Text Fields Research Articles

A comparison of mobile form controls for different tasks

Web forms are the design tools that are used for gathering inputs from users in interactive applications. These forms are mainly developed for desktop applications, and transferring them directly to a mobile context did not work well most of the time. Since user experience with mobile devices is different from with desktop computers, the efficiency and accuracy of form controls (buttons, text boxes, drop-down boxes, radio buttons, etc.) in the user interface is essential.In the scope of this research, a user study was conducted to compare the efficiency, effectiveness, and user satisfaction of mobile form controls. In the user study, an experiment was set up to compare four different form controls that were frequently used in mobile web applications, considering variation by task type and size of data set, in the context of two different touch-based smartphones, one running the iOS operating system and the other running the Android operating system. The form controls in the experiment were button, radio button/switch, spinner/picker, and text field. The task types were mutually exclusive selection, non-mutually exclusive selection, and set-a-value. Set sizes were designated as either small set size for less than 5 items or large set size for more than 5 items. There were 30 participants in the study. The task performance, accuracy, and satisfaction levels of the participants, as well as their perceived and actual performances, were recorded. The results showed that the button was fastest for small-set, mutually exclusive tasks while the spinner was fastest for large sets. In addition, it was found that the radio button/switch was the most effective controller for the non-mutually exclusive tasks. Based on the results of the experiment and analysis of the existing literature, five key issues for these form controls were identified to guide application developers in mobile form design: visibility, data set size, task type, users' experience level with a system, and screen layout.

Opioid Overdose Ambulance Runs: How Wisconsin Uses Free Text Data

Objective: 1. Develop an understanding of the benefits and challenges of analyzing free text fields on a population level.2. Observe how a complex surveillance definition can be created from free text fields.3. Observe how an ambulance data system can be used to describe the opioid epidemic.Introduction: In 2016, twelve states received Center for Disease Control and Prevention (CDC) Enhanced State Opioid Overdose Surveillance grants. The purpose of the grant is to explore enhanced data sources to track nonfatal opioid overdoses. One data source is ambulance runs. Wisconsin collects ambulance run information within the Wisconsin Ambulance Runs Data System (WARDS). Around 84% of all Wisconsin administrative services report into this electronic system. This is a timely, robust data system that has not been used previously to examine drug overdoses and presents an analytical challenge as it contains many free text fields.Methods: Wisconsin’s ambulance data system is robust, well-populated, and includes the majority of Emergency Medical Services (EMS) within the state. The analytic challenge with this data is that most of the reported fields are free text, which can be difficult to analyze on a population level. Wisconsin created a case definition using SAS regular expressions to take advantage of the free text fields. A combination of fields (chief complaint, secondary complaint, medications given, and the EMS narrative) were used to determine if the ambulance run was due to an opioid overdose. It was necessary to create a definition that used a diverse combination of phrases as free text fields are prone to spelling errors and there are many phrases used to identify opioid overdoses. It was also necessary to create a definition for unwanted phrases that signal a false positive, for example, “withdrawal”.Results: Wisconsin’s opioid definition uses regular expressions to search for the words “heroin”, “opioid”, “narcan”, or “methadone” (including various spellings). The overdose definition searches for words and phrases like “drug abuse”, “drug use”, “poisoning”, “drug ingestion”, and “overdose”. The medication administered fields are examined for “narcan”. In Wisconsin, the medication is listed every time it is used, so it is possible to determine the number of times Narcan was administered to a single person as well as how many ambulance runs used at least one dose of Narcan. False positives are identified with words and phrases like “withdrawal”, “detox”, and if Narcan was given but there is no indication that the ambulance run was due to drugs. From January 2016 – June 2017, Wisconsin had over 917,000 ambulance runs for people aged 11 years and older. We excluded non-emergency ambulance runs, like medical transports, and so our final denominator was 627,536 runs (32% of all runs were classified as non-emergencies). Suspected opioid overdoses were determined to be 1% of emergency ambulance runs. Narcan was administered in a total of 5,900 runs and the false positive flag picked up 10,399 runs that may not have been due to suspected opioid overdoses. Applying all of these components together, it was determined that in Wisconsin from January 2016 – June 2017, there were 4,041 emergency ambulance runs due to suspected, unintentional opioid overdoses for people 11 years and older (rate of 6 per 1,000 people).Conclusions: The use of regular expressions enables Wisconsin to extend analyses to data systems that contain robust information within free text fields. Within Wisconsin, this has been utilized to enhance opioid overdose surveillance with the use of a rapid data system previously not examined. Ambulance run information is a valuable resource to Wisconsin with the opioid epidemic. By creating case definitions with free text fields, we can quantify ambulance runs on a population level and create linkable analytic data sets to provide a more complete picture of the health of Wisconsin.

Evaluation of Syndromic Surveillance in Detecting Hepatitis A in Los Angeles County

ObjectiveTo create a hepatitis A virus (HAV) syndrome category with which to monitor emergency department (ED) visits for situational awareness during a currently emerging Hepatitis A community outbreak in Los Angeles County (LAC), and to evaluate its usefulness.IntroductionIn early 2017, HAV outbreaks were identified in San Diego County (490 cases)1 and Santa Cruz County (73 cases)2 in California, affecting primarily the homeless and/or illicit drug users. As of October 10, 2017, LAC had identified 12 outbreak-related HAV cases. Due to LAC’s proximity to San Diego County, and its own large homeless population, the syndromic surveillance team of the LAC Department of Public Health created a syndrome category and began querying its ED data to monitor for any increase in HAV-related visits.MethodsED data from 1/1/2017 to 10/10/2017 (CDC weeks 1-41) from all participating syndromic EDs in LAC were queried for patients who reported symptoms and signs of HAV. For comparison, ED data from calendar year 2016 was also queried. The query consisted of key word searches primarily within the chief complaint field, and, if available, the diagnosis and triage note fields. Based on the Centers for Disease Control and Prevention (CDC) clinical description of hepatitis A3, the HAV syndrome category was defined as: Jaundice (or elevated liver function tests) AND nausea or vomiting. Any ED visit that mentioned a diagnosis of hepatitis A also met the syndrome criteria. The resulting line lists were reviewed, and the query was periodically refined to exclude visits unrelated to hepatitis A (e.g., previous history of or vaccination for hepatitis A, other forms of hepatitis, and neonatal jaundice). The syndromic system was also queried for records that matched the 12 LAC cases by hospital and admission date. In addition, the chief complaint, diagnosis, and triage note fields were reviewed for any mention of homelessness or intravenous drug use.ResultsThe syndromic system detected 158 ED patients meeting the HAV syndrome category criteria. Of these, 12.7% had a diagnosis of HAV, 53.8% had jaundice, 36.7% had elevated liver enzymes, 65.2% had nausea, and 65.8% had vomiting. In 2016, 170 ED patients who met the syndrome criteria were detected: 8.2% had a diagnosis of HAV, 64.1% had jaundice, 32.4% had elevated liver enzymes, 63.5% had nausea, and 71.2% had vomiting. In both years, no indications of homelessness or IDU were found.Of the 12 LAC confirmed HAV outbreak-related cases, three did not go to a hospital, and thus did not have any syndromic data. Two went to non-participating syndromic EDs, but medical chart review showed that they would not have met the syndrome criteria. Of the remaining seven, all went to a participating syndromic ED, and three met the syndrome criteria, but none had any mention of homelessness or IDU.ConclusionsNo major change was seen in the trend of HAV syndrome visits in 2017 compared to 2016 (Figure 1). One of the challenges in monitoring HAV incidence is that the clinical signs and symptoms are very general, and may be shared with many other conditions. An emerging outbreak may not be detected above background levels unless the increase in ED patients with HAV is large, or consolidated over time. Variability in data quality in the free text fields such as chief complaint and triage notes may be problematic. Cases will be missed if data fields are not fully and accurately documented, and also if patients didn’t go to a participating syndromic hospital, or to a hospital at all. Though many syndromic hospitals now report diagnosis information, such information may be delayed, waiting for lab results. Using a stricter syndrome definition, such as requiring a specific diagnosis of HAV, may result in underreporting, but may provide a more accurate baseline for detecting increases and monitoring trends.While the query relied primarily on ED chief complaint, diagnosis and triage notes also proved useful in detecting HAV syndrome visits. None of the confirmed HAV cases that were known to be homeless had any mention of homelessness. The lack of any records indicating homelessness or IDU may indicate that these conditions are not currently reliably captured in the syndromic extract of ED patient records.LAC will continue to monitor for increases in HAV syndrome ED visits as a surrogate measure of HAV spread in the community. Syndromic surveillance, despite its limitations, remains a valuable complement to electronic lab reporting and other traditional reporting mechanisms.

Tracking drug-related overdoses at the local level: Using Syndromic Surveillance in the CO-NCR

Objective: In order to better describe local drug-related overdoses, we developed a novel syndromic case definition using discharge diagnosis codes from emergency department data in the Colorado North Central Region (CO-NCR). Secondarily, we used free text fields to understand the use of unspecified diagnosis fields.Introduction: The United States is in the midst of a drug crisis; drug-related overdoses are the leading cause of unintentional death in the country. In Colorado the rate of fatal drug overdose increased 68% from 2002-2014 (9.7 deaths per 100,000 to 16.3 per 100,000, respectively)1, and non-fatal overdose also increased during this time period (23% increase in emergency department visits since 2011)2. The CDC’s National Syndromic Surveillance Program (NSSP) provides near-real time monitoring of emergency department (ED) events across the country, with information uploaded daily on patient demographics, chief complaint for visit, diagnosis codes, triage notes, and more. Colorado North Central Region (CO-NCR) receives data for 4 local public health agencies from 25 hospitals across Adams, Arapahoe, Boulder, Denver, Douglas, and Jefferson Counties.Access to local syndromic data in near-real time provides valuable information for local public health program planning, policy, and evaluation efforts. However, use of these data also comes with many challenges. For example, we learned from key informant interviews with ED staff in Boulder and Denver counties, about concern with the accuracy and specificity of drug-related diagnosis codes, specifically for opioid-related overdoses.Methods: Boulder County Public Health (BCPH) and Denver Public Health (DPH) developed a query in Early Notification of Community Based Epidemics (ESSENCE) using ICD-10-CM codes to identify cases of drug-related overdose [T36-T51] from October 2016 to September 2017. The Case definition included unintentional, self-harm, assault and undetermined poisonings, but did not include cases coded as adverse effects or underdosing of medication. Cases identified in the query were stratified by demographic factors (i.e., gender and age) and substance used in poisoning. The first diagnosis code in the file was considered the primary diagnosis. Chief complaint and triage note fields were examined to further describe unspecified cases and to describe how patients present to emergency departments in the CO-NCR. We also explored whether detection of drug overdose visits captured by discharge diagnosis data varied by patient sex, age, or county.Results: The query identified 2,366 drug-related overdoses in the CO-NCR. The prevalence of drug overdoses differed across age groups. The detection of drug overdoses was highest among our youth and young adult populations; 16 to 20 year olds (16.0%), 21-25 year olds (11.4%), 26-30 year olds (11.4%). Females comprised 56.1% of probable general drug overdoses. The majority of primary diagnoses (31.0%) included poisonings related to diuretics and other unspecified drugs (T50), narcotics (T40) (12.6%), or non-opioid analgesics (T39) (7.8%). For some cases with unspecified drug overdose codes there was additional information about drugs used and narcan administration found in the triage notes and chief complaint fields.Conclusions: Syndromic surveillance offers the opportunity to capture drug-related overdose data in near-real time. We found variation in drug-related overdose by demographic groups. Unspecified drug overdose codes are extremely common, which likely negatively impacts the quality of drug-specific surveillance. Leveraging chief complaint and triage notes could improve our understanding of factors involved in drug-related overdose with limitations in discharge diagnosis. Chart reviews and access to more fields from the ED electronic health record could improve local drug surveillance.

Bias, Bullshit and Lies: Audience Perspectives on Low Trust in the Media

Even in a world where people increasingly get news from social media, the professional news media is still seen as largely to blame for low trust according to a new report from the Reuters Institute for the Study of Journalism, which examines the underlying reasons for trust and distrust in the news media (and in social media) across nine countries. Bias, spin and hidden agendas come across as the main reasons for lack of trust in the news media along with a perceived decline in journalistic standards driven by greater competition and some online business models. These concerns are strongest with the young and with those on low incomes. Trust in the news that people find in social media is lower still, but similar trends are at play - bias, agendas and low quality information. The report argues that this is largely a function of a model that allows anybody to publish without checks, and algorithms that sometimes favour extreme or contentious content. The study is based on analysing thousands of open-ended responses from the 2017 Reuters Institute Digital News Report, where respondents were asked to give their reasons for low trust in their own words, using open-ended text fields. By coding and analysing responses, the report categorises the specific issues that are driving public concern across countries as well as those that build trust such as journalistic processes, strong brands and quality journalism delivered over time.

Restoration of an academic historical gross pathology collection-refreshed impact on current medical teaching?

The declaration of Leiden pronounces the demand to conserve pathological-anatomical collections as cultural heritage. Likewise, the Institute of Pathology of the Friedrich-Alexander-University Erlangen-Nuremberg owns macroscopic pathological-anatomical specimens reaching back over 150years. The purpose of this work is to examine the impact, meaning, and perception of such historical preparations during the current medical curriculum. Additionally, the experiences from the renovation process can be used as a template for other institutes. All preparations were documented, photographed, and catalogued in an electronic database. During a restoration period, a series of didactically suitable specimens were professionally restored. Hereby, the help of a special course of interested students was admitted. In a second step, the specimens were integrated into the regular teaching of students in macroscopic pathology. An evaluation was carried out on two student cohorts with and without historical specimens by means of a questionnaire with 23 items and two free text fields. In total, 1261 specimens were registered covering diseases from almost the complete human body with a strong representation of the cardiovascular, urinary, gastrointestinal, and central nervous systems. Hereby, exceptional rare and untreated cases with medical relevance could be found and stepwise implemented into the curriculum. The student evaluation positively addressed that the courses became livelier and interactive. Furthermore, a more comprehensive overview and a better understanding of the macroscopic pathology were appreciated. However, more self-study time with the specimen was demanded. The authenticity of historical specimens contrasts with the tendency to carry out virtual "online" didactic methods. The stereoscopic view on often untreated and, therefore, unbiased cases enhances a skill-oriented deeper understanding of diseases. In conclusion, historical specimens regain interest and even didactic value, especially in an era of declining autopsy rates.

P091: Emergency Critical Care Ultrasound (ECCU) paramedical course: a novel curriculum for training paramedics in ultrasound

Introduction: Ultrasonography (US), performed in the Emergency Department (ED) by Emergency Physicians, is well established. Educational studies have shown some promise in training paramedics in US use. We have developed and piloted a novel curriculum for paramedic US education. Methods: Based on an informal needs assessment, an US curriculum for paramedics was developed to include: Basic principles, Focused assessment with sonography for trauma (FAST), cardiac, and vascular access. Participants included ED-based and pre-hospital paramedics including all paramedics with critical care training who routinely perform vascular access and procedural sedation within our ED. Comparisons were made using paired non-parametric tests (GraphPad). Results: Participants (N=9) were provided pre- reading materials prior to completing a 6-hour course, consisting of a mix of didactic and practical sessions with live models and vascular access phantoms. Each module was introduced with a 30 minute didactic session, led by an Emergency Physician trained in US, followed immediately by a 1 hour hands-on session lead by either an Emergency Physician or an Emergency Medicine Resident at a learner to instructor ratio of 3:1. At the end of the course, participants were asked to complete a short 10 minute survey that included (1) an assessment of the course quality with regard to preparatory material and course content/delivery (4 point Likert scale; excellent, good, fair, poor); (2) self reported US knowledge pre and post course on a scale of 1-10 (10 high, 1 low); (3) general yes/no questions related to the future of ECCU paramedical and (4) a subjective written section for additional comments. All participants rated the content favourably: 97% scoring it as excellent, and 3% as good. The participants median self-reported US knowledge score increased from 2/10 (IQR 2-3) to 8/10 (IQR 7.25-8; p=0.009) post- course. All comments from the text field were positive in nature. Conclusion: We report a paramedic US course curriculum, which when piloted resulted in high learner satisfaction and a high rate of self reported improvement in US knowledge. Further study will include an assessment of knowledge acquisition and practical performance. Future modifications in our curriculum will be based on needs assessment and may include additional modules.

Surgery and out-patient data collection and reporting using Filemaker Pro.

The application of an electronic database in clinical practice is used widespread in every field of medicine. The aim of the present study is to illustrate our experience to use a database software for documentation of two of our clinical activities, outpatient hysteroscopy and inpatient gynaecological surgery. In 2004, we designed two databases, the first one to document surgical procedures in the operating theatre, the second to document outpatient hysteroscopy procedures using FileMaker v.8.5. The data entry interface contains free text fields for patient demographic data and the description of the surgical procedure, supplemented by drop-down lists for items such as clinical findings, procedures, instrumentation, technique, and complications. Copies were filed in the main hospital notes, sent to General Practitioners, and also given to our patients. Since August 2004, we have used our two databases to document 2766 gynaecological operations and 3777 outpatient hysteroscopies. All users particularly liked the dropdown lists as their use greatly reduced the time taken to enter each patient's data. The databases were regularly used to select patients for audit projects and research data collection for prospective studies. FileMaker is an user-friendly and easily configured software, extremely valuable in everyday clinical work.

P094: A computerized provider order entry strategy to improve the quality of clinical information on neuroimaging requisitions from the emergency department: an interim analysis

Introduction: Clinical context is critical for accurate radiologic interpretation of neuroimaging investigations. The aim of this study was to determine the impact of a change in the Emergency Department (ED) computerized provider order entry (CPOE) interface on the quality of clinical information conveyed in ED neuroimaging requisitions for suspected stroke patients. Methods: Four local EDs utilizing a common CPOE ED Stroke order set were studied before and after the introduction of a mandatory blank free text field requiring clinical information for the radiologist before a computed tomography angiography (CTA) request could be submitted. Prior to this modification, the indication (acute stroke) was pre-filled in the CTA request for convenience with the option of providing additional information at the discretion of the ordering physician. ED physicians were informed of the change as well as the rationale for its implementation. A retrospective pre (90 days) post (30 days) analysis was conducted across four local EDs to evaluate the impact of the CPOE user interface change on the quality of clinical information provided on neuroimaging orders. Patients aged 18 with CTA head and/or neck orders submitted from the order set were included. Patients were excluded if the CTA order was submitted outside of the ED Stroke order set, if order entry was by non-physician personnel, or if the order was modified by the diagnostic imaging department after ED submission. Clinical information from CTA orders were scored as complete, partial, or absent/uninformative based on a standardized rubric of critical elements, including: description of neurological deficit(s), lateralization, and timing of symptom onset or duration. Results were analyzed using chi square analysis. Results: Pre-implementation data from Oct 1, 2015 Jan 1, 2016 (N=652) was compared to post-implementation data from Nov 1 30, 2016 (N=227). The proportion of complete, partial, and absent/uninformative clinical histories were: 45.3%, 31.4%, and 23.3% in the pre-implementation period and 62.6%, 37.4%, and 0% in the post-implementation period respectively. There was a 38.2% relative increase in complete clinical histories, a 19.1% relative increase in partial clinical histories, and a 100% reduction in absent/uninformative clinical histories (p<0.001). Conclusion: The introduction of a mandatory free text field significantly increased the overall quality of clinical information provided on ED neuroimaging orders. This CPOE strategy has the potential to improve diagnostic accuracy and reduce unnecessary delays to imaging interpretation caused by lack of clinical information.

СТРУКТУРНО-СЕМАНТИЧЕСКАЯ ТИПОЛОГИЗАЦИЯ ПРОИЗНЕСЁННОЙ НЕСОБСТВЕННО-ПРЯМОЙ РЕЧИ НА МАТЕРИАЛЕ ПРОЗЫ МАРИНЫ ЦВЕТАЕВОЙ

Статья посвящена теме чужой речи, которая остаётся актуальной в современном языкознании. Автор статьи исследует особенности функционирования произнесённой (внешней) несобственно-прямой речи (далее НПР) (а именно её структурно-семантических видов) на материале прозаических текстов Марины Цветаевой. Посредством произнесённой НПР отображается прямая речь литературных персонажей через авторскую перспективу, при этом фактическая речь персонажа в авторской речи иногда принимает форму прямой речи, либо маркируется за счёт использования кавычек, редко – двоеточия. Опираясь на структурно-семантическую классификацию, предложенную украинским лингвистом Е.Я. Кусько, автор статьи описывает отличительные черты различных видов произнесённой несобственно-прямой речи: тематической речи, скрытой речи, речи в речи, коллективной речи, цитатной речи. Делается вывод, что последняя разновидность внешней НПР находит наибольшее применение в прозаических текстах выдающегося русского поэта ХХ века, а произнесённая НПР используется М.И. Цветаевой очень редко, что связано со спецификой идиостиля поэта.Целью работы является описание характерных черт функционирования НПР, выявление механизмов реализации и маркеров выделяемых видов произнесённой НПР в прозаических текстах поэта.Метод или методология проведения работы. Основу исследования образует метод сплошной выборки иллюстративного материала, метод нарратологического анализа, метод семантического структурирования художественного текста, прием стилистической интерпретации.Результаты. В результате проведённого анализа теоретического и практического материала автор приходит к выводу о том, что в прозаических текстах М.И. Цветаева прибегает к трём разновидностям произнесённой НПР, а именно цитатной речи, реже речи в речи и коллективной речи. В качестве цитатной речи в прозе Цветаевой используется дословное цитирование, когда в структуре авторского повествования дословно цитируется речь литературных персонажей (в виде отдельных слов или выражений); и дословное или видоизменённое цитирование речи известных лиц.Область применения результатов. Результаты исследования могут быть применены исследователями творчества Марины Цветаевой, студентами филологических отделений при изучении таких дисциплин, как стилистика русского языка, синтаксис русского языка.

Tree-Related Injuries Associated With Response and Recovery From Hurricane Sandy, New Jersey, 2011-2014.

Extreme weather events require extensive tree removal and disposal, tasks associated with severe injury risks among workers and residents. To help understand the risks of such activities, we evaluated the impact of a large and destructive storm (Hurricane Sandy in 2012) on the incidence of tree-related injuries. We searched chief-complaint text fields for patients aged 18-65 from 2011-2014 emergency department visit records submitted by New Jersey hospitals through the state-based syndromic surveillance system. Tree-related keywords (eg, saw, branch, wood chip, woodchip, tree) identified possible injuries that we then reviewed to exclude unrelated cases and classify mechanisms of tree-related injury. We used Poisson regression analysis to evaluate changes in the rates of probable tree-related injuries, adjusting for total emergency department visits and seasonal variation. We identified 698 probable tree-related injuries from 2011-2014 among patients aged 18-65, including 104 (14.9%) falls, 241 (34.5%) machine-related injuries, 311 (44.6%) struck-by injuries, and 42 (6.0%) other tree-related injuries. Tree-related injuries increased significantly in the quarter immediately after Hurricane Sandy (November 2012-January 2013) compared with the same quarter the year before (rate ratio [RR] = 1.67; 95% confidence interval [CI], 1.13-2.47) and the year after (RR = 2.47; 95% CI, 1.62-3.78) Hurricane Sandy, especially for struck-by injuries compared with the year before (RR = 2.74; 95% CI, 1.47-5.12) and the year after (RR = 4.17; 95% CI, 2.09-8.32) Hurricane Sandy. More than one-third of the injuries (33.4%) involved chainsaws. A major hurricane was associated with an increase in tree-related injuries in emergency departments, especially for mechanisms consistent with handling downed and damaged trees. Further research should confirm these findings and evaluate opportunities for preventing tree-related injuries.

Developing program objectives in data analytic field of study using three frameworks

This paper presents a methodology and results in developing program as well as course objectives to be included in curriculum proposals. This study provide a literature review and uses steps of keyword indexing, thematic analysis, as well as the popular Bloom’s taxonomy field of text in effort to phrase program objectives that follow standards set for this purpose. A set of program objectives for a program in Data Analytics derived by this methodology is presented in the conclusions.

When Communication Meets Computation: Opportunities, Challenges, and Pitfalls in Computational Communication Science

ABSTRACTThe recent increase in digitally available data, tools, and processing power is fostering the use of computational methods to the study of communication. This special issue discusses the validity of using big data in communication science and showcases a number of new methods and applications in the fields of text and network analysis. Computational methods have the potential to greatly enhance the scientific study of communication because they allow us to move towards collaborative large-N studies of actual behavior in its social context. This requires us to develop new skills and infrastructure and meet the challenges of open, valid, reliable, and ethical “big data” research. By bringing together a number of leading scholars in one issue, we contribute to the increasing development and adaptation of computational methods in communication science.

An enhancement on Clinical Data Analytics Language (CliniDAL) by integration of free text concept search

Much of the important patient information can only be found in patient narratives or in free text fields of structural schema of the Clinical Information System (CIS). So, the integration of free text search facilities will improve question answering on CISs. This paper describes a method for integrating free text search facility to the proposed Data Analytics Language (CliniDAL) to improve its capabilities at answering more common clinical questions. The proposed language constructs in CliniDAL’s grammar enables its parser to recognize the part of the Restricted Natural Language Query (RNLQ) of the CliniDAL interface, which needs a free text resolution mechanism. Then the Natural Language Processing (NLP) approach of the CliniSearch tool finds the correct matches with the query. The search result is integrated into the translated CliniDAL query which can be executed to return a more comprehensive answer to the initial text query. 160 queries are tested in the current work to investigate the improvements on answering more common questions from a CIS, which result in a simple taxonomy of four query categories of: unanswerable queries, queries that require more evidence to be answered, queries requiring user interpretation and queries with suitable answers. Compatibility of query results between the structural schema and patient progress notes is examined which showed the usability of the approach in answering queries, confirming the results from different sources and finding any inconsistency in the stored data in the CIS. The proposed solution provides a simple mechanism for extracting knowledge from CISs.

Workarounds to Intended Use of Health Information Technology: A Narrative Review of the Human Factors Engineering Literature.

Objective To integrate and synthesize insights from recent studies of workarounds to the intended use of health information technology (HIT) by health care professionals. Background Systems are safest when the documentation of how work is done in policies and procedures closely matches what people actually do when they are working. Proactively identifying and managing workarounds to the intended use of technology, including deviations from expected workflows, can improve system safety. Method A narrative review of studies of workarounds with HIT was conducted to identify themes in the literature. Results Three themes were identified: (1) Users circumvented new additional steps in the workflow when using HIT, (2) interdisciplinary team members communicated via HIT in text fields that were intended for other purposes, and (3) locally developed paper-based and manual whiteboard systems were used instead of HIT to support situation awareness of individuals and groups; an example of a locally developed system was handwritten notes about a patient on a piece of paper folded up and carried in a nurse's pocket. Conclusion Workarounds were employed to avoid changes to workflow, enable interdisciplinary communication, coordinate activities, and have real-time portable access to summarized and synthesized information. Application Implications for practice include providing summary overview displays, explicitly supporting role-based communication and coordination through HIT, and reducing the risk to reputation due to electronic monitoring of individual performance.

Physicians’ perceptions of the impact of the EHR on the collection and retrieval of psychosocial information in outpatient diabetes care

BackgroundPsychosocial information informs clinical decisions by providing crucial context for patients’ barriers to recommended self-care; this is especially important in outpatient diabetes care because outcomes are largely dependent upon self-care behavior. Little is known about provider perceptions of use of psychosocial information. Further, while EHRs have dramatically changed how providers interact with patient health information, the EHRs’ role in collection and retrieval of psychosocial information is not understood. MethodsWe designed a qualitative study. We used semi-structured interviews to investigate physicians’ (N = 17) perspectives on the impact of EHR for psychosocial information use for outpatient Type II diabetes care decisions. We selected the constant comparative method to analyze the data. FindingsPsychosocial information is perceived as dissimilar from other clinical information such as HbA1c and prescribed medications. Its narrative form conveys the patient’s story, which elucidates barriers to following self-care recommendations. The narrative is abstract, and requires interpretation of patterns. Psychosocial information is also circumstantial; hence, the patients’ context determines influence on self-care. Furthermore, EHRs can impair the collection of psychosocial information because the designs of EHR tools make it difficult to document, search for, and retrieve it. Templates do not enable users from collecting the patient’s ‘story’, and using free text fields is time consuming. Providers therefore had low use of, and confidence in, the accuracy of psychosocial information in the EHR. Principal conclusionsWorkflows and EHR tools should be re-designed to better support psychosocial information collection and retrieval. Tools should enable recording and summarization of the patient’s story, and the rationale for treatment decisions.

A model of interprofessional problem-based learning for medical and nursing students: Implementation, evaluation and implications for future implementation.

Objective: In Germany there is little evidence of interprofessional problem-based learning (PBL) to date. For this reason a corresponding course for medical and nursing students was implemented. The goal was to analyse the feasibility and usefulness from the point of view of the students.Method: For the purposes of evaluation a quantitative observational study was conducted with a pre-course survey, a teaching evaluation and a post-course survey. The pre- and post-course surveys took place before the commencement of and after the conclusion of learning. The teaching evaluation was carried out after the conclusion of the interprofessional course. In total there were five medical students and five nursing students who took part. The descriptive data analysis was carried out using the statistics software package SPSS. The data from the open text fields was transcribed and evaluated using qualitative content analysis.Results: The students’ assessment of the interprofessional PBL was predominantly positive. They stated that they were able to expand their knowledge of the roles of the other profession, that the interprofessional course had a positive effect in terms of mutual appreciation and that the tutor had a positive influence on the interprofessional PBL due to his cooperative learning support. Suggestions for improvement were concerned with the expansion of the interprofessional exchange and the establishment of a set timetable for the tutorials.Conclusion: The first study results of the Bildungscluster study can be used to make adjustments to the interprofessional PBL in order to be able to implement the course with a greater number of participants. Further studies are needed in order to comprehensively examine the learning effects and the impact on daily practice.

New document scoring model based on interval tree

Classical BM25 scoring is designed for unstructured documents. In the past years, people try to adapt the BM25 ranking formula to deal with structured documents. Most works on structured document retrieval treat the combination of field scores, but it is hard to determine the field weights before the formation of document score. We aim to establish a new method to sort the field weights. The motivation comes from two aspects. On the one hand, the construction of interval tree reflects retrieval results with higher-order proximity for a text field. According to writing style, the important sentence or phrase for representing main idea frequently appear in the front or the rear part of a text-field. Therefore, the proximity scoring for different part in a text-field should be different. We thus take higher factor for calculating proximity scoring in the front and the rear parts than in the middle part. On the other hand, the more the interval length includes inquiring terms, the less the proximity scoring is, thereby the higher tf value for term appearing in an interval should affect the computation of proximity scoring. Therefore, we develop a new method for calculating the field weights based on the ranking score. The ranking score for each field can be calculated by interval tree based on terms relevance. Interval tree can be viewed as a tool of higher terms proximity in text visualization. This new field weights reflect the terms proximity and can be used to calculate document scoring for terms retrieval. Experimental results show that the new document scoring model well reflects the terms proximity, and the new document scoring scheme ScoreComp, combined with interval scoring, is more sensitive than scheme FreqComp combined with interval scoring.

A Supervised Bug Report Classification with Incorporate and Textual field Knowledge

Performance of the bug prediction model is directly depends on the misclassification of bug reports. Misclassification issue surely scarifies the accuracy of the system. To resolve this issue the manual examination of bug reports are required, but it is very time consuming and tedious job for a developer and tester. In this paper the hybrid approach of merging text mining, natural language processing and machine learning techniques is used to identify bug report as bug or non-bug. The four incorporates fields with textual fields are added to bug reports to improve the performance of classifier. TF-IDF and Bigram feature extraction methods are used with feature selection and K-nearest neighbor (K-NN) classifier. The performance of the proposed system is evaluated by using Precision, Recall and F-measure by using five datasets. It is observed that the performance of K-NN classifier is changed according to the dataset and addition of bigram method improve the performance of classifier.

К ПРОБЛЕМЕ ДАТИРОВКИ ПОЗДНИХ ОЙРАТСКИХ ТЕКСТОВ НА ОСНОВЕ ПАЛЕОГРАФИЧЕСКОГО И ГРАФИКО-ОРФОГРАФИЧЕСКОГО ОПИСАНИЯ (ПО АРХИВНЫМ МАТЕРИАЛАМ РЕСПУБЛИКИ КАЛМЫКИЯ)

The article deals with the problem of dating some late Oirat texts (lists, copies) compiked by Kalmyk Buddhist clerics. In terms of dating, researchers first turn to the data contained in colophons (afterwords) of texts. In addition, one can resort to various indirect data relevant to the original text (field labels, notes on margins, indication of the time of recitation, lists of family members, etc.), as well as those related to the storage of copies in archival funds - the time of receipt, the name of the description compiler, inventory number, etc. The time when Zaya Pandita and his closest disciples and followers created translations of well-known Buddhist works can be well determined to a certain extent, chronological frameworks can be identified. But when it comes to later texts - with the vague circumstances of their creation, conditions of their storage, absence of any related dates - those are very difficult to date. One more means to cope with the task can be analysis of their linguistic characteristics (lexical, graphic, orthographic ones, etc.).