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A comunicação clínica, com foco nas competências emocionais, é uma habilidade que requer treinamento devido à necessidade de reconhecer expressões emocionais dos pacientes e dar uma resposta adequada. O objetivo deste estudo é apresentar uma ferramenta para avaliação da comunicação emocional dos profissionais de saúde, abordando as principais definições teóri-cas sobre a temática e pesquisas baseadas em evidências que aplicaram a ferramenta Codifica-ção de Verona para Sequências Emocionais(VR-CoDES). Baseado numa pesquisa de levantamen-to bibliográfico, o estudo analisa a comunicação emocional dos profissionais de saúde e o uso dessa ferramenta, tendo em vista que a comunicação é a componente chave na alta qualidade do tratamento, com impacto na satisfação e adesão dos pacientes. O estudo discute a impor-tância do reconhecimento de pistas e preocupações emocionais de pacientes em tratamentos de saúde e destaca as lacunas e desafios sobre os treinamentos de habilidades de comunicação emocional nos contextos de saúde. (AU)

Emotional communication in health is a tool to improve communication skills regarding the need to recognize patients’ emotional expressions and give them an adequate response. This study aims to show a tool to assess the emotional communication of health professionals, ad-dressing the main theoretical definitions and evidence-based research that applied the Verona Coding Definitions of Emotional Sequences (VR-CoDES) methodology. Based on bibliographic research, the study analyzes the use of the VR-CoDES on emotional communication of health professionals, considering that communication is the key component in the high quality of treatment, with an impact on patient satisfaction and compliance. The study discusses the importance of recognizing patients’ emotional cues and concerns in health care and highlights the gaps and challenges in training emotional communication skills in health contexts. (AU)

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Background: Automatic cell type identification has been an urgent task for the rapid development of single-cell RNA-seq techniques. Generally, the current approach for cell type identification is to generate cell clusters by unsupervised clustering and later assign labels to each cell cluster with manual annotation. Methods: Here, we introduce LIDER (celL embeddIng based Deep nEural netwoRk classifier), a deep supervised learning method that combines cell embedding and deep neural network classifier for automatic cell type identification. Based on a stacked denoising autoencoder with a tailored and reconstructed loss function, LIDER identifies cell embedding and predicts cell types with a deep neural network classifier. LIDER was developed upon a stacked denoising autoencoder to learn encoder-decoder structures for identifying cell embedding. Results: LIDER accurately identifies cell types by using stacked denoising autoencoder. Benchmarking against state-of-the-art methods across eight types of single-cell data, LIDER achieves comparable or even superior enhancement performance. Moreover, LIDER suggests comparable robust to batch effects. Our results show a potential in deep supervised learning for automatic cell type identification of single-cell RNA-seq data. The LIDER codes are available at https://github.com/ShiMGLab/LIDER.

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PURPOSE: To estimate the positive predictive value (PPV) of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes for identifying HF subtypes. METHODS: We validated ICD-10-CM HF diagnosis codes among Kaiser Permanente Washington enrollees who were ≥18 years of age and had an ICD-10-CM HF diagnosis code during 2017-2018 and a procedure code for an echocardiogram in the 12 months before through 6 months after the HF code. Left ventricular ejection fraction (LVEF) ascertained from medical chart review was used as the gold standard for classifying patients as having reduced ejection fraction (rEF), mid-range ejection fraction (mEF), or preserved ejection fraction (pEF). RESULTS: Among 6194 eligible patients, we randomly sampled 1000 for medical chart review. A total of 974 patients had LVEF information in their chart. The ICD-10-CM HF code group with the highest PPV for rEF was I50.20-I50.23, "Systolic (congestive) heart failure," PPV = 41.4% (95% CI, 34.5-48.7%); and the highest PPV for mEF or rEF was also I50.20-I50.23, PPV = 70.2% (95% CI, 63.1-76.4%). The highest PPV for pEF was the I50.30-I50.33 group, "Diastolic (congestive) heart failure," PPV = 92.0% (95% CI, 88.1-94.7%); and the highest PPV for mEF or pEF was also I50.30-I50.33, PPV = 97.7% (95% CI, 95.1-99.0%). CONCLUSIONS: If the accuracy measure of greatest interest is PPV, our results suggest that ICD-10-CM HF codes alone may not be adequate for identifying patients with rEF but may be adequate for identifying patients with pEF. HF coding practices may vary across settings, which may impact generalizability of our findings.

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BACKGROUND: We aimed to identify and characterize adult population-based multimorbidity measures using health administrative data and the International Classification of Diseases (ICD) codes for disease identification. METHODS: We performed a narrative systematic review of studies using or describing development or validation of multimorbidity measures. We compared the number of diseases included in the measures, the process of data extraction (case definition) and the validation process. We assessed the methodological robustness using eight criteria, five based on general criteria for indicators (AIRE instrument) and three multimorbidity-specific criteria. RESULTS: Twenty-two multimorbidity measures were identified. The number of diseases they included ranged from 5 to 84 (median = 20), with 19 measures including both physical and mental conditions. Diseases were identified using ICD codes extracted from inpatient and outpatient data (18/22) and sometimes including drug claims (10/22). The validation process relied mainly on the capacity of the measures to predict health outcome (5/22), or on the validation of each individual disease against a gold standard (8/22). Six multimorbidity measures met at least six of the eight robustness criteria assessed. CONCLUSION: There is significant heterogeneity among the measures used to assess multimorbidity in administrative databases, and about a third are of low to moderate quality. A more consensual approach to the number of diseases or groups of diseases included in multimorbidity measures may improve comparison between regions, and potentially provide better control for multimorbidity-related confounding in studies.

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Introduction: Central cancer registries are responsible for managing appropriate research contacts and record releases. Do not contact (DNC) flags are used by some registries to indicate patients who should not be contacted or included in research. Longitudinal changes in DNC coding practices and definitions may result in a lack of code standardization and inaccurately include or exclude individuals from research. Purpose: We performed a comprehensive manual review of DNC cases in the Utah Cancer Registry to inform updates to standardization of DNC code definitions, and use of DNC codes for exclusion/inclusion in research. Methods: We identified 858 cases with a current or prior DNC flag in the SEER Data Management System (SEER*DMS) or a research database, with cancers diagnosed from 1957-2021. We reviewed scanned images of correspondence with cases and physicians, incident forms, and comments in SEER*DMS and research databases. We evaluated whether there was evidence to support the current DNC code, a different DNC code, or insufficient evidence for any code. Results: Of the 755 cases that had a current DNC flag and reason code in SEER*DMS, the distribution was as follows: 58%, Patient requested no contact; 20%, Physician denied; 13%, Patient is not aware they have cancer; 4%, Patient is mentally disabled [sic]; 4%, Other; and 1%, Unknown. In 5% of these cases, we found evidence supporting a different DNC reason code. Among cases included because of a prior DNC flag in SEER*DMS (n = 10) or a DNC flag in a research database (ie, cases with no current DNC flag or reason code in SEER*DMS, n = 93), we found evidence supporting the addition of a SEER*DMS DNC flag and reason code in 50% and 40% of cases, respectively. We identified DNC reason codes with outdated terminology (Patient is mentally disabled) and codes that may not accurately reflect patient research preferences (Physician denied without asking the patient). To address this, we identified new reason codes, retired old reason codes, and updated current reason code definitions and research handlings. Conclusion: The time and resource investment in manual review allowed us to identify and, in most cases, resolve discordance in DNC flags and reason codes, adding reason codes when they were missing. This process was valuable because it informed recommended changes to DNC code definitions and research handlings that will ensure more appropriate inclusion and exclusion of cancer cases in research.

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Over the past year, Quick Response (QR) codes have played a significant role in our day-to-day lives in reducing the transmission and tracking the spread of COVID-19. In this article, we share our innovation utilising QR codes to replace paper information leaflets allowing patients to immediately access the required information on their own personal device. This is contactless and therefore preferred to reduce viral transmission, as well as having several other advantages. Our findings demonstrate that QR codes are a familiar, easy-to-use system and a preferred tool for delivering patient information over paper leaflets. The findings and methodology may be of benefit to other units seeking to improve their infection control in the COVID-19 era.

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ABSTRACT: This article discusses the optimal ways to document and code for outpatient evaluation and management (E/M) codes. Since the changes for Current Procedural Terminology (CPT) codes 99202-99215 were finalized for 2021, they have been modified by the Centers for Medicare & Medicaid Services (CMS) in their Medicare Physician Fee Schedule and by technical corrections issued on March 9, 2021. The 21st Century Cures Act mandated that patients can access their notes and test results immediately. These developments have transformed medical documentation and coding for outpatient E/M services. One year in, the authors have a better understanding of the subtleties of documenting and accurately determining levels of service for outpatient encounters using these new rules and regulations, and they share key insights gained by experience with the new system.

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BACKGROUND: Most battlefield deaths occur in the prehospital setting prior to reaching surgical and hospital care. Described are casualties captured by the Joint Trauma System (JTS) in the Prehospital Trauma Registry (PHTR) module of the Department of Defense Trauma Registry (DoDTR), from inception through May 2019. METHODS: The JTS was queried for all PHTR encounters and associated data from inception (January 2003) through May 2019. The PHTR captures data on Role 1 prehospital care which encompasses treatment prior to arrival at a Role 2 with or without forward surgical team or Role 3 combat support hospital. Two unique patient identifiers were used to link DODTR outcome data to each PHTR encounter. Descriptive statistics were used to analyze the data. RESULTS: We obtained a total of 1,357 encounters from the PHTR. Of these encounters, we successfully linked 52.2% (709/1357) to the DODTR for outcome data. Encounters spanned from 2003 to 2019, with most (69.5%) occurring from 2012 to 2014. Many casualties were in the 18-25 (25.5%) or 26-33 (27.0%) age ranges, male (99.2%), injured by explosive (47.1%) or firearm (34.8%), enlisted (44.8%), and US military conventional (24.1%) and special operations (23.9%) forces. Of those linked to the DODTR, demographics were similar, most casualties sustained battle injuries (87.1%), the majority of which survived (99.1%). CONCLUSIONS: We described 1,357 encounters within the PHTR, most of which were US casualties and casualties injured by explosives. This renewed effort by the JTS to capture more casualties for inclusion into the registry has nearly doubled the proportion of available encounters for analysis. This analysis lays the foundation for in-depth analyses targeting areas for optimizing Role 1 prehospital combat casualty care.

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As a result of the COVID-19 public health emergency, the Centers for Medicare & Medicaid Services expanded its telehealth benefit on a temporary and emergency basis. Effective March 6, 2020, Medicare will pay for Medicare telehealth services at the same rate as regular, in-person visits. Medicare has prescribed specific guidance on the billing and coding of such services, having an impact on reimbursement for qualified providers. Additional guidance also exists on acceptable telehealth communication platforms and patient privacy.

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OBJECTIVE: The first objective of this scoping review was to identify all the tools designed to measure movement or mobility in adults. The second objective was to compare the tools to the conceptual definitions of movement and mobility by mapping them to the International Classification of Functioning, Disability and Health (ICF). INTRODUCTION: The concepts of movement and mobility are distinct concepts that are often conflated, and the differences are important to patient care. Movement is a change in the place or position of a part of the body or of the whole body. Mobility is derived from movement and is defined as the ability to move with ease. Researchers and clinicians, including nurses, physiotherapists, and occupational therapists who work with adults and in rehabilitation, need to be confident that they are measuring the outcome of interest. INCLUSION CRITERIA: This scoping review considered studies that included participants who are adults, aged 19 and older, with any level of ability or disability. The concepts of interest were tools that measured movement or mobility relative to the human body. Studies were considered regardless of country of origin, health care setting, or sociocultural setting. METHODS: CINAHL, Health and Psychosocial Instruments, MEDLINE, and Embase were searched in June 2018 and OpenGrey, Dissertation Abstracts International, and Google Scholar were searched in November 2018. The searches were limited to articles in English, and the date range was from the inception of the database to the current date. Data were extracted from the studies using a custom data extraction tool. Once tools were identified for analysis, they were coded using the table format developed by Cieza and colleagues. RESULTS: There were 702 unique tools identified, with 651 of them available to be coded for the ICF. There were 385 ICF codes used when coding the tools. From these codes, the percentage of codes of the defining attributes of movement and mobility that were covered could be calculated, as well as the percentage of tool items that were linked to the antecedents, consequences, or defining attributes of movement or mobility. CONCLUSIONS: Although there are many tools that measure only movement or mobility, there are many that measure a mixture of the defining attributes as well as the antecedents and consequences. The tool name alone should not be considered a guarantee of the concept measured, and tool selection should be done with a critical eye. This study provides a starting point from which clinicians and researchers can find tools that measure the concepts of movement and mobility of interest and importance to their patient population.

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Medical services can be conceptualized as falling into two categories: procedures and cognitive care. A procedure is defined as a surgical, medical, or diagnostic test performed on a patient, such as an x-ray, wound suture, surgery, or physical therapy treatment. Cognitive care, also known as Evaluation and Management (E/M) services, involves performing a medical history along with a physical examination and possibly ordering or reviewing diagnostic tests before formulating a medical opinion and initiating a care plan. The uniform language and categorization of all medical services is contained in the Current Procedural Terminology (CPT) manual by the American Medical Association, which precisely describes all medical services using non-overlapping definitions and descriptions. The codes defined by CPT are the most commonly accepted set of codes used to file medical claims. In 2000, the US Department of Health and Human Services designated CPT to be the national reporting standard used in conjunction with the Health Insurance Portability and Accountability Act (HIPAA). CPT codes used today for E/M services were established in 1995 and define the components of history, examination, and medical decision making necessary to determine the level of each cognitive care service as delivered by a physician or other qualified health care professionals (eg, advanced practice providers). E/M rules were modified in 1997 and allowed some specialty services, such as neurology, to substitute a single system examination for a general, multisystem physical examination. Although new E/M codes were added over the years, the code descriptions and documentation guidelines for E/M services for outpatient and inpatient care remained essentially unchanged from 1997 through 2020. Most of the work performed by neurologists is E/M services, and the rules for coding outpatient care will change dramatically on January 1, 2021. This article discusses the rationale for these coding changes and explains how they are to be applied in the clinical setting.

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Almost all medical care in the United States is delivered with the provider and patient in immediate proximity; this model is referred to as face-to-face care. Medical services can be apportioned as procedural care (eg, surgery, radiology, or laboratory testing and others) or cognitive care, also known as Evaluation and Management (E/M) services, in which the provider formulates an assessment and plan after obtaining information from the patient's history, examination, and diagnostic tests.Providing a medical opinion and plan using the telephone as the technology that links the provider and the patient is an example of a non-face-to-face E/M service. Common Procedural Terminology (CPT) codes and the details for how to provide telephone services have been available for decades but have not been reimbursed and therefore were rarely used. In recent years, as new technologies have evolved, there has been slow and steady acceptance that non-face-to-face E/M care can be an adjunct to or replacement for some face-to-face E/M services. These technologies and the descriptors for associated CPT and Healthcare Common Procedure Coding System (HCPCS) codes were introduced over the past few years and have become known by the generic term telehealth. They have been slowly incorporated into medical practice. Most of these services were introduced in the consumer retail market, in which the cost was borne directly by the patient, or as private contract services, in which the cost was borne by the consulting hospital, such as with telestroke services. In both the consumer retail model and private contract model, the care delivered usually did not involve CPT or HCPCS coding. The adoption of telehealth has been slow, in part because of the initial costs and several regulatory constraints, as well as the reluctance of patients, providers, and the insurance industry to change the concept that medical care could only be delivered when the patient and their provider were in physical proximity.After the COVID-19 pandemic reached the United States, the US Department of Health & Human Services issued a public health emergency and declared a Section 1135 Waiver that lifted many of the administrative constraints. With the need for near-absolute social distancing, this perfect storm has resulted in the immediate adoption of telemedicine, at least for the duration of the pandemic, for cognitive care to be delivered using communication technologies that are already in place. This article discusses the most common forms of non-face-to-face E/M care and the proper coding elements necessary to provide these services.

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PURPOSE: Health-care claims are of increasing utility as a rich, real-world data resource for conducting treatment-related cancer research. However, multiple dynamic coding nomenclatures exist, leading to study variability. To promote increased standardization and reproducibility, the National Cancer Institute (NCI) developed the Cancer Medications Enquiry Database (CanMED)-Healthcare Common Procedure Coding System (HCPCS) within the Observational Research in Oncology Toolbox. METHODS: The CanMED-HCPCS includes codes for oncology medications that a) have a US Food and Drug Administration-approved indication for cancer treatment or treatment-related symptom management; b) are present in National Comprehensive Cancer Network guidelines; or c) carry an orphan drug designation for treatment or management of cancer. Included medications and their HCPCS codes were primarily identified based on Center for Medicare and Medicaid Services annual HCPCS Indices (2012-2018). To demonstrate the utility of the CanMED-HCPCS, use of systemic treatment for stage II-IV colorectal cancer patients included in the Surveillance, Epidemiology, and End Results-Medicare data (2007-2013) was assessed. RESULTS: The CanMED-HCPCS (v2018) includes 332 HCPCS codes for cancer-related medications: chemotherapy (156), immunotherapy (74), hormonal therapy (54), and ancillary therapy (48). Observed treatment trends within the NCI Surveillance, Epidemiology, and End Results-Medicare data were as expected; utilization of each treatment type increased with stage, and immunotherapy was largely confined to use among stage IV patients. CONCLUSION: The CanMED-HCPCS provides a comprehensive resource that can be used by the research community to facilitate systematic identification of medications within claims or electronic health data using the HCPCS nomenclature and greater reproducibility of cancer surveillance and health services research.

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PURPOSE: To assess the capture of biologics (originator and biosimilar) in the Biologics and Biosimilars Collective Intelligence Consortium (BBCIC) Distributed Research Network (DRN), with a focus on medical claim National Drug Code (NDC), a new data field, and Healthcare Common Procedure Coding System (HCPCS) modifier. METHODS: We conducted a repeated cross-sectional study among patients with medical and pharmacy benefits enrolled in insurance plans participating in the BBCIC DRN between 1 January 2013 and 30 September 2017. We calculated the proportion of medical claims with ≥1 NDC and identified select biologics using four different approaches: (a) specific HCPCS alone, (b) specific HCPCS and NDC, (c) non-specific HCPCS with NDC, and (d) HCPCS with modifiers (applicable to biosimilars). Numbers of dispensings were calculated for each biologic by approach and select patient and claim characteristics. RESULTS: More than 1.5 million eligible participants contributed approximately 4 million person-years of data, including 1.2 billion medical claims. The proportion of medical claims with ≥1 NDC increased from 1.2% in 2013 to 3.0% in 2017. Medical claim NDCs identified 39% and 28% of vedolizumab dispensed in 2014 and 2015 and 30% of Epogen/Procrit dispensed overall. Out of 26,381 filgrastim biosimilar dispensings identified, 51% had a HCPCS modifier and 12% had a medical claim NDC for Zarxio. HCPCS modifiers and medical claim NDCs were present for 38% and 3% of all infliximab biosimilars dispensed (total n = 1,244). CONCLUSIONS: Medical claim NDC and HCPCS modifier improves identification of select biologics without product-specific HCPCS code, thereby facilitating product-specific biologic research.

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Women are often highly motivated to obtain contraception during the immediate postpartum period. However, not all women receive contraception, particularly long-acting reversible contraceptive methods, during this time. One barrier to immediate postpartum contraception is the cost of placing long-acting reversible contraceptives, because such devices have a large upfront cost and historically could not be charged separately from the global delivery fee. In 2017, Florida Medicaid unbundled the fee for the long-acting reversible contraceptive device and insertion from the Diagnosis Related Group and encouraged Medicaid Managed Care plans to do the same. The Florida Perinatal Quality Collaborative, in recognition of guidance put forth by other states, designed the Access LARC initiative to have 2 phases: the preimplementation phase and the implementation phase. After completing all steps in the preimplementation phase, 1 pilot hospital placed 195 long-acting reversible contraceptives during the first 5 months of the initiative. During this trial period, setbacks in the reimbursement process occurred for both the hospital and payer groups. The Agency for Health Care Administration was instrumental in providing assistance to overcome these setbacks. Although there were obstacles and setbacks along the way, this initiative was finally a success for our providers and patients. We encourage other hospitals and states to implement their own postpartum long-acting reversible contraceptive initiative with the use of the guidelines set forth by Florida's Access LARC initiative.

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BACKGROUND/AIMS: Proton pump inhibitors (PPIs) are widely used for the management of acid-related diseases. This study aimed to clarify the status of PPI use in hospitalized patients. METHOD: A retrospective observational study was performed. We analyzed PPI prescription data for the past 8 years (2009-2016) using Diagnosis Procedure Combination survey data from approximately 10,000 patients per year at Saga University Hospital. We investigated the trend in the number of hospitalized patients who were prescribed PPI for the past 8 years and the changes in patient characteristics. RESULTS: We identified 11,009 patients using PPIs throughout the study period. PPI prescription proportions significantly increased over the study period. The use of PPIs increased steadily with increasing age. The proportion of PPIs prescribed was 1.3-times higher among men than compared with women. In most clinical departments, the number of patients prescribed PPIs was up to 3 times higher in the second half of the period (2013-2016) compared with the first half of the period (2009-2012). The number of patients taking concomitant PPIs and anticoagulants or dual antithrombotic combination therapy increased. CONCLUSION: PPI use has increased substantially in hospitalized patients. The prevalence of PPI prescription by doctors other than gastroenterologists also increased.

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