Methods of Information in Medicine Methods of Information in Medicine mim de-de Sun, 23 Jul 17 02:54:56 +0200 Ahead of print: Back on Track S. Koch (1), O. Gefeller (2), I. N. Sarkar (3), R. Haux (4) 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. Bartels-Velthuis (1, 2), K. Van der Ploeg (1), E. H. Bos (2) 27735 2017-07-18 15:13:48 Ahead of print: 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 Ahead of print: An Environment for Guideline-based Decision Support Systems for Outpatients... 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 Ahead of print: Can Statistical Machine Learning Algorithms Help for Classification of Obstructive... 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 Ahead of print: 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 Ahead of print: Data Requirements for the Correct Identification of Medication Errors and Adverse... 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 Ahead of print: 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 Ahead of print: 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 Ahead of print: 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 Open Access: On Teaching International Courses on Health Information Systems Background: Health information systems (HIS) are one of the most important areas for biomedical and health informatics. In order to professionally deal with HIS well-educated informaticians are needed. Because of these reasons, in 2001 an international course has been established: The Frank – van Swieten Lectures on Strategic Information Management of Health Information Systems. Objectives: Reporting about the Frank – van Swieten Lectures and about our students‘ feedback on this course during the last 16 years. Summarizing our lessons learned and making recommendations for such international courses on HIS. Methods: The basic concept of the Frank – van Swieten lectures is to teach the theoretical background in local lectures, to organize practical exercises on modelling sub-information systems of the respective local HIS and finally to conduct Joint Three Days as an international meeting were the resulting models are introduced and compared. Results: During the last 16 years, the Universities of Amsterdam, Braunschweig, Heidelberg/Heilbronn, Leipzig as well as UMIT were involved in running this course. Overall, 517 students from these universities participated. Our students‘ feedback was clearly positive. The Joint Three Days of the Frank – van Swieten Lectures, where at the end of the course all students can meet, turned out to be an important component of this course. Based on the last 16 years, we recommend common teaching materials, agreement on equivalent clinical areas for the exercises, support of group building of international student groups, motivation of using a collaboration platform, ensuring quality management of the course, addressing different levels of knowledge of the students, and ensuring sufficient funding for joint activities. Conclusions: Although associated with considerable additional efforts, we can clearly recommend establishing such international courses on HIS, such as the Frank – van Swieten Lectures.... E. Ammenwerth (1), P. Knaup (2), A. Winter (3), A. W. Bauer (4), O. J. Bott (5, 6), M. Gietzelt (2), B. Haarbrandt (5), W. O. Hackl (1), N. Hellrung (5, 7), G. Hübner-Bloder (1), F. Jahn (3), M. W. Jaspers (8), U. Kutscha (9), C. Machan (1), B. Oppermann (5), J. Pilz (9), J. Schwartze (5), C. Seidel (10), J.-E. Slot (11, 12), S. Smers (13), K. Spitalewsky (2, 14), N. Steckel (5, 15), A. Strübing (3), M. van der Haak (2, 16), R. Haux (5), W. J. ter Burg (8) 27297 2017-03-08 15:04:46 Open Access: The Impact of Information Culture on Patient Safety Outcomes Background: An organization’s information culture and information management practices create conditions for processing patient information in hospitals. Information management incidents are failures that could lead to adverse events for the patient if they are not detected. Objectives: To test a theoretical model that links information culture in acute care hospitals to information management incidents and patient safety outcomes. Methods: Reason’s model for the stages of development of organizational accidents was applied. Study data were collected from a cross-sectional survey of 909 RNs who work in medical or surgical units at 32 acute care hospitals in Finland. Structural equation modeling was used to assess how well the hypothesized model fit the study data. Results: Fit indices indicated a good fit for the model. In total, 18 of the 32 paths tested were statistically significant. Documentation errors had the strongest total effect on patient safety outcomes. Organizational guidance positively affected information availability and utilization of electronic patient records, whereas the latter had the strongest total effect on the reduction of information delays. Conclusions: Patient safety outcomes are associated with information management incidents and information culture. Further, the dimensions of the information culture create work conditions that generate errors in hospitals.... V. Jylhä (1), S. Mikkonen (2), K. Saranto (1), D. W. Bates (3, 4) 27296 2017-03-08 15:01:25 Technologies Solutions Schemes for Patients’ Rehabilitation Objective: The present editorial is part of the focus theme of Methods of Information in Medicine entitled “Technologies solutions schemes for Patients’ Rehabilitation: Methodologies, Models and Algorithms”. The focus theme aims to present nowadays most innovative solutions to improve patients’ rehabilitation by applying and using sophisticated and pioneering Information and Communication Technologies (ICT) and human factors. Methods: The focus theme explores the different existent research works and tools used, applied and developed for incapable people in terms of rehabilitation and health care, as to look into the extent methodologies, models and algorithms by means of ICT in this process. Results: The focus theme lists a group of research works, which are presenting various solutions using ICT systems to improve the rehabilitation process of people with physical incapacities and to help them in carrying out their daily life. H. M. Fardoun (1), A. S. Mashat (2) 27263 2017-02-28 10:58:35 The MADE Reference Information Model for Interoperable Pervasive Telemedicine Systems Objectives: The main objective is to develop and validate a reference information model (RIM) to support semantic interoperability of pervasive telemedicine systems. The RIM is one component within a larger, computer-interpretable "MADE language" developed by the authors in the context of the MobiGuide project. To validate our RIM, we applied it to a clinical guideline for patients with gestational diabetes mellitus (GDM). Methods: The RIM is derived from a generic data flow model of disease management which comprises a network of four types of concurrent processes: Monitoring (M), Analysis (A), Decision (D) and Effectuation (E). This resulting MADE RIM, which was specified using the formal Vienna Development Method (VDM), includes six main, high-level data types representing measurements, observations, abstractions, action plans, action instructions and control instructions. Results: The authors applied the MADE RIM to the complete GDM guideline and derived from it a domain information model (DIM) comprising 61 archetypes, specifically 1 measurement, 8 observation, 10 abstraction, 18 action plan, 3 action instruction and 21 control instruction archetypes. It was observed that there are six generic patterns for transforming different guideline elements into MADE archetypes, although a direct mapping does not exist in some cases. Most notable examples are notifications to the patient and/or clinician as well as decision conditions which pertain to specific stages in the therapy. Conclusions: The results provide evidence that the MADE RIM is suitable for modelling clinical data in the design of pervasive telemedicine systems. Together with the other components of the MADE language, the MADE RIM supports development of pervasive telemedicine systems that are interoperable and independent of particular clinical applications.... N. L. S. Fung (1), V. M. Jones (1), H. J. Hermens (1, 2) 27262 2017-02-28 10:57:18 Relating Complexity and Error Rates of Ontology Concepts Objectives: Ontologies are knowledge structures that lend support to many health-information systems. A study is carried out to assess the quality of ontological concepts based on a measure of their complexity. The results show a relation between complexity of concepts and error rates of concepts. Methods: A measure of lateral complexity defined as the number of exhibited role types is used to distinguish between more complex and simpler concepts. Using a framework called an area taxonomy, a kind of abstraction network that summarizes the structural organization of an ontology, concepts are divided into two groups along these lines. Various concepts from each group are then subjected to a two-phase QA analysis to uncover and verify errors and inconsistencies in their modeling. A hierarchy of the National Cancer Institute thesaurus (NCIt) is used as our test-bed. A hypothesis pertaining to the expected error rates of the complex and simple concepts is tested. Results: Our study was done on the NCIt’s Biological Process hierarchy. Various errors, including missing roles, incorrect role targets, and incorrectly assigned roles, were discovered and verified in the two phases of our QA analysis. The overall findings confirmed our hypothesis by showing a statistically significant difference between the amounts of errors exhibited by more laterally complex concepts vis-à-vis simpler concepts. Conclusions: QA is an essential part of any ontology’s maintenance regimen. In this paper, we reported on the results of a QA study targeting two groups of ontology concepts distinguished by their level of complexity, defined in terms of the number of exhibited role types. The study was carried out on a major component of an important ontology, the NCIt. The findings suggest that more complex concepts tend to have a higher error rate than simpler concepts. These findings can be utilized to guide ongoing efforts in ontology QA.... H. Min (1), L. Zheng (2), Y. Perl (2), M. Halper (3), S. De Coronado (4), C. Ochs (2) 27261 2017-02-28 10:55:48