The Intellectual Structure of Health and Medical Informatics

This paper presents the results of an author co-citation analysis of the health and medical informatics discipline. It updates a smaller study that focused on health information systems. Drawing on such sub-fields as bio informatics, clinical decision support systems, computational genomics, e-health, health informatics, and others, this body of knowledge defines the core internal structure of the discipline and delineates its sub-fields. An author co-citation analysis was performed for a nine-year period using the members of editorial boards of several medical informatics-related journals as an initial author sample (N = 272). Several multivariate analyses, including cluster analysis, factor analysis and multidimensional scaling, were performed. The authors results confirm that several established sub-fields still stand but a number of new sub-fields are emerging. Future research can build on this work and examine other journals and additional authors to gain insights into the collaborative and interdisciplinary nature of the health and medical informatics discipline.

Adaptive Multi-Services System for Maternal and Child Health Care on Mobile Application (AM-Care)

Addressing efforts towards the improvement of maternal and child health management can often prove to be problematic in context to successfully obtaining healthcare and medical treatment information from health care professionals. In this regard, the authors propose an adaptive multi-service system that contains fully integrated health care services, medical treatment services, and maternal and child health management. The system utilized both web-based and mobile technology for implementing the application. A practical framework for generating individual maternal and child health care is also presented from data repositories and fully integrated functional health care services to support an improved quality of life for both mother and children. The application, namely AM-Care, consists of the three main components, i.e., Control Centre Component, Web-based Components, and Mobile Components. Also, AM-Care has the important add-on features such as emergency services and warning services.

Assessment of Anti-Angiogenic Drug in Cancer Therapy

It has been documented in the literature that a solid tumor survives by the generation of micro-vessels around it. This phenomenon is known as angiogenesis. Angiogenesis is governed by two factors, namely Tumor Angiogenic Factor (TAF) secreted by the tumor cells and tissue Fibronectin (FNT) concentration in the extra-cellular space. These two factors help in mobilization of endothelial cells from nearby blood vessels. At the initial phase of angiogenesis, neighboring blood vessels affect in formation of capillary sprouts. In this work, to the authors develop a clinically relevant analytical model that could act as an effective tracing system of tumor growth. The author has performed a quantitative assessment of tumor angiogenesis. This analytical method is a correlation between tumor system and vasculature system through an analytical assessment at peripheral blood circulatory of tumor milieu.

The SURE-LET Approach for MR Brain Image Denoising Using Different Shrinkage Rules

SURE-LET Approach is used for reducing or removing noise in brain Magnetic Resonance Images (MRI). Removing or reducing noise is an active research area in image processing. Rician noise is the dominant noise in MRIs. Due to this type of noise, the abnormal tissue (cancerous tissue) may be misclassified as normal tissue and introduces bias into MRI measurements that can have significant impact on the shapes and orientations of tensors in diffusion tensor MRIs. SURE is a new approach to Orthonormal wavelet image denoising. It is an image-domain minimization of an estimate of the mean squared error—Stein’s unbiased risk estimates (SURE). Here, the denoising process can be expressed as a linear combination of elementary denoising processes-linear expansion of thresholds (LET). Different Shrinkage functions such as Soft and Hard and Shrinkage rules and Universal and BayesShrink are used to remove noise and the performance of these results are compared. The algorithm is applied on brain MRIs with different noisy conditions by varying standard deviation of noise. The performance of this approach is compared with performance of the Curvelet transform.

Characterization of HRV by Poincare Plot Analysis among the Female Tea Garden Workers of Northern Hilly Regions of West Bengal

Recent research on Heart Rate Variability (HRV) has proven that Poincare Plot is a powerful tool to mark Short Term and Long Term Heart Rate Variability. This study focuses a comprehensive characterization of HRV among the Tea Garden Workers of the Northern Hilly Regions of West Bengal. The characterization, as available from the data sets, projects the average values of SD1 characteristics, that is, Short Term HRV in females as 58.265ms and SD2 as 149.474. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. The SDRR shows a mean value of 87.298 with a standard deviation of 119.669 and the S Characterization as 16505.99 ms and Standard deviation of 45882.31 ms. ApEn Characterization showed mean value of 0.961 and standard deviation of 0.274.

ECG Signal De-Noising with Asynchronous Averaging and Filtering Algorithm

In this study, a new algorithm is proposed—Asynchronous Averaging and Filtering (AAF) for ECG signal de-noising. R-peaks are detected with another proposed algorithm—Minimum Slot and Maximum Point selecting method (MSMP). AAF algorithm reduces random noise (major component of EMG noise) from ECG signal and provides comparatively good results for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered ECG signal, while signal shape remains undistorted. The authors conclude that R-peak detection with MSMP method gives comparable results from existing algorithm like Pan-Tomkins algorithm. AAF algorithm is advantageous over adaptation algorithms like Wiener and LMS algorithm. Overall performance of proposed algorithms is comparatively good.

Load Balancing Algorithms in Distributed Service Architectures for Medical Applications

This paper investigates the performance of two proposed load balancing algorithms for Object-Oriented Distributed Service Architectures (DSA) that are open and flexible, enabling rapid and easy development of new applications on various kinds of software and hardware platforms, catering for telecommunications and distributed medical applications. The proposed algorithms, namely, Node Status Algorithm and Random Sender Initiated Algorithm, have been developed as solutions to the performance problems faced by the DSA. The performance of the proposed algorithms have been tested and compared with baseline load balancing algorithms, namely the Random Algorithm and Shortest Queue Algorithm. Simulation results show that both the proposed algorithms perform better than the baseline algorithms, especially in heavily loaded conditions. This paper discusses the mechanisms of the algorithms and reports on the investigations that have been carried out in comparing the load balancing algorithms implemented on a DSA-based network, which is useful for the distributed computing requirements of the medical field.

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.

Measuring the Effects of Information Systems on the Performance of Operating Rooms (OR)

The paper applies various qualitative and quantitative methods to measure the influence of information technology on the performance of operating rooms (ORs). It provides a comprehensive set of indicators to evaluate the impact of IT on the quality, efficiency and performance of ORs. This set of indicators was further enriched by conducting interviews with hospital professionals. The result is a new set of performance indicators, divided into five major categories: productivity, efficiency, quality, cost savings and employee satisfaction. This set of indicators serves as a basis for a weighed performance model that can be applied as a useful tool for selecting new OR information systems. Additionally, the model can be used for improving existing applications. The research identifies time registration, integration with the medical and financial systems of the hospital and production of activity queries and reports as the most common functions of ORs systems. However, despite their potential to contribute to productivity, efficiency and quality of ORs, automatic conflict recognition, material and personnel planning are less frequently applied by hospitals. Finally, the paper highlights the links between efficient use of OR systems, larger volumes of operations and higher occupation rates, and assists in identifying positive effects of systems used in ORs on their performance.

Electronic Health Records

Electronic health records (EHRs) have been proposed as a sustainable solution for improving the quality of medical care. This study investigates how EHR use, as implemented and utilized, impacts patient safety and quality performance. Data in this paper include nonfederal acute care hospitals in the state of Texas, and the data sources include the American Hospital Association, the Dallas Fort Worth Hospital Council, and the American Hospital Directory. The authors use partial least squares modeling to assess the relationship between hospital EHR use, patient safety, and quality of care. Patient safety is measured using 11 indicators as identified by the Agency for Healthcare Research and Quality (AHRQ) and quality performance is measured by 11 mortality indicators as related to 2 constructs, that is, conditions and surgical procedures. Results identify positive significant relationships between EHR use, patient safety, and quality of care with respect to procedures. The authors conclude that there is sufficient evidence of the relationship between hospital EHR use and patient safety, and that sufficient evidence exists for the support of EHR use with hospital surgical procedures.