Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information ...exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the “Shared Nationwide Interoperability Roadmap” from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study.
Abstract
Emergent policy changes related to telemedicine and the Emergency Medical Treatment and Labor Act during the novel coronavirus disease 2019 (COVID-19) pandemic have created opportunities for ...technology-based clinical evaluation, which serves to conserve personal protective equipment (PPE) and protect emergency providers. We define electronic PPE as an approach using telemedicine tools to perform electronic medical screening exams while satisfying the Emergency Medical Treatment and Labor Act. We discuss the safety, legal, and technical factors necessary for implementing such a pathway. This approach has the potential to conserve PPE and protect providers while maintaining safe standards for medical screening exams in the emergency department for low-risk patients in whom COVID-19 is suspected.
Health care providers managing the complex health needs of adolescents must comply with state laws governing adolescent consent and right to privacy. However, these laws vary. Our objectives were to ...summarize consent and privacy laws state-by-state and assess the implications of variation for compliance with the 21st Century Cures Act and with evidence-based guidance on adolescent care.
We summarized state laws and regulations on minor consent for the following: health services, substance abuse treatment, prenatal care, mental health care, contraceptive management, immunizations, sexually transmitted infection management, human immunodeficiency viruses testing and treatment, dental care, and sexual assault evaluation. We compared state laws and regulations with American Academy of Pediatrics' evidence-based guidelines to assess consistencies in guidance.
We observed notable state-by-state variability in laws governing consent for adolescent patients. No states had identical policies for all services studied. For example, although all states had provisions for consent to management of sexually transmitted infections, there were variable specifications in the age and type of minor, whether this includes human immunodeficiency viruses, and whether confidentiality is protected. Providing confidential care to the adolescent patient has been set as a priority by medical societies; however, guidelines are limited by the need to comply with state laws and regulations.
State laws on consent and privacy for adolescents are highly variable, and many do not reflect pediatric professional standards of care. This inconsistency is a barrier to operationalizing a consistent and equitable experience providing evidence-based medical care and ensuring adolescent privacy protection.
Abstract
The COVID-19 national emergency has led to surging care demand and the need for unprecedented telehealth expansion. Rapid telehealth expansion can be especially complex for pediatric ...patients. From the experience of a large academic medical center, this report describes a pathway for efficiently increasing capacity of remote pediatric enrollment for telehealth while fulfilling privacy, security, and convenience concerns.
The design and implementation of the process took 2 days. Five process requirements were identified: efficient enrollment, remote ability to establish parentage, minimal additional work for application processing, compliance with guidelines for adolescent autonomy, and compliance with institutional privacy and security policies.
Weekly enrollment subsequently increased 10-fold for children (age 0–12 years) and 1.2-fold for adolescents (age 13–17 years). Weekly telehealth visits increased 200-fold for children and 90-fold for adolescents.
The obstacles and solutions presented in this report can provide guidance to health systems for similar challenges during the COVID-19 response and future disasters.
In response to mounting evidence that use of electronic medical record systems may cause unintended consequences, and even patient harm, the AMIA Board of Directors convened a Task Force on Usability ...to examine evidence from the literature and make recommendations. This task force was composed of representatives from both academic settings and vendors of electronic health record (EHR) systems. After a careful review of the literature and of vendor experiences with EHR design and implementation, the task force developed 10 recommendations in four areas: (1) human factors health information technology (IT) research, (2) health IT policy, (3) industry recommendations, and (4) recommendations for the clinician end-user of EHR software. These AMIA recommendations are intended to stimulate informed debate, provide a plan to increase understanding of the impact of usability on the effective use of health IT, and lead to safer and higher quality care with the adoption of useful and usable EHR systems.
•We applied automated classification methods to patient generated portal messages.•Classifier features included words from messages, NLP concepts, and NLP semantics.•Random forest and logistic ...regression approaches accurately classified messages.•Automated classification may aid in managing growing volumes of portal messages.
Secure messaging through patient portals is an increasingly popular way that consumers interact with healthcare providers. The increasing burden of secure messaging can affect clinic staffing and workflows. Manual management of portal messages is costly and time consuming. Automated classification of portal messages could potentially expedite message triage and delivery of care.
We developed automated patient portal message classifiers with rule-based and machine learning techniques using bag of words and natural language processing (NLP) approaches. To evaluate classifier performance, we used a gold standard of 3253 portal messages manually categorized using a taxonomy of communication types (i.e., main categories of informational, medical, logistical, social, and other communications, and subcategories including prescriptions, appointments, problems, tests, follow-up, contact information, and acknowledgement). We evaluated our classifiers’ accuracies in identifying individual communication types within portal messages with area under the receiver-operator curve (AUC). Portal messages often contain more than one type of communication. To predict all communication types within single messages, we used the Jaccard Index. We extracted the variables of importance for the random forest classifiers.
The best performing approaches to classification for the major communication types were: logistic regression for medical communications (AUC: 0.899); basic (rule-based) for informational communications (AUC: 0.842); and random forests for social communications and logistical communications (AUCs: 0.875 and 0.925, respectively). The best performing classification approach of classifiers for individual communication subtypes was random forests for Logistical-Contact Information (AUC: 0.963). The Jaccard Indices by approach were: basic classifier, Jaccard Index: 0.674; Naïve Bayes, Jaccard Index: 0.799; random forests, Jaccard Index: 0.859; and logistic regression, Jaccard Index: 0.861. For medical communications, the most predictive variables were NLP concepts (e.g., Temporal_Concept, which maps to ‘morning’, ‘evening’ and Idea_or_Concept which maps to ‘appointment’ and ‘refill’). For logistical communications, the most predictive variables contained similar numbers of NLP variables and words (e.g., Telephone mapping to ‘phone’, ‘insurance’). For social and informational communications, the most predictive variables were words (e.g., social: ‘thanks’, ‘much’, informational: ‘question’, ‘mean’).
This study applies automated classification methods to the content of patient portal messages and evaluates the application of NLP techniques on consumer communications in patient portal messages. We demonstrated that random forest and logistic regression approaches accurately classified the content of portal messages, although the best approach to classification varied by communication type. Words were the most predictive variables for classification of most communication types, although NLP variables were most predictive for medical communication types. As adoption of patient portals increases, automated techniques could assist in understanding and managing growing volumes of messages. Further work is needed to improve classification performance to potentially support message triage and answering.
Objective: The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) ...systems’ capability to handle abbreviations in clinical narratives.
Methods: We developed an open-source framework for clinical abbreviation recognition and disambiguation (CARD) that leverages our previously developed methods, including: (1) machine learning based approaches to recognize abbreviations from a clinical corpus, (2) clustering-based semiautomated methods to generate possible senses of abbreviations, and (3) profile-based word sense disambiguation methods for clinical abbreviations. We applied CARD to clinical corpora from Vanderbilt University Medical Center (VUMC) and generated 2 comprehensive sense inventories for abbreviations in discharge summaries and clinic visit notes. Furthermore, we developed a wrapper that integrates CARD with MetaMap, a widely used general clinical NLP system.
Results and Conclusion: CARD detected 27 317 and 107 303 distinct abbreviations from discharge summaries and clinic visit notes, respectively. Two sense inventories were constructed for the 1000 most frequent abbreviations in these 2 corpora. Using the sense inventories created from discharge summaries, CARD achieved an F1 score of 0.755 for identifying and disambiguating all abbreviations in a corpus from the VUMC discharge summaries, which is superior to MetaMap and Apache’s clinical Text Analysis Knowledge Extraction System (cTAKES). Using additional external corpora, we also demonstrated that the MetaMap-CARD wrapper improved MetaMap’s performance in recognizing disorder entities in clinical notes. The CARD framework, 2 sense inventories, and the wrapper for MetaMap are publicly available at https://sbmi.uth.edu/ccb/resources/abbreviation.htm. We believe the CARD framework can be a valuable resource for improving abbreviation identification in clinical NLP systems.
The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted ...status-from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge.
The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes.
Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge.
Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test data set in the challenge.
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