Information Systems in Healthcare

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 9423

Special Issue Editor


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Guest Editor
Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Campus of Arta, GR-47100 Arta, Greece
Interests: biomedical signal processing; computational intelligence; knowledge extraction; medical informatics; IoT; data mining; pervasive and mobile computing systems

Special Issue Information

Dear Colleagues,

Information systems in healthcare, or health information systems (HIS), refers to systems designed to manage healthcare data. This includes systems that collect, store, manage, and transmit a patient’s electronic medical record (EMR), a hospital’s operational management, or a system supporting healthcare policy decisions. Health information systems also include those systems that handle data related to the activities of providers and health organizations. As an integrated effort, these may be leveraged to improve patient outcomes, inform research, and influence policy-making and decision-making processes. Health information systems can be used by everyone in healthcare, including patients, clinicians, and public health officials. They collect data and compile it in a way that can be used to make healthcare decisions. Examples of health information systems include electronic medical record (EMR) and electronic health record (EHR), practice management software, master patient index (MPI), patient portals, remote patient monitoring (RPM), and clinical decision support (CDS) systems. Clinical decision support systems analyze data from various clinical and administrative systems to help healthcare providers make clinical decisions.

Information systems can improve cost control, increase the timeliness and accuracy of patient care and administration information, increase service capacity, reduce personnel costs and inventory levels, and improve the quality of patient care.

The main aim of this Special Issue is to seek high-quality submissions focusing on the theoretical and practical aspects of information systems in healthcare, focusing on all related research areas, such as medication management, preventive care, health conditions, data quality, and care process/outcome.

Dr. Evaggelos Karvounis
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. Information is an international peer-reviewed open access monthly 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 information technology
  • patient safety
  • health information systems
  • hospital information systems
  • clinical informatics
  • health informatics
  • medical informatics
  • clinical decision support
  • connected health
  • data analytics
  • electronic health record

Published Papers (6 papers)

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Research

16 pages, 2688 KiB  
Article
A Multimethod Approach for Healthcare Information Sharing Systems: Text Analysis and Empirical Data
by Amit Malhan, Robert Pavur, Lou E. Pelton and Ava Hajian
Information 2024, 15(6), 319; https://doi.org/10.3390/info15060319 - 29 May 2024
Viewed by 217
Abstract
This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of [...] Read more.
This paper provides empirical evidence using two studies to explain the primary factors facilitating electronic health record (EHR) systems adoption through the lens of the resource advantage theory. We aim to address the following research questions: What are the main organizational antecedents of EHR implementation? What is the role of monitoring in EHR system implementation? What are the current themes and people’s attitudes toward EHR systems? This paper includes two empirical studies. Study 1 presents a research model based on data collected from four different archival datasets. Drawing upon the resource advantage theory, this paper uses archival data from 200 Texas hospitals, thus mitigating potential response bias and enhancing the validity of the findings. Study 2 includes a text analysis of 5154 textual data, sentiment analysis, and topic modeling. Study 1’s findings reveal that joint ventures and ownership are the two main enablers of adopting EHR systems in 200 Texas hospitals. Moreover, the results offer a moderating role of monitoring in strengthening the relationship between joint-venture capability and the implementation of EHR systems. Study 2’s results indicate a positive attitude toward EHR systems. The U.S. was unique in the sample due to its slower adoption of EHR systems than other developed countries. Physician burnout also emerged as a significant concern in the context of EHR adoption. Topic modeling identified three themes: training, healthcare interoperability, and organizational barriers. In a multimethod design, this paper contributes to prior work by offering two new EHR antecedents: hospital ownership and joint-venture capability. Moreover, this paper suggests that the monitoring mechanism moderates the adoption of EHR systems in Texas hospitals. Moreover, this paper contributes to prior EHR works by performing text analysis of textual data to carry out sentiment analysis and topic modeling. Full article
(This article belongs to the Special Issue Information Systems in Healthcare)
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43 pages, 8370 KiB  
Article
Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction
by Fahim Sufi
Information 2024, 15(5), 264; https://doi.org/10.3390/info15050264 - 6 May 2024
Viewed by 1052
Abstract
This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves [...] Read more.
This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT’s aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients’ conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts. Full article
(This article belongs to the Special Issue Information Systems in Healthcare)
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23 pages, 6157 KiB  
Article
Advancing Tuberculosis Detection in Chest X-rays: A YOLOv7-Based Approach
by Rabindra Bista, Anurag Timilsina, Anish Manandhar, Ayush Paudel, Avaya Bajracharya, Sagar Wagle and Joao C. Ferreira
Information 2023, 14(12), 655; https://doi.org/10.3390/info14120655 - 10 Dec 2023
Cited by 1 | Viewed by 2632
Abstract
In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is [...] Read more.
In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesions and their detection, respectively. For this study, we experimented on the TBX11K dataset, which is a publicly available dataset. In our experiment, we employed class weights and data augmentation techniques to address the data imbalance present in the dataset. This technique shows a promising improvement in the model’s performance and thus better generalization. In addition, it also shows that the developed model achieved promising results with a mAP (mean average precision) of 0.587, addressing class imbalance and yielding a robust performance for both obsolete pulmonary TB and active TB detection. Thus, our CAD system, rooted in state-of-the-art deep-learning and computer vision methodologies, not only advances diagnostic accuracy but also contributes to the mitigation of TB transmission risks. The substantial improvement in the model’s performance and the ability to handle class imbalance underscore the potential of our approach for real-world TB detection applications. Full article
(This article belongs to the Special Issue Information Systems in Healthcare)
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13 pages, 1868 KiB  
Article
Semantic Integration of BPMN Models and FHIR Data to Enable Personalized Decision Support for Malignant Melanoma
by Catharina Lena Beckmann, Daniel Keuchel, Wa Ode Iin Arliani Soleman, Sylvia Nürnberg and Britta Böckmann
Information 2023, 14(12), 649; https://doi.org/10.3390/info14120649 - 6 Dec 2023
Cited by 1 | Viewed by 1648
Abstract
With digital patient data increasing due to new diagnostic methods and technology, showing the right data in the context of decision support at the point of care becomes an even greater challenge. Standard operating procedures (SOPs) modeled in BPMN (Business Process Model and [...] Read more.
With digital patient data increasing due to new diagnostic methods and technology, showing the right data in the context of decision support at the point of care becomes an even greater challenge. Standard operating procedures (SOPs) modeled in BPMN (Business Process Model and Notation) contain evidence-based treatment guidance for all phases of a certain diagnosis, while physicians need the parts relevant to a specific patient at a specific point in the clinical process. Therefore, integration of patient data from electronic health records (EHRs) providing context to clinicians is needed, which is stored and communicated in HL7 (Health Level Seven) FHIR (Fast Healthcare Interoperability Resources). To address this issue, we propose a method combining an integration of stored data into BPMN and a loss-free transformation from BPMN into FHIR, and vice versa. Based on that method, an identification of the next necessary decision point in a specific patient context is possible. We verified the method for treatment of malignant melanoma by using an extract of a formalized SOP document with predefined decision points and validated FHIR references with real EHR data. The patient data could be stored and integrated into the BPMN element ‘DataStoreReference’. Our loss-free transformation process therefore is the foundation for combining evidence-based knowledge from formalized clinical guidelines or SOPs and patient data from EHRs stored in FHIR. Processing the SOP with the available patient data can then lead to the next upcoming decision point, which will be displayed to the physician integrated with the corresponding data. Full article
(This article belongs to the Special Issue Information Systems in Healthcare)
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19 pages, 2438 KiB  
Article
A Deep Learning Approach for Predictive Healthcare Process Monitoring
by Ulises Manuel Ramirez-Alcocer, Edgar Tello-Leal, Gerardo Romero and Bárbara A. Macías-Hernández
Information 2023, 14(9), 508; https://doi.org/10.3390/info14090508 - 16 Sep 2023
Cited by 4 | Viewed by 1439
Abstract
In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a [...] Read more.
In this paper, we propose a deep learning-based approach to predict the next event in hospital organizational process models following the guidance of predictive process mining. This method provides value for the planning and allocating of resources since each trace linked to a case shows the consecutive execution of events in a healthcare process. The predictive model is based on a long short-term memory (LSTM) neural network that achieves high accuracy in the training and testing stages. In addition, a framework to implement the LSTM neural network is proposed, comprising stages from the preprocessing of the raw data to selecting the best LSTM model. The effectiveness of the prediction method is evaluated through four real-life event logs that contain historical information on the execution of the processes of patient transfer orders between hospitals, sepsis care cases, billing of medical services, and patient care management. In the test stage, the LSTM model reached values of 0.98, 0.91, 0.85, and 0.81 in the accuracy metric, and in the evaluation of the prediction of the next event using the 10-fold cross-validation technique, values of 0.94, 0.88, 0.84, and 0.81 were obtained for the four previously mentioned event logs. In addition, the performance of the LSTM prediction model was evaluated with the precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC) metrics, obtaining high scores very close to 1. The experimental results suggest that the proposed method achieves acceptable measures in predicting the next event regardless of whether an input event or a set of input events is used. Full article
(This article belongs to the Special Issue Information Systems in Healthcare)
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14 pages, 1838 KiB  
Article
Digital-Reported Outcome from Medical Notes of Schizophrenia and Bipolar Patients Using Hierarchical BERT
by Rezaul K. Khandker, Md Rakibul Islam Prince, Farid Chekani, Paul Richard Dexter, Malaz A. Boustani and Zina Ben Miled
Information 2023, 14(9), 471; https://doi.org/10.3390/info14090471 - 22 Aug 2023
Viewed by 1118
Abstract
Patient-reported (PRO) and clinician-reported (CRO) outcomes are assessment instruments that are completed by patients and trained healthcare professionals, respectively. A PRO is a report of the direct experience of the patient with a given disease condition. A CRO is an assessment of the [...] Read more.
Patient-reported (PRO) and clinician-reported (CRO) outcomes are assessment instruments that are completed by patients and trained healthcare professionals, respectively. A PRO is a report of the direct experience of the patient with a given disease condition. A CRO is an assessment of the condition of the patient by the healthcare provider. PROs may not be accessible to all patients, especially those suffering from severe disease conditions. CROs are time-consuming and therefore administered infrequently. In the present study, we introduce a new form of assessment, the digital-reported outcome (DRO), which is automatically derived from the medical notes of the patient. DROs have a low overhead and can be generated at each patient’s visit to complement other outcome-assessment instruments and enhance clinical decision support by identifying at-risk patients. In this study, a DRO is developed to evaluate the functional impairment in the daily activities of two cohorts of patients suffering from bipolar disorder and schizophrenia. The input of the DRO is a single medical note from the electronic medical record of the patient. This note is submitted to a hierarchical bidirectional encoder representations from transformers (BERT) model. First, a sentence-level embedding is produced for each sentence in the note using a token-level attention mechanism. Second, an embedding for the entire note is constructed using a sentence-level attention mechanism. Third, the final embedding is classified using a feed-forward neural network. The model is trained to classify patients into moderate or severe functioning impairment levels according to the general assessment of functioning (GAF) scale, a CRO instrument for the assessment of the impact of mental illness on the daily activities of the patient. The DRO is validated using medical notes that were labeled by multiple healthcare providers from different healthcare institutions. The results indicate that a general DRO is able to classify patients from the two cohorts according to the two functioning impairment levels (severe versus moderate) prior to the onset of disease with an AUC of 76%. Disease-specific DROs are only applicable after the onset of the disease and produced AUCs of nearly 85%. The methodology introduced in the present paper is practical and can support the automated monitoring of the severity of the functioning impairment of bipolar and schizophrenia patients. Extending the proposed DRO to other psychiatric conditions and types of impairments is the subject of ongoing research work. Full article
(This article belongs to the Special Issue Information Systems in Healthcare)
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