TY - JOUR AU - Rajkomar, Alvin AU - Yim, Lan Joanne Wing AU - Grumbach, Kevin AU - Parekh, Ami PY - 2016/10/14 TI - Weighting Primary Care Patient Panel Size: A Novel Electronic Health Record-Derived Measure Using Machine Learning JO - JMIR Med Inform SP - e29 VL - 4 IS - 4 KW - primary health care KW - risk adjustment KW - patient acceptance of health care KW - ambulatory care KW - health care economics and organizations KW - medical informatics KW - machine learning N2 - Background: Characterizing patient complexity using granular electronic health record (EHR) data regularly available to health systems is necessary to optimize primary care processes at scale. Objective: To characterize the utilization patterns of primary care patients and create weighted panel sizes for providers based on work required to care for patients with different patterns. Methods: We used EHR data over a 2-year period from patients empaneled to primary care clinicians in a single academic health system, including their in-person encounter history and virtual encounters such as telephonic visits, electronic messaging, and care coordination with specialists. Using a combination of decision rules and k-means clustering, we identified clusters of patients with similar health care system activity. Phenotypes with basic demographic information were used to predict future health care utilization using log-linear models. Phenotypes were also used to calculate weighted panel sizes. Results: We identified 7 primary care utilization phenotypes, which were characterized by various combinations of primary care and specialty usage and were deemed clinically distinct by primary care physicians. These phenotypes, combined with age-sex and primary payer variables, predicted future primary care utilization with R2 of .394 and were used to create weighted panel sizes. Conclusions: Individual patients? health care utilization may be useful for classifying patients by primary care work effort and for predicting future primary care usage. UR - http://medinform.jmir.org/2016/4/e29/ UR - http://dx.doi.org/10.2196/medinform.6530 UR - http://www.ncbi.nlm.nih.gov/pubmed/27742603 ID - info:doi/10.2196/medinform.6530 ER - TY - JOUR AU - Farhan, Wael AU - Wang, Zhimu AU - Huang, Yingxiang AU - Wang, Shuang AU - Wang, Fei AU - Jiang, Xiaoqian PY - 2016/11/25 TI - A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences JO - JMIR Med Inform SP - e39 VL - 4 IS - 4 KW - clinical decision support KW - early classification KW - temporal phenotyping KW - clinical event context embedding N2 - Background: Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction). Objective: Our work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation. Methods: We developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests). Each medical event is converted into a numerical vector that resembles its ?semantics,? via which the similarity between medical events can be easily measured. We developed simple and effective predictive models based on these vectors to predict novel diagnoses. Results: We evaluated our sequential prediction model (and standard learning methods) in estimating the risk of potential diseases based on our contextual embedding representation. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79 on chronic systolic heart failure and an average AUC of 0.67 (over the 80 most common diagnoses) using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Conclusions: We propose a general early prognosis predictor for 80 different diagnoses. Our method computes numeric representation for each medical event to uncover the potential meaning of those events. Our results demonstrate the efficiency of the proposed method, which will benefit patients and physicians by offering more accurate diagnosis. UR - http://medinform.jmir.org/2016/4/e39/ UR - http://dx.doi.org/10.2196/medinform.5977 UR - http://www.ncbi.nlm.nih.gov/pubmed/27888170 ID - info:doi/10.2196/medinform.5977 ER - TY - JOUR AU - Colorafi, Karen AU - Bailey, Bryan PY - 2016/11/02 TI - It?s Time for Innovation in the Health Insurance Portability and Accountability Act (HIPAA) JO - JMIR Med Inform SP - e34 VL - 4 IS - 4 KW - innovation KW - HIPAA KW - electronic health record demonstration UR - http://medinform.jmir.org/2016/4/e34/ UR - http://dx.doi.org/10.2196/medinform.6372 UR - http://www.ncbi.nlm.nih.gov/pubmed/27806923 ID - info:doi/10.2196/medinform.6372 ER - TY - JOUR AU - Kaufman, R. David AU - Sheehan, Barbara AU - Stetson, Peter AU - Bhatt, R. Ashish AU - Field, I. Adele AU - Patel, Chirag AU - Maisel, Mark James PY - 2016/10/28 TI - Natural Language Processing?Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study JO - JMIR Med Inform SP - e35 VL - 4 IS - 4 KW - electronic health records KW - natural language processing KW - medical transcription KW - user-computer interface N2 - Background: The process of documentation in electronic health records (EHRs) is known to be time consuming, inefficient, and cumbersome. The use of dictation coupled with manual transcription has become an increasingly common practice. In recent years, natural language processing (NLP)?enabled data capture has become a viable alternative for data entry. It enables the clinician to maintain control of the process and potentially reduce the documentation burden. The question remains how this NLP-enabled workflow will impact EHR usability and whether it can meet the structured data and other EHR requirements while enhancing the user?s experience. Objective: The objective of this study is evaluate the comparative effectiveness of an NLP-enabled data capture method using dictation and data extraction from transcribed documents (NLP Entry) in terms of documentation time, documentation quality, and usability versus standard EHR keyboard-and-mouse data entry. Methods: This formative study investigated the results of using 4 combinations of NLP Entry and Standard Entry methods (?protocols?) of EHR data capture. We compared a novel dictation-based protocol using MediSapien NLP (NLP-NLP) for structured data capture against a standard structured data capture protocol (Standard-Standard) as well as 2 novel hybrid protocols (NLP-Standard and Standard-NLP). The 31 participants included neurologists, cardiologists, and nephrologists. Participants generated 4 consultation or admission notes using 4 documentation protocols. We recorded the time on task, documentation quality (using the Physician Documentation Quality Instrument, PDQI-9), and usability of the documentation processes. Results: A total of 118 notes were documented across the 3 subject areas. The NLP-NLP protocol required a median of 5.2 minutes per cardiology note, 7.3 minutes per nephrology note, and 8.5 minutes per neurology note compared with 16.9, 20.7, and 21.2 minutes, respectively, using the Standard-Standard protocol and 13.8, 21.3, and 18.7 minutes using the Standard-NLP protocol (1 of 2 hybrid methods). Using 8 out of 9 characteristics measured by the PDQI-9 instrument, the NLP-NLP protocol received a median quality score sum of 24.5; the Standard-Standard protocol received a median sum of 29; and the Standard-NLP protocol received a median sum of 29.5. The mean total score of the usability measure was 36.7 when the participants used the NLP-NLP protocol compared with 30.3 when they used the Standard-Standard protocol. Conclusions: In this study, the feasibility of an approach to EHR data capture involving the application of NLP to transcribed dictation was demonstrated. This novel dictation-based approach has the potential to reduce the time required for documentation and improve usability while maintaining documentation quality. Future research will evaluate the NLP-based EHR data capture approach in a clinical setting. It is reasonable to assert that EHRs will increasingly use NLP-enabled data entry tools such as MediSapien NLP because they hold promise for enhancing the documentation process and end-user experience. UR - http://medinform.jmir.org/2016/4/e35/ UR - http://dx.doi.org/10.2196/medinform.5544 UR - http://www.ncbi.nlm.nih.gov/pubmed/27793791 ID - info:doi/10.2196/medinform.5544 ER - TY - JOUR AU - Chen, Jinying AU - Zheng, Jiaping AU - Yu, Hong PY - 2016/11/30 TI - Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations JO - JMIR Med Inform SP - e40 VL - 4 IS - 4 KW - electronic health records KW - natural language processing KW - information extraction KW - supervised learning KW - learning to rank N2 - Background: Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients? notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. Objective: We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. Methods: First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians? agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Results: Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen?s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P<.001). Rich learning features contributed to FOCUS?s performance substantially. Conclusions: FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care. UR - http://medinform.jmir.org/2016/4/e40/ UR - http://dx.doi.org/10.2196/medinform.6373 UR - http://www.ncbi.nlm.nih.gov/pubmed/27903489 ID - info:doi/10.2196/medinform.6373 ER - TY - JOUR AU - Zheng, Le AU - Wang, Yue AU - Hao, Shiying AU - Shin, Y. Andrew AU - Jin, Bo AU - Ngo, D. Anh AU - Jackson-Browne, S. Medina AU - Feller, J. Daniel AU - Fu, Tianyun AU - Zhang, Karena AU - Zhou, Xin AU - Zhu, Chunqing AU - Dai, Dorothy AU - Yu, Yunxian AU - Zheng, Gang AU - Li, Yu-Ming AU - McElhinney, B. Doff AU - Culver, S. Devore AU - Alfreds, T. Shaun AU - Stearns, Frank AU - Sylvester, G. Karl AU - Widen, Eric AU - Ling, Bruce Xuefeng PY - 2016/11/11 TI - Web-based Real-Time Case Finding for the Population Health Management of Patients With Diabetes Mellitus: A Prospective Validation of the Natural Language Processing?Based Algorithm With Statewide Electronic Medical Records JO - JMIR Med Inform SP - e37 VL - 4 IS - 4 KW - electronic medical record KW - natural language processing KW - diabetes mellitus KW - data mining N2 - Background: Diabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency. Objective: This study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs). Methods: This study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014). Results: Of the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days). Conclusions: The online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus. UR - http://medinform.jmir.org/2016/4/e37/ UR - http://dx.doi.org/10.2196/medinform.6328 UR - http://www.ncbi.nlm.nih.gov/pubmed/27836816 ID - info:doi/10.2196/medinform.6328 ER - TY - JOUR AU - Park, Sook Min AU - He, Zhe AU - Chen, Zhiwei AU - Oh, Sanghee AU - Bian, Jiang PY - 2016/11/24 TI - Consumers? Use of UMLS Concepts on Social Media: Diabetes-Related Textual Data Analysis in Blog and Social Q&A Sites JO - JMIR Med Inform SP - e41 VL - 4 IS - 4 KW - controlled vocabulary KW - consumer health vocabulary KW - concept coverage N2 - Background: The widely known terminology gap between health professionals and health consumers hinders effective information seeking for consumers. Objective: The aim of this study was to better understand consumers? usage of medical concepts by evaluating the coverage of concepts and semantic types of the Unified Medical Language System (UMLS) on diabetes-related postings in 2 types of social media: blogs and social question and answer (Q&A). Methods: We collected 2 types of social media data: (1) a total of 3711 blogs tagged with ?diabetes? on Tumblr posted between February and October 2015; and (2) a total of 58,422 questions and associated answers posted between 2009 and 2014 in the diabetes category of Yahoo! Answers. We analyzed the datasets using a widely adopted biomedical text processing framework Apache cTAKES and its extension YTEX. First, we applied the named entity recognition (NER) method implemented in YTEX to identify UMLS concepts in the datasets. We then analyzed the coverage and the popularity of concepts in the UMLS source vocabularies across the 2 datasets (ie, blogs and social Q&A). Further, we conducted a concept-level comparative coverage analysis between SNOMED Clinical Terms (SNOMED CT) and Open-Access Collaborative Consumer Health Vocabulary (OAC CHV)?the top 2 UMLS source vocabularies that have the most coverage on our datasets. We also analyzed the UMLS semantic types that were frequently observed in our datasets. Results: We identified 2415 UMLS concepts from blog postings, 6452 UMLS concepts from social Q&A questions, and 10,378 UMLS concepts from the answers. The medical concepts identified in the blogs can be covered by 56 source vocabularies in the UMLS, while those in questions and answers can be covered by 58 source vocabularies. SNOMED CT was the dominant vocabulary in terms of coverage across all the datasets, ranging from 84.9% to 95.9%. It was followed by OAC CHV (between 73.5% and 80.0%) and Metathesaurus Names (MTH) (between 55.7% and 73.5%). All of the social media datasets shared frequent semantic types such as ?Amino Acid, Peptide, or Protein,? ?Body Part, Organ, or Organ Component,? and ?Disease or Syndrome.? Conclusions: Although the 3 social media datasets vary greatly in size, they exhibited similar conceptual coverage among UMLS source vocabularies and the identified concepts showed similar semantic type distributions. As such, concepts that are both frequently used by consumers and also found in professional vocabularies such as SNOMED CT can be suggested to OAC CHV to improve its coverage. UR - https://medinform.jmir.org/2016/4/e41/ UR - http://dx.doi.org/10.2196/medinform.5748 UR - http://www.ncbi.nlm.nih.gov/pubmed/27884812 ID - info:doi/10.2196/medinform.5748 ER - TY - JOUR AU - Luo, Jake AU - Eldredge, Christina AU - Cho, C. Chi AU - Cisler, A. Ron PY - 2016/10/17 TI - Population Analysis of Adverse Events in Different Age Groups Using Big Clinical Trials Data JO - JMIR Med Inform SP - e30 VL - 4 IS - 4 KW - big data analysis KW - adverse events KW - clinical trial data KW - population health KW - clinical trial safety KW - data processing and integration N2 - Background: Understanding adverse event patterns in clinical studies across populations is important for patient safety and protection in clinical trials as well as for developing appropriate drug therapies, procedures, and treatment plans. Objectives: The objective of our study was to conduct a data-driven population-based analysis to estimate the incidence, diversity, and association patterns of adverse events by age of the clinical trials patients and participants. Methods: Two aspects of adverse event patterns were measured: (1) the adverse event incidence rate in each of the patient age groups and (2) the diversity of adverse events defined as distinct types of adverse events categorized by organ system. Statistical analysis was done on the summarized clinical trial data. The incident rate and diversity level in each of the age groups were compared with the lowest group (reference group) using t tests. Cohort data was obtained from ClinicalTrials.gov, and 186,339 clinical studies were analyzed; data were extracted from the 17,853 clinical trials that reported clinical outcomes. The total number of clinical trial participants was 6,808,619, and total number of participants affected by adverse events in these trials was 1,840,432. The trial participants were divided into eight different age groups to support cross-age group comparison. Results: In general, children and older patients are more susceptible to adverse events in clinical trial studies. Using the lowest incidence age group as the reference group (20-29 years), the incidence rate of the 0-9 years-old group was 31.41%, approximately 1.51 times higher (P=.04) than the young adult group (20-29 years) at 20.76%. The second-highest group is the 50-59 years-old group with an incidence rate of 30.09%, significantly higher (P<.001) when compared with the lowest incidence in the 20-29 years-old group. The adverse event diversity also increased with increase in patient age. Clinical studies that recruited older patients (older than 40 years) were more likely to observe a diverse range of adverse events (P<.001). Adverse event diversity increased at an average rate of 77% for each age group (older than 30 years) until reaching the 60-69 years-old group, which had a diversity level of 54.7 different types of adverse events per trial arm. The 70-100 years-old group showed the highest diversity level of 55.5 events per trial arm, which is approximately 3.44 times more than the 20-29 years-old group (P<.001). We also observe that adverse events display strong age-related patterns among different categories. Conclusion: The results show that there is a significant adverse event variance at the population level between different age groups in clinical trials. The data suggest that age-associated adverse events should be considered in planning, monitoring, and regulating clinical trials. UR - http://medinform.jmir.org/2016/4/e30/ UR - http://dx.doi.org/10.2196/medinform.6437 UR - http://www.ncbi.nlm.nih.gov/pubmed/27751983 ID - info:doi/10.2196/medinform.6437 ER - TY - JOUR AU - Kruse, Scott Clemens AU - Goswamy, Rishi AU - Raval, Yesha AU - Marawi, Sarah PY - 2016/11/21 TI - Challenges and Opportunities of Big Data in Health Care: A Systematic Review JO - JMIR Med Inform SP - e38 VL - 4 IS - 4 KW - big data KW - analytics KW - health care KW - human genome KW - electronic medical record N2 - Background: Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management. Objective: The purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care. Methods: A total of 3 searches were performed for publications between January 1, 2010 and January 1, 2016 (PubMed/MEDLINE, CINAHL, and Google Scholar), and an assessment was made on content germane to big data in health care. From the results of the searches in research databases and Google Scholar (N=28), the authors summarized content and identified 9 and 14 themes under the categories Challenges and Opportunities, respectively. We rank-ordered and analyzed the themes based on the frequency of occurrence. Results: The top challenges were issues of data structure, security, data standardization, storage and transfers, and managerial skills such as data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction. Conclusions: Big data analytics has the potential for positive impact and global implications; however, it must overcome some legitimate obstacles. UR - http://medinform.jmir.org/2016/4/e38/ UR - http://dx.doi.org/10.2196/medinform.5359 UR - http://www.ncbi.nlm.nih.gov/pubmed/27872036 ID - info:doi/10.2196/medinform.5359 ER - TY - JOUR AU - Lobach, F. David AU - Johns, B. Ellis AU - Halpenny, Barbara AU - Saunders, Toni-Ann AU - Brzozowski, Jane AU - Del Fiol, Guilherme AU - Berry, L. Donna AU - Braun, M. Ilana AU - Finn, Kathleen AU - Wolfe, Joanne AU - Abrahm, L. Janet AU - Cooley, E. Mary PY - 2016/11/08 TI - Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention JO - JMIR Med Inform SP - e36 VL - 4 IS - 4 KW - rule-based clinical decision support KW - clinical algorithms KW - Web services KW - software as a service KW - symptom management KW - patient-reported outcomes KW - lung cancer N2 - Background: Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care. Objective: The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care. Methods: This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires. Results: In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements. Conclusions: A rule-based CDS system for complex symptom management was systematically developed and tested. The complexity of the algorithms required extensive development and innovative testing. The Web service-based approach allowed remote access to CDS knowledge, and could enable scaling and sharing of this knowledge to accelerate availability, and reduce duplication of effort. Patients and HCPs found the system to be usable and useful. UR - http://medinform.jmir.org/2016/4/e36/ UR - http://dx.doi.org/10.2196/medinform.5728 UR - http://www.ncbi.nlm.nih.gov/pubmed/27826132 ID - info:doi/10.2196/medinform.5728 ER - TY - JOUR AU - Azizi, Amirabbas AU - Aboutorabi, Robab AU - Mazloum-Khorasani, Zahra AU - Afzal-Aghaea, Monavar AU - Tabesh, Hamed AU - Tara, Mahmood PY - 2016/10/21 TI - Evaluating the Effect of Web-Based Iranian Diabetic Personal Health Record App on Self-Care Status and Clinical Indicators: Randomized Controlled Trial JO - JMIR Med Inform SP - e32 VL - 4 IS - 4 KW - systematic review KW - diabetes mellitus, type 2 KW - health records, personal KW - electronic health records KW - self care KW - Iran N2 - Background: There are 4 main types of chronic or noncommunicable diseases. Of these, diabetes is one of the major therapeutic concerns globally. Moreover, Iran is among the countries with the highest incidence of diabetic patients. Furthermore, library-based studies by researchers have shown that thus far no study has been carried out to evaluate the relationship between Web-based diabetic personal health records (DPHR) and self-care indicators in Iran. Objective: The objective of this study is to examine the effect of Web-based DPHR on self-care status of diabetic patients in an intervention group as compared with a control group. Methods: The effect of DPHR on self-care was assessed by using a randomized controlled trial (RCT) protocol for a 2-arm parallel group with a 1:1 allocation ratio. During a 4-month trial period, the control group benefited from the routine care; the intervention group additionally had access to the Web-based DPHR app besides routine care. During the trial, 2 time points at baseline and postintervention were used to evaluate the impact of the DPHR app. A sample size of 72 people was randomly and equally assigned to both the control and intervention groups. The primary outcome measure was the self-care status of the participants. Results: Test results showed that the self-care status in the intervention group in comparison with the control group had a significant difference. In addition, the dimensions of self-care, including normal values, changes trend, the last measured value, and the last time measured values had a significant difference while other dimensions had no significant difference. Furthermore, we found no correlation between Web-based DPHR system and covariates, including scores of weight, glycated hemoglobin (HbA1c), serum creatinine, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol, and planned visit adherence, as well as the change trend of mean for blood glucose and blood pressure. Conclusions: We found that as a result of the Web-based DPHR app, the self-care scores in the intervention group were significantly higher than those of the control group. In total, we found no correlation between the Web-based DPHR app and covariates, including planned visit adherence, HbA1c, serum creatinine, HDL, LDL, total cholesterol, weight, and the change trend of mean for blood glucose and blood pressure. ClinicalTrial: Iranian Registry of Clinical Trials (IRCT): 2013082914522N1; http://www.irct.ir/searchresult.php?id= 14522&number=1 (Archived by WebCite at http://www.webcitation.org/6cC4PCcau) UR - http://medinform.jmir.org/2016/4/e32/ UR - http://dx.doi.org/10.2196/medinform.6433 UR - http://www.ncbi.nlm.nih.gov/pubmed/27769953 ID - info:doi/10.2196/medinform.6433 ER - TY - JOUR AU - Kamaleswaran, Rishikesan AU - McGregor, Carolyn PY - 2016/11/21 TI - A Review of Visual Representations of Physiologic Data JO - JMIR Med Inform SP - e31 VL - 4 IS - 4 KW - survey KW - human-centered computing KW - visualization application domains KW - information visualization KW - visualization systems and tools KW - visualization toolkits N2 - Background: Physiological data is derived from electrodes attached directly to patients. Modern patient monitors are capable of sampling data at frequencies in the range of several million bits every hour. Hence the potential for cognitive threat arising from information overload and diminished situational awareness becomes increasingly relevant. A systematic review was conducted to identify novel visual representations of physiologic data that address cognitive, analytic, and monitoring requirements in critical care environments. Objective: The aims of this review were to identify knowledge pertaining to (1) support for conveying event information via tri-event parameters; (2) identification of the use of visual variables across all physiologic representations; (3) aspects of effective design principles and methodology; (4) frequency of expert consultations; (5) support for user engagement and identifying heuristics for future developments. Methods: A review was completed of papers published as of August 2016. Titles were first collected and analyzed using an inclusion criteria. Abstracts resulting from the first pass were then analyzed to produce a final set of full papers. Each full paper was passed through a data extraction form eliciting data for comparative analysis. Results: In total, 39 full papers met all criteria and were selected for full review. Results revealed great diversity in visual representations of physiological data. Visual representations spanned 4 groups including tabular, graph-based, object-based, and metaphoric displays. The metaphoric display was the most popular (n=19), followed by waveform displays typical to the single-sensor-single-indicator paradigm (n=18), and finally object displays (n=9) that utilized spatiotemporal elements to highlight changes in physiologic status. Results obtained from experiments and evaluations suggest specifics related to the optimal use of visual variables, such as color, shape, size, and texture have not been fully understood. Relationships between outcomes and the users? involvement in the design process also require further investigation. A very limited subset of visual representations (n=3) support interactive functionality for basic analysis, while only one display allows the user to perform analysis including more than one patient. Conclusions: Results from the review suggest positive outcomes when visual representations extend beyond the typical waveform displays; however, there remain numerous challenges. In particular, the challenge of extensibility limits their applicability to certain subsets or locations, challenge of interoperability limits its expressiveness beyond physiologic data, and finally the challenge of instantaneity limits the extent of interactive user engagement. UR - http://medinform.jmir.org/2016/4/e31/ UR - http://dx.doi.org/10.2196/medinform.5186 UR - http://www.ncbi.nlm.nih.gov/pubmed/27872033 ID - info:doi/10.2196/medinform.5186 ER - TY - JOUR AU - Vannieuwenborg, Frederic AU - Van der Auwermeulen, Thomas AU - Van Ooteghem, Jan AU - Jacobs, An AU - Verbrugge, Sofie AU - Colle, Didier PY - 2016/10/31 TI - Evaluating the Economic Impact of Smart Care Platforms: Qualitative and Quantitative Results of a Case Study JO - JMIR Med Inform SP - e33 VL - 4 IS - 4 KW - medical informatics applications KW - home care services KW - ambulatory care information systems KW - evaluation studies as topic KW - cost-benefit analysis N2 - Background: In response to the increasing pressure of the societal challenge because of a graying society, a gulf of new Information and Communication Technology (ICT) supported care services (eCare) can now be noticed. Their common goal is to increase the quality of care while decreasing its costs. Smart Care Platforms (SCPs), installed in the homes of care-dependent people, foster the interoperability of these services and offer a set of eCare services that are complementary on one platform. These eCare services could not only result in more quality care for care receivers, but they also offer opportunities to care providers to optimize their processes. Objective: The objective of the study was to identify and describe the expected added values and impacts of integrating SCPs in current home care delivery processes for all actors. In addition, the potential economic impact of SCP deployment is quantified from the perspective of home care organizations. Methods: Semistructured and informal interviews and focus groups and cocreation workshops with service providers, managers of home care organizations, and formal and informal care providers led to the identification of added values of SCP integration. In a second step, process breakdown analyses of home care provisioning allowed defining the operational impact for home care organization. Impacts on 2 different process steps of providing home care were quantified. After modeling the investment, an economic evaluation compared the business as usual (BAU) scenario versus the integrated SCP scenario. Results: The added value of SCP integration for all actors involved in home care was identified. Most impacts were qualitative such as increase in peace of mind, better quality of care, strengthened involvement in care provisioning, and more transparent care communication. For home care organizations, integrating SCPs could lead to a decrease of 38% of the current annual expenses for two administrative process steps namely, care rescheduling and the billing for care provisioning. Conclusions: Although integrating SCP in home care processes could affect both the quality of life of the care receiver and informal care giver, only scarce and weak evidence was found that supports this assumption. In contrast, there exists evidence that indicates the lack of the impact on quality of life of the care receiver while it increases the cost of care provisioning. However, our cost-benefit quantification model shows that integrating SCPs in home care provisioning could lead to a considerable decrease of costs for care administrative tasks. Because of this cost decreasing impact, we believe that the integration of SCPs will be driven by home care organizations instead of the care receivers themselves. UR - http://medinform.jmir.org/2016/4/e33/ UR - http://dx.doi.org/10.2196/medinform.5012 UR - http://www.ncbi.nlm.nih.gov/pubmed/27799137 ID - info:doi/10.2196/medinform.5012 ER - TY - JOUR AU - Seth, Puneet AU - Abu-Abed, Ismail Mohamed AU - Kapoor, Vikram AU - Nicholson, Kathryn AU - Agarwal, Gina PY - 2016/12/22 TI - Email Between Patient and Provider: Assessing the Attitudes and Perspectives of 624 Primary Health Care Patients JO - JMIR Med Inform SP - e42 VL - 4 IS - 4 KW - electronic mail KW - email KW - communication KW - primary health care KW - surveys KW - patient engagement N2 - Background: Email between patients and their health care providers can serve as a continuous and collaborative forum to improve access to care, enhance convenience of communication, reduce administrative costs and missed appointments, and improve satisfaction with the patient-provider relationship. Objective: The main objective of this study was to investigate the attitudes of patients aged 16 years and older toward receiving email communication for health-related purposes from an academic inner-city family health team in Southern Ontario. In addition to exploring the proportion of patients with a functioning email address and interest in email communication with their health care provider, we also examined patient-level predictors of interest in email communication. Methods: A cross-sectional study was conducted using a self-administered, 1-page survey of attitudes toward electronic communication for health purposes. Participants were recruited from attending patients at the McMaster Family Practice in Hamilton, Ontario, Canada. These patients were aged 16 years and older and were approached consecutively to complete the self-administered survey (N=624). Descriptive analyses were conducted using the Pearson chi-square test to examine correlations between variables. A logistic regression analysis was conducted to determine statistically significant predictors of interest in email communication (yes or no). Results: The majority of respondents (73.2%, 457/624) reported that they would be willing to have their health care provider (from the McMaster Family Practice) contact them via email to communicate health-related information. Those respondents who checked their personal email more frequently were less likely to want to engage in this electronic communication. Among respondents who check their email less frequently (fewer than every 3 days), 46% (37/81) preferred to communicate with the McMaster Family Practice via email. Conclusions: Online applications, including email, are emerging as a viable avenue for patient communication. With increasing utility of mobile devices in the general population, the proportion of patients interested in email communication with their health care providers may continue to increase. When following best practices and appropriate guidelines, health care providers can use this resource to enhance patient-provider communication in their clinical work, ultimately leading to improved health outcomes and satisfaction with care among their patients. UR - http://medinform.jmir.org/2016/4/e42/ UR - http://dx.doi.org/10.2196/medinform.5853 UR - http://www.ncbi.nlm.nih.gov/pubmed/28007688 ID - info:doi/10.2196/medinform.5853 ER -