Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.8
3.1
Journals
Informatics
Special Issues
Innovations in Electronic Health Records to Improve Safety and Quality...
share announcement

Innovations in Electronic Health Records to Improve Safety and Quality and Reduce Clinician Burnout

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 14007

Special Issue Editor

Dr. Evan W. Orenstein

E-Mail Website
Guest Editor
Department of Biomedical and Health Informatics (DBHi), Emory University School of Medicine, Atlanta, GA, USA
Interests: clinical decision support; usability; quality improvement; medical education; global health

Special Issue Information

Dear Colleagues,

Implementation of electronic health records (EHRs) has reduced medication errors, improved chart legibility, created new forms of communication, and led to a proliferation of electronic data capture for secondary use. However, poor attention to EHR usability and interoperability has facilitated novel medical errors, incited patient and clinician frustration, and contributed to a crisis in burnout among healthcare workers. Overcoming these challenges will require innovations in health information technology that support the sociotechnical system comprising patients, their care providers, healthcare organizations, and their environment. This Special Issue of Informatics seeks contributions focused on innovations in EHR design, training, and secondary use of EHR data to improve health outcomes and health equity. Topics include but are not limited to:

  • Quality improvement projects involving clinical decision support or other EHR-based interventions to improve diagnosis, adherence to best practices, and health outcomes or reduce burnout;
  • Predictive models for deterioration, early detection of disease, patient flow, or other outcomes that leverage EHR data to improve healthcare. Interventions in which predictive models have been implemented and evaluated in live clinical settings will be given preference;
  • Qualitative studies describing how stakeholders work with EHRs or secondary data with a focus on insights for improvement to EHR design and evaluation;
  • EHR design principles and applications in specific healthcare settings including resource-limited settings;
  • Methodological advances in EHR design and evaluation of EHR-based interventions;
  • User-centered design of EHR interfaces, functions, and dashboards that re-use EHR data;
  • Innovations to promote interoperability in EHRs;
  • Studies focused on EHR training or use of EHRs and EHR data for medical education.

Dr. Evan W. Orenstein
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

  • electronic health records
  • quality improvement
  • clinical decision support
  • usability/user-centered design
  • interoperability
  • predictive models
  • training and medical education
  • simulation

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

19 pages, 1229 KiB  
Article
Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review
by Terrence C. Lee, Neil U. Shah, Alyssa Haack and Sally L. Baxter
Informatics 2020, 7(3), 25; https://doi.org/10.3390/informatics7030025 - 25 Jul 2020
Cited by 33 | Viewed by 5737
Abstract
Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic [...] Read more.
Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings. Full article
(This article belongs to the Special Issue Innovations in Electronic Health Records to Improve Safety and Quality and Reduce Clinician Burnout)
►▼ Show Figures

Figure 1

Review

Jump to: Research, Other

13 pages, 561 KiB  
Review
Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities
by Fernando Ribeiro, Filipe Fidalgo, Arlindo Silva, José Metrôlho, Osvaldo Santos and Rogério Dionisio
Informatics 2021, 8(4), 76; https://doi.org/10.3390/informatics8040076 - 10 Nov 2021
Cited by 14 | Viewed by 4269
Abstract
Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have [...] Read more.
Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities. Full article
(This article belongs to the Special Issue Innovations in Electronic Health Records to Improve Safety and Quality and Reduce Clinician Burnout)
►▼ Show Figures

Figure 1

Other

Jump to: Research, Review

10 pages, 492 KiB  
Concept Paper
Towards a New Paradigm of Federated Electronic Health Records in Palestine
by Carol El Jabari, Mario Macedo and Mohanad O. Al-jabari
Informatics 2020, 7(4), 41; https://doi.org/10.3390/informatics7040041 - 05 Oct 2020
Cited by 5 | Viewed by 3237
Abstract
While efforts are underway to create a sound system of electronic health records in Palestinian health institutions, there remain obstacles and challenges. Given modern day demands on health systems, we propose a federated electronic health system based on the clinical document architecture (CDA) [...] Read more.
While efforts are underway to create a sound system of electronic health records in Palestinian health institutions, there remain obstacles and challenges. Given modern day demands on health systems, we propose a federated electronic health system based on the clinical document architecture (CDA) that is compliant within the Palestine context. This architecture also brings a normalized electronic health record and a structure of blockchain to enhance interoperability with scalability, fault tolerance, privacy, and security. The new architecture and technologies will enhance services by allowing health care players, patients, and others to have the opportunity to obtain improved access and control of their health services. This may also serve as a useful model for other low-middle income countries. Full article
(This article belongs to the Special Issue Innovations in Electronic Health Records to Improve Safety and Quality and Reduce Clinician Burnout)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop