Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Monday, December 24 through Wednesday, December 26 inclusive. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Monday, December 24.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Monday, December 24.
Background: Access to specialty care remains a major challenge in the Canadian healthcare system. eConsult services allow primary care providers to seek specialist advice often without needing the patient to go for a face to face consultation. It improves overall access to specialists and referral process using an electronic care consultation service in urban and rural primary care clinics. This paper describes our preliminary results of a pilot study with an eConsult across three regions in the province of Quebec, Canada. Objective: The main objective of this pilot project is to describe the initial experience with the implementation of eConsult Quebec Service in rural and urban primary care clinics to improve access to care and specialty-referral process for primary care providers (PCPs). Methods: eConsult is a secure web-based platform that allows primary care providers (PCPs) to electronically consult with specialists regarding their patients’ medical issues in a wide range of specialty groups. We established an eConsult service that covers urban and rural communities in three regions of Quebec. We conducted a cross-sectional analysis of all eConsult cases submitted from July 4, 2017 to December 8, 2018. Results: For over a year, 1016 eConsults have been generated during the course of this study. 97 PCPs submitted requests to 22 specialty groups and answered by 40 different specialists. The most popular specialty was internal medicine (22%, N=224). Overall, 63% of completed cases did not require a face-to-face visit. PCPs rated the service as being of high or very high value for the patients in 96% of cases, and for themselves in 98% of cases. Conclusions: The preliminary data highlights the success of the implementation of the eConsult Quebec Service across 6 primary care clinics. The eConsult platform proves effective, efficient, and well received by both patients and physicians. If used more widely, eConsult could help reducing wait times significantly. Recently the Ministry of Health and Social Services of Quebec has identified as a top priority developing a strategic plan to scale eConsult throughout other regions of the province.
(Toronto, Dec 7, 2018) JMIR Publications is pleased to announce that JMIR Medical Informatics will have its inaugural "official" impact factor for 2018 released in mid-2019 by Clarivate Analytics. After a rigorous evaluation process over the past 5 years, Clarivate (formerly the IP division of Thomson Reuters, which is in charge of the calculation of "official" impact factors) has finally admitted JMIR Med Inform into the Science Citation Index Expanded (SCIE). The Journal Citations Report (JCR) 2019 will list an official impact factor for JMI (which will be the 2018 IF).
JMIR Medical Informatics (JMI, ISSN 2291-9694) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a Pubmed/SCIE-indexed, 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.
It is one of 29 journal titles published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2018: 4.671), and is the 4th JMIR title getting an impact factor. Other titles include JMIR mHealth & uHealth (Impact Factor 2018: 4.541) and JMIR Serious Games (Impact Factor 2018: 2.226), both journals which are top ranked in their respective fields.
JMIR Medical Informatics (JMI, ISSN 2291-9694) (Editor-in-chief: Christian Lovis MD MPH FACMI) is a Pubmed/SCIE-indexed, top-rated, tier A journal with impact factor expected in 2019, 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 2018: 4.671), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
JMIR Medical Informatics is indexed in PubMed Central/PubMed, and has also been accepted for SCIE, with an official Clarivate impact factor 2018 expected to be released in 2019 (see announcement).
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).
Background: Defining clinical phenotypes from electronic health record (EHR)–derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely grained clinical terminology—either native SNOMED CT or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets. The work of creating and maintaining list-based value sets proves daunting, as does insuring that their contents accurately represent the clinically intended condition. Objective: The goal of the research was to compare an intensional (concept hierarchy-based) versus extensional (list-based) value set approach to defining clinical phenotypes using SNOMED CT–encoded data from EHRs by evaluating value set conciseness, time to create, and completeness. Methods: Starting from published Centers for Medicare and Medicaid Services (CMS) high-priority eCQMs, we selected 10 clinical conditions referenced by those eCQMs. For each, the published SNOMED CT list-based (extensional) value set was downloaded from the Value Set Authority Center (VSAC). Ten corresponding SNOMED CT hierarchy-based intensional value sets for the same conditions were identified within our EHR. From each hierarchy-based intensional value set, an exactly equivalent full extensional value set was derived enumerating all included descendant SNOMED CT concepts. Comparisons were then made between (1) VSAC-downloaded list-based (extensional) value sets, (2) corresponding hierarchy-based intensional value sets for the same conditions, and (3) derived list-based (extensional) value sets exactly equivalent to the hierarchy-based intensional value sets. Value set conciseness was assessed by the number of SNOMED CT concepts needed for definition. Time to construct the value sets for local use was measured. Value set completeness was assessed by comparing contents of the downloaded extensional versus intensional value sets. Two measures of content completeness were made: for individual SNOMED CT concepts and for the mapped diagnosis clinical terms available for selection within the EHR by clinicians. Results: The 10 hierarchy-based intensional value sets proved far simpler and faster to construct than exactly equivalent derived extensional value set lists, requiring a median 3 versus 78 concepts to define and 5 versus 37 minutes to build. The hierarchy-based intensional value sets also proved more complete: in comparison, the 10 downloaded 2018 extensional value sets contained a median of just 35% of the intensional value sets’ SNOMED CT concepts and 65% of mapped EHR clinical terms. Conclusions: In the EHR era, defining conditions preferentially should employ SNOMED CT concept hierarchy-based (intensional) value sets rather than extensional lists. By doing so, clinical guideline and eCQM authors can more readily engage specialists in vetting condition subtypes to include and exclude, and streamline broad EHR implementation of condition-specific decision support promoting guideline adherence for patient benefit.
Background: In 2015, the Department of Veterans Affairs (VA) nationally implemented a transgender e-consultation (e-consult) program with expert clinical guidance for providers. Objective: This mixed-methods project aimed to describe providers’ program experiences, reasons for nonuse of the program, and ways to improve the program use. Methods: From January to May 2017, 15 urban and rural VA providers who submitted at least one e-consult in the last year participated in semistructured interviews about their program experiences, which were analyzed using content analysis. From November to December 2017, 53 providers who encountered transgender patients but did not utilize the program participated in a brief online survey on the reasons for nonuse of the program and the facilitators encouraging use. Results: Qualitative analysis showed that providers learned of the program through email; colleagues; the electronic health record (EHR) system; and participation in the VA Lesbian, Gay, Bisexual, and Transgender committees or educational trainings. Providers used the program to establish care plans, hormone therapy recommendations, sexual and reproductive health education, surgical treatment education, patient-provider communication guidance, and second opinions. The facilitators of program use included understandable recommendations, ease of use through the EHR system, and status as the only transgender resource for rural providers. Barriers to use included time constraints, communication-related problems with the e-consult, impractical recommendations for underresourced sites, and misunderstanding of the e-consult purpose. Suggestions for improvement included addition of concise or sectioned responses, expansion of program awareness among providers or patients, designation of a follow-up contact person, and increase in provider education about transgender veterans and related care. Quantitative analysis showed that the common reasons for nonuse of the program were no knowledge of the program (54%), no need of the program (32%), and receipt of help from a colleague outside of e-consult (24%). Common suggestions to improve the program use in quantitative analyses included provision of more information about where to find e-consult in the chart, guidance on talking with patients about the program, and e-mail announcements to improve provider awareness of the program. Post hoc exploratory analyses showed no differences between urban and rural providers. Conclusions: The VA transgender e-consult program is useful for providers, but there are several barriers to implementing recommendations, some of which are especially challenging for rural providers. Addressing the identified barriers and enhancing the facilitators may improve program use and quality care for transgender veterans.
Background: Digital transformation in health care is being driven by the need to improve quality, reduce costs, and enhance the patient experience of health care delivery. It does this through both the direct intervention of technology to create new diagnostic and treatment opportunities and also through the improved use of information to create more engaging and efficient care processes. Objective: In a modern digital hospital, improved clinical and business processes are often driven through enhancing the information flows that support them. To understand an organization’s ability to transform their information flows requires a clear understanding of the capabilities of an organization’s information technology infrastructure. To date, hospital facilities have been challenged by the absence of uniform ways of describing this infrastructure that would enable them to benchmark where they are and create a vision of where they would like to be. While there is an industry assessment measure for electronic medical record (EMR) adoption using the Healthcare Information and Management Systems Society Analytics EMR Adoption Model, there is no equivalent for assessing the infrastructure and associated technology capabilities for digital hospitals. Our aim is to fill this gap, as hospital administrators and clinicians need to know how and why to invest in information infrastructure to support health information technology that benefits patient safety and care. Methods: Based on an operational framework for the Capability Maturity Model, devised specifically for health care, we applied information use characteristics to define eight information systems maturity levels and associated technology infrastructure capabilities. These levels are mapped to user experiences to create a linkage between technology infrastructure and experience outcomes. Subsequently, specific technology capabilities are deconstructed to identify the technology features required to meet each maturity level. Results: The resulting assessment framework clearly defines 164 individual capabilities across the five technology domains and eight maturity levels for hospital infrastructure. These level-dependent capabilities characterize the ability of the hospital’s information infrastructure to support the business of digital hospitals including clinical and administrative requirements. Further, it allows the addition of a scoring calculation for each capability, domain, and the overall infrastructure, and it identifies critical requirements to meet each of the maturity levels. Conclusions: This new Infrastructure Maturity Assessment framework will allow digital hospitals to assess the maturity of their infrastructure in terms of their digital transformation aligning to business outcomes and supporting the desired level of clinical and operational competency. It provides the ability to establish an international benchmark of hospital infrastructure performance, while identifying weaknesses in current infrastructure capability. Further, it provides a business case justification through increased functionality and a roadmap for subsequent digital transformation while moving from one maturity level to the next. As such, this framework will encourage and guide information-driven, digital transformation in health care.
Background: The data missing from patient profiles in intensive care units (ICUs) are substantial and unavoidable. However, this incompleteness is not always random or because of imperfections in the data collection process. Objective: This study aimed to investigate the potential hidden information in data missing from electronic health records (EHRs) in an ICU and examine whether the presence or missingness of a variable itself can convey information about the patient health status. Methods: Daily retrieval of laboratory test (LT) measurements from the Medical Information Mart for Intensive Care III database was set as our reference for defining complete patient profiles. Missingness indicators were introduced as a way of representing presence or absence of the LTs in a patient profile. Thereafter, various feature selection methods (filter and embedded feature selection methods) were used to examine the predictive power of missingness indicators. Finally, a set of well-known prediction models (logistic regression [LR], decision tree, and random forest) were used to evaluate whether the absence status itself of a variable recording can provide predictive power. We also examined the utility of missingness indicators in improving predictive performance when used with observed laboratory measurements as model input. The outcome of interest was in-hospital mortality and mortality at 30 days after ICU discharge. Results: Regardless of mortality type or ICU day, more than 40% of the predictors selected by feature selection methods were missingness indicators. Notably, employing missingness indicators as the only predictors achieved reasonable mortality prediction on all days and for all mortality types (for instance, in 30-day mortality prediction with LR, we achieved area under the curve of the receiver operating characteristic [AUROC] of 0.6836±0.012). Including indicators with observed measurements in the prediction models also improved the AUROC; the maximum improvement was 0.0426. Indicators also improved the AUROC for Simplified Acute Physiology Score II model—a well-known ICU severity of illness score—confirming the additive information of the indicators (AUROC of 0.8045±0.0109 for 30-day mortality prediction for LR). Conclusions: Our study demonstrated that the presence or absence of LT measurements is informative and can be considered a potential predictor of in-hospital and 30-day mortality. The comparative analysis of prediction models also showed statistically significant prediction improvement when indicators were included. Moreover, missing data might reflect the opinions of examining clinicians. Therefore, the absence of measurements can be informative in ICUs and has predictive power beyond the measured data themselves. This initial case study shows promise for more in-depth analysis of missing data and its informativeness in ICUs. Future studies are needed to generalize these results.
Background: Blockchain technology is emerging as an innovative tool in data and software security. Objective: This study aims to explore the role of blockchain in supporting clinical trials data management and develop a proof-of-concept implementation of a patient-facing and researcher-facing system. Methods: Blockchain-based Smart Contracts were built using the Ethereum platform. Results: We described BlockTrial, a system that uses a Web-based interface to allow users to run trials-related Smart Contracts on an Ethereum network. Functions allow patients to grant researchers access to their data and allow researchers to submit queries for data that are stored off chain. As a type of distributed ledger, the system generates a durable and transparent log of these and other transactions. BlockTrial could be used to increase the trustworthiness of data collected during clinical research with benefits to researchers, regulators, and drug companies alike. In addition, the system could empower patients to become more active and fully informed partners in research. Conclusions: Blockchain technology presents an opportunity to address some of the common threats to the integrity of data collected in clinical trials and ensure that the analysis of these data comply with prespecified plans. Further technical work is needed to add additional functions. Policies must be developed to determine the optimal models for participation in the system by its various stakeholders.
Background: Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care 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: The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. Methods: On the basis of 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 multilevel 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), whereas 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. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. 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 health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. 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.
Background: Ontologies are key enabling technologies for the Semantic Web. The Web Ontology Language (OWL) is a semantic markup language for publishing and sharing ontologies. Objective: The supply of customizable, computable, and formally represented molecular genetics information and health information, via electronic health record (EHR) interfaces, can play a critical role in achieving precision medicine. In this study, we used cystic fibrosis as an example to build an Ontology-based Knowledge Base prototype on Cystic Fibrobis (OntoKBCF) to supply such information via an EHR prototype. In addition, we elaborate on the construction and representation principles, approaches, applications, and representation challenges that we faced in the construction of OntoKBCF. The principles and approaches can be referenced and applied in constructing other ontology-based domain knowledge bases. Methods: First, we defined the scope of OntoKBCF according to possible clinical information needs about cystic fibrosis on both a molecular level and a clinical phenotype level. We then selected the knowledge sources to be represented in OntoKBCF. We utilized top-to-bottom content analysis and bottom-up construction to build OntoKBCF. Protégé-OWL was used to construct OntoKBCF. The construction principles included (1) to use existing basic terms as much as possible; (2) to use intersection and combination in representations; (3) to represent as many different types of facts as possible; and (4) to provide 2-5 examples for each type. HermiT 1.3.8.413 within Protégé-5.1.0 was used to check the consistency of OntoKBCF. Results: OntoKBCF was constructed successfully, with the inclusion of 408 classes, 35 properties, and 113 equivalent classes. OntoKBCF includes both atomic concepts (such as amino acid) and complex concepts (such as “adolescent female cystic fibrosis patient”) and their descriptions. We demonstrated that OntoKBCF could make customizable molecular and health information available automatically and usable via an EHR prototype. The main challenges include the provision of a more comprehensive account of different patient groups as well as the representation of uncertain knowledge, ambiguous concepts, and negative statements and more complicated and detailed molecular mechanisms or pathway information about cystic fibrosis. Conclusions: Although cystic fibrosis is just one example, based on the current structure of OntoKBCF, it should be relatively straightforward to extend the prototype to cover different topics. Moreover, the principles underpinning its development could be reused for building alternative human monogenetic diseases knowledge bases.
Background: Health care maldistribution is a long-term problem in China. Telemedicine is an efficient way to deliver medical resources to remote areas; however, there are few studies on the effectiveness and challenges in providing health care from rural to urban areas in China. Objective: The objective was to describe the effectiveness and challenges of telemedicine for providing health care from Guangzhou to rural areas in Guangdong Province. Methods: We designed surveys and conducted them immediately after the consultation and 2-4 weeks later. Data were collected from June 2015 to May 2016 including patients’ demographic features, patient satisfaction, medicine effect, patient compliance, acceptability of prescription expenses, patients’ desire to revisit the department, the top 10 diseases, and self-reported difficulties in telemedicine experience. The monthly average prescription expense was described using a line chart. The monthly consultation and prescription, as well as monthly prescriptions of Western medicines and herbs, were described using a bar chart. Results: Women comprised majority (45,386/67,740, 67.00%) of participants and men comprised the minority (22,354/67,740, 33.00%). The top 3 diseases were upper respiratory diseases (12,371/36,311, 34.07%), laryngopharyngitis (4938/36,311, 13.60%), and menstrual disorders (4669/36,311, 12.86%). The monthly prescription for Western medicine was much more than that for Chinese herbs. The annual average medicine expense per prescription was 62.9 ¥. The participants’ perception of expense was acceptable (8775/12,450, 70.48%), mostly acceptable (2497/12,450, 20.01%), accepted but somewhat expensive (980/12,450, 7.9%), and unacceptable because of high cost (198/12,450, 1.6%). The surveys on patient satisfaction demonstrated very satisfied (55,687/67,740, 82.21%), satisfied (5891/67,740, 8.70%), basic satisfaction (3561/67,740, 5.26%), dissatisfaction (1454/67,740, 2.15%), and no comment (1147/67,740, 1.69%). Participants reported their treatment outcome as follows: full recovery (5008/12,450, 40.22%), recovering (4873/12,450, 39.14%), no effect (2153/12,450, 17.29%), or worsening (416/12,450, 3.3%). Approximately 89.01% (20,240/22,740) of participants will revisit the department, whereas 10.99% (2500/22,740) will not. Most patients complied with the doctors’ advice completely (5430/10,290, 52.77%), whereas the rest reported partial compliance (3684/10,290, 35.80%) or no compliance at all (1176/10,290, 11.43%). The participants reported poor computer skills (4980/22,740, 21.90%), transportation inconvenience (4670/22,740, 20.50%), unstable internet connection (3820/22,740, 16.80%), language barriers (3708/22,740, 16.30%), medication and medical hardware shortage (2459/22,740, 10.82%), tiring commute (2068/22,740, 9.08%), family care burdens (679/22,740, 3.0%), and other unclassified difficulties (356/22,740, 1.6%) as difficulties in using telemedicine. Conclusions: Telemedicine has a wide disease spectrum, similar to ordinary medicine in China. It saves costs, has high patient satisfaction and price acceptability, and can relieve disease and syndromes. However, certain problems need to be resolved. Telemedicine could be a feasible approach to address the health care maldistribution in rural China. This study may provide useful information for policy making and guidance for further telemedicine practice in China and other developing countries.
Background: Electronic health records (EHRs) are important data resources for clinical studies and applications. Physicians or clinicians describe patients’ disorders or treatment procedures in EHRs using free text (unstructured) clinical notes. The narrative information plays an important role in patient treatment and clinical research. However, it is challenging to make machines understand the clinical narratives. Objective: This study aimed to automatically identify Chinese clinical entities from free text in EHRs and make machines semantically understand diagnoses, tests, body parts, symptoms, treatments, and so on. Methods: The dataset we used for this study is the benchmark dataset with human annotated Chinese EHRs, released by the China Conference on Knowledge Graph and Semantic Computing 2017 clinical named entity recognition challenge task. Overall, 2 machine learning models, the conditional random fields (CRF) method and bidirectional long short-term memory (LSTM)-CRF, were applied to recognize clinical entities from Chinese EHR data. To train the CRF–based model, we selected features such as bag of Chinese characters, part-of-speech tags, character types, and the position of characters. For the bidirectional LSTM-CRF–based model, character embeddings and segmentation information were used as features. In addition, we also employed a dictionary-based approach as the baseline for the purpose of performance evaluation. Precision, recall, and the harmonic average of precision and recall (F1 score) were used to evaluate the performance of the methods. Results: Experiments on the test set showed that our methods were able to automatically identify types of Chinese clinical entities such as diagnosis, test, symptom, body part, and treatment simultaneously. With regard to overall performance, CRF and bidirectional LSTM-CRF achieved a precision of 0.9203 and 0.9112, recall of 0.8709 and 0.8974, and F1 score of 0.8949 and 0.9043, respectively. The results also indicated that our methods performed well in recognizing each type of clinical entity, in which the “symptom” type achieved the best F1 score of over 0.96. Moreover, as the number of features increased, the F1 score of the CRF model increased from 0.8547 to 0.8949. Conclusions: In this study, we employed two computational methods to simultaneously identify types of Chinese clinical entities from free text in EHRs. With training, these methods can effectively identify various types of clinical entities (eg, symptom and treatment) with high accuracy. The deep learning model, bidirectional LSTM-CRF, can achieve better performance than the CRF model with little feature engineering. This study contributed to translating human-readable health information into machine-readable information.
Background: Several models suggest how the qualities of a product or service influence user satisfaction. Models such as the Customer Satisfaction Index (CSI), Technology Acceptance Model (TAM), and Delone and McLean Information Systems Success demonstrate those relations and have been used in the context of health information systems. Objective: This study aimed to investigate which qualities foster greater satisfaction among patient and professional users. In addition, we are interested in knowing to what extent improvement in those qualities can explain user satisfaction and whether this makes user satisfaction a proxy indicator of those qualities. Methods: The Unified eValuation using ONtology (UVON) method was used to construct an ontology of the required qualities for 7 electronic health (eHealth) apps being developed in the Future Internet Social and Technological Alignment Research (FI-STAR) project, a European Union (EU) project in electronic health (eHealth). The eHealth apps were deployed across 7 EU countries. The ontology included and unified the required qualities of those systems together with the aspects suggested by the Model for ASsessment of Telemedicine apps (MAST) evaluation framework. Moreover, 2 similar questionnaires for 87 patient users and 31 health professional users were elicited from the ontology. In the questionnaires, the user was asked if the system has improved the specified qualities and if the user was satisfied with the system. The results were analyzed using Kendall correlation coefficients matrices, incorporating the quality and satisfaction aspects. For the next step, 2 partial least squares structural equation modeling (PLS-SEM) path models were developed using the quality and satisfaction measure variables and the latent construct variables that were suggested by the UVON method. Results: Most of the quality aspects grouped by the UVON method are highly correlated. Strong correlations in each group suggest that the grouped qualities can be measures that reflect a latent quality construct. The PLS-SEM path analysis for the patients reveals that the effectiveness, safety, and efficiency of treatment provided by the system are the most influential qualities in achieving and predicting user satisfaction. For the professional users, effectiveness and affordability are the most influential. The parameters of the PLS-SEM that are calculated allow for the measurement of a user satisfaction index similar to CSI for similar health information systems. Conclusions: For both patients and professionals, the effectiveness of systems highly contributes to their satisfaction. Patients care about improvements in safety and efficiency, whereas professionals care about improvements in the affordability of treatments with health information systems. User satisfaction is reflected more in the users’ evaluation of system output and fulfillment of expectations but slightly less in how far the system is from ideal. Investigating satisfaction scores can be a simple and fast way to infer if the system has improved the abovementioned qualities in treatment and care.
Background: The increasing use of social media and mHealth apps has generated new opportunities for health care consumers to share information about their health and well-being. Information shared through social media contains not only medical information but also valuable information about how the survivors manage disease and recovery in the context of daily life. Objective: The objective of this study was to determine the feasibility of acquiring and modeling the topics of a major online breast cancer support forum. Breast cancer patient support forums were selected to discover the hidden, less obvious aspects of disease management and recovery. Methods: First, manual topic categorization was performed using qualitative content analysis (QCA) of each individual forum board. Second, we requested permission from the Breastcancer.org Community for a more in-depth analysis of the postings. Topic modeling was then performed using open source software Machine Learning Language Toolkit, followed by multiple linear regression (MLR) analysis to detect highly correlated topics among the different website forums. Results: QCA of the forums resulted in 20 categories of user discussion. The final topic model organized >4 million postings into 30 manageable topics. Using qualitative analysis of the topic models and statistical analysis, we grouped these 30 topics into 4 distinct clusters with similarity scores of ≥0.80; these clusters were labeled Symptoms & Diagnosis, Treatment, Financial, and Family & Friends. A clinician review confirmed the clinical significance of the topic clusters, allowing for future detection of actionable items within social media postings. To identify the most significant topics across individual forums, MLR demonstrated that 6 topics—based on the Akaike information criterion values ranging from −642.75 to −412.32—were statistically significant. Conclusions: The developed method provides an insight into the areas of interest and concern, including those not ascertainable in the clinic. Such topics included support from lay and professional caregivers and late side effects of therapy that consumers discuss in social media and may be of interest to clinicians. The developed methods and results indicate the potential of social media to inform the clinical workflow with regards to the impact of recovery on daily life.
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 health care 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 (PICC). Objective: The objective of this study was to examine the effectiveness of a multimedia PtDA in supplementing 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 PICC multimedia PtDA. Methods: This pre-post quasi-experimental study 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 those in the intervention group (n=65) received the multimedia PtDA to support the consent process of a PICC. All patients were surveyed 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: Compared with the control group, patients in the intervention group scored around 2 points higher on knowledge recall (t125=4.9, P<.001) and knowledge retention (t126=4.8, P<.001). All patients in the intervention group were highly satisfied with the multimedia PtDA, with a mean score of >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 to learn (mean 4.8 [SD 0.5]), complete understanding of the procedure-related 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, 15% (10/65) patients in the control group reported that the following information was omitted from the discussion: patient and provider roles in the safety of the PICC, other treatment options, and common side effects. Furthermore, 2 patients commented that they were not ready to engage in the discussion. Conclusions: The 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.
Background: Access to specialty care remains a major challenge in the Canadian healthcare system. eConsult services allow primary care providers to seek specialist advice often without needing the pat...
Background: Access to specialty care remains a major challenge in the Canadian healthcare system. eConsult services allow primary care providers to seek specialist advice often without needing the patient to go for a face to face consultation. It improves overall access to specialists and referral process using an electronic care consultation service in urban and rural primary care clinics. This paper describes our preliminary results of a pilot study with an eConsult across three regions in the province of Quebec, Canada. Objective: The main objective of this pilot project is to describe the initial experience with the implementation of eConsult Quebec Service in rural and urban primary care clinics to improve access to care and specialty-referral process for primary care providers (PCPs). Methods: eConsult is a secure web-based platform that allows primary care providers (PCPs) to electronically consult with specialists regarding their patients’ medical issues in a wide range of specialty groups. We established an eConsult service that covers urban and rural communities in three regions of Quebec. We conducted a cross-sectional analysis of all eConsult cases submitted from July 4, 2017 to December 8, 2018. Results: For over a year, 1016 eConsults have been generated during the course of this study. 97 PCPs submitted requests to 22 specialty groups and answered by 40 different specialists. The most popular specialty was internal medicine (22%, N=224). Overall, 63% of completed cases did not require a face-to-face visit. PCPs rated the service as being of high or very high value for the patients in 96% of cases, and for themselves in 98% of cases. Conclusions: The preliminary data highlights the success of the implementation of the eConsult Quebec Service across 6 primary care clinics. The eConsult platform proves effective, efficient, and well received by both patients and physicians. If used more widely, eConsult could help reducing wait times significantly. Recently the Ministry of Health and Social Services of Quebec has identified as a top priority developing a strategic plan to scale eConsult throughout other regions of the province.
Background: Electronic Health Record Systems have become ubiquitous in the delivery of patient care. While the implementation has brought safety and efficiency boosts to the industry, it has also expo...
Background: Electronic Health Record Systems have become ubiquitous in the delivery of patient care. While the implementation has brought safety and efficiency boosts to the industry, it has also exposed patients and their data to new risks in the form of downtime. Downtimes are any period where the computer systems are unavailable and these periods occur for updates or upgrades, but can also be triggered by deliberate cyber-attack. During an unexpected downtime, healthcare workers are forced to fall back to rarely practiced paper-based methods for healthcare delivery, while at the same time, patient data is potentially exposed to parties seeking to profit from its sale. Objective: We sought to provide a foundational perspective of the current state of downtime readiness in light of the growing cyber-attack threat on healthcare data and hospital networks. Methods: A search of technical news media related to healthcare informatics and a scoping review of research literature were conducted. Following the ENTEREQ framework, 1,651 records were retrieved, of which 16 were included in the final review. Results: 164 US-based hospitals experienced a total of 670 days of downtime in 41 events between 2012 and 2018. Almost half (48.8%) of the published downtime events involved some form of cyber-attack. 1,651 studies matching downtime search strings were found, 16 of which were found to meet inclusion criteria. Few research studies have a downtime emphasis; those that do are predominantly focused on a top-down approach. They were found to have a range of focus from the theoretical exploration of downtime to direct empirical comparison of downtime versus normal operation. Conclusions: Downtime contingency planning is still predominantly considered in abstract or top-down organizational focus. It is proposed that a bottom-up approach to comprehending and addressing downtime will be beneficial due to the complicated nature of patient care and computer downtime events. A bottom-up approach would involve the front-line clinical staff responsible for executing the downtime procedure and directly caring for the patients. EHR downtime events will continue to be a complication to hospital and healthcare operations. Significant new research support for the development of contingency plans will be needed as the cyber-attack threat continues to grow.
Background: Many theories/ models adopted from behavioral sciences literature or developed within the field of information technologies could help in understanding the technology acceptance, usage, an...
Background: Many theories/ models adopted from behavioral sciences literature or developed within the field of information technologies could help in understanding the technology acceptance, usage, and effective adoption. Objective: The main aim of this paper is to review the different theories/ models that can help in understanding information technology/system acceptance and use, and to choose the most appropriate theoretical framework that could be applied to understand the factors influencing physicians’ use of the Electronic Health Record system (EHR) at King Fahd Military Medical Complex (KFMMC) in Dhahran city, Saudi Arabia. Methods: The theories/ models were reviewed using scientific databases. The inclusion criteria were if the theories/ models used to explain individual behaviors toward accepting and using of information technology including the once conducted within the healthcare. Results: The review showed that there were five theories/ models were used within information technology studies to understand the technology acceptance and used. There were Theory of Reasoned Action, Theory of Planned Behaviour, Innovation Diffusion Theory, Unified theory of acceptance and use of technology, and Technology Acceptance Model. Each has different explanatory power of technology use. The most appropriate theoretical framework to understand the reason behind physician use of the EHR at KFMMC would be the Technology Acceptance Model (TAM). TAM model could explain up to 75% of the variation in the behavioral intention (acceptance), and up to 62% of the variation in the actual use. It is the gold standard for assessing the usage of health technologies and systems. In fact, the TAM model is one of the core models used to explore the physician’s perceptions of the Electronic Health Record system adoption. Conclusions: This review showed that there are different theories available in the literature can be used to justify the reason behind electronic health record acceptance. TAM is one of the effective, simplest models used to understand the factors influencing physicians to use the EHR-system. Further studies need to apply the TAM model to check its ability in explaining the reason behind EHR within different hospitals in Saudi Arabia
Background: Background: Governments and healthcare providers are keen to find innovative ways to more efficiently deliver care. Interest in e-consultation has grown, but evidence of benefit is uncerta...
Background: Background: Governments and healthcare providers are keen to find innovative ways to more efficiently deliver care. Interest in e-consultation has grown, but evidence of benefit is uncertain. Objective: Aims: To assess the evidence of delivering e-consultation using secure email/messaging or video links in primary care. Methods: Design & Methods: A systematic review was conducted focusing on the use and application of e-consultations in primary care. A systematic review of seven international databases was searched (Medline, Embase, CINAHL, Cochrane Library, PsycINFO, Econlit and Web of Science) (1999-2017), identifying 52 relevant studies. Screening was conducted against a detailed inclusion and exclusion criteria. Independent dual data extraction was conducted and assessed for quality. The resulting evidence was synthesised using thematic analysis. Results: Results: Patient responses to e-consultation are mixed. Patients report satisfaction with services, and improved self-care, communication and engagement with clinicians. Evidence for the acceptability and ease of use was strong, especially for those with long-term conditions and patients located in remote regions. However, patients were concerned about the privacy and security of their records. For primary healthcare staff, e-consultation delivers challenges around time-management, having the correct technological infrastructure, whether it offers a comparable standard of clinical quality, and whether it impacts on health outcomes. Conclusions: Conclusion: The emerging evidence around e-consultation is promising, with patients especially those with long-term conditions, finding this method of delivery useful. The small, pilot nature of many of the studies and low adoption rates result in unanswered questions about usage, quality, cost and sustainability. Clinical Trial: PROSPERO (International Prospective Register of Systematic Reviews) Registration Number: CRD42015019152
Background: Diabetes is a serious chronic disease marked by high levels of blood glucose. It results from issues related to how insulin is produced and/or how insulin functions in the body. In the lon...
Background: Diabetes is a serious chronic disease marked by high levels of blood glucose. It results from issues related to how insulin is produced and/or how insulin functions in the body. In the long run, uncontrolled blood sugar can damage the vessels that supply blood to important organs such as heart, kidneys, eyes, and nerves. Currently there are no effective algorithms to automatically recommend insulin dosage level considering the characteristics of a diabetic patient. Objective: The objective of this work is to develop and validate a general reinforcement learning framework and a related learning model for personalized treatment and management of Type 1 diabetes and its complications. Methods: This research presents a model-free reinforcement learning (RL) algorithm to recommend insulin level to regulate the blood glucose level of a diabetic patient considering his/her state defined by A1C level, alcohol usage, activity level, and BMI value. In this approach, an RL agent learns from its exploration and response of diabetic patients when they are subject to different actions in terms of insulin dosage level. As a result of a treatment action at time step t, the RL agent receives a numeric reward depending on the response of the patient’s blood glucose level. At each stage the reward for the learning agent is calculated as a function of the difference between the glucose level in the patient body and its target level. The RL algorithm is trained on ten years of the clinical data of 87 patients obtained from the Mass General Hospital. Demographically, 59% of patients are male and 41% of patients are female; the median of age is 54 years and mean is 52.92 years; 86% of patients are white and 47% of 87 patients are married. Results: The performance of the algorithm is evaluated on 60 test cases. Further the performance of Support Vector Machine (SVM) has been applied for Lantus class prediction and results has been compared with Q-learning algorithm recommendation. The results show that the RL recommendations of insulin levels for test patients match with the actual prescriptions of the test patients. The RL gave prediction with an accuracy of 88% and SVM shows 80% accuracy. Conclusions: Since the RL algorithm can select actions that improve patient condition by taking into account delayed effects, it has a good potential to control blood glucose level in diabetic patients.
Background: Evidence-based practice (EBP) is necessary to improve the practice of physical therapy (PT). However, a lack of knowledge and skills among physical therapists (PTs) and the presence of bar...
Background: Evidence-based practice (EBP) is necessary to improve the practice of physical therapy (PT). However, a lack of knowledge and skills among physical therapists (PTs) and the presence of barriers may hinder the implementation of EBP in the State of Kuwait. Objective: The objectives of this study were to extensively 1) investigate attitudes toward EBP, 2) assess the current level of knowledge and skills necessary for EBP, and 3) identify the barriers to EBP among PTs in the State of Kuwait. Methods: The following methods were used: 1) a previously validated self-reported questionnaire and 2) a face-to-face semi-structured interview. The questionnaire, which was distributed to 200 PTs, examined the attitudes and beliefs of PTS about EBP; the interest in and motivation to engage in EBP; educational background, knowledge and skills related to accessing and interpreting information; level of attention to and use of the literature; access to and availability of information to promote EBP; and the perceived barriers to using EBP. The interview explored the factors that promote or discourage EBP. Descriptive statistics and logistic regression analyses were used. Results: Of the 200 non-randomly distributed questionnaires, 184 (92%) were completed and returned. In general, the PTs had positive attitudes, beliefs, and interests in EBP. Their educational background knowledge and skills related to assessing and interpreting information were well founded. The top three barriers included insufficient time (59%), lack of information resources (49%) and inapplicability of the research findings to the patient population (40.6%). Conclusions: EBP lacks support from superiors at work. Thus, identifying methods and strategies to support PTs in adopting EBP in the State of Kuwait is necessary.
