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There is a lack of research on adverse event (AE) detection in oncology patients, despite the propensity for iatrogenic harm. Two common methods include voluntary safety reporting (VSR) and chart review tools, such as the Institute for Healthcare Improvement's Global Trigger Tool (GTT). Our objective was to compare frequency and type of AEs detected by a modified GTT compared with VSR for identifying AEs in oncology patients in a larger clinical trial.Patients across 6 oncology units (from July 1, 2013, through May 29, 2015) were randomly selected. Retrospective chart reviews were conducted by a team of nurses and physicians to identify AEs using the GTT. The VSR system was queried by the department of quality and safety of the hospital. Adverse event frequencies, type, and harm code for both methods were compared.The modified GTT detected 0.90 AEs per patient (79 AEs in 88 patients; 95% [0.71-1.12] AEs per patient) that were predominantly medication AEs (53/79); more than half of the AEs caused harm to the patients (41/79, 52%), but only one quarter were preventable (21/79; 27%). The VSR detected 0.24 AEs per patient (21 AEs in 88 patients; 95% [0.15-0.37] AEs per patient), a large plurality of which were medication/intravenous related (8/21); more than half did not cause harm (70%). Only 2% of the AEs (2/100) were detected by both methods.Neither the modified GTT nor the VSR system alone is sufficient for detecting AEs in oncology patient populations. Further studies exploring methods such as automated AE detection from electronic health records and leveraging patient-reported AEs are needed.

Background: MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward.Objective: To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance.Methods: MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (β =1) were calculated.Results: The percentages of exact matches and missing gold-standard terms were 0.6–0.79 and 0.09–0.3 for one behavior option, and 0.56–0.8 and 0.09–0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively.Conclusion: We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.

Interoperable clinical decision support system (CDSS) rules are a pathway to achieving interoperability which is a well-recognized challenge in health information technology. Building an ontology facilitates the creation of interoperable CDSS rules, which can be achieved by identifying the keyphrases (KP) from the existing literature. However, KP identification for labeling the data requires human expertise, consensus, and contextual understanding. This paper aims to present a semi-supervised framework for the CDSS using minimal labeled data based on hierarchical attention over the documents fused with domain adaptation approaches. Then, evaluate the effectiveness of KP identification with this framework. In the view of semi-supervised learning, our methodology toward building this framework outperforms the prior neural architectures by learning with document-level context, no explicit hand-crafted features, knowledge transfer from pre-trained models (on unlabeled corpus), and post-fine-tuning with smaller gold standard-labeled data. To the best of our knowledge, this is the first functional framework for the CDSS sub-domain to identify the KP, which is trained on limited labeled data. It contributes to the general natural language processing (NLP) architectures in areas such as clinical NLP, where manual data labeling is challenging.

Clinical decision support systems (CDSSs) are important for the quality and safety of healthcare delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused.Ontology has the potential to promote the reuse of CDSS rules. Therefore, we systematically screened literature to elaborate on the current status of ontology applied in CDSS rules, such as rule management (which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate) and maintenance (which updates CDSS rules). Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules.The literature search was focused on the intersection of ontology, clinical decision support, and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the NursingAllied Health Database. Grounded theory and PRISMA 2020 guidelines were followed. One author initiated the screening and literature review, while the other two validated the processes and results independently. Inclusion and exclusion criteria were developed and refined iteratively.CDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among the 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules.Ontologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the CDSS rule reusability and interoperability. This paper identified and described the ontologies that, despite their limitations, enable Semantic Web technology and their applications in CDSS rules.

BACKGROUND Clinical decision support systems (CDSS) play a critical role in the quality and safety of healthcare delivery. Although CDSS rules direct CDSS behavior, CDSS rules have not been routinely shared and reused. OBJECTIVE Ontology has the potential to promote the reusing of CDSS rules. Therefore, we systematically screened literature to elaborate on the current status of ontology applied in CDSS rules, e.g., rule management (uses the captured CDSS rule usage data and users’ feedback data to customize CDSS services to be more accurate) and maintenance (updates CDSS rules). We aim to delineate the frontlines of ontologies applied in CDSS rules via this systematic literature review. METHODS The literature search was focused on ontology, clinical decision support, and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA 2020 guidelines were followed. One author started the screening and literature analysis, and two validated the processes and results. Inclusion and exclusion criteria were developed and refined iteratively. RESULTS Among the 81 included publications, CDSS were mainly applied to managing chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite ontologies being used to supply medical knowledge, CDSS rules, and terminologies, ontologies have not been used in CDSS rule management or facilitate the reusing of CDSS rules. CONCLUSIONS Ontologies have been used to organize and represent medical knowledge and controlled vocabularies that contribute to CDSS and the content of CDSS rules. So far, there is a lack of reusing CDSS rules. More efforts are needed to improve the reusability and interoperability of CDSS rules. This paper delineated the ontologies, enabling Semantic Web technology, despite inadequate, and their applications in CDSS rules.

ObjectiveClinical decision support systems (CDSS) have a critical role in improving the quality and safety of health care delivery. CDSS rules direct the behavior of CDSS. However, the CDSS rules have not been routinely shared and reused, and ontology can promote the reusing of CDSS rules. We systematically screened literature to elaborate on the current status of ontology applied in CDSS rule management.MethodsWe searched PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database for publications focusing on ontology, clinical decision support, and rules. Grounded theory and PRISMA 2020 guidelines were followed. One author started the screening and literature analysis, and two authors validated the processes and results. Inclusion and exclusion criteria were developed and refined iteratively.ResultsAmong 81 included publications, the identified CDSS were mainly applied to managing chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite ontology was used to supply medical knowledge, CDSS rules, and terminologies to CDSS, ontology has not been used in CDSS rule management.ConclusionsAlthough ontology can facilitate the reuse, management, and maintenance of CDSS rules, CDSS ontology remains unavailable indicating that more efforts are needed to improve the reusability and interoperability of CDSS rules.

Interdisciplinary team communication in eHealth development is challenging because all disciplines have unique, intrinsic discursive practices, theories and artefacts. Due to these factors, members of interdisciplinary teams can experience problems in communication and collaboration. Through a centered focus, members can benefit individually, inspire one another, and ultimately reach a timely delivery of their common pedagogical goal(s). Using the lens of dialogism, this paper aims to identify the conceptual considerations that arose during the development of a Massive Open Online Course (MOOC) for higher education in eHealth. Methods included auto-ethnography and interdisciplinary dialogue supported by literacy artefacts, including visual material. Results yielded a visual tool for meta-assessment of team communication, and an organizing principle for topics in the MOOC. A major implication is that especially for eHealth, scientific communicative competence of experts—while establishing a common understanding—can lead to a unique and meaningful delivery of high pedagogical quality.

BACKGROUND Increased digitization of health care might challenge some of the trust functions that are established in a traditional health care system. We have, with the concept of VerifyMed, developed a decentralized service for work history and competence verification, as a means to increase trust in the virtual interaction between a patient and a caregiver, mitigate administrative burden, and provide patient-reported outcomes seamlessly for health professionals. OBJECTIVE This research aimed to validate the use case of a decentralized credentials service for health care professionals in Norway. We also aimed to evaluate the proof-of-concept of VerifyMed, a blockchain-based credential service for health care professionals. METHODS A qualitative approach was applied with data collection through 9 semistructured interviews and 2 focus groups (one with 4 participants and the other with 5 participants). The System Usability Scale (SUS) was used as a part of the interviews. Data were analyzed through the principles of systematic text condensation. The recruitment of participants ended when it was concluded that the data had reached saturation. RESULTS The following 5 themes were identified from the interviews and focus groups: (1) the need for aggregated storage of work- and study-related verification, (2) trust in a virtual health care environment, (3) the potential use of patient feedback, (4) trust in blockchain technology, and (5) improvements of the VerifyMed concept. The SUS questionnaire gave a score of 69.7. CONCLUSIONS This study has validated the need for a decentralized system where health care professionals can control their credentials and, potentially, their reputation. Future work should update the VerifyMed system according to this input. We concluded that a decentralized system for the storage of work-related verifiable credentials could increase trust in a virtualized health care system.

Background Population based reference intervals are fundamental for interpreting results for quantitative laboratory tests. In patients with a specific chronic disorder, however, results of various tests may regularly be different than in healthy individuals. Health-associated reference intervals may therefore have limited value in such patients. Instead, disease-associated reference intervals may be useful, as they describe the results distribution in populations resembling the specific patients. Few disease-associated reference intervals are available in the literature. The aim of this study was to estimate reference intervals for common laboratory tests for patient populations with rheumatoid arthritis, Crohn’s disease or ulcerative colitis without significant comorbidity, using a novel algorithm. Material and methods Laboratory test results and hospital discharge diagnoses were collected for relevant patients. An algorithm was developed to identify discharge diagnoses significantly associated with high or low results for specific tests. After excluding patients with such diagnoses, reference intervals were estimated, representing results distributions in patients with each of the specific chronic disorders, but without significant comorbidity. Results Disease-associated reference intervals were estimated for 20 common laboratory tests. Most of the estimated reference limits were significantly different from corresponding health-associated reference limits. Thirty percent of the estimated reference intervals were different from estimates based on crude patient populations, indicating that the algorithm applied managed to exclude patients with relevant comorbidity. Conclusion Disease-associated reference intervals could be estimated for a number of tests in patients with rheumatoid arthritis, ulcerative colitis or Crohn’s disease using a highly automated algorithm based on routinely recorded patient data., Highlights • Patients with chronic disorders have different lab results than healthy individuals. • Reference intervals for patients with chronic disorders are often lacking. • We estimated reference intervals for patients with chronic inflammatory disorders.

BACKGROUND Health care systems are currently undergoing a digital transformation that has been primarily triggered by emerging technologies, such as artificial intelligence, the Internet of Things, 5G, blockchain, and the digital representation of patients using (mobile) sensor devices. One of the results of this transformation is the gradual virtualization of care. Irrespective of the care environment, trust between caregivers and patients is essential for achieving favorable health outcomes. Given the many breaches of information security and patient safety, today’s health information system portfolios do not suffice as infrastructure for establishing and maintaining trust in virtual care environments. OBJECTIVE This study aims to establish a theoretical foundation for a complex health care system intervention that aims to exploit a cryptographically secured infrastructure for establishing and maintaining trust in virtualized care environments and, based on this theoretical foundation, present a proof of concept that fulfills the necessary requirements. METHODS This work applies the following framework for the design and evaluation of complex intervention research within health care: a review of the literature and expert consultation for technology forecasting. A proof of concept was developed by following the principles of design science and requirements engineering. RESULTS This study determined and defined the crucial functional and nonfunctional requirements and principles for enhancing trust between caregivers and patients within a virtualized health care environment. The cornerstone of our architecture is an approach that uses blockchain technology. The proposed decentralized system offers an innovative governance structure for a novel trust model. The presented theoretical design principles are supported by a concrete implementation of an Ethereum-based platform called VerifyMed. CONCLUSIONS A service for enhancing trust in a virtualized health care environment that is built on a public blockchain has a high fit for purpose in Healthcare 4.0.

Background Health care systems are currently undergoing a digital transformation that has been primarily triggered by emerging technologies, such as artificial intelligence, the Internet of Things, 5G, blockchain, and the digital representation of patients using (mobile) sensor devices. One of the results of this transformation is the gradual virtualization of care. Irrespective of the care environment, trust between caregivers and patients is essential for achieving favorable health outcomes. Given the many breaches of information security and patient safety, today’s health information system portfolios do not suffice as infrastructure for establishing and maintaining trust in virtual care environments. Objective This study aims to establish a theoretical foundation for a complex health care system intervention that aims to exploit a cryptographically secured infrastructure for establishing and maintaining trust in virtualized care environments and, based on this theoretical foundation, present a proof of concept that fulfills the necessary requirements. Methods This work applies the following framework for the design and evaluation of complex intervention research within health care: a review of the literature and expert consultation for technology forecasting. A proof of concept was developed by following the principles of design science and requirements engineering. Results This study determined and defined the crucial functional and nonfunctional requirements and principles for enhancing trust between caregivers and patients within a virtualized health care environment. The cornerstone of our architecture is an approach that uses blockchain technology. The proposed decentralized system offers an innovative governance structure for a novel trust model. The presented theoretical design principles are supported by a concrete implementation of an Ethereum-based platform called VerifyMed. Conclusions A service for enhancing trust in a virtualized health care environment that is built on a public blockchain has a high fit for purpose in Healthcare 4.0.

Background Recently, there has been an increasing focus among healthcare organisations on implementing patient portals. Previous studies have mainly focussed on the experiences of patient portal use. Few have investigated the processes of deciding what content and features to make available, in particular for shared portals across healthcare domains. The aim of the study was to investigate views on content and experiences from the configuration process among participants involved in setting up a shared patient portal for primary and specialist health services. Methods A qualitative study including 15 semi-structured interviews with persons participating in patient portal configuration was conducted from October 2019 to June 2020. Results Whether a shared patient portal for all the health services in the region should be established was not questioned by any of the informants. It was experienced as a good thing to have numerous participants present in the discussions on configuration, but it also was said to increase the complexity of the work. The informants considered a patient portal to be of great value for patient care, among other things because it would lead to improvements in patient follow-up and increased patient empowerment. Nevertheless, some informants advocated caution as they thought the patient portal possibly could lead to an increase in healthcare providers’ workloads and to anxiety and worries, as well as to inequality in access to health care among patients. The findings were categorized into the themes ‘A tool for increased patient involvement’, ‘Which information should be available for the patient’, ‘Concerns about increased workload’, ‘Too complex to use versus not interesting enough’, ‘Involving all services’ and ‘Patient involvement’. Conclusions Establishing a shared patient portal for primary and specialist health services was considered unproblematic. There was, however, variation in opinions on which content and features to include. This variation was related to concerns about increasing the workload for health care providers, causing anxiety and inequality among patients, and ensuring that the solution would be interesting enough to adopt. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

BACKGROUND Oral anticancer therapies can be self-administered by patients outside the hospital setting, which poses challenges of adherence to a drug plan and monitoring of side effects. Modern information technology may be developed and implemented to address these pertinent issues. OBJECTIVE The aim of this study was to explore how a smartphone app developed through a stepwise, iterative process can help patients using oral chemotherapy to take their drug, and to report adherence and side effects in a reliable and verifiable manner. METHODS Fourteen patients starting capecitabine treatment were included in this study and used the smartphone app in addition to regular follow up of capecitabine treatment. Nine of these patients fulfilled the treatment plan and were interviewed based on a semistructured interview guide and the System Usability Scale (SUS). In addition, two focus groups were completed with 7 oncologists and 7 oncology nurses, respectively. Interview data were analyzed in accordance with the principles of systematic text condensation. Features of the app were also assessed. RESULTS The smartphone app provided the patients with a feeling of reassurance regarding correct adherence of their oral chemotherapy treatment. They used the app as a memory tool about their treatment and possible serious side effects, as well as for treatment education. Patients expressed concerns about using the app to report side effects that were not considered to be obviously serious, fearing overreporting. The health personnel expressed an overall positive attitude to integrate this new tool in their everyday work. CONCLUSIONS Patients on oral chemotherapy treatment at home felt safe and found the app to be helpful. The app promoted learning about their treatment and made the patients more independent of the cancer clinic, reducing the need for the clinic’s limited resources for follow up of patients on oral anticancer medications.