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Clinical paper searching is a major task for clinical researchers to collect authoritative and up-to-date evidences to support their research works and clinical practices. Currently, this task needs huge amount of labor work. Researchers usually spend a lot of time searching on the online repository and iterating many times to get the final paper list. Systematic review is a special case, in which the paper searching process is a critical step. To address this challenge, this paper introduces a method to streamline the iterative paper searching process. It automatically selects the most probably matched papers, and then generates new search strategy. All the intermediate results are visualized based on the paper citation graph. It assembles technologies such as Page Rank and Topic-based clustering to accelerate the paper searching tasks. The precision, recall, and execution time of the proposed method are then evaluated by comparing with published systematic reviews [ABSTRACT FROM AUTHOR]

This protocol paper explores the initiative to build and implement a National Trial Overview to make clinical trials more accessible to patients and health professionals in Denmark. The paper address how a user-centered evaluation of the platform will be conducted and how the National Trial Overview can contribute to enhancing diversity, equity, and inclusion of patients and health professionals, and make access to clinical trials more patient-centered. [ABSTRACT FROM AUTHOR]

The research field of this paper is an analysis of the application of modern information technologies in e-health. The application of web, mobile and Internet of things technologies in the field of ehealth will be analyzed. Development of e-health applications enables the provision of health services and expert assistance to patients in remote locations. The paper presents the model of ehealth based on Internet of things. A web application has been developed to allow patients greater access to health services, providing expert assistance, advice and participation in the treatment process, clinical trials and early diagnosis of the disease. [ABSTRACT FROM AUTHOR]

Nurses are a vital resource of health care systems, a resource that must be valued, bearing in mind that their work has a role in social indicators of health, and also determines topics such as equality and equity, fairness and justice, which support the kind of societies we all want to live in. In the training process, they learn, in the middle of a team, to plan, to put into practice and evaluate general medical care based on acquired knowledge and skills. Future nurses must not only be prepared to meet the needs of the individual, the family, the community, but also to have confidence in their own intellectual and clinical abilities so that they can work responsibly and autonomously, facing at the same time with increasingly complex requirements of healthcare. The refinement of medical training is subsequently achieved by developing the ability to solve problems, to make decisions, to practice leadership in medical care. The acquisition of leadership skills has been identified as a need in training nurses, both from the researches of internal and international professional bodies and from those of clinical units. By harmonizing this need with public educational policies, the unit of learning outcomes "Leadership and professional communication" was implemented in the professional training standard. This paper aims to gain insight into the relationship between the values of leadership and the development of a good educational policy in training nurses, with a fundamental impact on the provision of health care in an efficient, integrated and safe manner for the patient. [ABSTRACT FROM AUTHOR]

Natural Language Processing (NLP) has been adopted widely in clinical trial matching for its ability to process unstructured text that is often found in electronic health records. Despite the rise in the new tools that use NLP to match patients to eligible clinical trials, the comparison of these tools is difficult due to the lack of consistency in how these tools are evaluated. The ground truth or reference that the tools use to assess results varies, making it difficult to compare the robustness of the tools against each other. This paper alarms the lack of definition and consistency of ground truth data used to evaluate such tools and suggests two ways to define a gold standard for the ground truth in small and large-scale studies. [ABSTRACT FROM AUTHOR]

The semi-automatic evaluation of eligibility criteria can facilitate the recruitment for clinical trials, timely completion of studies and generation of clinical evidence about new approaches to treatment, prevention and diagnosis. Because eligibility criteria are represented as free text, automatically extracting their meaning and evaluating them for a particular patient is challenging. This paper presents our approach to the problem of automatic interpretation of criteria meaning. It is based on detecting in text semantic entities (diseases, treatment, measurements etc.) using ontology annotators and semantic taggers, and detecting predefined patterns providing the contextual information in which these entities occur. Evaluation of the approach is the main subject of the paper. It covers several aspects: precision and recall of the pattern detection algorithm and the assessment of the implications of using the identified patterns to find potential candidates. It was performed manually using a subset of patterns and randomly selected 33 trials from ClinicalTrials.gov. The average precision and recall of pattern detection algorithm calculated for selected patterns is 0.9 and 0.91, meaning that in most cases using the patterns can lead to correct interpretation of criteria and can support patient recruitment. [ABSTRACT FROM PUBLISHER]

There are many serious and acute physiological conditions about which we have incomplete medical knowledge. To address this and develop effective treatments it is often the case that a wealth of clinical data is required for collection, analysis and feedback. Whilst such data often exists it is typically held in a variety of different formats and locations. This paper describes the EU FP7-funded Avert-IT project (www.avert-it.org), which has developed an integrated, real-time physiological data infrastructure (ICUnet) to address the specific issue of prediction of hypotensive events in the brain trauma domain. This system has been used to support a major multi-centre clinical trial. In this paper, the implementation and application of the ICUnet system is described, followed by the design and results of the clinical trial. [ABSTRACT FROM PUBLISHER]

The Australian Health Information Workforce is a critical discipline in the health sector as the investment in digital technologies increases. Yet historically there was no standardized reporting about the workforce and its six professional areas: clinical coding, costing analysts, data analysts, health informaticians, health information managers and health librarians. This paper presents the findings from the inaugural Australian Health Information Workforce Census. Analysis of 1,596 responses indicates this is an aging (56.1% ≥45 years) workforce with a large (78.1%) female population. Working in permanent (82%), public hospital (72%) roles, in professional or managerial roles (84%). The majority (93.2%) of respondents hold a tertiary qualification in health information, one-quarter of these at masters or doctoral level. Fewer than 30% of respondents hold a health information credential from a professional or industry association. The data from the ongoing national census will inform workforce planning and enable forecasting of the future workforce needs. [ABSTRACT FROM AUTHOR]

Data-driven decision support, which refers to the reliance on knowledge Discovery and Data mining (KDD) via statistical, mathematical, and machine learning algorithms for abstractions has enhanced clinical and non-clinical decisions. Thus, this paper relies on Action Design Research (ADR) for designing, implementing, and understanding Information Technology (IT) artifacts for the risk of 30-days Unplanned Readmission (URA) of comorbid patients of diabetes from diverse cultural backgrounds. The analysis of 17933 patients records at the building, intervention, and evaluation phases of ADR showed a 30-days URA rate of 10.71% for all patients, 10.98% rate for Caucasians (non Black, Indigenous, & people of Colour (BIPOC)), 9.94% for AA-BIPOC – African Americans, and 9.63% for BIPOC- Asians, Hispanics, and other races. This study leads to a better clinical practice via targeted and reflective management of hospitalized comorbid patients of diabetes to forestall early URA. [ABSTRACT FROM AUTHOR]

Because of recent replacement of physical documents with electronic medical records (EMR), the importance of information processing in the medical field has increased. In light of this trend, we have been developing MedEx/J, which retrieves important Japanese language information from medical reports. MedEx/J executes two tasks simultaneously: (1) term extraction and (2) positive and negative event classification. We designate this approach as a one-scan approach, providing simplicity of systems and reasonable accuracy. MedEx/J performance on the two tasks is described herein:(1) term extraction (F β-1 = 0.87) and (2) positive-negative classification (F β-1= 0.63). This paper also presents discussion and explains remaining issues in the medical natural language processing field. [ABSTRACT FROM AUTHOR]

Recruitment of patients in clinical trials is nowadays preoccupying, as the inclusion rate is particularly low. The main identified factors are the multiplicity of open clinical trials, the high number and complexity of eligibility criteria, and the additional workload that a systematic search of the clinical trials a patient could be enrolled in for a physician. The principal objective of the ASTEC project is to automate the prescreening phase during multidisciplinary meetings (MDM). This paper presents the evaluation of a computerized recruitment support systems (CRSS) based on semantic web approach. The evaluation of the system was based on data collected retrospectively from a 6 month period of MDM in Urology and on 4 clinical trials of prostate cancer. The classification performance of the ASTEC system had a precision of 21%, recall of 93%, and an error rate equal to 37%. Missing data was the main issue encountered. The system was designed to be both scalable to other clinical domains and usable during MDM process. [ABSTRACT FROM AUTHOR]

This paper describes our experience in designing a clinical trial protocol and its registration in Clinical Trials Registry- India (CTRI). The aim of our trial is to assess efficacy of a newly developed indigenous, low cost functional electrical stimulator (FES) device for correcting foot drop in stroke survivors. We started with shortlisting the existing trials from the search result of different trial databases and published literatures. We picked those trials which are related to our research question and useful for designing our protocol. We compared our trial with shortlisted trials and explained the significance of various trial characteristics in designing a medical device trial. With this model study we intend to explain the processes involved in designing a clinical trial for devices from initiation of idea to registration in clinical trials registry. [ABSTRACT FROM PUBLISHER]

Biomarkers detectable by imaging methods are increasingly used as surrogate endpoints to establish the severity of a disease to evaluate the efficacy of a treatment method or chemotherapy drug in cancer clinical trials. In therapeutic cancer clinical trials, the tumors are measured from medical images such as CT or MR at a baseline time point and the treatment response assessment is followed-up on a frequent basis at subsequent time intervals. In this paper, we will try to evaluate to which degree the measurements from medical images are comparable for clinical trials by specifying sources of uncertainties of image measurements in order to support both clinical trial investigators by giving them confidence in their interpretation of the measurements results and implementers to follow the best-practice guidelines to be taken into consideration to minimize sources of such variability. [ABSTRACT FROM PUBLISHER]

The recruitment of human subjects for clinical trials research is a critically important step in the discovery of new cures for diseases. However, the current major recruitment methodologies are inherently inefficient. Considerable resources are expended in efforts to recruit adequate numbers of patient volunteers who meet the inclusion/exclusion criteria for clinical trials. Virtual worlds such as Second Life have great potential to help improve the clinical trial recruitment process and overcome some of the limitations. In this paper, we present a virtual depiction of clinical trials, called VirtualMindTrial. With an increasing digital lifestyle, this can enhance the efficiency and quality of recruitment of patients with psychiatric disorders for clinical research. The VirtualMindTrial system is developed through Second Life technologies as an alternative to the traditional recruitment model for clinical trials. The VirtualMindTrial system offers a simulation of clinical trials workflow and can educate potential participants with visualization of the processes involved. We believe such systems are particularly valuable in increasing self-enrollment of sufficient numbers of patient volunteers and enhancing recruitment for clinical trial studies for development of new drugs. [ABSTRACT FROM PUBLISHER]

Clinical trials are key and essential processes for researchers to develop new treatments as well as evaluate their effectiveness and safety, whilst more than half of all clinical trials experience delays, which leads to a considerable amount of cost. In this paper, we present a cost-effective framework to reduce the time and monetary cost in the stage of recruiting and screening eligible clinical trial participants. By leveraging patients’ observed conditions and the cost of medical examinations, the proposed framework uses collaborative filtering techniques to predict the utilized cost for the to-do medical examinations and then rank patients and medical examinations. The preliminary experiment results indicate that the framework is promising to reduce the cost spent on medical examinations by three quarters or even more and accelerate the recruitment process in the screening stage. [ABSTRACT FROM AUTHOR]

Clinical information in electronic health records (EHRs) is mostly unstructured. With the ever-increasing amount of information in patients' EHRs, manual extraction of clinical information for data reuse can be tedious and time-consuming without dedicated tools. In this paper, we present SmartCRF, a prototype to visualize, search and ease the extraction and structuration of information from EHRs stored in an i2b2 data warehouse. [ABSTRACT FROM AUTHOR]

Algorithm of diagnosis and treatment of patients with chronic hemorrhoids including any others steps: General Considerations, Clinical Findings, Differential Diagnosis, Complications, Treatment, Prognosis. Essentials of Diagnosis “Hemorrhoids”: a) rectal bleeding, protrusion, discomfort; b) mucoid discharge from rectum; c) possible secondary anemia; d) characteristic findings on external anal inspection and anoscopic examination. General Considerations for chronic hemorrhoids including investigations of external hemorrhoids and internal hemorrhoids. Clinical Findings including analysis of symptoms and signs on examination. Differential Diagnosis patients with chronic hemorrhoids in first time application with similar manifestation for rectal cancer and other diseases for anorectal zone. Complications of chronic hemorrhoids: bleeding, prolapse recti and acute phlebotrombosis. Treatment may be any other for concrete of studies chronic hemorrhoids: Firstdegree, Second-Degree, Third-Degree and Fourth-Degree. Concrete of treatment program may be for patients with complications: for bleeding, for prolapse and for acute phlebotrombosis. Treatment Program for patients with hemorrhoids may be: Medical Treatment with application medicament's drugs, Rubber Band Ligation, Hemorrhoidectomy with application traditional and any others modern methods for excision, Sclerotherapy and Cryosurgery with application more any others mini-invasive methods. Prognosis for patients with chronic hemorrhoids after medicaments or surgical treatment is favorable. [ABSTRACT FROM AUTHOR]

Health data collected in clinical trials and epidemiological as well as public health studies cannot be freely published, but are valuable datasets whose subsequent use is of high importance for health research. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) aims to promote the publication of such health data without compromising privacy. Based on existing international standards, NFDI4Health has established a generic information model for the description and preservation of high-level metadata describing health-related studies, covering both clinical and epidemiological studies. As an infrastructure for publishing such preservation metadata as well as more detailed representation information of study data (e.g. questionaries and data dictionaries), NFDI4Health has developed the German Central Health Study Hub. Content is either harvested from existing distributed sources or entered directly via a user interface. This metadata makes health studies more discoverable, and researchers can use the published metadata to evaluate the content of data collections, learn about access conditions and how and where to request data access. The goal of NFDI4Health is to establish interoperable and internationally accepted standards and processes for the publication of health data sets to make health data FAIR. [ABSTRACT FROM AUTHOR]

The Healthcare Supply Chain (HSC) plays a key role in the safety and quality of healthcare services. A growing awareness of the complexities of the HSC has highlighted the potential usefulness of digital systems to deliver efficiencies which may lead to improved clinical safety. The business benefits of digital transformation of the HSC have been identified however there has been no clear methodology proposed to the author’s knowledge to measure the clinical safety benefits. This study will examine the performance of a Clinical Safety Evaluation Model (CSEM) created to provide healthcare organisations a measure of clinical safety through digital transformation of their supply chain. The CSEM uses an adaptation of the DeLone and McLean Information Success Model blended with proven clinical safety criterion within the healthcare supply chain sphere. The study will provide measures on both the effectiveness of the model and the potential for use of the CSEM in industry application to improve clinical safety. [ABSTRACT FROM AUTHOR]

Doctoral dissertations and master's theses of "hepatitis B"(HB) in CNKI database were retrieved to explore the hot spots on HB research field in China, we processed, visualized the data above and analyzed those data by using co-word analysis informetrics method. Then, the figure for co-occurrence analysis of high-frequency keywords in theses on HB were plotted, which represented eight topics, could help health staff to understand hot topics in this field. [ABSTRACT FROM AUTHOR]

Having precisely analyzed the data from patients with specific diseases, we could obtain not only the patterns and knowledge of these diseases, but also the specific characteristics of patients. It is usually considered hypotheses in medical studies. Then, the data is prospectively collected to prove or deny this hypothesis, but in many cases, there may be relationships between patient data, which has not even been conjectured about them, and no hypothesis naturally has been considered. Hence, the purpose of this study is to discover the hidden patterns of tuberculosis(TB) patients' datasets. According to various assessment indicators of TB, first, the Entropy-Shannon method was used to identify the most important features. After discovering the existing association rule of data by using the APRIORI technique, the R software was then used to implement these techniques on 548 data of TB patients referred. Following this, the results of the Entropy-Shannon method have identified 18 factors. Also, the APRIORI algorithm was discovered nine association rules between the highest values of lift and the minimum support and confidence value equaled to 0.5 and 0.9, respectively. The results could be considered for further studies, particularly clinical trials as a primary hypothesis or apply to analyze patients' clinical status. [ABSTRACT FROM AUTHOR]

Clinical data, such as laboratory test results, is increasingly being made available to patients through patient portals. However, patients often have difficulties understanding and acting upon the clinical data presented in portals. As such, many turn to online resources to fill their knowledge gaps and obtain actionable advice. In this work, we present a content analysis of the questions posted in a major social Q&A site to characterize lay people’s general information needs concerning laboratory test results and to inform the design of patient portals for supporting patients’ understanding of clinical data. We identified 15 information needs related to laboratory test results, and clustered them under four themes: understanding the results of lab test, interpreting doctor’s diagnosis, learning about lab tests, and consulting the next steps. We draw on our findings to discuss design opportunities for supporting the understanding of laboratory results. [ABSTRACT FROM AUTHOR]

Changing physician behaviors is difficult. Electronic health record (EHR) clinical decision support (CDS) offers an opportunity to promote guideline adherence. Behavioral economics (BE) has shown success as an approach to supporting evidence-based decision-making with little additional cognitive burden. We applied a user-centered approach to incorporate BE “nudges” into a CDS module in two “vanguard” sites utilizing: (1) semi-structured interviews with key informants (n=8); (2) a design thinking workshop; and (3) semi-structured group interviews with clinicians. In the 133 day development phase at two clinics, the navigator section fired 299 times for 27 unique clinicians. The inbasket refill alert fired 124 times for 22 clinicians. Fifteen prescriptions for metformin were written by 11 clinicians. Our user-centered approach yielded a BE-driven CDS module with relatively high utilization by clinicians. Next steps include the addition of two modules and continued tracking of utilization, and assessment of clinical impact of the module. [ABSTRACT FROM AUTHOR]

Alzheimer's disease and other dementias are becoming more prevalent and placing increasing burdens on the community. The ADNeT Screening and Trials initiative aims to improve research outcomes by identifying people with an increased risk of developing these diseases and directing them to suitable clinical trials. To support the initiative, we have developed a modular informatics platform utilizing private cloud deployment to securely manage operational and research data across six clinical sites. [ABSTRACT FROM AUTHOR]

One of the challenges to using electronic health record (EHR) repositories for research is the difficulty mapping study subject eligibility criteria to the query capabilities of the repository. We sought to characterize criteria as "easy" (searchable in a typical repository), "hard" (requiring manual review of the record data), and "impossible" (not typically available in EHR repositories). We obtained 292 criteria from 20 studies available from Clinical Trials.gov and rated them according to our three types, plus a fourth "mixed" type. We had good agreement among three independent reviewers and chose 274 criteria that were characterized by single types for further analysis. The resulting analysis showed typical features of criteria that do and don't map to repositories. We propose that these features be used to guide researchers in specifying eligibility criteria to improve development of enrollment workflow, including the definition of EHR repository queries for self-service or analyst-mediated retrievals. [ABSTRACT FROM AUTHOR]

Clinical trials are studies designed to assess whether a new intervention is better than the current alternatives. However, most of them fail to recruit participants on schedule. It is hard to use Electronic Health Record (EHR) data to find eligible patients, therefore studies rely on manual assessment, which is time consuming, inefficient and requires specialized training. In this work we describe the design and development of an information retrieval system with the objective of finding eligible patients for cancer trials. The Recruit system has been in use at A. C. Camargo Cancer Center since August/2014 and contains data from more than 500,000 patients and 9 databases. It uses ontologies to integrate data from several sources and represent medical knowledge, which helps enhance results. One can search both in structured data and inside free text reports. The preliminary quality assessments shows excellent recall rates. Recruit proved to be an useful tool for researchers and its modular design could be applied to other clinical conditions and hospitals. [ABSTRACT FROM AUTHOR]

Randomized controlled trials generate high-quality medical evidence. However, the use of unjustified inclusion/exclusion criteria may compromise the external validity of a study. We have introduced a method to assess the population representativeness of related clinical trials using electronic health record (EHR) data. As EHR data may not perfectly represent the real-world patient population, in this work, we further validated the method and its results using the National Health and Nutrition Examination Survey (NHANES) data. We visualized and quantified the differences in the distributions of age, HbA1c, and BMI among the target population of Type 2 diabetes trials, diabetics in NHANES databases, and a convenience sample of patients enrolled in selected Type 2 diabetes trials. The results are consistent with the previous study. [ABSTRACT FROM AUTHOR]

We describe our system for visualizing data from clinical trials. The system is intended for clinicians with little or no knowledge of statistics, data mining, etc. The system is built using pluggable components, and it is easy to add more types of visualization. Currently the system supports interactive data exploration using for instance parallel coordinates, image maps, charts, and life tables. Grouping and filtering patients or subsets of data is easy and any changes are immediately reflected in all visualization tools currently used. [ABSTRACT FROM PUBLISHER]

Integrating evidence from systematic research in daily clinical practice is one of the pillars of evidence-based medicine. Electronic data capture tools simplify data collection from different centers and supports the management of multicenter clinical trials. The Ligurian HIV Network (LHN) is one such tool, originating from a regional effort to integrate clinical trial capabilities for HIV and other chronic infectious diseases. In order to manually collect a complete report of all clinical tests on patients enrolled in a trial, a strenuous human effort and the allocation of great resources would be necessary. Moreover, the risk of error in a manual system is very high. The proposed system automatically extracts clinical data from the EHR of three hospitals of the LHN in a standardized way, and enhance their re-use in clinical trials. Through dedicated questionnaires, physicians reported a strongly positive feedback about the efficacy of the platform in supporting clinical research. [ABSTRACT FROM AUTHOR]

The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. [ABSTRACT FROM AUTHOR]