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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 2017: 4.671), 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 (http://www.jmir.org/issue/current).

 

Recent Articles:

  • Source: StockSnap; Copyright: William Stitt; URL: https://stocksnap.io/photo/F3BFC12YTH; License: Public Domain (CC0).

    Uncovering a Role for Electronic Personal Health Records in Reducing Disparities in Sexually Transmitted Infection Rates Among Students at a Predominantly...

    Abstract:

    Background: Black youth continue to bear an overwhelming proportion of the United States sexually transmitted infection (STI) burden, including HIV. Several studies on web-based and mobile health (mHealth) STI interventions have focused on characterizing strategies to improve HIV-related prevention and treatment interventions, risk communication, and stigma among men who have sex with men (MSM), people who use substances, and adolescent populations. The Electronic Sexual Health Information Notification and Education (eSHINE) Study was an exploratory mixed-methods study among students at a historically black university exploring perceptions on facilitating STI testing conversations with partners using electronic personal health records (PHRs). Objective: The purpose of this paper is to use eSHINE Study results to describe perceived impacts of electronic PHRs on facilitating STI testing discussions between sexual partners. Methods: Semistructured focus groups and individual in-depth interviews were conducted on a heterogeneous sample of students (n=35) between May and July 2014. Qualitative phase findings guided development of an online survey instrument for quantitative phase data collection. Online surveys were conducted using a convenience sample of students (n=354) between January and May 2015. Online survey items collected demographic information, sexual behaviors, beliefs and practices surrounding STI testing communication between partners, and beliefs about the impact of electronic PHR access on facilitating these discussions with partners. Chi-square analysis was performed to assess gender differences across quantitative measures. A Wilcoxon signed rank sum test was used to test the null hypothesis that electronic PHRs are believed to have no effect on the timing of dyadic STI health communication. Results: Participants described multiple individual and dyadic-level factors that inhibit initiating discussions about STI testing and test results with partners. Electronic PHRs were believed to improve ability to initiate conversations and confidence in STI screening information shared by partners. Among online survey participants, men were more likely to believe electronic PHRs make it easier to facilitate STI talks with potential partners (59.9% vs 51.9%; χ2=3.93, P=.05). The Wilcoxon signed-rank test results indicate significant increases in perceived discussion timing before sex with electronic PHR access (61.0% vs 40.4%; P<.001). Conclusions: Findings suggest that electronic PHR access in STI screening settings among similar populations of Black youth may improve both motivation and personal agency for initiating dyadic STI health communication. Results from this study will likely inform novel interventions that use access to electronic PHRs to stimulate important health-related discussions between sexual partners. Moving forward requires studying strategies for implementing interventions that leverage electronic PHRs to create new sexual health communication channels with providers, peers, and family among black youth.

  • Source: Freepik; Copyright: pressfoto; URL: https://www.freepik.com/free-photo/doctors-looking-through-the-medical-record_862391.htm; License: Licensed by JMIR.

    Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey With Experts and Older Adults

    Abstract:

    Background: Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists. Objective: The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization. Methods: Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations. Results: Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (χ24=14.1, P=.002) and monitoring (χ24=22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for “monitoring” between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t96=2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups. Conclusions: We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine—chosen as the basis for the construction of present taxonomy—was confirmed.

  • Source: Flickr; Copyright: US Department of Agriculture; URL: https://www.flickr.com/photos/usdagov/8576196340; License: Creative Commons Attribution (CC-BY).

    Adverse Drug Event Reporting From Clinical Care: Mixed-Methods Analysis for a Minimum Required Dataset

    Abstract:

    Background: Patients commonly transition between health care settings, requiring care providers to transfer medication utilization information. Yet, information sharing about adverse drug events (ADEs) remains nonstandardized. Objective: The objective of our study was to describe a minimum required dataset for clinicians to document and communicate ADEs to support clinical decision making and improve patient safety. Methods: We used mixed-methods analysis to design a minimum required dataset for ADE documentation and communication. First, we completed a systematic review of the existing ADE reporting systems. After synthesizing reporting concepts and data fields, we conducted fieldwork to inform the design of a preliminary reporting form. We presented this information to clinician end-user groups to establish a recommended dataset. Finally, we pilot-tested and refined the dataset in a paper-based format. Results: We evaluated a total of 1782 unique data fields identified in our systematic review that describe the reporter, patient, ADE, and suspect and concomitant drugs. Of these, clinicians requested that 26 data fields be integrated into the dataset. Avoiding the need to report information already available electronically, reliance on prospective rather than retrospective causality assessments, and omitting fields deemed irrelevant to clinical care were key considerations. Conclusions: By attending to the information needs of clinicians, we developed a standardized dataset for adverse drug event reporting. This dataset can be used to support communication between care providers and integrated into electronic systems to improve patient safety. If anonymized, these standardized data may be used for enhanced pharmacovigilance and research activities.

  • Source: Wikimedia Commons; Copyright: Kgbo; URL: https://commons.wikimedia.org/wiki/File:Princess_Alexandra_Hospital_nurse_with_mobile_computer_work_station.jpg; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Nurses’ Experience With Health Information Technology: Longitudinal Qualitative Study

    Abstract:

    Background: Nurses are the largest group of health information technology (HIT) users. As such, nurses’ adaptations are critical for HIT implementation success. However, longitudinal approaches to understanding nurses’ perceptions of HIT remain underexplored. Previous studies of nurses’ perceptions demonstrate that the progress and timing for acceptance of and adaptation to HIT varies. Objective: This study aimed to explore nurses’ experience regarding implementation of HIT over time. Methods: A phenomenological approach was used for this longitudinal qualitative study to explore nurses’ perceptions of HIT implementation over time, focusing on three time points (rounds) at 3, 9, and 18 months after implementation of electronic health records and bar code medication administration. The purposive sample was comprised of clinical nurses who worked on a medical-surgical unit in an academic center. Results: Major findings were categorized into 7 main themes with 54 subthemes. Nurses reported personal-level and organizational-level factors that facilitated HIT adaptation. We also generated network graphs to illustrate the occurrence of themes. Thematic interconnectivity differed due to nurses’ concerns and satisfaction at different time points. Equipment and workflow were the most frequent themes across all three rounds. Nurses were the most dissatisfied approximately 9 months after HIT implementation. Eighteen months after HIT implementation, nurses’ perceptions appeared more balanced. Conclusions: It is recommended that organizations invest in equipment (ie, wireless barcode scanners), refine policies to reflect nursing practice, and improve systems to focus on patient safety. Future research is necessary to confirm patterns of nurses’ adaptation to HIT in other samples.

  • Preterm infant in the NICU. Source: Flickr; Copyright: The Hudson Family; URL: https://www.flickr.com/photos/hudsonthego/157033260/; License: Creative Commons Attribution (CC-BY).

    The Impact of Implementation of a Clinically Integrated Problem-Based Neonatal Electronic Health Record on Documentation Metrics, Provider Satisfaction, and...

    Abstract:

    Background: A goal of effective electronic health record 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: The aim is to fully redesign and standardize the provider documentation process, seeking improvement in documentation based on ongoing All Patient Refined Diagnosis Related Group–based coding records, while maintaining noninferiority comparing provider satisfaction to our existing documentation process. We estimated the fiscal impact of improved documentation based on 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, neonatal intensive care unit (NICU) nursing and respiratory therapy staff. It provided 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. The interventions were to (1) improve provider awareness of appropriate documentation through a provider education handout and follow-up group discussion and (2) fully redesign and standardize the provider documentation process building from the native Epic-based software. The measures were (1) hospital coding department review of all NICU admissions and 3M All Patient Refined Diagnosis Related Group–based calculations of severity of illness, risk of mortality, and case mix index scores; (2) balancing measure: provider time utilization case study and survey; and (3) average expected hospital payment based on acuity-based clinical logic algorithm and payer mix. Results: We compared preintervention (October 2015-October 2016) to postintervention (November 2016-May 2017) time periods and saw: (1) significant improvement in All Patient Refined Diagnosis Related Group–derived severity of illness, risk of mortality, and case mix index (monthly average severity of illness scores increased by 11.1%, P=.008; monthly average risk of mortality scores increased by 13.5%, P=.007; and monthly average case mix index scores increased by 7.7%, P=.009); (2) time study showed increased time to complete history and physical and progress notes and decreased time to complete discharge summary (history and physical exam: time allocation increased by 47%, P=.05; progress note: time allocation increased by 91%, P<.001; discharge summary: time allocation decreased by 41%, P=.03); (3) survey of all providers: overall there was positive provider perception of the new documentation process based on a survey of the provider group; (4) significantly increased hospital average expected payments: comparing the preintervention and postintervention study periods, there was a US $14,020 per month per patient increase in average expected payment for hospital charges (P<.001). There was no difference in payer mix during this time period. Conclusions: A problem-based NICU documentation electronic health record more effectively improves documentation without dissatisfaction by the participating providers and improves hospital estimations of All Patient Refined Diagnosis Related Group–based revenue.

  • Source: Rawpixel; Copyright: Rawpixel; URL: https://www.rawpixel.com/image/380187/chess-game-business-strategy-concept; License: Public Domain (CC0).

    Validation of a Natural Language Processing Algorithm for Detecting Infectious Disease Symptoms in Primary Care Electronic Medical Records in Singapore

    Abstract:

    Background: Free-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms. Objective: The aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records. Methods: CHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration. Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation. Results: The symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status. Conclusions: We have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations.

  • Source: Freepik; Copyright: peoplecreations; URL: https://www.freepik.com/free-photo/doctor-interacting-with-patient-in-medical-office-at-hospital_1006264.htm; License: Licensed by JMIR.

    Health Information Technology in Healthcare Quality and Patient Safety: Literature Review

    Abstract:

    Background: The area of healthcare quality and patient safety is starting to use health information technology to prevent reportable events, identify them before they become issues, and act on events that are thought to be unavoidable. As healthcare organizations begin to explore the use of health information technology in this realm, it is often unclear where fiscal and human efforts should be focused. Objective: The purpose of this study was to provide a foundation for understanding where to focus health information technology fiscal and human resources as well as expectations for the use of health information technology in healthcare quality and patient safety. Methods: A literature review was conducted to identify peer-reviewed publications reporting on the actual use of health information technology in healthcare quality and patient safety. Inductive thematic analysis with open coding was used to categorize a total of 41 studies. Three pre-set categories were used: prevention, identification, and action. Three additional categories were formed through coding: challenges, outcomes, and location. Results: This study identifies five main categories across seven study settings. A majority of the studies used health IT for identification and prevention of healthcare quality and patient safety issues. In this realm, alerts, clinical decision support, and customized health IT solutions were most often implemented. Implementation, interface design, and culture were most often noted as challenges. Conclusions: This study provides valuable information as organizations determine where they stand to get the most “bang for their buck” relative to health IT for quality and patient safety. Knowing what implementations are being effectivity used by other organizations helps with fiscal and human resource planning as well as managing expectations relative to cost, scope, and outcomes. The findings from this scan of the literature suggest that having organizational champion leaders that can shepherd implementation, impact culture, and bridge knowledge with developers would be a valuable resource allocation to consider.

  • Source: Flickr; Copyright: aaron gilson; URL: https://www.flickr.com/photos/aarongilson/5419284543/; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Development and Validation of a Functional Behavioural Assessment Ontology to Support Behavioural Health Interventions

    Abstract:

    Background: In the cognitive-behavioral approach, Functional Behavioural Assessment is one of the most effective methods to identify the variables that determine a problem behavior. In this context, the use of modern technologies can encourage the collection and sharing of behavioral patterns, effective intervention strategies, and statistical evidence about antecedents and consequences of clusters of problem behaviors, encouraging the designing of function-based interventions. Objective: The paper describes the development and validation process used to design a specific Functional Behavioural Assessment Ontology (FBA-Ontology). The FBA-Ontology is a semantic representation of the variables that intervene in a behavioral observation process, facilitating the systematic collection of behavioral data, the consequential planning of treatment strategies and, indirectly, the scientific advancement in this field of study. Methods: The ontology has been developed deducing concepts and relationships of the ontology from a gold standard and then performing a machine-based validation and a human-based assessment to validate the Functional Behavioural Assessment Ontology. These validation and verification processes were aimed to verify how much the ontology is conceptually well founded and semantically and syntactically correct. Results: The Pellet reasoner checked the logical consistency and the integrity of classes and properties defined in the ontology, not detecting any violation of constraints in the ontology definition. To assess whether the ontology definition is coherent with the knowledge domain, human evaluation of the ontology was performed asking 84 people to fill in a questionnaire composed by 13 questions assessing concepts, relations between concepts, and concepts’ attributes. The response rate for the survey was 29/84 (34.52%). The domain experts confirmed that the concepts, the attributes, and the relationships between concepts defined in the FBA-Ontology are valid and well represent the Functional Behavioural Assessment process. Conclusions: The new ontology developed could be a useful tool to design new evidence-based systems in the Behavioral Interventions practices, encouraging the link with other Linked Open Data datasets and repositories to provide users with new models of eHealth focused on the management of problem behaviors. Therefore, new research is needed to develop and implement innovative strategies to improve the poor reproducibility and translatability of basic research findings in the field of behavioral assessment.

  • Source: Flickr; Copyright: Tunstall; URL: https://www.flickr.com/photos/tunstalltelehealthcare/6850760963; License: Creative Commons Attribution (CC-BY).

    Perspectives of Nurses Toward Telehealth Efficacy and Quality of Health Care: Pilot Study

    Abstract:

    Background: Telehealth nursing, or the delivery, management, and coordination of nursing care services provided via telecommunications technology, is one of the methods of delivering health care to patients in the United States. It is important to assess the service quality of the involved health professionals as well as the telehealth nursing process. The focus of this study is the innovative model of telehealth care delivery by nurses for managing patients with chronic disease while they are living in their own residence. Objective: The primary objective of this pilot study was to examine whether telehealth technology impacts the perceived level of internal service quality delivered by nurses within a telehealth organization. To address this research goal, the notion of telehealth nursing service quality (TNSQ) is empirically tested and validated with a survey instrument. Methods: Data were collected from nurses belonging to a home care agency based on interview questions inquiring about facilitators and inhibitors to TNSQ. A survey to measure TNSQ based on the SERVQUAL instrument was completed by adjusting descriptions of the original instrument to suit the context. Follow-up interviews were conducted to validate questions on the revised instrument. Results: The findings of this survey research were positive, based on mean differences between expectations and perceptions of TNSQ. This indicates satisfaction with TNSQ and shows that the quality of the service is higher than what the respondents expect. The Wilcoxon signed-rank test using the P value for the test, which is .35, did not show a statistically significant change between the median differences of perception and expectation. The total number of respondents was 13. Results indicate that overall perceived service quality is a positive value (0.05332). This means the perceptions of the level of service are slightly higher than what they expect, indicating there is satisfaction with TNSQ. Conclusions: The responses to the interview questions and data gathered from the survey showed overall satisfaction with TNSQ. The SERVQUAL instrument was a good framework to assess TNSQ. In a nutshell, the study highlighted how the telehealth process provides daily monitoring of patient health, leading to the benefits of immediate feedback for patients, family, and caregivers as well as convenience of scheduling.

  • Source: Flickr.com; Copyright: Anoto AB; URL: https://www.flickr.com/photos/anotogroup/5703053431/in/photolist-9FXD6r-7eBNui-68rQsa-4k62X8-e5jtT9-exQtz6-7NvHSv-7NvHXv-7NvGEV-aWeqag-7NvHdn-7NzGE3-7NzGUN-7NzGom-7NvGSx-7NzHKC-7NvJmv-7NvJ6F-7NvJeX-7NvHu4-7NzHdj-doiTpG-xzbkL5-DBZvKq-ExpcXh-DCkPAp-E2epbn-E; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    Privacy-Preserving Predictive Modeling: Harmonization of Contextual Embeddings From Different Sources

    Abstract:

    Background: Data sharing has been a big challenge in biomedical informatics because of privacy concerns. Contextual embedding models have demonstrated a very strong representative capability to describe medical concepts (and their context), and they have shown promise as an alternative way to support deep-learning applications without the need to disclose original data. However, contextual embedding models acquired from individual hospitals cannot be directly combined because their embedding spaces are different, and naive pooling renders combined embeddings useless. Objective: The aim of this study was to present a novel approach to address these issues and to promote sharing representation without sharing data. Without sacrificing privacy, we also aimed to build a global model from representations learned from local private data and synchronize information from multiple sources. Methods: We propose a methodology that harmonizes different local contextual embeddings into a global model. We used Word2Vec to generate contextual embeddings from each source and Procrustes to fuse different vector models into one common space by using a list of corresponding pairs as anchor points. We performed prediction analysis with harmonized embeddings. Results: We used sequential medical events extracted from the Medical Information Mart for Intensive Care III database to evaluate the proposed methodology in predicting the next likely diagnosis of a new patient using either structured data or unstructured data. Under different experimental scenarios, we confirmed that the global model built from harmonized local models achieves a more accurate prediction than local models and global models built from naive pooling. Conclusions: Such aggregation of local models using our unique harmonization can serve as the proxy for a global model, combining information from a wide range of institutions and information sources. It allows information unique to a certain hospital to become available to other sites, increasing the fluidity of information flow in health care.

  • Ortho Clinical Diagnostics e-Connectivity® Predictive Technology Center. Source: Image created by Ortho Clinical Diagnostics; Copyright: Ortho Clinical Diagnostics; URL: http://medinform.jmir.org/2018/2/e34/; License: Licensed by the authors.

    Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning

    Abstract:

    Background: Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. Objective: The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. Methods: QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. Results: The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potential cause of customer complaints. Performance was improved by shortening the training period when the volume of complaints increased. Denoising filters that reduced the number of categories to predict further improved performance, as their application simplified the prediction problem. Conclusions: This novel approach to predicting customer complaints based on QC data may allow the diagnostic industry, the expected end user of our approach, to proactively identify potential product quality issues and fix these before receiving customer complaints. This represents a new step in the direction of using big data toward product quality improvement.

  • Source: BC Children's Hospital Research Institute; Copyright: BC Children's Hospital Research Institute; URL: http://medinform.jmir.org/2018/2/e32/; License: Licensed by the authors.

    Data Access and Usage Practices Across a Cohort of Researchers at a Large Tertiary Pediatric Hospital: Qualitative Survey Study

    Abstract:

    Background: Health and health-related data collected as part of clinical care is a foundational component of quality improvement and research. While the importance of these data is widely recognized, there are many challenges faced by researchers attempting to use such data. It is crucial to acknowledge and identify barriers to improve data sharing and access practices and ultimately optimize research capacity. Objective: To better understand the current state, explore opportunities, and identify barriers, an environmental scan of investigators at BC Children’s Hospital Research Institute (BCCHR) was conducted to elucidate current local practices around data access and usage. Methods: The Clinical and Community Data, Analytics and Informatics group at BCCHR comprises over 40 investigators with diverse expertise and interest in data who share a common goal of facilitating data collection, usage, and access across the community. Semistructured interviews with 35 of these researchers were conducted, and data were summarized qualitatively. A total impact score, considering both frequency with which a problem occurs and the impact of the problem, was calculated for each item to prioritize and rank barriers. Results: Three main themes for barriers emerged: the lengthy turnaround time before data access (18/35, 51%), inconsistent and opaque data access processes (16/35, 46%), and the inability to link data (15/35, 43%) effectively. Less frequent themes included quality and usability of data, ethics and privacy review barriers, lack of awareness of data sources, and efforts required duplicating data extraction and linkage. The two main opportunities for improvement were data access facilitation (14/32, 44%) and migration toward a single data platform (10/32, 31%). Conclusions: By identifying the current state and needs of the data community onsite, this study enables us to focus our resources on combating the challenges having the greatest impact on researchers. The current state parallels that of the national landscape. By ensuring protection of privacy while achieving efficient data access, research institutions will be able to maximize their research capacity, a crucial step towards achieving the ultimate and shared goal between all stakeholders—to better health outcomes.

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  • SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From EHR Data: Comparison of Intensional vs. Extensional Value Sets

    Date Submitted: Jul 15, 2018

    Open Peer Review Period: Jul 15, 2018 - Jul 24, 2018

    Background: Defining clinical phenotypes from EHR-derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw fr...

    Background: Defining clinical phenotypes from EHR-derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely-grained clinical terminology—either native SNOMED CT, or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does vetting that their contents accurately represent the clinically-intended condition. Objective: To compare an intensional (concept hierarchy-based) vs. extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT encoded data from EHRs, by evaluating value set conciseness, time to create, and completeness. Methods: Starting from published CMS 2018 high-priority eCQMs, we selected ten clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (a) (VSAC) downloaded list-based (extensional) value sets, (b) corresponding hierarchy-based intensional value sets for the same conditions, and (c) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional vs. intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts, and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians. Results: The 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 vs. 78 concepts to define, and 5 vs. 37 min to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms. Conclusions: In the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets, rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to include and exclude, and streamline broad EHR implementation of condition-specific decision support promoting guideline adherence for patient benefit.

  • Facilitators and Barriers to the Adoption of Telemonitoring to Manage COPD: A Systematic Literature Review

    Date Submitted: Jul 5, 2018

    Open Peer Review Period: Jul 9, 2018 - Sep 3, 2018

    Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases, and its prevalence is increasing in the U.S...

    Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases, and its prevalence is increasing in the U.S. Telemonitoring of patients with COPD has the potential to help patients manage disease and predict exacerbations. The objective of this review is to evaluate the effectiveness of telemonitoring to manage the chronic disease of COPD. Objective: Researchers want to look at how telemonitoring has been used to observe COPD, and we’re hoping this will lead to more research in telemonitoring of this disease. Methods: The review was conducted and reported in accordance with Assessment for Multiple Systematic Reviews (AMSTAR) and Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), respectively. Authors performed a systematic review of Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PubMed databases to obtain relevant articles. Then, articles were accepted or rejected by group consensus. Each article was read and authors identified barriers and facilitators to effectiveness of telemonitoring of COPD. Results: The review’s results indicate that conflicting information exists for the effectiveness of telemonitoring of patients with COPD. Primarily, 13 of 29 articles stated that patient outcomes were improved overall with telemonitoring, while 11 of 29 indicated no improvement. For facilitators, authors recognized reduced need for in-person visits, better disease management, and bolstered patient-provider relationship. Important barriers included low-quality data, increased workload for providers, and cost. Conclusions: The high variability between the articles and the ways they provided telemonitoring services created conflicting results from the literature review. Future research should emphasize standardization of telemonitoring services and predictability of exacerbations.

  • Health data for research: a nationwide privacy-proof system in Belgium

    Date Submitted: Jun 29, 2018

    Open Peer Review Period: Jul 3, 2018 - Aug 28, 2018

    Background: Health data collected during routine care has important potential for reuse for other purposes, especially as part of a learning health system to advance quality of care. Many sources of b...

    Background: Health data collected during routine care has important potential for reuse for other purposes, especially as part of a learning health system to advance quality of care. Many sources of bias have been identified through the lifecycle of health data that could compromise the scientific integrity of these data. New data protection legislation requires research facilities to improve safety measures to ensure privacy. Objective: In this paper we address the question on how health data can be transferred from various sources and using multiple systems to a centralized platform, called Healthdata.be, while ensuring accuracy, validity, safety and privacy. We also demonstrate how these processes can be used in various research designs relevant for learning health systems. Methods: The Healthdata.be platform urges uniformity of data registration at the primary source through the use of detailed clinical models. Data retrieval and transfer is organized through end-to-end encrypted eHealth channels and data is encoded using token keys. Patient identifiers are pseudonymised so that health data from the same patient collected across various sources can still be linked without compromising the deidentification. Results: The Healthdata.be platform currently collects data for more than 150 clinical registries in Belgium. We demonstrate how the data collection for the Belgian primary care morbidity register INTEGO is organized and how the Healthdata.be platform can be used for a cluster randomized trial. Conclusions: Collecting health data in various sources and linking this data to a single patient is a promising feature that can potentially address important concerns on validity and quality of health data. Safe methods of data transfer without compromising privacy are capable of transporting these data from the primary data provider or clinician to the research facility. More research is required to demonstrate that these methods improve the quality of data collection allowing researchers to rely on electronic health records as a valid source for scientific data. Clinical Trial: N/A

  • Requirement Specification of wearable blanket for monitoring patients in ambulance

    Date Submitted: Jun 23, 2018

    Open Peer Review Period: Jun 25, 2018 - Aug 20, 2018

    Objective: Todays, the smart systems and developed tools such as wearable systems have significantly increased for monitoring the patients and controlling their conditions consistently. The present st...

    Objective: Todays, the smart systems and developed tools such as wearable systems have significantly increased for monitoring the patients and controlling their conditions consistently. The present study aimed at determining the requirements for designing a wearable smart blanket system that is able to monitoring the condition of patients in ambulance. Method: After identifying the features of wearable systems based on comparative study, the description of the requirements for creating the proposed system in ambulance was considered. Firstly, some studies were conducted for identifying the wearable system development. Secondly, the questionnaire elicited from the studies was distributed among the physicians and specialists. Results: Wearable smart blanket system has some specific functional features such as monitoring the vital signs, communicating with the surroundings, processing the vital signals instantly, warning when the vital signs exceed the threshold, and storing all vital signs. In addition, they should have the non-functional features such as easy installment and function, interactivity, error fault tolerance, low energy consumption, the accuracy of signs stability, and data analysis. Conclusion: Wearable smart blanket system records all the required vital signs for controlling the individuals in an integrative way and provides the interpreted data for the treatment team in ambulance. Thus, all medical, diagnostic, and monitoring data related to the individuals are stored in the physician assistant system enabling the ambulance physician to take the early diagnosis without delay. The benefits of wearable smart blankets can be converted as an alternative to the current equipment in ambulance.

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