Correction: A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

[This corrects the article DOI: 10.2196/27990.].

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

BACKGROUND: Due to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits. OBJECTIVE: The aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care. METHODS: A study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study. RESULTS: This project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase. CONCLUSIONS: An ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.

A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study.

BACKGROUND: The evidence-based medicine (EBM) paradigm requires the development of health care professionals' skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients' health care, increase patient safety, and optimize resources use. OBJECTIVE: The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice. METHODS: Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy. RESULTS: The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times. CONCLUSIONS: The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future.

Diagnostic accuracy of a telemedicine tool for acute burns diagnosis.

BACKGROUND: Telemedicine technologies have a valuable potential when it comes to improving the accuracy of triage protocols in selecting severely injured patients who may benefit from transportation. The main objective of this study was to evaluate the correlation of the urgent diagnosis made by telemedicine through an App with traditional face-to-face urgent care and the final diagnosis, made by scheduled consultation. METHODS: We carried out a descriptive cross-sectional study to evaluate the accuracy of telemedicine in burns evaluation compared to gold standard (in-person consultation). An App was designed. All patients enrolled were evaluated by both teleconsultation and face-to-face approach on burn emergencies. Diagnosis on presentation made by a physician constituted the gold standard. RESULTS: A total of 202 patients were included in the study. The use of TM was able to detect that 83.17% of the patients attending the BU could have been managed on as outpatient basis. The intra-observer concordance was k=0.94 (95% CI: 0.90-0.97). These results highlight a very high sensitivity and specificity (99.40 and 100% respectively). CONCLUSIONS: The telemedicine system for planning referrals is a useful tool that may make significant differences in the management of burned patients although further research needs to be taken in that direction.

A Mobile Health Solution Complementing Psychopharmacology-Supported Smoking Cessation: Randomized Controlled Trial.

BACKGROUND: Smoking cessation is a persistent leading public health challenge. Mobile health (mHealth) solutions are emerging to improve smoking cessation treatments. Previous approaches have proposed supporting cessation with tailored motivational messages. Some managed to provide short-term improvements in smoking cessation. Yet, these approaches were either static in terms of personalization or human-based nonscalable solutions. Additionally, long-term effects were neither presented nor assessed in combination with existing psychopharmacological therapies. OBJECTIVE: This study aimed to analyze the long-term efficacy of a mobile app supporting psychopharmacological therapy for smoking cessation and complementarily assess the involved innovative technology. METHODS: A 12-month, randomized, open-label, parallel-group trial comparing smoking cessation rates was performed at Virgen del Rocío University Hospital in Seville (Spain). Smokers were randomly allocated to a control group (CG) receiving usual care (psychopharmacological treatment, n=120) or an intervention group (IG) receiving psychopharmacological treatment and using a mobile app providing artificial intelligence-generated and tailored smoking cessation support messages (n=120). The secondary objectives were to analyze health-related quality of life and monitor healthy lifestyle and physical exercise habits. Safety was assessed according to the presence of adverse events related to the pharmacological therapy. Per-protocol and intention-to-treat analyses were performed. Incomplete data and multinomial regression analyses were performed to assess the variables influencing participant cessation probability. The technical solution was assessed according to the precision of the tailored motivational smoking cessation messages and user engagement. Cessation and no cessation subgroups were compared using t tests. A voluntary satisfaction questionnaire was administered at the end of the intervention to all participants who completed the trial. RESULTS: In the IG, abstinence was 2.75 times higher (adjusted OR 3.45, P=.01) in the per-protocol analysis and 2.15 times higher (adjusted OR 3.13, P=.002) in the intention-to-treat analysis. Lost data analysis and multinomial logistic models showed different patterns in participants who dropped out. Regarding safety, 14 of 120 (11.7%) IG participants and 13 of 120 (10.8%) CG participants had 19 and 23 adverse events, respectively (P=.84). None of the clinical secondary objective measures showed relevant differences between the groups. The system was able to learn and tailor messages for improved effectiveness in supporting smoking cessation but was unable to reduce the time between a message being sent and opened. In either case, there was no relevant difference between the cessation and no cessation subgroups. However, a significant difference was found in system engagement at 6 months (P=.04) but not in all subsequent months. High system appreciation was reported at the end of the study. CONCLUSIONS: The proposed mHealth solution complementing psychopharmacological therapy showed greater efficacy for achieving 1-year tobacco abstinence as compared with psychopharmacological therapy alone. It provides a basis for artificial intelligence-based future approaches. TRIAL REGISTRATION: ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/NCT03553173. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/12464.

Comparison of Feature Selection Methods for Predicting RT-Induced Toxicity.

This work addresses a scoping review of Feature Selection (FS) methods applied to a Lung Cancer dataset to elucidate parameters' relevance when predicting radiotherapy (RT) induced toxicity. Subsetting-based and Ranking-based FS methods were implemented along with 4 advanced classifiers to predict the onset of RT-induced acute esophagitis, cough, pneumonitis and dyspnea. Their prediction performance was measured in terms of the AUC for each model to find the best FS.

Evaluation of a Clinical Decision Support System for the Prescription of Genetic Tests in the Gynecological Cancer Risk.

Clinical Decision Support System (CDSS) has been implemented to support physicians about the medical prescription of genetic testing. CDSS is based on open source software. A CDSS for prescribing these genetic tests in BRCA1 and BRCA2 and preventing gynecological cancer risks has been designed and performed in the 'Virgen del Rocío' University Hospital. Clinical evidence demonstrates that BRCA1 and BRCA2 mutations can develop gynecological cancer, but genetic testing has a high cost to the healthcare system. The developed technological architecture integrates open source tools like Mirth Connect and OpenClinica. The system allows general practitioners and gynecologists to classify patients as low risk (they do not require a specific treatment) or high risk (they should be attended by the Genetic Council), According to their genetic risk, recommending the prescription of genetic tests. The aim main of this paper is the evaluation of the developed CDSS, getting positive outcomes.

Using the Social-Local-Mobile App for Smoking Cessation in the SmokeFreeBrain Project: Protocol for a Randomized Controlled Trial.

BACKGROUND: Smoking is considered the main cause of preventable illness and early deaths worldwide. The treatment usually prescribed to people who wish to quit smoking is a multidisciplinary intervention, combining both psychological advice and pharmacological therapy, since the application of both strategies significantly increases the chance of success in a quit attempt. OBJECTIVE: We present a study protocol of a 12-month randomized open-label parallel-group trial whose primary objective is to analyze the efficacy and efficiency of usual psychopharmacological therapy plus the Social-Local-Mobile app (intervention group) applied to the smoking cessation process compared with usual psychopharmacological therapy alone (control group). METHODS: The target population consists of adult smokers (both male and female) attending the Smoking Cessation Unit at Virgen del Rocío University Hospital, Seville, Spain. Social-Local-Mobile is an innovative intervention based on mobile technologies and their capacity to trigger behavioral changes. The app is a complement to pharmacological therapies to quit smoking by providing personalized motivational messages, physical activity monitoring, lifestyle advice, and distractions (minigames) to help overcome cravings. Usual pharmacological therapy consists of bupropion (Zyntabac 150 mg) or varenicline (Champix 0.5 mg or 1 mg). The main outcomes will be (1) the smoking abstinence rate at 1 year measured by means of exhaled carbon monoxide and urinary cotinine tests, and (2) the result of the cost-effectiveness analysis, which will be expressed in terms of an incremental cost-effectiveness ratio. Secondary outcome measures will be (1) analysis of the safety of pharmacological therapy, (2) analysis of the health-related quality of life of patients, and (3) monitoring of healthy lifestyle and physical exercise habits. RESULTS: Of 548 patients identified using the hospital's electronic records system, we excluded 308 patients: 188 declined to participate and 120 did not meet the inclusion criteria. A total of 240 patients were enrolled: the control group (n=120) will receive usual psychopharmacological therapy, while the intervention group (n=120) will receive usual psychopharmacological therapy plus the So-Lo-Mo app. The project was approved for funding in June 2015. Enrollment started in October 2016 and was completed in October 2017. Data gathering was completed in November 2018, and data analysis is under way. The first results are expected to be submitted for publication in early 2019. CONCLUSIONS: Social networks and mobile technologies influence our daily lives and, therefore, may influence our smoking habits as well. As part of the SmokeFreeBrain H2020 European Commission project, this study aims at elucidating the potential role of these technologies when used as an extra aid to quit smoking. TRIAL REGISTRATION: ClinicalTrials.gov NCT03553173; https://clinicaltrials.gov/ct2/show/record/NCT03553173 (Archived by WebCite at http://www.webcitation.org/74DuHypOW). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/12464.

Learning Healthcare System for the Prescription of Genetic Testing in the Gynecological Cancer Risk.

Clinical evidence demonstrates that BRCA 1 and BRCA2 mutations can develop a gynecological cancer but genetic testing has a high cost to the healthcare system. Besides, several studies in the literature indicate that performing these genetic tests to the population is not cost-efficient. Currently, our physicians do not have a system to provide them the support for prescribing genetic tests. A Decision Support System for prescribing these genetic tests in BRCA1 and BRCA2 and preventing gynecological cancer risks has been designed, developed and deployed in the Virgen del Rocío University Hospital (VRUH). The technological architecture integrates a set of open source tools like Mirth Connect, OpenClinica, OpenCDS, and tranSMART in addition to several interoperability standards. The system allows general practitioners and gynecologists to classify patients as low risk (they do not require a specific treatment) or high risk (they should be attended by the Genetic Council). On the other hand, by means of this system we are also able to standardize criteria among professionals to prescribe these genetic tests. Finally, this system will also contribute to improve the assistance for this kind of patients.

Evaluation of clinical information modeling tools.

OBJECTIVE: Clinical information models are formal specifications for representing the structure and semantics of the clinical content within electronic health record systems. This research aims to define, test, and validate evaluation metrics for software tools designed to support the processes associated with the definition, management, and implementation of these models. METHODOLOGY: The proposed framework builds on previous research that focused on obtaining agreement on the essential requirements in this area. A set of 50 conformance criteria were defined based on the 20 functional requirements agreed by that consensus and applied to evaluate the currently available tools. RESULTS: Of the 11 initiative developing tools for clinical information modeling identified, 9 were evaluated according to their performance on the evaluation metrics. Results show that functionalities related to management of data types, specifications, metadata, and terminology or ontology bindings have a good level of adoption. Improvements can be made in other areas focused on information modeling and associated processes. Other criteria related to displaying semantic relationships between concepts and communication with terminology servers had low levels of adoption. CONCLUSIONS: The proposed evaluation metrics were successfully tested and validated against a representative sample of existing tools. The results identify the need to improve tool support for information modeling and software development processes, especially in those areas related to governance, clinician involvement, and optimizing the technical validation of testing processes. This research confirmed the potential of these evaluation metrics to support decision makers in identifying the most appropriate tool for their organization. OBJECTIVO: Los Modelos de Información Clínica son especificaciones para representar la estructura y características semánticas del contenido clínico en los sistemas de Historia Clínica Electrónica. Esta investigación define, prueba y valida un marco para la evaluación de herramientas informáticas diseñadas para dar soporte en la en los procesos de definición, gestión e implementación de estos modelos. METODOLOGIA: El marco de evaluación propuesto se basa en una investigación previa para obtener consenso en la definición de requisitos esenciales en esta área. A partir de los 20 requisitos funcionales acordados, un conjunto de 50 criterios de conformidad fueron definidos y aplicados en la evaluación de las herramientas existentes. RESULTADOS: Un total de 9 de las 11 iniciativas identificadas desarrollando herramientas para el modelado de información clínica fueron evaluadas. Los resultados muestran que las funcionalidades relacionadas con la gestión de tipos de datos, especificaciones, metadatos y mapeo con terminologías u ontologías tienen un buen nivel de adopción. Se identifican posibles mejoras en áreas relacionadas con los procesos de modelado de información. Otros criterios relacionados con presentar las relaciones semánticas entre conceptos y la comunicación con servidores de terminología tienen un bajo nivel de adopción. CONCLUSIONES: El marco de evaluación propuesto fue probado y validado satisfactoriamente contra un conjunto representativo de las herramientas existentes. Los resultados identifican la necesidad de mejorar el soporte de herramientas a los procesos de modelado de información y desarrollo de software, especialmente en las áreas relacionadas con gobernanza, participación de profesionales clínicos y la optimización de la validación técnica en los procesos de pruebas técnicas. Esta investigación ha confirmado el potencial de este marco de evaluación para dar soporte a los usuarios en la toma de decisiones sobre que herramienta es más apropiadas para su organización.

Contextual cloud-based service oriented architecture for clinical workflow.

Given that acceptance of systems within the healthcare domain multiple papers highlighted the importance of integrating tools with the clinical workflow. This paper analyse how clinical context management could be deployed in order to promote the adoption of cloud advanced services and within the clinical workflow. This deployment will be able to be integrated with the eHealth European Interoperability Framework promoted specifications. Throughout this paper, it is proposed a cloud-based service-oriented architecture. This architecture will implement a context management system aligned with the HL7 standard known as CCOW.