bluuebunny/crossref_metadata_2025 huggingface dataset:
TermiCron – Bridging the Gap Between FHIR Terminology Servers and Metadata Repositories
Authors: Wiedekopf Joshua , Ulrich Hannes , Drenkhahn Cora , Kock-Schoppenhauer Ann-Kristin , Ingenerf Josef
Published: June 2022

The large variability of data models, specifications, and interpretations of data elements is particular to the healthcare domain. Achieving semantic interoperability is the first step to enable reuse of healthcare data. To ensure interoperability, metadata repositories (MDR) are increasingly used to manage data elements on a structural level, while terminology servers (TS) manage the ontologies, terminologies, coding systems and value sets on a semantic level. In practice, however, this strict separation is not always followed; instead, semantical information is stored and maintained directly in the MDR, as a link between both systems is missing. This may be reasonable up to a certain level of complexity, but it quickly reaches its limitations with increasing complexity. The goal of this approach is to combine both components in a compatible manner. We present TermiCron, a synchronization engine that provides synchronized value sets from TS in MDRs, including versioning and annotations. Prototypical results were shown for the terminology server Ontoserver and two established MDR systems. Bridging the semantic and structural gap between the two infrastructure components, this approach enables shared use of metadata and reuse of corresponding health information by establishing a clear separation of the two systems and thus serves to strengthen reuse as well as to increase quality.
Using an Ontological Representation of Chemotherapy Toxicities for Guiding Information Extraction and Integration from EHRs
Authors: Rogier Alice , Coulet Adrien , Rance Bastien
Published: June 2022

Introduction: Chemotherapies against cancers are often interrupted due to severe drug toxicities, reducing treatment opportunities. For this reason, the detection of toxicities and their severity from EHRs is of importance for many downstream applications. However toxicity information is dispersed in various sources in the EHRs, making its extraction challenging. Methods: We introduce OntoTox, an ontology designed to represent chemotherapy toxicities, its attributes and provenance. We illustrated the interest of OntoTox by integrating toxicities and grading information extracted from three heterogeneous sources: EHR questionnaires, semi-structured tables, and free-text. Results: We instantiated 53,510, 2,366 and 54,420 toxicities from questionnaires, tables and free-text respectively, and compared the complementarity and redundancy of the three sources. Discussion: We illustrated with this preliminary study the potential of OntoTox to guide the integration of multiple sources, and identified that the three sources are only moderately overlapping, stressing the need for a common representation.
A Semi-Automatic Data Cleaning & Coding Tool for Chinese Clinical Data Standardization
Authors: Chen Yani , Tian Qi , Cai Hailing , Lu Xudong
Published: June 2022

The clinical data often have limited usefulness because of the diversified expression. Chinese clinical data standardization can improve the usability of clinical data. The complexity of data cleaning and coding for Chinese clinical data prompted the turn of low-effective manual coding into the computer-aided tool. This study established the universal data cleaning and coding process and tool for Chinese clinical data standardization, which can greatly improve human efficiency. The process included the preprocessing, text similarity algorithm, and manual review. The standardization process proved effective for the diagnosis, drug, and examination data standardization task and can be used gradually in other clinical domains. The semi-automatic data cleaning and coding can reduce the half time for standardization, and it was used in hospitals in Beijing.
Mobile Application for Home Healthcare: Physician’s Expectations and Perceptions
Authors: Senillosa Mónica B. , Saimovici Javier , Mandirola Brieux Humberto F. , Rabinovich Diego , Galván Camila , Jauregui Oscar I. , Rizzato Lede Daniel A. , Otero Carlos M. , Luna Daniel R.
Published: June 2022

The aging of the population and the increase in chronic diseases generated the need for care at home for pluripathological patients, who can no longer access outpatient care due to functional and social problems. The use of Electronic Medical Records (EMR) improves continuity of care, simplifies data collection, decreases overhead costs, and reduces mortality in chronically ill patients. The use of an App to check and record data in the EMR during the home visit saves time for professionals and helps to avoid transcription errors. This article shares our experience with the design and implementation of a Mobile Application with EMR functionalities for the Homecare setting of the Hospital Italiano de Buenos Aires network
Mobilemicroservices Architecture for Remote Monitoring of Patients: A Feasibility Study
Authors: Ruokolainen Jari , Haladijan Juan , Juutinen Milla , Puustinen Juha , Holm Anu , Vehkaoja Antti , Nieminen Hannu
Published: June 2022

Recent developments in smart mobile devices (SMDs), wearable sensors, the Internet, mobile networks, and computing power provide new healthcare opportunities that are not restricted geographically. This paper aims to introduce Mobilemicroservices Architecture (MMA) based on a study on architectures. In MMA, an HTTP-based Mobilemicroservivce (MM) is allocated to each SMD’s sensor. The key benefits are extendibility, scalability, ease of use for the patient, security, and the possibility to collect raw data without the necessity to involve cloud services. Feasibility was investigated in a two-year project, where MMA-based solutions were used to collect motor function data from patients with Parkinson’s disease. First, we collected motor function data from 98 patients and healthy controls during their visit to a clinic. Second, we monitored the same subjects in real-time for three days in their everyday living environment. These MMA applications represent HTTP-based business-logic computing in which the SMDs’ resources are accessible globally.
Information Assessment for the Implementation of Electronic Informed Consent for Genetic Studies in a High Complexity Hospital
Authors: Descalzo Juan , Frutos Eliana , Rebrij Romina , Luna Daniel , Benítez Sonia
Published: June 2022

The objective of this study was to investigate and analyze the most relevant aspects that influence the development and implementation of electronic informed consent for genetic studies. Interviews were conducted with experts in the area within our institution, the different informed consents available and the number of genetic studies requested in the last 5 years were analyzed. Professionals acknowledged the ethical dilemmas related to the genetic studies and the importance of having an electronic informed consent that not only provides the patient with the information necessary to understand the implications of the study, but also be flexible enough to adapt to the various genetic studies today. The development of informed consent is a challenge for health IT professionals, due to the complexity of the information it contains and the ethical implications it represents.
Towards a Toolbox for Privacy-Preserving Computation on Health Data
Authors: Yazijy Suhail , Schölly Reto , Kellmeyer Philipp
Published: June 2022

Substantial advances in methods of collecting and aggregating large amounts of biomedical data have been met with insufficient measures of protecting it from unwarranted access and use. Most of the current layers of protection are merely aimed at ensuring compliance with regulations (e.g., the EU’s General Data Protection Regulation) but do not represent a vision of privacy-by-design as an efficient and ethical advantage in biomedical research and clinical applications. This not only slows down the pace of such efforts but also leaves the data exposed to a wide spectrum of cyberattacks. This work presents an overview of recent advancements in data and compuation security, along with a discussion of their limitations and potential for deployement in both health care and research settings.
Design and Implementation of a Unique Patient Identification Model in Information Systems in Burkina Faso
Authors: Golo Barro Seydou , Ugon Adrien , Nana Nadège R. , Staccini Pascal
Published: June 2022

The implementation of a reliable identity process is the basis of any secure patient information sharing system. Indeed, each individual is unique and should be identified by a unique number (identifier). It is with these issues in mind that we have designed and implemented a unique patient identification method adapted to the context of Burkina Faso. The recommended method is inspired by the French method based on the work of the Group for the Modernization of the Hospital Information System (GMSIH) [1]. The developed model allows to assign a “Unique Identifier” (PatientID) to each patient from his profile of identification features (name, date of birth, gender,…). The patient ID is a sequence of 20 characters plus a security “key” of 2 characters. A reliability test of the model has been performed to take into account identity anomalies (duplicate, collision).
POKR: Building a Computable Heterogeneous Knowledge Resource for Precision Oncology
Authors: Guo Yanbo , Wang Chen , Moore Raymond , Liu Hongfang , Shen Feichen
Published: June 2022

Precision oncology is expected to improve selection of targeted therapies, tailored to individual patients and ultimately improve cancer patients’ outcomes. Several cancer genetics knowledge databases have been successfully developed for such purposes, including CIViC and OncoKB, with active community-based curations and scoring of genetic-treatment evidences. Although many studies were conducted based on each knowledge base respectively, the integrative analysis across both knowledge bases remains largely unexplored. Thus, there exists an urgent need for a heterogeneous precision oncology knowledge resource with computational power to support drug repurposing discovery in a timely manner, especially for life-threatening cancer. In this pilot study, we built a heterogeneous precision oncology knowledge resource (POKR) by integrating CIViC and OncoKB, in order to incorporate unique information contained in each knowledge base and make associations amongst biomedical entities (e.g., gene, drug, disease) computable and measurable via training POKR graph embeddings. All the relevant codes, database dump files, and pre-trained POKR embeddings can be accessed through the following URL: https://github.com/shenfc/POKR.
The Classification of Scientific Abstracts Using Text Statistical Features
Authors: Ishankulov Timur , Danilov Gleb , Kotik Konstantin , Orlov Yuriy , Shifrin Mikhail , Potapov Alexander
Published: June 2022

Automated abstracts classification could significantly facilitate scientific literature screening. The classification of short texts could be based on their statistical properties. This research aimed to evaluate the quality of short medical abstracts classification primarily based on text statistical features. Twelve experiments with machine learning models over the sets of text features were performed on a dataset of 671 article abstracts. Each experiment was repeated 300 times to estimate the classification quality, ending up with 3600 tests total. We achieved the best result (F1 = 0.775) using a random forest machine learning model with keywords and three-dimensional Word2Vec embeddings. The classification of scientific abstracts might be implemented using straightforward and computationally inexpensive methods presented in this paper. The approach we described is expected to facilitate literature selection by researchers.
The Effect of Data Augmentation in Deep Learning Approach for Peripheral Blood Leukocyte Recognition
Authors: Nozaka Hiroyuki , Oda Miku , Sasaki Ami , Harako Honoka , Miyazaki Mae , Kaga Suzuka , Sakaiya Niina , Kudo Kyouka , Kimura Shou , Nakano Manabu , Fujioka Miyuki , Yamagata Kazufumi
Published: June 2022

Data augmentation is reported as a useful technique to generate a large amount of image datasets from a small image dataset. The aim of this study is to clarify the effect of data augmentation for leukocyte recognition with deep learning. We performed three different data augmentation methods (rotation, scaling, and distortion) as pretreatment on the original images. The subjects of clinical assessment were 51 healthy persons. The thin-layer blood smears were prepared from peripheral blood and stained with MG. The effect of data augmentation with rotation was the only significant effective technique in AI model generation for leukocyte recognition. On contrast, the effect of data augmentation with image distortion or image scaling was poor, and accuracy improvement was limited to specific leukocyte categories. Although data augmentation is one effective method for high accuracy in AI training, we consider that a highly effective method should be selected.
Using Deep Learning to Improve Phenotyping from Clinical Reports
Authors: Vincent Marc , Douillet Maxime , Lerner Ivan , Neuraz Antoine , Burgun Anita , Garcelon Nicolas
Published: June 2022

With the development of clinical databases and the ubiquity of EHRs, physicians and researchers alike have access to an unprecedented amount of data. Complexity of the available data has also increased since clinical reports are also included and require frameworks with natural language processing capabilities in order to process them and extract information not found in other types of documents. In the following work we implement a data processing pipeline performing phenotyping, disambiguation, negation and subject prediction on such reports. We compare it to an existing solution routinely used in a children’s hospital with special focus on genetic diseases. We show that by replacing components based on rules and pattern matching with components leveraging deep learning models and fine-tuned word embeddings we obtain performance improvements of 7%, 10% and 27% in terms of F1 measure for each task. The solution we devised will help build more reliable decision support systems.
Learning Insurance Benefit Rules from Policy Texts with Small Labeled Data
Authors: Picco Gabriele , Lam Hoang Thanh , Lopez Vanessa , Segrave-Daly John , Wei Miao , Sbodio Marco , Vejsbjerg Inge , Brady Seamus , Kristiansen Morten
Published: June 2022

To protect vital health program funds from being paid out on services that are wasteful and inconsistent with medical practices, government healthcare insurance programs need to validate the integrity of claims submitted by providers for reimbursement. However, due the complexity of healthcare billing policies and the lack of coded rules, maintaining “integrity” is a labor-intensive task, often narrow-scope and expensive. We propose an approach that combines deep learning and an ontology to support the extraction of actionable knowledge on benefit rules from regulatory healthcare policy text. We demonstrate its feasibility even in the presence of small ground truth labeled data provided by policy investigators. Leveraging deep learning and rich ontological information enables the system to learn from human corrections and capture better benefit rules from policy text, beyond just using a deterministic approach based on pre-defined textual and semantic pattterns.
OpenMRS Analytics Engine: A FHIR Based Approach
Authors: Kimaina Allan , Dick Jonathan , Sadjad Bashir
Published: June 2022

As the Electronic Health Record (EHR) data keeps growing in volume at an unprecedented rate, there is an increasing need for a more collaborative and scalable approach for designing and engineering clinical data pipelines. To address these two critical needs, we present a scalable analytics pipeline architecture, designed from the bottom-up to harness the power of FHIR (Fast Healthcare Interoperability Resources) for improving collaborative efforts in health data analytics and indicator reporting.
Estimation and Monitoring of Operating Room Utilization by a Distributed Streaming and Analytics Architecture Deployed at Heidelberg University Hospital’s Medical Data Integration Center
Authors: Klar Oliver , Klass Maximilian , Schneider Gerd , Kenngott Hannes , Heinze Oliver
Published: June 2022

Operating rooms are a major cost factor in a hospital’s budget. Therefore, there is a need for process optimization related to the operating rooms (OR). However, the collection of key figures for process optimization is often done manually by medical staff. This can be erroneous, inaccurate, time consuming, and incomplete. Automated, data-driven approaches are intended to address these problems and help to get the most precise picture possible of what is happening within the OR. At Heidelberg University Hospital (UKHD), a distributed AI based streaming analytics architecture was set up and integrated into the Medical Data Integration Center (MeDIC). This architecture can process, store, and visualize heterogeneous data from different sources. Data from medical devices and the video stream of the wall mounted cameras of four integrated operating rooms are ingested into our system. Aggregated and analyzed in real-time computed key figures including OR state and utilization numbers are visualized in a dashboard for monitoring and decision support. Because of high data protection hurdles the proposed system, especially the video analytics, was trained and tested with statists and did not run during real procedures. Studies to evaluate and test the system during live surgeries are planned.
Recurrence of Hypoglycemia in Hospitalized Adult Patients in Non-Critical Areas: An Opportunity to Improve Patient Care Using Electronic Health Records
Authors: Llera Lucía , Grande Ratti María Florencia , Jauregui Oscar Ignacio , Otero Carlos Martin , Luna Daniel R. , Burgos Mariana , Russo María Paula
Published: June 2022

Electronic Health Records offer an opportunity to improve patient care (in terms of quality and/or safety) by making available patient health information stored in a single Clinical Data Repository. We aimed to estimate the frequency of hypoglycemic recurrences in hospitalized adult patients in non-critical areas. We designed a cross sectional study with hospitalizations between 2017 and 2018, which included patients with at least one hypoglycemia health record (defined as a value <70 mg/dL, either by capillary glycemic monitoring or serum measurement). Recurrence was defined as those who presented a second event, with at least 2 hours of difference. We included 1884 patients, and 573 presented recurrences, yielding a global prevalence of 30.41% (95%CI 28.34–32.54). Due to the high frequency, it is important to identify vulnerable populations, to implement preventive measures to assist clinicians for decision-making tasks, as a clinical decision support system.
Impact of COVID-19 Pandemic on Telehealth and In-Person Visits: Implications from an Emergency Department in Argentina
Authors: Grande Ratti Maria Florencia , Frid Santiago Andrés , Rodriguez Paz , Pollan Javier Alberto , Rovegno Luciana , Esteban Jorge Ariel , Luna Daniel , Martinez Bernardo Julio , Pedretti Ana Soledad , Plazzotta Fernando
Published: June 2022

Since Argentina’s government declared a national emergency to combat the COVID-19 pandemic with a lockdown status, it has produced consequences on the healthcare system. We aimed to quantify the effect on the Emergency Department (ED) visits at Hospital Italiano de Buenos Aires. Our electronic health data showed that ED in-person visits declined 46% during the COVID-19 pandemic, from an overall of 176,370 visits during 2019 to 95,421 visits during 2020. Simultaneously, there was a telehealth visits boom when mandatory quarantine began (March 20, 2020): from a median of 12 daily in February 2020 to a median of 338 daily in April 2020; reaching a maximum daily peak of 1,132 on March 26 2020. For a while, teleconsultations replaced ED visits. Then, when face-to-face visits began to increase, teleconsultations began to decrease slowly, as the phenomenon reversed.
Overriding Drug-Drug Interaction Alerts in Clinical Decision Support Systems: A Scoping Review
Authors: Villa Zapata Lorenzo , Subbian Vignesh , Boyce Richard D. , Hansten Philip D. , Horn John R. , Gephart Sheila M. , Romero Andrew , Malone Daniel C.
Published: June 2022

Ineffective computerized alerts for potential Drug-Drug Interactions (DDI) is a longstanding informatics issue. Prescribing clinicians often ignore or override such alerts due to lack of context and clinical relevance, among various other reasons. In this study, we reveiwed published data on the rate of DDI alert overrides and medications involved in the overrides. We identified 34 eligible studies from sites across Asia, Europe, the United States, and the United Kingdom. The override rate of DDI alerts ranged from 55% to 98%, with more than half of the studies reporting the most common drug pairs or medications involved in acceptance or overriding of alerts. The high prevalance of alert overrides highlights the need for decision support systems that take user, drug, and institutional factors into consideration, as well as actionable metrics to better characterize harm associated with overrides.
Testing a Novel Inpatient Respiratory Depression Electronic Clinical Quality Measure (eCQM) for Orthopedic Practice in Two Large U.S. Health Systems
Authors: Syrowatka Ania , Li Troy , Curtin-Bowen Mica , Pullman Avery , Lipsitz Stuart R. , Sainlaire Michael , Song Wenyu , Thai Tien , Businger Alexandra , Bozic Kevin J. , Jiranek William A. , Lieberman Jay R. , Bates David W. , Dykes Patricia C.
Published: June 2022

The objective of this study was to assess the feasibility of using an electronic clinical quality measure (eCQM) to assess inpatient respiratory depression rates following elective primary total hip or total knee arthroplasty using data routinely collected in electronic health records. Measure testing was conducted at two large urban, academic health systems – Mass General Brigham and a geographically distant system in southern U.S. The risk-adjusted inpatient respiratory depression rates were 3.83 and 2.73% for the two health systems, respectively. Clinician group rates ranged from 1.40 to 4.35%, demonstrating opportunity for improvement. Both the data and measure specifications showed strong reliability and validity to allow for calculation of accurate and comparable rates of inpatient respiratory depression.
Electronic Reporting of Workplace Violence Incidents: Improving the Usability, and Optimizing Healthcare Workers’ Cognitive Workload, and Performance
Authors: Foster Meagan , Adapa Karthik , Soloway Anna , Francki Jeffrey , Stokes Sara , Mazur Lukasz
Published: June 2022

A majority of healthcare workers (HCWs) experience workplace violence (WPV) but most WPV events go unreported. Underreporting of WPV is well documented in the literature as a barrier to identifying underlying causes and to evaluating the effectiveness of WPV interventions. Previous studies suggest that WPV reporting data is fragmentary, unreliable, and inconsistent. Also, WPV reporting systems are suboptimally designed making it difficult for healthcare workers to report WPV incidents. This study aims to assess the usability of an electronic WPV report in a large academic medical center and the perceived cognitive workload (CWL) and performance of HCWs associated with reporting WPV events. Findings from this study suggest that our institutional WPV report has suboptimal perceived usability and suboptimal perceived cognitive workload. Further, participants with training reported lower error rates in comparison to participants without training on performance.
Proposed Patient-Inclusive Methodology for Developing and Validating Patient Experience Surveys
Authors: Izower Mitchell , Quintana Yuri
Published: June 2022

Patient experience while using telehealth services impacts patient care, and health system incentives and reimbursement. Therefore, the patient experience should be continuously improved, which requires evaluation. Surveying patients is integral to this process. Ideally, patient experience surveys used to evaluate telehealth services should cover patient experience sub-themes, be validated, and minimize bias. This paper evaluates how current validated patient experience surveys cover patient experience sub-themes, and how many are telehealth-specific. We collected 11 validated patient experience surveys. We then extracted five themes and 114 sub-themes of the patient experience. We evaluated survey questions against patient experience sub-themes. We found that current validated patient experience surveys cover, at most, 20.2% of patient experience sub-themes, with the most common sub-themes being “Psychosocial Needs” (81.8%) and “Information: Treatment” (72.7%). We cross-referenced validated patient experience surveys against validated telehealth-specific surveys. Only one validated patient experience survey (PEQ) was also telehealth-specific.
SHACL-Based Report Quality Evaluation for Health IT-Induced Medication Errors
Authors: Keuchel Daniel , Spicher Nicolai , Wang Ju , Völcker Michael , Gong Yang , Deserno Thomas M.
Published: June 2022

Patient safety event (PSE) reports are an important source of information for analyzing risks in healthcare processes. However, the reports’ quality is often low due to missing or imprecise information. We work towards an automatic analysis of reports and quality evaluation. To leverage a suitable data representation of health IT-induced medication error reports, we apply the Shapes Constraint Language (SHACL). We define an ontology representing these reports and construct a corresponding SHACL graph. Three authors manually annotate and transform 20 textual reports to the SHACL representation. Furthermore, we use this representation to compute a quality score for each report. The results indicate the suitability of SHACL as a representation of health IT-induced medication error reports, which paves a path of automatically extracting information from PSE reports using text mining and transform them to SHACL for quality evaluation.
The Role of Informatics in Implementing Guidelines for Chronic Opioid Therapy Risk Assessment in Primary Care: A Narrative Review Informed by the Socio-Technical Model
Authors: Mishra Meenakshi , Pickett Mary , Weiskopf Nicole G.
Published: June 2022

Approximately 2 million Americans live with opioid use disorder (OUD), most of whom also have chronic pain. The economic burden of chronic pain and prescription opioid misuse runs into billions of dollars. Patients on prescription opioids for chronic non-cancer pain (CNCP) are at increased risk for OUD and overdose. By adhering to the Center for Disease Control and Prevention (CDC) opioid prescribing guidelines, primary care providers (PCPs) have the potential to improve patient outcomes. But numerous provider, patient, and practice-specific factors challenge adherence to guidelines in primary care. Many of the barriers may be mediated by informatics interventions, but gaps in knowledge and unmet needs exist. This narrative review examines the risk assessment and harm reduction process in a socio-technical context to highlight the gaps in knowledge and unmet needs that can be mediated through informatics intervention.
Barriers to Seasonal Influenza Vaccine Uptake in a Pediatric Inpatient Healthcare Setting After Implementation of Clinical Decision Support
Authors: Kandaswamy Swaminathan , Masterson Erin , Blanco Reena , Shah Pareen , Lantis Patricia , Iyer Srikant , Shane Andi , Jernigan Stephanie , Orenstein Evan
Published: June 2022

Every heatlhcare encounter is an opportunity to provide both acute care and health maintenance to children. A Clinical Decision Support (CDS) intervention was instituted in a tertiary pediatric health system to improve influenza vaccination rates during the 2019–2020 season among eligible children receiving care in an acute care inpatient healthcare setting. This study explores reasons for low vaccine uptake following implementation of a CDS aimed at improving vaccine administration as well as identifying possible solutions to improve flu vaccine coverage.
Feature Engineering for Interpretable Machine Learning for Quality Assurance in Radiation Oncology
Authors: Pillai Malvika , Adapa Karthik , Shumway John W. , Dooley John , Das Shiva K. , Chera Bhishamjit S. , Mazur Lukasz
Published: June 2022

Chart checking is a time intensive process with high cognitive workload for physicists. Previous studies have partially automated and standardized chart checking, but limited studies implement data-driven approaches to reduce cognitive workload for quality assurance processes. This study aims to evaluate feature selection methods to improve the interpretability and transparency of machine learning models in predicting the degree of difficulty for a pretreatment physics chart check. We compare chi-square, mutual information, feature importance thresholding, and greedy feature selection for four different classifiers. Random forest has the highest performance with SMOTE oversampling using mutual information for feature selection (accuracy 84.0%, AUC 87.0%, precision 80.0%, recall 80.0%). This study demonstrates that feature selection methods can improve model interpretability and transparency.
Assessing Patient Satisfaction with Teledermatology Implementation During the COVID-19 Pandemic
Authors: Hamad Judy , Fox Amy , Kammire Maria Suzanne , Hollis Alison Nancy , Khairat Saif
Published: June 2022

The aim of this study was to assess the patient experience with teledermatology among new versus existing clinic patients in the context of the rapid practice shift to teledermatology during the COVID-19 pandemic. We analyzed survey responses from 184 teledermatology patients seen during COVID-19 at a major Southeastern medical center from May 13th to June 5th 2020. Overall patient-reported satisfaction with teledermatology was high with the majority of respondents rating their overall satisfaction as excellent (68%) or very good (18%). As teledermatology experiences wider adoption with the COVID-19 pandemic, it is essential to examine patient experience and satisfaction with teledermatology.
Compiling Criteria for Assessing Essential Aspects of Home Exercise Performance: A Questionnaire-Based Approach
Authors: Saalfeld Birgit , Elgert Lena , Steiner Bianca , Wolf Klaus-Hendrik
Published: June 2022

Health-enabling technologies (HET) have high potential in rehabilitation to support patients performing their home exercises. The modeling of human movements as well as the modelling of quality criteria of an exercise performance remains challenging when implementing HETs. A combination of data-driven approaches and knowledge-based methods may deliver new insights. This requires structured quality assessments of concrete exercise performances from a therapists’ point of view. Therefore, a structured, easy to use questionnaire to assess home exercise performances is developed and implemented. The questionnaire consists of eight items in three categories: (1–4) overall assessment of quality and quantity, (5–7) need for correction, and (8) correction hints. The collected data will be the basis for mathematical modeling of home exercise performance assessment as foundation for the development of patient supporting HETs.
ISO/IEC-Standards on Quality and Safety of Telehealth Services and Mobile Medical Apps
Authors: Meijer Wouter , Taylor Alan
Published: June 2022

This paper investigates consistency of ISO/IEC-standards on quality and safety of telehealth services and mobile medical apps. Since mobile medical apps are ‘software as a medical device’, requirements for these apps are in software standards, in particular health software standards, as well as in medical device standards. These requirements were analyzed within an analysis model that has three domains: quality and safety management, core healthcare processes and resources. Telehealth services are healthcare processes and mobile medical apps are resources. There is good alignment of the standards that pertain to quality and safety management, the healthcare processes and medical devices. However, there is a lack of alignment, consistency and transparency of the ISO/IEC-standards on quality and safety of telehealth services and mobile medical apps. An international effort to address these issues is recommended.
User-Centered Evaluation of a Visual Annotation Tool for Rapid Assessment of Pediatric Weight Entry Errors
Authors: Wu Danny T.Y. , Van Camp P.J. , Kim Abraham , Parikh Milan , Liu Lei , Mahdi Monifa , Ni Yizhao , Spooner S. Andrew
Published: June 2022

Weight entry errors can cause significant patient harm in pediatrics due to pervasive weight-based dosing practices. While computerized algorithms can assist in error detection, they have not achieved high sensitivity and specificity to be further developed as a clinical decision support tool. To train an advanced algorithm, expert-annotated weight errors are essential but difficult to collect. In this study, we developed a visual annotation tool to gather large amounts of expertly annotated pediatric weight charts and conducted a formal user-centered evaluation. Key features of the tool included configurable grid sizes and annotation styles. The user feedback was collected through a structured survey and user clicks on the interface. The results show that the visual annotation tool has high usability (average SUS=86.4). Different combinations of the key features, however, did not significantly improve the annotation efficiency and duration. We have used this tool to collect expert annotations for algorithm development and benchmarking.
An Online Community for Patient with Psoriasis with Built-in Self-Reported Questionnaires
Authors: Colussi Giuliana , Inda Delfina , Bruchanski Lucila , Binder Fernando , Hernandez Barbara Agustina , Bollea Maria Luz , Cura María Julia , Galimberti María Laura , Mazzuoccolo Luis , Sommer Janine , Plazzotta Fernando , Luna Daniel
Published: June 2022

This study presents an online psoriasis community developed with dermatologists in a PHR. We describe the interaction of users with this platform and the relationship between the use of self-report questionnaires, their results and users’ subsequent contact with the healthcare system. Out of 2175 users that interacted with the platform, 477 visited the forums. 60% of those who completed questionnaires presented at least one abnormal result that prompted a recommendation for an outpatient visit. Although our data suggest a trend, we failed to find a statistically significant association between questionnaire severity and visits scheduling. To our knowledge, this is the first study that analyses the relationship between patient self-reported disease severity and the subsequent contact with the healthcare system.
Designing an Atopic Dermatitis Community that Integrates Patient Self-Uploaded Information into the EHR to Optimize Follow-up
Authors: Colussi Giuliana , Inda Delfina , Zin Yael , De Matos Lima Santiago , Angles Maria Valeria , Bruey Silvina , Lanteri Sambrizzi Ana , Mazzuoccolo Luis , Luna Daniel , Plazzotta Fernando
Published: June 2022

Atopic dermatitis is a common chronic dermatological disease in childhood that can affect people’s quality of life. The aim of this study was to inquire about the difficulties, needs and interests related to the disease that people with eczema and their caregivers have; in order to develop a tool that is useful for the follow-up of the illness. Electronic surveys were sent to potential users and interviews were conducted with professionals who are specialized on the subject. The main findings allowed us to understand the challenges and situations they face on a daily basis, such as the difficulties related to the family support, the queries on the eczema flare-ups, the struggles with the adherence to treatment and the needs of optimizing their quality of life. These results helped us design a tool that allows patients and their companions to better monitor their disease while optimizing communication with their health professionals.
Augmentation of Electronic Medical Record Data for Deep Learning
Authors: Kennedy Georgina , Dras Mark , Gallego Blanca
Published: June 2022

Data imbalance is a well-known challenge in the development of machine learning models. This is particularly relevant when the minority class is the class of interest, which is frequently the case in models that predict mortality, specific diagnoses or other important clinical end-points. Typical methods of dealing with this include over- or under-sampling training data, or weighting the loss function in order to boost the signal from the minority class. Data augmentation is another frequently employed method — particularly for models that use images as input data. For discrete time-series data, however, there is no consensus method of data augmentation. We propose a simple data augmentation strategy that can be applied to discrete time-series data from the EMR. This strategy is then demonstrated using a publicly available data-set, in order to provide proof of concept for the work undertaken in [1], where data is unable to be made open.
AUTOMETA: Automatic Meta-Analysis System Employing Natural Language Processing
Authors: Mutinda Faith W. , Yada Shuntaro , Wakamiya Shoko , Aramaki Eiji
Published: June 2022

Meta-analyses examine the results of different clinical studies to determine whether a treatment is effective or not. Meta-analyses provide the gold standard for medical evidence. Despite their importance, meta-analyses are time-consuming and this poses a challenge where timeliness is important. Research articles are also increasing rapidly and most meta-analyses become outdated after publication since they have not incorporated new evidence. Therefore, there is increasing interest to automate meta-analysis so as to speed up the process and allow for automatic update when new results are available. In this preliminary study we present AUTOMETA, our proposed system for automating meta-analysis which employs existing natural language processing methods for identifying Participants, Intervention, Control, and Outcome (PICO) elements. We show that our system can perform advanced meta-analyses by parsing numeric outcomes to identify the number of patients having certain outcomes. We also present a new dataset which improves previous datasets by incorporating additional tags to identify detailed information.
Accuracies of Training Labels and Machine Learning Models: Experiments on Delirium and Simulated Data
Authors: Cheng Yan , Shao Yijun , Rudolph James , Weir Charlene R. , Sahlmann Beth , Zeng-Treitler Qing
Published: June 2022

Supervised predictive models require labeled data for training purposes. Complete and accurate labeled data is not always available, and imperfectly labeled data may need to serve as an alternative. An important question is if the accuracy of the labeled data creates a performance ceiling for the trained model. In this study, we trained several models to recognize the presence of delirium in clinical documents using data with annotations that are not completely accurate. In the external evaluation, the support vector machine model with a linear kernel performed best, achieving an area under the curve of 89.3% and accuracy of 88%, surpassing the 80% accuracy of the training sample. We then generated a set of simulated data and carried out a series of experiments which demonstrated that models trained on imperfect data can (but do not always) outperform the accuracy of the training data.
Study of 2D Feature Extraction Techniques for Classification of Spinocerebellar Ataxia Type 12 (SCA12)
Authors: Agrawal Snigdha , Kumaran Senthil S. , Srivastava Achal Kumar , Agrawal Ramesh Kumar , Narang Manpreet Kaur
Published: June 2022

Spinocerebellar ataxia type 12 (SCA12) is a neurodegenerative genetic disorder triggered by abnormal CAG repeat expansion at locus 5q32. MRI recognises dissimilarities in affected areas of SCA12 patients and healthy subjects. But manual diagnosis is time-consuming and prone to subjective errors. This has motivated us in developing a systematic and authentic decision model for computer-aided diagnosis (CAD) of SCA12. Four different feature extraction techniques are examined in this research work, such as First Order Statistics, GLRLM, GLCM, and GLGCM, to obtain distinguishable characteristics for differentiating SCA12 patients from healthy subjects. The model’s performance is measured using sensitivity, specificity, accuracy and F1-score with leave-one-out cross-validation scheme. Our experimental results show that features based on the GLRLM can distinguish SCA12 from healthy subjects with a maximum classification accuracy of 85% among all the different function extraction techniques used.
The Pitfalls of Using Open Data to Develop Deep Learning Solutions for COVID-19 Detection in Chest X-Rays
Authors: Harkness Rachael , Hall Geoff , Frangi Alejandro F. , Ravikumar Nishant , Zucker Kieran
Published: June 2022

Since the emergence of COVID-19, deep learning models have been developed to identify COVID-19 from chest X-rays. With little to no direct access to hospital data, the AI community relies heavily on public data comprising numerous data sources. Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak. In this study impactful models are trained on a widely used open-source data and tested on an external test set and a hospital dataset, for the task of classifying chest X-rays into one of three classes: COVID-19, non-COVID pneumonia and no-pneumonia. Classification performance of the models investigated is evaluated through ROC curves, confusion matrices and standard classification metrics. Explainability modules are implemented to explore the image features most important to classification. Data analysis and model evalutions show that the popular open-source dataset COVIDx is not representative of the real clinical problem and that results from testing on this are inflated. Dependence on open-source data can leave models vulnerable to bias and confounding variables, requiring careful analysis to develop clinically useful/viable AI tools for COVID-19 detection in chest X-rays.
Correlation Between Quality Evaluation Metrics and Teeth Detection Results in Panoramic X-Rays Using Deep Learning
Authors: Giardina Claudia L. , Legal Horacio , Vázquez Noguera José Luis , Salgueiro Luis , Fretes Vicente R. , Defazio Diego
Published: June 2022

Panoramic images are one of the most requested exams by dentists for allowing the visualization of the entire mouth. Interpreting X-ray images is a time-consuming task in which misdiagnoses can occur due to the inexperience or fatigue of professionals. In this work, we applied different image enhancement techniques as a pre-processing step to determine which image features correlate with improvements in teeth detection in panoramic images using deep learning architectures. We contrasted the performance of five object-detection architectures using 300 panoramic images of a public dataset. We evaluated the enhancement in the pre-processing step and the detection performance. Quality and detection metrics were considered, and the cross-correlation between them was computed for every object-detection method contemplated. We observe the dependence of the detection performance with some image enhancement techniques, especially those that introduce less noise and preserve the global contrast of the image.
Who Influences Cancer Conversations on Twitter?: A Comparative Surveillance of Cancer Communications
Authors: Jain Nishant , Zachary Iris , Boren Suzanne
Published: June 2022

In this study, we used social network analysis to compare the Twitter social networks of top five cancers in the United States (as ranked by the CDC) to determine the key influencers in cancer-related conversations. We find that organizations and groups geared toward patients that provide patient support, promote cancer awareness, cancer prevention and cancer management comprised up to 40% of influencers. Researchers (24%) and physicians (14%) were also found to be influential participants; the extent of influence varying by each cancer, being as high as 40% research influence for colorectal cancer. Notably, scientific organizations (JAMA, CDC_cancer, AACR) played a key role in conversations about colorectal cancer whereas patient-focused organizations played a greater influencing role in conversations about prostate cancer and skin cancer. This study shows that Twitter data can be a valuable source of cancer surveillance data, and has potential to influence policies, strategies, and research directions around each cancer.
Automatic Patient Fall Outcome Extraction Using Narrative Incident Reports
Authors: Liu Jiaxing , Wong Zoie S.Y. , So H.Y.
Published: June 2022

Patient outcome is one of the key information categories in incident reporting. Being able to extract meaningful patient fall outcomes would allow better analysis of the consequences and possible mitigating actions for in-hospital fall incidents. This study aims to automate the extraction of patient outcomes from narrative fall incident reports by decomposing this into three classification subtasks: injured or not, injury types, and the number of injuries. Implementing an existing incident report classification (IRC) framework, the experimental results demonstrated that oversampling and structured features were effective to achieve better overall performances across all three subtasks. The study further validated the application of an IRC framework to deal with imbalanced classification problems found in fall patient outcome classification and advanced the science of automatic patient outcomes extraction.
Characterizing Infant Mortality Using Data Mining – A Case Study in Two Brazilian States – Santa Catarina and Amapá
Authors: Soares Wanderson L. , Song Mark A.J. , Zárate Luis E. , Nobre Cristiane N.
Published: June 2022

Infant mortality is characterized by the death of young children under the age of one, and it is an issue affecting millions of children in the world. The objective of this article is to employ concepts of knowledge discovery in databases, specifically of machine learning in the data mining phase, to characterize infant mortality in two states of Brazil: Santa Catarina, with the lowest infant mortality rate of the country’s states, and Amapá, with the highest. The classifiers C4.5, JRip, Random Forest, SVM, and Multilayer Perceptron were used, and a brief comparison of the results obtained by the classifiers in both states is made. In addition, the dataset preprocessing is detailed, which includes attribute selection and class balancing. The results show that the features APGAR5, WEIGHT, and CONGENITAL ANOMALY stood out the most from the rules generated by the tree-based classifiers.
Identification of Transportation Barriers in Patient Portal Messages via Deep Semantic Embeddings and Clustering
Authors: Huang Ming , Khurana Aditya , Mastorakos George M. , Fan Jungwei
Published: June 2022

Patient portals have been widely used by patients to enable timely communications with their providers via secure messaging for various issues including transportation barriers. The large volume of portal messages offers an invaluable opportunity for studying transportation barriers reported by patients. In this work, we explored the feasibility of cutting-edge deep learning techniques for identifying transportation issues mentioned in patient portal messages with deep semantic embeddings. The successful creation of annotated corpus and identification of 7 transportation issues showed the feasibility of this strategy. The developed annotated corpus could aid in developing an artificial intelligence tool to automatically identify transportation issues from millions of patient portal messages. The identified specific transportation issues and the analysis of patient demographics could shed light on how to reduce transportation gaps for patients.