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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: 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.

  • Mobilizing health information nationwide (montage). Source: The Author / Placeit.net; Copyright: JMIR Publications; URL: http://medinform.jmir.org/2018/2/e29/; License: Creative Commons Attribution (CC-BY).

    An Early Model for Value and Sustainability in Health Information Exchanges: Qualitative Study

    Authors List:

    Abstract:

    Background: The primary value relative to health information exchange has been seen in terms of cost savings relative to laboratory and radiology testing, emergency department expenditures, and admissions. However, models are needed to statistically quantify value and sustainability and better understand the dependent and mediating factors that contribute to value and sustainability. Objective: The purpose of this study was to provide a basis for early model development for health information exchange value and sustainability. Methods: A qualitative study was conducted with 21 interviews of eHealth Exchange participants across 10 organizations. Using a grounded theory approach and 3.0 as a relative frequency threshold, 5 main categories and 16 subcategories emerged. Results: This study identifies 3 core current perceived value factors and 5 potential perceived value factors—how interviewees predict health information exchanges may evolve as there are more participants. These value factors were used as the foundation for early model development for sustainability of health information exchange. Conclusions: Using the value factors from the interviews, the study provides the basis for early model development for health information exchange value and sustainability. This basis includes factors from the research: fostering consumer engagement; establishing a provider directory; quantifying use, cost, and clinical outcomes; ensuring data integrity through patient matching; and increasing awareness, usefulness, interoperability, and sustainability of eHealth Exchange.

  • Image shows positron emission tomography (PET) viewer’s full interface. Source: Figure 5 from http://medinform.jmir.org/2018/2/e26; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    A Neuroimaging Web Services Interface as a Cyber Physical System for Medical Imaging and Data Management in Brain Research: Design Study

    Abstract:

    Background: Structural and functional brain images are essential imaging modalities for medical experts to study brain anatomy. These images are typically visually inspected by experts. To analyze images without any bias, they must be first converted to numeric values. Many software packages are available to process the images, but they are complex and difficult to use. The software packages are also hardware intensive. The results obtained after processing vary depending on the native operating system used and its associated software libraries; data processed in one system cannot typically be combined with data on another system. Objective: The aim of this study was to fulfill the neuroimaging community’s need for a common platform to store, process, explore, and visualize their neuroimaging data and results using Neuroimaging Web Services Interface: a series of processing pipelines designed as a cyber physical system for neuroimaging and clinical data in brain research. Methods: Neuroimaging Web Services Interface accepts magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and functional magnetic resonance imaging. These images are processed using existing and custom software packages. The output is then stored as image files, tabulated files, and MySQL tables. The system, made up of a series of interconnected servers, is password-protected and is securely accessible through a Web interface and allows (1) visualization of results and (2) downloading of tabulated data. Results: All results were obtained using our processing servers in order to maintain data validity and consistency. The design is responsive and scalable. The processing pipeline started from a FreeSurfer reconstruction of Structural magnetic resonance imaging images. The FreeSurfer and regional standardized uptake value ratio calculations were validated using Alzheimer’s Disease Neuroimaging Initiative input images, and the results were posted at the Laboratory of Neuro Imaging data archive. Notable leading researchers in the field of Alzheimer’s Disease and epilepsy have used the interface to access and process the data and visualize the results. Tabulated results with unique visualization mechanisms help guide more informed diagnosis and expert rating, providing a truly unique multimodal imaging platform that combines magnetic resonance imaging, positron emission tomography, diffusion tensor imaging, and resting state functional magnetic resonance imaging. A quality control component was reinforced through expert visual rating involving at least 2 experts. Conclusions: To our knowledge, there is no validated Web-based system offering all the services that Neuroimaging Web Services Interface offers. The intent of Neuroimaging Web Services Interface is to create a tool for clinicians and researchers with keen interest on multimodal neuroimaging. More importantly, Neuroimaging Web Services Interface significantly augments the Alzheimer’s Disease Neuroimaging Initiative data, especially since our data contain a large cohort of Hispanic normal controls and Alzheimer’s Disease patients. The obtained results could be scrutinized visually or through the tabulated forms, informing researchers on subtle changes that characterize the different stages of the disease.

  • The Actionable Intime Insights homepage (montage). Source: Aisquared.co / Placeit.net; Copyright: JMIR Publications; URL: http://medinform.jmir.org/2018/2/e28/; License: Creative Commons Attribution (CC-BY).

    Developing a Third-Party Analytics Application Using Australia’s National Personal Health Records System: Case Study

    Abstract:

    Background: My Health Record (MyHR) is Australia’s national electronic health record (EHR) system. Poor usability and functionality have resulted in low utility, affecting enrollment and participation rates by both patients and clinicians alike. Similar to apps on mobile phone app stores, innovative third-party applications of MyHR platform data can enhance the usefulness of the platform, but there is a paucity of research into the processes involved in developing third-party applications that integrate and use data from EHR systems. Objective: The research describes the challenges involved in pioneering the development of a patient and clinician Web-based software application for MyHR and insights resulting from this experience. Methods: This research uses a case study approach, investigating the development and implementation of Actionable Intime Insights (AI2), a third-party application for MyHR, which translates Medicare claims records stored in MyHR into a clinically meaningful timeline visualization of health data for both patients and clinicians. This case study identifies the challenges encountered by the Personal Health Informatics team from Flinders University in the MyHR third-party application development environment. Results: The study presents a nuanced understanding of different data types and quality of data in MyHR and the complexities associated with developing secondary-use applications. Regulatory requirements associated with utilization of MyHR data, restrictions on visualizations of data, and processes of testing third-party applications were encountered during the development of the application. Conclusions: This study identified several processes, technical and regulatory barriers which, if addressed, can make MyHR a thriving ecosystem of health applications. It clearly identifies opportunities and considerations for the Australian Digital Health Agency and other national bodies wishing to encourage the development of new and innovative use cases for national EHRs.

  • The DWISE mobile app (montage). Source: The Authors / Smartmockups.com; Copyright: JMIR Publications; URL: http://medinform.jmir.org/2018/2/e25/; License: Creative Commons Attribution (CC-BY).

    Diabetes-Related Behavior Change Knowledge Transfer to Primary Care Practitioners and Patients: Implementation and Evaluation of a Digital Health Platform

    Abstract:

    Background: Behavioral science is now being integrated into diabetes self-management interventions. However, the challenge that presents itself is how to translate these knowledge resources during care so that primary care practitioners can use them to offer evidence-informed behavior change support and diabetes management recommendations to patients with diabetes. Objective: The aim of this study was to develop and evaluate a computerized decision support platform called “Diabetes Web-Centric Information and Support Environment” (DWISE) that assists primary care practitioners in applying standardized behavior change strategies and clinical practice guidelines–based recommendations to an individual patient and empower the patient with the skills and knowledge required to self-manage their diabetes through planned, personalized, and pervasive behavior change strategies. Methods: A health care knowledge management approach is used to implement DWISE so that it features the following functionalities: (1) assessment of primary care practitioners’ readiness to administer validated behavior change interventions to patients with diabetes; (2) educational support for primary care practitioners to help them offer behavior change interventions to patients; (3) access to evidence-based material, such as the Canadian Diabetes Association’s (CDA) clinical practice guidelines, to primary care practitioners; (4) development of personalized patient self-management programs to help patients with diabetes achieve healthy behaviors to meet CDA targets for managing type 2 diabetes; (5) educational support for patients to help them achieve behavior change; and (6) monitoring of the patients’ progress to assess their adherence to the behavior change program and motivating them to ensure compliance with their program. DWISE offers these functionalities through an interactive Web-based interface to primary care practitioners, whereas the patient’s self-management program and associated behavior interventions are delivered through a mobile patient diary via mobile phones and tablets. DWISE has been tested for its usability, functionality, usefulness, and acceptance through a series of qualitative studies. Results: For the primary care practitioner tool, most usability problems were associated with the navigation of the tool and the presentation, formatting, understandability, and suitability of the content. For the patient tool, most issues were related to the tool’s screen layout, design features, understandability of the content, clarity of the labels used, and navigation across the tool. Facilitators and barriers to DWISE use in a shared decision-making environment have also been identified. Conclusions: This work has provided a unique electronic health solution to translate complex health care knowledge in terms of easy-to-use, evidence-informed, point-of-care decision aids for primary care practitioners. Patients’ feedback is now being used to make necessary modification to DWISE.

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  • 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.

  • Validating a framework for coding patient-reported health information to MedDRA® terminology

    Date Submitted: May 16, 2018

    Open Peer Review Period: May 17, 2018 - May 26, 2018

    Background: The availability of and interest in patient-generated health data (PGHD) have grown steadily. Patients describe medical experiences differently than a clinician or researcher describes the...

    Background: The availability of and interest in patient-generated health data (PGHD) have grown steadily. Patients describe medical experiences differently than a clinician or researcher describes their observations. Patients may find non-serious, known adverse drug events (ADE) to be an ongoing concern, which impacts tolerability and adherence. Clinicians must be vigilant for medically serious, potentially fatal ADEs. Having both perspectives provides patients and clinicians with a more complete picture of what to expect from drug therapies. Multiple initiatives seek to incorporate patient perspectives into drug development, including PGHD exploration for pharmacovigilance. FDA Adverse Event Reporting System (FAERS) contains case reports of post-marketing ADEs. To facilitate analysis of FAERS case reports, case details are coded using the Medical Dictionary for Regulatory Activities (MedDRA®). PatientsLikeMe (PLM) is an online network where patients report, track, share, and discuss their health information. PLM captures PGHD through free-text and structured data fields. PLM codes structured data to an internal terminology which incorporates multiple terminologies, including MedDRA®. Standardization of PLM PGHD enables electronic accessibility and enhances patient engagement. Objective: A Food and Drug Administration (FDA) MedDRA® coding expert reviewed a retrospective data file containing patient-reported symptoms and ADEs and the PLM-assigned MedDRA® terms to determine the medical accuracy and appropriateness of the selected MedDRA® terms, applying the International Council for Harmonisation (ICH) MedDRA® Term Selection: Points to Consider (MTS:PTC) guides. Methods: An FDA coding expert reviewed retrospective data containing patient-reported symptoms and adverse drugs events to determine medical accuracy and appropriateness of MedDRA terms assigned by PatientsLikeMe, applying the International Council for Harmonisation MedDRA Term Selection: Points to Consider (MTS:PTC) guides. Results: The FDA MedDRA® coding expert reviewed 3,234 PLM-assigned MedDRA® codes and the patient-reported verbatim text. FDA and PLM were concordant on 97.09% (3,140/3,234) of the PLM-assigned MedDRA® codes. The 2.91% discordant subset was analyzed to identify reasons for differences. Coding differences were consequent to several reasons, but mostly driven by PLM’s approach of assigning a more general MedDRA® term to enable patient-to-patient engagement, while FDA assigned a more specific term. Conclusions: PLM MedDRA® coding of PGHD was generally comparable to how FDA would code similar data, applying the MTS:PTC. Discordant coding was consequent to several reasons, but mostly reflected a difference in purpose. The intent of MTS:PTC coding principles is to capture the most specific reported information about an ADE whereas PLM may code patient-reported symptoms and ADEs to more general MedDRA® terms to support patient engagement amongst a larger group of patients. This study demonstrates that most symptoms and ADEs collected by a PGHD source such as the PLM platform can be reliably coded to MedDRA® terminology by applying the MTS:PTC guide. As with all secondary use of novel data, understanding coding and standardization principles applied to these data types is important.

  • 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.

  • Development of an Educational Program to Increase Patient Involvement in a Healthcare Patient Portal: A Quality Improvement Project

    Date Submitted: Apr 20, 2018

    Open Peer Review Period: Apr 23, 2018 - Jun 18, 2018

    Background: Efforts in the United States to improve patient engagement and communication with health care providers have led to the creation of the meaningful use program. [1] The Centers for Medicare...

    Background: Efforts in the United States to improve patient engagement and communication with health care providers have led to the creation of the meaningful use program. [1] The Centers for Medicare and Medicaid Services have created a three-stage process to encourage the adoption and use of electronic health records (EHR). Benefits of EHR use include the ability to provide accurate, up-to-date, legible, and complete information about patients at the point of care. One important component of EHR is the patient portal. Patient portals provide 24-hour access to portions of a patient medical record as well as a secure pathway to send messages to providers, ask for refills of medications, and schedule appointments. Objective: To assess if patients who have not used the patient portal will engage in using the portal after an in-office orientation on how to access and the benefits of using the patient portal. Methods: A quality improvement project was performed using a convenience sample of 60 participants who were scheduled for an appointment in an outpatient cardiology office and had not accessed the patient portal in the past 12 months. The participants were given a survey regarding their computer and internet access as well as their level of comfort using a computer. Each participant was assisted in creating a username and password as well as a security question and answer. The participant then accessed the portal and navigated through the portal with the guidance of the nurse practitioner. They also sent a message via the portal to the provider they were assigned to that day. Each participant was given a pamphlet and a printed power point to reinforce what they had learned. After two months, the nurse practitioner accessed the portal to determine if the enrolled participants had accessed the portal. The reasons for access and frequency were recorded. If there was no access, the participant was called by the nurse practitioner to determine the reason they had not accessed the portal. Results: Of the 60 participants, 54% were women, 46% men, 93% were Caucasian. Fifty-six point seven accessed the portal from home. Reasons for access included: 85% reviewed labs, 53% reviewed messages sent to them from the office and 23% sent messages to the office. Twenty-four participants did not access the portal. Of those participants, 33% stated that they had no clear reason to access the portal, 25% stated that they forgot their login information and 17% stated they no interest in the portal. Conclusions: Patient portals are a useful tool for communication between patients and their providers. Providing an in-office orientation to the portal increased patient access to the portal.

  • Integrated decision support software and data feedback can improve sexual orientation recording, comprehensive sexual health testing and detection of infections among gay and bisexual men attending general practice

    Date Submitted: Apr 17, 2018

    Open Peer Review Period: Apr 18, 2018 - Jun 13, 2018

    Background: Gay and bisexual men are disproportionately affected by HIV and other sexually transmissible infections (STIs) yet opportunities for STI testing of this population are often missed or inco...

    Background: Gay and bisexual men are disproportionately affected by HIV and other sexually transmissible infections (STIs) yet opportunities for STI testing of this population are often missed or incomplete in general practice settings. Strategies are needed for improving the uptake and completeness of sexual health testing in this setting. Objective: We evaluated an intervention centred around integrated decision support software and routine data feedback to improve the collection of sexual orientation data and increase sexual health screening among gay and bisexual men attending general practice. Methods: A study using before/after and case/comparison methods was undertaken to assess the interventions impact in seven Australian general practice clinics. The software was introduced in 2012 and used patient records to prompt clinicians to record sexual orientation and, along with pathology testing history, generated prompts when sexual health testing was overdue or incomplete. It also facilitated the routine extraction of clinical data, which was regularly reported to clinicians. We calculated summary rate ratios (SRRs) based on quarterly trends in the 12-month before and 24-month intervention periods and compared those to four comparison clinics that did not receive the intervention. Results: Among 32,276 attending male patients, sexual orientation recording increased 19% (from 47% to 56%) during the intervention period (SRR=1.10, P<0.001). Comprehensive STI testing increased by 89% during the intervention (26-49%; SRR=1.38, P<0.001). While comprehensive testing increased slightly in comparison sites, the increase was comparatively greater in intervention sites (SRR=1.12, P<0.001). There was also an increase in detection of chlamydia and gonorrhoea after the intervention’s introduction, which was not observed in the comparison sites. Conclusions: Integrated decision support software and data feedback were associated with modest increases in sexual orientation recording, comprehensive screening among gay and bisexual men, and the detection of STIs. Decision support software can be utilised to enhance the delivery of sexual health care in general practice. Clinical Trial: N/A

  • Is Adherence to Cancer Screening Associated with Knowledge of Screening Guidelines? A Feasibility Study of Linking Self-reported Survey Data with Medical Records

    Date Submitted: Mar 28, 2018

    Open Peer Review Period: Mar 31, 2018 - May 26, 2018

    Background: It is feasible that patients who are more aware of cancer screening guidelines may be more likely to adhere to them. Objective: In this study, we aimed to determine if screening knowledge...

    Background: It is feasible that patients who are more aware of cancer screening guidelines may be more likely to adhere to them. Objective: In this study, we aimed to determine if screening knowledge was associated with documented screening participation. We also assessed the feasibility and acceptability of linking electronic survey data with clinical data in the primary care setting. Methods: An electronic survey was conducted at two sites in Toronto, Canada. At one site, eligible patients were approached in the waiting room to complete the survey; at the second site, eligible patients were sent an email inviting them to participate. Participants were asked to consent to linkage of their survey results with their electronic medical record. Results: Overall, 1683 participants responded to the survey: 1436 responded via email (response rate 24.8%) whereas 247 responded to the survey in the waiting room (response rate 67.5%). The majority of patients consented to linking their survey data to their medical record. Knowledge of cancer screening guidelines was generally low. Although the majority of participants were able to identify the recommended tests for breast and cervical screening, very few participants correctly identified the recommended age and frequency of screening, with a maximum of 22% of screen-eligible women answering all three questions correctly for breast cancer screening. However, this low level of knowledge among patients was not significantly associated with screening uptake, particularly after adjustment for sociodemographic characteristics. Conclusions: Although knowledge of screening guidelines was low among patients in our study, this was not associated with screening participation. Participants were willing to link self-reported data with their medical record data, which has significant implications for future research.

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