JMIR Publications

We are scheduled to perform a server upgrade on Thursday, November 30, 2017 between 4 and 6 PM Eastern Time.

Please refrain from submitting support requests related to server downtime during this window.

JMIR Medical Informatics

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.


Journal Description

JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.

Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.

JMIR Medical Informatics journal features a rapid and thorough peer-review process, professional copyediting, professional production of PDF, XHTML, and XML proofs (ready for deposit in PubMed Central/PubMed). The site is optimized for mobile and iPad use.

JMIR Medical Informatics adheres to the same quality standards as JMIR and all articles published here are also cross-listed in the Table of Contents of JMIR, the worlds' leading medical journal in health sciences / health services research and health informatics (


Recent Articles:

  • Source:; Copyright: hamiltonpaviana; URL:; License: Public Domain (CC0).

    A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation


    Background: A computerized physician order entry (CPOE) system combined with a clinical decision support system can reduce duplication of medications and thus adverse drug reactions. However, without infrastructure that supports patients’ integrated medication history across health care facilities nationwide, duplication of medication can still occur. In Taiwan, the National Health Insurance Administration has implemented a national medication repository and Web-based query system known as the PharmaCloud, which allows physicians to access their patients’ medication records prescribed by different health care facilities across Taiwan. Objective: This study aimed to develop a scalable, flexible, and thematic design-based clinical decision support (CDS) engine, which integrates a national medication repository to support CPOE systems in the detection of potential duplication of medication across health care facilities, as well as to analyze its impact on clinical encounters. Methods: A CDS engine was developed that can download patients’ up-to-date medication history from the PharmaCloud and support a CPOE system in the detection of potential duplicate medications. When prescribing a medication order using the CPOE system, a physician receives an alert if there is a potential duplicate medication. To investigate the impact of the CDS engine on clinical encounters in outpatient services, a clinical encounter log was created to collect information about time, prescribed drugs, and physicians’ responses to handling the alerts for each encounter. Results: The CDS engine was installed in a teaching affiliate hospital, and the clinical encounter log collected information for 3 months, during which a total of 178,300 prescriptions were prescribed in the outpatient departments. In all, 43,844/178,300 (24.59%) patients signed the PharmaCloud consent form allowing their physicians to access their medication history in the PharmaCloud. The rate of duplicate medication was 5.83% (1843/31,614) of prescriptions. When prescribing using the CDS engine, the median encounter time was 4.3 (IQR 2.3-7.3) min, longer than that without using the CDS engine (median 3.6, IQR 2.0-6.3 min). From the physicians’ responses, we found that 42.06% (1908/4536) of the potential duplicate medications were recognized by the physicians and the medication orders were canceled. Conclusions: The CDS engine could easily extend functions for detection of adverse drug reactions when more and more electronic health record systems are adopted. Moreover, the CDS engine can retrieve more updated and completed medication histories in the PharmaCloud, so it can have better performance for detection of duplicate medications. Although our CDS engine approach could enhance medication safety, it would make for a longer encounter time. This problem can be mitigated by careful evaluation of adopted solutions for implementation of the CDS engine. The successful key component of a CDS engine is the completeness of the patient’s medication history, thus further research to assess the factors in increasing the PharmaCloud consent rate is required.

  • Source: Pexels; Copyright:; URL:; License: Public Domain (CC0).

    The Use of Technology in Identifying Hospital Malnutrition: Scoping Review


    Background: Malnutrition is a condition most commonly arising from the inadequate consumption of nutrients necessary to maintain physiological health and is associated with the development of cardiovascular disease, osteoporosis, and sarcopenia. Malnutrition occurring in the hospital setting is caused by insufficient monitoring, identification, and assessment efforts. Furthermore, the ability of health care workers to identify and recognize malnourished patients is suboptimal. Therefore, interventions focusing on the identification and treatment of malnutrition are valuable, as they reduce the risks and rates of malnutrition within hospitals. Technology may be a particularly useful ally in identifying malnutrition due to scalability, timeliness, and effectiveness. In an effort to explore the issue, this scoping review synthesized the availability of technological tools to detect and identify hospital malnutrition. Objective: Our objective was to conduct a scoping review of the different forms of technology used in addressing malnutrition among adults admitted to hospital to (1) identify the extent of the published literature on this topic, (2) describe key findings, and (3) identify outcomes. Methods: We designed and implemented a search strategy in 3 databases (PubMed, Scopus, and CINAHL). We completed a descriptive numerical summary and analyzed study characteristics. One reviewer independently extracted data from the databases. Results: We retrieved and reviewed a total of 21 articles. We categorized articles by the computerized tool or app type: malnutrition assessment (n=15), food intake monitoring (n=5), or both (n=1). Within those categories, we subcategorized the different technologies as either hardware (n=4), software (n=13), or both (n=4). An additional subcategory under software was cloud-based apps (n=1). Malnutrition in the acute hospital setting was largely an unrecognized problem, owing to insufficient monitoring, identification, and initial assessments of identifying both patients who are already malnourished and those who are at risk of malnourishment. Studies went on to examine the effectiveness of health care workers (nurses and doctors) with a knowledge base focused on clinical care and their ability to accurately and consistently identify malnourished geriatric patients within that setting. Conclusions: Most articles reported effectiveness in accurately increasing malnutrition detection and awareness. Computerized tools and apps may also help reduce health care workers’ workload and time spent assessing patients for malnutrition. Hospitals may also benefit from implementing malnutrition technology through observing decreased length of stay, along with decreased foregone costs related to missing malnutrition diagnoses. It is beneficial to study the impact of these technologies to examine possible areas of improvement. A future systematic review would further contribute to the evidence and effectiveness of the use of technologies in assessing and monitoring hospital malnutrition.

  • Source:; Copyright: Veterans Health; URL:; License: Public Domain (CC0).

    Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs


    Background: We developed an accurate, stakeholder-informed, automated, natural language processing (NLP) system to measure the quality of heart failure (HF) inpatient care, and explored the potential for adoption of this system within an integrated health care system. Objective: To accurately automate a United States Department of Veterans Affairs (VA) quality measure for inpatients with HF. Methods: We automated the HF quality measure Congestive Heart Failure Inpatient Measure 19 (CHI19) that identifies whether a given patient has left ventricular ejection fraction (LVEF) <40%, and if so, whether an angiotensin-converting enzyme inhibitor or angiotensin-receptor blocker was prescribed at discharge if there were no contraindications. We used documents from 1083 unique inpatients from eight VA medical centers to develop a reference standard (RS) to train (n=314) and test (n=769) the Congestive Heart Failure Information Extraction Framework (CHIEF). We also conducted semi-structured interviews (n=15) for stakeholder feedback on implementation of the CHIEF. Results: The CHIEF classified each hospitalization in the test set with a sensitivity (SN) of 98.9% and positive predictive value of 98.7%, compared with an RS and SN of 98.5% for available External Peer Review Program assessments. Of the 1083 patients available for the NLP system, the CHIEF evaluated and classified 100% of cases. Stakeholders identified potential implementation facilitators and clinical uses of the CHIEF. Conclusions: The CHIEF provided complete data for all patients in the cohort and could potentially improve the efficiency, timeliness, and utility of HF quality measurements.

  • Source:; Copyright: tmeier1964; URL:; License: Public Domain (CC0).

    A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis


    Background: With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective: This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods: We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results: The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions: In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge.

  • Source:; Copyright: Pina Messina; URL:; License: Public Domain (CC0).

    Stage-Based Mobile Intervention for Substance Use Disorders in Primary Care: Development and Test of Acceptability


    Background: In 2016, 21 million Americans aged 12 years and older needed treatment for a substance use disorder (SUD). However, only 10% to 11% of individuals requiring SUD treatment received it. Given their access to patients, primary care providers are in a unique position to perform universal Screening, Brief Intervention, and Referral to Treatment (SBIRT) to identify individuals at risk, fill gaps in services, and make referrals to specialty treatment when indicated. Major barriers to SBIRT include limited time among providers and low motivation to change among many patients. Objective: The objective of this study was to develop and test the acceptability of a prototype of a mobile-delivered substance use risk intervention (SURI) for primary care patients and a clinical dashboard for providers that can address major barriers to SBIRT for risky drug use. The SURI delivers screening and feedback on SUD risk via mobile tools to patients at home or in the waiting room; for patients at risk, it also delivers a brief intervention based on the transtheoretical model of behavior change (TTM) to facilitate progress through the stages of change for quitting the most problematic drug and for seeking treatment if indicated. The prototype also delivers 30 days of stage-matched text messages and 4 Web-based activities addressing key topics. For providers, the clinical dashboard summarizes the patient’s SUD risk scores and stage of change data, and provides stage-matched scripts to guide in-person sessions. Methods: A total of 4 providers from 2 federally qualified health centers (FQHCs) were recruited for the pilot test, and they in turn recruited 5 patients with a known SUD. Furthermore, 3 providers delivered dashboard-guided SBIRT sessions and completed a brief acceptability survey. A total of 4 patients completed a Web-based SURI session and in-person SBIRT session, accessed other program components, and completed 3 acceptability surveys over 30 days. Questions in the surveys were adapted from the National Cancer Institute’s Education Materials Review Form. Response options ranged from 1=strongly disagree to 5=strongly agree. The criterion for establishing acceptability was an overall rating of 4.0 or higher across items. Results: For providers, the overall mean acceptability rating was 4.4 (standard deviation [SD] 0.4). Notably, all providers gave a rating of 5.0 for the item, “The program can give me helpful information about my patient.” For patients, the overall mean acceptability rating was 4.5 (SD 0.3) for the mobile- and provider-delivered SBIRT sessions and 4.0 (SD 0.4) for the text messages and Web-based activities. One highly rated item was “The program could help me make some positive changes” (4.5). Conclusions: The SURI program and clinical dashboard, developed to reduce barriers to SBIRT in primary care, were well received by providers and patients.

  • Radiology CT technician and patient being scanned and diagnosed on CT. Source: iStock by Getty Images; Copyright: John Kellerman; URL:; License: Licensed by the authors.

    Standard Anatomic Terminologies: Comparison for Use in a Health Information Exchange–Based Prior Computed Tomography (CT) Alerting System


    Background: A health information exchange (HIE)–based prior computed tomography (CT) alerting system may reduce avoidable CT imaging by notifying ordering clinicians of prior relevant studies when a study is ordered. For maximal effectiveness, a system would alert not only for prior same CTs (exams mapped to the same code from an exam name terminology) but also for similar CTs (exams mapped to different exam name terminology codes but in the same anatomic region) and anatomically proximate CTs (exams in adjacent anatomic regions). Notification of previous same studies across an HIE requires mapping of local site CT codes to a standard terminology for exam names (such as Logical Observation Identifiers Names and Codes [LOINC]) to show that two studies with different local codes and descriptions are equivalent. Notifying of prior similar or proximate CTs requires an additional mapping of exam codes to anatomic regions, ideally coded by an anatomic terminology. Several anatomic terminologies exist, but no prior studies have evaluated how well they would support an alerting use case. Objective: The aim of this study was to evaluate the fitness of five existing standard anatomic terminologies to support similar or proximate alerts of an HIE-based prior CT alerting system. Methods: We compared five standard anatomic terminologies (Foundational Model of Anatomy, Systematized Nomenclature of Medicine Clinical Terms, RadLex, LOINC, and LOINC/Radiological Society of North America [RSNA] Radiology Playbook) to an anatomic framework created specifically for our use case (Simple ANatomic Ontology for Proximity or Similarity [SANOPS]), to determine whether the existing terminologies could support our use case without modification. On the basis of an assessment of optimal terminology features for our purpose, we developed an ordinal anatomic terminology utility classification. We mapped samples of 100 random and the 100 most frequent LOINC CT codes to anatomic regions in each terminology, assigned utility classes for each mapping, and statistically compared each terminology’s utility class rankings. We also constructed seven hypothetical alerting scenarios to illustrate the terminologies’ differences. Results: Both RadLex and the LOINC/RSNA Radiology Playbook anatomic terminologies ranked significantly better (P<.001) than the other standard terminologies for the 100 most frequent CTs, but no terminology ranked significantly better than any other for 100 random CTs. Hypothetical scenarios illustrated instances where no standard terminology would support appropriate proximate or similar alerts, without modification. Conclusions: LOINC/RSNA Radiology Playbook and RadLex’s anatomic terminologies appear well suited to support proximate or similar alerts for commonly ordered CTs, but for less commonly ordered tests, modification of the existing terminologies with concepts and relations from SANOPS would likely be required. Our findings suggest SANOPS may serve as a framework for enhancing anatomic terminologies in support of other similar use cases.

  • A senior resident during tele-consultation process. Source: Image created by the Authors; Copyright: Kolsoum Deldar; URL:; License: Creative Commons Attribution (CC-BY).

    A Data Model for Teleconsultation in Managing High-Risk Pregnancies: Design and Preliminary Evaluation


    Background: Teleconsultation is a guarantor for virtual supervision of clinical professors on clinical decisions made by medical residents in teaching hospitals. Type, format, volume, and quality of exchanged information have a great influence on the quality of remote clinical decisions or tele-decisions. Thus, it is necessary to develop a reliable and standard model for these clinical relationships. Objective: The goal of this study was to design and evaluate a data model for teleconsultation in the management of high-risk pregnancies. Methods: This study was implemented in three phases. In the first phase, a systematic review, a qualitative study, and a Delphi approach were done in selected teaching hospitals. Systematic extraction and localization of diagnostic items to develop the tele-decision clinical archetypes were performed as the second phase. Finally, the developed model was evaluated using predefined consultation scenarios. Results: Our review study has shown that present medical consultations have no specific structure or template for patient information exchange. Furthermore, there are many challenges in the remote medical decision-making process, and some of them are related to the lack of the mentioned structure. The evaluation phase of our research has shown that data quality (P<.001), adequacy (P<.001), organization (P<.001), confidence (P<.001), and convenience (P<.001) had more scores in archetype-based consultation scenarios compared with routine-based ones. Conclusions: Our archetype-based model could acquire better and higher scores in the data quality, adequacy, organization, confidence, and convenience dimensions than ones with routine scenarios. It is probable that the suggested archetype-based teleconsultation model may improve the quality of physician-physician remote medical consultations.

  • Source:; Copyright: George Hodan; URL:; License: Public Domain (CC0).

    Examining Tensions That Affect the Evaluation of Technology in Health Care: Considerations for System Decision Makers From the Perspective of Industry and...


    Virtual technologies have the potential to mitigate a range of challenges for health care systems. Despite the widespread use of mobile devices in everyday life, they currently have a limited role in health service delivery and clinical care. Efforts to integrate the fast-paced consumer technology market with health care delivery exposes tensions among patients, providers, vendors, evaluators, and system decision makers. This paper explores the key tensions between the high bar for evidence prior to market approval that guides health care regulatory decisions and the “fail fast” reality of the technology industry. We examine three core tensions: balancing user needs versus system needs, rigor versus responsiveness, and the role of pre- versus postmarket evidence generation. We use these to elaborate on the structure and appropriateness of evaluation mechanisms for virtual care solutions. Virtual technologies provide a foundation for personalized, patient-centered medicine on the user side, coupled with a broader understanding of impact on the system side. However, mechanisms for stakeholder discussion are needed to clarify the nature of the health technology marketplace and the drivers of evaluation priorities.

  • Source: Image created by the Authors; Copyright: Ahmad P Tafti; URL:; License: Creative Commons Attribution (CC-BY).

    Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure


    Background: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. Objective: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. Methods: We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. Results: The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. Conclusions: To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.

  • Source: The Authors /; Copyright: JMIR Publications; URL:; License: Creative Commons Attribution (CC-BY).

    Search and Graph Database Technologies for Biomedical Semantic Indexing: Experimental Analysis


    Background: Biomedical semantic indexing is a very useful support tool for human curators in their efforts for indexing and cataloging the biomedical literature. Objective: The aim of this study was to describe a system to automatically assign Medical Subject Headings (MeSH) to biomedical articles from MEDLINE. Methods: Our approach relies on the assumption that similar documents should be classified by similar MeSH terms. Although previous work has already exploited the document similarity by using a k-nearest neighbors algorithm, we represent documents as document vectors by search engine indexing and then compute the similarity between documents using cosine similarity. Once the most similar documents for a given input document are retrieved, we rank their MeSH terms to choose the most suitable set for the input document. To do this, we define a scoring function that takes into account the frequency of the term into the set of retrieved documents and the similarity between the input document and each retrieved document. In addition, we implement guidelines proposed by human curators to annotate MEDLINE articles; in particular, the heuristic that says if 3 MeSH terms are proposed to classify an article and they share the same ancestor, they should be replaced by this ancestor. The representation of the MeSH thesaurus as a graph database allows us to employ graph search algorithms to quickly and easily capture hierarchical relationships such as the lowest common ancestor between terms. Results: Our experiments show promising results with an F1 of 69% on the test dataset. Conclusions: To the best of our knowledge, this is the first work that combines search and graph database technologies for the task of biomedical semantic indexing. Due to its horizontal scalability, ElasticSearch becomes a real solution to index large collections of documents (such as the bibliographic database MEDLINE). Moreover, the use of graph search algorithms for accessing MeSH information could provide a support tool for cataloging MEDLINE abstracts in real time.

  • Mother and daughter with staff in Intensive Care Unit. Source:; Copyright: Monkey Business Images; URL:; License: Licensed by the authors.

    Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements


    Background: Early warning scores aid in the detection of pediatric clinical deteriorations but include limited data inputs, rarely include data trends over time, and have limited validation. Objective: Machine learning methods that make use of large numbers of predictor variables are now commonplace. This work examines how different types of predictor variables derived from the electronic health record affect the performance of predicting unplanned transfers to the intensive care unit (ICU) at three large children’s hospitals. Methods: We trained separate models with data from three different institutions from 2011 through 2013 and evaluated models with 2014 data. Cases consisted of patients who transferred from the floor to the ICU and met one or more of 5 different priori defined criteria for suspected unplanned transfers. Controls were patients who were never transferred to the ICU. Predictor variables for the models were derived from vitals, labs, acuity scores, and nursing assessments. Classification models consisted of L1 and L2 regularized logistic regression and neural network models. We evaluated model performance over prediction horizons ranging from 1 to 16 hours. Results: Across the three institutions, the c-statistic values for our best models were 0.892 (95% CI 0.875-0.904), 0.902 (95% CI 0.880-0.923), and 0.899 (95% CI 0.879-0.919) for the task of identifying unplanned ICU transfer 6 hours before its occurrence and achieved 0.871 (95% CI 0.855-0.888), 0.872 (95% CI 0.850-0.895), and 0.850 (95% CI 0.825-0.875) for a prediction horizon of 16 hours. For our first model at 80% sensitivity, this resulted in a specificity of 80.5% (95% CI 77.4-83.7) and a positive predictive value of 5.2% (95% CI 4.5-6.2). Conclusions: Feature-rich models with many predictor variables allow for patient deterioration to be predicted accurately, even up to 16 hours in advance.

  • Source: Pixabay; Copyright: Sasint; URL:; License: Public Domain (CC0).

    Patient Portal Utilization Among Ethnically Diverse Low Income Older Adults: Observational Study


    Background: Patient portals can improve patient communication with providers, provide patients with greater health information access, and help improve patient decision making, if they are used. Because research on factors facilitating and limiting patient portal utilization has not been conceptually based, no leverage points have been indicated for improving utilization. Objective: The primary objective for this analysis was to use a conceptual framework to determine potentially modifiable factors affecting patient portal utilization by older adults (aged 55 years and older) who receive care at clinics that serve low income and ethnically diverse communities. The secondary objective was to delineate how patient portal utilization is associated with perceived usefulness and usability. Methods: Patients from one urban and two rural clinics serving low income patients were recruited and completed interviewer-administered questionnaires on patient portal utilization. Results: A total of 200 ethnically diverse patients completed questionnaires, of which 41 (20.5%) patients reported utilizing portals. Education, social support, and frequent Internet utilization improve the odds of patient portal utilization; receiving health care at a rural clinic decreases the odds of portal utilization. Conclusions: Leverage points to address disparities in patient portal utilization include providing training for older adults in patient portal utilization, involving spouses or other care partners in this training, and making information technology access available at public places in rural and urban communities.

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Latest Submissions Open for Peer-Review:

View All Open Peer Review Articles
  • The Impact of Implementation of a Clinically-Integrated Problem-based Neonatal Electronic Medical Record- Documentation Metrics, Provider Satisfaction and Hospital Reimbursement. A Quality Improvement Project

    Date Submitted: Jan 8, 2018

    Open Peer Review Period: Jan 10, 2018 - Mar 7, 2018

    Background: A goal of effective EMR provider documentation platforms is to provide an efficient, concise and comprehensive notation system that will effectively reflect the clinical course, including...

    Background: A goal of effective EMR provider documentation platforms is to provide an efficient, concise and comprehensive notation system that will effectively reflect the clinical course, including the diagnoses, treatments and interventions. Objective: Fully redesign and standardize the provider documentation process, seeking improvement in documentation based upon ongoing APR-DRG-based coding records, while maintaining non-inferiority comparing provider satisfaction to our existing documentation process. We estimated the fiscal impact of improved documentation based upon changes in expected hospital payments. Methods: Employing a multidisciplinary collaborative approach, we created an integrated clinical platform that captures data entry from the obstetrical suite, delivery room, NICU nursing and respiratory therapy staff. It provides the sole source for hospital provider documentation in the form of a history and physical exam, daily progress notes, and discharge summary. Health maintenance information, follow-up appointments and running contemporaneous updated hospital course information have selected shared entry and common viewing by the NICU team. Interventions: (1) Improve provider awareness of appropriate documentation through a provider education hand-out and follow-up group discussion. (2) Fully redesign and standardize the provider documentation process building from the native Epic-based software. Measures: (1) Hospital coding department review of all NICU admissions and 3M APR-DRG based calculations of Severity of illness (SOI), risk of mortality (ROM) and case mix index (CMI) scores. (2) Balancing measure: Provider time utilization case study and survey; (3) Average expected hospital payment based upon acuity-based clinical logic algorithm and Payor mix. Results: We compared Pre-intervention (October 2015-October 2016) to Post-intervention (November 2016-May 2017) time-periods and demonstrated: (1) Significant improvement in APR-DRG derived SOI, ROM, CMI: Monthly average SOI scores increased by 11.1% (p = 0.008); Monthly average ROM scores increased by 13.5% ((p = 0.007); Monthly average CMI scores increased by 7.7% (p=0.009). (2) Time study showed increased time to complete H&P and progress notes and decreased time to complete discharge summary: H&P: time allocation increased by 47% (p = 0.053); Progress Note time allocation significantly increased by 91% (p < 0.001); Discharge summary time allocation significantly decreased by 41% (p = 0.032). (3) Survey of all providers: Overall there was positive provider perception of the new documentation process based upon a survey of the provider group. (4) Significantly increased hospital average expected payments: Comparing the PRE- and POST-intervention study periods, there was a $14,020/ month/ patient increase in Average Expected Payment for hospital charges (p < 0.001). There was no difference in payer mix during this time-period. Conclusions: A problem-based NICU documentation EMR more effectively improves documentation, without dissatisfaction by the participating providers, and improves hospital estimations of APR-DRG-based revenue.

  • Assessment of Informatics Competencies Among Nursing Students in Saudi Arabia

    Date Submitted: Dec 26, 2017

    Open Peer Review Period: Dec 26, 2017 - Feb 20, 2018

    Background: In response to the increased use of computers and technology in health-care settings and the development of communication technologies, nursing educators are constantly seeking ways to imp...

    Background: In response to the increased use of computers and technology in health-care settings and the development of communication technologies, nursing educators are constantly seeking ways to improve the informatics competencies, skills, and knowledge of undergraduate nurses. Indeed, informatics competencies impact quality of care and patient safety. Objective: We assessed informatics competencies among nursing undergraduates in Saudi Arabia and provided recommendations to improve informatics training for nurses. Methods: We conducted a cross-sectional survey of 108 female fourth-year undergraduate nurses using the 30-item Self-Assessment of Nursing Informatics Competencies Likert Scale, which evaluated basic computer knowledge and skills, attitudes to clinical informatics, and wireless device skills. Data were collected between October and December 2016, and analyzed using descriptive statistics. The response rate was 100%. Most respondents (98%) were aged 20–22 years. Results: All students reported that they used the computer several times a day, and that they had more than 2 years of computer experience. They reported competencies in attitudes to clinical informatics (mean: 4.6 ± 0.71), basic computer knowledge and skills (mean: 4.4 ± 0.69), and wireless device skills (mean: 4.3 ± 0.41). They exhibited least competency in applied computer skills (mean: 4.03 ± 0.9), which include the collection, interpretation, and extraction of patient care data. Conclusions: Our results highlight the informatics competencies of undergraduate nurses, and reveal how medical technologies and informatics applications can improve their future working experience. Improving informatics competencies will lead to a better, error-free service and a safer environment for patients.

  • Hot Topics and Fronts in E-health Research: A Scientometric Analysis in CiteSpace

    Date Submitted: Dec 20, 2017

    Open Peer Review Period: Dec 21, 2017 - Feb 15, 2018

    Background: E-health is the use of information and communication technology to treat patients. It has many benefits like cost reduction (e.g., health delivery cost), convenience for users, and health...

    Background: E-health is the use of information and communication technology to treat patients. It has many benefits like cost reduction (e.g., health delivery cost), convenience for users, and health policy system improvement. Several literature reviews have included one part or the other of the field, but an overall review is lacking possibly due to the field’s constant evolution. An overview of E-health research is needed. Objective: To show an over view of E-health research and lay a foundation for futhers research on health IT policy. Methods: We selected the related literature on E-health downloaded from Web of Science as data source and used the visualization analysis function of CiteSpace. Literature information would be converted into precise mapping knowledge domain. Through further analysis of mappings, we explored the theoretical framework and the forefront in the field of E-health. Results: Over the past 15 years, USA, England, and Australia were the top three countries that published the largest number of papers. Researches about Internet technology, telemedicine,m-health, and healthcare lay the basis of E-health research development. Particularly, m-health, health system management, and experimental intervention have emerged and formed the new study frontier in recent 3-5 years. With the advancement of E-health projects, an increasing number of scholars have been studying the commercialization of E-health. Conclusions: We analyzed the international studies from the aspects of the references’ quantity, country, author, keywords, and co-cited references. This paper provides a reference for scholars working on this field and lays a foundation for further research on health IT policy.

  • Development and evaluation methods of applications for Augmented Reality in nursing: a systematic review

    Date Submitted: Dec 18, 2017

    Open Peer Review Period: Dec 19, 2017 - Feb 13, 2018

    Background: Inclusion of information technology into nursing is rising. Furthermore, technological advancements lead to an increased attention for Augmented Reality (AR). As AR is implemented on smart...

    Background: Inclusion of information technology into nursing is rising. Furthermore, technological advancements lead to an increased attention for Augmented Reality (AR). As AR is implemented on smart devices and therefore part of a pervasive system, it can highly influence the daily working process. Thus, values should be taken into account when designing and implementing an AR application for nursing. Objective: The aim of this review is to provide an overview about the current research on AR in nursing with special focus on design and evaluation methods as well as on the integration of values. This led to the following research question: “Which research according to the topic of AR in nursing exists?” With focus on the topics use cases, evaluation, devices used, and ethics. Methods: We searched eight databases of the areas of nursing and informatics including PubMed, Web of Science and ACM. We used the keywords ‘Nurs’, ‘Care’ OR ‘Caring’ in combination with the phrasings ‘Augmented Reality’, ‘AR device’, ‘AR glass’, ‘Smart device’, ‘Smart glass’, ‘Smart watch’ OR ‘Google glass’. We included studies concerning the topic of AR in nursing. Quantitative as well as qualitative and mixed methods studies were included. We conducted a critical interpretive synthesis to synthesize the results. Results: The search led to 434 articles of which 13 were then included into the final analysis. According to our research question we defined four topics deductively and identified nine subtopics inductively. The subtopics are use case identification, setting/use case description, requirements elicitation, evaluation goals, evaluation methods, evaluation outcome, technical challenges, ethical approval, and values. Whereas reviewed publications evaluated the use of AR in nursing as merely positive they identified technical challenges as well. Usage of devices varies and values are hardly considered in application design and evaluation. Conclusions: Our results show, that research according to AR in nursing exists. Methods to identify use cases and to evaluate applications differ between the studies. Furthermore, devices used vary. Examples are smart glasses, tablets, and smart watches. Reviewed studies predominantly evaluated usage of google glass. Results provided show that design and evaluation of smart devices for nursing to date is conducted without explicitly taking values into account. Furthermore, evaluation does not consider framing conditions. Our study findings are important and informative to the nurses and technicians who are included in the development of new technologies. They can use this review to reflect on their own design of use case identification, requirements elicitation and evaluation.

  • Development of an eHealth system to capture and analyze patient sensor and self-report data: Potential applications to improve cancer care delivery

    Date Submitted: Nov 29, 2017

    Open Peer Review Period: Nov 30, 2017 - Jan 25, 2018

    Background: “COMPASS” (“Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers) is an eHealth platform designed to improve cancer care delivery through passive monito...

    Background: “COMPASS” (“Capturing and Analyzing Sensor and Self-Report Data for Clinicians and Researchers) is an eHealth platform designed to improve cancer care delivery through passive monitoring of patients’ health status and delivering customizable reports to clinicians. Based on data from sensors and context-driven administration of patient-reported outcome (PRO) measures, key indices of patients’ functional status can be collected between regular clinic visits, supporting clinicians in the delivery of patient care. Objective: The aim of the first phase of this project was to systematically collect input from oncology providers and patients on potential clinical applications for COMPASS in order to refine the system. Methods: Ten clinicians representing various oncology specialties and disciplines completed semi-structured interviews designed to solicit clinician input on how COMPASS can best support clinical care delivery. Three cancer patients tested a prototype of COMPASS for 7 days and provided feedback. Interview data was tabulated using thematic content analysis (TCA) to identify the most clinically relevant objective and PRO domains. Results: TCA revealed that clinicians were most interested in monitoring vital statistics, symptoms and functional status, including physical activity level (n=9), weight (n=5), fatigue (n=9), sleep quality (n=8) and anxiety. Patients (2 in active treatment, 1 in remission) reported that they would use such a device, were enthusiastic about their clinicians monitoring their health status, especially the tracking of symptoms, and felt knowing their clinicians were monitoring and reviewing their health status provided valuable reassurance. Patients would however like to provide some context to their data. Conclusions: Clinicians and patients both articulated potential benefits of the COMPASS system in improving cancer care. From a clinician standpoint, data needs to be easily interpretable and actionable. The fact that patients and clinicians both see potential value in eHealth systems suggests wider adoption and utilization could prove to be a useful tool for improving care delivery.