Current recording of long COVID in primary care is very low, and variable between practices. This may reflect patients not presenting; clinicians and patients holding different diagnostic thresholds; or challenges with the design and communication of diagnostic codes. Increased awareness of diagnostic codes is recommended to facilitate research and planning of services, and also surveys with qualitative work to better evaluate clinicians' understanding of the diagnosis.


Subject(s)
COVID-19 , Clinical Coding , COVID-19/complications , Cohort Studies , England , Female , Humans , Male , Primary Health Care
9.
Am J Public Health ; 111(S2): S101-S106, 2021 07.
Article in English | MEDLINE | ID: covidwho-1328027

ABSTRACT

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.


Subject(s)
COVID-19/mortality , Clinical Coding/standards , Forms and Records Control/standards , Quality Assurance, Health Care/standards , Adolescent , Aged , COVID-19/epidemiology , Cause of Death , Child , Child, Preschool , Humans , Male , Middle Aged , Minority Groups/statistics & numerical data , Quality Control , Sex Distribution , United States , Young Adult
10.
J Biomed Inform ; 116: 103728, 2021 04.
Article in English | MEDLINE | ID: covidwho-1131454

ABSTRACT

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.


Subject(s)
COVID-19 , Clinical Coding/methods , Neural Networks, Computer , SARS-CoV-2 , COVID-19/epidemiology , Clinical Coding/statistics & numerical data , Deep Learning , Electronic Health Records/statistics & numerical data , Humans , Medical Informatics , Pandemics/statistics & numerical data , United Kingdom/epidemiology
14.
Fertil Steril ; 114(6): 1129-1134, 2020 12.
Article in English | MEDLINE | ID: covidwho-959774

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has resulted in paradigm shifts in the delivery of health care. Lockdowns, quarantines, and local mandates forced many physician practices around the United States to move to remote patient visits and adoption of telemedicine. This has several long-term implications in the future practice of medicine. In this review we outline different models of integrating telemedicine into both male and female fertility practices and recommendations on performing video physical examinations. Moving forward we foresee two general models of integration: one conservative, where initial intake and follow-up is performed remotely, and a second model where most visits are performed via video and patients are only seen preoperatively if necessary. We also discuss the impact THAT telemedicine has on coding and billing and our experience with patient satisfaction.


Subject(s)
COVID-19 , Delivery of Health Care/methods , Reproductive Medicine/methods , SARS-CoV-2 , Telemedicine , Clinical Coding , Delivery of Health Care/economics , Delivery of Health Care/trends , Female , Health Care Costs , Humans , Insurance, Health, Reimbursement , Male , Patient Satisfaction , Reproductive Medicine/economics , Telemedicine/economics , Telemedicine/trends
15.
MMW Fortschr Med ; 162(20): 35, 2020 11.
Article in German | MEDLINE | ID: covidwho-938632
16.
JMIR Mhealth Uhealth ; 8(9): e22321, 2020 09 07.
Article in English | MEDLINE | ID: covidwho-771629

ABSTRACT

We discuss a pandemic management framework using symptom-based quick response (QR) codes to contain the spread of COVID-19. In this approach, symptom-based QR health codes are issued by public health authorities. The codes do not retrieve the location data of the users; instead, two different colors are displayed to differentiate the health status of individuals. The QR codes are officially regarded as electronic certificates of individuals' health status, and can be used for contact tracing, exposure risk self-triage, self-update of health status, health care appointments, and contact-free psychiatric consultations. This approach can be effectively deployed as a uniform platform interconnecting a variety of responders (eg, individuals, institutions, and public authorities) who are affected by the pandemic, which minimizes the errors of manual operation and the costs of fragmented coordination. At the same time, this approach enhances the promptness, interoperability, credibility, and traceability of containment measures. The proposed approach not only provides a supplemental mechanism for manual control measures but also addresses the partial failures of pandemic management tools in the abovementioned facets. The QR tool has been formally deployed in Fujian, a province located in southeast China that has a population of nearly 40 million people. All individuals aged ≥3 years were officially requested to present their QR code during daily public activities, such as when using public transportation systems, working at institutions, and entering or exiting schools. The deployment of this approach has achieved sizeable containment effects and played remarkable roles in shifting the negative gross domestic product (-6.8%) to a positive value by July 2020. The number of cumulative patients with COVID-19 in this setting was confined to 363, of whom 361 had recovered (recovery rate 99.4%) as of July 12, 2020. A simulation showed that if only partial measures of the framework were followed, the number of cumulative cases of COVID-19 could potentially increase ten-fold. This approach can serve as a reliable solution to counteract the emergency of a public health crisis; as a routine tool to enhance the level of public health; to accelerate the recovery of social activities; to assist decision making for policy makers; and as a sustainable measure that enables scalability.


Subject(s)
Clinical Coding , Contact Tracing/methods , Coronavirus Infections/prevention & control , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , COVID-19 , Coronavirus Infections/epidemiology , Humans , Pneumonia, Viral/epidemiology
17.
J Allergy Clin Immunol Pract ; 8(8): 2461-2473.e3, 2020 09.
Article in English | MEDLINE | ID: covidwho-764972

ABSTRACT

Telemedicine adoption has rapidly accelerated since the onset of the COVID-19 pandemic. Telemedicine provides increased access to medical care and helps to mitigate risk by conserving personal protective equipment and providing for social/physical distancing to continue to treat patients with a variety of allergic and immunologic conditions. During this time, many allergy and immunology clinicians have needed to adopt telemedicine expeditiously in their practices while studying the complex and variable issues surrounding its regulation and reimbursement. Some concerns have been temporarily alleviated since March 2020 to aid with patient care in the setting of COVID-19. Other changes are ongoing at the time of this publication. Members of the Telemedicine Work Group in the American Academy of Allergy, Asthma & Immunology (AAAAI) completed a telemedicine literature review of online and Pub Med resources through May 9, 2020, to detail Pre-COVID-19 telemedicine knowledge and outline up-to-date telemedicine material. This work group report was developed to provide guidance to allergy/immunology clinicians as they navigate the swiftly evolving telemedicine landscape.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Telemedicine/organization & administration , Allergy and Immunology/organization & administration , Betacoronavirus , COVID-19 , Clinical Coding , Computer Security , Health Services Accessibility/organization & administration , Humans , Hypersensitivity/therapy , Infection Control/organization & administration , Insurance, Health, Reimbursement , Pandemics , SARS-CoV-2 , Societies, Medical , Telemedicine/economics
19.
JAMA Netw Open ; 3(8): e2017703, 2020 08 03.
Article in English | MEDLINE | ID: covidwho-713159

ABSTRACT

Importance: International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes are used to characterize coronavirus disease 2019 (COVID-19)-related symptoms. Their accuracy is unknown, which could affect downstream analyses. Objective: To compare the performance of fever-, cough-, and dyspnea-specific ICD-10 codes with medical record review among patients tested for COVID-19. Design, Setting, and Participants: This cohort study included patients who underwent quantitative reverse transcriptase-polymerase chain reaction testing for severe acute respiratory syndrome coronavirus 2 at University of Utah Health from March 10 to April 6, 2020. Data analysis was performed in April 2020. Main Outcomes and Measures: The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of ICD-10 codes for fever (R50*), cough (R05*), and dyspnea (R06.0*) were compared with manual medical record review. Performance was calculated overall and stratified by COVID-19 test result, sex, age group (<50, 50-64, and >64 years), and inpatient status. Bootstrapping was used to generate 95% CIs, and Pearson χ2 tests were used to compare different subgroups. Results: Among 2201 patients tested for COVD-19, the mean (SD) age was 42 (17) years; 1201 (55%) were female, 1569 (71%) were White, and 282 (13%) were Hispanic or Latino. The prevalence of fever was 66% (1444 patients), that of cough was 88% (1930 patients), and that of dyspnea was 64% (1399 patients). For fever, the sensitivity of ICD-10 codes was 0.26 (95% CI, 0.24-0.29), specificity was 0.98 (95% CI, 0.96-0.99), PPV was 0.96 (95% CI, 0.93-0.97), and NPV was 0.41 (95% CI, 0.39-0.43). For cough, the sensitivity of ICD-10 codes was 0.44 (95% CI, 0.42-0.46), specificity was 0.88 (95% CI, 0.84-0.92), PPV was 0.96 (95% CI, 0.95-0.97), and NPV was 0.18 (95% CI, 0.16-0.20). For dyspnea, the sensitivity of ICD-10 codes was 0.24 (95% CI, 0.22-0.26), specificity was 0.97 (95% CI, 0.96-0.98), PPV was 0.93 (95% CI, 0.90-0.96), and NPV was 0.42 (95% CI, 0.40-0.44). ICD-10 code performance was better for inpatients than for outpatients for fever (χ2 = 41.30; P < .001) and dyspnea (χ2 = 14.25; P = .003) but not for cough (χ2 = 5.13; P = .16). Conclusions and Relevance: These findings suggest that ICD-10 codes lack sensitivity and have poor NPV for symptoms associated with COVID-19. This inaccuracy has implications for any downstream data model, scientific discovery, or surveillance that relies on these codes.


Subject(s)
Clinical Coding/standards , Coronavirus Infections/diagnosis , Cough/diagnosis , Dyspnea/diagnosis , Electronic Health Records , Fever/diagnosis , International Classification of Diseases , Pneumonia, Viral/diagnosis , Adult , Aged , Betacoronavirus , COVID-19 , Clinical Coding/methods , Cohort Studies , Coronavirus Infections/complications , Coronavirus Infections/epidemiology , Coronavirus Infections/virology , Cough/etiology , Dyspnea/etiology , Female , Fever/etiology , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/epidemiology , Pneumonia, Viral/virology , Polymerase Chain Reaction , Reproducibility of Results , SARS-CoV-2 , Sensitivity and Specificity , Utah/epidemiology
20.
Curr Allergy Asthma Rep ; 20(10): 60, 2020 07 27.
Article in English | MEDLINE | ID: covidwho-679786

ABSTRACT

PURPOSE OF REVIEW: Telemedicine is a rapidly growing healthcare sector that can improve access to care for underserved populations and offer flexibility and convenience to patients and clinicians alike. However, uncertainty about insurance coverage and reimbursement policies for telemedicine has historically been a major barrier to adoption, especially among physicians in private practice (the majority of practicing allergists). RECENT FINDINGS: The COVID-19 public health emergency has highlighted the importance of telehealth as a safe and effective healthcare delivery model, with governments and payers rapidly expanding coverage and payment in an effort to ensure public access to healthcare in the midst of an infectious pandemic. This comprehensive review of updated telemedicine coverage and payment policies will include a tabular guide on how to appropriately bill and optimize reimbursement for telemedicine services. This review of current trends in telemedicine coverage, billing, and reimbursement will outline the historical and current state of telemedicine payment policies in the USA, with special focus on recent policy changes implemented in light of COVID-19. The authors will also explore the potential future landscape of telehealth coverage and reimbursement beyond the resolution of the public health emergency.


Subject(s)
Coronavirus Infections/diagnosis , Coronavirus Infections/therapy , Pneumonia, Viral/diagnosis , Pneumonia, Viral/therapy , Telemedicine/economics , Telemedicine/methods , Betacoronavirus/isolation & purification , COVID-19 , Clinical Coding , Coronavirus Infections/economics , Humans , Insurance, Health, Reimbursement/economics , Pandemics/economics , Pneumonia, Viral/economics , SARS-CoV-2
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