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JMIR Medical Informatics

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

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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 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 (http://www.jmir.org/issue/current).

 

Recent Articles:

  • 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.

  • AutoTransMan app (montage). Source: The Authors / Placeit.net; Copyright: The Authors; URL: http://medinform.jmir.org/2018/2/e27/; License: Creative Commons Attribution (CC-BY).

    The Importance of Nonlinear Transformations Use in Medical Data Analysis

    Abstract:

    Background: The accumulation of data and its accessibility through easier-to-use platforms will allow data scientists and practitioners who are less sophisticated data analysts to get answers by using big data for many purposes in multiple ways. Data scientists working with medical data are aware of the importance of preprocessing, yet in many cases, the potential benefits of using nonlinear transformations is overlooked. Objective: Our aim is to present a semi-automated approach of symmetry-aiming transformations tailored for medical data analysis and its advantages. Methods: We describe 10 commonly encountered data types used in the medical field and the relevant transformations for each data type. Data from the Alzheimer’s Disease Neuroimaging Initiative study, Parkinson’s disease hospital cohort, and disease-simulating data were used to demonstrate the approach and its benefits. Results: Symmetry-targeted monotone transformations were applied, and the advantages gained in variance, stability, linearity, and clustering are demonstrated. An open source application implementing the described methods was developed. Both linearity of relationships and increase of stability of variability improved after applying proper nonlinear transformation. Clustering simulated nonsymmetric data gave low agreement to the generating clusters (Rand value=0.681), while capturing the original structure after applying nonlinear transformation to symmetry (Rand value=0.986). Conclusions: This work presents the use of nonlinear transformations for medical data and the importance of their semi-automated choice. Using the described approach, the data analyst increases the ability to create simpler, more robust and translational models, thereby facilitating the interpretation and implementation of the analysis by medical practitioners. Applying nonlinear transformations as part of the preprocessing is essential to the quality and interpretability of results.

  • Source: Freepik; Copyright: Freepik; URL: https://www.freepik.com/free-photo/woman-in-glasses-browsing-tablet_2146194.htm#term=elderly%20woman%20computer&page=1&position=10; License: Licensed by JMIR.

    Capturing a Patient-Reported Measure of Physical Function Through an Online Electronic Health Record Patient Portal in an Ambulatory Clinic: Implementation...

    Abstract:

    Background: Despite significant interest in the collection of patient-reported outcomes to make care more patient-centered, few studies have evaluated implementation efforts to collect patient-reported outcomes from diverse patient populations Objective: We assessed the collection of patient-reported outcomes from rheumatoid arthritis patients in an academic rheumatology clinic, using a paper and an online form through the electronic health record patient portal. Methods: We identified patients seen between 2012-2016 with ≥2 face-to-face encounters with a rheumatology provider and International Classification of Diseases codes for RA, ≥30 days apart. In 2013, our clinic implemented a paper version of the Patient Reported Outcome Measurement Information System (PROMIS) physical function form that was administered to patients upon their check-in at the clinic. In 2015, an online version of the form became available by way of the electronic health record patient portal to patients with active portal accounts. We compared the proportion of visits with documented PROMIS scores across age, race and ethnicity, and language and examined trends over time using a control chart. Results: We included 1078 patients with rheumatoid arthritis with 7049 in-person encounters at the rheumatology clinic over 4 years, with an average of 168 visits per month. Of the included patients, 80.4% of patients (867/1078) were female and the mean age was 58 (SD 16) years. The overall PROMIS physical function score documentation increased from 60.4% (1081/1791) of visits in 2013 to 74.4% (905/1217) of visits in 2016. Online score documentation increased from 10.0% (148/1473) in 2015 to 19.3% (235/1217) in 2016. African American patients were least likely to have a PROMIS physical function score recorded (55/88, 62.5% compared to 792/990, 80.0% for other racial or ethnic groups; P<.001). Compared with white patients, both African American and Hispanic patients were less likely to have active online electronic health record portal accounts (44/88, 50% and 90/157, 57.3% respectively, compared to 437/521, 83.9% of white patients; P<.001) and, once activated, less likely to use the online survey (6/44, 13.6% and 16/90, 17.8% respectively, compared to 135/437, 30.9% of white patients; P=.02). There was no significant difference in the proportion of any PROMIS physical function forms recorded between non-English vs English preferred patients. No significant differences were found across age or gender. Conclusions: PROMIS physical function form completion improved overall from 2012-2016 but lagged among racial and ethnic minorities and non-English preferred patients. Future studies should address issues of portal access, enrollment, satisfaction, and persistence and focus on developing PRO implementation strategies that accommodate the needs and preferences of diverse populations.

  • Multitasking clinician. Source: Image created by the Authors; Copyright: The Authors; URL: http://medinform.jmir.org/2018/2/e10167/; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Finding Meaning in Medication Reconciliation Using Electronic Health Records: Qualitative Analysis in Safety Net Primary and Specialty Care

    Abstract:

    Background: Safety net health systems face barriers to effective ambulatory medication reconciliation for vulnerable populations. Although some electronic health record (EHR) systems offer safety advantages, EHR use may affect the quality of patient-provider communication. Objective: This mixed-methods observational study aimed to develop a conceptual framework of how clinicians balance the demands and risks of EHR and communication tasks during medication reconciliation discussions in a safety net system. Methods: This study occurred 3 to 16 (median 9) months after new EHR implementation in five academic public hospital clinics. We video recorded visits between English-/Spanish-speaking patients and their primary/specialty care clinicians. We analyzed the proportion of medications addressed and coded time spent on nonverbal tasks during medication reconciliation as “multitasking EHR use,” “silent EHR use,” “non-EHR multitasking,” and “focused patient-clinician talk.” Finally, we analyzed communication patterns to develop a conceptual framework. Results: We examined 35 visits (17%, 6/35 Spanish) between 25 patients (mean age 57, SD 11 years; 44%, 11/25 women; 48%, 12/25 Hispanic; and 20%, 5/25 with limited health literacy) and 25 clinicians (48%, 12/25 primary care). Patients had listed a median of 7 (IQR 5-12) relevant medications, and clinicians addressed a median of 3 (interquartile range [IQR] 1-5) medications. The median duration of medication reconciliation was 2.1 (IQR 1.0-4.2) minutes, comprising a median of 10% (IQR 3%-17%) of visit time. Multitasking EHR use occurred in 47% (IQR 26%-70%) of the medication reconciliation time. Silent EHR use and non-EHR multitasking occurred a smaller proportion of medication reconciliation time, with a median of 0% for both. Focused clinician-patient talk occurred a median of 24% (IQR 0-39%) of medication reconciliation time. Five communication patterns with EHR medication reconciliation were observed: (1) typical EHR multitasking for medication reconciliation, (2) dynamic EHR use to negotiate medication discrepancies, (3) focused patient-clinician talk for medication counseling and addressing patient concerns, (4) responding to patient concerns while maintaining EHR use, and (5) using EHRs to engage patients during medication reconciliation. We developed a conceptual diagram representing the dilemma of the multitasking clinician during medication reconciliation. Conclusions: Safety net visits involve multitasking EHR use during almost half of medication reconciliation time. The multitasking clinician balances the cognitive and emotional demands posed by incoming information from multiple sources, attempts to synthesize and act on this information through EHR and communication tasks, and adopts strategies of silent EHR use and focused patient-clinician talk that may help mitigate the risks of multitasking. Future studies should explore diverse patient perspectives about clinician EHR multitasking, clinical outcomes related to EHR multitasking, and human factors and systems engineering interventions to improve the safety of EHR use during the complex process of medication reconciliation.

  • Medical Image Region of interest analysis tool and Repository (MIROR) user-interface intravoxel incoherent motion (IVIM) maps tabs. This figure represents data for a malignant tumor case. Source: Image created by the authors; Copyright: The Authors; URL: http://medinform.jmir.org/2018/2/e30/; License: Creative Commons Attribution (CC-BY).

    Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

    Abstract:

    Background: Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care. Objective: The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data. Methods: The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors. Results: Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters. Conclusions: MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians’ skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments.

  • Assistive Dressing System: A Capabilities Study for Personalized Support of Dressing Activities for People Living with Dementia. Source: image created by the authors; Copyright: Winslow Burleson; License: Fair use/fair dealings.

    An Assistive Technology System that Provides Personalized Dressing Support for People Living with Dementia: Capability Study

    Abstract:

    Background: Individuals living with advancing stages of dementia (persons with dementia, PWDs) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, which in turn makes them increasingly dependent on the assistance of caregivers. Dressing is one of the most common and stressful activities provided by caregivers because of its complexity and privacy challenges posed during the process. Objective: In preparation for in-home trials with PWDs, the aim of this study was to develop and evaluate a prototype intelligent system, the DRESS prototype, to assess its ability to provide automated assistance with dressing that can afford independence and privacy to individual PWDs and potentially provide additional freedom to their caregivers (family members and professionals). Methods: This laboratory study evaluated the DRESS prototype’s capacity to detect dressing events. These events were engaged in by 11 healthy participants simulating common correct and incorrect dressing scenarios. The events ranged from donning a shirt and pants inside out or backwards to partial dressing—typical issues that challenge a PWD and their caregivers. Results: A set of expected detections for correct dressing was prepared via video analysis of all participants’ dressing behaviors. In the initial phases of donning either shirts or pants, the DRESS prototype missed only 4 out of 388 expected detections. The prototype’s ability to recognize other missing detections varied across conditions. There were also some unexpected detections such as detection of the inside of a shirt as it was being put on. Throughout the study, detection of dressing events was adversely affected by the relatively smaller effective size of the markers at greater distances. Although the DRESS prototype incorrectly identified 10 of 22 cases for shirts, the prototype preformed significantly better for pants, incorrectly identifying only 5 of 22 cases. Further analyses identified opportunities to improve the DRESS prototype’s reliability, including increasing the size of markers, minimizing garment folding or occlusions, and optimal positioning of participants with respect to the DRESS prototype. Conclusions: This study demonstrates the ability to detect clothing orientation and position and infer current state of dressing using a combination of sensors, intelligent software, and barcode tracking. With improvements identified by this study, the DRESS prototype has the potential to provide a viable option to provide automated dressing support to assist PWDs in maintaining their independence and privacy, while potentially providing their caregivers with the much-needed respite.

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  • Use of Telemedicine to Screen Patients in the Emergency Department: An Evaluation of Efficiency and Patient Safety

    Date Submitted: Jun 5, 2018

    Open Peer Review Period: Jun 10, 2018 - Aug 5, 2018

    Background: Early efforts to incorporate telemedicine in the Emergency Department (ED) focused on providing remote consultation services to EDs with limited resources. With continued ED overcrowding,...

    Background: Early efforts to incorporate telemedicine in the Emergency Department (ED) focused on providing remote consultation services to EDs with limited resources. With continued ED overcrowding, some EDs have used telemedicine to increase providers during surges of patient visits and offer scheduled “home” face to face on-screen visits. We used remote on-screen telemedicine providers in the “screening-in-triage” role during hours in which our ED did not previously have in-person screening. Objective: Compare efficiency and patient safety of in-person screening vs telescreening Methods: This is a retrospective, matched cohort study to compare the performance of real-time remote telescreening to in-person screening at a single urban academic ED. 337 screening hours were compared to 315 equivalent proximate hours of telescreening. Participants were selected from an ED convenience sample. Using electronic medical records we compared patients screened per hour, rates of patients leaving without being seen (LWBS), rates of analgesia ordered and proportion of patients with chest pain being ordered a standard set of tests and medications. Results: Over the 22 weeks of our study in-person screeners evaluated 1933 participants over 337 hours, 5.7 patients-per-hour, while telescreeners saw 1497 patients over 315 hours, 4.9 patients per hour, (p<0.001). However, for the final three weeks of the evaluation, the rates were not statistically different (5.5 v 5.3) (p=0.66). Fewer patients LWBS during in-person screening compared to telescreening hours, (2.6 vs. 3.8%; p=0.03). Yet, when compared to prior year date and time-matched data, implementation of telescreening from 1-3am, a time when in-person screening was not used, decreased the rate of LWBS from 25.1% to 4.5%, p<0.001. Analgesia was ordered more frequently by telescreeners, 51 vs 32%, p<0.001. Screeners ordered a full chest pain bundle for those presenting with chest pain and triaged to an ESI level 3-5 34% of the time compared to telescreeners ordering 37%, p=0.18. Conclusions: While the rate of patients seen per hour during telescreening was lower throughout the course of our study, this effect was driven by early differences and by the last three weeks the rates of patients screened was not statistically different between in-person and telescreening. Significantly, adding telescreening during 1-3am on weekdays, a time when screening did not previously occur, we were able to decrease the rates of LWBS from 25.1% to 4.5%. More analgesia was ordered by telescreeners but this is driven by established practice patterns of the telescreening providers. No difference was observed for rates of ordering a standard set of labs, imaging and medications for those screened with a chief complaint of chest pain. Telescreening was an effective and safe way for this ED to expand the hours in which patients were screened by a health care provider in triage.

  • Effect of a Multimedia Patient Decision Aid to Supplement the Informed Consent Process of a Peripherally Inserted Central Venous Catheter Procedure: Part 2

    Date Submitted: May 15, 2018

    Open Peer Review Period: May 18, 2018 - Jul 13, 2018

    Background: Informed consent is a complex process to help patients engage in care processes and reach the best treatment decisions. There are many limitations to the conventional consent process that...

    Background: Informed consent is a complex process to help patients engage in care processes and reach the best treatment decisions. There are many limitations to the conventional consent process that is based on oral discussion of information related to treatment procedures by the healthcare provider. A conclusive body of research supports the effectiveness of multimedia patient decision aids (PtDAs) in the consent process in terms of patient satisfaction, increased knowledge about the procedure, reduced anxiety level, and higher engagement in the decision-making. little information is available about the effectiveness of multimedia PtDAs in the consent process of invasive therapeutic procedures such as the peripherally inserted central venous catheter or PICC. Objective: This study examined the effectiveness of a multimedia PtDA to supplement the consent process of the PICC for patients in 10 acute and intensive care units in terms of knowledge recall, knowledge retention, satisfaction with the consent process, and satisfaction with the multimedia PtDA. Methods: This is a pre-post study that included 130 patients for whom a PICC was ordered. Patients in the control group (N= 65) received the conventional consent process for the PICC, while patients in the intervention group (N= 65) received the multimedia PtDA to support the consent process of a PICC. All patients were surveys for knowledge recall and retention about the procedure and satisfaction with the consent process. Patients in the intervention group were also surveyed for their satisfaction with the multimedia PtDA. Results: In comparison to the control group, the intervention group scored around 2 points higher on knowledge recall (t = 4.9, P = .0001) and knowledge retention (t = 4.8, P = .0001). All patients in the intervention group were highly satisfied with the multimedia PtDA with a mean score above 4.5 out of 5 on all items. Items with the highest mean scores were related to the effect of the multimedia PtDA on knowledge retention (mean=4.9, SD=0.2), patient readiness (mean=4.8, SD= 0.5), and complete understanding of procedure complications (mean=4.8, SD= 0.4) and patient role in maintaining the safety of the PICC (mean=4.8, SD= 0.5). Patients in the two groups were highly satisfied with the consent process. However, 10 (out of 65) patients in the control group (15%) reported the followings were omitted from the discussion: patient and provider role in the safety of the PICC, other treatment options, and common side effects. Two of the patients also commented that they were not ready to engage in the discussion. Conclusions: Multimedia PtDA is an effective standardized, structured, self-paced learning tool to supplement the consent process of the PICC and improve patient satisfaction with the process, knowledge recall, and knowledge retention.

  • Processing of Electronic Medical Record for Health Services Research in Academic Medical Centre: Methods and Validation

    Date Submitted: May 2, 2018

    Open Peer Review Period: May 5, 2018 - Jun 30, 2018

    Background: Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in healthcare policy design and service planning. Although research using EMR...

    Background: Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in healthcare policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity and lack of suitable measures in important domains still hinder the progress. Objective: Our objective is to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. Methods: Based on a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multi-level views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), while socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. Results: Visit level (N=7,778,761) and patient level records (n=549,109) were generated. Diagnosis codes were standardized to ICD-9-CM with a mapping rate of 97.5%. 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software (CCS). Diagnosis codes that underwent modification (truncation or zero-addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other, and positively correlated with healthcare utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with lower SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity and hospital utilization was found in those aged above 65 and those below. Conclusions: The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

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