Data Mining for Adverse Drug Events: Impact on Six Learning Styles.

Emerging technologies and big data influence the role of nurses, calling for new ways of thinking and teaching. Innovative educational methods are needed to prepare students for providing evidence-based care in today's complex healthcare environment. Active learning methods appeal to tech-savvy, self-directed learners who desire instant results during the learning process. The aim of this pretest/posttest study was to evaluate the impact of active learning methods on student attitudes and feelings, using the Grasha-Riechmann Student Learning Style Scale. Results were used to tailor active learning interventions using Twitter and Federal Adverse Event Reporting System data, for a research and evidence-based practice nursing course. Participants (N = 126) evaluated tweets describing adverse drug events and their concordance with federal reporting system data. Paired-samples t test results revealed significant differences (P < .05) between pretest/posttest for five of the six learning style preferences. Active learning methods resulted in high levels of student engagement and satisfaction. Data mining as an active learning intervention is popular with learners and offers a quick, valuable way to reveal real-world adverse drug event experiences while introducing basic research principles.

Decision Support for Excipient Risk Assessment in Pharmaceutical Manufacturing.

Excipients have always been a key input into pharmaceutical products, profoundly affecting product quality. Currently, most of our knowledge of excipient critical quality attributes is empirical, gained through experience, and shared through publications and other sources. The behavior of excipients is complicated, with many different failure modes that depend on the type of dosage form. Even within the same dosage form, there can be multiple failure modes depending on the manufacturing method. This complex behavior creates many possible combinations to assess when designing a formulation or evaluating regulatory submissions. Formulation science could be improved if data from different sources could be made widely available through an interactive system using a consistent, structured format to help formulators and regulators assess the risk of excipient usage for a particular dosage form with a particular manufacturing method. This paper describes a decision support system that was created for assessing excipient risk in different types of formulations and considering different types of manufacturing methods, dosage forms, and excipient functionality. The Excipient Risk Assessment System consists of a database that stores knowledge about factors that affect formulation design and a decision support processor that manages selections for creating formulation scenarios and assigning risk. Formulation and risk assessment data are provided by formulation science experts. This enables the system to assess compatibility among excipients, functionality, dosage forms, and manufacturing methods selected for formulations. The interface guides users through the creation of formulation scenarios and displays customized, interactive risk assessment reports for users to search and explore.

Changing Smart Pump Vendors: Lessons Learned.

Smart infusion pump technology is a mainstay in health care, and the integration and use of those pumps is crucial for patient safety. An institution purchasing smart infusion pumps has the ability to trial the various vendors before purchase, however literature that documents a conversion from one pump to another is lacking. This article describes the conversion from one smart infusion pump platform to another at a government institution and a large multisite facility. The differences in 2 smart infusion pumps are described as well as lessons learned following the conversion in both organizations.

Sankofa pediatric HIV disclosure intervention cyber data management: building capacity in a resource-limited setting and ensuring data quality.

Prevalence of pediatric HIV disclosure is low in resource-limited settings. Innovative, culturally sensitive, and patient-centered disclosure approaches are needed. Conducting such studies in resource-limited settings is not trivial considering the challenges of capturing, cleaning, and storing clinical research data. To overcome some of these challenges, the Sankofa pediatric disclosure intervention adopted an interactive cyber infrastructure for data capture and analysis. The Sankofa Project database system is built on the HUBzero cyber infrastructure ( https://hubzero.org ), an open source software platform. The hub database components support: (1) data management - the "databases" component creates, configures, and manages database access, backup, repositories, applications, and access control; (2) data collection - the "forms" component is used to build customized web case report forms that incorporate common data elements and include tailored form submit processing to handle error checking, data validation, and data linkage as the data are stored to the database; and (3) data exploration - the "dataviewer" component provides powerful methods for users to view, search, sort, navigate, explore, map, graph, visualize, aggregate, drill-down, compute, and export data from the database. The Sankofa cyber data management tool supports a user-friendly, secure, and systematic collection of all data. We have screened more than 400 child-caregiver dyads and enrolled nearly 300 dyads, with tens of thousands of data elements. The dataviews have successfully supported all data exploration and analysis needs of the Sankofa Project. Moreover, the ability of the sites to query and view data summaries has proven to be an incentive for collecting complete and accurate data. The data system has all the desirable attributes of an electronic data capture tool. It also provides an added advantage of building data management capacity in resource-limited settings due to its innovative data query and summary views and availability of real-time support by the data management team.

Positron Emission Tomography in Thymic Tumors: Analysis Using a Prospective Research Database.

BACKGROUND: Positron emission tomography may have a role in the pretreatment workup of patients with thymic malignancies. This study was undertaken to determine the utility of the maximum standardized uptake value (SUVmax) in predicting histologic type and tumor stage in a large cohort of thymic epithelial tumors. METHODS: The large, multiinstitutional, prospective database of The International Thymic Malignancy Interest Group (ITMIG) was queried for the use of positron emission tomography in the pretreatment workup of patients with thymic tumors. Data analyzed included demographics, SUVmax, histologic tumor type, and tumor stage. The distribution of SUVmax according to histologic type and Masaoka-Koga pathologic stage was determined, and the ability of SUVmax to predict these two variables was calculated using analysis of receiver operating characteristic curves. RESULTS: Since 2012, data from 926 patients with thymic malignancies were entered into the ITMIG prospective database, of which 154 had a reported value for SUVmax. The area under the receiver operating characteristic curve for SUVmax in predicting histologic type and pathologic stage was 0.79 (95% confidence interval, 0.70 to 0.88; p < 0.001) and 0.81 (95% confidence interval, 0.73 to 0.88; p < 0.001), respectively. In addition, there was a significant relationship between SUVmax and histologic type (p < 0.001) as well as Masaoka-Koga pathologic stage (p < 0.001). CONCLUSIONS: Positron emission tomography has utility in predicting clinicopathologic features of thymic malignancies. These results may have clinical application in the pretreatment workup of patients with these rare tumors.

SANKOFA: a multisite collaboration on paediatric HIV disclosure in Ghana.

With the scale-up of effective antiretroviral therapy in resource-limited settings, many HIV-infected children are now able to survive into adulthood. To achieve this potential, children must navigate normative developmental processes and challenges while living with an unusually complex, stigmatizing, potentially fatal chronic illness and meeting the demands of treatment.Yet many of these children, especially preadolescents, do not know they are HIV-infected. Despite compelling evidence supporting the merits of informing children of their HIV status, there has been little emphasis on equipping the child's caregiver with information and skills to promote disclosure, particularly, when the caregiver faces a variety of sociocultural barriers and is reluctant to do so. In this study, we present the background, process and methods for a first of its kind collaboration that is examining the efficacy of an intervention developed to facilitate the engagement of caregivers in the process of disclosure in a manner suitable to the sociocultural context and developmental age and needs of the child in Ghana. We also report preliminary data that supported the design of the intervention approach and currently available domains of the data system. Finally, we discuss challenges and implications for future research.

Comparative analytics of infusion pump data across multiple hospital systems.

PURPOSE: A Web-based analytics system for conducting inhouse evaluations and cross-facility comparisons of alert data generated by smart infusion pumps is described. SUMMARY: The Infusion Pump Informatics (IPI) project, a collaborative effort led by research scientists at Purdue University, was launched in 2009 to provide advanced analytics and tools for workflow analyses to assist hospitals in determining the significance of smart-pump alerts and reducing nuisance alerts. The IPI system allows facility-specific analyses of alert patterns and trends, as well as cross-facility comparisons of alert data uploaded by more than 55 participating institutions using different types of smart pumps. Tools accessible through the IPI portal include (1) charts displaying aggregated or breakout data on the top drugs associated with alerts, numbers of alerts per device or care area, and override-to-alert ratios, (2) investigative reports that can be used to characterize and analyze pump-programming errors in a variety of ways (e.g., by drug, by infusion type, by time of day), and (3) "drill-down" workflow analytics enabling users to evaluate alert patterns­both internally and in relation to patterns at other hospitals­in a quick and efficient stepwise fashion. CONCLUSION: The formation of the IPI analytics system to support a community of hospitals has been successful in providing sophisticated tools for member facilities to review, investigate, and efficiently analyze smart-pump alert data, not only within a member facility but also across other member facilities, to further enhance smart pump drug library design.

Development of the international thymic malignancy interest group international database: an unprecedented resource for the study of a rare group of tumors.

BACKGROUND: Our knowledge of thymic malignancies has largely been derived from small, single-institution series. Recognition of the need for broad collaboration led to the creation of the International Thymic Malignancy Interest Group (ITMIG) and the development of a large, centralized database to advance knowledge of these rare tumors. METHODS: A multidisciplinary Database Committee was convened to define a common set of data elements a priori. Retrospective data were solicited from ITMIG members and collated using standardized fields. Patients with thymoma, thymic carcinoma, or thymic carcinoid were included. RESULTS: Over a 6-month period, 47 institutions spanning 15 countries contributed a total of 6097 cases (mean, 129 [range, 10-1209]). The sex distribution was equal for thymomas, but there was a greater proportion of men with thymic carcinoma and thymic carcinoid (p < 0.0001). Nearly all cases (99%) were treated surgically. WHO type B2 was the most frequent histologic classification among thymomas, whereas squamous was the most common among thymic carcinomas. In total, 38% of patients with thymoma had myasthenia gravis compared with less than or equal to 5% for thymic carcinoma and thymic carcinoid. Median overall survival was 18.9 years (95% confidence interval [CI], 17.4-20.3) for thymoma, 6.8 years (95% CI, 5.5-7.9) for thymic carcinoma, and 7.5 years (95% CI, 6.5-8.5) for thymic carcinoid. CONCLUSIONS: The rapid creation of the ITMIG database demonstrates the feasibility of international collaboration for this rare set of malignancies and attests to the engagement of its membership. This database represents the largest collective data set ever assembled and provides an unprecedented resource for research of these tumors.