Will Precision Medicine Meet Digital Health? A Systematic Review of Pharmacogenomics Clinical Decision Support Systems Used in Clinical Practice.

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto "the right drug, for the right patient, at the right dose, and the right time." PGx takes into consideration patients' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.

Real-World Data in Greece: Mapping the Papageorgiou General Hospital Data to the OMOP Common Data Model.

This paper showcases the results of the Extract-Transform-Load process mapping the Electronic Health Record of Papageorgiou General Hospital in Thessaloniki, Greece, to the Observational Medical Outcomes Partnership Common Data Model. We describe the staged process utilized to account for the intricate structure of the database, along with some general findings from the mapping. Finally, we investigate potential directions for future research.

Mapping OMOP-CDM to RDF: Bringing Real-World-Data to the Semantic Web Realm.

Real-world data (RWD) (i.e., data from Electronic Healthcare Records - EHRs, ePrescription systems, patient registries, etc.) gain increasing attention as they could support observational studies on a large scale. OHDSI is one of the most prominent initiatives regarding the harmonization of RWD and the development of relevant tools via the use of a common data model, OMOP-CDM. OMOP-CDM is a crucial step towards syntactic and semantic data interoperability. Still, OMOP-CDM is based on a typical relational database format, and thus, the vision of a fully connected semantically enriched model is not fully realized. This work presents an open-source effort to map the OMOP-CDM model and the data it hosts, to an ontological model using RDF to support the FAIRness of RWD and their interlinking with Linked Open Data (LOD) towards the vision of the Semantic Web.

Exploring User Views on Telemedicine to Support Cancer Patients: Insights from Focus Group Discussions in the Context of the eCAN Joint Action.

This paper provides insights into user perspectives on telemedicine for cancer based on Focus Group Discussions (FGDs) within the eCAN Joint Action. Two FGDs centered on the eCAN mobile app and the eCAN dashboard, aiming to confirm user acceptance and understand cancer patients' and healthcare professionals' views. The findings highlight the importance of personalized deployment of telemedicine technologies to meet the specific needs of end users.

Physicians' Views of a Digital Intervention to Support Respiratory Patients: Take-a-Breath Pre-Pilot Usability Study.

Although eHealth interventions are increasingly recognized as a useful tool to support healthcare, relatively few studies focus on the physician-end's usability. This study aims to evaluate the Healthcare Professional's (HCP) platform of the Take-A-Breath project, a Greek initiative for personalized respiratory disease monitoring, training and self-management. The pre-pilot usability study, involving 10 participants, combines qualitative methods, behavioral observations, and standardized measures of user experience and usability. While relatively high scores indicate overall acceptance, concerns are also discussed, particularly related with the volume of information provided and actions available to the users, hindering the usability of the system due to an overload effect. Findings emphasize also the need for more tailored in-app wordings as well as the integration of similar systems with the already set up electronic health record systems. This study contributes to understanding digital intervention success among HCPs in respiratory healthcare.

Pre-Pilot Usability Study of the Take-A-Breath Project: A Personalized Platform for Respiratory Disease Management.

This study aims to evaluate the patient's platform of the Take-A-Breath project-a Greek project for personalized respiratory disease support. The pre-pilot usability study, involving 11 participants, employed a mixed-methods approach. While calculated scores indicate overall acceptance, concerns are identified, particularly regarding the learning curve needed for the guided inhalation feature, the application's core functionality. Users appreciate the feature's utility and design after repeated use. Findings recommend user manuals and healthcare professional training, providing essential insights for the upcoming RCT.

Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study.

Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine explainable causal relationships between data features, providing useful insights for various applications, e.g. Pharmacovigilance (PV) signal detection upon Real-World Data. The objective of this study is to demonstrate the use of CDL for potential PV signal validation using Electronic Health Records as input data source.

Machine Learning upon RDF Knowledge Graphs for Drug Safety: A Case Study on Reactome Data.

Artificial Intelligence (AI), particularly Machine Learning (ML), has gained attention for its potential in various domains. However, approaches integrating symbolic AI with ML on Knowledge Graphs have not gained significant focus yet. We argue that exploiting RDF/OWL semantics while conducting ML could provide useful insights. We present a use case using signaling pathways from the Reactome database to explore drug safety. Promising outcomes suggest the need for further investigation and collaboration with domain experts.

The PrescIT platform: An interoperable Clinical Decision Support System for ePrescription to Prevent Adverse Drug Reactions and Drug-Drug Interactions.

INTRODUCTION: Preventable medication errors have been proven to cause significant public health burden, and ePrescription is a key part of the process where medication errors and adverse effects could be prevented. Information systems and "intelligent" computational approaches could provide a valuable tool to prevent such errors with profound impact in clinical practice. OBJECTIVES: The PrescIT platform is a Clinical Decision Support System (CDSS) that aims to facilitate the prevention of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) in the phase of ePrescription in Greece. The proposed platform could be relatively easily localized for use in other contexts too. METHODS: The PrescIT platform is based on the use of Knowledge Engineering (ΚΕ) approaches, i.e., the use of Ontologies and Knowledge Graphs (KGs) developed upon openly available data sources. Open standards (i.e., RDF, OWL, SPARQL) are used for the development of the platform enabling the integration with already existing IT systems or for standalone use. The main KG is based on the use of DrugBank, MedDRA, SemMedDB and OpenPVSignal. In addition, the Business Process Management Notation (BPMN) has been used to model long-term therapeutic protocols used during the ePrescription process. Finally, the produced software has been pilot tested in three hospitals by 18 clinical professionals via in-person think-aloud sessions. RESULTS: The PrescIT platform has been successfully integrated in a transparent fashion in a proprietary Hospital Information System (HIS), and it has also been used as a standalone application. Furthermore, it has been successfully integrated with the Greek National ePrescription system. During the pilot phase, one psychiatric therapeutic protocol was used as a testbed to collect end-users' feedback. Summarizing the feedback from the end-users, they have generally acknowledged the usefulness of such a system while also identifying some challenges in terms of usability and the overall user experience. CONCLUSIONS: The PrescIT platform has been successfully deployed and piloted in real-world environments to evaluate its ability to support safer medication prescriptions.

Electronic prescription systems in Greece: a large-scale survey of healthcare professionals' perceptions.

BACKGROUND: The national e-prescription system in Greece is one of the most important achievements in the e-health sector. Healthcare professionals' feedback is essential to ensure the introduced system tends to their needs and reduces their everyday workload. The number of surveys collecting the users' views is limited, while the existing studies include only a small number of participants. METHODS: In this study, healthcare professionals' perceptions on e-prescription are explored. For this, a questionnaire was distributed online, containing closed- and open-ended questions aiming to address strengths and identify drawbacks in e-prescription. Answers were collected from primary health care physicians, specialized medical doctors and pharmacists. RESULTS: In total, 430 answers were collected (129 from primary health care physicians, 164 responses from specialized medical doctors and 137 pharmacists). Analysis of the collected answers reveals that the views of the three groups of healthcare professionals mostly converge. The positive impact e-prescribing systems have on the overall prescribing procedure in preventing errors and providing automation is commented. Among gaps identified and proposed improvements, health care professionals note the need for access to information on adverse drug reactions, side effects, drug-to-drug interactions and allergies. Flexible interaction with Therapeutic Prescription Protocols is desired to ameliorate monitoring and decision-making, while drug dosing features, and simplified procedures for copying, repeating, canceling a prescription, are perceived as useful to incorporate. CONCLUSIONS: Collecting healthcare professionals' feedback is important, as their views can be transcribed to system requirements, to further promote e-prescribing and improve the provided health care services by facilitating decision making through safer and more efficient e-prescription. Introduction of the identified improvements can simplify the everyday workflow of healthcare professionals. To the best of our knowledge, a survey with more than 400 answered questionnaires on the use of e-prescription systems by healthcare professionals has never been conducted in Greece before.

Evaluation of an electronic prescription platform: Clinicians' feedback on three distinct services aiming to facilitate clinical decision and safer e-prescription.

BACKGROUND: Health Care Professionals (HCPs) are the main end-users of digital clinical tools such as electronic prescription systems. For this reason, it is of high importance to include HCPs throughout the design, development and evaluation of a newly introduced system to ensure its usefulness, as well as confirm that it tends to their needs and can be integrated in their everyday clinical practice. METHODS: In the context of the PrescIT project, an electronic prescription platform with three services was developed (i.e., Prescription Check, Prescription Suggestion, Therapeutic Prescription Monitoring). To allow an iterative process of discovery through user feedback, design and implementation, a two-phase evaluation was carried out, with the participation of HCPs from three hospitals in Northern Greece. The two-phase evaluation included presentations of the platform, followed by think-aloud sessions, individual platform testing and the collection of qualitative as well as quantitative feedback, through standard questionnaires (e.g., SUS, PSSUQ). RESULTS: Twenty one HCPs (8 in the first, 18 in the second phase, and five present in both) participated in the two-phase evaluation. HCPs comprised clinicians varying in their specialty and one pharmacist. Clinicians' feedback during the first evaluation phase already deemed usability as "excellent" (with SUS scores ranging from 75 to 95/100, showing a mean value of 86.6 and SD of 9.2) but also provided additional user requirements, which further shaped and improved the services. In the second evaluation phase, clinicians explored the system's usability, and identified the services' strengths and weaknesses. Clinicians perceived the platform as useful, as it provides information on potential adverse drug reactions, drug-to-drug interactions and suggests medications that are compatible with patients' comorbidities and current medication. CONCLUSIONS: The developed PrescIT platform aims to increase overall safety and effectiveness of healthcare services. Therefore, including clinicians in a two-phase evaluation confirmed that the introduced system is useful, tends to the users' needs, does not create fatigue and can be integrated in their everyday clinical practice to support clinical decision and e-prescribing.

Applying Digital Health in Cancer and Palliative Care in Europe: Policy Recommendations from an International Expert Workshop (MyPal Project).

Background: Digital health interventions are becoming increasingly important for adults, children, and young people with cancer and palliative care needs, but there is little research to guide policy and practice. Objectives: To identify recommendations for policy development of digital health interventions in cancer and palliative care. Design: Expert elicitation workshop. Setting: European clinical (cancer and palliative care, adult and pediatric), policy, technical, and research experts attended a one-day workshop in London, England, in October 2022, along with MyPal research consortium members. Methods: As part of the European Commission-funded MyPal project, we elicited experts' views on global, national, and institutional policies within structured facilitated groups, and conducted qualitative analysis on these discussions. Results/Implementation: Thirty-two experts from eight countries attended. Key policy drivers and levers in digital health were highlighted. Global level: global technology regulation, definitions, access to information technology, standardizing citizens' rights and data safety, digital infrastructure and implementation guidance, and incorporation of technology into existing health systems. National level: country-specific policy, compatibility of health apps, access to digital infrastructure including vulnerable groups and settings, development of guidelines, and promoting digital literacy. Institutional level: undertaking a needs assessment of service users and clinicians, identifying best practice guidelines, providing education and training for clinicians on digital health and safe digital data sharing, implementing plans to minimize barriers to accessing digital health care, minimizing bureaucracy, and providing technical support. Conclusions: Developers and regulators of digital health interventions may find the identified recommendations useful in guiding policy making and future research initiatives. MyPal child study Clinical Trial Registration NCT04381221; MyPal adult study Clinical Trial Registration NCT04370457.

Use of Real-World Data to Support Adverse Drug Reactions Prevention During ePrescription.

Adverse Drug Reactions (ADRs) are a crucial public health issue due to the significant health and monetary burden that they can impose. Real-World Data (RWD), e.g., Electronic Health Records, claims data, etc., can support the identification of potentially unknown ADRs and thus, they could provide raw data to mine ADR prevention rules. The PrescIT project aims to create a Clinical Decision Support System (CDSS) for ADR prevention during ePrescription and uses OMOP-CDM as the main data model to mine ADR prevention rules, based on the software stack provided by the OHDSI initiative. This paper presents the deployment of OMOP-CDM infrastructure using the MIMIC-III as a testbed.

Questionnaire to Capture the Over Time User Preference During the Comparison of Pharmacovigilance Software Systems.

The study aimed to assess the usability of the PVClinical platform, which is designed for detecting and managing Adverse Drug Reactions (ADRs). A "slider" type comparative questionnaire was designed to capture the preferences of six end-users over time between PVC clinical platform and the established clinical and pharmaceutical ADR detection software tools. The results of the questionnaire were cross-examined with the results of the usability study. The questionnaire was a quick preference-capturing tool over time and provided impactful insights. Coherence in participants' preferences for PVClinical platform was observed, but further research is needed to establish the effectiveness of the questionnaire as a preference-capturing tool.

Physician-Reported Experience and Usability of the MyPal Platform: A Palliative Care Digital Health Intervention.

There is a lack of research focusing on the physician-end, their experiences, and their perception of usability with an eHealth intervention. The aim of this study was to evaluate physician satisfaction, and perception of usability following the use of the MyPal platform, a digital health intervention to foster palliative care for hematological cancer patients. Participants were healthcare professionals active in the project's multinational randomized clinical trial evaluating the impact of the MyPal platform. A post-study electronic questionnaire was administered comprised of; 2 standardized questionnaires (PSSUQ, UEQ) and a feature satisfaction questionnaire, and an open ended question. All questionnaire scores were relatively high and the platform was more than marginally accepted by all participants.

A Platform Promoting Inter-Physician Interaction to Support the Management of Adverse Drug Reactions for CLL Patients.

Adverse Drug Reactions (ADRs) cause significant impact for patients' Quality of Life (QoL) and vastly increase costs, especially regarding chronic diseases. To this end, we propose a platform that aims at supporting the management of patients with Chronic Lymphocytic Leukemia (CLL), via an eHealth platform facilitating inter-physician interaction and the provision of treatment consultation by a specialized ADR management team comprised of CLL experts.

Transfer Learning for Early Prediction of Adverse Drug Reactions: Docetaxel and Alopecia in Breast Cancer as a Case Study.

Transfer Learning (TL) is an approach which has not yet been widely investigated in healthcare, mostly applied in image data. This study outlines a TL pipeline leveraging Individual Case Safety reports (ICSRs) and Electronic Health Records (EHR), applied for the early detection Adverse Drug Reactions (ADR), evaluated using of alopecia and docetaxel on breast cancer patients as use case.

The PrescIT Knowledge Graph: Supporting ePrescription to Prevent Adverse Drug Reactions.

Adverse Drug Reactions (ADRs) are an important public health issue as they can impose significant health and monetary burdens. This paper presents the engineering and use case of a Knowledge Graph, supporting the prevention of ADRs as part of a Clinical Decision Support System (CDSS) developed in the context of the PrescIT project. The presented PrescIT Knowledge Graph is built upon Semantic Web technologies namely the Resource Description Framework (RDF), and integrates widely relevant data sources and ontologies, i.e., DrugBank, SemMedDB, OpenPVSignal Knowledge Graph and DINTO, resulting in a lightweight and self-contained data source for evidence-based ADRs identification.

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even death. In this article, an ensemble of Machine Learning (ML) algorithms is evaluated in terms of predicting the severity of their condition using plasma proteomics and clinical data as input. An overview of AI-based technical developments to support COVID-19 patient management is presented outlining the landscape of relevant technical developments. Based on this review, the use of an ensemble of ML algorithms that analyze clinical and biological data (i.e., plasma proteomics) of COVID-19 patients is designed and deployed to evaluate the potential use of AI for early COVID-19 patient triage. The proposed pipeline is evaluated using three publicly available datasets for training and testing. Three ML "tasks" are defined, and several algorithms are tested through a hyperparameter tuning method to identify the highest-performance models. As overfitting is one of the typical pitfalls for such approaches (mainly due to the size of the training/validation datasets), a variety of evaluation metrics are used to mitigate this risk. In the evaluation procedure, recall scores ranged from 0.6 to 0.74 and F1-score from 0.62 to 0.75. The best performance is observed via Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) algorithms. Additionally, input data (proteomics and clinical data) were ranked based on corresponding Shapley additive explanation (SHAP) values and evaluated for their prognosticated capacity and immuno-biological credence. This "interpretable" approach revealed that our ML models could discern critical COVID-19 cases predominantly based on patient's age and plasma proteins on B cell dysfunction, hyper-activation of inflammatory pathways like Toll-like receptors, and hypo-activation of developmental and immune pathways like SCF/c-Kit signaling. Finally, the herein computational workflow is corroborated in an independent dataset and MLP superiority along with the implication of the abovementioned predictive biological pathways are corroborated. Regarding limitations of the presented ML pipeline, the datasets used in this study contain less than 1000 observations and a significant number of input features hence constituting a high-dimensional low-sample (HDLS) dataset which could be sensitive to overfitting. An advantage of the proposed pipeline is that it combines biological data (plasma proteomics) with clinical-phenotypic data. Thus, in principle, the presented approach could enable patient triage in a timely fashion if used on already trained models. However, larger datasets and further systematic validation are needed to confirm the potential clinical value of this approach. The code is available on Github: https://github.com/inab-certh/Predicting-COVID-19-severity-through-interpretable-AI-analysis-of-plasma-proteomics.

Hellenic registry of patients with home mechanical ventilation (HR-HMV): profiling sleep Apnea-Hypopnea syndrome patients across Greece.

BACKGROUND: Chronic respiratory conditions are a prominent public health issue and thus, building a patient registry might facilitate both policy decision making and improvement of clinical management processes. Hellenic Registry of patients with Home Mechanical Ventilation (HR-HMV) was initiated in 2017 and a web-based platform is used to support patient data collection. Eighteen hospital departments (including sleep labs) across Greece participate in this initiative, focusing on recording data for both children and adult patients supported by mechanical ventilation at home, including patients with Sleep Apnea-Hypopnea Syndrome (SAHS) under Positive Airway Pressure (PAP) therapy. METHODS: The HR-HMV initiative ultimately aims to provide a database for evidence-based care and policy making in this specific domain. To this end, a web information system was developed and data were manually collected by clinics and hospital departments. Legal and privacy issues (such as General Data Protection Rule compliance and technical information security measures) have been considered while designing the web application. Based on the collected data, an exploratory statistical report of SAHS patients in Greece is presented. RESULTS: Eleven out of the eighteen participating clinics and hospital departments have contributed with data by the time of the current study. More than 5000 adult and children patient records have been collected so far, the vast majority of which (i.e., 4900 patients) diagnosed with SAHS. CONCLUSION: The development and maintenance of patient registries is a valuable tool for policy decision making, observational/epidemiological research and beyond (e.g., health technology assessment procedures). However, as all data collection and processing approaches, registries are also related with potential biases. Along these lines, strengths and limitations must be considered when interpreting the collected data, and continuous validation of the collected clinical data per se should be emphasized. Especially for Greece, where the lack of national registries is eminent, we argue that HR-HMV could be a useful tool for the development and the update of related policies regarding the healthcare services for patients with home mechanical ventilation support and SAHS patients, which could be useful for related initiatives at a European level as well.