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Special Issue "Feature Papers: Health Informatics"

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A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Health Informatics".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 43718

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Dr. Kamran Sedig

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Department of Computer Science and The Faculty of Information & Media Studies, Western University, London, ON N6A 5B7, Canada
Interests: computer science; information science; design; human-computer interaction; visualization; cognition, learning, and motivation sciences
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Dear Colleagues,

Health informatics (also called health care informatics, healthcare informatics, medical informatics, nursing informatics, clinical informatics, or biomedical informatics) is information engineering applied to the field of health care—essentially the management and use of patient health care information. It is a multidisciplinary field that uses health information technology (HIT) to improve health care via any combination of higher quality, higher efficiency (spurring lower cost and thus greater availability), and new opportunities. The disciplines involved include information science, computer science, social science, behavioral science, management science, and others.

Our Special Issue is keen to receive and publish high-quality submissions on any subject relevant to health informatics. For well-prepared papers and those approved for further publication, authors might be eligible for discounts for publication.

Dr. Kamran Sedig
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Informatics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

health informatics
health care informatics
healthcare informatics
medical informatics
nursing informatics
clinical informatics
biomedical informatics

Published Papers (15 papers)

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Research

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Open AccessArticle

Lean ICU Layout Re-Design: A Simulation-Based Approach

Elpidio Romano

Andrea Falegnami

Anna Corinna Cagliano

and

Carlo Rafele

Informatics 2022, 9(2), 35; https://doi.org/10.3390/informatics9020035 - 22 Apr 2022

Cited by 2 | Viewed by 1675

Abstract

Healthcare facilities require flexible layouts that can adapt quickly in the face of various disruptions. COVID-19 confirmed this need for both healthcare and manufacturing systems. Starting with the transfer of decision support systems from manufacturing, this paper generalizes layout re-design activities for complex systems by presenting a simulation framework. Through a real case study concerning the proliferation of nosocomial cross-infection in an intensive care unit (ICU), the model developed in systems dynamics, based on a zero order immediate logic, allows reproducing the evolution of the different agencies (e.g., physicians, nurses, ancillary workers, patients), as well as of the cyber-technical side of the ICU, in its general but also local aspects. The entire global workflow is theoretically founded on lean principles, with the goal of balancing the need for minimal patient throughput time and maximum efficiency by optimizing the resources used during the process. The proposed framework might be transferred to other wards with minimal adjustments; hence, it has the potential to represent the initial step for a modular depiction of an entire healthcare facility. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessArticle

Extending the UTAUT2 Model with a Privacy Calculus Model to Enhance the Adoption of a Health Information Application in Malaysia

Ismail Bile Hassan

Masrah Azrifah Azmi Murad

Ibrahim El-Shekeil

and

Jigang Liu

Informatics 2022, 9(2), 31; https://doi.org/10.3390/informatics9020031 - 28 Mar 2022

Cited by 4 | Viewed by 3112

Abstract

This study validates and extends the latest unified theory of acceptance and use of technology (UTAUT2) with the privacy calculus model. To evaluate the adoption of healthcare and e-government applications, researchers have recommended—in previous literature—the application of technology adoption models with privacy, trust, and security-related constructs. However, the current UTAUT2 model lacks privacy, trust, and security-related constructs. Therefore, the proposed UTAUT2 with the privacy calculus model is incorporated into four constructs: privacy concern, perceived risk, trust in the smart national identity card (SNIC), and perceived credibility. Results from a survey data of 720 respondents show that habit, effort expectancy, performance expectancy, social influence, hedonic motivation, and price value are direct determinants that influence behavioral intentions to use. Results also revealed that behavioral intentions, facilitating conditions, habits, perceived risks, and privacy concerns are direct predictors of ‘use behavior’. The authors also analyzed the interrelationships among the research constructs. The extended model may lead toward establishing better innovative e-health services to cover the desires of the citizens through the use of health information applications embedded in an all-in-one card. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessFeature PaperArticle

Social Media and Social Support: A Framework for Patient Satisfaction in Healthcare

and

Informatics 2022, 9(1), 22; https://doi.org/10.3390/informatics9010022 - 04 Mar 2022

Viewed by 2886

Abstract

Social media has been a powerful source of social support for health consumers. In the healthcare sector, social media has thrived, building on various dynamic platforms supporting the connection between social relationships, health, and wellbeing. While prior research has shown that social support exerts a positive impact on health outcomes, there is scant literature examining the implications of social support for patient satisfaction, which suggests that there is a profound gap in the extant literature. The objective of this study is to develop and test a theoretical model for understanding the relationship between different dimensions of social support and patient empowerment. The study further investigates the debated relationship between patient empowerment and patient satisfaction. The measurement model indicated an acceptable fit (χ² = 260.226; df, 107, χ²/df = 2.432, RMSEA = 0.07, GFI = 0.90, IFI = 0.95, TLI = 0.94, and CFI = 0.95). Findings indicate that emotional support (p < 0.001), information support (p < 0.05), and network support (p < 0.001) positively influence the notion of patient empowerment. In turn, patient empowerment positively influences patient satisfaction (p < 0.001). The proposed framework contributes to the health communication literature by introducing a novel framework for patient satisfaction in the social media context, which provides important inputs for healthcare service providers in developing patient empowerment strategies. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessArticle

Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results

and

Informatics 2022, 9(1), 17; https://doi.org/10.3390/informatics9010017 - 25 Feb 2022

Viewed by 1835

Abstract

Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user’s confidence in the generated predictions and provides them the means to validate the model’s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user’s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessArticle

Machine Learning and IoT Applied to Cardiovascular Diseases Identification through Heart Sounds: A Literature Review

Ivo Sérgio Guimarães Brites

Lídia Martins da Silva

Jorge Luis Victória Barbosa

Sandro José Rigo

Sérgio Duarte Correia

and

Valderi Reis Quietinho Leithardt

Informatics 2021, 8(4), 73; https://doi.org/10.3390/informatics8040073 - 30 Oct 2021

Cited by 8 | Viewed by 3830

Abstract

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR—Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessArticle

A Self-Adaptive and Efficient Context-Aware Healthcare Model for COPD Diseases

Hamid Mcheick

and

John Sayegh

Informatics 2021, 8(3), 41; https://doi.org/10.3390/informatics8030041 - 22 Jun 2021

Cited by 3 | Viewed by 1601

Abstract

The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessFeature PaperArticle

Using Mobiles to Monitor Respiratory Diseases

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Informatics 2020, 7(4), 56; https://doi.org/10.3390/informatics7040056 - 16 Dec 2020

Cited by 4 | Viewed by 2776

Abstract

In this work, a mobile application is developed to assist patients suffering from chronic obstructive pulmonary disease (COPD) or Asthma that will reduce the dependency on hospital and clinic based tests and enable users to better manage their disease through increased self-involvement. Due to the pervasiveness of smartphones, it is proposed to make use of their built-in sensors and ever increasing computational capabilities to provide patients with a mobile-based spirometer capable of diagnosing COPD or asthma in a reliable and cost effective manner. Data collected using an experimental setup consisting of an airflow source, an anemometer, and a smartphone is used to develop a mathematical model that relates exhalation frequency to air flow rate. This model allows for the computation of two key parameters known as forced vital capacity (FVC) and forced expiratory volume in one second (FEV1) that are used in the diagnosis of respiratory diseases. The developed platform has been validated using data collected from 25 subjects with various conditions. Results show that an excellent match is achieved between the FVC and FEV1 values computed using a clinical spirometer and those returned by the model embedded in the mobile application. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessFeature PaperArticle

Toward Evaluation of the Subjective Experience of a General Class of User-Controlled, Robot-Mediated Rehabilitation Technologies for Children with Neuromotor Disability

Manon Maitland Schladen

and

Informatics 2020, 7(4), 45; https://doi.org/10.3390/informatics7040045 - 19 Oct 2020

Cited by 6 | Viewed by 1925

Abstract

Technological advances in game-mediated robotics provide an opportunity to engage children with cerebral palsy (CP) and other neuromotor disabilities in more frequent and intensive therapy by making personalized, programmed interventions available 24/7 in children’s homes. Though shown to be clinically effective and feasible to produce, little is known of the subjective factors impacting acceptance of what we term assistive/rehabilitative (A/R) gamebots by their target populations. This research describes the conceptualization phase of an effort to develop a valid and reliable instrument to guide the design of A/R gamebots. We conducted in-depth interviews with 8 children with CP and their families who had trialed an exemplar A/R gamebot, PedBotHome, for 28 days in their homes. The goal was to understand how existing theories and instruments were either appropriate or inappropriate for measuring the subjective experience of A/R gamebots. Key findings were the importance of differentiating the use case of therapy from that of assistance in rehabilitative technology assessment, the need to incorporate the differing perspectives of children with CP and those of their parents into A/R gamebot evaluation, and the potential conflict between the goals of preserving the quality of the experience of game play for the child while also optimizing the intensity and duration of therapy provided during play. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessFeature PaperArticle

Usability in Patient-Oriented Drug Interaction Checkers—A Scandinavian Sampling and Heuristic Evaluation

David Vingen

Elias J. Andrews

and

Mexhid Ferati

Informatics 2020, 7(4), 42; https://doi.org/10.3390/informatics7040042 - 09 Oct 2020

Cited by 5 | Viewed by 2314

Abstract

Drug interactions are an important source of medical error and a topic of particular interest to patient audiences. Patients must be informed to be able to participate in decision-making affecting their health. This paper explores the availability of drug interaction checkers in Scandinavia and the prevalence and characteristics of usability issues preventing patients from benefiting from them. Drug interaction checkers were sampled and evaluated through heuristic evaluations. Issue-based data were analyzed through descriptive statistics, as well as single-case and cross-case qualitative analyses. The findings were interpreted side-by-side using a mixed-methods approach. The results showed a multitude of usability issues. Catastrophic issues indicating the safety of dangerous drug pairings were found in two of the checkers. Results also showed that the checkers lacked adaptive design, patient-oriented content, and adherence to basic design principles. A positive correlation was observed between system complexity and number of usability issues. We suggest that this comes from a lack of systematic design approach. The market for Scandinavian drug interaction checkers was as such characterized by a limited selection of checkers known to be used by patients for their utility, but failing to accommodate them in terms of system quality. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessArticle

Building a Persuasive Virtual Dietitian

Luca Anselma

and

Alessandro Mazzei

Informatics 2020, 7(3), 27; https://doi.org/10.3390/informatics7030027 - 30 Jul 2020

Cited by 7 | Viewed by 2908

Abstract

This paper describes the Multimedia Application for Diet Management (MADiMan), a system that supports users in managing their diets while admitting diet transgressions. MADiMan consists of a numerical reasoner that takes into account users’ dietary constraints and automatically adapts the users’ diet, and of a natural language generation (NLG) system that automatically creates textual messages for explaining the results provided by the reasoner with the aim of persuading users to stick to a healthy diet. In the first part of the paper, we introduce the MADiMan system and, in particular, the basic mechanisms related to reasoning, data interpretation and content selection for a numeric data-to-text NLG system. We also discuss a number of factors influencing the design of the textual messages produced. In particular, we describe in detail the design of the sentence-aggregation procedure, which determines the compactness of the final message by applying two aggregation strategies. In the second part of the paper, we present the app that we developed, CheckYourMeal!, and the results of two human-based quantitative evaluations of the NLG module conducted using CheckYourMeal! in a simulation. The first evaluation, conducted with twenty users, ascertained both the perceived usefulness of graphics/text and the appeal, easiness and persuasiveness of the textual messages. The second evaluation, conducted with thirty-nine users, ascertained their persuasive power. The evaluations were based on the analysis of questionnaires and of logged data of users’ behaviour. Both evaluations showed significant results. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessFeature PaperArticle

Investigation of Women’s Health on Wikipedia—A Temporal Analysis of Women’s Health Topic

Yanyan Wang

and

Jin Zhang

Informatics 2020, 7(3), 22; https://doi.org/10.3390/informatics7030022 - 17 Jul 2020

Cited by 2 | Viewed by 2648

Abstract

New health-related concepts, terms, and topics emerge, and the meanings of existing terms and topics keep changing. This study investigated and explored the evolutions of the women’s health topic on Wikipedia. The creation time, page views data, page edits data, and text of historical versions of 207 women-health-related entries from 2010 to 2017 on Wikipedia were collected. Coding, subject analysis, descriptive and inferential statistical analysis, and Self-Organizing Map and n-gram approaches were employed to explore the characteristics and evolutions of the entries for the women’s health topic. The results show that the number of the women-health-related entries kept increasing from 2010 to 2017, and nearly half of them were related to the supports and protection of women’s health. The total number of page views of the investigated items increased from 2011 to 2013, but it decreased from 2013 to 2017, while the total number of page edits stayed stable from 2010 to 2017. Growing subjects were found during the investigated period, such as abuse and violence, and family planning and reproduction. However, the entries related to the economy and politics were diminishing. There was no association between the internal characteristic evolution and the external popularity evolution of the women’s health topic. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessArticle

Machine Learning for Identifying Medication-Associated Acute Kidney Injury

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Informatics 2020, 7(2), 18; https://doi.org/10.3390/informatics7020018 - 31 May 2020

Cited by 7 | Viewed by 3586

Abstract

One of the prominent problems in clinical medicine is medication-induced acute kidney injury (AKI). Avoiding this problem can prevent patient harm and reduce healthcare expenditures. Several researches have been conducted to identify AKI-associated medications using statistical, data mining, and machine learning techniques. However, these studies are limited to assessing the impact of known nephrotoxic medications and do not comprehensively explore the relationship between medication combinations and AKI. In this paper, we present a population-based retrospective cohort study that employs automated data analysis techniques to identify medications and medication combinations that are associated with a higher risk of AKI. By integrating multivariable logistic regression, frequent itemset mining, and stratified analysis, this study is designed to explore the complex relationships between medications and AKI in such a way that has never been attempted before. Through an analysis of prescription records of one million older patients stored in the healthcare administrative dataset at ICES (an independent, non-profit, world-leading research organization that uses population-based health and social data to produce knowledge on a broad range of healthcare issues), we identified 55 AKI-associated medications among 595 distinct medications and 78 AKI-associated medication combinations among 7748 frequent medication combinations. In addition, through a stratified analysis, we identified 37 cases where a particular medication was associated with increasing the risk of AKI when used with another medication. We have shown that our results are consistent with previous studies through consultation with a nephrologist and an electronic literature search. This research demonstrates how automated analysis techniques can be used to accomplish data-driven tasks using massive clinical datasets. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessFeature PaperArticle

Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records

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Informatics 2020, 7(2), 17; https://doi.org/10.3390/informatics7020017 - 27 May 2020

Cited by 15 | Viewed by 5013

Abstract

Recent advancement in EHR-based (Electronic Health Record) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional data available in EHRs creates great opportunities for machine learning techniques such as clustering. Cluster analysis often requires dimension reduction to achieve efficient processing time and mitigate the curse of dimensionality. Given a wide range of techniques for dimension reduction and cluster analysis, it is not straightforward to identify which combination of techniques from both families leads to the desired result. The ability to derive useful and precise insights from EHRs requires a deeper understanding of the data, intermediary results, configuration parameters, and analysis processes. Although these tasks are often tackled separately in existing studies, we present a visual analytics (VA) system, called Visual Analytics for Cluster Analysis and Dimension Reduction of High Dimensional Electronic Health Records (VALENCIA), to address the challenges of high-dimensional EHRs in a single system. VALENCIA brings a wide range of cluster analysis and dimension reduction techniques, integrate them seamlessly, and make them accessible to users through interactive visualizations. It offers a balanced distribution of processing load between users and the system to facilitate the performance of high-level cognitive tasks in such a way that would be difficult without the aid of a VA system. Through a real case study, we have demonstrated how VALENCIA can be used to analyze the healthcare administrative dataset stored at ICES. This research also highlights what needs to be considered in the future when developing VA systems that are designed to derive deep and novel insights into EHRs. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Review

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Open AccessReview

Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review

Deepika Verma

Kerstin Bach

and

Paul Jarle Mork

Informatics 2021, 8(3), 56; https://doi.org/10.3390/informatics8030056 - 25 Aug 2021

Cited by 8 | Viewed by 2386

Abstract

The field of patient-centred healthcare has, during recent years, adopted machine learning and data science techniques to support clinical decision making and improve patient outcomes. We conduct a literature review with the aim of summarising the existing methodologies that apply machine learning methods on patient-reported outcome measures datasets for predicting clinical outcomes to support further research and development within the field. We identify 15 articles published within the last decade that employ machine learning methods at various stages of exploiting datasets consisting of patient-reported outcome measures for predicting clinical outcomes, presenting promising research and demonstrating the utility of patient-reported outcome measures data for developmental research, personalised treatment and precision medicine with the help of machine learning-based decision-support systems. Furthermore, we identify and discuss the gaps and challenges, such as inconsistency in reporting the results across different articles, use of different evaluation metrics, legal aspects of using the data, and data unavailability, among others, which can potentially be addressed in future studies. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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Open AccessReview

Visual Analytics for Electronic Health Records: A Review

Neda Rostamzadeh

Sheikh S. Abdullah

and

Kamran Sedig

Informatics 2021, 8(1), 12; https://doi.org/10.3390/informatics8010012 - 23 Feb 2021

Cited by 9 | Viewed by 3511

Abstract

The increasing use of electronic health record (EHR)-based systems has led to the generation of clinical data at an unprecedented rate, which produces an untapped resource for healthcare experts to improve the quality of care. Despite the growing demand for adopting EHRs, the large amount of clinical data has made some analytical and cognitive processes more challenging. The emergence of a type of computational system called visual analytics has the potential to handle information overload challenges in EHRs by integrating analytics techniques with interactive visualizations. In recent years, several EHR-based visual analytics systems have been developed to fulfill healthcare experts’ computational and cognitive demands. In this paper, we conduct a systematic literature review to present the research papers that describe the design of EHR-based visual analytics systems and provide a brief overview of 22 systems that met the selection criteria. We identify and explain the key dimensions of the EHR-based visual analytics design space, including visual analytics tasks, analytics, visualizations, and interactions. We evaluate the systems using the selected dimensions and identify the gaps and areas with little prior work. Full article

(This article belongs to the Special Issue Feature Papers: Health Informatics)

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