Josette Jones

Using bibliometrics to support your selection of a nursing terminology set.

Nurses are being pressured to integrate standardized nursing terminology into the electronic health record to enable the representation and evaluation of nursing practice. Five terminology sets are recognized by the American Nurses Association that contain terms to represent nursing diagnoses, outcomes, and intervention: CCC, ICNP, NANDA/NOC/NIC, Omaha System, and PNDS. Key criteria for choosing the most suitable include demonstrated use and testing under real-time clinical conditions, scope of terms, cost, and the administrative infrastructure to sustain and evolve the terminology. Likelihood of survival is also critical and was evaluated here by examining the diffusion pattern of each terminology set through bibliometric analysis. Each of the five sets had a unique diffusion pattern, with NANDA/NOC/NIC demonstrating the most extensive penetration and author network in the CINAHL literature examined from 1982 to 2006.

Using a mobile application to self‐monitor diet and fluid intake among adults receiving hemodialysis

Hemodialysis patients have difficulty self-managing a complex dietary and fluid regimen. The purpose of this feasibility study was to pilot test an electronic self-monitoring intervention based on social cognitive theory. During a 6-week intervention, 24 participants self-monitored diet and fluid intake using the Dietary Intake Monitoring Application (DIMA), and 20 participants served as controls by monitoring their activity using the Daily Activity Monitor Application (DAMA). Results from this pilot study suggest the intervention is feasible and acceptable, although few significant effects on outcomes were found in this small sample. The DIMA has potential to facilitate dietary and fluid self-monitoring but requires additional refinement and further testing.

Knowledge Discovery and Data Mining of Free Text Radiology Reports

Medical Knowledge Discovery and Data Mining (KDD) over text is a promising yet difficult technology for unlocking meaning and uncovering associations in vast clinical text repositories. We report our experience in developing a new text analytic system called MEDAT or Medical Exploratory Data Analysis over Text, which overcomes several problems in text mining. The MEDAT system employs an annotated semantic index with a large number of assertions (propositions). The semantic index is able to capture complex assertions which encapsulate conceptual relationships including their modifiers at a granular level. The index represents semantically equivalent sentences with the same symbols, a necessary component for KDD semantic queries, including semantic Boolean and correlation queries. The graphical user interface enables users to perform complex semantic analysis of the Roentgen corpus, consisting of 594,000 de-identified radiology reports with 4.3 million sentences, without having to learn a programming language. The MEDAT architecture offers a novel framework for text mining in other medical domains.

Human Factors Engineering and Human–Computer Interaction: Supporting User Performance and Experience

Clinical informatics systems should be designed to support the performance and experience of its users, allowing them to perform their tasks effectively, efficiently, and with the highest possible satisfaction. The disciplines of human factors engineering and human–computer interaction offer a set of models, practices, and principles for improving the design and usability of interactive systems. In this chapter, we describe models such as the TURF usability framework for electronic health records systems, practices such as user-centered design and testing, and principles such as user interface design heuristics. We cite additional resources for information, education, and tools to enhance performance and user experience. Lastly, we discuss relevant emerging trends such as team-based care, personal informatics, mobile computing, and big data visualization for learning health systems.

Patient education and the use of the World Wide Web.

Wilkins, Inc. C trends in health care include increased consumerism and increased efforts to control the escalating costs of medical care while maintaining its quality. Patients are expected to be informed about health matters and to have active involvement in their own health care. Managed care organizations, in their attempts to limit health care use, emphasize patient education efforts that encourage self-care and the effective use of medical resources. Patient education to assist patients in managing their symptoms and recovery process is becoming increasingly complex; the shorter length of time allotted to hospital stay or clinic visits combined with the absence of work restructuring limit nurses’ opportunity to provide thorough patient education as well as to adapt this education to patient’s readiness to fully participate. Of particular concern here is the increased patient acuity and the increasing numbers of older patients with multiple health problems. These factors can have a dramatic impact on the patient’s physical and emotional readiness to learn. In addition, the information available in standard educational material usually is not organized in a manner that is suitable for time-relevant use or staged to the individual recovery process. Above all, such information is not based on the patient’s individual priorities and characteristics; thus nurses are frequently confronted with requests from patients or their caregivers for unique explanations to complex questions not included in the standardized educational packages. This information may be needed in a variety of forms, such as text, video, and audio, and in different organizational structures and access pathways to account for the user’s preferences and characteristics. Although it is difficult to accommodate differences and preferences in the print and graphics media of patient brochures or commercial audio-visual materials, it may be possible with interactive information technology such as browser-based information resources. Even if these computer-stored and displayed information resources offer nurses what they like in relation to information resources for patient care, their use in clinical practice is rather inconsistent and poses several challenges:

AutoBayesian: developing bayesian networks based on text mining

Bayesian network is a widely used tool for data analysis, modeling and decision support in various domains. There is a growing need for techniques and tools which can automatically construct Bayesian networks from massive text or literature data. In practice, Bayesian networks also need be updated when new data is observed, and literature mining is a very important source of new data after the initial network is constructed. Information closely related to Bayesian network usually includes the causal associations, statistics information and experimental results. However, these associations and numerical results cannot be directly integrated with the Bayesian network. The source of the literature and the perceived quality of research needs to be factored into the process of integration. In this demo, we will present a general methodology and toolkit called AutoBayesian that we developed to automatically build and update a Bayesian network based on the casual relationships derived from text mining.

Bridging Text Mining and Bayesian Networks

Bayesian networks need to be updated as and when new data is observed. Literature mining is a very important source of this new data after the initial network is constructed using the expert’s knowledge. In this work, we specifically interested in the causal associations and experimental results obtained from literature mining. However, these associations and numerical results cannot be directly integrated with the Bayesian network. The source of the literature and the perceived quality of research needs to be factored into the process of integration, just like a human, reading the literature, would. We present a general methodology for deriving a confidence measure for the mined data and provide inputs to the expert for resolving the modeling issues in integrating it with the existing network.

Privacy, security and interoperability of mobile health applications

This paper will discuss the security, privacy and interoperability of mobile health applications (MHAs) and how these issues must be reconciled in order for MHA devices to be implemented in the most robust fashion. Balance is needed between privacy and accessibility, between security and interoperability and between flexibility and standardization. The interoperability of diverse MHA devices must be a goal for the future in order to realize portability, true continuity and quality of care across a wide spectrum of health services. A pilot project to determine potential threats to the privacy of personal health information on an iPad will be described.

Using podcasts to help students apply health informatics concepts: benefits and unintended consequences.

Despite requirements for robust health informatics education, a multitude of educators and policy analysts report that programs are not adequately preparing nurses to handle the bevy of technologies that will be a part of their practice. A series of 14 “Podcasted” exemplars were developed to help graduate online students visualize the application of health informatics concepts in real-world settings and to determine the impact of podcasting on student cognition, engagement, and satisfaction. Although no significant differences in student cognition scores or student engagement were found between course conditions, course satisfaction was significantly higher in Podcasted weeks of the course. Also, student engagement was positively correlated with aspects of course satisfaction and overall cognition scores under both course conditions. This result suggests that student engagement plays an important mediating role in improving cognition. Students’ use of podcasting did produce a temporary drop in scores for one group; therefore, more research is needed to understand these unintended consequences. With distance/online education becoming mainstream, it is imperative that faculty deploy and confirm ways to improve student cognition, engagement, and satisfaction.

Dietary intake of isoflavones and coumestrol and the risk of prostate cancer in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial

Experimental studies have revealed that phytoestrogens may modulate the risk of certain sites of cancer due to their structural similarity to 17β‐estradiol. The present study investigates whether intake of these compounds may influence prostate cancer risk in human populations. During a median follow up of 11.5 years, 2,598 cases of prostate cancer (including 287 advanced cases) have been identified among 27,004 men in the intervention arm of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. Dietary intake of phytoestrogens (excluding lignans) was assessed with a food frequency questionnaire. Cox proportional hazards regression analysis was performed to estimate hazard ratios (HRs) and 95% confidence intervals (CI) for dietary isoflavones and coumestrol in relation to prostate cancer risk. After adjustment for confounders, an increased risk of advanced prostate cancer [HR (95% CI) for quintile (Q) 5 vs. Q1] was found for the dietary intake of total isoflavones [1.91 (1.25–2.92)], genistein [1.51 (1.02–2.22), daidzein [1.80 (1.18–2.75) and glycitein [1.67 (1.15–2.43)] (p‐trend for all associations ≤0.05). For example, HR (95% CI) for comparing the Q2, Q3, Q4 and Q5 with Q1 of daidzein intake was 1.45 (0.93–2.25), 1.65 (1.07–2.54), 1.73 (1.13–2.66) and 1.80 (1.18–2.75), respectively (p‐trend: 0.013). No statistically significant associations were observed between the intake of total isoflavones and individual phytoestrogens and non‐advanced and total prostate cancer after adjustment for confounders. This study revealed that dietary intake of isoflavones was associated with an elevated risk of advanced prostate cancer.