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Background: Despite improvements over the past decade, children continue to experience significant pain and distress surrounding invasive procedures in the emergency department (ED). To assess the impact of newly developed interventions, we must create more reliable and valid behavioral assessment tools that have been validated for the unique settings of pediatric EDs., Objective: This study aimed to create and test the Emergency Department Child Behavior Coding System (ED-CBCS) for the assessment of child distress and nondistress behaviors surrounding pediatric ED procedures., Methods: Via an iterative process, a multidisciplinary expert panel developed the ED-CBCS, an advanced time-based behavioral coding measure. Inter-rater reliability and concurrent validity were examined using 38 videos of children aged from 2 to 12 years undergoing laceration procedures. Face, Legs, Activity, Cry, Consolability (FLACC) scale scores were used to examine concurrent validity., Results: The final ED-CBCS included 27 child distress and nondistress behaviors. Time-unit κ values from 0.64 to 0.98 and event alignment κ values from 0.62 to 1.00 indicated good to excellent inter-rater reliability for all but one of the individual codes. ED-CBCS distress (B = 1.26; p < 0.001) and nondistress behaviors (B = -0.69, p = 0.025) were independently significantly associated with FLACC scores, indicating concurrent validity., Conclusions: We developed a psychometrically sound tool tailored for pediatric ED procedures. Future work could use this measure to better identify behavioral targets and test the effects of interventions to relieve pediatric ED pain and distress., Competing Interests: Declaration of competing interest Zeev N. Kain is the president of the American College of Perioperative Medicine and has served as a consultant for Edwards Lifesciences and Pacira. The remaining authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Machine learning methods hold the promise of leveraging available data and generating higher-quality data while alleviating the data collection burden on healthcare professionals. International Classification of Diseases (ICD) diagnoses data, collected globally for billing and epidemiological purposes, represents a valuable source of structured information. However, ICD coding is a challenging task. While numerous previous studies reported promising results in automatic ICD classification, they often describe input data specific model architectures, that are heterogeneously evaluated with different performance metrics and ICD code subsets. This study aims to explore the evaluation and construction of more effective Computer Assisted Coding (CAC) systems using generic approaches, focusing on the use of ICD hierarchy, medication data and a feed forward neural network architecture., Methods: We conduct comprehensive experiments using the MIMIC-III clinical database, mapped to the OMOP data model. Our evaluations encompass various performance metrics, alongside investigations into multitask, hierarchical, and imbalanced learning for neural networks., Results: We introduce a novel metric, , tailored to the ICD coding task, which offers interpretable insights for healthcare informatics practitioners, aiding them in assessing the quality of assisted coding systems. Our findings highlight that selectively cherry-picking ICD codes diminish retrieval performance without performance improvement over the selected subset. We show that optimizing for metrics such as NDCG and AUPRC outperforms traditional F1-based metrics in ranking performance. We observe that Neural Network training on different ICD levels simultaneously offers minor benefits for ranking and significant runtime gains. However, our models do not derive benefits from hierarchical or class imbalance correction techniques for ICD code retrieval., Conclusion: This study offers valuable insights for researchers and healthcare practitioners interested in developing and evaluating CAC systems. Using a straightforward sequential neural network model, we confirm that medical prescriptions are a rich data source for CAC systems, providing competitive retrieval capabilities for a fraction of the computational load compared to text-based models. Our study underscores the importance of metric selection and challenges existing practices related to ICD code sub-setting for model training and evaluation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes a pretrained Transformer model to represent the tokens of clinical documents. We subsequently employ a two-level hierarchical label-wise attention mechanism that creates label-specific document representations. These representations are in turn used by a feed-forward neural network to predict whether a specific ICD code is assigned to the input clinical document of interest. We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database. To investigate the performance of different types of Transformer models, we develop ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using all the MIMIC-III clinical notes. The experiment results show that the F1 scores of the HiLAT + ClinicalplusXLNet outperform the previous state-of-the-art models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

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 F 1 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 F 1 . 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., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Purpose: The optimal approach to managing incident learning system (ILS) reports remains unclear. Here, we describe our experience with prospective coding of events reported to the ILS with comparisons of risk scores on the basis of event type and process map location., Methods and Materials: Reported events were coded by type, origin, and method of discovery. Events were given a risk priority number (RPN) and near-miss risk index (NMRI) score. We compared workflow versus near-miss events with respect to origin and detection in the process map and by risk scores. A χ 2 test was used to compare the differences between workflow and near-miss events. A comparison of RPN scores was done by independent t test., Results: During 2016, 1351 events were reported. Of these events, 1300 (96.2%) were workflow and 51 (3.8%) near-miss events. Workflow events were more likely to both originate (1041 of 1300 events; 81.2%) compared with near-miss events (31 of 51 events; 62.7%; P = .005) and be detected in pre-treatment (997 of 1300 events; 76.7%) compared with near-miss events (24 of 51 events; 47%; P < .001). Average occurrence (scale: 1-10) was 6.14 for workflow versus 3.33 for near-miss events (P < .001), average severity was 2.94 versus 7.35 (P < .001), and average detectability was 1.33 versus 4.67 (P < .001). Mean overall RPN was 22.4 for workflow versus 108.4 for near-miss events (P = .07) and mean NMRI was 1.16 versus 3.19, respectively. Events that originated and were detected in treatment delivery had the greatest mean overall RPN (38.2 and 32.1, respectively) and NMRI scores (1.62 and 1.6, respectively)., Conclusions: Our experience demonstrates that workflow event reports are far more common than near-misses and that near-miss events are more likely to both originate and be discovered in later treatment phases. The frequency of workflow reports highlights the imperative need for safety and operational teams to work collaboratively to maximize the benefit of ILS. We suggest a potential utility of the RPN system to guide mitigation strategies for future near-miss events., (Copyright © 2018 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.)

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples., (Copyright © 2018. Published by Elsevier Inc.)

The International Classification of Disease (ICD) coding system is broadly used by health-care providers, hospitals, health-care payers, and governments to track health trends and statistics at the global, national, and local levels and to provide a reimbursement framework for medical care based on diagnosis and severity of illness. The current iteration of the ICD system, the ICD, Tenth Revision (ICD-10), was implemented in 2015. Although many changes to the prior ICD, Ninth Revision system were included in the ICD-10 system, the newer revision failed to adequately reflect advances in the clinical classification of certain diseases such as pulmonary hypertension (PH). Recently, a proposal to modify the ICD-10 codes for PH was considered and ultimately adopted for inclusion as an update to the ICD-10 coding system. Although these revisions better reflect the current clinical classification of PH, in the future, further changes should be considered to improve the accuracy and ease of coding for all forms of PH., (Copyright © 2018 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Introduction: Clinical coding is often a mystery to us surgeons, but in actuality, it has a huge bearing on the financial sustainability of our services. Given the rapid innovations in plastic surgical procedures, clinical coders often struggle to decipher the extent of surgery. Meeting midway is the way forward here., Methods: In a prospective audit over a six-month period, we analysed data from 2586 patients in our practice: a combination of general plastic surgery and specialist facial reanimation services. This involved comparing data from the first three months where coding was performed by clinical coders based on operating notes per se (phase I) and the subsequent three months when the operating surgeon filled in the OPCS 4.7 (version 2014) codes at the time of completing the operating notes; the clinical coders then vetted this information (phase II) as part of a sequential TBS coding system., Results: In terms of outpatient income, there was a 3% increase in facial palsy income and 6% increase in general plastic services, but the most significant improvement was in terms of procedural income per case. General plastic surgery cases saw an increase of 49%, while facial palsy income increased by 58% over the same period. Greater insight into OPCS and HRG codes also allowed for the calculation of the actual tariffs for specific procedures., Conclusions: Having the operating surgeon as the primary coder, using a template, with subsequent vetting by the clinical coders, improves data capture, and this in turn increases income. Future recommendations include the use of proforma-based operating notes for workhorse procedures., (Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.)

We address the assignment of ICD-10 codes for causes of death by analyzing free-text descriptions in death certificates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. We leverage a deep neural network that combines word embeddings, recurrent units, and neural attention, for the generation of intermediate representations of the textual contents. The neural network also explores the hierarchical nature of the input data, by building representations from the sequences of words within individual fields, which are then combined according to the sequences of fields that compose the inputs. Moreover, we explore innovative mechanisms for initializing the weights of the final nodes of the network, leveraging co-occurrences between classes together with the hierarchical structure of ICD-10. Experimental results attest to the contribution of the different neural network components. Our best model achieves accuracy scores over 89%, 81%, and 76%, respectively for ICD-10 chapters, blocks, and full-codes. Through examples, we also show that our method can produce interpretable results, useful for public health surveillance., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Changes in coding and coverage generate an uncertain reimbursement environment for molecular pathology laboratories. We analyzed our experience with two representative molecular oncology tests: a T-cell receptor (TCR) β rearrangement test and a large (467-gene) cancer next-generation sequencing panel, the Columbia Combined Cancer Panel (CCCP). Before 2013, the TCR β test was coded using stacked current procedural terminology codes and subsequently transitioned to a tier 1 code. CCCP was coded using a combination of tier 1 and 2 codes until 2015, when a new Genomic Sequencing Procedure code was adopted. A decrease in reimbursement of 61% was observed for the TCR β test on moving from stacking to tier 1 codes. No initial increase in total rejection rate was observed, but a subsequent increase in rejection rates in 2015 and 2016 was noted. The CCCP test showed a similar decrease (48%) in reimbursement after adoption of the new Genomic Sequencing Procedure code and was accompanied by a sharp increase in rejection rates both on implementation of the new code and over time. Changes in coding can result in substantial decreases in reimbursement. This may be a barrier to patient access because of the high cost of molecular diagnostics. Revisions to the molecular code set will continue. These findings help laboratories and manufacturers prepare for the financial impact and advocate appropriately., (Copyright © 2018 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.)

Objectives: To estimate how direct health care costs resulting from adverse drug events (ADEs) and cost distribution are affected by methodological decisions regarding identification of ADEs, assigning relevant resource use to ADEs, and estimating costs for the assigned resources., Methods: ADEs were identified from medical records and diagnostic codes for a random sample of 4970 Swedish adults during a 3-month study period in 2008 and were assessed for causality. Results were compared for five cost evaluation methods, including different methods for identifying ADEs, assigning resource use to ADEs, and for estimating costs for the assigned resources (resource use method, proportion of registered cost method, unit cost method, diagnostic code method, and main diagnosis method). Different levels of causality for ADEs and ADEs' contribution to health care resource use were considered., Results: Using the five methods, the maximum estimated overall direct health care costs resulting from ADEs ranged from Sk10,000 (Sk = Swedish krona; ~€1,500 in 2016 values) using the diagnostic code method to more than Sk3,000,000 (~€414,000) using the unit cost method in our study population. The most conservative definitions for ADEs' contribution to health care resource use and the causality of ADEs resulted in average costs per patient ranging from Sk0 using the diagnostic code method to Sk4066 (~€500) using the unit cost method., Conclusions: The estimated costs resulting from ADEs varied considerably depending on the methodological choices. The results indicate that costs for ADEs need to be identified through medical record review and by using detailed unit cost data., (Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)

Objective: To discuss the theoretical and empirical framework of VR-CoDES and potential future direction in research based on the coding system., Methods: The paper is based on selective review of papers relevant to the construction and application of VR-CoDES., Results: VR-CoDES system is rooted in patient-centered and biopsychosocial model of healthcare consultations and on a functional approach to emotion theory. According to the VR-CoDES, emotional interaction is studied in terms of sequences consisting of an eliciting event, an emotional expression by the patient and the immediate response by the clinician. The rationale for the emphasis on sequences, on detailed classification of cues and concerns, and on the choices of explicit vs. non-explicit responses and providing vs. reducing room for further disclosure, as basic categories of the clinician responses, is described., Conclusions: Results from research on VR-CoDES may help raise awareness of emotional sequences. Future directions in applying VR-CoDES in research may include studies on predicting patient and clinician behavior within the consultation, qualitative analyses of longer sequences including several VR-CoDES triads, and studies of effects of emotional communication on health outcomes., Practice Implications: VR-CoDES may be applied to develop interventions to promote good handling of patients' emotions in healthcare encounters., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: The Verona Coding Definitions of Emotional sequences (VR-CoDES) system has been applied in a wide range of studies, in some of these, because of its attention on healthcare provider's ability to respond to patient emotions, it has been used as a proxy of patient-centeredness. The paper aims to discuss how the VR-CoDES can contribute to the broader concept of patient-centeredness and its limitations., Methods: VR-CoDES and patient-centeredness concept are briefly described, trying to detect commonalities and distinctions. The VR-CoDES dimensions of Explicit/non explicit responding and Providing or Reducing Space are analysed in relation to relevant aspects of patient-centred communication., Results: Emotional aspects are encompassed within patient-centeredness model, but they represent only one of the numerous dimensions that contribute to define patient-centeredness as well as Explicit/non explicit responding and Providing or Reducing Space serve different functions during communication., Conclusion: The VR-CoDES can contribute to operationalize the description of emotional aspects emerging in a consultation, by inducing coders to adopt a factual attitude in assessing how health providers react to patient's expression of emotions., Practice Implications: To better define empirically which measure affective aspects and dimensions of health provider responses are relevant and may contribute to patient-centeredness in different clinical settings., (Copyright © 2017. Published by Elsevier B.V.)

Background: Most residency programs still lack formal education and training on the basic clinical documentation and coding principles. Today's physicians are continuously being held to increasing standards for correct coding and documentation, yet little has changed in the residency training curricula to keep pace with these increasing standards. Although there are many barriers to implementing these topics formally, the main concern has been the lack of time and resources. Thus, simple models may have the best chance for success at widespread implementation., Purpose: The first goal of the study was to assess a group of orthopedic residents' fund of knowledge regarding basic clinical documentation guidelines, coding principles, and their ability to appropriately identify cases of Medicare fraud. The second goal was to analyze a single, high-yield educational session's effect on overall resident knowledge acquisition and awareness of these concepts., Subject Selection and Study Protocol: Orthopedic residents belonging to 1 of 2 separate residency programs voluntarily and anonymously participated. All were asked to complete a baseline assessment examination, followed by attending a 45-minute lecture given by the same orthopedic faculty member who remained blinded to the test questions. Each resident then completed a postsession examination. Each resident was also asked to self-rate his or her documentation and coding level of comfort on a Likert scale (1-5). Statistical significance was set at p < 0.05., Main Findings: A total of 32 orthopedic residents were participated. Increasing postgraduate year-level of training correlated with higher Likert-scale ratings for self-perceived comfort levels with documentation and coding. However, the baseline examination scores were no different between senior and junior residents (p > 0.20). The high-yield teaching session significantly improved the average total examination scores at both sites (p < 0.01), with overall improvement being similar between the 2 groups (p > 0.10)., Principal Conclusions: The current healthcare environment necessitates better physician awareness regarding clinical documentation guidelines and coding principles. Very few adjustments to incorporate these teachings have been made to most residency training curricula, and the lack of time and resources remains the concern of many surgical programs. We have demonstrated that orthopedic resident knowledge in these important areas drastically improves after a single, high-yield 45-minute teaching session., (Copyright © 2017 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

Objective: To investigate whether the Verona Coding Definitions of Emotional Sequences to code health providers' responses (VR-CoDES-P) can be used for assessment of medical students' responses to patients' cues and concerns provided in written case vignettes., Methods: Student responses in direct speech to patient cues and concerns were analysed in 21 different case scenarios using VR-CoDES-P., Results: A total of 977 student responses were available for coding, and 857 responses were codable with the VR-CoDES-P. In 74.6% of responses, the students used either a "reducing space" statement only or a "providing space" statement immediately followed by a "reducing space" statement. Overall, the most frequent response was explicit information advice (ERIa) followed by content exploring (EPCEx) and content acknowledgement (EPCAc)., Discussion: VR-CoDES-P were applicable to written responses of medical students when they were phrased in direct speech. The application of VR-CoDES-P is reliable and feasible when using the differentiation of "providing" and "reducing space" responses. Communication strategies described by students in non-direct speech were difficult to code and produced many missings., Practice Implications: VR-CoDES-P are useful for analysis of medical students' written responses when focusing on emotional issues. Students need precise instructions for their response in the given test format., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Background: Having a robust Emergency Department Presenting Problem Code Set (EDPPCS) is important for collecting and analysing data around Emergency Department (ED) activity, funding, bio-surveillance and research. This paper analyses the clinical utilisation of the current EDPPCS using two years worth of ED data collected as part of the larger state-wide Demand for Emergency Services Trends in Years 2010-2014 (DESTINY) project. This project proposes potential improvements in the current EDPPCS including a reduction in duplication and redundant clinical terms., Methods: ED presenting problem fields were abstracted from the Emergency Department Data Collection (EDCC) Registry as entered by trained triage nurses. Frequencies of presenting problems were calculated and cross referenced with the EDPPCS. These were then categorised into clinically meaningful groups., Results: There were 1,746,635million eligible ED presentations during January 2013 and December 2014 to 23 level 5 or 6 EDs. Of these, there were 64,849 unique presenting problem entries with 450 terms being used more than 100 times during the study period. Of those 450 terms, only 177 (39.3%) matched the current EDPPCS., Conclusion: Future iterations of the EDPPCS should be based on the evidence presented making it shorter, more comprehensive and systematic leading to improved triage performance, usefulness in research and bio-surveillance., (Copyright © 2016 College of Emergency Nursing Australasia. Published by Elsevier Ltd. All rights reserved.)

Introduction: The Scottish Transfusion and Laboratory Support in Trauma Group (TLSTG) have introduced a unified National pre-hospital Code Red protocol. This paper reports the results of a study aiming to establish whether current pre-hospital Code Red activation criteria for trauma patients successfully predict need for in hospital transfusion or haemorrhagic death, the current admission coagulation profile and Concentrated Red Cell (CRC): Fresh Frozen Plasma (FFP) ratio being used, and whether use of the protocol leads to increased blood component discards?, Methods: Prospective cohort study. Clinical and transfusion leads for each of Scotland's pre-hospital services and their receiving hospitals agreed to enter data into the study for all trauma patients for whom a pre-hospital Code Red was activated. Outcome data collected included survival 24h after Code Red activation, survival to hospital discharge, death in the Emergency Department and death in hospital., Results: Between June 1st 2013 and October 31st 2015 there were 53 pre-hospital Code Red activations. Median Injury Severity Score (ISS) was 24 (IQR 14-37) and mortality 38%. 16 patients received pre-hospital blood. The pre-hospital Code Red protocol was sensitive for predicting transfusion or haemorrhagic death (89%). Sensitivity, specificity, positive and negative predictive values of the pre-hospital SBP <90mmHg component were 63%, 33%, 86% and 12%. 19% had an admission prothrombin time >14s and 27% had a fibrinogen <1.5g/L. CRC: FFP ratios did not drop to below 2:1 until 150min after arrival in the ED. 16 red cell units, 33 FFP and 6 platelets were discarded. This was not significantly increased compared to historical data., Conclusions: A National pre-hospital Code Red protocol is sensitive for predicting transfusion requirement in bleeding trauma patients and does not lead to increased blood component discards. A significant number of patients are coagulopathic and there is a need to improve CRC: FFP ratios and time to transfusion support especially FFP provision. Training clinicians to activate pre-hospital Code Red earlier during the pre-hospital phase may give blood bank more time to thaw and prepare FFP and may improve FFP administration times and ratios so long as components are used upon their availability., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Objective: Little is known about how older persons in home care express their concerns. Emotional cues and concerns can be identified by the Verona coding definitions of emotional sequences (VR-CoDES), but the method gives no insight into what causes the distress and the emotions involved. The aims of this study are to explore (1) older persons' worries and (2) the content of these expressions., Methods: An observational exploratory two-step approach was used to investigate audiotaped recordings from 38 Norwegian home care visits with older persons and nurse assistants. First, 206 cues and concerns were identified using VR-CoDES. Second, the content and context of these expressions were analysed inductively., Results: Four main categories emerged: worries about relationships with others, worries about health care-related issues, worries about aging and bodily impairment, and life narratives and value issues, with several subcategories showing the causes of worry and emotions involved., Conclusion: The two-step approach provides an in-depth knowledge of older persons' worries, causes of worries, and their related emotions., Practice Implications: The subcategories described in a language close to the experience can be useful in practice development and communication training for students and health care providers., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Introduction: The Accreditation Council for Graduate Medical Education (ACGME) case log captures resident operative experience based on Current Procedural Terminology (CPT) codes and is used to track operative experience during residency. With increasing emphasis on resident operative experiences, coding is more important than ever. It has been shown in other surgical specialties at similar institutions that the residents' ACGME case log may not accurately reflect their operative experience. What barriers may influence this remains unclear. As the only objective measure of resident operative experience, an accurate case log is paramount in representing one's operative experience. This study aims to determine the accuracy of procedural coding by general surgical residents at a single institution., Methods: Data were collected from 2 consecutive graduating classes of surgical residents' ACGME case logs from 2008 to 2014. A total of 5799 entries from 7 residents were collected. The CPT codes entered by residents were compared to departmental billing records submitted by the attending surgeon for each procedure. Assigned CPT codes by institutional American Academy of Professional Coders certified abstract coders were considered the "gold standard." A total of 4356 (75.12%) of 5799 entries were identified in billing records. Excel 2010 and SAS 9.3 were used for analysis. In the event of multiple codes for the same patient, any match between resident codes and billing record codes was considered a "correct" entry. A 4-question survey was distributed to all current general surgical residents at our institution for feedback on coding habits, limitations to accurate coding, and opinions on ACGME case log representation of their operative experience., Results: All 7 residents had a low percentage of correctly entered CPT codes. The overall accuracy proportion for all residents was 52.82% (range: 43.32%-60.07%). Only 1 resident showed significant improvement in accuracy during his/her training (p = 0.0043). The survey response rate was 100%. Survey results indicated that inability to find the precise code within the ACGME search interface and unfamiliarity with available CPT codes were by far the most common perceived barriers to accuracy. Survey results also indicated that most residents (74%) believe that they code accurately most of the time and agree that their case log would accurately represent their operative experience (66.6%)., Conclusion: This is the first study to evaluate correctness of residents' ACGME case logs in general surgery. The degree of inaccuracy found here necessitates further investigation into the etiology of these discrepancies. Instruction on coding practices should also benefit the residents after graduation. Optimizing communication among attendings and residents, improving ACGME coding search interface, and implementing consistent coding practices could improve accuracy giving a more realistic view of residents' operative experience., (Published by Elsevier Inc.)

Aims: The reduction of delay times as well as the rate of false alarms (FA) have become some of the main points of the different infarction networks. We propose a simple way of classifying patients derived for primary PCI (pPCI) into well-defined simple groups by colors, where we can assess real delays of each clinical presentation, define the FA and, furthermore, establish their immediate and short term prognosis., Methods and Results: Prospective study of STEMI consecutive patients derived for pPCI during 2014. Patients were categorized into one of the 3 predesigned groups [(i) Green: diagnostic-ECG with compatible clinical presentation for pPCI; (ii) Yellow: LBBB, pacemaker rate or non-diagnostic ECG; and (iii) Red: very complex patients], always before performing the angiography in 518 patients. Delay times were highest in the Yellow group, with much longer first medical contact (FMC) to balloon time (median Green 118'; Yellow 163'; Red 130'; p<0.001) mainly due to higher times from the first medical contact to the diagnosis and team activation (median Green 30'; Yellow 70'; Red 39'; p<0.001). In the whole cohort, pPCI was performed in 80.2% of patients, with 11.9% of FA. The Green group had only a 2.5% FA rate, in contrast to the Yellow group where FA were 43.2%., Conclusions: This simple classification differentiates the 3 very clear groups in which delay times and prognosis are very different. This classification allows us to measure, evaluate and compare the performance of each of our pPCI networks with others and within different periods of times., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

The immense volume of cases signed out by surgical pathologists on a daily basis gives little time to think about exactly how data are stored. An understanding of the basics of data representation has implications that affect a pathologist's daily practice. This article covers the basics of data representation and its importance in the design of electronic medical record systems. Coding in surgical pathology is also discussed. Finally, a summary of communication standards in surgical pathology is presented, including suggested resources that establish standards for select aspects of pathology reporting., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: Accurate International Classification of Diseases (ICD) diagnosis coding is critical for patient care, billing purposes, and research endeavors. In this single-institution study, we evaluated our baseline ICD-9 (9th revision) diagnosis coding accuracy, identified the most common errors contributing to inaccurate coding, and implemented a multimodality strategy to improve radiation oncology coding., Methods and Materials: We prospectively studied ICD-9 coding accuracy in our radiation therapy--specific electronic medical record system. Baseline ICD-9 coding accuracy was obtained from chart review targeting ICD-9 coding accuracy of all patients treated at our institution between March and June of 2010. To improve performance an educational session highlighted common coding errors, and a user-friendly software tool, RadOnc ICD Search, version 1.0, for coding radiation oncology specific diagnoses was implemented. We then prospectively analyzed ICD-9 coding accuracy for all patients treated from July 2010 to June 2011, with the goal of maintaining 80% or higher coding accuracy. Data on coding accuracy were analyzed and fed back monthly to individual providers., Results: Baseline coding accuracy for physicians was 463 of 661 (70%) cases. Only 46% of physicians had coding accuracy above 80%. The most common errors involved metastatic cases, whereby primary or secondary site ICD-9 codes were either incorrect or missing, and special procedures such as stereotactic radiosurgery cases. After implementing our project, overall coding accuracy rose to 92% (range, 86%-96%). The median accuracy for all physicians was 93% (range, 77%-100%) with only 1 attending having accuracy below 80%. Incorrect primary and secondary ICD-9 codes in metastatic cases showed the most significant improvement (10% vs 2% after intervention)., Conclusions: Identifying common coding errors and implementing both education and systems changes led to significantly improved coding accuracy. This quality assurance project highlights the potential problem of ICD-9 coding accuracy by physicians and offers an approach to effectively address this shortcoming., (Copyright © 2015. Published by Elsevier Inc.)

Medication exposure is an important variable in virtually all clinical research, yet there is great variation in how the data are collected, coded, and analyzed. Coding and classification systems for medication data are heterogeneous in structure, and there is little guidance for implementing them, especially in large research networks and multi-site trials. Current practices for handling medication data in clinical trials have emerged from the requirements and limitations of paper-based data collection, but there are now many electronic tools to enable the collection and analysis of medication data. This paper reviews approaches to coding medication data in multi-site research contexts, and proposes a framework for the classification, reporting, and analysis of medication data. The framework can be used to develop tools for classifying medications in coded data sets to support context appropriate, explicit, and reproducible data analyses by researchers and secondary users in virtually all clinical research domains., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Objective: The VR-CoDES has been previously applied in the dental context. However, we know little about how dental patients with intellectual disabilities (ID) and complex communication needs express their emotional distress during dental visits. This is the first study explored the applicability of the VR-CoDES to a dental context involving patients with ID., Methods: Fourteen dental consultations were video recorded and coded using the VR-CoDES, assisted with the additional guidelines for the VR-CoDES in a dental context. Both inter- and intra-coder reliabilities were checked on the seven consultations where cues were observed., Results: Sixteen cues (eight non-verbal) were identified within seven of the 14 consultations. Twenty responses were observed (12 reducing space) with four multiple responses. Cohen's Kappa were 0.76 (inter-coder) and 0.88 (intra-coder)., Conclusion: With the additional guidelines, cues and responses were reliably identified. Cue expression was exhibited by non-verbal expression of emotion with people with ID in the literature. Further guidance is needed to improve the coding accuracy on multiple providers' responses and to investigate potential impacts of conflicting responses on patients., Practice Implications: The findings provided a useful initial step towards an ongoing exploration of how healthcare providers identify and manage emotional distress of patients with ID., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Over 1.5 million pleural effusions occur in the United States every year as a consequence of a variety of inflammatory, infectious, and malignant conditions. Although rarely fatal in isolation, pleural effusions are often a marker of a serious underlying medical condition and contribute to significant patient morbidity, quality-of-life reduction, and mortality. Pleural effusion management centers on pleural fluid drainage to relieve symptoms and to investigate pleural fluid accumulation etiology. Many recent studies have demonstrated important advances in pleural disease management approaches for a variety of pleural fluid etiologies, including malignant pleural effusion, complicated parapneumonic effusion and empyema, and chest tube size. The last decade has seen greater implementation of real-time imaging assistance for pleural effusion management and increasing use of smaller bore percutaneous chest tubes. This article will briefly review recent pleural effusion management literature and update the latest changes in common procedural terminology billing codes as reflected in the changing landscape of imaging use and percutaneous approaches to pleural disease management.

Objectives: Traditional Chinese medicines in treating systemic lupus erythematosus are not uncommon. However, logic of pattern diagnosis and consensus on treating this disease is lacking. This study aimed to explore the correlation between the pattern diagnosis of and its corresponding prescription in systemic lupus erythematosus., Design: Clinical data including patterns and prescriptions from the electronic medical records of lupus patients in a medical center were collected. Using a specific coding system (B-code) to encode the patterns and prescriptions provided a platform for data processing and statistics, and nonlinear canonical correlation analysis was employed to examine the correlation between them., Results: In the 261 valid visits collected, a total of 46 patterns and 193 prescriptions were encoded into 57 B-codes. In our database, "Yin, Vacuity, Heat, and Dampness" were the most common B-codes (more than 90%) in both patterns and prescriptions. "Anemarrhena, Phellodendron, and Rehmannia Pill (Zhi-Bai-Di-Huang-Wan)" and "Miltiorrhizae Radix (Dan-Shen)" were the most frequently used formula (52%) and herb (48%), respectively. Water-rheum, kidney, and stasis were among the three most effective variable sets for correlating the patterns and prescriptions., Conclusion: By using B-code with nonlinear canonical correlation analysis, the clinical dataset can be examined to shed light on the logic of pattern differentiation and principle of treatment for a specific disease., (Copyright © 2014. Published by Elsevier Ltd.)

Fingerprinting is the most widely accepted method of identification of people. But in cases of disfigured, decomposed, burnt or fragmented bodies, it is of limited value. Teeth and dental restorations on the other hand are extremely resistant to destruction by fire. They retain a number of their original characteristics, which are often unique and hence offer a possibility of rather accurate and legally acceptable identification of such remains. This study was undertaken to evaluate the utility of orthopantomography for human identification and propose a coding system for orthopantomogram (OPG), which can be utilized as an identification tool in forensic sciences., (Copyright © 2013 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.)

Clinical documentation improvement is an important aspect to achieve top performance. Clinical documentation in a patient's record includes any and all documentation that relates to the care of the patient during the patient's stay or encounter at the hospital. Documentation is key to accurate clinical coding, validating length of stay, resource utilization, physician profiling, case management, severity of illness, risk of mortality, quality management, risk management, clinical outcomes, critical pathways, regulatory compliance, Joint Commission accreditation, managed care, and reimbursement. Good documentation minimizes coding errors, reduces claim denials, and optimizes reimbursement. Implementing quality improvement strategies that make documentation and coding an organizational priority can positively influence operations, services, and revenue. Other external and internal coding audits show that the cause of improper coding is due to lack of proper physician documentation to support reimbursement at the appropriate level. The purpose of this article is to provide tips for documenting pulmonary diagnoses that not only would ensure appropriate reimbursement but also would accurately represent the severity of a patient's condition.

Objective: To show if cues, concerns and provider responses (defined in VR-CoDES and VR-CoDES-P manuals) are present, can be reliably coded and require additional advice for adoption in a dental context., Methods: Thirteen patients in a dental practice setting were videoed with either their dentist or hygienist and dental nurse present in routine treatment sessions. All utterances were coded using the Verona systems: VR-CoDES and the VR-CoDES-P. Rates of cue, concerns and provider responses described and reliability tested., Results: The VR-CoDES and VR-CoDES-P were successfully applied in the dental context. The intra-rater ICCs for the detection of cues and concerns and provider response were acceptable and above 0.75. A similar satisfactory result was found for the inter-rater reliability., Conclusion: The VR-CoDES and the VR-CoDES-P are applicable in the dental setting with minor supporting guidelines and show evidence of reliable coding., Practice Implications: The VR-CoDES and the VR-CoDES-P may be helpful tools for analysing patient cues and concerns and the dental professionals' responses in the dental context., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Objectives: To describe the development and refinement of a scheme, detail of essential elements and participants in shared decision making (DEEP-SDM), for coding shared decision making (SDM) while reporting on the characteristics of decisions in a sample of patients with metastatic breast cancer., Methods: The evidence-based patient choice instrument was modified to reflect Makoul and Clayman's integrative model of SDM. Coding was conducted on video recordings of 20 women at the first visit with their medical oncologists after suspicion of disease progression. Noldus Observer XT v.8, a video coding software platform, was used for coding., Results: The sample contained 80 decisions (range: 1-11), divided into 150 decision making segments. Most decisions were physician-led, although patients and physicians initiated similar numbers of decision-making conversations., Conclusion: DEEP-SDM facilitates content analysis of encounters between women with metastatic breast cancer and their medical oncologists. Despite the fractured nature of decision making, it is possible to identify decision points and to code each of the essential elements of shared decision making. Further work should include application of DEEP-SDM to non-cancer encounters., Practice Implications: A better understanding of how decisions unfold in the medical encounter can help inform the relationship of SDM to patient-reported outcomes., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Objectives: We wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: (1) Determine if any contradictory knowledge exists in LOINC. (2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. (3) Provide suggestions for improving the semantic interoperability of LOINC., Methods: We collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness., Results: The number of LOINC codes evaluated were 1917, 1267 and 1693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2022, 2030, and 2301 version spaces among ARUP and Intermountain, Intermountain and Regenstrief and ARUP and Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1165, 765 and 1121 semantically interoperable pairs. The interoperable pairs were classified into three levels: (1) Level I - No loss of meaning, complete information was exchanged by identical codes. (2) Level II - No loss of meaning, but processing of data was needed to make the data completely comparable. (3) Level III - Some loss of meaning. For example, tests with a specific 'method' could be rolled-up with tests that were 'methodless'., Conclusions: There are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Objective: To create an interoperable set of nursing diagnoses for use in the patient problem list in the EHR to support interoperability., Design: Queries for nursing diagnostic concepts were executed against the UMLS Metathesaurus to retrieve all nursing diagnoses across four nursing terminologies where the concept was also represented in SNOMED CT. A candidate data set was retrieved and included the nursing diagnoses and corresponding SNOMED CT concepts from the UMLS Metathesaurus. The team members identified the concepts that met the semantic selection criteria for inclusion in the nursing problem list., Results: 1320 concepts were returned in the initial UMLS Metathesaurus query of nursing diagnostic concepts. Further analysis was conducted to identify those nursing diagnostic concepts mapped to SNOMED CT and duplicate concepts were removed resulting in 591 unique UMLS Metathesaurus concepts. The query extracted all concepts from two of the nursing terminologies that contained interventions and outcomes. After cleaning the dataset, the final count of SNOMED CT concepts in the nursing problem list subset is 369., Conclusions: The problem list is a key component of the patient care and has been acknowledged as critical by the EHR Meaningful Use criteria. Nursing diagnoses on the problem list are foundational for constructing a nursing care plan. A multidisciplinary patient problem list will facilitate communication and evaluation of the contribution of nursing care to the patient's clinical care experiences and outcomes., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: Acute exacerbations of COPD (AE-COPD) are a leading cause of hospitalizations in the United States. To estimate the burden of disease (eg, prevalence and cost), identify opportunities to improve care quality (eg, performance measures), and conduct observational comparative effectiveness research studies, various algorithms based on the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes have been used to identify patients with COPD. However, the validity of these algorithms remains unclear., Methods: We compared the test characteristics (sensitivity, specificity, positive predictive value, and negative predictive value) of four different coding algorithms for identifying patients hospitalized for an exacerbation of COPD with chart review (reference standard) using a stratified probability sample of 200 hospitalizations at two urban academic medical centers. Sampling weights were used when calculating prevalence and test characteristics., Results: The prevalence of COPD exacerbations (based on the reference standard) was 7.9% of all hospitalizations. The sensitivity of all ICD-9-CM algorithms was very low and varied by algorithm (12%-25%), but the negative predictive value was similarly high across algorithms (93%-94%). The specificity was > 99% for all algorithms, but the positive predictive value varied by algorithm (81%-97%)., Conclusions: Algorithms based on ICD-9-CM codes will undercount hospitalizations for AE-COPD, and as many as one in five patients identified by these algorithms may be misidentified as having a COPD exacerbation. These findings suggest that relying on ICD-9-CM codes alone to identify patients hospitalized for AE-COPD may be problematic.

Objective: To investigate whether different measures of inter-rater reliability will compute similar estimates with nominal data commonly encountered in communication studies. To make recommendations how reliability should be computed and described for communication coding instruments., Methods: The raw data from an inter-rater study with three coders were analysed with; Cohen's κ, sensitivity and specificity measures, Fleiss's multirater κj, and an intraclass correlation coefficient (ICC)., Results: Minor differences were found between Cohen's κ and an ICC model across paired data (largest margin=0.01). There were negligible differences between the multirater estimates e.g. κj (0.52) and ICC (0.53). Sensitivity analyses were in general agreement with the multirater estimates., Conclusion: It is more practical to analyse nominal data with >2 raters with an appropriate model ICC for inter-rater studies, and little difference exists between Cohen's κ or an ICC., Practice Implication: Alternatives to Cohen's κ are readily available, but researchers need to be aware of the different ICC definitions. An ICC model should be fully described in reports. Investigators are encouraged to supply confidence limits with inter-rater data, and to revisit guidance regarding the relative strengths of agreement of reliability coefficients., (Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.)