Hasil untuk "Computer applications to medicine. Medical informatics"

Yigit Yazarkan, Gamze Sonmez, Cem Simsek

Patricia A. Braun, Kimberly T. Wiggins, Cherith Flowerday et al.

Objective: Evaluate healthcare providers’ and staffs’ knowledge, self-reported abilities, activities, and barriers to providing preventive oral health services (POHS) at primary care medical visits before and after participation in the Rocky Mountain Network of Oral Health (RoMoNOH) project. Methods: The RoMoNOH project integrated POHS into primary care medical visits of young children at 22 community health centers (CHCs) in Arizona, Colorado, Montana, and Wyoming by medical team members and/or by embedded dental hygienists (DHs). Twelve CHCs embedded DHs onto their teams. In an observational pre/post evaluation, a convenience sample of healthcare providers’ characteristics were surveyed at baseline and 3 years across 4 oral health domains: knowledge, self-reported abilities, behaviors, and barriers. Each domain was scored from 0% to 100%, with 100% being optimal. Differences between pre- and post-project domain scores were assessed using chi-square, t-tests, and linear and logistic regression adjusting for providers’ age. Results: Embedding DHs into CHCs and staff turnover impacted pre/post survey participants. The final analytic cohort included 213 (pre-survey response rate: 71%) and 165 (post-survey response rate: 52%) healthcare providers who worked with children < age 3. Participants were female (pre: 81%, post: 81%) and aged >35 years (pre: 39%, post: 41%). Unadjusted mean differences across surveys improved across all oral health domains (pre/post): knowledge: 65%/81%, P  < .001; self-reported ability: 52%/71%, P  < .001; activities: 32%/57%, P  < .001; barriers: 27%/21%, P  = .011. After adjustment for age, these improvements remained significant (all P  ≤ .011). Conclusions: Healthcare providers’ oral health practices improved over a multi-year oral health integration project aimed at increasing delivery of POHS at medical visits.

Annika Sannes, Erling W Rognli, Ketil Hanssen-Bauer et al.

Abstract BackgroundInternet-delivered therapist-guided therapy (e-therapy) represents a promising approach for enhancing accessibility, treatment fidelity, and scalability within child and adolescent mental health services (CAMHS). ObjectiveThis systematic review aimed to (1) identify and synthesize determinants of implementation, specifically barriers to and facilitators of e-therapy in CAMHS structured according to the Consolidated Framework of Implementation Research (CFIR); and (2) provide pooled benchmark estimates of key implementation outcomes for fidelity, cost-effectiveness, and acceptability. MethodsA PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)–compliant systematic review was performed across PsycINFO, MEDLINE, Web of Science, CINAHL, Embase, Cochrane, and ProQuest Dissertations & Thesis on June 6, 2025—to identify peer-reviewed studies assessing implementation outcomes or determinants of e-therapy in the context of outpatient CAMHS (ages 8‐18 years). Barriers and facilitators were synthesized qualitatively with thematic analysis applying CFIR. A parallel quantitative synthesis of Proctor et al’s taxonomy of implementation outcomes was performed using Bayesian multilevel random-effects meta-analyses to estimate pooled effect sizes and 95% credible intervals (CIs). By combining quantitative benchmarks of implementation success with qualitative insights into contextual determinants, the review provides an integrated understanding of what drives effective e-therapy implementation in CAMHS. Study quality was assessed using the CASP (Critical Appraisal Skills Programme) checklist, Cochrane Risk of Bias tool, and Risk Of Bias In Non-randomized Studies–of Interventions tool. Small study effects were evaluated using funnel plots, sensitivity analyses, and the Egger test. ResultsFrom 50,026 screened reports, 50 studies published between 2007 and 2025 were included: 18 randomized controlled trials, 17 cohort, and 15 qualitative or mixed methods studies. Most studies originated from Western Europe (n=34), Northern America (n=11), and Oceania (n=5), targeting anxiety (n=24) and depression (n=9), through cognitive behavioral therapy–based programs (n=47), with parallel parent content (n=31). Therapist guidance was primarily asynchronous (n=43). Among the 39 studies reporting determinants, common barriers and facilitators were identified across intervention, organization, therapist, and patient domains, structured via CFIR. Pooled implementation outcomes showed modest dropout rates (~20%, CI 14%‐27%), high module completion (~68%, CI 60%‐75%), low therapist time (24 min per wk per patient, 95% CI 19‐28), and high patient satisfaction (24/32 on Client Satisfaction Questionnaire-8, 95% CI 22‐27; and 76% satisfaction rate, 95% CI 62%‐87%), suggesting e-therapy is resource efficient and acceptable if implemented successfully. ConclusionsThis review provided the first integrated synthesis of pooled benchmarks for implementation outcomes of e-therapy in CAMHS and modifiable determinants to inform future service planning and scale-up. These findings highlighted service-level enablers, such as leadership anchoring, targeted use, technical stability, structured patient flow, and therapist training, that organizations could prioritize to strengthen sustainable e-therapy implementation in CAMHS.

Janaka Godevithana, Champa Jayalakshmie Wijesinghe, Millawage Supun Dilara Wijesinghe

BackgroundAn unhealthy diet is a well-established risk factor for the development of noncommunicable diseases, and office workers are at a higher risk of noncommunicable diseases due to their sedentary work style. Stage of change (SOC) theory–based and stage-matched interventions effectively influence dietary and behavior changes. The effectiveness of such interventions in the context of low- and middle-income countries is yet to be assessed. ObjectiveThis protocol describes a cluster randomized trial planned to evaluate the effectiveness of an intervention for changing dietary behavior among government office workers in the Galle district in Sri Lanka. MethodsA cluster randomized trial was conducted in 20 clusters divided into intervention and control arms. A cluster was an office with 30 clerical-type workers who were sedentary at work. A stage-matched intervention based on behavior change processes was implemented in the intervention clusters for 3 months. Participants were provided with an intervention matched to their SOC at baseline. Precontemplators and contemplators received awareness-raising and emotional arousal interventions. Others received goal setting and self-monitoring interventions. The SOC and dietary intake were assessed at baseline and the postintervention stage through a staging algorithm, and 24-hour dietary recall was supplemented with a picture guide and computer software. Adherence to the intervention was assessed monthly. We hypothesized that participants would achieve a progressive change in the SOC and healthy dietary intake in the intervention clusters compared to the control clusters. ResultsBy December 2024, the planned intervention was completed. Data analysis on the effectiveness of the intervention is to be completed and published in 2025. ConclusionsThis protocol reports a stage-matched intervention based on SOC theory, enriching the current knowledge base with new evidence from office workers in a low- to middle-income country. Trial RegistrationSri Lanka Clinical Trials Registry SLCTR/2020/025; https://slctr.lk/trials/slctr-2020-025 International Registered Report Identifier (IRRID)DERR1-10.2196/70293

Andrés Anaya-Isaza, Leonel Mera-Jiménez, Martha Zequera-Diaz

Samantha Eve Smith, Scott McColgan-Smith, Fiona Stewart et al.

Abstract Background Behavioural marker systems are used across several healthcare disciplines to assess behavioural (non-technical) skills, but rater training is variable, and inter-rater reliability is generally poor. Inter-rater reliability provides data about the tool, but not the competence of individual raters. This study aimed to test the inter-rater reliability of a new behavioural marker system (PhaBS — pharmacists’ behavioural skills) with clinically experienced faculty raters and near-peer raters. It also aimed to assess rater competence when using PhaBS after brief familiarisation, by assessing completeness, agreement with an expert rater, ability to rank performance, stringency or leniency, and avoidance of the halo effect. Methods Clinically experienced faculty raters and near-peer raters attended a 30-min PhaBS familiarisation session. This was immediately followed by a marking session in which they rated a trainee pharmacist’s behavioural skills in three scripted immersive acute care simulated scenarios, demonstrating good, mediocre, and poor performances respectively. Inter-rater reliability in each group was calculated using the two-way random, absolute agreement single-measures intra-class correlation co-efficient (ICC). Differences in individual rater competence in each domain were compared using Pearson’s chi-squared test. Results The ICC for experienced faculty raters was good at 0.60 (0.48–0.72) and for near-peer raters was poor at 0.38 (0.27–0.54). Of experienced faculty raters, 5/9 were competent in all domains versus 2/13 near-peer raters (difference not statistically significant). There was no statistically significant difference between the abilities of clinically experienced versus near-peer raters in agreement with an expert rater, ability to rank performance, stringency or leniency, or avoidance of the halo effect. The only statistically significant difference between groups was ability to compete the assessment (9/9 experienced faculty raters versus 6/13 near-peer raters, p = 0.0077). Conclusions Experienced faculty have acceptable inter-rater reliability when using PhaBS, consistent with other behaviour marker systems; however, not all raters are competent. Competence measures for other assessments can be helpfully applied to behavioural marker systems. When using behavioural marker systems for assessment, educators must start using such rater competence frameworks. This is important to ensure fair and accurate assessments for learners, to provide educators with information about rater training programmes, and to provide individual raters with meaningful feedback.

Benedetta Gottardelli, Roberto Gatta, Leonardo Nucciarelli et al.

Abstract Background Artificial intelligence (AI) has become a pivotal tool in advancing contemporary personalised medicine, with the goal of tailoring treatments to individual patient conditions. This has heightened the demand for access to diverse data from clinical practice and daily life for research, posing challenges due to the sensitive nature of medical information, including genetics and health conditions. Regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe aim to strike a balance between data security, privacy, and the imperative for access. Results We present the Gemelli Generator - Real World Data (GEN-RWD) Sandbox, a modular multi-agent platform designed for distributed analytics in healthcare. Its primary objective is to empower external researchers to leverage hospital data while upholding privacy and ownership, obviating the need for direct data sharing. Docker compatibility adds an extra layer of flexibility, and scalability is assured through modular design, facilitating combinations of Proxy and Processor modules with various graphical interfaces. Security and reliability are reinforced through components like Identity and Access Management (IAM) agent, and a Blockchain-based notarisation module. Certification processes verify the identities of information senders and receivers. Conclusions The GEN-RWD Sandbox architecture achieves a good level of usability while ensuring a blend of flexibility, scalability, and security. Featuring a user-friendly graphical interface catering to diverse technical expertise, its external accessibility enables personnel outside the hospital to use the platform. Overall, the GEN-RWD Sandbox emerges as a comprehensive solution for healthcare distributed analytics, maintaining a delicate equilibrium between accessibility, scalability, and security.

Talha Ibn Mahmud, Sheikh Asif Imran, Celia Shahnaz

Determining the severity level of hypoxemia, the scarcity of saturated oxygen (SpO2) in the human body, is very important for the patients, a matter which has become even more significant during the outbreak of Covid-19 variants. Although the widespread usage of Pulse Oximeter has helped the doctors aware of the current level of SpO2 and thereby determine the hypoxemia severity of a particular patient, the high sensitivity of the device can lead to the desensitization of the care-givers, resulting in slower response to actual hypoxemia event. There has been research conducted for the detection of severity level using various parameters and bio-signals and feeding them in a machine learning algorithm. However, in this paper, we have proposed a new residual-squeeze-excitation-attention based convolutional network (Res-SE-ConvNet) using only Photoplethysmography (PPG) signal for the comfortability of the patient. Unlike the other methods, the proposed method has outperformed the standard state-of-art methods as the result shows 96.5&#x0025; accuracy in determining 3 class severity problems with 0.79 Cohen Kappa score. This method has the potential to aid the patients in receiving the benefit of an automatic and faster clinical decision support system, thus handling the severity of hypoxemia.

Alexey V. Dimov, Kelly M. Gillen, Thanh D. Nguyen et al.

Quantitative susceptibility mapping (QSM) facilitates mapping of the bulk magnetic susceptibility of tissue from the phase of complex gradient echo (GRE) MRI data. QSM phase processing combined with an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi mathvariant="bold-italic">R</mi><mn mathvariant="bold">2</mn><mo>*</mo></msubsup></mrow></semantics></math></inline-formula> model of magnitude of multiecho gradient echo data (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi mathvariant="bold-italic">R</mi><mn mathvariant="bold">2</mn><mo>*</mo></msubsup><mi mathvariant="bold-italic">Q</mi><mi mathvariant="bold-italic">S</mi><mi mathvariant="bold-italic">M</mi></mrow></semantics></math></inline-formula>) allows separation of dia- and para-magnetic components (e.g., myelin and iron) that contribute constructively to<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msubsup><mi mathvariant="bold-italic">R</mi><mn mathvariant="bold">2</mn><mo>*</mo></msubsup></mrow></semantics></math></inline-formula> value but destructively to the QSM value of a voxel. This <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi mathvariant="bold-italic">R</mi><mn mathvariant="bold">2</mn><mo>*</mo></msubsup><mi mathvariant="bold-italic">Q</mi><mi mathvariant="bold-italic">S</mi><mi mathvariant="bold-italic">M</mi></mrow></semantics></math></inline-formula> technique is validated against quantitative histology—optical density of myelin basic protein and Perls’ iron histological stains of rim and core of 10 ex vivo multiple sclerosis lesions, as well as neighboring normal appearing white matter. We found that <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi mathvariant="bold-italic">R</mi><mn mathvariant="bold">2</mn><mo>*</mo></msubsup><mi mathvariant="bold-italic">Q</mi><mi mathvariant="bold-italic">S</mi><mi mathvariant="bold-italic">M</mi></mrow></semantics></math></inline-formula> source maps are in good qualitative agreement with histology, e.g., showing increased iron concentration at the edge of the rim+ lesions and myelin loss in the lesions’ core. Furthermore, our results indicate statistically significant correlation between paramagnetic and diamagnetic tissue components estimated with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi mathvariant="bold-italic">R</mi><mn mathvariant="bold">2</mn><mo>*</mo></msubsup><mi mathvariant="bold-italic">Q</mi><mi mathvariant="bold-italic">S</mi><mi mathvariant="bold-italic">M</mi></mrow></semantics></math></inline-formula> and optical densities of Perls’ and MPB stains. These findings provide direct support for the use of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mi mathvariant="bold-italic">R</mi><mn mathvariant="bold">2</mn><mo>*</mo></msubsup><mi mathvariant="bold-italic">Q</mi><mi mathvariant="bold-italic">S</mi><mi mathvariant="bold-italic">M</mi></mrow></semantics></math></inline-formula> magnetic source separation based solely on GRE complex data to characterize MS lesion composition.

Lianxin Zhong, Qingfang Meng, Yuehui Chen et al.

Abstract Background Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. Results In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. Conclusion The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

Lehmann, Jens, Buhl, Petra, Giesinger, Johannes M et al.

BackgroundPatient portals offer the possibility to assess patient-reported outcome measures (PROMs) remotely, and first evidence has demonstrated their potential benefits. ObjectiveIn this study, we evaluated patient use of a web-based patient portal that provides patient information and allows online completion of PROMs. A particular focus was on patient motivation for (not) using the portal. The portal was developed to supplement routine monitoring at the Department of Internal Medicine V in Innsbruck. MethodsWe included patients with multiple myeloma and chronic lymphocytic leukemia who were already participating in routine monitoring at the hospital for use of the patient portal. Patients were introduced to the portal and asked to complete questionnaires prior to their next hospital visits. We used system access logs and 3 consecutive semistructured interviews to analyze patient use and evaluation of the portal. ResultsBetween July 2017 and August 2020, we approached 122 patients for participation in the study, of whom 83.6% (102/122) consented to use the patient portal. Patients were on average 60 (SD 10.4) years old. Of patients providing data at all study time points, 37% (26/71) consistently used the portal prior to their hospital visits. The main reason for not completing PROMs was forgetting to do so in between visits (25/84, 29%). During an average session, patients viewed 5.3 different pages and spent 9.4 minutes logged on to the portal. Feedback from interviews was largely positive with no patients reporting difficulties navigating the survey and 50% of patients valuing the self-management tools provided in the portal. Regarding the portal content, patients were interested in reviewing their own results and reported high satisfaction with the dynamic self-management advice, also reflected in the high number of clicks on those pages. ConclusionsPatient portals can contribute to patient empowerment by offering sought-after information and self-management advice. In our study, the majority of our patients were open to using the portal. The low number of technical complaints and average time spent in the portal demonstrate the feasibility of our patient portal. While initial interest was high, long-term use was considerably lower and identified as the main area for improvement. In a next step, we will improve several aspects of the patient portal (eg, including a reminder to visit the portal before the next appointment and closer PROM symptom monitoring via an onconurse).

Bardadym T.O., G. V.M., N. N.A. et al.

The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. At the conditions of pandemic, the success of health care system depends significantly on the regular implementation of effective research tools and population monitoring. The earlier the risks of disease can be identified, the more effective process of preventive measures or treatments can be. This publication is about the creation of a prototype for such a tool within the project «Development of methods, algorithms and intelligent analytical system for processing and analysis of heterogeneous clinical and biomedical data to improve the diagnosis of complex diseases» (M/99-2019, M/37-2020 with support of the Ministry of Education and Science of Ukraine), implementted by the V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, together with the United Institute of Informatics Problems, National Academy of Sciences of Belarus (F19UKRG-005 with support of the Belarussian Republican Foundation for Fundamental Research). The insurers, entering the market, can insure mostly low risks by facilitating more frequent changes of insurers by consumers (policyholders) and mixing the overall health insurance market. Socio-demographic variables can be risk adjusters. Since age and gender have a relatively small explanatory power, other socio-demographic variables were studied – marital status, retirement status, disability status, educational level, income level. Because insurers have an interest in beneficial diagnoses for their policyholders, they are also interested in the ability to interpret relevant information – upcoding: insurers can encourage their policyholders to consult with doctors more often to select as many diagnoses as possible. Many countries and health care systems use diagnostic information to determine the reimbursement to a service provider, revealing the necessary data. For processing and analysis of these data, software implementations of construction for classifiers, allocation of informative features, processing of heterogeneous medical and biological variables for carrying out scientific research in the field of clinical medicine are developed. The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. Particularly, attention is paid to containerization of biomedical applications (Docker, Singularity containerization technology), this permits to get reproducibility of the conditions in which the calculations took place (invariability of software including software and libraries), technologies of software pipelining of calculations, that allows to organize flow calculations, and technologies for parameterization of software environment, that allows to reproduce, if necessary, an identical computing environment. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. The experience of using the developed linear classifier, gained during its testing on artificial and real data, allows us to conclude about several advantages provided by the containerized form of the created application: it permits to provide access to real data located in cloud environment; it is possible to perform calculations to solve research problems on cloud resources both with the help of developed tools and with the help of cloud services; such a form of research organization makes numerical experiments reproducible, i.e. any other researcher can compare the results of their developments on specific data that have already been studied by others, in order to verify the conclusions and technical feasibility of new results; there exists a universal opportunity to use the developed tools on technical devices of various classes from a personal computer to powerful cluster.

T. Shirai, T. Terada

Kaspar, Kai

BackgroundThe current COVID-19 pandemic is showing negative effects on human health as well as on social and economic life. It is a critical and challenging task to revive public life while minimizing the risk of infection. Reducing interactions between people by social distancing is an effective and prevalent measure to reduce the risk of infection and spread of the virus within a community. Current developments in several countries show that this measure can be technologically accompanied by mobile apps; meanwhile, privacy concerns are being intensively discussed. ObjectiveThe aim of this study was to examine central cognitive variables that may constitute people’s motivations for social distancing, using an app, and providing health-related data requested by two apps that differ in their direct utility for the individual user. The results may increase our understanding of people’s concerns and convictions, which can then be specifically addressed by public-oriented communication strategies and appropriate political decisions. MethodsThis study refers to the protection motivation theory, which is adaptable to both health-related and technology-related motivations. The concept of social trust was added. The quantitative survey included answers from 406 German-speaking participants who provided assessments of data security issues, trust components, and the processes of threat and coping appraisal related to the prevention of SARS-CoV-2 infection by social distancing. With respect to apps, one central focus was on the difference between a contact tracing app and a data donation app. ResultsMultiple regression analyses showed that the present model could explain 55% of the interindividual variance in the participants’ motivation for social distancing, 46% for using a contact tracing app, 42% for providing their own infection status to a contact tracing app, and 34% for using a data donation app. Several cognitive components of threat and coping appraisal were related to motivation measurements. Trust in other people’s social distancing behavior and general trust in official app providers also played important roles; however, the participants’ age and gender did not. Motivations for using and accepting a contact tracing app were higher than those for using and accepting a data donation app. ConclusionsThis study revealed some important cognitive factors that constitute people’s motivation for social distancing and using apps to combat the COVID-19 pandemic. Concrete implications for future research, public-oriented communication strategies, and appropriate political decisions were identified and are discussed.

Richard McKinley, Rik Wepfer, Lorenz Grunder et al.

The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multiple sclerosis. However, the definition of ‘new or enlarged’ is not fixed, and it is known that lesion-counting is highly subjective, with high degree of inter- and intra-rater variability. Automated methods for lesion quantification, if accurate enough, hold the potential to make the detection of new and enlarged lesions consistent and repeatable. However, the majority of lesion segmentation algorithms are not evaluated for their ability to separate radiologically progressive from radiologically stable patients, despite this being a pressing clinical use-case. In this paper, we explore the ability of a deep learning segmentation classifier to separate stable from progressive patients by lesion volume and lesion count, and find that neither measure provides a good separation. Instead, we propose a method for identifying lesion changes of high certainty, and establish on an internal dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable time-points with a very high level of discrimination (AUC = 0.999), while changes in lesion volume are much less able to perform this separation (AUC = 0.71). Validation of the method on two external datasets confirms that the method is able to generalize beyond the setting in which it was trained, achieving an accuracies of 75 % and 85 % in separating stable and progressive time-points. Keywords: Deep Learning, Multiple Sclerosis, MRI, Longitudinal Imaging

Akthi, Umar, Rul et al.

Yuehwern Yih

Healthcare Delivery System Overview Health Care: How Did We Get Here and Where Are We Going? David M. Lawrence Engineering and the Healthcare Delivery System, Proctor P. Reid and W. Dale Compton The VA Healthcare Delivery System, Elizabeth M. Yano Outpatient Clinics: Primary and Specialty Care, Deanna R. Willis Designing a Nurse-Managed Healthcare Delivery System, Julie Cowan Novak Long-Term Care, Kathleen Abrahamson and Karis Pressler Healthcare Insurance: An Introduction, Vinod K. Sahney and Ann Tousignant An Integrated Model of Healthcare Delivery, Steven M. Witz and Kenneth J. Musselman Performance Assessment and Process Improvement Management Performance Assessment for Healthcare Organizations, Peter Arthur Woodbridge and George Oscar Allen Managing Physician Panels in Primary Care, Hari Balasubramanian, Brian T. Denton, and Qi Ming Lin Lean in Health Care, Philip C. Jones and Barrett W. Thomas Patient Safety and Proactive Risk Assessment, Pascale Carayon, Helene Faye, Ann Schoofs Hundt, Ben-Tzion Karsh, and Tosha B. Wetterneck Toward More Effective Implementation of Evidence-Based Practice: Relational and Contextual Considerations, Priscilla A. Arling, Rebekah L. Fox, and Bradley N. Doebbeling System Engineering: Technologies and Methodologies Computer Simulation in Health Care, Sean Carr and Stephen D. Roberts An Introduction to Optimization Models and Applications in Healthcare Delivery Systems, Wenhua Cao and Gino J. Lim Queueing Theory and Modeling, Linda V. Green Markov Decision Processes and Their Applications in Health Care, Jonathan Patrick and Mehmet A. Begen Statistical-Based Analysis and Modeling, Min Zhang and Mark E. Cowen Analyzing Decisions Using Datasets with Multiple Attributes: A Machine Learning Approach, Janusz Wojtusiak and Farrokh Alemi The Shaping of Inventory Systems in Health Services, Jan de Vries and Karen Spens Facility Planning and Design, Richard L. Miller Work Design, Task Analysis, and Value Stream Mapping, Cindy Jimmerson Scheduling and Sequencing, Edmund K. Burke, Timothy Curtois, Tomas Eric Nordlander, and Atle Riise Data Mining, Niels Peek Applications to HIV Prevention Strategies, Paul G. Farnham and Arielle Lasry Causal Risk Analysis: Revisiting the Vioxx Study, Farrokh Alemi and Manaf Zargoush Design, Planning, Control, and Management of Healthcare Systems Preventive Care Vaccine Production, Karen M. Polizzi and Cleo Kontoravdi Economic Implications of Preventive Care, George H. Avery, Kara E. Leonard, and Steve P. McKenzie Telemedicine Interactive Medicine, Pamela Whitten, Samantha A. Nazione, and Jennifer Cornacchione Transplant Services The U.S. Organ Transplant Network: History, Structure, and Functions, Timothy L. Pruett and Joel D. Newman Pharmacy Operation Pharmacoeconomics and the Drug Development Process, Stephanie R. Earnshaw, Thomas N. Taylor, and Cheryl McDade ED/ICU Operation Emergency Department Crowding, Joshua A. Hilton and Jesse M. Pines Emergency Department Throughput from a Healthcare Leader's Perspective, Airica Steed OR Management Capacity Planning in Operating Rooms, John T. Blake Managing Critical Resources through an Improved Surgery Scheduling Process, Erik Demeulemeester, Jeroen Belien, and Brecht Cardoen Anesthesia Group Management and Strategies, William H. Hass, Alex Macario, and Randal G. Garner Decontamination Service Turnovers and Turnarounds in the Healthcare System, June M. Worley and Toni L. Doolen Decontamination Service, Peter F. Hooper Laboratories Quality Control in Hospital Clinical Laboratories: A System Approach, George G. Klee Emergency Response and Pandemics Planning Emergency Planning Model for Pandemics, J. Eric Dietz, David R. Black, Julia E. Drifmeyer, and Jennifer A. Smock Public Health and Medical Preparedness, Eva K. Lee, Anna Yang Yang, Ferdinand Pietz, and Bernard Benecke Mental Health Mental Health Allocation and Planning Simulation Model, H. Stephen Leff, David R. Hughes, Clifton M. Chow, Steven Noyes, and Laysha Ostrow Correlation with Social and Medical Factors, Kathleen Abrahamson, Karis Pressler, and Melissa Grabner-Hagen Food and Supplies Healthcare Foodservice, L. Charnette Norton Healthcare-Product Supply Chains: Medical-Surgical Supplies, Pharmaceuticals, and Orthopedic Devices: The Flows of Product, Information, and Dollars, Leroy B. Schwarz Tracking and Information Systems Wireless Sensor Network, James A.C. Patterson, Raza Ali, and Guang-Zhong Yang Bar Coding in Medication Administration, Ben-Tzion Karsh, Tosha B. Wetterneck, Richard J. Holden, A. Joy Rivera-Rodriguez, Helene Faye, Matthew C. Scanlon, Pascale Carayon, and Samuel J. Alper Clinical Decision Support Systems, Sze-jung Sandra Wu, Mark Lehto, and Yuehwern Yih Health Informatics: Systems and Design Considerations, Jose Antonio Valdez and Rupa Sheth Valdez Privacy/Security/Personal Health Record Service, Jeff Donnell Index

S. N. Aken

With a myriad of relatively recent books on health communication and related topics available, there would need to be a pressing need to publish yet another one. In her introduction, Schiavo states that such a need became evident during her search for a textbook to use in the course that she was teaching. In short, she wanted a book that “combined a theoretical and practice-based overview of current issues and topics … with a step-by-step practical section that would help readers acquire technical skills.” At well over 400 pages, including a glossary, a lengthy list of references, subject and name indexes, abundant tables and figures, and two appendixes, this book seems a likely candidate to fill any void in previous offerings. Although this book appears to be the author's first major foray into print publishing, she has written several unpublished reports and has been an active contributor to The Health Communications Initiative [1], where she first presented some of the concepts later included in the book. She continues to teach numerous workshops in the field and is actively involved in the American College of Public Health. The book itself is divided into three parts. Part one, “Introduction to Health Communication,” defines the concepts and clearly establishes the importance of various sociocultural/socioeconomic influences on health beliefs and models. A major strength of the book is the liberal use of practical examples to illustrate and reinforce the theoretical issues being presented, as these may be minimal in less exhaustive texts, such as Berry's Health Communication: Theory and Practice [2]. Health sciences librarians involved in outreach activities or those who work with patients or the public will find chapter 3, “Cultural, Gender, Ethnic, Religious, and Geographical Influences on Conceptions of Health and Illness,” of particular interest. Part two, “Health Communication Approaches and Action Areas,” provides a more in-depth look at the various types of communication presented in part one, including interpersonal communication, public relations/public advocacy, community mobilization, professional medical communications, and constituency relations, where the constituents range from patients to health care providers, drug companies, policymakers, and other key stakeholders. Again, the numerous case studies, lists (do versus don't, definitions of communication formats, etc.), and even testimonials from other experts are particularly helpful in the selection and integration of the most appropriate tools for engaging constituents and for implementing action programs. Part three, “Planning, Implementing, and Evaluating a Health Communication Program,” outlines practical methods for converting communication initiatives into actions. While most readers would not need a rationale for the need to plan, the differentiation between traditional (vertical or centralized) and participatory (horizontal or decentralized) planning may be of interest because of the cultural implications. As in the previous two parts, this section of the book offers practical examples and applications, complete with a review of preferred channels and barriers and strengths and weaknesses of actual projects. A review of qualitative versus quantitative research methods is somewhat cursory but may be helpful to those not familiar with the concepts. Unfortunately, libraries are mentioned only in passing as “good places to start a search” as they “provide access to databases and online journals” (p. 269). The author then advises, however, that “most health organizations now have the internal capability to conduct these searches from their offices.” MEDLINE receives but a token nod here, along with Lexis-Nexis and “several commercial databases to which users can subscribe or access using a public library system” in an equally abbreviated section on database and Internet searching. The author instead focuses on the Internet and the evaluation of websites, including Table 10.2, reprinted from a 1999 BMJ article (p. 271). Literature reviews of peer-reviewed and trade publications, newsletters, and so on are once again suggested as a means of monitoring results in Table 13.1, along with “internet searches” (p. 344), but there is neither any mention of electronic alerting services by journal or topic nor any direction given in the text on how or in what resources such searches should be performed. The book is clearly designed to be a used as a teaching tool, and, in this regard, it is successful. Each chapter ends with a “Key Concepts” review that reiterates the main points. Suggested activities in the “For Discussion and Practice” sections could be used in a classroom or workshop setting to help learners apply the principles to real-life situations. Most disappointing, but probably not surprising to most health sciences librarians, is the conspicuous absence of the roles that they or libraries, including the National Library of Medicine (NLM), play in any of the types of presented communication; the planning, design, and implementation of action plans; or the subsequent evaluation process. In chapter 7, “Professional Medical Communications,” for example, neither librarians nor informationists appear in the pathways described, although the author emphasizes that professional communication is an important component of health communications programs and that it requires specialized skills and tools that may not always be used with other audiences (p. 178). Moreover, clinical librarian and reference services are not mentioned in the discussion of effective ways through which health care provider behavior can be influenced, particularly in encouraging evidence-based practice (pp. 182–5). This oversight may touch a nerve with librarians who routinely teach, use, and promote evidence-based health care. Academic health sciences librarians may find this book quite useful, as the author has provided a thorough overview of factors that must be considered to achieve success in implementing instructional, patient/consumer health, or outreach programs. A sampling of similar current texts reveals a similar disregard for the profession, so librarians clearly need to apply the principles outlined here in marketing themselves and their abilities. Readers seeking more in-depth coverage of professional and patient or consumer communication or health literacy may want to consider Consumer Health Informatics: Informing Consumers and Improving Health Care [3]—which includes chapters on NLM outreach efforts, patient-to-patient or patient-to-computer communication, and design of consumer health websites— or the American Medical Association's Understanding Health Literacy: Implications for Medicine and Public Health [4]. Health Communication in the 21st Century [5] explores the impact that new technologies have had on health communication. Chapters on interdisciplinary and synergistic health care teams and on interpersonal communication issues related to hospice or palliative care, caregivers, and support providers may make it of more practical value in these settings.

Anders K. Mortensen, Henrik Karstoft, Karen Søegaard et al.

The clover-grass ratio is an important factor in composing feed ratios for livestock. Cameras in the field allow the user to estimate the clover-grass ratio using image analysis; however, current methods assume the total dry matter is known. This paper presents the preliminary results of an image analysis method for non-destructively estimating the total dry matter of clover-grass. The presented method includes three steps: (1) classification of image illumination using a histogram of the difference in excess green and excess red; (2) segmentation of clover and grass using edge detection and morphology; and (3) estimation of total dry matter using grass coverage derived from the segmentation and climate parameters. The method was developed and evaluated on images captured in a clover-grass plot experiment during the spring growing season. The preliminary results are promising and show a high correlation between the image-based total dry matter estimate and the harvested dry matter ( R 2 = 0.93 ) with an RMSE of 210 kg ha − 1 .

Hui-Mei Hsu, I-Chiu Chang, Ta-Wei Lai