Arrhythmia is one of the preventive cardiac problems frequently occurs all over the globe. In order to screen such disease at early stage, this work attempts to develop a system approach based on registration, feature extraction using discrete wavelet transform (DWT), feature validation and classification of electrocardiogram (ECG). This diagnostic issue is set as a two-class pattern classification problem (normal sinus rhythm versus arrhythmia) where MIT-BIH database is considered for training, testing and clinical validation. Here DWT is applied to extract multi-resolution coefficients followed by registration using Pan Tompkins algorithm based R point detection. Moreover, feature space is compressed using sub-band principal component analysis (PCA) and statistically validated using independent sample t-test. Thereafter, the machine learning algorithms viz., Gaussian mixture model (GMM), error back propagation neural network (EBPNN) and support vector machine (SVM) are employed for pattern classification. Results are studied and compared. It is observed that both supervised classifiers EBPNN and SVM lead to higher (93.41% and 95.60% respectively) accuracy in comparison with GMM (87.36%) for arrhythmia screening.

This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.

Patient safety has been regarded as the most important quality policy of hospital management. The medicine dispensing definitely plays an influential role in the Joint Commission International Accreditation Standards. The problem we are going to discuss in this paper is that the function of detecting mistakes does not exist in the Automatic Tablets packaging machine (ATPM) in the hospital pharmacy department when the pharmacists implement the replenishment of cassettes. In this situation, there are higher possibilities of placing the wrong cassettes back to the wrong positions, so that the human errors will lead to a crucial impact on total inpatients undoubtedly. Therefore, this study aims to design the RFID (Radio frequency identification) position based system (PBS) for the ATPM with passive high frequency (HF) model. At first, we placed the HF tags on each cassette and installed the HF readers on the cabinets for each position. Then, the system works on the reading loop to verify ID numbers and positions on each cassette. Next, the system would detect whether the orbit opens or not and controls the readers’ working power consumption for drug storage temperature. Finally, we use the RFID PBS of the ATPM to achieve the goal of avoiding the medication errors at any time for patient safety.

An automated diagnostic system for diabetes mellitus (DM), from heart rate variability (HRV) measures, using feed forward neural network has been developed. Changes in autonomic nervous system activity caused by DM are quantified by means of time domain and frequency domain analysis of HRV. Electrocardiograms of 70 DM patients and 65 healthy volunteers were recorded. Nine time domain measures—standard deviation of all NN intervals, square root of mean of sum of squares of differences between adjacent NN interval (RMSSD), number of adjacent NN intervals differing more than 50 ms. (NN50 count), percentage of NN50 count, R-R triangular index, triangular interpolation of NN intervals (TINN), standard deviation of the mean heart rate, mean R-R interval and mean heart rate—were used as the input features to the neural network. This diagnostic system classifies DM patients and normal volunteers from morphologically identical ECGs. Diagnostic results show that the system is performing well with an accuracy of 93.08%, specificity of 96.92% and sensitivity of 89.23%.

SPECT(single photon emission computed tomography) myocardial imaging is a diagnosis technique that images the region of interest and examines any change induced by disease using a computer after injects intravenously a radiopharmaceutical drug emitting gamma ray and the drug has dispersed evenly in the heart . Myocardial perfusion imaging, which contains functional information, is useful for non–invasive diagnosis of myocardial disease but noises caused by physical factors and low resolution give difficulty in reading the images. In order to help reading myocardial images, this study proposed a method that segments myocardial images and reconstructs the segmented region into a 3D image. To resolve difficulty in reading, we segmented the left ventricle, the region of interest, using a level set and modeled the segmented region into a 3D image.

The computerization of Intensive Care Units provides an overwhelming amount of electronic data for both medical and financial analysis. However, the current tarification, which is the process to tick and count patients’ procedures, is still a repetitive, time-consuming process on paper. Nurses and secretaries keep track manually of the patients’ medical procedures. This paper describes the design methodology and implementation of automated tarification services. In this study we investigate if the tarification can be modeled in service oriented architecture as a composition of interacting services. Services are responsible for data collection, automatic assignment of records to physicians and application of rules. Performance is evaluated in terms of execution time, cost evaluation and return on investment based on tracking of real procedures. The services provide high flexibility in terms of maintenance, integration and rules support. It is shown that services offer a more accurate, less time-consuming and cost-effective tarification.

Prescription medicine for asthma at primary stages is based on asthma severity level. Despite major progress in discovering various variables affecting asthma severity levels, disregarding some of these variables by physicians, variables’ inherent uncertainty, and assigning patients to limited categories of decision making are the major causes of underestimating asthma severity, and as a result low quality of life in asthmatic patients. In this paper, we provide a solution of intelligence fuzzy system for this problem. Inputs of this system are organized in five modules of respiratory symptoms, bronchial obstruction, asthma instability, quality of life, and asthma severity. Output of this system is degree of asthma severity in score (0–10). Evaluating performance of this system by 28 asthmatic patients reinforces that the system’s results not only correspond with evaluations of physicians, but represent the slight differences of asthmatic patients placed in specific category introduced by guidelines.

The purpose of this study is to build an integrated medical information system for effective database management of clinical information and to improve the existing Electronic Medical Record (EMR)-based system that is currently being used in hospitals. The integrated medical information system of hospitals consists of an Order Communication System (OCS), Picture Archiving Communication System (PACS), and Laboratory Information System (LIS), as well as Electronic Medical Record (EMR). It is designed so that remote health screening and patient data search can be accessed through a high speed network—even in remote areas—in order to effectively manage data on medical treatment that patients received at their respective hospitals. The existing oriental treatment system is one in which the doctor requires the patient to visit the hospital in person, so as to be able to check the patient’s pulse and measure it with his hand for proper diagnosis and treatment. However, due to the recent development of digitalized medical measurement equipment, not only can doctors now check a patient’s pulse without touching it directly, but the measured data are computerized and stored into the database as the electronic obligation record. Thus, even if a patient cannot visit the hospital, proper medical treatment is available by analyzing the patient’s medical history and diagnosis process in the remote area. Furthermore, when a comprehensive medical testing center system including the people medical examination and diverse physical examination is established, the quality of medical service is expected to be improved than now.

This work explores the design of mammography-based machine learning classifiers (MLC) and proposes a new method to build MLC for breast cancer diagnosis. We massively evaluated MLC configurations to classify features vectors extracted from segmented regions (pathological lesion or normal tissue) on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views, providing BI-RADS diagnosis. Previously, appropriate combinations of image processing and normalization techniques were applied to reduce image artifacts and increase mammograms details. The method can be used under different data acquisition circumstances and exploits computer clusters to select well performing MLC configurations. We evaluated 286 cases extracted from the repository owned by HSJ-FMUP, where specialized radiologists segmented regions on CC and/or MLO images (biopsies provided the golden standard). Around 20,000 MLC configurations were evaluated, obtaining classifiers achieving an area under the ROC curve of 0.996 when combining features vectors extracted from CC and MLO views of the same case.

Recent advances in medical imaging modality have enabled us to identify new features in retinal vasculature. One of the features is retinal vascular tortuosity which has been shown to become a predictive factor for cardiovascular diseases and diabetes. The changes in retinal vascular tortuosity might be a sign of severity or improvement of the disease. In this paper, we propose a new method for measuring retinal vascular tortuosity. We adopt a new technique to analyze tortuosity that consider vessel-segment’s width simultaneously. Our proposed method measures vessel-segment’s tortuosity on its edge. A qualitative assessment shows that the method is appropriate for measuring the tortuosity of the vessels in different widths and directions in the image. Finally, a comparison distinguishing tortuous vs. non tortuous vessels demonstrates that the proposed approach may be suitable for predicting or earlier diagnosis of diabetes or cardiovascular diseases.

The Intensive Care Unit is a data intensive environment where large volumes of patient monitoring and observational data are daily generated. Today, there is a lack of an integrated clinical platform for automated decision support and analysis. Despite the potential of electronic records for infection surveillance and antibiotic management, different parts of the clinical data are stored across databases in their own formats with specific parameters, making access to all data a complex and time-consuming challenge. Moreover, the motivation behind physicians’ therapy decisions is currently not captured in existing information systems. The COSARA research project offers automated data integration and services for infection control and antibiotic management for Ghent University Hospital. The platform not only gathers and integrates all relevant data, it also presents the information visually at the point of care. In this paper, we describe the design and value of COSARA for clinical treatment and infectious diseases monitoring. On the one hand, this platform can facilitate daily bedside follow-up of infections, antibiotic therapies and clinical decisions for the individual patient, while on the other hand, the platform serves as management view for infection surveillance and care quality improvement within the complete ICU ward. It is shown that COSARA is valuable for registration, real-time presentation and management of infection-related and antibiotics data.

In this study, it has been intended to perform an automatic classification of Electroencephalography (EEG) signals via Artificial Neural Networks (ANN) and to investigate these signals using Wavelet Transform (WT) for diagnosing epilepsy syndrome. EEG signals have been decomposed into frequency sub-bands using WT and a set of feature vectors which were extracted from the sub-bands. Dimensions of these feature vectors have been reduced via Principal Component Analysis (PCA) method and then classified as epileptic or healthy using Multilayer Perceptron (MLP) and ELMAN ANN. Performance evaluation of the used ANN models have been carried out by performing Receiver Operation Characteristic (ROC) analysis.

Ventriculomegaly is the most commonly detected abnormality in neonatal brain. It can be defined as a condition when the human brain ventricle system becomes dilated. This in turn increases the intracranial pressure inside the skull resulting in progressive enlargement of the head. Sometimes it may also cause mental disability or death. For these reasons early detection of ventriculomegaly has become an important task. In order to identify ventriculomegaly from neonatal brain ultrasound images, we propose an automated image processing based approach that measures the anterior horn width as the distance between medial wall and floor of the lateral ventricle at the widest point. Measurement is done in the plane of the scan at the level of the intraventricular foramina. Our study is based on neonatal brain ultrasound images in the midline coronal view. In addition to ventriculomegaly detection, this work also includes both cross sectional and longitudinal study of anterior horn width of lateral ventricles. Experiments were carried out on brain ultrasound images of 96 neonates with gestational age ranging from 26 to 39 weeks and results have been verified with the ground truth provided by doctors. Accuracy of the proposed scheme is quite promising.

Choosing the most suitable treatment for the scoliosis relies heavily on accurate and reproducible spinal curvature measurement from radiographs. Our objective is to reduce the variability in spinal curvature measurement by reducing the user intervention and bias. In order to determine the reliability of the spinal curvature measurement as it is in the clinical measurement of scoliosis a methodological survey has been carried out that concludes with inter and intra observer error variation. The proposed method list out horizontal inclination of all the vertebrae’s in terms of slopes using active contour models and morphological operators. This facilitates the radiologist to decide end vertebrae and hence inter/intra observer variation is completely eliminated. Tables  and shows the observer error variation between manual and proposed methods in terms of mean and standard deviation.

A conventional Nursing Information System (NIS), which supports the role of nurse in some areas, is typically deployed as an immobile system. However, the traditional information system can’t response to patients’ conditions in real-time, causing delays on the availability of this information. With the advances of information technology, mobile devices are increasingly being used to extend the human mind’s limited capacity to recall and process large numbers of relevant variables and to support information management, general administration, and clinical practice. Unfortunately, there have been few studies about the combination of a well-designed small-screen interface with a personal digital assistant (PDA) in clinical nursing. Some researchers found that user interface design is an important factor in determining the usability and potential use of a mobile system. Therefore, this study proposed a systematic approach to the development of a mobile nursing information system (MNIS) based on Mobile Human-Computer Interaction (M-HCI) for use in clinical nursing. The system combines principles of small-screen interface design with user-specified requirements. In addition, the iconic functions were designed with metaphor concept that will help users learn the system more quickly with less working-memory. An experiment involving learnability testing, thinking aloud and a questionnaire investigation was conducted for evaluating the effect of MNIS on PDA. The results show that the proposed MNIS is good on learning and higher satisfaction on symbol investigation, terminology and system information.

Breast Cancer is an important disease that affects many women, excluding self-examination and screening by mammogram, nothing helps women or their physicians to know what risk they run of suffering from breast cancer during the course of their lives. There have been many studies detailing the relative risks of breast cancer based on different factors and applications to calculate the breast cancer risk, but none implemented in a way to show lifetime risk. This paper presents an on-line tool (called Breast Alert) to calculate the lifetime breast cancer risk for women using a proposed model. With Breast Alert, physicians can make a quick screening for women when they consult. It is easy to use and intuitive. In a few minutes, physicians can have a lifetime breast cancer risk. This tool does not replace tests like self-examination, breast screening or detection by other options, but allows for the proper precautions to be taken and calls attention to the expected lifetime risk. Nowadays, 300 women (between 20 and 75 years old) from different countries have used the system and most of them (80%) have a higher than normal chance of contracting breast cancer. With these results, it is important to alert of the importance to make an early prevention of breast cancer in different women groups.

In this paper, we present MedWise, a high level design of a medical information infrastructure, and its architecture. The proposed system offers a comprehensive, modular, robust and extensible infrastructure to be used in public health care systems. The system gathers reliable and evidence based health data, which it then classifies, interprets and stores into a particular database. It creates a healthcare ecosystem that aids the medical community by providing for less error prone diagnoses and treatment of diseases. This system will be standards-compliant; therefore it would be complementary to the existing healthcare and clinical information systems. The key objective of the proposed system is to provide as much medical historical and miscellaneous data as possible about the patients with minimal consultation, thus allowing physicians to easily access Patients’ Ancillary Data (PAD) such as hereditary, residential, travel, custom, meteorological, biographical and demographical data before the consultation. In addition, the system can help to diminish problems and misdiagnosis situations caused by language barriers-disorders and misinformation. MedWise can assist physicians to shorten time for diagnosis and consultations, therefore dramatically improving quality and quantity of the physical examinations of patients. Furthermore, since it intends to supply a significant amount of data, it may be used to improve skills of students in medical education.

In August 2009, Miyazaki Health and Welfare Network (Haniwa Net, hereafter referred to as “the Net”), centrally led by University of Miyazaki Hospital (UMH), adopted a center hospital-based system offering a unilateral linkage that enables the viewing of UMH’s medical records through a web-based browser (electronic medical records (EMR)). By the end of December 2010, the network had developed into a system of 79 collaborating physicians from within the prefecture. Beginning in August 2010, physicians in 12 medical institutions were visited and asked to speak freely on the operational issues concerning the Net. Recordings and written accounts were coded using the text analysis software MAXQDA 10 to understand the actual state of operations. Analysis of calculations of Kendall’s rank correlation confirmed that the interdependency between human networks and information networks is significant. At the same time, while the negative opinions concerning the functions of the Net were somewhat conspicuous, the results showed a correlation between requests and proposals for operational improvements of the Net, clearly indicating the need for a more user-friendly system and a better viewer.

The aim of this study is to develop a Financial Early Warning System (FEWS) for hospitals by using data mining. A data mining method, Chi-Square Automatic Interaction Detector (CHAID) decision tree algorithm, was used in the study for financial profiling and developing FEWS. The study was conducted in Turkish Ministry of Health’s public hospitals which were in financial distress and in need of urgent solutions for financial issues. 839 hospitals were covered and financial data of the year 2008 was obtained from Ministry of Health. As a result of the study, it was determined that 28 hospitals (3.34%) had good financial performance, and 811 hospitals (96.66%) had poor financial performance. According to FEWS, the covered hospitals were categorized into 11 different financial risk profiles, and it was found that 6 variables affected financial risk of hospitals. According to the profiles of hospitals in financial distress, one early warning signal was detected and financial road map was developed for risk mitigation.

Based on users’ health issues, an intelligent personal health record (iPHR) system can automatically recommend home medical products (HMPs) and display them in a sequential order. However, the sequential output interface does not categorize search results and is not easy for users to quickly navigate to their desired HMPs. To address this problem, we developed a navigation interface for retrieved HMPs. Our idea is to use medical knowledge and nursing knowledge to construct a navigation hierarchy based on product categories. This hierarchy is added to the left side of each search result Web page to help users move through retrieved HMPs. We demonstrate the effectiveness of our techniques using USMLE medical exam cases.

The division of breast cancer cells results in regions of electrical depolarisation within the breast. These regions extend to the skin surface from where diagnostic information can be obtained through measurements of the skin surface electropotentials using sensors. This technique is used by the Biofield Diagnostic System (BDS) to detect the presence of malignancy. This paper evaluates the efficiency of BDS in breast cancer detection and also evaluates the use of classifiers for improving the accuracy of BDS. 182 women scheduled for either mammography or ultrasound or both tests participated in the BDS clinical study conducted at Tan Tock Seng hospital, Singapore. Using the BDS index obtained from the BDS examination and the level of suspicion score obtained from mammography/ultrasound results, the final BDS result was deciphered. BDS demonstrated high values for sensitivity (96.23%), specificity (93.80%), and accuracy (94.51%). Also, we have studied the performance of five supervised learning based classifiers (back propagation network, probabilistic neural network, linear discriminant analysis, support vector machines, and a fuzzy classifier), by feeding selected features from the collected dataset. The clinical study results show that BDS can help physicians to differentiate benign and malignant breast lesions, and thereby, aid in making better biopsy recommendations.

The basic principle of graph-based approaches for image segmentation is to interpret an image as a graph, where the nodes of the graph represent 2D pixels or 3D voxels of the image. The weighted edges of the graph are obtained by intensity differences in the image. Once the graph is constructed, the minimal cost closed set on the graph can be computed via a polynomial time s-t cut, dividing the graph into two parts: the object and the background. However, no segmentation method provides perfect results, so additional manual editing is required, especially in the sensitive field of medical image processing. In this study, we present a manual refinement method that takes advantage of the basic design of graph-based image segmentation algorithms. Our approach restricts a graph-cut by using additional user-defined seed points to set up fixed nodes in the graph. The advantage is that manual edits can be integrated intuitively and quickly into the segmentation result of a graph-based approach. The method can be applied to both 2D and 3D objects that have to be segmented. Experimental results for synthetic and real images are presented to demonstrate the feasibility of our approach.

In this paper, we present a three-stage expert system based on a hybrid support vector machines (SVM) approach to diagnose thyroid disease. Focusing on feature selection, the first stage aims at constructing diverse feature subsets with different discriminative capability. Switching from feature selection to model construction, in the second stage, the obtained feature subsets are fed into the designed SVM classifier for training an optimal predictor model whose parameters are optimized by particle swarm optimization (PSO). Finally, the obtained optimal SVM model proceeds to perform the thyroid disease diagnosis tasks using the most discriminative feature subset and the optimal parameters. The effectiveness of the proposed expert system (FS-PSO-SVM) has been rigorously evaluated against the thyroid disease dataset, which is commonly used among researchers who use machine learning methods for thyroid disease diagnosis. The proposed system has been compared with two other related methods including the SVM based on the Grid search technique (Grid-SVM) and the SVM based on Grid search and principle component analysis (PCA-Grid-SVM) in terms of their classification accuracy. Experimental results demonstrate that FS-PSO-SVM significantly outperforms the other ones. In addition, Compared to the existing methods in previous studies, the proposed system has achieved the highest classification accuracy reported so far by 10-fold cross-validation (CV) method, with the mean accuracy of 97.49% and with the maximum accuracy of 98.59%. Promisingly, the proposed FS-PSO-SVM expert system might serve as a new candidate of powerful tools for diagnosing thyroid disease with excellent performance.

Continuous monitoring of EEG is essential for the neurologist to detect the epileptic seizures that occur at various intervals. Since large volume of data need to be analyzed, visual analysis has been proven to be time consuming and subsequently automated detection techniques have gained importance in the recent years. For the biomedical research community, the major challenge lies in providing a solution to neurologists in terms of diagnosis and EEG database management. This paper discusses the automated detection of epileptic seizure using frequency domain and entropy parameters which helps in the construction of epileptic database for handling EEG data. Experimental study indicates that the suggested mode of operation can be used for internet based framework which contains pure epileptic patterns in the server. This can be retrieved and analyzed for detection and annotation of epileptic spikes in extensive EEG recordings.

Toothache is the most common symptom encountered in dental practice. It is subjective and hence, there is a possibility of under or over diagnosis of oral pathologies where patients present with only toothache. Addressing the issue, the paper proposes a methodology to develop a Bayesian classifier for diagnosing some common dental diseases (D = 10) using a set of 14 pain parameters (P = 14). A questionnaire is developed using these variables and filled up by ten dentists (n = 10) with various levels of expertise. Each questionnaire is consisted of 40 real-world cases. Total 14*10*10 combinations of data are hence collected. The reliability of the data (P and D sets) has been tested by measuring (Cronbach’s alpha). One-way ANOVA has been used to note the intra and intergroup mean differences. Multiple linear regressions are used for extracting the significant predictors among P and D sets as well as finding the goodness of the model fit. A naïve Bayesian classifier (NBC) is then designed initially that predicts either presence/absence of diseases given a set of pain parameters. The most informative and highest quality datasheet is used for training of NBC and the remaining sheets are used for testing the performance of the classifier. Hill climbing algorithm is used to design a Learned Bayes’ classifier (LBC), which learns the conditional probability table (CPT) entries optimally. The developed LBC showed an average accuracy of 72%, which is clinically encouraging to the dentists.

The classification of epileptic electroencephalogram (EEG) signals is challenging because of high nonlinearity, high dimensionality, and hidden states in EEG recordings. The detection of the preictal state is difficult due to its similarity to the ictal state. We present a framework for using principal components analysis (PCA) and a classification method for improving the detection rate of epileptic classes. To unearth the nonlinearity and high dimensionality in epileptic signals, we extract principal component features using PCA on the 15 high-order spectra (HOS) features extracted from the EEG data. We evaluate eight classifiers in the framework using true positive (TP) rate and area under curve (AUC) of receiver operating characteristics (ROC). We show that a simple logistic regression model achieves the highest TP rate for class “preictal” at 97.5% and the TP rate on average at 96.8% with PCA variance percentages selected at 100%, which also achieves the most AUC at 99.5%.

Nowadays with the growing of the wireless connections people can access all the resources hosted in the Cloud almost everywhere. In this context, organisms can take advantage of this fact, in terms of e-Health, deploying Cloud-based solutions on e-Health services. In this paper two Cloud-based solutions for different scenarios of Electronic Health Records (EHRs) management system are proposed. We have researched articles published between the years 2005 and 2011 about the implementation of e-Health services based on the Cloud in Medline. In order to analyze the best scenario for the deployment of Cloud Computing two solutions for a large Hospital and a network of Primary Care Health centers have been studied. Economic estimation of the cost of the implementation for both scenarios has been done via the Amazon calculator tool. As a result of this analysis two solutions are suggested depending on the scenario: To deploy a Cloud solution for a large Hospital a typical Cloud solution in which are hired just the needed services has been assumed. On the other hand to work with several Primary Care Centers it’s suggested the implementation of a network, which interconnects these centers with just one Cloud environment. Finally it’s considered the fact of deploying a hybrid solution: in which EHRs with images will be hosted in the Hospital or Primary Care Centers and the rest of them will be migrated to the Cloud.

This paper presents the impact of the Electronic Health Records (EHRs) systems jointly in the Spanish Primary Public Health System. Different EHRs that exist in each of the Spanish regions are discussed. Moreover, other purpose of this analysis is to identify the current state of knowledge about health information systems adoption in primary care in Spain. For the analysis and study of EHRs systems in Spain we have relied on the use of different sources, mostly items related to the study of EHRs systems in different areas. We will analyze some technical aspects of these and some of their major implications, both positive and negative. Moreover, we have resorted to make direct contact with the organizations that have implemented the EHRs systems. The result of this study leads to a main idea, the need for interoperability between different systems. We will delve into how we have reached this conclusion and that is the key to EHRs systems homogenization of Spanish territory. EHR systems used in different regions of Spain offer the access to medical information as well as provide a clinical analysis of each patient more quickly. The adoption of health information systems is seen world wide as one method to mitigate the widening health care demand and supply gap.

Computer and information technology popularizes in the medicine manufacturing enterprise for its potentials in working efficiency and service quality. In allusion to the explosive data and information of application system in current medicine manufacturing enterprise, we desire to propose a novel application information system integration platform in medicine manufacturing enterprise, which based on a combination of RFID technology and SOA, to implement information sharing and alternation. This method exploits the application integration platform across service interface layer to invoke the RFID middleware. The loose coupling in integration solution is realized by Web services. The key techniques in RFID event components and expanded role-based security access mechanism are studied in detail. Finally, a case study is implemented and tested to evidence our understanding on application system integration platform in medicine manufacturing enterprise.

This paper is focused on the factors causing long patient waiting time/clinic overtime in outpatient clinics and how to mitigate them using discrete event simulation. A two-week period of data collection is conducted in an outpatient clinic of a Singapore government hospital. Detailed time study from patient arrival to patient departure is conducted, and the possible factors causing long patient waiting time/clinic overtime are discussed. A discrete simulation model is constructed to illustrate how to improve the clinic performance by mitigating the detected factors. Simulation and implementation results show that significant improvement is achieved if the factors are well addressed.

Due to increasing occurrence of accidents and illness during business trips, travel, or overseas studies, the requirement for portable EMR (Electronic Medical Records) has increased. This study proposes integrating streaming media technology into the EMR system to facilitate referrals, contracted laboratories, and disease notification among hospitals. The current study encoded static and dynamic medical images of patients into a streaming video format and stored them in a Flash Media Server (FMS). Based on the Taiwan Electronic Medical Record Template (TMT) standard, EMR records can be converted into XML documents and used to integrate description fields with embedded streaming videos. This investigation implemented a web-based portable EMR interchanging system using streaming media techniques to expedite exchanging medical image information among hospitals. The proposed architecture of the portable EMR retrieval system not only provides local hospital users the ability to acquire EMR text files from a previous hospital, but also helps access static and dynamic medical images as reference for clinical diagnosis and treatment. The proposed method protects property rights of medical images through information security mechanisms of the Medical Record Interchange Service Center and Health Certificate Authorization to facilitate proper, efficient, and continuous treatment of patients.

To investigate the impacts of availability of pre-mixed solutions and computerized order entry on nephrologists’ choice of the initial mode of renal replacement therapy in acute renal failure. We studied 898 patients with acute renal failure in 3 consecutive eras: era 1 (custom-mixed solution; n = 309), era 2 (pre-mixed commercial solution; n = 324), and era 3 (post-computerized order entry; n = 265). The proportion of patients treated with renal replacement therapy and the time from consult to initiation of continuous renal replacement therapy was similar in the 3 eras. Following introduction of the pre-mixed solution, the proportion of patients treated with continuous renal replacement therapy increased (20% vs. 33%; p < 0.05), it was initiated at a lower serum creatinine (353 ± 123 μmol/L vs. 300 ± 80 μmol/L; p < 0.05) and in older patients (53 ± 12 vs. 61 ± 14 years; p < 0.05). There was a progressive increase in the use of continuous veno-venous hemodialysis (18% vs. 79% vs. 100%; p < 0.05) and in the total prescribed flow rate (1,382 ± 546 vs. 2,324 ± 737 vs. 2,900 ± 305 mL/hr 3; p < 0.05). There was no significant impact on mortality. The availability of a pre-mixed solution increases the likelihood of initiating continuous renal replacement therapy in acute renal failure, initiating it at a lower creatinine and for older patients, use of continuous veno-venous hemodialysis and higher prescribed continuous renal replacement therapy dose. Computerized order entry implementation is associated with an additional increase in the use of continuous veno-venous hemodialysis, higher total prescribed dialysis dose, and use of CRRT among an increasing number of patients not on mechanical ventilation. The effect of these changes on patient survival is not significant.

In this paper, we classify the breast cancer of medical diagnostic data. Information gain has been adapted for feature selections. Neural fuzzy (NF), k-nearest neighbor (KNN), quadratic classifier (QC), each single model scheme as well as their associated, ensemble ones have been developed for classifications. In addition, a combined ensemble model with these three schemes has been constructed for further validations. The experimental results indicate that the ensemble learning performs better than individual single ones. Moreover, the combined ensemble model illustrates the highest accuracy of classifications for the breast cancer among all models.

Prior research on technology usage had largely overlooked the issue of user resistance or barriers to technology acceptance. Prior research on the Electronic Medical Records had largely focused on technical issues but rarely on managerial issues. Such oversight prevented a better understanding of users’ resistance to new technologies and the antecedents of technology rejection. Incorporating the enablers and the inhibitors of technology usage intention, this study explores physicians’ reactions towards the electronic medical record. The main focus is on the barriers, perceived threat and perceived inequity. 115 physicians from 6 hospitals participated in the questionnaire survey. Structural Equation Modeling was employed to verify the measurement scale and research hypotheses. According to the results, perceived threat shows a direct and negative effect on perceived usefulness and behavioral intentions, as well as an indirect effect on behavioral intentions via perceived usefulness. Perceived inequity reveals a direct and positive effect on perceived threat, and it also shows a direct and negative effect on perceived usefulness. Besides, perceived inequity reveals an indirect effect on behavioral intentions via perceived usefulness with perceived threat as the inhibitor. The research finding presents a better insight into physicians’ rejection and the antecedents of such outcome. For the healthcare industry understanding the factors contributing to physicians’ technology acceptance is important as to ensure a smooth implementation of any new technology. The results of this study can also provide change managers reference to a smooth IT introduction into an organization. In addition, our proposed measurement scale can be applied as a diagnostic tool for them to better understand the status quo within their organizations and users’ reactions to technology acceptance. By doing so, barriers to physicians’ acceptance can be identified earlier and more effectively before leading to technology rejection.

Medical Records Department (MRD) is an important unit for evaluating and planning of care services. The goal of this study is evaluating the performance of the Medical Records Departments (MRDs) of the selected hospitals in Isfahan, Iran by using Analytical Hierarchy Process (AHP). This was an analytic of cross-sectional study that was done in spring 2008 in Isfahan, Iran. The statistical population consisted of MRDs of Alzahra, Kashani and Khorshid Hospitals in Isfahan. Data were collected by forms and through brainstorm technique. To analyze and perform AHP, Expert Choice software was used by researchers. Results were showed archiving unit has received the largest importance weight with respect to information management. However, on customer aspect admission unit has received the largest weight. Ordering weights of Medical Records Departments’ Alzahra, Kashani and Khorshid Hospitals in Isfahan were with 0.394, 0.342 and 0.264 respectively. It is useful for managers to allocate and prioritize resources according to AHP technique for ranking at the Medical Records Departments.

In recent years interest in the application of Wireless Body Area Network (WBAN) for patient monitoring applications has grown significantly. A WBAN can be used to develop patient monitoring systems which offer flexibility to medical staff and mobility to patients. Patients monitoring could involve a range of activities including data collection from various body sensors for storage and diagnosis, transmitting data to remote medical databases, and controlling medical appliances, etc. Also, WBANs could operate in an interconnected mode to enable remote patient monitoring using telehealth/e-health applications. A WBAN can also be used to monitor athletes’ performance and assist them in training activities. For such applications it is very important that a WBAN collects and transmits data reliably, and in a timely manner to a monitoring entity. In order to address these issues, this paper presents WBAN design techniques for medical applications. We examine the WBAN design issues with particular emphasis on the design of MAC protocols and power consumption profiles of WBAN. Some simulation results are presented to further illustrate the performances of various WBAN design techniques.

This research work presents a quantitative approach for analysis of histomorphometric features of the basal cell nuclei in respect to their size, shape and intensity of staining, from surface epithelium of Oral Submucous Fibrosis showing dysplasia (OSFD) to that of the Normal Oral Mucosa (NOM). For all biological activity, the basal cells of the surface epithelium form the proliferative compartment and therefore their morphometric changes will spell the intricate biological behavior pertaining to normal cellular functions as well as in premalignant and malignant status. In view of this, the changes in shape, size and intensity of staining of the nuclei in the basal cell layer of the NOM and OSFD have been studied. Geometric, Zernike moments and Fourier descriptor (FD) based as well as intensity based features are extracted for histomorphometric pattern analysis of the nuclei. All these features are statistically analyzed along with 3D visualization in order to discriminate the groups. Results showed increase in the dimensions (area and perimeter), shape parameters and decreasing mean nuclei intensity of the nuclei in OSFD in respect to NOM. Further, the selected features are fed to the Bayesian classifier to discriminate normal and OSFD. The morphometric and intensity features provide a good sensitivity of 100%, specificity of 98.53% and positive predicative accuracy of 97.35%. This comparative quantitative characterization of basal cell nuclei will be of immense help for oral onco-pathologists, researchers and clinicians to assess the biological behavior of OSFD, specially relating to their premalignant and malignant potentiality. As a future direction more extensive study involving more number of disease subjects is observed.

Personal Health Record (PHR) enables patients to access their health information and improves care quality by supporting self-care. The purpose of this study is to provide a comparative analysis of the concept of PHRs in selected countries and Iran in order to investigate the gaps between Iran and more advanced countries in terms of PHRs. The study was carried out in 2008-2009 using a descriptive—comparative method in Australia, the United States, England and Iran. Data was gathered from articles, books, journals and reputed websites in English and Persian published between 1995 and September 2009. After collecting the data, both advantages and disadvantages of each of concepts were analyzed. In the three countries considered in the present study the concepts of PHR, extracted from the literature, are that; a)patient/person be recognized as the owner of PHR; b)information be disclosed only to those authorized by the patient; c) and that PHR is created upon request and consent of the individual involved. Before PHRs can be profitably used in the health administration of a (developing) country, the necessary knowledge, infrastructures, and rules need to be developed.

With the advancement of pharmaceutical development, drug interactions have become increasingly complex. As a result, a computer-based drug interaction search system is required to organize the whole of drug interaction data. To overcome problems faced with the existing systems, we developed a drug interaction search system using a hash table, which offers higher processing speeds and easier maintenance operations compared with relational databases (RDB). In order to compare the performance of our system and MySQL RDB in terms of search speed, drug interaction searches were repeated for all 45 possible combinations of two out of a group of 10 drugs for two cases: 5,604 and 56,040 drug interaction data. As the principal result, our system was able to process the search approximately 19 times faster than the system using the MySQL RDB. Our system also has several other merits such as that drug interaction data can be created in comma-separated value (CSV) format, thereby facilitating data maintenance. Although our system uses the well-known method of a hash table, it is expected to resolve problems common to existing systems and to be an effective system that enables the safe management of drugs.

This study explores whether Internet users have different privacy concerns regarding the information contained in electronic medical records (EMRs) according to gender, age, occupation, education, and EMR awareness. Based on the Concern for Information Privacy (CFIP) scale developed by Smith and colleagues in 1996, we conducted an online survey using 15 items in four dimensions, namely, collection, unauthorized access, secondary use, and errors, to investigate Internet users’ concerns regarding the privacy of EMRs under health information exchanges (HIE). We retrieved 213 valid questionnaires. The results indicate that the respondents had substantial privacy concerns regarding EMRs and their educational level and EMR awareness significantly influenced their privacy concerns regarding unauthorized access and secondary use of EMRs. This study recommends that the Taiwanese government organizes a comprehensive EMR awareness campaign, emphasizing unauthorized access and secondary use of EMRs. Additionally, to cultivate the public’s understanding of EMRs, the government should employ various media, especially Internet channels, to promote EMR awareness, thereby enabling the public to accept the concept and use of EMRs. People who are highly educated and have superior EMR awareness should be given a comprehensive explanation of how hospitals protect patients’ EMRs from unauthorized access and secondary use to address their concerns. Thus, the public can comprehend, trust, and accept the use of EMRs, reducing their privacy concerns, which should facilitate the future implementation of HIE.

RFID technology, an automatic identification and data capture technology to provide identification, tracing, security and so on, was widely applied to healthcare industry in these years. Employing HEPA ventilation system in hospital is a way to ensure healthful indoor air quality to protect patients and healthcare workers against hospital-acquired infections. However, the system consumes lots of electricity which cost a lot. This study aims to apply the RFID technology to offer a unique medical staff and patient identification, and reacting HEPA air ventilation system in order to reduce the cost, save energy and prevent the prevalence of hospital-acquired infection. The system, reacting HEPA air ventilation system, contains RFID tags (for medical staffs and patients), sensor, and reacting system which receives the information regarding the number of medical staff and the status of the surgery, and controls the air volume of the HEPA air ventilation system accordingly. A pilot program was carried out in a unit of operation rooms of a medical center with 1,500 beds located in central Taiwan from Jan to Aug 2010. The results found the air ventilation system was able to function much more efficiently with less energy consumed. Furthermore, the indoor air quality could still keep qualified and hospital-acquired infection or other occupational diseases could be prevented.

Electronic medical record (EMR) systems have been proposed as technology to improve the quality of patient care, decrease medical errors, control and reduce medical expenditure, however the financial effects have not yet been as well documented in China. We presented a net financial cost-benefit analysis of implementing electronic medical record systems in general hospital in China. The data, which were obtained from studies of the general hospital and the published literature, collected from 15 consecutive fiscal months from May 1, 2009 to August 30, 2010. We performed a perspective cost-benefit study to analyze the financial effects of EMR system implementing. The reference strategy for comparisons was the traditional paper-based medical record. The net financial benefits or costs for a 6-year period were calculated. All data were adjusted for inflation. The totally assessed net benefit from implementing an EMR system for a 6-year period was $559,025 in the general hospital. Benefits accrue primarily from savings in new medical record creation, decreased full-time-equivalent (FTE) employees, saving of adverse drug events (ADEs) and dose errors, improved charge capture and decreased billing errors. In this model, the time of return on investment is 3.00 years. In one-way sensitivity analysis, the model was most sensitive in new medical record creation; the net benefit varied from $398,057 to $719,992. The five-way sensitivity analysis with the most pessimistic and optimistic assumptions showed results ranging from a $76,970 net cost to a $1,062,122 net benefit; the pessimistic time of return on investment is 5.38 years. An EMR system cost-benefit analysis can rapidly demonstrate a positive return on investment when implemented in hospitals. The magnitude of the return is sensitive to several key factors.

Breast cancer is becoming a leading cause of death among women in the whole world, meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. In this paper, a swarm intelligence technique based support vector machine classifier (PSO_SVM) is proposed for breast cancer diagnosis. In the proposed PSO-SVM, the issue of model selection and feature selection in SVM is simultaneously solved under particle swarm (PSO optimization) framework. A weighted function is adopted to design the objective function of PSO, which takes into account the average accuracy rates of SVM (ACC), the number of support vectors (SVs) and the selected features simultaneously. Furthermore, time varying acceleration coefficients (TVAC) and inertia weight (TVIW) are employed to efficiently control the local and global search in PSO algorithm. The effectiveness of PSO-SVM has been rigorously evaluated against the Wisconsin Breast Cancer Dataset (WBCD), which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The proposed system is compared with the grid search method with feature selection by F-score. The experimental results demonstrate that the proposed approach not only obtains much more appropriate model parameters and discriminative feature subset, but also needs smaller set of SVs for training, giving high predictive accuracy. In addition, Compared to the existing methods in previous studies, the proposed system can also be regarded as a promising success with the excellent classification accuracy of 99.3% via 10-fold cross validation (CV) analysis. Moreover, a combination of five informative features is identified, which might provide important insights to the nature of the breast cancer disease and give an important clue for the physicians to take a closer attention. We believe the promising result can ensure that the physicians make very accurate diagnostic decision in clinical breast cancer diagnosis.

Knee sound signals generated by knee movement are sometimes associated with degeneration of the knee joint surface and such sounds may be a useful index for early disease. In this study, we detected the acoustical parameters, such as the fundamental frequency (F0), mean amplitude of the pitches, and jitter and shimmer of knee sounds, and compared them according to the pathological conditions. Six normal subjects (4 males and 2 females, age: 28.3 ± 2.3 years) and 11 patients with knee problems were enrolled. The patients were divided into the 1st patient group (5 males and 1 female, age: 30.2 ± 10.3 years) with ruptured wounds of the meniscus and 2nd patient group (2 males and 3 females, age: 42.1 ± 16.2 years) with osteoarthritis. The mean values of F0, jitter and shimmer of the 2nd patient group were larger than those of the normal group, whereas those of the 1st patient group were smaller (p < 0.05). Also, the F0 and jitter in the standing position were larger than those in the sitting position in both the 1st and 2nd patient groups (p < 0.05). These results showed good potential for the non-invasive diagnosis and early detection of articular pathologies via an auscultation.

This paper describes a large resource of multi-center and multi-topic heart sound databases, which were based on the measured data from more than 9,000 heart sound samples (saved in WAV file format). According to different research topics, these samples were respectively stored in different folders (corresponding to different research topics and distributed over various cooperative research centers), most of which as subfolds were stored in a pooled folder in the principal center. According to different research topics, the measured data from these samples were used to create different databases. Relevant data for a specific topic can be pooled in a large database for further analysis. This resource is shared by members of related centers for their own specific topic. The applications of this resource include evaluation of cardiac safety of pregnant women, evaluation of cardiac reserve for children, athletes, addicts, astronauts, and general populations, as well as studies on a bedside method for evaluating cardiac energy, reversal of S1-S2 ratio, etc.

The potential of computer based tools to assist physicians in medical decision making, was envisaged five decades ago. Apart from factors like usability, integration with work-flow and natural language processing, lack of decision accuracy of the tools has hindered their utility. Hence, research to develop accurate algorithms for medical decision support tools, is required. Pioneering research in last two decades, has demonstrated the utility of fuzzy set theory for medical domain. Recently, Wagholikar and Deshpande proposed a fuzzy relation based method (FR) for medical diagnosis. In their case studies for heart and infectious diseases, the FR method was found to be better than naive bayes (NB). However, the datasets in their studies were small and included only categorical symptoms. Hence, more evaluative studies are required for drawing general conclusions. In the present paper, we compare the classification performance of FR with NB, for a variety of medical datasets. Our results indicate that the FR method is useful for classification problems in the medical domain, and that FR is marginally better than NB. However, the performance of FR is significantly better for datasets having high proportion of unknown attribute values. Such datasets occur in problems involving linguistic information, where FR can be particularly useful. Our empirical study will benefit medical researchers in the choice of algorithms for decision support tools.

The low birth rate has led to an aging society; the burgeoning number of elderly patients may affect the medical quality and result in negative medical incidents. There are many factors that lead to medical errors, such as similar medication names, erroneous labels and packaging, as well as staff shortages, fatigue and carelessness. Determining how to reduce medical errors has become an important issue. As RFID exhibits powerful identification characteristics, it can help nurses to quickly identify patients and their corresponding medicine. Currently, there are numerous practical applications for improving the efficiency of Radio Frequency Identification (RFID) systems. In this paper, we use an RFID yoking proof mechanism which conforms to EPCglobal Class 1 Generation 2 standards to improve patient safety and reduce medical errors. Our scheme can achieve different goals such as resist numerous known attacks, achieve mutual authentication, anonymity and non-repudiation. It also provides a practical medical care and offer higher quality of medical care. The pharmacist cannot deny that this prescription was confirmed for the patient and the nurse cannot also deny he or she dispensed this medicine to the patient for protecting the patients’ interests.

Electronic medical records are generally used by nurses in hospitals. However, studies investigating views on and evaluations of electronic medical records by nurses are limited in Turkey and in other countries around the world. Thus, in this study, nurses’ views on electronic medical record systems will be investigated in terms of use, quality and user satisfaction. Our goal was to investigate the views on electronic medical records used by nurses working at hospital clinics (inpatient care units). Moreover, in this study, we will examine whether there are relationships among the use, quality and user satisfaction of electronic medical records. This study is composed of field research conducted using questionnaires. To prepare the data-measuring instrument, the literature on electronic medical records was reviewed. In addition, during the pilot run of the questionnaire, some revisions were made to the measuring instrument to account for the views of nurse managers in the field. The questionnaire consists of 35 items: 12 items for use, 12 items for quality and 11 items for user satisfaction. A Likert scale type was used in this questionnaire. Responses for usage and quality were assigned a value of 1–5 (‘never/almost never/not at all’ to ‘always/almost always’) for each item. Responses for user satisfaction were assigned a value of 1–5 (‘not at all’, to ‘very great’) for each item. The study was planned and conducted on nurses working at inpatient care units at one public university hospital, one Turkish Ministry of Health hospital and one private hospital in Kocaeli. A sampling technique was not used because we aimed to conduct the questionnaire among all nurses. At the end of this study, the questionnaire had been conducted on 200 nurses. We found that the average score for the nurses’ satisfaction with electronic medical records was 3.28, the average score for using electronic medical records was 1.96, and the average score for the quality of electronic medical records was 3.16. We also determined that there are significant relationships among the use, quality and user satisfaction of electronic medical records. This study revealed that there are significant differences among the mean quality scores for the EMR systems in the Ministry of health hospital, the university hospital and the private hospital. Interestingly, 59.0% of all participants in this study felt that EMR systems were not well integrated into their workflow. In addition, half of all respondents had not been trained in using EMR systems.

In pattern classification problems, feature extraction is an important step. Quality of features in discriminating different classes plays an important role in pattern classification problems. In real life, pattern classification may require high dimensional feature space and it is impossible to visualize the feature space if the dimension of feature space is greater than four. In this paper, we have proposed a Similarity-Dissimilarity plot which can project high dimensional space to a two dimensional space while retaining important characteristics required to assess the discrimination quality of the features. Similarity-dissimilarity plot can reveal information about the amount of overlap of features of different classes. Separable data points of different classes will also be visible on the plot which can be classified correctly using appropriate classifier. Hence, approximate classification accuracy can be predicted. Moreover, it is possible to know about whom class the misclassified data points will be confused by the classifier. Outlier data points can also be located on the similarity-dissimilarity plot. Various examples of synthetic data are used to highlight important characteristics of the proposed plot. Some real life examples from biomedical data are also used for the analysis. The proposed plot is independent of number of dimensions of the feature space.

Expert or knowledge-based systems are the most common type of AIM (artificial intelligence in medicine) system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusion. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules. Arthritis is a chronic disease and about three fourth of the patients are suffering from osteoarthritis and rheumatoid arthritis which are undiagnosed and the delay of detection may cause the severity of the disease at higher risk. Thus, earlier detection of arthritis and treatment of its type of arthritis and related locomotry abnormalities is of vital importance. Thus the work was aimed to design a system for the diagnosis of Arthitis using fuzzy logic controller (FLC) which is, a successful application of Zadeh’s fuzzy set theory. It is a potential tool for dealing with uncertainty and imprecision. Thus, the knowledge of a doctor can be modelled using an FLC. The performance of an FLC depends on its knowledge base which consists of a data base and a rule base. It is observed that the performance of an FLC mainly depends on its rule base, and optimizing the membership function distributions stored in the data base is a fine tuning process.

In the breast-cancer image detection device field, remarkable advancements have been made in the breast cancer detection method using microwave radiation that satisfies the conditions required by Institute of Medicine (IOM). This paper is for embodying the microwave analysis breast tumor detection system that can analyze the permittivity and the conductivity of the breast inside, discover breast tumors, and easily check the various analytical information of the scatter and size of tumors inside breasts. The microwave breast tumor detection system is composed of an antenna array and the RF transceiver for the acquiring of microwave exposure information; the inverse scattering algorithm for searching the permitivity, conductivity and position of a material, and graphic user interface software that includes the visualization and various analyses of acquired data. The embodied system has shown the same-level function of tumor detection even in the type of heterogeneously dense material that is difficult to detect through mammography by experimentations with four kinds of classifications according to the distribution of lactiferous duct inside the breast.

Web-based personal health records (PHRs) are being widely deployed. To improve PHR’s capability and usability, we proposed the concept of intelligent PHR (iPHR). In this paper, we use automatic home medical product recommendation as a concrete application to demonstrate the benefits of introducing intelligence into PHRs. In this new application domain, we develop several techniques to address the emerging challenges. Our approach uses treatment knowledge and nursing knowledge, and extends the language modeling method to (1) construct a topic-selection input interface for recommending home medical products, (2) produce a global ranking of Web pages retrieved by multiple queries, and (3) provide diverse search results. We demonstrate the effectiveness of our techniques using USMLE medical exam cases.

Many medical organizations have deployed electronic medical record (EMR) information systems (IS) to improve medical decision-making and increase efficiency. Despite their advantages, however, EMR IS may make less of a contribution in the stressful environment of an emergency department (ED) that operates under tight time constraints. The high level of crowdedness in the EDs itself can cause physicians to make medical decisions resulting in more unnecessary admissions and fewer necessary admissions. Thus this study evaluated the contribution of an EMR IS to physicians by investigating whether EMR IS leads to improved medical outcomes in points of care in EDs under different levels of crowdedness. For this purpose a track log-file analysis of a database containing 3.2 million ED referrals in seven main hospitals in Israel (the whole population in these hospitals) was conducted. The findings suggest that viewing medical history via the EMR IS leads to better admission decisions, and reduces the number of possibly avoidable single-day admissions. Furthermore, although the ED can be very stressful especially on crowded days, physicians used EMR IS more on crowded days than on non-crowded days. These results have implications as regards the viability of EMR IS in complex, fast-paced environments.

The Department of Health of Executive Yuan in Taiwan (R.O.C.) is implementing a five-stage project entitled Electronic Medical Record (EMR) converting all health records from written to electronic form. Traditionally, physicians record patients’ symptoms, related examinations, and suggested treatments on paper medical records. Currently when implementing the EMR, all text files and image files in the Hospital Information System (HIS) and Picture Archiving and Communication Systems (PACS) are kept separate. The current medical system environment is unable to combine text files, hand-drafted files, and photographs in the same system, so it is difficult to support physicians with the recording of medical data. Furthermore, in surgical and other related departments, physicians need immediate access to medical records in order to understand the details of a patient’s condition. In order to address these problems, the Department of Health has implemented an EMR project, with the primary goal of building an electronic hand-drafting and picture management system (HDP system) that can be used by medical personnel to record medical information in a convenient way. This system can simultaneously edit text files, hand-drafted files, and image files and then integrate these data into Portable Document Format (PDF) files. In addition, the output is designed to fit a variety of formats in order to meet various laws and regulations. By combining the HDP system with HIS and PACS, the applicability can be enhanced to fit various scenarios and can assist the medical industry in moving into the final phase of EMR.

A quantitative graduation system based on Grey Relational Analysis is proposed to recognize fatty livers in B-scan ultrasonic images. We evaluated ultrasonography liver images from 95 subjects having fatty livers (Grade I, II, III) and 45 normal subjects, as diagnosed by an expert radiologist. In practice, ultrasonographical findings of fatty liver are based on the brightness level of the liver in comparison to the renal parenchyma. The development of a non-invasive and accurate method would be of great clinical value as an alternative to diagnosing fatty liver based on the radiologist’s visual perception. In this study, we also evaluated AST and ALT liver enzymes for fatty liver having different grades. A high correlation between enzymes and Grey Relational Grades were found. The Receiver Operating Characteristic (ROC) curves were obtained and yielded satisfactory classification results using sensitivity, specificity and area under the curve for computing graduation and distinguishing fatty livers from healthy livers. With the proposed method based on Grey Relational Analysis, not only misdiagnosis caused by subjective differences in clinical evaluation will be reduced, but also the early diagnosis fatty liver and quantitative assessment of its degree will be achieved.

A natural disaster is a consequence of a natural hazard, such as a tsunami, earthquake or volcanic eruption, affecting humans. In order to support emergency medical communication services in natural disaster areas where the telecommunications facility has been seriously damaged, an ad hoc communication network backbone should be build to support emergency medical services. Combinations of requirements need to be considered before deciding on the best option. In the present study we have proposed a Low Altitude Platform consisting of tethered balloons combined with Wireless Fidelity (WiFi) 802.11 technology. To confirm that the suggested network would satisfy the emergency medical service requirements, a communications experiment, including performance service measurement, was carried out.

This systematic review of mixed methods studies focuses on factors that can facilitate or limit the implementation of information and communication technologies (ICTs) in clinical settings. Systematic searches of relevant bibliographic databases identified studies about interventions promoting ICT adoption by healthcare professionals. Content analysis was performed by two reviewers using a specific grid. One hundred and one (101) studies were included in the review. Perception of the benefits of the innovation (system usefulness) was the most common facilitating factor, followed by ease of use. Issues regarding design, technical concerns, familiarity with ICT, and time were the most frequent limiting factors identified. Our results suggest strategies that could effectively promote the successful adoption of ICT in healthcare professional practices.

A new algorithm for medical image retrieval is presented in the paper. An 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT) which is similar to the lifting scheme in real wavelet transform (RWT) is performed on each bitplane to extract the multi-resolution binary images. The local binary pattern (LBP) features are extracted from the resultant BWT sub-bands. Three experiments have been carried out for proving the effectiveness of the proposed algorithm. Out of which two are meant for medical image retrieval and one for face retrieval. It is further mentioned that the database considered for three experiments are OASIS magnetic resonance imaging (MRI) database, NEMA computer tomography (CT) database and PolyU-NIRFD face database. The results after investigation shows a significant improvement in terms of their evaluation measures as compared to LBP and LBP with Gabor transform.

The rapid rise and development of the internet has made digitization of our everyday life common. E-medicine, including electronic prescription records, electronic prescriptions, diagnosis information systems, and others are now being regarded as future trends. As development on the structure and format of electronic patient records and prescriptions matures, the implementation of a comprehensive medical information system is imperative, one which is constructed from integrating the various electronic information systems that is being developed. It is important to allow the implementation of such a system applicable to the present medical environment, which facilitates the integration of electronic patient record from all levels of medical centers and clinics, secures the transmission of these integrated patient records between them, enables the combined use of electronic prescriptions with patients’ medications, and permits anonymous or confidential transmission of patients’ private data. To put the ideas into practice, in this study, we would like to propose an Integrated Medical Information System.

It is important to guarantee the privacy and the security of the users in the telecare medicine information system. Recently, Wu et al.’s proposed an authentication scheme for mobile devices in telecare medicine information system. They added the pre-computing idea within the communication process to avoid the time-consuming exponential computations. They also claimed their scheme can withstand various attacks. We will show that their scheme suffers from the impersonation attack to the insider’s attack. In order to overcome the weaknesses, we propose an improved scheme to eliminate the weakness. Our scheme is not only more secure than Wu et al.’s scheme, but also has better performance. Then our scheme is more efficient and appropriate to collocating with low power mobile devices for the telecare medicine information system.

This study evaluated the effectiveness of implementation of an improved storage label and an error-reducing process on the incidence of drug-dispensing errors. A total of 27 pharmacists (11 male and 16 female) were included. Questionnaires were distributed to pharmacists to measure their degree of satisfaction with the format and content of the labels. The questionnaires were completed before and one month after implementation of new label. Pharmacists were also requested to follow a new error-reducing dispensing process by circling the following items on the new storage label: drug name, appearance, packaging, dose, and formulation. The pharmacists’ degrees of satisfaction increased significantly after implementation of the new label with respect to these questionnaire items: all label format items, edition appropriateness, use of capital fonts to distinguish similar drug names, reminder images to help with drug differentiation, and complete label information. The outpatient monthly drug-dispensing error rate was significantly decreased.

Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

The Technology Safeguard in Health Insurance Portability and Accountability Act (HIPAA) Title II has addressed a way to maintain the integrity and non-repudiation of Electronic Medical Record (EMR). One of the important cryptographic technologies is mentioned in the ACT is digital signature; however, the ordinary digital signature (e.g. DSA, RSA, GQ...) has an inherent weakness: if the key (certificate) is updated, than all signatures, even the ones generated before the update, are no longer trustworthy. Unfortunately, the current most frequently used digital signature schemes are categorized into the ordinary digital signature scheme; therefore, the objective of this paper is to analyze the shortcoming of using ordinary digital signatures in EMR and to propose a method to use forward secure digital signature to sign EMR to ensure that the past EMR signatures remain trustworthy while the key (certificate) is updated.

This article aims at building clinical data groups for Electronic Medical Records (EMR) in China. These data groups can be reused as basic information units in building the medical sheets of Electronic Medical Record Systems (EMRS) and serve as part of its implementation guideline. The results were based on medical sheets, the forms that are used in hospitals, which were collected from hospitals. To categorize the information in these sheets into data groups, we adopted the Health Level 7 Clinical Document Architecture Release 2 Model (HL7 CDA R2 Model). The regulations and legal documents concerning health informatics and related standards in China were implemented. A set of 75 data groups with 452 data elements was created. These data elements were atomic items that comprised the data groups. Medical sheet items contained clinical records information and could be described by standard data elements that exist in current health document protocols. These data groups match different units of the CDA model. Twelve data groups with 87 standardized data elements described EMR headers, and 63 data groups with 405 standardized data elements constituted the body. The later 63 data groups in fact formed the sections of the model. The data groups had two levels. Those at the first level contained both the second level data groups and the standardized data elements. The data groups were basically reusable information units that served as guidelines for building EMRS and that were used to rebuild a medical sheet and serve as templates for the clinical records. As a pilot study of health information standards in China, the development of EMR data groups combined international standards with Chinese national regulations and standards, and this was the most critical part of the research. The original medical sheets from hospitals contain first hand medical information, and some of their items reveal the data types characteristic of the Chinese socialist national health system. It is possible and critical to localize and stabilize the adopted international health standards through abstracting and categorizing those items for future sharing and for the implementation of EMRS in China.

This paper introduces a new integrated approach to measure unified efficiency of the healthcare systems. Health centers as an important part of the healthcare systems are considered for evaluation. For this purpose, we define two categories of inputs to measure performance of health centers based on medical human resources and characteristics of spatial information by using geographic information system (GIS). Catching the balance in the spatial distribution of populations and services is one of the main problems in health centers evaluation. On the other hand, data envelopment analysis (DEA) is widely applied for measuring efficiency of the healthcare systems. But, the conventional DEA models may fail to integrated several categories of measures. In this paper, DEA and bargaining game model are integrated for evaluation of health centers. In other words, two categories of measures are used to measure unified efficiency for each health center in the competitive environment. Two models according to constant return to scale (CRS) and variable return to scale (VRS) assumptions are developed. The case study of health centers under supervising of Tehran university of medical sciences (TMUS) is presented to show the abilities of the proposed approach.

We address in the present paper a medical monitoring system designed as a multi-agent based approach. Our system includes mainly numerous agents that act as correlated multi-agent sub-systems at the three layers of the whole monitoring infrastructure, to avoid non informative alarms and send effective alarms at time. The intelligence in the proposed monitoring system is provided by the use of time series technology. In fact, the capability of continuous learning of time series from the physiological variables allows the design of a system that monitors patients in real-time. Such system is a contrast to the classical threshold-based monitoring system actually present in the Intensive Care Units (ICUs) which causes a huge number of irrelevant alarms.

Effective communication is the most important part of any healthcare organization. For many years, hospital nurse call solutions had been stand-alone systems with occasional integration to pocket paging for outputting patient call alerts to mobile staff. In the late 1990’s, technology enabled in-building wireless phones to supplement or replace paging systems as a means of not only sending alerts, but also enabling voice communication between mobile staff and patients. Today’s nurse call market requires integration of additional information from location and ADT (admit, discharge, transfer) systems into what have traditionally been nurse call applications. This system information is required not only at the nursing station, pagers, and phones, but also at PC’s placed on each patient care floor in hallways, nurse stations, and offices, and at areas away from the patients, including administrator and clinical engineering offices. It is crucial that nurses have the latest patient information in their hand wherever they go in the hospital. In this paper, MatchMaker.NET has been developed to integrate all these technologies into the hospital’s LAN to improve nurse-patient communication.

Industry predictions focus on future e-hospitals that will integrate all stakeholders into a seamless network, allowing data to be shared. The Health Level Seven (HL7) is a standard for the interchange of data within the healthcare industry. It simplifies communication interfaces and allows the interoperability among heterogeneous applications. Although the benefits of adopting HL7 are well known, only a few hospitals in Taiwan have actually adopted it. What are the reasons behind the hospitals’ lack of intention to adopt HL7? Most prior studies on HL7 have focused on technical issues and general overlooked the managerial side. This has caused a lack of understanding of factors influencing hospitals’ decision on HL7 adoption. In fact, main reasons behind a hospital’s decision on whether to adopt an innovative technology are more often related to organizational than purely technical issues. Hence, we pay our attention to these organizational considerations over HL7 adoption. Based on the Innovation Diffusion Theory, we proposed a research model to explore the critical factors influencing Taiwan hospitals’ adoption intention of HL7. 472 questionnaires were distributed to all accredited hospitals in Taiwan and 122 were returned. The valid response rate was 25.21% (119). Factor analysis, logistic regression and Pearson Chi-square test were conducted to verify the research model. The results showed that environmental pressure, top management attitude towards HL7, staff’s technology capability, system integrity, and hospital’s scale were critical factors influencing hospitals’ intention on whether to adopt HL7. The research findings provided the government, the healthcare industry, the hospital administrators and the academia with practical and theoretical references. These factors should be considered in planning promotion plan to encourage hospital adoption of HL7. This study also opens up a new research direction as well as a new viewpoint, and consequentially improves the completeness of related researches in the medical informatics discipline.

In this paper we present a system using Semantic Web by which applications can be effectively constructed for clinical research purposes. We are aware of the immense difficulties and variations involved in clinical research applications. With a purpose of mitigating some of these difficulties in the process of developing clinical research applications we are presenting an approach for building information systems based on Semantic Web. We have developed a working prototype using C-Map tools leveraging the underlying principles of Abstract Software Design Framework to convert domain knowledge into machine-actable information.

This paper presents automatic detection and localization of myocardial infarction (MI) using K-nearest neighbor (KNN) classifier. Time domain features of each beat in the ECG signal such as T wave amplitude, Q wave and ST level deviation, which are indicative of MI, are extracted from 12 leads ECG. Detection of MI aims to classify normal subjects without myocardial infarction and subjects suffering from Myocardial Infarction. For further investigation, Localization of MI is done to specify the region of infarction of the heart. Total 20,160 ECG beats from PTB database available on Physio-bank is used to investigate the performance of extracted features with KNN classifier. In the case of MI detection, sensitivity and specificity of KNN is found to be 99.9% using half of the randomly selected beats as training set and rest of the beats for testing. Moreover, Arif-Fayyaz pruning algorithm is used to prune the data which will reduce the storage requirement and computational cost of search. After pruning, sensitivity and specificity are dropped to 97% and 99.6% respectively but training is reduced by 93%. Myocardial Infarction beats are divided into ten classes based on the location of the infarction along with one class of normal subjects. Sensitivity and Specificity of above 90% is achieved for all eleven classes with overall classification accuracy of 98.8%. Some of the ECG beats are misclassified but interestingly these are misclassified to those classes whose location of infarction is near to the true classes of the ECG beats. Pruning is done on the training set for eleven classes and training set is reduced by 70% and overall classification accuracy of 98.3% is achieved. The proposed method due to its simplicity and high accuracy over the PTB database can be very helpful in correct diagnosis of MI in a practical scenario.

This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms a method based on watershed segmentation.

As seen in the spring 2009 A/H1N1 influenza outbreak, influenza pandemics can have profound social, legal and economic effects. This experience has also made the importance of public health preparedness exercises more evident. Universities face unique challenges with respect to pandemic preparedness due to their dense student populations, location within the larger community and frequent student/faculty international travel. Depending on the social structure of the community, different mitigation strategies should be applied for decreasing the severity and transmissibility of the disease. To this end, Arizona State University has developed a simulation model and tabletop exercise that facilitates decision-maker interactions around emergency-response scenarios. This simulation gives policy makers the ability to see the real-time impact of their decisions. Therefore, tabletop exercises with computer simulations are developed to practice these decisions, which can possibly give opportunities for practicing better policy implementations. This paper introduces a new method of designing and performing table-top exercises for pandemic influenza via state-of-the-art technologies including system visualization and group decision making with a supporting simulation model. The presented exercise methodology can increase readiness for a pandemic through the support of computer and information technologies and the survey results that we include in this paper certainly support this result. The video scenarios and the computer simulation model make the exercise appear very compelling and real, which makes this presented method of exercising different than the other table-top exercises in the literature. Finally, designing a pandemic preparedness exercise with supporting technologies can help identifying the communication gaps between responsible authorities and advance the table-top exercising methodology.

Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.

Nowadays, patients usually take more than three drugs for diseases such as hypertension, diabetes, and dyslipidemia. Hence, nuclear medicine physicians should be very careful about the medication history of each patient and ensure that their medication will not cause false positive or false negative imaging results, because either condition will interfere with adequate treatment of the patient and result in a wrong diagnosis. The aim of the present paper is to develop an ontology-based medication search and alert system for scintiphotography of Chang Gung Memorial hospital at Kaohsiung. Composed of four sub-systems, including Medication History Collect Agent (MHCA), Medication History Search System (MHSS), Patient Medication Consultation System (PMCS), and Patient Medication Alert System (PMAS), this medication search and alert system for scintiphotography is expected to support decision making of nuclear medicine examination, improve accuracy of image reading, and offer detailed data for further research. The ultimate goal of this system is to ensure patient safety.

In the rapidly developing world of information technology, computers have been used in various settings for clinical medicine application. Studies have focused on computerized physician order entry (CPOE) system interface design and functional development to achieve a successful technology adoption process. Therefore, the purpose of this study was to evaluate physician satisfaction with the CPOE system. This survey included user attitude toward interface design, operation functions/usage effectiveness, interface usability, and user satisfaction. We used questionnaires for data collection from June to August 2008, and 225 valid questionnaires were returned with a response rate of 84.5 %. Canonical correlation was applied to explore the relationship of personal attributes and usability with user satisfaction. The results of the data analysis revealed that certain demographic groups showed higher acceptance and satisfaction levels, especially residents, those with less pressure when using computers or those with less experience with the CPOE systems. Additionally, computer use pressure and usability were the best predictors of user satisfaction. Based on the study results, it is suggested that future CPOE development should focus on interface design and content links, as well as providing educational training programs for the new users; since a learning curve period should be considered as an indespensible factor for CPOE adoption.

This is a study that describes the prevalence and patterns of constructing virtual subject in hospital in China. It is a high risk for hospital to invest greatly for innovation of hospital disciplines, so we want to establish some new comprehensive platforms which based on some informational systems that involve diseases treatment, medical research, diseases recoveries, prevent diseases and medicine developments. But the virtual subject platform could afford a superior chance for cooperation between interior and exterior medical organizations. This article discusses the subject’s structure, the construction’s principles, cooperation advantages and clarifies that the platform could boost the efficiency of hospital to do some medical research.

Disease identification in public health monitoring routinely employs analyte detection systems capable of discriminating mixtures of analytes, toxins, cells and/or bacteria in medical and/or environmental solutions. The development of smart sensors capable of discriminating such compounds has become increasingly important for clinical, environmental, and health applications. While some sensors have been fashioned for single analyte detection, methods and systems that facilitate rapid screening of multiple clinical components are needed, serving as triggers for potential epidemics or more specific confirmatory testing. In public health applications, there is like need for immediate collection of geocoded data tagged by disease identification characteristics, with corresponding alerting capabilities. In this technology review we propose one promising model for using a combination of emerging systems-based technologies in multi sensor cartridges, integrated with GPS-enabled, alert-capable mobile phone devices.

Current EMR system benefits physicians by facilitating order entry and reducing errors. It can improve the safety and effectiveness of medical services, but cannot manage the whole medical process and the quality of medical services. In addition to physicians, EMR should be designed for all medical professionals because medical services cannot be accomplished by physicians alone, but also requires the involvement of other medical professionals. Therefore, we applied PDCA, the famous quality management cycle to design a comprehensive and coherent EMR system which can be used throughout the entire treatment process. EMR with the PDCA Cycle can record every order state and every treatment procedure in order to monitor the whole medical process. This extends the safety from planning the treatment to fulfilling it. By analyzing the records, doctors and hospital managers can perfect the medical process and improve healthcare quality. The EMR we designed with the PDCA Cycle provides a record entry interface for physicians and a worksheet interface for nurses and other professionals. Every treatment procedure and every change of orders or tasks will be fed back to medical professionals. So information generated from the beginning to the end of treatment will link with each other to avoid any information islands. Furthermore, the EMR can display the additional information intuitively and real-timely without increasing the burden of medical professionals’ work.

Listening via stethoscope is a primary method, being used by physicians for distinguishing normally and abnormal cardiac systems. Listening to the voices, coming from the cardiac valves via stethoscope, upon the flow of the blood running in the heart, physicians examine whether there is any abnormality with regard to the heart. However, listening via stethoscope has got a number of limitations, for interpreting different heart sounds depends on hearing ability, experience, and respective skill of the physician. Such limitations may be reduced by developing biomedical based decision support systems. In this study, a biomedical-based decision support system was developed for the classification of heart sound signals, obtained from 120 subjects with normal, pulmonary and mitral stenosis heart valve diseases via stethoscope. Developed system was mainly comprised of three stages, namely as being feature extraction, dimension reduction, and classification. At feature extraction stage, applying Discrete Fourier Transform (DFT) and Burg autoregressive (AR) spectrum analysis method, features, representing heart sounds in frequency domain, were obtained. Obtained features were reduced in lower dimensions via Principal Component Analysis (PCA), being used as a dimension reduction technique. Heart sounds were classified by having the features applied as input to Artificial Neural Network (ANN). Classification results have shown that, dimension reduction, being conducted via PCA, has got positive effects on the classification of the heart sounds.

Personally controlled health records (PCHR) systems have emerged to allow patients to control their own medical data. In a PCHR system, all the access privileges to a patient’s data are granted by the patient. However, in many emergency cases, it is impossible for the patient to participate in access authorization on site when immediate medical treatment is needed. To solve the emergency access authorization problem in the absence of patients, we consider two cases: a) the requester is already in the PCHR system but has not obtained the access privilege of the patient’s health records, and b) the requester does not even have an account in the PCHR system to submit its request. For each of the two cases, we present a method for emergency access authorization, utilizing the weighted voting and source authentication cryptographic techniques. Our methods provide an effective, secure and private solution for emergency access authorization, that makes the existing PCHR system frameworks more practical and thus improves the patients’ experiences of health care when using PCHR systems. We have implemented a prototype system as a proof of concept.

The aim of this study was to explore the importance of service need along with perceived technology attributes in potentially influence the acceptance of teleconsultation. The study was conducted based on the concurrent triangulation design involving qualitative and quantitative study methods. These entailed interviews with key informants and questionnaires survey of health care providers who practiced in the participating hospitals in Malaysia. Thematic analysis involving iterative coding was conducted on qualitative data. Scale reliability test and hypothesis testing procedures were performed on quantitative data. Subsequently, both data were merged, compared and interpreted. In particular, this study utilized a qualitative priority such that a superior emphasis was placed on the qualitative method to demonstrate an overall understanding. Based on the responses of 20 key informants, there was a significant need for teleconsultation as a tool to extend health services to patients under constrained resources and critical conditions. Apparently, the latest attributes of teleconsultation technology have generally met users’ expectation but rather perceived as supportive facets in encouraging the usage. Concurrently, based on the survey engaging 72 health care providers, teleconsultation acceptance was statistically proven to be strongly associated with service need and not originated exclusively from the technological attributes. Additionally, the results of this study can be used to promote teleconsultation as an effective means in delivering better health services. Thus, the categories emerged from this study may be further revised and examined for explaining the acceptance of teleconsultation technology in other relevant contexts.

Digital signature is an important cryptography technology to be used to provide integrity and non-repudiation in electronic medical record systems (EMRS) and it is required by law. However, digital signatures normally appear in forms unrecognizable to medical staff, this may reduce the trust from medical staff that is used to the handwritten signatures or seals. Therefore, in this paper we propose a dual function seal to extend user trust from a traditional seal to a digital signature. The proposed dual function seal is a prototype that combines the traditional seal and digital seal. With this prototype, medical personnel are not just can put a seal on paper but also generate a visualized digital signature for electronic medical records. Medical Personnel can then look at the visualized digital signature and directly know which medical personnel generated it, just like with a traditional seal. Discrete wavelet transform (DWT) is used as an image processing method to generate a visualized digital signature, and the peak signal to noise ratio (PSNR) is calculated to verify that distortions of all converted images are beyond human recognition, and the results of our converted images are from 70 dB to 80 dB. The signature recoverability is also tested in this proposed paper to ensure that the visualized digital signature is verifiable. A simulated EMRS is implemented to show how the visualized digital signature can be integrity into EMRS.

Developing standards and technology models that will facilitate e-prescribing is one of the key action items in the federal government’s plan to build a nationwide electronic health information infrastructure in the United States. E-prescribing has the potential to drive change in the healthcare industry, but the unavailability of diagnostic testing and detection equipment outside of clinical settings makes expanded collection and use of information problematic. Most solutions are provider-based, and limited by organization-wide startup & maintenance costs, and risk-averse data distribution policies. Objective, consumer-provided standardized data can facilitate the use of distributed information networks in polypharmacy detection and avoidance. In this technology review we propose here one promising model for polypharmacy management and integrated diagnostics through the use of breath-based, multiple array sensing and data capture.

We introduce a method for extracting hidden patterns seen in rheumatic diseases by using articles from the widely used biomedical database MEDLINE. Rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. Diagnosing rheumatic diseases can be difficult because some symptoms are common to many of them. We use Facta system as a biomedical text mining tool for finding symptoms and then create a dataset with the frequencies of symptoms for each disease and apply hierarchical clustering analysis to find similarities between diseases. Clustering analysis yields four distinct types or groups of rheumatic diseases. Although our results cannot remove all the uncertainty for the diagnosis of rheumatic diseases, we believe they can contribute to the diagnosis of rheumatic diseases to a certain extent. We hope that some similarities exposed can provide additional information at the stage of decision-making.

Development of virtual-reality medical applications is usually a complicated and labour intensive task. This paper explores the feasibility of using commodity physics engine to develop a suturing simulator prototype for manual skills training in the fields of nursing and medicine, so as to enjoy the benefits of rapid development and hardware-accelerated computation. In the prototype, spring-connected boxes of finite dimension are used to simulate soft tissues, whereas needle and thread are modelled with chained segments. Spherical joints are used to simulate suture’s flexibility and to facilitate thread cutting. An algorithm is developed to simulate needle insertion and thread advancement through the tissue. Two-handed manipulations and force feedback are enabled with two haptic devices. Experiments on the closure of a wound show that the prototype is able to simulate suturing procedures at interactive rates. The simulator is also used to study a curvature-adaptive suture modelling technique. Issues and limitations of the proposed approach and future development are discussed.

This paper presents the framework for forecasting the surgery time by taking into account the surgical environment in an ophthalmology department (experience of surgeon in years, experience of anesthetist in years, staff experience in years, type of anesthesia etc.). The estimation of surgery times is done using three techniques, such as the Adaptive Neuro Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN) and Multiple Linear Regression Analysis (MLRA) and the results of estimation accuracy were compared. Though the developed framework is general, it is illustrated for three ophthalmologic surgeries such as the cataract surgery, corneal transplant surgery and Oculoplastic surgery. The framework is validated by using data obtained from a local hospital. It is hypothesized that by accurately knowing the surgery times, one can schedule the operations optimally resulting in the efficient utilization of the operating rooms. This increase in the efficiency is demonstrated through computer simulations of the operating theater.

The shape prior segmentation procedure and pruned association rule with ImageApriori algorithm has been used to develop an improved brain image classification system are presented in this paper. The CT scan brain images have been classified into three categories namely normal, benign and malignant, considering the low-level features extracted from the images and high level knowledge from specialists to enhance the accuracy in decision process. The experimental results on pre-diagnosed brain images showed 97% sensitivity, 91% specificity and 98.5% accuracy. The proposed algorithm is expected to assist the physicians for efficient classification with multiple key features per image.

Adverse drug events are largely considered to be errors in which the severity of effects could be lessened or even prevented through more effective medication reconciliation practices. Transitions of care, particularly at the time of discharge from the hospital, represent a time of heightened error vulnerability that contributes to medication discrepancy occurrences. The observed vulnerability can be attributed to communication and care continuity gaps across health care settings and can often lead to preventable errors. Health IT tools developed through research can identify factors which increase the risk of medication discrepancies. Additionally, the implementations of optimized clinical workflow processes to form effective transitions of care are approaches to decreasing medication discrepancies which may lead to adverse drug events. While federal policies and certifying organizations have implemented quality initiatives to increase focus on medication reconciliation practices in the hospital and primary care settings, the same practices must be implemented after a patient is discharged to their homes or another health care facility in order to mitigate error vulnerabilities that occur at the transition of care. This paper provides an overview of health IT system capabilities and their applications within and across health care delivery settings to facilitate care coordination to ensure continuity of care.

This paper presents a case study of a hospital operating room that evaluated a Radio-Frequency Identification (RFID)-enabled traceability system for the management of consignment and high value products requiring item level traceability. Results indicate that the traceability system in conjunction with the redesign of replenishment processes facilitates item level traceability, improves financial controls and case costing, upgrades service levels and reduces inventory shrinkage. Other benefits include time saved from non-value-added activities that can be transferred to patient care activities. The solution can be considered (i) as an alternative to RFID-enabled cabinets used in the replenishment of consignment and high value supplies in certain operating rooms, cardiac catheterization laboratories and interventional radiology departments, or (ii) as a complementary solution facilitating the tracking of medical devices removed from RFID-enabled cabinets. In short, the end-to-end traceability of medical products in the healthcare supply chain can be significantly enhanced.

With the rapid development of the Internet, both digitization and electronic orientation are required on various applications in the daily life. For hospital-acquired infection control, a Web-based Hospital-acquired Infection Surveillance System was implemented. Clinical data from different hospitals and systems were collected and analyzed. The hospital-acquired infection screening rules in this system utilized this information to detect different patterns of defined hospital-acquired infection. Moreover, these data were integrated into the user interface of a signal entry point to assist physicians and healthcare providers in making decisions. Based on Service-Oriented Architecture, web-service techniques which were suitable for integrating heterogeneous platforms, protocols, and applications, were used. In summary, this system simplifies the workflow of hospital infection control and improves the healthcare quality. However, it is probable for attackers to intercept the process of data transmission or access to the user interface. To tackle the illegal access and to prevent the information from being stolen during transmission over the insecure Internet, a password-based user authentication scheme is proposed for information integrity.

In nowadays, there are many various diseases, whose diagnosis is very hardly. Lung cancer is one of this type diseases. It begins in the lungs and spreads to other organs of human body. In this paper, an expert diagnostic system based on General Discriminant Analysis (GDA) and Least Square Support Vector Machine (LS-SVM) Classifier for diagnosis of lung cancer. This expert diagnosis system is called as GDA-LS-SVM in rest of this paper. The GDA-LS-SVM expert diagnosis system has two stages. These are 1. Feature extraction and feature reduction stage and 2. Classification stage. In feature extraction and feature reduction stage, lung cancer dataset is obtained and dimension of this lung cancer dataset, which has 57 features, is reduced to eight features using Generalized Discriminant Analysis (GDA) method. Then, in classification stage, these reduced features are given to Least Squares Support Vector Machine (LS-SVM) classifier. The lung cancer dataset used in this study was taken from the UCI machine learning database. The classification accuracy of this GDA-LS-SVM expert system was obtained about 96.875% from results of these experimental studies.

Under the constraints of limited medical resources and severe competition among hospitals, administrators have begun to pay attention to the opportunities of cost reduction and quality improvement in hospital management, in order to find methods to increase hospital revenue and improve service quality. The operating room should be one of the most important sources of hospital income, yet it is both costly to run and constrictive to inpatient flow. Successful hospital management necessitates the construction of cost-effective and quality operating room scheduling. This paper attempts to model the scheduling problem in the form of mathematical programming with the objective being to minimize the deviation between the total operation time and the total available time in operating rooms. Urgent revisions to the model in consideration of such factors as doctor’s availability, outpatient consulting hours and unfavorable surgery hours can be achieved in a timely manner. With the present approach, surgical procedures can start punctually, inpatient waiting time for surgery and length of stay can be reduced, and staff morale can be enhanced. These improvements will result in cost reduction, and increased hospital revenue without sacrificing the quality of medical care.

MELD score is a formula based on laboratory variables used as a predictor of short-term mortality index in cirrhotic patients. It is applied to allocate patients in liver transplantation waiting list in many countries. However, MELD score cutoff point accuracy to predict long term mortality has not been statistically evaluated. The aim of this study was to analyze the MELD score and other variables related to long-term mortality using a new model: the Survival Tree analysis. The variables considered in this study were obtained at the time of liver transplantation list enrollment. The graphical representation of the survival trees showed that MELD 16 was the most statistically significant mortality cutoff point. The results were compatible with the MELD cutoff point reported in the clinical literature. This methodology can be extended to identify significant cutoff points related to other diseases whose severity is not necessarily expressed by MELD.

Detecting lower limb peripheral vascular occlusive disease (PVOD) early is important for patients to prevent disabling claudication, ischaemic rest pain and gangrene. According to previous research, the pulse timing and shape distortion characteristics of photoplethysmography (PPG) signals tend to increase with disease severity and calibrated amplitude decreases with vascular diseases. However, this is not a reliable method of evaluating the condition of PVOD because of noise effect. In this paper, an adaptive network-based fuzzy inference system (ANFIS) is proposed to assess lower limb PVOD based on PPG signals. PPG signals are non-invasively recorded from the right and left sides at the big toe sites from twenty subjects, including normal condition (Nor), lower-grade disease (LG), and higher-grade disease (HG) groups. The number of each group is 10, 8 and 2 respectively, and the ages ranged from 24 to 65 years. With the time-domain technique, the parameters for the absolute bilateral differences (right-to-left side of foot) in pulse delay and amplitude were extracted for analyzing ANFIS. The results indicated that ANFIS based on three timing parameters base bilateral differences, including ΔPTTf and ΔPTTp, and ΔRT has a high rate and noise tolerance of PVOD assessment.

A Wireless Body Area Network (WBAN) is expected to play a significant role in future healthcare system. It interconnects low-cost and intelligent sensor nodes in, on, or around a human body to serve a variety of medical applications. It can be used to diagnose and treat patients with chronic diseases such as hypertensions, diabetes, and cardiovascular diseases. The lightweight sensor nodes integrated in WBAN require low-power operation, which can be achieved using different optimization techniques. We introduce a Traffic-adaptive MAC protocol (TaMAC) for WBAN that supports dual wakeup mechanisms for normal, emergency, and on-demand traffic. In this letter, the TaMAC protocol is simulated using a well-known Network Simulator 2 (NS-2). The problem of multiple emergency nodes is solved using both wakeup radio and CSMA/CA protocol. The power consumption, delay, and throughput performance are closely compared with beacon-enabled IEEE 802.15.4 MAC protocol using extensive simulations.

This paper discusses the capacities of artificial intelligence in the process of asthma diagnosing and asthma treatment. Developed intelligent systems for asthma disease have been classified in five categories including diagnosing, evaluating, management, communicative facilities, and prediction. Considering inputs, results, and methodologies of the systems show that by focusing on meticulous analysis of quality of life as an input variable and developing patient-based systems, under-diagnosing and asthma morbidity and mortality would decrease significantly. Regard to the importance of accurate evaluation in accurate prescription and expeditious treatment, the methodology of developing a fuzzy expert system for evaluating level of asthma exacerbation is presented in this paper too. The performance of this system has been tested in Asthma, Allergy, and Immunology Center of Iran using 25 asthmatic patients. Comparison between system’s results and physicians’ evaluations using Kappa coefficient (K) reinforces the value of K = 1. In addition this system assigns a degree in gradation (0–10) to every patient representing the slight differences between patients assigned to a specific category.