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Informatics
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Data-Driven Healthcare Research
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Data-Driven Healthcare Research

A special issue of Informatics (ISSN 2227-9709).

Deadline for manuscript submissions: closed (31 July 2018) | Viewed by 39711

Special Issue Editor

Prof. Dr. You Chen

E-Mail Website
Guest Editor
Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave., Nashville, TN 37203, USA
Interests: data mining and machine learning in healthcare; predictive analytics in healthcare; value-based care; patient centered care; healthcare organization modeling; clinical workflow modeling; clinical phenotype learning; patient outcome prediction; drug reposition and health data security and privacy

Special Issue Information

Dear Colleagues,

We cordially invite you to submit your contribution to Informatics. Informatics (ISSN 2227-9709) is an international, open access journal on information and communication technologies, human–computer interaction, and social informatics, and is published quarterly online by MDPI. This Special Issue highlights the most novel data-driven healthcare research to discover useful and actionable knowledge and gain insights from raw data to improve human health, such as saving lives, reducing medical errors, increasing efficiency, reducing costs, and improving patient outcome.

The theme of this Special Issue is “Health Data Science”. We have been witnessing a digital revolution in healthcare, such as the development of various emerging technologies, including electronic medical record systems, patient engagement systems, healthcare social media systems, wearable devices, smart phones, cloud computing, and big data analytics tools. The amount of healthcare data is growing every second in these systems. We invite the submission of papers on novel methods for exploring and analyzing healthcare data, such as data mining, machine learning, predictive analytics and knowledge discovery from medical data, disease diagnostic predictive models, disease profiling and personalized medicine, healthcare workflow mining, healthcare organization modeling, social media and web data analytics for public health, hospital readmission and patient length of stay analytics, natural language processing and text mining, recommender systems, clinical phenotyping, and innovative visualization techniques for the query and analysis of medical data. Papers may also report studies on data quality assurance, data cleaning, pre-processing, and ensuring quality and integrity of healthcare data. Additionally, we encourage submission on privacy and security in sharing healthcare data, as well as evaluation and validation of methods in healthcare data analytics.

Prof. Dr. You Chen
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 1800 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 data science
  • data mining
  • machine learning
  • predictive analytics
  • personalized medicine
  • electronic medical records
  • healthcare social media
  • healthcare operational action logs
  • clinical workflow modeling
  • helathcare organizaiton modleing
  • patient centered care
  • clincial phenotyping algorithms
  • visualization techniques
  • readmission
  • patient length of stay
  • data security and privacy
  • data quality
  • natrual language processing
  • evaluation and validation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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22 pages, 365 KiB  
Article
Understanding the EMR-Related Experiences of Pregnant Japanese Women to Redesign Antenatal Care EMR Systems
by Samar Helou, Victoria Abou-Khalil, Goshiro Yamamoto, Eiji Kondoh, Hiroshi Tamura, Shusuke Hiragi, Osamu Sugiyama, Kazuya Okamoto, Masayuki Nambu and Tomohiro Kuroda
Informatics 2019, 6(2), 15; https://doi.org/10.3390/informatics6020015 - 4 Apr 2019
Cited by 4 | Viewed by 8105
Abstract
Woman-centered antenatal care necessitates Electronic Medical Record (EMR) systems that respect women’s preferences. However, women’s preferences regarding EMR systems in antenatal care remain unknown. This work aims to understand the EMR-related experiences that pregnant Japanese women want. First, we conducted a field-based observational [...] Read more.
Woman-centered antenatal care necessitates Electronic Medical Record (EMR) systems that respect women’s preferences. However, women’s preferences regarding EMR systems in antenatal care remain unknown. This work aims to understand the EMR-related experiences that pregnant Japanese women want. First, we conducted a field-based observational study at an antenatal care clinic at a Japanese university hospital. We analyzed the data following a thematic analysis approach and found multiple EMR-related experiences that pregnant women encounter during antenatal care. Based on the observations’ findings, we administered a web survey to 413 recently pregnant Japanese women to understand their attitudes regarding the EMR-related experiences. Our results show that pregnant Japanese women want accessible, exchangeable, and biopsychosocial EMRs. They also want EMR-enabled explanations and summaries. Interestingly, differences in their demographics and stages of pregnancy affected their attitudes towards some EMR-related experiences. To respect their preferences, we propose amplifying the roles of EMR systems as tools that promote communication and woman-centeredness in antenatal care. We also propose expanding the EMR design mindset from a biomedical to a biopsychosocial-oriented one. Finally, to accommodate the differences in individual needs and preferences, we propose the design of adaptable person-centered EMR systems. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Research)
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11 pages, 1681 KiB  
Article
Unstructured Text in EMR Improves Prediction of Death after Surgery in Children
by Oguz Akbilgic, Ramin Homayouni, Kevin Heinrich, Max Raymond Langham and Robert Lowell Davis
Informatics 2019, 6(1), 4; https://doi.org/10.3390/informatics6010004 - 10 Jan 2019
Cited by 4 | Viewed by 8604
Abstract
Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur [...] Read more.
Text fields in electronic medical records (EMR) contain information on important factors that influence health outcomes, however, they are underutilized in clinical decision making due to their unstructured nature. We analyzed 6497 inpatient surgical cases with 719,308 free text notes from Le Bonheur Children’s Hospital EMR. We used a text mining approach on preoperative notes to obtain a text-based risk score to predict death within 30 days of surgery. In addition, we evaluated the performance of a hybrid model that included the text-based risk score along with structured data pertaining to clinical risk factors. The C-statistic of a logistic regression model with five-fold cross-validation significantly improved from 0.76 to 0.92 when text-based risk scores were included in addition to structured data. We conclude that preoperative free text notes in EMR include significant information that can predict adverse surgery outcomes. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Research)
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5 pages, 190 KiB  
Communication
The STELAR ICU: Leveraging Electronic Health Record Data to Foster Research and Optimize Patient Care
by Christopher M. Horvat, Srinivasan Suresh and Robert S. B. Clark
Informatics 2018, 5(3), 37; https://doi.org/10.3390/informatics5030037 - 7 Sep 2018
Viewed by 7138
Abstract
Electronic health records (EHR) combined with robust data collection systems can be used to simultaneously drive research and performance improvement initiatives. Our Smart, Transformative, EHR-based Approaches to Revolutionizing the Intensive Care Unit (STELAR ICU) consists of a framework of five best practices that [...] Read more.
Electronic health records (EHR) combined with robust data collection systems can be used to simultaneously drive research and performance improvement initiatives. Our Smart, Transformative, EHR-based Approaches to Revolutionizing the Intensive Care Unit (STELAR ICU) consists of a framework of five best practices that make optimal use of objective data to guide clinicians caring for the sickest patients in our quaternary center. Our strategy has relied on an accessible data infrastructure, standardizing without protocolizing care, using technology to increase patient contact and time spent at the bedside, continuously re-evaluating performance in real-time, and acknowledging uncertainty by using electronic data to provide probabilistic weight to clinical decision-making. These strategies blur the lines between research and quality improvement, with the aim of achieving truly stellar patient outcomes. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Research)
11 pages, 1095 KiB  
Article
Temporal and Atemporal Provider Network Analysis in a Breast Cancer Cohort from an Academic Medical Center (USA)
by Bryan D. Steitz and Mia A. Levy
Informatics 2018, 5(3), 34; https://doi.org/10.3390/informatics5030034 - 6 Aug 2018
Cited by 4 | Viewed by 7660
Abstract
Social network analysis (SNA) is a quantitative approach to study relationships between individuals. Current SNA methods use static models of organizations, which simplify network dynamics. To better represent the dynamic nature of clinical care, we developed a temporal social network analysis model to [...] Read more.
Social network analysis (SNA) is a quantitative approach to study relationships between individuals. Current SNA methods use static models of organizations, which simplify network dynamics. To better represent the dynamic nature of clinical care, we developed a temporal social network analysis model to better represent care temporality. We applied our model to appointment data from a single institution for early stage breast cancer patients. Our cohort of 4082 patients were treated by 2190 providers. Providers had 54,695 unique relationships when calculated using our temporal method, compared to 249,075 when calculated using the atemporal method. We found that traditional atemporal approaches to network modeling overestimate the number of provider-provider relationships and underestimate common network measures such as care density within a network. Social network analysis, when modeled accurately, is a powerful tool for organizational research within the healthcare domain. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Research)
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10 pages, 1004 KiB  
Perspective
Large Scale Advanced Data Analytics on Skin Conditions from Genotype to Phenotype
by Maryam Panahiazar, Darya Fadavi, Jihad Aljabban, Laraib Safeer, Imad Aljabban and Dexter Hadley
Informatics 2018, 5(4), 39; https://doi.org/10.3390/informatics5040039 - 23 Oct 2018
Cited by 1 | Viewed by 7214
Abstract
A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for [...] Read more.
A crucial factor in Big Data is to take advantage of available data and use that for new discovery or hypothesis generation. In this study, we analyzed Large-scale data from the literature to OMICS, such as the genome, proteome or metabolome, respectively, for skin conditions. Skin acts as a natural barrier to the world around us and protects our body from different conditions, viruses, and bacteria, and plays a big part in appearance. We have included Hyperpigmentation, Postinflammatory Hyperpigmentation, Melasma, Rosacea, Actinic keratosis, and Pigmentation in this study. These conditions have been selected based on reasoning of big scale UCSF patient data of 527,273 females from 2011 to 2017, and related publications from 2000 to 2017 regarding skin conditions. The selected conditions have been confirmed with experts in the field from different research centers and hospitals. We proposed a novel framework for large-scale available public data to find the common genotypes and phenotypes of different skin conditions. The outcome of this study based on Advance Data Analytics provides information on skin conditions and their treatments to the research community and introduces new hypotheses for possible genotype and phenotype targets. The novelty of this work is a meta-analysis of different features on different skin conditions. Instead of looking at individual conditions with one or two features, which is how most of the previous works are conducted, we looked at several conditions with different features to find the common factors between them. Our hypothesis is that by finding the overlap in genotype and phenotype between different skin conditions, we can suggest using a drug that is recommended in one condition, for treatment in the other condition which has similar genes or other common phenotypes. We identified common genes between these skin conditions and were able to find common areas for targeting between conditions, such as common drugs. Our work has implications for discovery and new hypotheses to improve health quality, and is geared towards making Big Data useful. Full article
(This article belongs to the Special Issue Data-Driven Healthcare Research)
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