bluuebunny/crossref_metadata_2025 huggingface dataset:
Hypergraph Based Data Model for Complex Health Data Exploration and Its Implementation in PREDIMED Clinical Data Warehouse
Authors: Cancé Christophe ,   PREDIMED Group BedouchPierrickaef BlatierJean-Françoisac BossonJean-Lucabcf CaporossiAlbanabfc ChanoineSébastienaeg CharrièreKatiaab CohardBrigittea FauconnierJérômeacf GiaiJorisabcf MadiotPierre-Ephrema, Lenne Christian , Artemova Svetlana , Mossuz Pascal , Moreau-Gaudry Alexandre
Published: June 2022

Within the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field. This flexibility provides adaptability to the evolution of interoperability standards.
Evaluation of Telehealth Service for COVID-19 Outpatients: A Dashboard to Measure Healthcare Quality and Safety
Authors: Pedretti Ana Soledad , Fosser Santiago Márquez , Mercau Cossio Roberto José , Esteban Jorge Ariel , Rodriguez Paz , Martinez Bernardo Julio , Frid Santiago Andres , Luna Daniel , Iannicelli Adriana , Plazzotta Fernando , Grande Ratti Maria Florencia
Published: June 2022

Due to the COVID-19 pandemic, changes and improvements regarding the organization have been made to adapt quickly at the Emergency Department (ED) of the Hospital Italiano de Buenos Aires, Argentina. This article describes the design, implementation, and use of an electronic dashboard which provided monitoring of patients discharged home, during follow-up with telehealth. It was useful to access essential information to organize and coordinate professional work and patients’ surveillance, providing highly relevant data in real-time as proxy variables for quality and safety during home isolation. The implemented tool innovated in the integration of technologies within a real context. The information management was crucial to optimize services and decision-making, as well to guarantee safety for healthcare workers and patients.
Just Talk to Me – A Qualitative Study of Patient Satisfaction in Emergency Departments
Authors: Haug Maximilian , Dahm Maria , Gewald Heiko , Georgiou Andrew
Published: June 2022

Communication between patients and hospital staff is a vital part of patient satisfaction and can contribute to better healthcare outcomes. Especially in emergency departments, where the workload is high, it is difficult to always address the communication needs of patients. In a qualitative study, we interviewed 32 patients in emergency departments in Australia. We found that, in the context of the emergency department, the characteristics of the source assumes an essential role in the appraisal of information. Especially if patients show low health literacy hospital staff needs to engage with them. It is important that patients feel informed as this increases patient satisfaction, even though they may not fully understand the delivered information.
Implementation and Analysis of an Institution-Wide EHR-Integrated Handoff Note
Authors: Arsoniadis Elliot G. , Jahansouz Cyrus , Olsen Rohini , Skube Steven J. , Marquard Jenna , Kim Michael , Bjerke Treva , Kaun Therese , Melton Genevieve B.
Published: June 2022

EHR-Integrated Handoff Notes are becoming increasingly prevalent, especially among inpatient clinical service teams composed of physicians in training (resident physicians). We describe the implementation of such a tool at our institution, and then describe the usage of the tool, as well as changes made to its content, structure, and format, years after original implementation. We also describe frequency and temporal distribution of updates made to free text elements of the Handoff Note. At the initial implementation there were three versions available; medical/surgical, pediatric, and ICU. Years after implementation, 57% of clinical service teams continue to utilize the note, including all medical, pediatric, behavioral health, obstetrics/gynecology, and neurology services, as well as most surgical services, that serve as “primary” teams. Several interesting changes were noted to the content and structure of the Handoff Note, namely that more complicated versions were abandoned in favor of simpler versions. The Patient Summary and To Do free text boxes are updated an average of 1.0 and 1.6 times per day. Around 60% of updates to both free text boxes occur between 12 pm – 5:59 pm, likely in preparation for the daytime-nightime team handoff.
Modification and Validation of a Maturity Assessment Tool for Public Health Information System Implementations in Sri Lanka
Authors: Wijayarathne Jayathri , Hewapathirana Roshan , Dissanayake Vajira H.W.
Published: June 2022

Introduction: Maturity models assess the snapshot view of an organization and simultaneously guides the organization to advance on a road map towards ultimate levels of maturity. The health industry has recently embraced maturity models as a tool to improve the management of health information systems. Most electronic health information systems in Sri Lanka need assessment and monitoring and can benefit vastly by adopting maturity models. This study was conducted to modify and adopt a maturity model for public health institutions in Sri Lanka. Methods: A review of the literature was done to identify a suitable model to measure the maturity of the public health information system implementations. A Modified Delphi study was then carried out with six experts to adapt the selected maturity tool, Public Health Information Technology (PHIT) maturity index, to the Sri Lankan context. Necessary modifications to the PHIT tool were done according to the comments gathered in the Modified Delphi rounds, and the validity of the tool was established. Finally, Key Informant Interviews were carried out with nine interviewees to qualitatively validate the instrument. Results: The Public Health Information Technology maturity index developed by the University of Maryland, USA, was modified to suit the Sri Lankan context. Comments from the experts were accommodated during the initial rounds of the Modified Delphi study. It further derived the following values, indicating excellent content validity: I-CVI > 0.8 for 57 total items, S-CVI/Avg = 0.988, S-CVI/UA = 0.929 and Free-marginal kappa = 0.95. Discussion and Conclusions: Modified and validated PHIT tool can be used to measure the maturity of public health institutions in similar contexts.
Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up
Authors: Diamond Courtney J. , Laurentiev John , Yang Jie , Wint Amy , Harris Kimberly A. , Dang Tin H. , Mecker Amrita , Carpenter Emily B. , Tosteson Anna N. , Wright Adam , Haas Jennifer S. , Atlas Steven J. , Zhou Li
Published: June 2022

Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. While EHRs have coded results for certain tests, cancer screening results are often stored in free-text reports, which limit capabilities for automated decision support. As part of the multilevel Follow-up of Cancer Screening (mFOCUS) trial, we developed and implemented a natural language processing (NLP) tool to assist with real-time detection of abnormal cancer screening test results (including mammograms, low-dose chest CT scans, and Pap smears) and identification of gynecological follow-up for higher risk abnormalities (i.e. colposcopy) from free-text reports. We demonstrate the integration and implementation of NLP, within the mFOCUS system, to improve the follow-up of abnormal cancer screening results in a large integrated healthcare system. The NLP pipelines have detected scenarios when guideline-recommended care was not delivered, in part because the provider mis-identified the text-based result reports.
Using Informatics Framework in Immunosuppressed or Transplanted Patients for a Preferential Care Route at the Emergency Department
Authors: Ungaro Catalina Maria , Binder Fernando , Luna Daniel Roberto , Pollan Javier Alberto , Martinez Bernardo Julio , Jáuregui Oscar Ignacio , Grande Ratti María Florencia
Published: June 2022

For immunosuppressed or transplanted patients, appropriate triage is a timely topic, especially in the Emergency Department (ED) of a high-volume referral center. We implemented a new Program called Rapid Clinical Care by Internal Medicine Specialists, as a preferential care route for these patients, which combines the proposed informatics framework in the field of total quality management in the healthcare units, as an example of digital technologies that can improve processes in the clinical routine. Our study aimed to describe waiting-time and attention-time in ED and to explore the effect on patients’ clinical outcomes after discharge. Findings were: shortened waiting time (median of 8 minutes versus 21, p<0.001), improved ED on-call time (median of 2 hours compared to 4, p<0.001), and greater follow-up after discharge, measured as 1-week scheduled-visits rate (69% with 95%CI 63-75; compared to 43% with 95%CI 35-51; p<0.001).
Introducing Augmented Reality Technique to Enhance the Preparation Circuit of Injectable Chemotherapy Drugs
Authors: Othman Sarah Ben , Zgaya Hayfa , Vasseur Michèle , Décaudin Bertrand , Odou Pascal , Hammadi Slim
Published: June 2022

Chemotherapy preparations are often complex and subject to a strict regulatory context. The existing control methods are often limited to Double Visual Control (DVC). In this paper, the preparation circuit of chemotherapy drugs is evaluated through data collection and statistical analysis in order to highlight the difficulties encountered. The results regarding preparation and control times and the number of task interruptions highlight the unreliability of the DVC and its impact on processing time. As a solution, we propose a decision support system “Smart Prep” based on Augmented Reality (AR), co-developed, and commercialized by the Faculty of Pharmacy of Lille, Ecole Centrale de Lille and the company Computer Engineering. This system allows the preparation of chemotherapy drugs according to a step-by-step mode, a traceability of the preparation steps and a reduction of tasks’ interruptions.
Telehealth Use in the COVID-19 Pandemic: A Retrospective Study of Prenatal Care
Authors: Gao Cheng , Osmundson Sarah , Malin Bradley A. , Chen You
Published: June 2022

Telehealth is an alternative care delivery model to in-person care. It uses electronic information and telecommunication technologies to provide remote clinical care to patients, especially those living in rural areas that lack sufficient access to health care services. Like other areas of care affected by the COVID-19 pandemic, the prevalence of telehealth has increased in prenatal care. This study reports on telehealth use in prenatal care at a large academic medical center in Middle Tennessee, USA. We examine the electronic health records of over 2500 women to characterize 1) the volume of prenatal visits participating in telehealth, 2) disparities in obstetric patients using telehealth, and 3) the impact of telehealth use on obstetric outcomes, including duration of intrapartum hospital stays, preterm birth, Cesarean rate, and newborn birthweight. Our results show that telehealth mainly was used in the second and third trimesters, especially for consulting services. In addition, we found that certain demographics correlated with lower telehealth utilization, including patients who were under 26 years old, were Black and/or Hispanic, were on a state-sponsored health insurance program, and those who lived in urban areas. Furthermore, no significant differences were found on preterm birth and Cesarean between the patients who used telehealth in their prenatal care and those who did not.
A Mobile Health Application to Enhance Self-Management Skills of Patients with Shoulder Impingement Syndrome During Rehabilitation
Authors: Hacke Sara , Krämer Paul , Steiner Bianca
Published: June 2022

Especially in chronic diseases, such as shoulder impingement syndrome (SIS), good self-management is important for patients to take personal responsibility for their treatment and make informed decisions in rehabilitation processes. Mobile apps integrating game design elements have great potential to increase patients self-management skills. A total of 21 functionalities for a self-management app were derived from semi-structured interviews with six patients and three therapists. Thereby, welcome messages, a personalized home-screen, and training plans are rated as particular useful. Ten of these functionalities could be implemented in a first prototype of SISco: your shoulder impingement syndrome companion. SISco provides possibilities for creating and executing training programs, learning about SIS, checking daily challenges, and making diary entries. Thereby, SISco motivates patients via progress bars, collecting XP, unlocking content and Dr. SISco the virtual therapist. Future work includes the implementation of further functionalities and the evaluation of SISco for usability and user acceptance.
Obstructive Sleep Apnea Home-Monitoring Using a Commercial Wearable Device
Authors: Mokhtaran Mehrshad , Sacchi Lucia , Tibollo Valentina , Risi Irene , Ramella Vittoria , Quaglini Silvana , Fanfulla Francesco
Published: June 2022

Obstructive sleep apnea (OSA) is a common sleep disorder and polysomnography (PSG) is the gold standard for its diagnosis and treatment monitoring. There are nowadays several activity trackers measuring sleep quality through the detection of sleep stages. To allow an easier monitoring of the treatment efficacy at home, this work explores the possibility of using one of those commercial smart-bands. To this aim, we studied the signals provided by PSG and a Fitbit smart-band on 26 consecutive patients, admitted to the hospital after the diagnosis of OSA, and submitted to ventilation or positional treatment. They underwent monitoring for three nights (basal, titration, and control). We developed both a visualization software allowing doctors to visually compare the two hypnograms, and a set of statistics for assessing the concordance of the two methods. Results indicate that Fitbit can detect normal sleep patterns, while it is less able to detect the abnormal ones.
To Improve Supportive Care for Patients Taking Oral Anticancer Agents
Authors: He Lu , Song Ting , Jiang Yun , Yu Ping , Song Lixin , Gong Yang
Published: June 2022

Oral anticancer agents (OAA) are increasingly prescribed to treat cancer because they are flexible and convenient to use. However, managing complex OAA regimens and life-threatening toxicities at home can be challenging for patients and their caregivers. It is urgent to better understand the supportive care needs for OAA and develop novel approaches to facilitating self-management and communicating about OAA. Guided by the chronic care model (CCM), we conducted a grounded theory-based study to analyze OAA-related online discussions and potential mHealth interventions. We found that patients and caregivers commonly used the online community to share personal experiences and concerns, exchange emotional and informational support, identify relevant resources, and obtain benefits of peer coaching. The findings deepen the understanding of the needs for OAA self-management and mHealth interventions, contributing to the development of mHealth models to enhance supportive care and improve communication among peer patients and between patients and providers.
Implementing Predictive Models Within an Electronic Health Record System: Lessons from an External Validation of a Suicide Risk Model
Authors: Sequeira Lydia , McNair Douglas , Wiljer David , Strudwick Gillian , Deluca Vincenzo , Kailasam Kanakasaba , Thompson Michael , Chou Brian , Strauss John
Published: June 2022

Over the past 5 years, there has been an increase in the development of EHR-based models for predicting suicidal behaviour. Using the McGinn (2000) framework for creating clinical prediction rules, this study discusses the broad validation of one such predictive model in a context external to its derivation. Along with reporting performance metrics, our paper high-lights five practical challenges that arise when trying to undertake such a project including (i) validation sample sizes, (ii) availability and timeliness of data, (iii) limited or incomplete documentation for predictor variables, (iv) reliance on structured data and (v) differences in the source context of algorithms. We also discuss our study in the context of the current literature.
Medical Workflow Design and Planning Using Node-RED Data Fusion
Authors: Ewen Lisa , Mohammed Sabah , Kim Arnold
Published: June 2022

The space of clinical planning requires a complex arrangement of information, often not capable of being captured in a singular dataset. As a result, data fusion techniques can be used to combine multiple data sources as a method of enriching data to mimic and compliment the nature of clinical planning. These techniques are capable of aiding healthcare providers to produce higher quality clinical plans and better progression monitoring techniques. Clinical planning and monitoring are important facets of healthcare which are essential to improving the prognosis and quality of life of patients with chronic and debilitating conditions such as COPD. To exemplify this concept, we utilize a Node-Red-based clinical planning and monitoring tool that combines data fusion techniques using the JDL Model for data fusion and a domain specific language which features a self-organizing abstract syntax tree.
Improving Keyword-Based Topic Classification in Cancer Patient Forums with Multilingual Transformers
Authors: Buonocore T.M. , Parimbelli E. , Sacchi L. , Bellazzi R. , del Campo L. , Quaglini S.
Published: June 2022

Online forums play an important role in connecting people who have crossed paths with cancer. These communities create networks of mutual support that cover different cancer-related topics, containing an extensive amount of heterogeneous information that can be mined to get useful insights. This work presents a case study where users’ posts from an Italian cancer patient community have been classified combining both count-based and prediction-based representations to identify discussion topics, with the aim of improving message reviewing and filtering. We demonstrate that pairing simple bag-of-words representations based on keywords matching with pre-trained contextual embeddings significantly improves the overall quality of the predictions and allows the model to handle ambiguities and misspellings. By using non-English real-world data, we also investigated the reusability of pretrained multilingual models like BERT in lower data regimes like many local medical institutions.
Does Enrichment of Clinical Texts by Ontology Concepts Increases Classification Accuracy?
Authors: Denecke Kerstin
Published: June 2022

In the medical domain, multiple ontologies and terminology systems are available. However, existing classification and prediction algorithms in the clinical domain often ignore or insufficiently utilize semantic information as it is provided in those ontologies. To address this issue, we introduce a concept for augmenting embeddings, the input to deep neural networks, with semantic information retrieved from ontologies. To do this, words and phrases of sentences are mapped to concepts of a medical ontology aggregating synonyms in the same concept. A semantically enriched vector is generated and used for sentence classification. We study our approach on a sentence classification task using a real world dataset which comprises 640 sentences belonging to 22 categories. A deep neural network model is defined with an embedding layer followed by two LSTM layers and two dense layers. Our experiments show, classification accuracy without content enriched embeddings is for some categories higher than without enrichment. We conclude that semantic information from ontologies has potential to provide a useful enrichment of text. Future research will assess to what extent semantic relationships from the ontology can be used for enrichment.
NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov
Authors: Shah-Mohammadi Fatemeh , Parvanova Irena , Finkelstein Joseph
Published: June 2022

Core outcome sets (COS) are necessary to ensure the systematic collection, metadata analysis and sharing the information across studies. However, development of an area-specific clinical research is costly and time consuming. ClinicalTrials.gov, as a public repository, provides access to a vast collection of clinical trials and their characteristics such as primary outcomes. With the growing number of COVID-19 clinical trials, identifying COSs from outcomes of such trials is crucial. This paper introduces a semi-automatic pipeline that can efficiently identify, aggregate and rank the COS from the primary outcomes of COVID-19 clinical trials. Using Natural language processing (NLP) techniques, our proposed pipeline successfully downloads and processes 5090 trials from all over the world and identifies COVID-19-specific outcomes that appeared in more than 1% of the trials. The top-of-the-list outcomes identified by the pipeline are mortality due to COVID-19, COVID-19 infection rate and COVID-19 symptoms.
Predictions of Academic Performance of Children and Adolescents with ADHD Using the SHAP Approach
Authors: Balbino Marcelo , Jandre Caroline , de Miranda Débora , Nobre Cristiane
Published: June 2022

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neuro-developmental disorder characterized by inattention and/or impulsivity-hyperactivity symptoms. Through Machine Learning methods and the SHAP approach, this work aims to discover which features have the most significant impact on the students’ performance with ADHD in arithmetic, writing and reading. The SHAP allowed us to deepen the model’s understanding and identify the most relevant features for academic performance. The experiments indicated that the Raven_Z IQ test score is the factor with the most significant impact on academic performance in all disciplines. Then, the mother’s schooling, being from a private school, and the student’s social class were the most frequently highlighted features. In all disciplines, the student having ADHD emerged as an important feature with a negative impact but less relevance than the previous features.
Using Data-Driven Machine Learning to Predict Unplanned ICU Transfers with Critical Deterioration from Electronic Health Records
Authors: Shi Lingyun , Muthu Naveen , Shaeffer Gerald P. , Sun Yujie , Ruiz Herrera Victor M. , Tsui Fuchiang R.
Published: June 2022

Objective: We aimed to develop a data-driven machine learning model for predicting critical deterioration events from routinely collected EHR data in hospitalized children. Materials: This retrospective cohort study included all pediatric inpatients hospitalized on a medical or surgical ward between 2014–2018 at a quaternary children’s hospital. Methods: We developed a large data-driven approach and evaluated three machine learning models to predict pediatric critical deterioration events. We evaluated the models using a nested, stratified 10-fold cross-validation. The evaluation metrics included C-statistic, sensitivity, and positive predictive value. We also compared the machine learning models with patients identified as high-risk Watchers by bedside clinicians. Results: The study included 57,233 inpatient admissions from 34,976 unique patients. 3,943 variables were identified from the EHR data. The XGBoost model performed best (C-statistic=0.951, CI: 0.946 ∼ 0.956). Conclusions: Our data-driven machine learning models accurately predicted patient deterioration. Future sociotechnical analysis will inform deployment within the clinical setting.
Phylogenetic Analyses of SARS-CoV-2 Strains Reveal Its Link to the Spread of COVID-19 Across the Globe
Authors: Sawmya Shashata , Saha Arpita , Tasnim Sadia , Anjum Naser , Toufikuzzaman Md. , Rafid Ali Haisam Muhamad , Rahman Mohammad Saifur , Rahman M. Sohel , Alam Tanvir
Published: June 2022

This study leveraged the phylogenetic analysis of more than 10K strains of novel coronavirus (SARS-CoV-2) from 67 countries. Due to the requirement of high-end computational power for phylogenetic analysis, we leverage a fast yet highly accurate alignment-free method to develop the phylogenetic tree out of all the strains of novel coronavirus. K-Means clustering and PCA-based dimension reduction technique were used to identify a representative strain from each location. The resulting phylogenetic tree was able to highlight evolutionary relationships of SARS-CoV-2 genome and, subsequently, linked to the interpretation of facts and figures across the globe for the spread of COVID-19. Our analysis revealed that the geographical boundaries could not be explained by the phylogenetic analysis of novel coronavirus as it placed different countries from Asia, Europe and the USA in very close proximity in the tree. Instead, the commute of people from one country to another is the key to the spread of COVID-19. We believe our study will support the policymakers to contain the spread of COVID-19 globally.
AuguR: A Scalable Open-Source Interactive Web Application for Routinely Collected Data
Authors: Zucker Kieran , Wagstaff Millie , Tomson Cathy , Beecham Roger , Hall Geoff
Published: June 2022

Most data collected by hospitals as a consequence of the delivery of routine care is not utilised for analytics or organisational intelligence. This project aims to develop tools to enhance the utilisation of routinely collected cancer data within hospitals across England. This was achieved by developing a web application using open source tools to provide health care professionals and hospital managers with easy to use, interactive analytics for cancer data. The application uses data items hospitals in England are mandated to collect as part of the Cancer Outcomes and Services Dataset (COSD), to provide clinical insight into survival outcomes, population distributions, service demands, waiting times, geographical case distributions and treatment information in real-time or near real-time. Development was guided by end user needs through the use of panels of clinical and non-clinical end users.
Patients’ and Clinicians’ Perspectives on the Acceptability of Completing Digital Quality of Life Questionnaires During Routine Haemodialysis Clinics: A Mixed-Methods Study
Authors: Meiklem Ramsay , Stevenson Karen , Richarz Sabine , Kingsmore David , Bouamrane Matt-Mouley , Dunlop Mark , Thomson Peter
Published: June 2022

Advanced chronic kidney disease is a serious and common medical condition with significant treatment choices incurring varying high treatment burdens. Better understanding on how to best collect quality of life (QoL) in this unique situation may help guide critical decisions. This study investigates the acceptability of digital QoL questionnaires completed during routine haemodialysis sessions. Qualitative data was collected from patient (n=23) and clinical researcher (n=2) interviews alongside analysis of data from questionnaires completed by patients (n=101) during a 6-week validation study. Interviews refined the content and format of digital QoL questionnaires and provided novel insights regarding how patients assessed and completed QoL questions. This study suggests collecting QoL data using digital tablet technology during routine haemodialysis clinics is both feasible and acceptable to patients and has provided novel insights that are not routinely available with traditional methods.
Extraction of Medication-Effect Relations in Twitter Data with Neural Embedding and Recurrent Neural Network
Authors: Jiang Keyuan , Zhang Dingkai , Bernard Gordon R.
Published: June 2022

Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.
Characteristics of Electronic Informed Consent Platforms for Consenting Patients to Research Studies: A Scoping Review
Authors: Guarino Jennifer , Parvanova Irena , Finkelstein Joseph
Published: June 2022

Informed consent process assures that research study participants are properly informed about the study prior to their consent. Due to the increasing significance of electronic informed consent (eIC) platforms, particularly during the COVID-19 pandemic, we conducted a scoping review of eIC systems to address the following characteristics: 1) technological features of current eIC platforms, 2) eIC platforms usability and efficacy, and 3) areas for future eIC research. We performed a literature search using publically available PubMed repository, where we included studies discussing an eIC platform or multimedia educational module given to patients prior to signing a consent form. In addition, we tracked first author, year of publication, sample size, study location, eIC procedure, methodology, and eIC’s comparison to paper consent. Our results showed that with a few noted exceptions, electronic consent improves patient usability, satisfaction, knowledge, and trust scores when compared to traditional paper consent.
Hierarchical Representation of Complex Intervention Sequences for Automated Subgroup Analysis in Critical Care Settings
Authors: Trivedi Amee , Ogallo William , Tadesse Girmaw Abebe
Published: June 2022

Our understanding of the impact of interventions in critical care is limited by the lack of techniques that represent and analyze complex intervention spaces applied across heterogeneous patient populations. Existing work has mainly focused on selecting a few interventions and representing them as binary variables, resulting in oversimplification of intervention representation. The goal of this study is to find effective representations of sequential interventions to support intervention effect analysis. To this end, we have developed Hi-RISE (Hierarchical Representation of Intervention Sequences), an approach that transforms and clusters sequential interventions into a latent space, with the resulting clusters used for heterogeneous treatment effect analysis. We apply this approach to the MIMIC III dataset and identified intervention clusters and corresponding subpopulations with peculiar odds of 28-day mortality. Our approach may lead to a better understanding of the subgroup-level effects of sequential interventions and improve targeted intervention planning in critical care settings.
Automatic Integration of Clinical and Genetic Data Using cBioPortal
Authors: Brunner Mauricio , Mullen Lucas , Jauk Federico , Oliver Javier , Cayol Federico , Minata José , Herrera Victor , Pavicic Walter , Luna Daniel , Risk Marcelo , Garcia Rivello Hernan , Benitez Sonia
Published: June 2022

Precision medicine seeks to improve the prevention, diagnosis and treatment of patients based on genetic characteristics unique to each person. In oncology, therapeutic decisions have been established based on the genomic characteristics of each patient’s tumor. Data integration is key for the successful implementation of precision medicine since it is necessary for both studying a large volume of data from different sources and working with an interdisciplinary and translational vision. In this work, a bioinformatic process was successfully implemented that allows the integration of patients’ genomic data, from two molecular biology laboratories, with their clinical data provided by their electronic medical records. For this, the REDCap data capture software, the cBioPortal visualization and analysis software, and a computer tool developed to automate the processing and annotation of the information in REDCap were used to be included in cBioPortal, for the “Map of Tumor Genomic Actionability of Argentina” project.
A Design Thinking Approach to Developing a Clinical Decision Support System for Breathlessness in Primary Care
Authors: Sunjaya Anthony Paulo , Martin Allison , Jenkins Christine
Published: June 2022

Clinical decision support systems (CDSS) have the potential to support guideline implementation and bridge the research translation chasm. However, clinician barriers to uptake remain strongly reported in previous studies. This study aims to utilise a design thinking approach to develop a CDSS for breathlessness in primary care. A low fidelity mockup was developed based on an exploratory focus group to elucidate clinician needs and assess responses to key features. The low fidelity prototype was then developed and tested through two rounds of Think-Aloud testing. Post each Think-Aloud, changes were made and split-run (A/B) testing conducted in the second round in response to user interface concerns raised in the first round. Overall, GPs find the CDSS to be a useful addition to their breathlessness assessment and are open to its use. This study showed that utilising a design thinking and practice-oriented approach with rapid usability testing, it was possible to gain crucial insight in a more rapid and cost effective way.
Pre- and Post-Redesign Usability Assessment of a Telemedicine Interface Based on Subjective Metrics
Authors: Campbell Jessica Lynn , Monkman Helen
Published: June 2022

Usability problems in the interaction between patients and telemedicine platforms has been recognized as a deterrent in the public’s acceptance and use of this alternative healthcare delivery method. Therefore, evaluating the usability of telemedicine provider websites, with a focus on potential patients’ first interaction with telemedicine, is a critical research inquiry. To this end, a novel survey was developed to conduct an unmoderated remote usability test (URUT) of the Teladoc website. Teladoc is one of the largest providers of Direct-to-Consumer (DTC) telemedicine. The Teladoc Website Usability Survey (TWUS) instrument collected both objective task completion success metrics and subjective user feedback. A codebook was developed to categorize design features and user interface aspects that affected usability. The TWUS and codebook demonstrated value in identifying usability problems with the Teladoc interface and can be applied in other telemedicine or Health Information Technology (HIT) usability studies. Identifying and addressing usability issues is an important approach to increase the widespread acceptance and adoption of these healthcare delivery technologies.
Medical Scribes Have a Variable Impact on Documentation Workflows
Authors: Rule Adam , Chiang Michael F. , Hribar Michelle R.
Published: June 2022

Physicians can reduce their documentation time by working with a scribe. However, what scribes document and how their actions affect existing documentation workflows is unclear. This study leverages electronic health record (EHR) audit logs to observe how scribes affected the documentation workflows of seven physicians and their staff across 13,000 outpatient ophthalmology visits. In addition to editing progress notes, scribes routinely edited exam findings and diagnoses. Scribes with clinical training also edited items such as vital signs that a scribe without clinical training did not. Every physician edited patient records later in the day when working with a scribe and those who deferred their editing the most had some of the largest reductions in EHR time. These results suggest that what scribes document, how physicians work with scribes, and scribe impact on documentation time are all highly variable, highlighting the need for evidence-based best practices.
The BioVisualSpeech Serious Game with Voice Exercises for People with Parkinson’s Disease with Hypokinetic Dysarthria
Authors: Viana Tiago , Cavaco Sofia
Published: June 2022

People with Parkinson’s disease (PD) can have dysarthria, a voice disorder that affects speech intelligibility. To fight this disorder people may resort to speech and language therapy. Unfortunately, weekly speech therapy sessions may not be enough, because to achieve and maintain good voice quality, intensive training is required. Additionally, the COVID-19 pandemic brought attention to the need for alternative speech therapy treatments that complement face-to-face appointments. Here, we propose a serious therapy game to improve voice loudness that can be used for intensive therapy or when face-to-face appointments are not possible. The game integrates three voice exercises used in speech therapy sessions for people with PD and aims to provide motivation for patients to perform the exercises on a daily basis. This application evaluates the vocal intensity, vocal frequency and maximum phonation time, offering real-time visual feedback. It also allows pathologists to customize the exercises difficulty to the needs of each patient.
Educating the Healthcare Workforce to Support Digital Transformation
Authors: Davies Angela C. , Davies Alan , Abdulhussein Hatim , Hooley Frances , Eleftheriou Iliada , Hassan Lamiece , Bromiley Paul A. , Couch Philip , Wasiuk Catherine , Brass Andy
Published: June 2022

Digital transformation of the healthcare workforce is a priority if we are to leverage the potential of digital technologies, artificial intelligence in clinical decision support and the potential of data captured within electronic health records. Educational programmes need to be diverse and support the digital novices through to the champions whom will be responsible for procuring and implementing digital solutions. In order to professionalise the workforce in this area, digital competencies need to be built into training from early on and be underpinned by frameworks that help to guide regulators and professional bodies and support educational providers to deliver them. Here we describe Manchester’s involvement in the development of digital competency frameworks and our digital transformation education programmes that we have created, including a Massive Online Open Course and a professional development course for England’s Topol Digital Fellows.
Ensuring Care Continuity in Extreme Crises: A Participatory Action Research Approach
Authors: Daou Melissa , Helou Samar , El Helou Jeanine , El Hachem Charline , El Helou Elie
Published: June 2022

The Lebanese healthcare system has been facing major challenges due to an unprecedented financial crisis, socio-political instability, and the COVID-19 pandemic. This study aims to examine the impact of overlapping major crises on care continuity and to propose IT-based solutions to address current challenges and build future resilience. To this end, we adopted a participatory action research approach and conducted a two-phase qualitative study – six semi-structured interviews followed by three future workshops with local stakeholders including physicians and interns practicing in Lebanon. Through the interviews, we identified the primary consequences of the crises and the ways they impacted the continuity of care. We also identified adaptation mechanisms used by physicians and patients to ensure continuity of care. Through the future workshops, we identified various IT-based solutions that could be implemented to tackle existing challenges and support local adaptation attempts.
Impact of the Automation of Inpatient Bed Management to Reduce the Emergency Service Waiting Time
Authors: Ajmi Faiza , Ajmi Faten , Ben Othman Sarah , Zgaya Hayfa , Smith Gregoire , Renard Jean-Marie , Hammadi Slim
Published: June 2022

The patient waiting time to be transferred for hospitalization is the time that the patient waits between the decision to hospitalize and the actual admission to an inpatient hospital bed. One of the difficulties encountered in qualifying waiting time for inpatient bed is the inability of hospital information systems to measure it. Hospitals in France have a specialized bed allocation team. This team must manage the bed allocation problem between different hospital departments using phone communication to assign patients to the adapted service. This kind of communication represents a lengthy additional workload in which effectiveness is uncertain. This paper presents a new approach to automate bed management in downstream service. For that, we have implemented algorithms based on artificial intelligent integrated in an inpatient web platform using IoT-Beacons, which is implemented to improve and facilitate the exchange of availability information of downstream beds within the Lille university hospital center (LUHC).
Ontology for Overcrowding Management in Emergency Department
Authors: Fakhfakh Maala Khouloud , Ben-Othman Sarah , Jourdan Laetitia , Smith Grégoire , Renard Jean-Marie , Hammadi Slim , Zgaya Biau Hayfa
Published: June 2022

Emergency department (ED) overcrowding is an ongoing problem worldwide. Scoring systems are available for the detection of this problem. This study aims to combine a model that allows the detection and management of overcrowding. Therefore, it is crucial to implement a system that can reason model, rank ED resources and ED performance indicators based on environmental factors. Thus, we propose in this paper a new domain ontology (EDOMO) based on a new overcrowding estimation score (OES) to detect critical situations, specify the level of overcrowding and propose solutions to deal with these situations. Our approach is based on a real database created during more than four years from the Lille University Hospital Center (LUHC) in France. The resulting ontology is capable of modeling complete domain knowledge to enable semantic reasoning based on SWRL rules. The evaluation results show that the EDOMO is complete that can enhance the functioning of the ED.
Simple Heuristics for Near-Optimal Appointment Scheduling in Primary Care
Authors: Meckoni Prashant , Balasubramanian Hari
Published: June 2022

In primary care allocating appointments to sequential requests can result in sub-optimal scheduling. Optimal scheduling requires hiring of consultants to analyze historical patterns. Many practices focus their resources on larger problems instead of optimizing appointment schedules. We simulate simple heuristics to compare their performance with optimal schedules uncovered using offline optimization models. We use uncapacitated appointment calendars for a nationally representative heterogeneous primary care panel to meet all patients’ requests. The stochastic nature of appointment requests gives a distribution for daily appointments and for the uncovered optimal capacity. The First Minimum heuristic gives near-optimal schedules and can be easily implemented in small practices using pen-and-paper, without any investment in computer-systems.
Interoperable Medical Application for CTC Counting
Authors: Cal-González Victoria M. , González-Castro Lorena
Published: June 2022

In the present poster we will explain how the development of an interoperable AI-powered application for Circulating Tumor Cells (CTCs) counting is addressed. We will explain the selection of the most appropriate information for early detection of distant metastasis, local recurrence and the data structure definition to be compliant with international standards and ontologies.
Using SPI-Hub™ to Promote the Key Role of Prepublishing in Healthcare
Authors: Koonce Taneya Y. , Blasingame Mallory N. , Williams Annette M. , Zhao Jerry , Su Jing , Epelbaum Marcia I. , Clark John D. , Naylor Helen M. , Kusnoor Sheila V. , DesAutels Spencer J. , Giuse Nunzia Bettinsoli
Published: June 2022

With the need to quickly advance knowledge dissemination in rapid-paced fields, and more recently in response to the urgency of the COVID-19 pandemic, prepublishing has been brought to the forefront. SPI-Hub™, a publicly available journal selection decision support tool, is being strategically enhanced to address prospective authors’ critical needs in navigating and selecting the most appropriate preprint or traditional publication venue.
A Comparison of Recommendations from a Computer-Based Decision System and Tobacco Treatment Specialists: Insights and Lessons Learned
Authors: Soares Andrey , Khamesipour Alireza , Davis Joan M.
Published: June 2022

This pilot study examines the similarities and differences between treatment recommendations offered by a decision system and trained tobacco treatment specialists. Using a sample of ten de-identified patient cases who used tobacco, we compared recommendations from the manual and preliminary review of cases by four tobacco specialists with the automated analysis of patient cases using both the first version of the rule-based system and the second version with improved and additional rules.
Exploration of the Decision Model and Notation (DMN) Standard for Representation of Knowledge Bases Encoded in the Arden Syntax
Authors: Jenders Robert A.
Published: June 2022

Context: Arden Syntax encodes knowledge as Medical Logic Modules (MLMs). Current work involves improvement of its representation of business processes. Objective: Assess how DMN can represent decision-making in MLMs. Method: A sample of 340 MLMs was tabulated for DMN representation. Result: While DMN can represent all MLMs at the decision requirements level, some are not supported at the decision logic level. Conclusion: DMN facilitates representation of business processes but would not address all decision logic.
Developing a Pediatric EHR Testing & Certification Program in the USA: Initial Phase
Authors: Thompson C. , Margo T. , Yu F.B. , Lehmann C.U.
Published: June 2022

In 2016, the 21st Century Cures Act included ONC’s 10 functionality recommendations for voluntary testing and certification of pediatric EHRs. Since 2019, Drummond has been developing a new pediatric EHR testing and certification program with pediatric and industry experts. The testing criteria are based upon AAP’s 47 requirements that include ONC’s 10 recommendations. It is anticipated the program will be operationalized and ready for EHR vendors in 1Q2022.
Consideration of Usefulness of Leakage Prevention System for Critical Diagnostic Information Transmission
Authors: Tani Yuji , Hayashi Tatsuya , Iwata Tatsuya , Hirokawa Hiroyuki
Published: June 2022

In our country, overlooking and failing to communicate the results of imaging examinations and pathological examinations to patients is a problem. This is because it affects the prognosis of the patients. With the introduction of this system, the situation improved within six months, so this system was useful. However, there are some things that remain unaddressed even after notification by the system, so human intervention as well as the system is considered necessary.
COVID 19 Care Call-in Helpline. A Report from India
Authors: Khan Naved , Sen Meghdeep , Gogia Shashi , Garodia Vinay , Maheshwari J. , Jaiya G.S.
Published: June 2022

A COVID 19 call helpline was established by a noted TV channel along with a group of consultant doctors at the height of the second wave COVID 19 pandemic in India. It helped the general public to understand dos and donts as well as long term implications. We retrospectively analysed the questions asked to get a feel of the misunderstandings and perceptions within the general public. Many quereies related to the vaccination drive.