TY - JOUR AU - Deliberato, Octávio Rodrigo AU - Celi, Anthony Leo AU - Stone, J. David PY - 2017/08/03 TI - Clinical Note Creation, Binning, and Artificial Intelligence JO - JMIR Med Inform SP - e24 VL - 5 IS - 3 KW - electronic health records KW - artificial Intelligence KW - clinical informatics UR - http://medinform.jmir.org/2017/3/e24/ UR - http://dx.doi.org/10.2196/medinform.7627 UR - http://www.ncbi.nlm.nih.gov/pubmed/28778845 ID - info:doi/10.2196/medinform.7627 ER - TY - JOUR AU - Yen, Po-Yin AU - McAlearney, Scheck Ann AU - Sieck, J. Cynthia AU - Hefner, L. Jennifer AU - Huerta, R. Timothy PY - 2017/09/07 TI - Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation JO - JMIR Med Inform SP - e28 VL - 5 IS - 3 KW - health information technology KW - adaptation KW - adoption KW - acceptance UR - http://medinform.jmir.org/2017/3/e28/ UR - http://dx.doi.org/10.2196/medinform.7476 UR - http://www.ncbi.nlm.nih.gov/pubmed/28882812 ID - info:doi/10.2196/medinform.7476 ER - TY - JOUR AU - Kaiser, Tim AU - Laireiter, Rupert Anton PY - 2017/07/20 TI - DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy JO - JMIR Med Inform SP - e20 VL - 5 IS - 3 KW - health information management KW - mental health KW - mental disorders KW - psychotherapeutic processes KW - algorithms N2 - Background: In recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners. Objective: Software packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes. Methods: In this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible. Results: The capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice. Conclusions: With the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy. UR - http://medinform.jmir.org/2017/3/e20/ UR - http://dx.doi.org/10.2196/medinform.6808 UR - http://www.ncbi.nlm.nih.gov/pubmed/28729233 ID - info:doi/10.2196/medinform.6808 ER - TY - JOUR AU - Oreskovic, Michel Nicolas AU - Maniates, Jennifer AU - Weilburg, Jeffrey AU - Choy, Garry PY - 2017/08/14 TI - Optimizing the Use of Electronic Health Records to Identify High-Risk Psychosocial Determinants of Health JO - JMIR Med Inform SP - e25 VL - 5 IS - 3 KW - word recognition KW - Medicaid KW - psychosocial determinants of health KW - social determinants of health KW - care coordination N2 - Background: Care coordination programs have traditionally focused on medically complex patients, identifying patients that qualify by analyzing formatted clinical data and claims data. However, not all clinically relevant data reside in claims and formatted data. Recently, there has been increasing interest in including patients with complex psychosocial determinants of health in care coordination programs. Psychosocial risk factors, including social determinants of health, mental health disorders, and substance abuse disorders, are less amenable to rapid and systematic data analyses, as these data are often not collected or stored as formatted data, and due to US Health Insurance Portability and Accountability Act (HIPAA) regulations are often not available as claims data. Objective: The objective of our study was to develop a systematic approach using word recognition software to identifying psychosocial risk factors within any part of a patient?s electronic health record (EHR). Methods: We used QPID (Queriable Patient Inference Dossier), an ontology-driven word recognition software, to scan adult patients? EHRs to identify terms predicting a high-risk patient suitable to be followed in a care coordination program in Massachusetts, USA. Search terms identified high-risk conditions in patients known to be enrolled in a care coordination program, and were then tested against control patients. We calculated precision, recall, and balanced F-measure for the search terms. Results: We identified 22 EHR-available search terms to define psychosocial high-risk status; the presence of 9 or more of these terms predicted that a patient would meet inclusion criteria for a care coordination program. Precision was .80, recall .98, and balanced F-measure .88 for the identified terms. For adult patients insured by Medicaid and enrolled in the program, a mean of 14 terms (interquartile range [IQR] 11-18) were present as identified by the search tool, ranging from 2 to 22 terms. For patients enrolled in the program but not insured by Medicaid, a mean of 6 terms (IQR 3-8) were present as identified by the search tool, ranging from 1 to 21. Conclusions: Selected informatics tools such as word recognition software can be leveraged to improve health care delivery, such as an EHR-based protocol that identifies psychosocially complex patients eligible for enrollment in a care coordination program. UR - http://medinform.jmir.org/2017/3/e25/ UR - http://dx.doi.org/10.2196/medinform.8240 UR - http://www.ncbi.nlm.nih.gov/pubmed/28807893 ID - info:doi/10.2196/medinform.8240 ER - TY - JOUR AU - Hefner, L. Jennifer AU - Sieck, J. Cynthia AU - Walker, M. Daniel AU - Huerta, R. Timothy AU - McAlearney, Scheck Ann PY - 2017/09/14 TI - System-Wide Inpatient Portal? Implementation: Survey of Health Care Team Perceptions JO - JMIR Med Inform SP - e31 VL - 5 IS - 3 KW - patient portals KW - hospitalization KW - medical informatics KW - patient participation N2 - Background: Inpatient portals, a new type of patient portal tailored specifically to the hospital setting, can allow patients to access up-to-date health information and exchange secure communications with their care team. As such, inpatient portals present an opportunity for patients to increase engagement in their care during a time of acute crisis that emphasizes focus on a patient?s health. While there is a large body of research on patient portals in the outpatient setting, questions are being raised specifically about inpatient portals, such as how they will be incorporated into the flow of patient care in hectic, stressed, team-based hospital settings. Objective: Our aim is to improve understanding about hospital care team members? perceptions of the value of an interactive patient portal for admitted patients, as well as to ascertain staff orientation toward this new technology. Methods: Throughout the course of 2016, an inpatient portal, MyChart Bedside (MCB) was implemented across a five-hospital health system. The portal is a tablet-based app that includes a daily schedule, lab/test results, secure messaging with the care team, a place to take notes, and access to educational materials. Within a month of initial rollout, hospital care team members completed a 5-minute, anonymous online survey to assess attitudes and perceptions about MCB use and staff training for the new technology. Results: Throughout the health system, 686 staff members completed the survey: 193 physicians (23.6%), 439 nurses (53.7%), and 186 support staff (22.7%). Questions about the importance of MCB, self-efficacy in using MCB with patients, and feelings about sufficient training and resources showed that an average of 40-60% of respondents in each group reported a positive orientation toward the MCB technology and training received. This positive orientation was highest among support staff, lower among nurses, and lowest for physicians (all differences by staff role were statistically significant at P<.001). Additionally, 62.0% of respondents reported ?not enough? training. Conclusions: Despite the robust training effort, similar to that used in previous health information technology implementations at this health system, hospital care team members reported only a moderately positive orientation toward MCB and its potential, and the majority wanted more training. We propose that due to the unique elements of the inpatient portal?interactive features used by patients and providers requiring explanation and collaboration?traditional training approaches may be insufficient. Introduction of the inpatient portal as a new collaborative tool may thus require new methods of training to support enhanced engagement between patients and their care team. UR - http://medinform.jmir.org/2017/3/e31/ UR - http://dx.doi.org/10.2196/medinform.7707 UR - http://www.ncbi.nlm.nih.gov/pubmed/28912115 ID - info:doi/10.2196/medinform.7707 ER - TY - JOUR AU - Elmessiry, Adel AU - Cooper, O. William AU - Catron, F. Thomas AU - Karrass, Jan AU - Zhang, Zhe AU - Singh, P. Munindar PY - 2017/07/31 TI - Triaging Patient Complaints: Monte Carlo Cross-Validation of Six Machine Learning Classifiers JO - JMIR Med Inform SP - e19 VL - 5 IS - 3 KW - natural language processing KW - NLP KW - machine learning KW - patient complaints N2 - Background: Unsolicited patient complaints can be a useful service recovery tool for health care organizations. Some patient complaints contain information that may necessitate further action on the part of the health care organization and/or the health care professional. Current approaches depend on the manual processing of patient complaints, which can be costly, slow, and challenging in terms of scalability. Objective: The aim of this study was to evaluate automatic patient triage, which can potentially improve response time and provide much-needed scale, thereby enhancing opportunities to encourage physicians to self-regulate. Methods: We implemented a comparison of several well-known machine learning classifiers to detect whether a complaint was associated with a physician or his/her medical practice. We compared these classifiers using a real-life dataset containing 14,335 patient complaints associated with 768 physicians that was extracted from patient complaints collected by the Patient Advocacy Reporting System developed at Vanderbilt University and associated institutions. We conducted a 10-splits Monte Carlo cross-validation to validate our results. Results: We achieved an accuracy of 82% and F-score of 81% in correctly classifying patient complaints with sensitivity and specificity of 0.76 and 0.87, respectively. Conclusions: We demonstrate that natural language processing methods based on modeling patient complaint text can be effective in identifying those patient complaints requiring physician action. UR - http://medinform.jmir.org/2017/3/e19/ UR - http://dx.doi.org/10.2196/medinform.7140 UR - http://www.ncbi.nlm.nih.gov/pubmed/28760726 ID - info:doi/10.2196/medinform.7140 ER - TY - JOUR AU - Tapi Nzali, Donald Mike AU - Bringay, Sandra AU - Lavergne, Christian AU - Mollevi, Caroline AU - Opitz, Thomas PY - 2017/07/31 TI - What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer JO - JMIR Med Inform SP - e23 VL - 5 IS - 3 KW - breast cancer KW - text mining KW - social media KW - unsupervised learning N2 - Background: Social media dedicated to health are increasingly used by patients and health professionals. They are rich textual resources with content generated through free exchange between patients. We are proposing a method to tackle the problem of retrieving clinically relevant information from such social media in order to analyze the quality of life of patients with breast cancer. Objective: Our aim was to detect the different topics discussed by patients on social media and to relate them to functional and symptomatic dimensions assessed in the internationally standardized self-administered questionnaires used in cancer clinical trials (European Organization for Research and Treatment of Cancer [EORTC] Quality of Life Questionnaire Core 30 [QLQ-C30] and breast cancer module [QLQ-BR23]). Methods: First, we applied a classic text mining technique, latent Dirichlet allocation (LDA), to detect the different topics discussed on social media dealing with breast cancer. We applied the LDA model to 2 datasets composed of messages extracted from public Facebook groups and from a public health forum (cancerdusein.org, a French breast cancer forum) with relevant preprocessing. Second, we applied a customized Jaccard coefficient to automatically compute similarity distance between the topics detected with LDA and the questions in the self-administered questionnaires used to study quality of life. Results: Among the 23 topics present in the self-administered questionnaires, 22 matched with the topics discussed by patients on social media. Interestingly, these topics corresponded to 95% (22/23) of the forum and 86% (20/23) of the Facebook group topics. These figures underline that topics related to quality of life are an important concern for patients. However, 5 social media topics had no corresponding topic in the questionnaires, which do not cover all of the patients? concerns. Of these 5 topics, 2 could potentially be used in the questionnaires, and these 2 topics corresponded to a total of 3.10% (523/16,868) of topics in the cancerdusein.org corpus and 4.30% (3014/70,092) of the Facebook corpus. Conclusions: We found a good correspondence between detected topics on social media and topics covered by the self-administered questionnaires, which substantiates the sound construction of such questionnaires. We detected new emerging topics from social media that can be used to complete current self-administered questionnaires. Moreover, we confirmed that social media mining is an important source of information for complementary analysis of quality of life. UR - http://medinform.jmir.org/2017/3/e23/ UR - http://dx.doi.org/10.2196/medinform.7779 UR - http://www.ncbi.nlm.nih.gov/pubmed/28760725 ID - info:doi/10.2196/medinform.7779 ER - TY - JOUR AU - Wang, Yaogang AU - Sun, Li AU - Hou, Jie PY - 2017/08/08 TI - Hierarchical Medical System Based on Big Data and Mobile Internet: A New Strategic Choice in Health Care JO - JMIR Med Inform SP - e22 VL - 5 IS - 3 KW - medical services KW - continuity of patient care KW - mobile health UR - http://medinform.jmir.org/2017/3/e22/ UR - http://dx.doi.org/10.2196/medinform.6799 UR - http://www.ncbi.nlm.nih.gov/pubmed/28790024 ID - info:doi/10.2196/medinform.6799 ER - TY - JOUR AU - Zolhavarieh, Seyedjamal AU - Parry, David AU - Bai, Quan PY - 2017/07/05 TI - Issues Associated With the Use of Semantic Web Technology in Knowledge Acquisition for Clinical Decision Support Systems: Systematic Review of the Literature JO - JMIR Med Inform SP - e18 VL - 5 IS - 3 KW - semantic web technology KW - clinical decision support system KW - systematic review KW - medical informatics KW - knowledge KW - Internet N2 - Background: Knowledge-based clinical decision support system (KB-CDSS) can be used to help practitioners make diagnostic decisions. KB-CDSS may use clinical knowledge obtained from a wide variety of sources to make decisions. However, knowledge acquisition is one of the well-known bottlenecks in KB-CDSSs, partly because of the enormous growth in health-related knowledge available and the difficulty in assessing the quality of this knowledge as well as identifying the ?best? knowledge to use. This bottleneck not only means that lower-quality knowledge is being used, but also that KB-CDSSs are difficult to develop for areas where expert knowledge may be limited or unavailable. Recent methods have been developed by utilizing Semantic Web (SW) technologies in order to automatically discover relevant knowledge from knowledge sources. Objective: The two main objectives of this study were to (1) identify and categorize knowledge acquisition issues that have been addressed through using SW technologies and (2) highlight the role of SW for acquiring knowledge used in the KB-CDSS. Methods: We conducted a systematic review of the recent work related to knowledge acquisition MeM for clinical decision support systems published in scientific journals. In this regard, we used the keyword search technique to extract relevant papers. Results: The retrieved papers were categorized based on two main issues: (1) format and data heterogeneity and (2) lack of semantic analysis. Most existing approaches will be discussed under these categories. A total of 27 papers were reviewed in this study. Conclusions: The potential for using SW technology in KB-CDSS has only been considered to a minor extent so far despite its promise. This review identifies some questions and issues regarding use of SW technology for extracting relevant knowledge for a KB-CDSS. UR - http://medinform.jmir.org/2017/3/e18/ UR - http://dx.doi.org/10.2196/medinform.6169 UR - http://www.ncbi.nlm.nih.gov/pubmed/28679487 ID - info:doi/10.2196/medinform.6169 ER - TY - JOUR AU - Hao, Shiying AU - Fu, Tianyun AU - Wu, Qian AU - Jin, Bo AU - Zhu, Chunqing AU - Hu, Zhongkai AU - Guo, Yanting AU - Zhang, Yan AU - Yu, Yunxian AU - Fouts, Terry AU - Ng, Phillip AU - Culver, S. Devore AU - Alfreds, T. Shaun AU - Stearns, Frank AU - Sylvester, G. Karl AU - Widen, Eric AU - McElhinney, B. Doff AU - Ling, B. Xuefeng PY - 2017/07/26 TI - Estimating One-Year Risk of Incident Chronic Kidney Disease: Retrospective Development and Validation Study Using Electronic Medical Record Data From the State of Maine JO - JMIR Med Inform SP - e21 VL - 5 IS - 3 KW - electronic medical record KW - chronic kidney disease KW - risk model KW - retrospective study N2 - Background: Chronic kidney disease (CKD) is a major public health concern in the United States with high prevalence, growing incidence, and serious adverse outcomes. Objective: We aimed to develop and validate a model to identify patients at risk of receiving a new diagnosis of CKD (incident CKD) during the next 1 year in a general population. Methods: The study population consisted of patients who had visited any care facility in the Maine Health Information Exchange network any time between January 1, 2013, and December 31, 2015, and had no history of CKD diagnosis. Two retrospective cohorts of electronic medical records (EMRs) were constructed for model derivation (N=1,310,363) and validation (N=1,430,772). The model was derived using a gradient tree-based boost algorithm to assign a score to each individual that measured the probability of receiving a new diagnosis of CKD from January 1, 2014, to December 31, 2014, based on the preceding 1-year clinical profile. A feature selection process was conducted to reduce the dimension of the data from 14,680 EMR features to 146 as predictors in the final model. Relative risk was calculated by the model to gauge the risk ratio of the individual to population mean of receiving a CKD diagnosis in next 1 year. The model was tested on the validation cohort to predict risk of CKD diagnosis in the period from January 1, 2015, to December 31, 2015, using the preceding 1-year clinical profile. Results: The final model had a c-statistic of 0.871 in the validation cohort. It stratified patients into low-risk (score 0-0.005), intermediate-risk (score 0.005-0.05), and high-risk (score ? 0.05) levels. The incidence of CKD in the high-risk patient group was 7.94%, 13.7 times higher than the incidence in the overall cohort (0.58%). Survival analysis showed that patients in the 3 risk categories had significantly different CKD outcomes as a function of time (P<.001), indicating an effective classification of patients by the model. Conclusions: We developed and validated a model that is able to identify patients at high risk of having CKD in the next 1 year by statistically learning from the EMR-based clinical history in the preceding 1 year. Identification of these patients indicates care opportunities such as monitoring and adopting intervention plans that may benefit the quality of care and outcomes in the long term. UR - http://medinform.jmir.org/2017/3/e21/ UR - http://dx.doi.org/10.2196/medinform.7954 UR - http://www.ncbi.nlm.nih.gov/pubmed/28747298 ID - info:doi/10.2196/medinform.7954 ER - TY - JOUR AU - Reis, Nogueira Zilma Silveira AU - Maia, Abreu Thais AU - Marcolino, Soriano Milena AU - Becerra-Posada, Francisco AU - Novillo-Ortiz, David AU - Ribeiro, Pinho Antonio Luiz PY - 2017/08/29 TI - Is There Evidence of Cost Benefits of Electronic Medical Records, Standards, or Interoperability in Hospital Information Systems? Overview of Systematic Reviews JO - JMIR Med Inform SP - e26 VL - 5 IS - 3 KW - electronic medical records KW - standards KW - medical information exchange KW - health information exchange KW - cost KW - benefits and costs N2 - Background: Electronic health (eHealth) interventions may improve the quality of care by providing timely, accessible information about one patient or an entire population. Electronic patient care information forms the nucleus of computerized health information systems. However, interoperability among systems depends on the adoption of information standards. Additionally, investing in technology systems requires cost-effectiveness studies to ensure the sustainability of processes for stakeholders. Objective: The objective of this study was to assess cost-effectiveness of the use of electronically available inpatient data systems, health information exchange, or standards to support interoperability among systems. Methods: An overview of systematic reviews was conducted, assessing the MEDLINE, Cochrane Library, LILACS, and IEEE Library databases to identify relevant studies published through February 2016. The search was supplemented by citations from the selected papers. The primary outcome sought the cost-effectiveness, and the secondary outcome was the impact on quality of care. Independent reviewers selected studies, and disagreement was resolved by consensus. The quality of the included studies was evaluated using a measurement tool to assess systematic reviews (AMSTAR). Results: The primary search identified 286 papers, and two papers were manually included. A total of 211 were systematic reviews. From the 20 studies that were selected after screening the title and abstract, 14 were deemed ineligible, and six met the inclusion criteria. The interventions did not show a measurable effect on cost-effectiveness. Despite the limited number of studies, the heterogeneity of electronic systems reported, and the types of intervention in hospital routines, it was possible to identify some preliminary benefits in quality of care. Hospital information systems, along with information sharing, had the potential to improve clinical practice by reducing staff errors or incidents, improving automated harm detection, monitoring infections more effectively, and enhancing the continuity of care during physician handoffs. Conclusions: This review identified some benefits in the quality of care but did not provide evidence that the implementation of eHealth interventions had a measurable impact on cost-effectiveness in hospital settings. However, further evidence is needed to infer the impact of standards adoption or interoperability in cost benefits of health care; this in turn requires further research. UR - http://medinform.jmir.org/2017/3/e26/ UR - http://dx.doi.org/10.2196/medinform.7400 UR - http://www.ncbi.nlm.nih.gov/pubmed/28851681 ID - info:doi/10.2196/medinform.7400 ER - TY - JOUR AU - Peissig, Peggy AU - Schwei, M. Kelsey AU - Kadolph, Christopher AU - Finamore, Joseph AU - Cancel, Efrain AU - McCarty, A. Catherine AU - Okorie, Asha AU - Thomas, L. Kate AU - Allen Pacheco, Jennifer AU - Pathak, Jyotishman AU - Ellis, B. Stephen AU - Denny, C. Joshua AU - Rasmussen, V. Luke AU - Tromp, Gerard AU - Williams, S. Marc AU - Vrabec, R. Tamara AU - Brilliant, H. Murray PY - 2017/09/13 TI - Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application JO - JMIR Med Inform SP - e27 VL - 5 IS - 3 KW - electronic health records KW - ophthalmology KW - data acquisition KW - extensible markup language XML KW - data collection N2 - Background: The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. Objective: The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Methods: Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. Results: The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. Conclusions: This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. UR - http://medinform.jmir.org/2017/3/e27/ UR - http://dx.doi.org/10.2196/medinform.7465 UR - http://www.ncbi.nlm.nih.gov/pubmed/28903894 ID - info:doi/10.2196/medinform.7465 ER - TY - JOUR AU - Pranaat, Robert AU - Mohan, Vishnu AU - O'Reilly, Megan AU - Hirsh, Maxwell AU - McGrath, Karess AU - Scholl, Gretchen AU - Woodcock, Deborah AU - Gold, A. Jeffrey PY - 2017/09/20 TI - Use of Simulation Based on an Electronic Health Records Environment to Evaluate the Structure and Accuracy of Notes Generated by Medical Scribes: Proof-of-Concept Study JO - JMIR Med Inform SP - e30 VL - 5 IS - 3 KW - simulation training KW - documentation KW - electronic health record N2 - Background: The increasing adoption of electronic health records (EHRs) has been associated with a number of unintended negative consequences with provider efficiency and job satisfaction. To address this, there has been a dramatic increase in the use of medical scribes to perform many of the required EHR functions. Despite this rapid growth, little has been published on the training or assessment tools to appraise the safety and efficacy of scribe-related EHR activities. Given the number of reports documenting that other professional groups suffer from a number of performance errors in EHR interface and data gathering, scribes likely face similar challenges. This highlights the need for new assessment tools for medical scribes. Objective: The objective of this study was to develop a virtual video-based simulation to demonstrate and quantify the variability and accuracy of scribes? transcribed notes in the EHR. Methods: From a pool of 8 scribes in one department, a total of 5 female scribes, intent on pursuing careers in health care, with at least 6 months of experience were recruited for our simulation study. We created three simulated patient-provider scenarios. Each scenario contained a corresponding medical record in our simulation instance of our EHR. For each scenario, we video-recorded a standardized patient-provider encounter. Five scribes with at least 6 months of experience both with our EHR and in the specialty of the simulated cases were recruited. Each scribe watched the simulated encounter and transcribed notes into a simulated EHR environment. Transcribed notes were evaluated for interscribe variability and compared with a gold standard for accuracy. Results: All scribes completed all simulated cases. There was significant interscribe variability in note structure and content. Overall, only 26% of all data elements were unique to the scribe writing them. The term data element was used to define the individual pieces of data that scribes perceived from the simulation. Note length was determined by counting the number of words varied by 31%, 37%, and 57% between longest and shortest note between the three cases, and word economy ranged between 23% and 71%. Overall, there was a wide inter- and intrascribe variation in accuracy for each section of the notes with ranges from 50% to 76%, resulting in an overall positive predictive value for each note between 38% and 81%. Conclusions: We created a high-fidelity, video-based EHR simulation, capable of assessing multiple performance indicators in medical scribes. In this cohort, we demonstrate significant variability both in terms of structure and accuracy in clinical documentation. This form of simulation can provide a valuable tool for future development of scribe curriculum and assessment of competency. UR - http://medinform.jmir.org/2017/3/e30/ UR - http://dx.doi.org/10.2196/medinform.7883 UR - http://www.ncbi.nlm.nih.gov/pubmed/28931497 ID - info:doi/10.2196/medinform.7883 ER - TY - JOUR AU - Kruse, Scott Clemens AU - Mileski, Michael AU - Vijaykumar, Ganta Alekhya AU - Viswanathan, Vishnampet Sneha AU - Suskandla, Ujwala AU - Chidambaram, Yazhini PY - 2017/09/29 TI - Impact of Electronic Health Records on Long-Term Care Facilities: Systematic Review JO - JMIR Med Inform SP - e35 VL - 5 IS - 3 KW - electronic health record KW - long-term care KW - benefits KW - patient outcome KW - quality KW - nursing home KW - electronic medical record N2 - Background: Long-term care (LTC) facilities are an important part of the health care industry, providing care to the fastest-growing group of the population. However, the adoption of electronic health records (EHRs) in LTC facilities lags behind other areas of the health care industry. One of the reasons for the lack of widespread adoption in the United States is that LTC facilities are not eligible for incentives under the Meaningful Use program. Implementation of an EHR system in an LTC facility can potentially enhance the quality of care, provided it is appropriately implemented, used, and maintained. Unfortunately, the lag in adoption of the EHR in LTC creates a paucity of literature on the benefits of EHR implementation in LTC facilities. Objective: The objective of this systematic review was to identify the potential benefits of implementing an EHR system in LTC facilities. The study also aims to identify the common conditions and EHR features that received favorable remarks from providers and the discrepancies that needed improvement to build up momentum across LTC settings in adopting this technology. Methods: The authors conducted a systematic search of PubMed, Cumulative Index of Nursing and Allied Health (CINAHL), and MEDLINE databases. Papers were analyzed by multiple referees to filter out studies not germane to our research objective. A final sample of 28 papers was selected to be included in the systematic review. Results: Results of this systematic review conclude that EHRs show significant improvement in the management of documentation in LTC facilities and enhanced quality outcomes. Approximately 43% (12/28) of the papers reported a mixed impact of EHRs on the management of documentation, and 33% (9/28) of papers reported positive quality outcomes using EHRs. Surprisingly, very few papers demonstrated an impact on patient satisfaction, physician satisfaction, the length of stay, and productivity using EHRs. Conclusions: Overall, implementation of EHRs has been found to be effective in the few LTC facilities that have implemented them. Implementation of EHRs in LTC facilities caused improved management of clinical documentation that enabled better decision making. UR - http://medinform.jmir.org/2017/3/e35/ UR - http://dx.doi.org/10.2196/medinform.7958 UR - http://www.ncbi.nlm.nih.gov/pubmed/28963091 ID - info:doi/10.2196/medinform.7958 ER - TY - JOUR AU - de Lusignan, Simon AU - Shinneman, Stacy AU - Yonova, Ivelina AU - van Vlymen, Jeremy AU - Elliot, J. Alex AU - Bolton, Frederick AU - Smith, E. Gillian AU - O'Brien, Sarah PY - 2017/09/28 TI - An Ontology to Improve Transparency in Case Definition and Increase Case Finding of Infectious Intestinal Disease: Database Study in English General Practice JO - JMIR Med Inform SP - e34 VL - 5 IS - 3 KW - dysentery KW - enteritis KW - enterobacteriaceae KW - enterocolitis KW - gastritis KW - gastroenteritis KW - intestinal diseases KW - medical records systems, computerized KW - norovirus KW - primary health care N2 - Background: Infectious intestinal disease (IID) has considerable health impact; there are 2 billion cases worldwide resulting in 1 million deaths and 78.7 million disability-adjusted life years lost. Reported IID incidence rates vary and this is partly because terms such as ?diarrheal disease? and ?acute infectious gastroenteritis? are used interchangeably. Ontologies provide a method of transparently comparing case definitions and disease incidence rates. Objective: This study sought to show how differences in case definition in part account for variation in incidence estimates for IID and how an ontological approach provides greater transparency to IID case finding. Methods: We compared three IID case definitions: (1) Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC) definition based on mapping to the Ninth International Classification of Disease (ICD-9), (2) newer ICD-10 definition, and (3) ontological case definition. We calculated incidence rates and examined the contribution of four supporting concepts related to IID: symptoms, investigations, process of care (eg, notification to public health authorities), and therapies. We created a formal ontology using ontology Web language. Results: The ontological approach identified 5712 more cases of IID than the ICD-10 definition and 4482 more than the RCGP RSC definition from an initial cohort of 1,120,490. Weekly incidence using the ontological definition was 17.93/100,000 (95% CI 15.63-20.41), whereas for the ICD-10 definition the rate was 8.13/100,000 (95% CI 6.70-9.87), and for the RSC definition the rate was 10.24/100,000 (95% CI 8.55-12.12). Codes from the four supporting concepts were generally consistent across our three IID case definitions: 37.38% (3905/10,448) (95% CI 36.16-38.5) for the ontological definition, 38.33% (2287/5966) (95% CI 36.79-39.93) for the RSC definition, and 40.82% (1933/4736) (95% CI 39.03-42.66) for the ICD-10 definition. The proportion of laboratory results associated with a positive test result was 19.68% (546/2775). Conclusions: The standard RCGP RSC definition of IID, and its mapping to ICD-10, underestimates disease incidence. The ontological approach identified a larger proportion of new IID cases; the ontology divides contributory elements and enables transparency and comparison of rates. Results illustrate how improved diagnostic coding of IID combined with an ontological approach to case definition would provide a clearer picture of IID in the community, better inform GPs and public health services about circulating disease, and empower them to respond. We need to improve the Pathology Bounded Code List (PBCL) currently used by laboratories to electronically report results. Given advances in stool microbiology testing with a move to nonculture, PCR-based methods, the way microbiology results are reported and coded via PBCL needs to be reviewed and modernized. UR - http://medinform.jmir.org/2017/3/e34/ UR - http://dx.doi.org/10.2196/medinform.7641 UR - http://www.ncbi.nlm.nih.gov/pubmed/28958989 ID - info:doi/10.2196/medinform.7641 ER - TY - JOUR AU - Sieck, J. Cynthia AU - Hefner, L. Jennifer AU - Schnierle, Jeanette AU - Florian, Hannah AU - Agarwal, Aradhna AU - Rundell, Kristen AU - McAlearney, Scheck Ann PY - 2017/07/04 TI - The Rules of Engagement: Perspectives on Secure Messaging From Experienced Ambulatory Patient Portal Users JO - JMIR Med Inform SP - e13 VL - 5 IS - 3 KW - patient portals KW - secure messaging KW - patient-provider communication KW - qualitative study N2 - Background: Patient portals have shown promise in engaging individuals in self-management of chronic conditions by allowing patients to input and track health information and exchange secure electronic messages with their providers. Past studies have identified patient barriers to portal use including usability issues, low health literacy, and concerns about loss of personal contact as well as provider concerns such as increased time spent responding to messages. However, to date, studies of both patient and provider perspectives on portal use have focused on the pre-implementation or initial implementation phases and do not consider how these issues may change as patients and providers gain greater experience with portals. Objective: Our study examined the following research question: Within primary care offices with high rates of patient-portal use, what do experienced physician and patient users of the ambulatory portal perceive as the benefits and challenges of portal use in general and secure messaging in particular? Methods: This qualitative study involved 42 interviews with experienced physician and patient users of an ambulatory patient portal, Epic?s MyChart. Participants were recruited from the Department of Family Medicine at a large Academic Medical Center (AMC) and included providers and their patients, who had been diagnosed with at least one chronic condition. A total of 29 patients and 13 primary care physicians participated in the interviews. All interviews were conducted by telephone and followed a semistructured interview guide. Interviews were transcribed verbatim to permit rigorous qualitative analysis. Both inductive and deductive methods were used to code and analyze the data iteratively, paying particular attention to themes involving secure messaging. Results: Experienced portal users discussed several emergent themes related to a need for greater clarity on when and how to use the secure messaging feature. Patient concerns included worry about imposing on their physician?s time, the lack of provider compensation for responding to secure messages, and uncertainty about when to use secure messaging to communicate with their providers. Similarly, providers articulated a lack of clarity as to the appropriate way to communicate via MyChart and suggested that additional training for both patients and providers might be important. Patient training could include orienting patients to the ?rules of engagement? at portal sign-up, either in the office or through an online tutorial. Conclusions: As secure messaging through patient portals is increasingly being used as a method of physician-patient communication, both patients and providers are looking for guidance on how to appropriately engage with each other using this tool. Patients worry about whether their use is appropriate, and providers are concerned about the content of messages, which allow them to effectively manage patient questions. Our findings suggest that additional training may help address the concerns of both patients and providers, by providing ?rules of engagement? for communication via patient portals. UR - http://medinform.jmir.org/2017/3/e13/ UR - http://dx.doi.org/10.2196/medinform.7516 UR - http://www.ncbi.nlm.nih.gov/pubmed/28676467 ID - info:doi/10.2196/medinform.7516 ER - TY - JOUR AU - Celler, Branko AU - Varnfield, Marlien AU - Nepal, Surya AU - Sparks, Ross AU - Li, Jane AU - Jayasena, Rajiv PY - 2017/09/08 TI - Impact of At-Home Telemonitoring on Health Services Expenditure and Hospital Admissions in Patients With Chronic Conditions: Before and After Control Intervention Analysis JO - JMIR Med Inform SP - e29 VL - 5 IS - 3 KW - telehealth KW - telemonitoring KW - chronic disease management KW - healthcare outcomes KW - BACI analysis N2 - Background: Telemonitoring is becoming increasingly important for the management of patients with chronic conditions, especially in countries with large distances such as Australia. However, despite large national investments in health information technology, little policy work has been undertaken in Australia in deploying telehealth in the home as a solution to the increasing demands and costs of managing chronic disease. Objective: The objective of this trial was to evaluate the impact of introducing at-home telemonitoring to patients living with chronic conditions on health care expenditure, number of admissions to hospital, and length of stay (LOS). Methods: A before and after control intervention analysis model was adopted whereby at each location patients were selected from a list of eligible patients living with a range of chronic conditions. Each test patient was case matched with at least one control patient. Test patients were supplied with a telehealth vital signs monitor and were remotely managed by a trained clinical care coordinator, while control patients continued to receive usual care. A total of 100 test patients and 137 control patients were analyzed. Primary health care benefits provided to Australian patients were investigated for the trial cohort. Time series data were analyzed using linear regression and analysis of covariance for a period of 3 years before the intervention and 1 year after. Results: There were no significant differences between test and control patients at baseline. Test patients were monitored for an average of 276 days with 75% of patients monitored for more than 6 months. Test patients 1 year after the start of their intervention showed a 46.3% reduction in rate of predicted medical expenditure, a 25.5% reduction in the rate of predicted pharmaceutical expenditure, a 53.2% reduction in the rate of predicted unscheduled admission to hospital, a 67.9% reduction in the predicted rate of LOS when admitted to hospital, and a reduction in mortality of between 41.3% and 44.5% relative to control patients. Control patients did not demonstrate any significant change in their predicted trajectory for any of the above variables. Conclusions: At-home telemonitoring of chronically ill patients showed a statistically robust positive impact increasing over time on health care expenditure, number of admissions to hospital, and LOS as well as a reduction in mortality. Trial Registration: Retrospectively registered with the Australian and New Zealand Clinical Trial Registry ACTRN12613000635763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364030 (Archived by WebCite at http://www.webcitation.org/6sxqjkJHW) UR - http://medinform.jmir.org/2017/3/e29/ UR - http://dx.doi.org/10.2196/medinform.7308 UR - http://www.ncbi.nlm.nih.gov/pubmed/28887294 ID - info:doi/10.2196/medinform.7308 ER - TY - JOUR AU - Luo, Gang AU - Sward, Katherine PY - 2017/09/26 TI - A Roadmap for Optimizing Asthma Care Management via Computational Approaches JO - JMIR Med Inform SP - e32 VL - 5 IS - 3 KW - patient care management KW - clinical decision support KW - machine learning UR - http://medinform.jmir.org/2017/3/e32/ UR - http://dx.doi.org/10.2196/medinform.8076 UR - http://www.ncbi.nlm.nih.gov/pubmed/28951380 ID - info:doi/10.2196/medinform.8076 ER -