Methods of Information in Medicine Methods of Information in Medicine mim de-de Wed, 23 May 18 22:08:30 +0200 The Charlson Comorbidity Index in Registry-based Research Background: Comorbidities may have an important impact on survival, and comorbidity scores are often implemented in studies assessing prognosis. The Charlson Comorbidity index is most widely used, yet several adaptations have been published, all using slightly different conversions of the International Classification of Diseases (ICD) coding. Objective: To evaluate which coding should be used to assess and quantify comorbidity for the Charlson Comorbidity Index for registry-based research, in particular if older ICD versions will be used. Methods: A systematic literature search was used to identify adaptations and modifications of the ICD-coding of the Charlson Comorbidity Index for general purpose in adults, published in English. Back-translation to ICD version 8 and version 9 was conducted by means of the ICD-code converter of Statistics Sweden. Results: In total, 16 studies were identified reporting ICD-adaptations of the Charlson Comorbidity Index. The Royal College of Surgeons in the United Kingdom combined 5 versions into an adapted and updated version which appeared appropriate for research purposes. Their ICD-10 codes were back-translated into ICD-9 and ICD-8 according to their proposed adaptations, and verified with previous versions of the Charlson Comorbidity Index. Conclusion: Many versions of the Charlson Comorbidity Index are used in parallel, so clear reporting of the version, exact ICD-coding and weighting is necessary to obtain transparency and reproducibility in research. Yet, the version of the Royal College of Surgeons is up-to-date and easy-to-use, and therefore an acceptable co-morbidity score to be used in registry-based research especially for surgical patients.... N. Brusselaers (1, 2), J. Lagergren (3, 4) 28005 2017-10-12 15:45:32 Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors? Objectives: The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment-person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users’ own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual’s stress-behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior. Methods: This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior. Results: Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points. Conclusions: Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stress-behavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.... Y. Cheung (1), P. S: Hsueh (2), M. Qian (1), S. Yoon (3), L. Meli (4), K. M. Diaz (4), J. Schwartz (4, 5), I. M. Kronish (4), K. W. Davidson (4) 28004 2017-10-12 08:29:07 Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions* Objectives: Evolution of multiple chronic conditions (MCC) follows a complex stochastic process, influenced by several factors including the inter-relationship of existing conditions, and patient-level risk factors. Nearly 20% of citizens aged 18 years and older are burdened with two or more (multiple) chronic conditions (MCC). Treatment for people living with MCC currently accounts for an estimated 66% of the Nation's healthcare costs. However, it is still not known precisely how MCC emerge and accumulate among individuals or in the general population. This study investigates major patterns of MCC transitions in a diverse population of patients and identifies the risk factors affecting the transition process. Methods: A Latent regression Markov clustering (LRMCL) algorithm is proposed to identify major transitions of four MCC that include hypertension (HTN), depression, Post-Traumatic Stress Disorder (PTSD), and back pain. A cohort of 601,805 individuals randomly selected from the population of Iraq and Afghanistan war Veterans (IAVs) who received VA care during three or more years between 2002-2015, is used for training the proposed LRMCL algorithm. Results: Two major clusters of MCC transition patterns with 78% and 22% probability of membership respectively were identified. The primary cluster demonstrated the possibility of improvement when the number of MCC is small and an increase in probability of MCC accumulation as the number of comorbidities increased. The second cluster showed stability (no change) of MCC overtime as the major pattern. Age was the most significant risk factor associated with the most probable cluster for each IAV. Conclusions: These findings suggest that our proposed LRMCL algorithm can be used to describe and understand MCC transitions, which may ultimately allow healthcare systems to support optimal clinical decision-making. This method will be used to describe a broader range of MCC transitions in this and non-VA populations, and will add treatment information to see if models including treatments and MCC emergence can be used to support clinical decision-making in patient care.... A. Alaeddini (1), C. A. Jaramillo (2), S. H. A. Faruqui (1), M. J. Pugh (2) 28003 2017-10-12 08:27:37 Development and Usability of a Smartphone Application for Tracking Antiretroviral Medication Refill... Background: Adherence to antiretroviral medication leads to HIV suppression and decreased morbidity and mortality. In resource-limited settings, the dependence on paper medical charts and unstable electronic health records creates a challenge to monitoring medication adherence. A pharmacy-based strategy that utilizes existing cellular phone infrastructure may lead to a more stable system to monitor adherence. Objectives: To develop and evaluate the usability of a smartphone-based software application (app) for tracking antiretroviral medication refill data in a resource-limited setting. Methods: A pharmacy-based smartphone app for tracking HIV medication adherence was developed through a multi-step rapid prototyping process. The usability of the app was assessed during the daily activities of pharmacy dispensers at HIV clinics in and around Gaborone, Botswana using a validated computer usability survey. Results: The study demonstrated the effective development of and favorable end-user responses to a pharmacy-based HIV medication adherence app. End users had suggestions for minor changes to improve the app’s functionality. Conclusions: In resource-limited settings where electronic health record support is limited, such a system was feasible and appealing. In the future, this system may allow for improved HIV medication adherence tracking and be applied to medications beyond antiretrovirals.... D. Coppock (1), D. Zambo (2), D. Moyo (3), G. Tanthuma (4), J. Chapman (5), V. Lo Re III (1, 6), A. Graziani (1, 7), E. Lowenthal (5, 6), N. Hanrahan (8), R. Littman-Quinn (2), C. Kovarik (5, 9), D. Albarracin (10), J. H. Holmes (6), R. Gross (6, 1) 28002 2017-10-12 08:26:22 Addressing the Data Linking Challenges Background: PARENT JA (cross-border Patient Registries iNiTiative Joint Action), a joint EU and Member States project, has conducted a research among EU patient registries aimed at gathering information on the registries’ interoperability readiness. Leaning on the information and data collected through the previous PARENT JA research, this study aims to provide more detailed view into the registry holders’ practical challenges with data linking. Since the studies which dealt with patient data exchange have often neglected the registry holders’ performance of data exchange, we wanted to put a spotlight on various EU registry holders‘ practices and operations, aiming to detect their needs and concerns in the process of running an interoperable registry. The focus of this study was identifying the main practices and challenges in patient registries interoperability improvement. Methods: The basis for this analysis were the data collected in the series of structured interviews. The size of the interview sample was 13 patient registries, each from a different EU country. The structured interview consisted of nine questions and was conducted in two parts: oral and written. The answers were analysed using open coding. Results: Results are interpreted in the context of the six main themes that emerged through a comprehensive analysis. (1) Examples of data exchange: The most common reported data exchange practices were seen only as a way to achieve the most immediate needs and interests of the individual registries. (2) Awareness and use of international standards: International data and clinical standards were not widely used by the interviewed registries. (3) Use of data models and formats: In the area of data models and formats there is no universally used practice. (4) Data request protocols and procedures: Procedures and protocols varied, mostly depending on the national legal systems in which the patient registries operated. (5) Data security and integrity: Security of personal data was a universal concern for all registry holders that were interviewed; identifiable individual data was shared only in one case. (6) Opportunities and challenges of registry interoperability: most registry holders responded that their registries were well prepared for interoperability practices and that data exchange has never been their primary operative concern. Conclusions: Most of the difficulties regarding data linking were not necessarily associated with technical issues, which registry holders listed outright. Our analysis showed that the lack of interoperability came as a result of organizational or legal constraints that made the registries unable to process and conduct data linking quickly and effectively with other sources.... M. Valentic (1), B. Plese (1), I. Pristas (1), D. Ivankovic (1) 28001 2017-10-12 08:24:44 Application of N-of-1 Experiments to Test the Efficacy of Inactivity Alert Features in Fitness... 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 A Bag of Concepts Approach for Biomedical Document Classification Using Wikipedia Knowledge* 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 Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and... 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 &ldquo;mHealth&rdquo;, 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&rsquo;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&rsquo;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. H&uuml;bner (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 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 Chronic Disease Registries – Trends and Challenges Background: This accompanying editorial is&nbsp;an introduction to the focus theme of &ldquo;chronic disease registries &ndash; trends and challenges&rdquo;. Methods: A call for papers was announced on the website of Methods of Information in&nbsp;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. Sch&uuml;z (1), M. Fored (2) 27734 2017-07-18 15:13:29 Use of an Activity Tracker to Test for a Possible Correlation of Resting Heart Rate with Life Events P. C. Cooper (1), N. Wickramasinghe (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&rsquo;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&rsquo; 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&rsquo;s diabetes. Methods: 7,437 handwritten entries of the third author&rsquo;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&rsquo;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&rsquo;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 &ldquo;one-off&ldquo; 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&nbsp;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 &ndash; either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM) &ndash; were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined &ldquo;grains&rdquo; 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 &ldquo;sprints&rdquo; 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&nbsp;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&nbsp;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 A Randomized Trial Comparing Classical Participatory Design to VandAID, an Interactive CrowdSourcing... 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