Methods of Information in Medicine Methods of Information in Medicine mim de-de Sat, 23 Sep 17 09:33:38 +0200 Ahead of print: Application of N-of-1 Experiments to Test the Efficacy of Inactivity Alert Features... Background: Frequent breaks from sitting could improve health. Many commercially available fitness trackers deliver vibration alerts that could be used to cue sitting breaks. As a potentially pragmatic approach to promote frequent breaks from sitting, we tested the efficacy of inactivity alerts among obese older adults, a highly sedentary population. Methods: We conducted 10 sequential N-of-1 (single-case) experimental ABA trials. Participants (mean age = 68, mean BMI = 35) were monitored for a baseline phase (“A1”) followed by an intervention phase (“B”). The intervention was then removed and participants were monitored to test an experimental effect (reversal “A2” phase). Total time in the study was limited to 25 days. During the intervention phase (“B”), participants used fitness trackers to stand up or move every time they received an alert (every 15 or 20 minutes of inactivity). Participants wore activPAL devices to measure breaks from sitting each day. Randomization tests were used to determine whether the number of breaks was significantly higher during the “B” phase than the two “A” phases. Results: Breaks were higher by 7.2 breaks per day during the “B” phase compared to the mean of the “A” phases. Seven out of 10 participants had more sitting breaks during the intervention phase which subsequently decreased during the reversal “A2” phase (combined p-value < .05). Conclusion: Inactivity alert features within commercially available devices are efficacious for promoting modest improvements in breaks from sitting among older adults with obesity and could be a simple health-promoting strategy in this population.... D. E. Rosenberg (1), E. Kadokura (2), M. E. Morris (3), A. Renz (4), R. M. Vilardaga (5) 27827 2017-08-16 12:10:54 Ahead of print: A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia... Objectives: The ability to efficiently review the existing literature is essential for the rapid progress of research. This paper describes a classifier of text documents, represented as vectors in spaces of Wikipedia concepts, and analyses its suitability for classification of Spanish biomedical documents when only English documents are available for training. We propose the cross-language concept matching (CLCM) technique, which relies on Wikipedia interlanguage links to convert concept vectors from the Spanish to the English space. Methods: The performance of the classifier is compared to several baselines: a classifier based on machine translation, a classifier that represents documents after performing Explicit Semantic Analysis (ESA), and a classifier that uses a domain-specific semantic annotator (MetaMap). The corpus used for the experiments (Cross-Language UVigoMED) was purpose-built for this study, and it is composed of 12,832 English and 2,184 Spanish MEDLINE abstracts. Results: The performance of our approach is superior to any other state-of-the art classifier in the benchmark, with performance increases up to: 124% over classical machine translation, 332% over MetaMap, and 60 times over the classifier based on ESA. The results have statistical significance, showing p-values < 0.0001. Conclusion: Using knowledge mined from Wikipedia to represent documents as vectors in a space of Wikipedia concepts and translating vectors between language-specific concept spaces, a cross-language classifier can be built, and it performs better than several state-of-the-art classifiers.... M. A. Mouriño-García (1), R. Pérez-Rodríguez (1), L. E. Anido-Rifón (1) 27826 2017-08-16 12:10:30 Ahead of print: Prediction of Emergency Department Hospital Admission Based on Natural Language... Objective: To describe and compare logistic regression and neural network modeling strategies to predict hospital admission or transfer following initial presentation to Emergency Department (ED) triage with and without the addition of natural language processing elements. Methods: Using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), a cross-sectional probability sample of United States EDs from 2012 and 2013 survey years, we developed several predictive models with the outcome being admission to the hospital or transfer vs. discharge home. We included patient characteristics immediately available after the patient has presented to the ED and undergone a triage process. We used this information to construct logistic regression (LR) and multilayer neural network models (MLNN) which included natural language processing (NLP) and principal component analysis from the patient’s reason for visit. Ten-fold cross validation was used to test the predictive capacity of each model and receiver operating curves (AUC) were then calculated for each model. Results: Of the 47,200 ED visits from 642 hospitals, 6,335 (13.42%) resulted in hospital admission (or transfer). A total of 48 principal components were extracted by NLP from the reason for visit fields, which explained 75% of the overall variance for hospitalization. In the model including only structured variables, the AUC was 0.824 (95% CI 0.818-0.830) for logistic regression and 0.823 (95% CI 0.817-0.829) for MLNN. Models including only free-text information generated AUC of 0.742 (95% CI 0.731- 0.753) for logistic regression and 0.753 (95% CI 0.742-0.764) for MLNN. When both structured variables and free text variables were included, the AUC reached 0.846 (95% CI 0.839-0.853) for logistic regression and 0.844 (95% CI 0.836-0.852) for MLNN. Conclusions: The predictive accuracy of hospital admission or transfer for patients who presented to ED triage overall was good, and was improved with the inclusion of free text data from a patient’s reason for visit regardless of modeling approach. Natural language processing and neural networks that incorporate patient-reported outcome free text may increase predictive accuracy for hospital admission.... X. Zhang (1), J. Kim (2), R. E. Patzer (1, 3), S. R. Pitts (4), A. Patzer (5), J. D. Schrager (4) 27825 2017-08-16 12:09:55 Open Access: mHealth Application Areas and Technology Combinations Background: With the continuous and enormous spread of mobile technologies, mHealth has evolved as a new subfield of eHealth. While eHealth is broadly focused on information and communication technologies, mHealth seeks to explore more into mobile devices and wireless communication. Since mobile phone penetration has exceeded other infrastructure in low and middle-income countries (LMICs), mHealth is seen as a promising component to provide pervasive and patient-centered care. Objectives: The aim of our research work for this paper is to examine the mHealth literature to identify application areas, target diseases, and mHealth service and technology types that are most appropriate for LMICs. Methods: Based on the 2011 WHO mHealth report, a combination of search terms, all including the word “mHealth”, was identified. A literature review was conducted by searching the PubMed and IEEE Xplore databases. Articles were included if they were published in English, covered an mHealth solution/ intervention, involved the use of a mobile communication device, and included a pilot evaluation study. Articles were excluded if they did not provide sufficient detail on the solution covered or did not focus on clinical efficacy/effectiveness. Cross-referencing was also performed on included articles. Results: 842 articles were retrieved and analyzed, 255 of which met the inclusion criteria. North America had the highest number of applications (n=74) followed by Europe (n=50), Asia (n=44), Africa (n=25), and Australia (n=9). The Middle East (n=5) and South America (n=3) had the least number of studies. The majority of solutions addressed diabetes (n=51), obesity (n=25), CVDs (n=24), HIV (n=18), mental health (n=16), health behaviors (n=16), and maternal and child’s health (MCH) (n=11). Fewer solutions addressed asthma (n=7), cancer (n=5), family health planning (n=5), TB (n=3), malaria (n=2), chronic obtrusive pulmonary disease (COPD) (n=2), vision care (n=2), and dermatology (n=2). Other solutions targeted stroke, dental health, hepatitis vaccination, cold and flu, ED prescribed antibiotics, iodine deficiency, and liver transplantation (n=1 each). The remainder of solutions (n=14) did not focus on a certain disease. Most applications fell in the areas of health monitoring and surveillance (n=93) and health promotion and raising awareness (n=88). Fewer solutions addressed the areas of communication and reporting (n=11), data collection (n=6), telemedicine (n=5), emergency medical care (n=3), point of care support (n=2), and decision support (n=2). The majority of solutions used SMS messaging (n=94) or mobile apps (n=71). Fewer used IVR/phone calls (n=8), mobile website/email (n=5), videoconferencing (n=2), MMS (n=2), or video (n=1) or voice messages (n=1). Studies were mostly RCTs, with the majority suffering from small sample sizes and short study durations. Problems addressed by solutions included travel distance for reporting, self-management and disease monitoring, and treatment/medication adherence. Conclusions: SMS and app solutions are the most common forms of mHealth applications. SMS solutions are prevalent in both high and LMICs while app solutions are mostly used in high income countries. Common application areas include health promotion and raising awareness using SMS and health monitoring and surveillance using mobile apps. Remaining application areas are rarely addressed. Diabetes is the most commonly targeted medical condition, yet remains deficient in LMICs.... H. Abaza (1), M. Marschollek (1) 27809 2017-08-08 13:49:40 Open Access: Quality Requirements for Electronic Health Record Systems Background: For more than 30 years, there has been close cooperation between Japanese and German scientists with regard to information systems in health care. Collaboration has been formalized by an agreement between the respective scientific associations. Following this agreement, two joint workshops took place to explore the similarities and differences of electronic health record systems (EHRS) against the background of the two national healthcare systems that share many commonalities. Objectives: To establish a framework and requirements for the quality of EHRS that may also serve as a basis for comparing different EHRS. Methods: Donabedian’s three dimensions of quality of medical care were adapted to the outcome, process, and structural quality of EHRS and their management. These quality dimensions were proposed before the first workshop of EHRS experts and enriched during the discussions. Results: The Quality Requirements Framework of EHRS (QRF-EHRS) was defined and complemented by requirements for high quality EHRS. The framework integrates three quality dimensions (outcome, process, and structural quality), three layers of information systems (processes and data, applications, and physical tools) and three dimensions of information management (strategic, tactical, and operational information management). Conclusions: Describing and comparing the quality of EHRS is in fact a multidimensional problem as given by the QRF-EHRS framework. This framework will be utilized to compare Japanese and German EHRS, notably those that were presented at the second workshop.... A. Winter (1), K. Takabayashi (2), F. Jahn (1), E. Kimura (3), R. Engelbrecht (4), R. Haux (5), M. Honda (6), U. H. Hübner (7), S. Inoue (8), C. D. Kohl (9), T. Matsumoto (10), Y. Matsumura (11), K. Miyo (12), N. Nakashima (13), H.-U. Prokosch (14), M. Staemmler (15) 27796 2017-08-07 15:26:31 Back on Track S. Koch, O. Gefeller (1), I. N. Sarkar (2), R. Haux (3) 27736 2017-07-18 15:14:51 Ahead of print: Heart Rate Variability Biofeedback Stress Relief Program for Depression Background: Depressive disorders often have a chronic course and the efficacy of evidence-based treatments may be overestimated. Objective: To examine the effectiveness of the Heart Rate Variability Stress Reduction Program (SRP) as a supplement to standard treatment in patients with depressive disorders. Methods: The SRP was individually administered in eight weekly sessions. Seven participants completed the full protocol and were enrolled in a single-subject ABA multiple baseline experimental design. To perform interrupted time-series analyses, daily measures were completed in a diary (depression, resilience, happiness, heart coherence and a personalized outcome measure). Results: Five out of seven patients improved in depressed mood and/or a personalized outcome measure. The effect of treatment was reversed in four patients during the withdrawal phase. One patient reliably improved on depression, whereas two patients recovered on autonomy and one on social optimism. No consistent relationship was found between the heart rate variability-related level of coherence and self-reported mood levels. Conclusions: The SRP is beneficial in some domains and for some patients. A prolonged treatment or continued home practice may be required for enduring effects. The intervention had more clinical impact on resilience-related outcome measures than on symptoms. The small sample size does not permit generalization of the results. We recommend future investigation of the underlying mechanisms of the SRP.... B. M. A. Hartogs (1), A. A. Bartels-Velthuis (1, 2), K. Van der Ploeg (1), E. H. Bos (2) 27735 2017-07-18 15:13:48 Chronic Disease Registries – Trends and Challenges Background: This accompanying editorial is an introduction to the focus theme of “chronic disease registries – trends and challenges”. Methods: A call for papers was announced on the website of Methods of Information in Medicine in April 2016 with submission deadline in September 2016. A peer review process was established to select the papers for the focus theme, managed by two guest editors. Results: Three papers were selected to be included in the focus theme. Topics range from contributions to patient care through implementation of clinical decision support functionality in clinical registries; analysing similar-purposed acute coronary syndrome registries of two countries and their registry-to-SNOMED CT maps; and data extraction for speciality population registries from electronic health record data rather than manual abstraction. Conclusions: The focus theme gives insight into new developments related to disease registration. This applies to technical challenges such as data linkage and data as well as data structure abstraction, but also the utilisation for clinical decision making.... J. Schüz (1), M. Fored (2) 27734 2017-07-18 15:13:29 Ahead of print: Use of an Activity Tracker to Test for a Possible Correlation of Resting Heart Rate... P. Cooper (1), N. (1) 27733 2017-07-18 15:12:55 An Environment for Guideline-based Decision Support Systems for Outpatients Monitoring Objectives: We propose an architecture for monitoring outpatients that relies on mobile technologies for acquiring data. The goal is to better control the onset of possible side effects between the scheduled visits at the clinic. Methods: We analyze the architectural components required to ensure a high level of abstraction from data. Clinical practice guidelines were formalized with Alium, an authoring tool based on the PROforma language, using SNOMED-CT as a terminology standard. The Alium engine is accessible through a set of APIs that may be leveraged for implementing an application based on standard web technologies to be used by doctors at the clinic. Data sent by patients using mobile devices need to be complemented with those already available in the Electronic Health Record to generate personalized recommendations. Thus a middleware pursuing data abstraction is required. To comply with current standards, we adopted the HL7 Virtual Medical Record for Clinical Decision Support Logical Model, Release 2. Results: The developed architecture for monitoring outpatients includes: (1) a guideline-based Decision Support System accessible through a web application that helps the doctors with prevention, diagnosis and treatment of therapy side effects; (2) an application for mobile devices, which allows patients to regularly send data to the clinic. In order to tailor the monitoring procedures to the specific patient, the Decision Support System also helps physicians with the configuration of the mobile application, suggesting the data to be collected and the associated collection frequency that may change over time, according to the individual patient’s conditions. A proof of concept has been developed with a system for monitoring the side effects of chemo-radiotherapy in head and neck cancer patients. Conclusions: Our environment introduces two main innovation elements with respect to similar works available in the literature. First, in order to meet the specific patients’ needs, in our work the Decision Support System also helps the physicians in properly configuring the mobile application. Then the Decision Support System is also continuously fed by patient-reported outcomes.... E. M. Zini (1), G. Lanzola (1), P. Bossi (2), S. Quaglini (1) 27732 2017-07-18 15:12:19 Open Access: A Comparison of Discovered Regularities in Blood Glucose Readings across Two Data... Background: Type 1 diabetes requires frequent testing and monitoring of blood glucose levels in order to determine appropriate type and dosage of insulin administration. This can lead to thousands of individual measurements over the course of a lifetime of a single individual, of which very few are retained as part of a permanent record. The third author, aged 9, and his family have maintained several years of written records since his diagnosis with Type 1 diabetes at age 20 months, and have also recently begun to obtain automated records from a continuous glucose monitor. Objectives: This paper compares regularities identified within aggregated manually-collected and automatically-collected blood glucose data visualizations by the family involved in monitoring the third author’s diabetes. Methods: 7,437 handwritten entries of the third author’s blood sugar readings were obtained from a personal archive, digitized, and visualized in Tableau data visualization software. 6,420 automatically collected entries from a Dexcom G4 Platinum continuous glucose monitor were obtained and visualized in Dexcom’s Clarity data visualization report tool. The family was interviewed three times about diabetes data management and their impressions of data as presented in data visualizations. Interviews were audiorecorded or recorded with handwritten notes. Results: The aggregated visualization of manually-collected data revealed consistent habitual times of day when blood sugar measurements were obtained. The family was not fully aware that their existing life routines and the third author’s entry into formal schooling had created critical blind spots in their data that were often unmeasured. This was realized upon aggregate visualization of CGM data, but the discovery and use of these visualizations were not realized until a new healthcare provider required the family to find and use them. The lack of use of CGM aggregate visualization was reportedly because the default data displays seemed to provide already abundant information for in-the-moment decision making for diabetes management. Conclusions: Existing family routines and school schedules can shape if and when blood glucose data are obtained for T1D youth. These routines may inadvertently introduce blind spots in data, even when it is collected and recorded systematically. Although CGM data may be superior in its overall density of data collection, families do not necessarily discover nor use the full range of useful data visualization features. To support greater awareness of youth blood sugar levels, families that manually obtain youth glucose data should be advised to avoid inadvertently creating data blind spots due to existing schedules and routines. For families using CGM technology, designers and healthcare providers should consider implementing better cues and prompts that will encourage families to discover and utilize aggregate data visualization capabilities.... V. Lee, T. Thurston, C. Thurston 27704 2017-07-04 15:59:55 Open Access: Rapid Development of Specialty Population Registries and Quality Measures from... Background: Creation of a new electronic health record (EHR)-based registry often can be a “one-off“ complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch. Such an approach will not scale to meet the emerging demand for specialty registries to support population health and value-based care. Objective: To determine if the creation of EHR-based specialty registries could be markedly accelerated by employing (a) a finite core set of EHR data collection principles and methods, (b) concurrent engineering of data extraction and data warehouse design using a common dimensional data model for all registries, and (c) agile development methods commonly employed in new product development. Methods: We adopted as guiding principles to (a) capture data as a byproduct of care of the patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust set of EHR data capture tool types, and (d) leverage our existing technology toolkit. Registries were defined by a shared condition (recorded on the Problem List) or a shared exposure to a procedure (recorded on the Surgical History) or to a medication (recorded on the Medication List). Any EHR fields needed – either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM) – were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined “grains” from the EHR into the EDW model. All calculated CQM values were stored in a single Fact table in the EDW crossing all registries. Registry-specific dashboards were created in the EHR to display both (a) real-time patient lists of registry patients and (b) EDW-generated CQM data. Agile project management methods were employed, including co-development, lightweight requirements documentation with User Stories and acceptance criteria, and time-boxed iterative development of EHR features in 2-week “sprints” for rapid-cycle feedback and refinement. Results: Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic disease registries, with 111 new EHR data collection and clinical decision support tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting on both real-time patient care gaps and summarized and vetted CQM measure performance trends. Conclusions: This study suggests concurrent design of EHR data collection tools and reporting can quickly yield useful EHR structured data for chronic disease registries, and bodes well for efforts to migrate away from manual abstraction. This work also supports the view that in new EHR-based registry development, as in new product development, adopting agile principles and practices can help deliver valued, high-quality features early and often.... V. Kannan (1), J. S. Fish (1), J. M. Mutz (1), A. R. Carrington (1), K. Lai (1), L. S. Davis (1), J. E. Youngblood (1), M. R. Rauschuber (1), K. A. Flores (1), E. J. Sara (1), D. G. Bhat (1), D. L. Willett (1) 27646 2017-06-14 12:11:00 Can Statistical Machine Learning Algorithms Help for Classification of Obstructive Sleep Apnea... Objectives: The goal of this study is to evaluate the results of machine learning methods for the classification of OSA severity of patients with suspected sleep disorder breathing as normal, mild, moderate and severe based on non-polysomnographic variables: 1) clinical data, 2) symptoms and 3) physical examination. Methods: In order to produce classification models for OSA severity, five different machine learning methods (Bayesian network, Decision Tree, Random Forest, Neural Networks and Logistic Regression) were trained while relevant variables and their relationships were derived empirically from observed data. Each model was trained and evaluated using 10-fold cross-validation and to evaluate classification performances of all methods, true positive rate (TPR), false positive rate (FPR), Positive Predictive Value (PPV), F measure and Area Under Receiver Operating Characteristics curve (ROC-AUC) were used. Results: Results of 10-fold cross validated tests with different variable settings promisingly indicated that the OSA severity of suspected OSA patients can be classified, using non-polysomnographic features, with 0.71 true positive rate as the highest and, 0.15 false positive rate as the lowest, respectively. Moreover, the test results of different variables settings revealed that the accuracy of the classification models was significantly improved when physical examination variables were added to the model. Conclusions: Study results showed that machine learning methods can be used to estimate the probabilities of no, mild, moderate, and severe obstructive sleep apnea and such approaches may improve accurate initial OSA screening and help referring only the suspected moderate or severe OSA patients to sleep laboratories for the expensive tests.... S. Bozkurt (1), A. Bostanci (2), M. Turhan (2) 27605 2017-06-07 14:06:24 A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression* Objectives: Whether they have been engineered for it or not, most healthcare systems experience a variety of unexpected events such as appointment miss-opportunities that can have significant impact on their revenue, cost and resource utilization. In this paper, a multi-way multi-task learning model based on multinomial logistic regression is proposed to jointly predict the occurrence of different types of miss-opportunities at multiple clinics. Methods: An extension of L1 / L2 regularization is proposed to enable transfer of information among various types of miss-opportunities as well as different clinics. A proximal algorithm is developed to transform the convex but non-smooth likelihood function of the multi-way multi-task learning model into a convex and smooth optimization problem solvable using gradient descent algorithm. Results: A dataset of real attendance records of patients at four different clinics of a VA medical center is used to verify the performance of the proposed multi-task learning approach. Additionally, a simulation study, investigating more general data situations is provided to highlight the specific aspects of the proposed approach. Various individual and integrated multinomial logistic regression models with/without LASSO penalty along with a number of other common classification algorithms are fitted and compared against the proposed multi-way multi-task learning approach. Fivefold cross validation is used to estimate comparing models parameters and their predictive accuracy. The multi-way multi-task learning framework enables the proposed approach to achieve a considerable rate of parameter shrinkage and superior prediction accuracy across various types of miss-opportunities and clinics. Conclusions: The proposed approach provides an integrated structure to effectively transfer knowledge among different miss-opportunities and clinics to reduce model size, increase estimation efficacy, and more importantly improve predictions results. The proposed framework can be effectively applied to medical centers with multiple clinics, especially those suffering from information scarcity on some type of disruptions and/or clinics.... A. Alaeddini (1), S. H. Hong (1) 27604 2017-06-07 14:06:16 Open Access: mosaicQA – A General Approach to Facilitate Basic Data Quality Assurance for... Background: Epidemiological studies are based on a considerable amount of personal, medical and socio-economic data. To answer research questions with reliable results, epidemiological research projects face the challenge of providing high quality data. Consequently, gathered data has to be reviewed continuously during the data collection period. Objectives: This article describes the development of the mosaicQA-library for non-statistical experts consisting of a set of reusable R functions to provide support for a basic data quality assurance for a wide range of application scenarios in epidemiological research. Methods: To generate valid quality reports for various scenarios and data sets, a general and flexible development approach was needed. As a first step, a set of quality-related questions, targeting quality aspects on a more general level, was identified. The next step included the design of specific R-scripts to produce proper reports for metric and categorical data. For more flexibility, the third development step focussed on the generalization of the developed R-scripts, e.g. extracting characteristics and parameters. As a last step the generic characteristics of the developed R functionalities and generated reports have been evaluated using different metric and categorical datasets. Results: The developed mosaicQA-library generates basic data quality reports for multivariate input data. If needed, more detailed results for single-variable data, including definition of units, variables, descriptions, code lists and categories of qualified missings, can easily be produced. Conclusions: The mosaicQA-library enables researchers to generate reports for various kinds of metric and categorical data without the need for computational or scripting knowledge. At the moment, the library focusses on the data structure quality and supports the assessment of several quality indicators, including frequency, distribution and plausibility of research variables as well as the occurrence of missing and extreme values. To simplify the installation process, mosaicQA has been released as an official R-package.... M. Bialke (1), H. Rau (1), T. Schwaneberg (1), R. Walk (2), T. Bahls (1), W. Hoffmann (1) 27573 2017-05-29 12:50:03 Open Access: Utilizing Electronic Medical Records to Discover Changing Trends of Medical Behaviors... Objectives: Medical behaviors are playing significant roles in the delivery of high quality and cost-effective health services. Timely discovery of changing frequencies of medical behaviors is beneficial for the improvement of health services. The main objective of this work is to discover the changing trends of medical behaviors over time. Methods: This study proposes a two-steps approach to detect essential changing patterns of medical behaviors from Electronic Medical Records (EMRs). In detail, a probabilistic topic model, i.e., Latent Dirichlet allocation (LDA), is firstly applied to disclose yearly treatment patterns in regard to the risk stratification of patients from a large volume of EMRs. After that, the changing trends by comparing essential/critical medical behaviors in a specific time period are detected and analyzed, including changes of significant patient features with their values, and changes of critical treatment interventions with their occurring time stamps. Results: We verify the effectiveness of the proposed approach on a clinical dataset containing 12,152 patient cases with a time range of 10 years. Totally, 135 patients features and 234 treatment interventions in three treatment patterns were selected to detect their changing trends. In particular, evolving trends of yearly occurring probabilities of the selected medical behaviors were categorized into six content changing patterns (i.e, 112 growing, 123 declining, 43 up-down, 16 down-up, 35 steady, and 40 jumping), using the proposed approach. Besides, changing trends of execution time of treatment interventions were classified into three occurring time changing patterns (i.e., 175 early-implemented, 50 steady-implemented and 9 delay-implemented). Conclusions: Experimental results show that our approach has an ability to utilize EMRs to discover essential evolving trends of medical behaviors, and thus provide significant potential to be further explored for health services redesign and improvement.... L. Yin (1), Z. Huang (1, 2), W. Dong (3), C. He (2), H. Duan (1, 2) 27493 2017-05-05 09:19:25 Data Requirements for the Correct Identification of Medication Errors and Adverse Drug Events in... Background: Adverse drug events (ADE) involving or not involving medication errors (ME) are common, but frequently remain undetected as such. Presently, the majority of available clinical decision support systems (CDSS) relies mostly on coded medication data for the generation of drug alerts. It was the aim of our study to identify the key types of data required for the adequate detection and classification of adverse drug events (ADE) and medication errors (ME) in patients presenting at an emergency department (ED). Methods: As part of a prospective study, ADE and ME were identified in 1510 patients presenting at the ED of an university teaching hospital by an interdisciplinary panel of specialists in emergency medicine, clinical pharmacology and pharmacy. For each ADE and ME the required different clinical data sources (i.e. information items such as acute clinical symptoms, underlying diseases, laboratory values or ECG) for the detection and correct classification were evaluated. Results: Of all 739 ADE identified 387 (52.4%), 298 (40.3%), 54 (7.3%), respectively, required one, two, or three, more information items to be detected and correctly classified. Only 68 (10.2%) of the ME were simple drug-drug interactions that could be identified based on medication data alone while 381 (57.5%), 181 (27.3%) and 33 (5.0%) of the ME required one, two or three additional information items, respectively, for detection and clinical classification. Conclusions: Only 10% of all ME observed in emergency patients could be identified on the basis of medication data alone. Focusing electronic decisions support on more easily available drug data alone may lead to an under-detection of clinically relevant ADE and ME.... B. Plank-Kiegele (1), T. Bürkle (2), F. Müller (1), A. Patapovas (3), A. Sonst (4), B. Pfistermeister (1), H. Dormann (4), R. Maas (1) 27469 2017-04-28 07:49:00 Ahead of print: A Randomized Trial Comparing Classical Participatory Design to VandAID, an... Background: Early involvement of stakeholders in the design of medical software is particularly important due to the need to incorporate complex knowledge and actions associated with clinical work. Standard user-centered design methods include focus groups and participatory design sessions with individual stakeholders, which generally limit user involvement to a small number of individuals due to the significant time investments from designers and end users. Objectives: The goal of this project was to reduce the effort for end users to participate in co-design of a software user interface by developing an interactive web-based crowdsourcing platform. Methods: In a randomized trial, we compared a new web-based crowdsourcing platform to standard participatory design sessions. We developed an interactive, modular platform that allows responsive remote customization and design feedback on a visual user interface based on user preferences. The responsive canvas is a dynamic HTML template that responds in real time to user preference selections. Upon completion, the design team can view the user’s interface creations through an administrator portal and download the structured selections through a REDCap interface. Results: We have created a software platform that allows users to customize a user interface and see the results of that customization in real time, receiving immediate feedback on the impact of their design choices. Neonatal clinicians used the new platform to successfully design and customize a neonatal handoff tool. They received no specific instruction and yet were able to use the software easily and reported high usability. Conclusions: VandAID, a new web-based crowdsourcing platform, can involve multiple users in user-centered design simultaneously and provides means of obtaining design feedback remotely. The software can provide design feedback at any stage in the design process, but it will be of greatest utility for specifying user requirements and evaluating iterative designs with multiple options.... K. R. Dufendach (1), S. Koch (2), K. M. Unertl (3), C. U. Lehmann (3) 27468 2017-04-28 07:48:09 Reconstruction of 12-lead ECG Using a Single-patch Device Objectives: The aim of this study is to develop an optimal electrode system in the form of a small and wearable single-patch ECG monitoring device that allows for the faithful reconstruction of the standard 12-lead ECG. Methods: The optimized universal electrode positions on the chest and the personalized transformation matrix were determined using linear regression as well as artificial neural networks (ANNs). A total of 24 combinations of 4 neighboring electrodes on 35 channels were evaluated on 19 subjects. Moreover, we analyzed combinations of three electrodes within the four-electrode combination with the best performance. Results: The mean correlation coefficients were all higher than 0.95 in the case of the ANN method for the combinations of four neighboring electrodes. The reconstructions obtained using the three and four sensing electrodes showed no significant differences. The reconstructed 12-lead ECG obtained using the ANN method is better than that using the MLR method. Therefore, three sensing electrodes and one ground electrode (forming a square) placed below the clavicle on the left were determined to be suitable for ensuring good reconstruction performance. Conclusions: Since the interelectrode distance was determined to be 5 cm, the suggested approach can be implemented in a single-patch device, which should allow for the continuous monitoring of the standard 12-lead ECG without requiring limb contact, both in daily life and in clinical practice.... H. J. Lee (1), D. S. Lee (1), H. B. Kwon (1), D. Y. Kim (2), K. S. Park (3) 27467 2017-04-28 07:47:17 Boosting Quality Registries with Clinical Decision Support Functionality* Background: The care of HIV-related tuberculosis (HIV/TB) is complex and challenging. Clinical decision support (CDS) systems can contribute to improve quality of care, but more knowledge is needed on factors determining user acceptance of CDS. Objectives: To analyze physicians’ and nurses’ acceptance of a CDS prototype for evidence-based drug therapy recommendations for HIV/TB treatment. Methods: Physicians and nurses were involved in designing a CDS prototype intended for future integration with the Swedish national HIV quality registry. Focus group evaluation was performed with ten nurses and four physicians, respectively. The Unified Theory of Acceptance and Use of Technology (UTAUT) was used to analyze acceptance. Results: We identified several potential benefits with the CDS prototype as well as some concerns that could be addressed by redesign. There was also concern about dependence on physician attitudes, as well as technical, organizational, and legal issues. Conclusions: Acceptance evaluation at a prototype stage provided rich data to improve the future design of a CDS prototype. Apart from design and development efforts, substantial organizational efforts are needed to enable the implementation and maintenance of a future CDS system.... C. Wannheden (1), H. Hvitfeldt-Forsberg (1), E. Eftimovska (1), K. Westling (2, 3), J. Ellenius (4) 27466 2017-04-28 07:46:55 Tool-supported Interactive Correction and Semantic Annotation of Narrative Clinical Reports Objectives: Our main objective is to design a method of, and supporting software for, interactive correction and semantic annotation of narrative clinical reports, which would allow for their easier and less erroneous processing outside their original context: first, by physicians unfamiliar with the original language (and possibly also the source specialty), and second, by tools requiring structured information, such as decision-support systems. Our additional goal is to gain insights into the process of narrative report creation, including the errors and ambiguities arising therein, and also into the process of report annotation by clinical terms. Finally, we also aim to provide a dataset of ground-truth transformations (specific for Czech as the source language), set up by expert physicians, which can be reused in the future for subsequent analytical studies and for training automated transformation procedures. Methods: A three-phase preprocessing method has been developed to support secondary use of narrative clinical reports in electronic health record. Narrative clinical reports are narrative texts of healthcare documentation often stored in electronic health records. In the first phase a narrative clinical report is tokenized. In the second phase the tokenized clinical report is normalized. The normalized clinical report is easily readable for health professionals with the knowledge of the language used in the narrative clinical report. In the third phase the normalized clinical report is enriched with extracted structured information. The final result of the third phase is a semi-structured normalized clinical report where the extracted clinical terms are matched to codebook terms. Software tools for interactive correction, expansion and semantic annotation of narrative clinical reports has been developed and the three-phase preprocessing method validated in the cardiology area. Results: The three-phase preprocessing method was validated on 49 anonymous Czech narrative clinical reports in the field of cardiology. Descriptive statistics from the database of accomplished transformations has been calculated. Two cardiologists participated in the annotation phase. The first cardiologist annotated 1500 clinical terms found in 49 narrative clinical reports to codebook terms using the classification systems ICD 10, SNOMED CT, LOINC and LEKY. The second cardiologist validated annotations of the first cardiologist. The correct clinical terms and the codebook terms have been stored in a database. Conclusions: We extracted structured information from Czech narrative clinical reports by the proposed three-phase preprocessing method and linked it to electronic health records. The software tool, although generic, is tailored for Czech as the specific language of electronic health record pool under study. This will provide a potential etalon for porting this approach to dozens of other less-spoken languages. Structured information can support medical decision making, quality assurance tasks and further medical research.... K. Zvára (1, 2), M. Tomečková (2), J. Peleška (2), V. Svátek (3), J. Zvárová (1, 2) 27465 2017-04-28 07:45:06 Mapping Acute Coronary Syndrome Registries to SNOMED CT Background: Malaysia and Sweden have mapped their acute coronary syndrome registries using SNOMED CT. Since similar-purposed patient registries can be expected to collect similar data, these data should be mapped to the same SNOMED CT codes despite the different languages used. Previous studies have however shown variations in mapping between different mappers but the reasons behind these variations and the influence of different mapping approaches are still unknown. Objectives: To analyze similar-purposed registries and their registry-to-SNOMED CT maps, using two national acute coronary syndrome registries as examples, to understand the reasons for mapping similarities and differences as well as their implications. Methods: The Malaysian National Cardiovascular Disease – Acute Coronary Syndrome (NCVD-ACS) registry was compared to the Swedish Register of Information and Knowledge about Swedish Heart Intensive Care Admissions (RIKS-HIA). The structures of NCVD-ACS and RIKS-HIA registry forms and their distributions of headings, variables and values were studied. Data items with equivalent meaning (EDIs) were paired and their mappings were categorized into match, mismatch, and non-comparable mappings. Reasons for match, mismatch and non-comparability of each paired EDI were seen as factors that contributed to the similarities and differences between the maps. Results: The registries and their respective maps share a similar distribution pattern regarding the number of headings, variables and values. The registries shared 101 EDIs, whereof 42 % (42) were mapped to SNOMED CT. 45 % (19) of those SNOMED CT coded EDIs had matching codes. The matching EDIs occurred only in pre-coordinated SNOMED CT expressions. Mismatches occurred due to challenges arising from the mappers themselves, limitations in SNOMED CT, and complexity of the registries. Non-comparable mappings appeared due to the use of other coding systems, unmapped data items, as well as requests for new SNOMED CT concepts. Conclusions: To ensure reproducible and reusable maps, the following three actions are recommended: (i) develop a specific mapping guideline for patient registries; (ii) openly share maps; and (iii) establish collaboration between clinical research societies and the SNOMED CT community.... I. Mohd Sulaiman (1), D. Karlsson (2), S. Koch (3) 27378 2017-03-31 10:39:29 Evaluation of Adjusted and Unadjusted Indirect Comparison Methods in Benefit Assessment Background: With the Act on the Reform of the Market for Medicinal Products (AMNOG) in Germany, pharmaceutical manufacturers are obliged to submit a dossier demonstrating added benefit of a new drug compared to an appropriate comparator. Underlying evidence was planned for registration purposes and therefore often does not meet the appropriate comparator as defined by the Federal Joint Committee (G-BA). For this reason AMNOG allows indirect comparisons to assess the extent of added benefit. Objectives: The aim of this study is to evaluate the characteristics and applicability of adjusted indirect comparison described by Bucher and Matching-Adjusted Indirect Comparison (MAIC) in various situations within the early benefit assessment according to §35a Social Code Book 5. In particular, we consider time-to-event endpoints. Methods: We conduct a simulation study where we consider three different scenarios: I) similar study populations, II) dissimilar study populations without interactions and III) dissimilar study populations with interactions between treatment effect and effect modifiers. We simulate data from a Cox model with Weibull distributed survival times. Desired are unbiased effect estimates. We compare the power and the proportion of type 1 errors of the methods. Results: I) Bucher and MAIC perform equivalently well and yield unbiased effect estimates as well as proportions of type 1 errors below the significance level of 5 %. II) Both Bucher and MAIC yield unbiased effect estimates, but Bucher shows a higher power for detection of true added benefit than MAIC. III) Only MAIC, but not Bucher yields unbiased effect estimates. When using robust variance estimation MAIC yields a proportion of type 1 error close to 5 %. In general, power of all methods for indirect comparisons is low. An increasing loss of power for the indirect comparisons can be observed as the true treatment effects decrease. Conclusion: Due to the great loss of power and the potential bias for indirect comparisons, head-to-head trials using the appropriate comparator as defined by the Federal Joint Committee should be conducted whenever possible. However, indirect comparisons are needed if no such direct evidence is available. To conduct indirect comparisons in case of a present common comparator and similar study populations in the trials to be compared, both Bucher and MAIC can be recommended. In case of using adjusted effect measures (such as Hazard Ratio), the violation of the similarity assumption has no relevant effect on the Bucher approach as long as interactions between treatment effect and effect modifiers are absent. Therefore Bucher can still be considered appropriate in this specific situation. In the authors’ opinion, MAIC can be considered as an option (at least as sensitivity analysis to Bucher) if such interactions are present or cannot be ruled out. Nevertheless, in practice MAIC is potentially biased and should always be considered with utmost care.... S. Kühnast (1, 2), J. Schiffner-Rohe (1), J. Rahnenführer (2), F. Leverkus (1) 27377 2017-03-31 10:38:50 A Machine Learning-based Method for Question Type Classification in Biomedical Question Answering Background and Objective: Biomedical question type classification is one of the important components of an automatic biomedical question answering system. The performance of the latter depends directly on the performance of its biomedical question type classification system, which consists of assigning a category to each question in order to determine the appropriate answer extraction algorithm. This study aims to automatically classify biomedical questions into one of the four categories: (1) yes/no, (2) factoid, (3) list, and (4) summary. Methods: In this paper, we propose a biomedical question type classification method based on machine learning approaches to automatically assign a category to a biomedical question. First, we extract features from biomedical questions using the proposed handcrafted lexico-syntactic patterns. Then, we feed these features for machine-learning algorithms. Finally, the class label is predicted using the trained classifiers. Results: Experimental evaluations performed on large standard annotated datasets of biomedical questions, provided by the BioASQ challenge, demonstrated that our method exhibits significant improved performance when compared to four baseline systems. The proposed method achieves a roughly 10-point increase over the best baseline in terms of accuracy. Moreover, the obtained results show that using handcrafted lexico-syntactic patterns as features’ provider of support vector machine (SVM) lead to the highest accuracy of 89.40 %. Conclusion: The proposed method can automatically classify BioASQ questions into one of the four categories: yes/no, factoid, list, and summary. Furthermore, the results demonstrated that our method produced the best classification performance compared to four baseline systems.... M. Sarrouti (1), S. Ouatik El Alaoui (1) 27376 2017-03-31 10:37:51 Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of... Background: The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse. Objectives: In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration. Materials and Methods: Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS. Results: Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records’ normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users. Conclusions: The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid data set with known structure and constraints. The standard based nature of the archetype approach allows an easy integration of CDSSs with existing EHR systems.... G. Kopanitsa (1, 2) 27375 2017-03-31 10:37:03