Showing papers in "Journal of Medical Systems in 2011"

The Advantages of Wearable Green Reflected Photoplethysmography

Abstract: This report evaluates the efficacy of reflected-type green light photoplethysmography (green light PPG). Transmitted infrared light was used for PPG and the arterial pulse was monitored transcutaneously. The reflected PPG signal contains AC components based on the heartbeat-related signal from the arterial blood flow and DC components, which include reflectance and scattering from tissue. Generally, changes in AC components are monitored, but the DC components play an important role during heat stress. In this study, we compared the signal of green light PPG to infrared PPG and ECG during heat stress. The wavelengths of the green and infrared light were 525 nm and 880 nm, respectively. Experiments were performed on young healthy subjects in cold (10°C), hot (45°C), and normal environments. The pulse rates were compared among three measurement devices and the AC and DC components of the PPG signal were evaluated during heat stress. The pulse rates obtained from green light PPG were strongly correlated with the R---R interval of an electrocardiogram in all environments, but those obtained from infrared light PPG displayed a weaker correlation with cold exposure. The AC components were of similar signal output for both wavelengths during heat stress. Also, the DC components for green light PPG were similar during heat stress, but showed less signal output for infrared light PPG during hot exposure. The main reason for the reduced DC components was speculated to be the increased blood flow at the vascular bed. Therefore, reflected green light PPG can be useful for pulse rate monitoring because it is less influenced by the tissue and vein region.

Body Area Networks for Ubiquitous Healthcare Applications: Opportunities and Challenges

Abstract: Body Area Networks integrated into mHealth systems are becoming a mature technology with unprecedented opportunities for personalized health monitoring and management. Potential applications include early detection of abnormal conditions, supervised rehabilitation, and wellness management. Such integrated mHealth systems can provide patients with increased confidence and a better quality of life, and promote healthy behavior and health awareness. Automatic integration of collected information and user's inputs into research databases can provide medical community with opportunity to search for personalized trends and group patterns, allowing insights into disease evolution, the rehabilitation process, and the effects of drug therapy. A new generation of personalized monitoring systems will allow users to customize their systems and user interfaces and to interact with their social networks. With emergence of first commercial body area network systems, a number of system design issues are still to be resolved, such as seamless integration of information and ad-hoc interaction with ambient sensors and other networks, to enable their wider acceptance. In this paper we present state of technology, discuss promising new trends, opportunities and challenges of body area networks for ubiquitous health monitoring applications.

A Remote Patient Monitoring System for Congestive Heart Failure

Abstract: Congestive heart failure (CHF) is a leading cause of death in the United States affecting approximately 670,000 individuals. Due to the prevalence of CHF related issues, it is prudent to seek out methodologies that would facilitate the prevention, monitoring, and treatment of heart disease on a daily basis. This paper describes WANDA (Weight and Activity with Blood Pressure Monitoring System); a study that leverages sensor technologies and wireless communications to monitor the health related measurements of patients with CHF. The WANDA system is a three-tier architecture consisting of sensors, web servers, and back-end databases. The system was developed in conjunction with the UCLA School of Nursing and the UCLA Wireless Health Institute to enable early detection of key clinical symptoms indicative of CHF-related decompensation. This study shows that CHF patients monitored by WANDA are less likely to have readings fall outside a healthy range. In addition, WANDA provides a useful feedback system for regulating readings of CHF patients.

Relationship Between Measurement Site and Motion Artifacts in Wearable Reflected Photoplethysmography

Abstract: Pulse rates obtained from wearable photoplethysmography (PPG) sensors are important for monitoring cardiovascular condition, especially during exercise. However, it is difficult to precisely count pulse rates during exercise because PPG is sensitive to body movement. The artifacts from body movement are caused by a change in the blood volume at the measurement site, in addition to pulsatile changes. Here, we investigated the influence of motion artifact with respect to light source and anatomical sites. In this study, we compared the signal from green-light PPG to that from infrared PPG at different anatomical sites. In these experiments, 11 subjects were asked to either assume a resting position or generate spontaneous motion artifact by jumping and swinging their arm. As a result, pulse rates obtained from green-light PPG showed a higher correlation with the ECG R-R interval as compared to those obtained with infrared. Additionally, the signal from the upper arm showed less artifact than did the peripheral one. Therefore, the green-light PPG may be useful for pulse rate monitoring.

Application of Higher Order Spectra to Identify Epileptic EEG

Abstract: Epilepsy is characterized by the spontaneous and seemingly unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into this phenomenon. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, we made a comparative study of the performance of Gaussian mixture model (GMM) and Support Vector Machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Results show that the selected HOS based features achieve 93.11% classification accuracy compared to 88.78% with features derived from the power spectrum for a GMM classifier. The SVM classifier achieves an improvement from 86.89% with features based on the power spectrum to 92.56% with features based on the bispectrum.

Critical Elements and Lessons Learnt from the Implementation of an RFID-enabled Healthcare Management System in a Medical Organization

Abstract: Healthcare services are complex and life-critical. One mistake in any procedure may lead to irremediable consequences; numerous researchers, thus, introduce information and communication technology to improve quality of services and enhance patient safety by reducing the medical errors. Radio frequency identification (RFID) is considered as one of the emerging tool assist in meeting the challenges of the present situation. In recent years, RFID has been applied in medical organizations for the purpose of managing and tracking medical equipment, monitoring and identifying patients, ensuring that the right medication is given to the right patient, and preventing the use of counterfeit medicine. However, most of the existing literature focuses on demonstrating how RFID can benefit the healthcare industry, whereas little attention has been given to the management issues involved in constructing an RFID project in medical organizations. In this paper, an exploratory case study is conducted in a medical organization to illustrate the development framework and critical issues that should be taken into consideration in the preparation, implementation and maintenance stage of constructing such a project. All the experiences and results discussed in this paper offer valuable and useful insights to steer those who would like to start their journey using RFID in medical organizations.

Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network

Abstract: Wireless Capsule Endoscopy (WCE), which allows clinicians to inspect the whole gastrointestinal tract (GI) noninvasively, has bloomed into one of the most efficient technologies to diagnose the bleeding in GI tract. However WCE generates large amount of images in one examination of a patient. It is hard for clinicians to leave continuous time to examine the full WCE images, and this is the main factor limiting the wider application of WCE in clinic. A novel intelligent bleeding detection based on Probabilistic Neural Network (PNN) is proposed in this paper. The features of bleeding region in WCE images distinguishing from non-bleeding region are extracted. A PNN classifier is built to recognize bleeding regions in WCE images. Finally the intelligent bleeding detection method is implemented through programming. The experiments show this method can correctly recognize the bleeding regions in WCE images and clearly mark them out. The sensitivity and specificity on image level are measured as 93.1% and 85.6% respectively.

Two RFID-based Solutions to Enhance Inpatient Medication Safety

Abstract: Owing to the low cost and convenience of identifying an object without physical contact, Radio Frequency Identification (RFID) systems provide innovative, promising and efficient applications in many domains. An RFID grouping protocol is a protocol that allows an off-line verifier to collect and verify the evidence of two or more tags simultaneously present. Recently, Huang and Ku (J. Med. Syst, 2009) proposed an efficient grouping protocol to enhance medication safety for inpatients based on low-cost tags. However, the Huang---Ku scheme is not secure; an attacker can easily make up fake grouping records to cheat the verifier. This weakness would seriously endanger the safety of inpatient medication safety. This paper will show the weaknesses, and then propose two RFID-based solutions to enhance medication safety for two different scenarios. The proposed schemes are practical, secure and efficient for medication applications.

Systematic Review of the Use of Computer Simulation Modeling of Patient Flow in Surgical Care

Abstract: Computer simulation has been employed to evaluate proposed changes in the delivery of health care. However, little is known about the utility of simulation approaches for analysis of changes in the delivery of surgical care. We searched eight bibliographic databases for this comprehensive review of the literature published over the past five decades, and found 34 publications that reported on simulation models for the flow of surgical patients. The majority of these publications presented a description of the simulation approach: 91% outlined the underlying assumptions for modeling, 88% presented the system requirements, and 91% described the input and output data. However, only half of the publications reported that models were constructed to address the needs of policy-makers, and only 26% reported some involvement of health system managers and policy-makers in the simulation study. In addition, we found a wide variation in the presentation of assumptions, system requirements, input and output data, and results of simulation-based policy analysis.

Designing a Personal Health Application for Older Adults to Manage Medications: A Comprehensive Case Study

Abstract: Older adults with multiple chronic conditions often go through care transitions where they move between care facilities or providers during their treatment. These transitions are often uncoordinated and can imperil patients by omitted, duplicative, or contradictory care plans. Older adults sometimes feel overwhelmed with the new responsibility of coordinating the care plan with providers and changing their medication regimes. In response, we developed a Lesser General Public License (LGPL) open source, web-based Personal Health Application (PHA) using an iterative participatory design process that provided older adults and their caregivers the ability to manage their personal health information. In this paper, we document the PHA design process from low-fidelity prototypes to high-fidelity prototypes over the course of six user studies. Our findings establish the imperative need for interdisciplinary research and collaboration among all stakeholders to create effective PHAs. We conclude with design guidelines that encourage researchers to gradually increase functionality as users become more proficient.

Tuberculosis Disease Diagnosis Using Artificial Neural Network Trained with Genetic Algorithm

Abstract: Tuberculosis is a common and often deadly infectious disease caused by mycobacterium; in humans it is mainly Mycobacterium tuberculosis (Wikipedia 2009) It is a great problem for most developing countries because of the low diagnosis and treatment opportunities Tuberculosis has the highest mortality level among the diseases caused by a single type of microorganism Thus, tuberculosis is a great health concern all over the world, and in Turkey as well This article presents a study on tuberculosis diagnosis, carried out with the help of multilayer neural networks (MLNNs) For this purpose, an MLNN with two hidden layers and a genetic algorithm for training algorithm has been used The tuberculosis dataset was taken from a state hospital's database, based on patient's epicrisis reports

The Effect of Computerized Physician Order Entry and Decision Support System on Medication Errors in the Neonatal Ward: Experiences from an Iranian Teaching Hospital

Abstract: Medication dosing errors are frequent in neonatal wards. In an Iranian neonatal ward, a 7.5 months study was designed in three periods to compare the effect of Computerized Physician Order Entry (CPOE) without and with decision support functionalities in reducing non-intercepted medication dosing errors in antibiotics and anticonvulsants. Before intervention (Period 1), error rate was 53%, which did not significantly change after the implementation of CPOE without decision support (Period 2). However, errors were significantly reduced to 34% after that the decision support was added to the CPOE (Period 3; P?

A New QRS Detection Method Using Wavelets and Artificial Neural Networks

Abstract: We present a new method for detection and classification of QRS complexes in ECG signals using continuous wavelets and neural networks. Our wavelet method consists of four wavelet basis functions that are suitable in detection of QRS complexes within different QRS morphologies in the signal and thresholding technique for denoising and feature extraction. The results demonstrate that the proposed method is not only efficient for normal ECG signal analysis but also for various types of arrhythmic cardiac signals embedded in noise. For the classification stage, a feedforward neural network was trained with standard backpropagation algorithm. The classifier input features consisted of compact wavelet coefficients of QRS complexes that resulted in higher classification rates. We demonstrate the efficiency of our method with the average accuracy 97.2% in classification of normal and abnormal QRS complexes.

Computerized Wrist Pulse Signal Diagnosis Using Modified Auto-Regressive Models

Abstract: The wrist pulse signals can be used to analyze a person's health status in that they reflect the pathologic changes of the person's body condition. This paper aims to present a novel time series analysis approach to analyze wrist pulse signals. First, a data normalization procedure is proposed. This procedure selects a reference signal that is `closest' to a newly obtained signal from an ensemble of signals recorded from the healthy persons. Second, an auto-regressive (AR) model is constructed from the selected reference signal. Then, the residual error, which is the difference between the actual measurement for the new signal and the prediction obtained from the AR model established by reference signal, is defined as the disease-sensitive feature. This approach is based on the premise that if the signal is from a patient, the prediction model previously identified using the healthy persons would not be able to reproduce the time series measured from the patients. The applicability of this approach is demonstrated using a wrist pulse signal database collected using a Doppler Ultrasound device. The classification accuracy is over 82% in distinguishing healthy persons from patients with acute appendicitis, and over 90% for other diseases. These results indicate a great promise of the proposed method in telling healthy subjects from patients of specific diseases.

A Smart-Phone Application and a Companion Website for the Improvement of the Communication Skills of Children with Autism: Clinical Rationale, Technical Development and Preliminary Results

Abstract: Autism is a complex neurobiological disorder that is part of a group of disorders known as autism spectrum disorders (ASD). Today, one in 150 individuals is diagnosed with autism. Lack of social interaction and problems with communication are the main characteristics displayed by children with ASD. The Picture Exchange Communication System (PECS) is a communication system where children exchange visual symbols as a form of communication. The visual symbols are laminated pictures stored in a binder. We have designed, developed and are currently testing a software application, called PixTalk which works on any Windows Mobile Smart-phone. Teachers and caregivers can access a web site and select from an online library the images to be downloaded on to the Smart-phone. Children can browse and select images to express their intentions, desires, and emotions using PixTalk. Case study results indicate that PixTalk can be used as part of ongoing therapy.

Data Envelopment Analysis Model for the Appraisal and Relative Performance Evaluation of Nurses at an Intensive Care Unit

Abstract: The appraisal and relative performance evaluation of nurses are very important and beneficial for both nurses and employers in an era of clinical governance, increased accountability and high standards of health care services. They enhance and consolidate the knowledge and practical skills of nurses by identification of training and career development plans as well as improvement in health care quality services, increase in job satisfaction and use of cost-effective resources. In this paper, a data envelopment analysis (DEA) model is proposed for the appraisal and relative performance evaluation of nurses. The model is validated on thirty-two nurses working at an Intensive Care Unit (ICU) at one of the most recognized hospitals in Lebanon. The DEA was able to classify nurses into efficient and inefficient ones. The set of efficient nurses was used to establish an internal best practice benchmark to project career development plans for improving the performance of other inefficient nurses. The DEA result confirmed the ranking of some nurses and highlighted injustice in other cases that were produced by the currently practiced appraisal system. Further, the DEA model is shown to be an effective talent management and motivational tool as it can provide clear managerial plans related to promoting, training and development activities from the perspective of nurses, hence increasing their satisfaction, motivation and acceptance of appraisal results. Due to such features, the model is currently being considered for implementation at ICU. Finally, the ratio of the number DEA units to the number of input/output measures is revisited with new suggested values on its upper and lower limits depending on the type of DEA models and the desired number of efficient units from a managerial perspective.

Morphological Multiscale Enhancement, Fuzzy Filter and Watershed for Vascular Tree Extraction in Angiogram

Abstract: This paper presented an automatic morphological method to extract a vascular tree using an angiogram. Under the assumption that vessels are connected in a local linear pattern in a noisy environment, the algorithm decomposes the vessel extraction problem into several consecutive morphological operators, aiming to characterize and distinguish different patterns on the angiogram: background, approximate vessel region and the boundary. It started with a contrast enhancement and background suppression process implemented by subtracting the background from the original angiogram. The background was estimated using multiscale morphology opening operators by varying the size of structuring element on each pixel. Subsequently, the algorithm simplified the enhanced angiogram with a combined fuzzy morphological opening operation, with linear rotating structuring element, in order to fit the vessel pattern. This filtering process was then followed by simply setting a threshold to produce approximate vessel region. Finally, the vessel boundaries were detected using watershed techniques with the obtained approximate vessel centerline, thinned result of the obtained vessel region, as prior marker for vessel structure. Experimental results using clinical digitized vascular angiogram and some comparative performance of the proposed algorithm were reported.

A Study on Hepatitis Disease Diagnosis Using Multilayer Neural Network with Levenberg Marquardt Training Algorithm

Abstract: In this study, a hepatitis disease diagnosis study was realized using neural network structure. For this purpose, a multilayer neural network structure was used. Levenberg---Marquardt algorithm was used as training algorithm for the weights update of the neural network. The results of the study were compared with the results of the previous studies reported focusing on hepatitis disease diagnosis and using same UCI machine learning database. We obtained a classification accuracy of 91.87% via tenfold cross validation.

Extracting Insights from Electronic Health Records: Case Studies, a Visual Analytics Process Model, and Design Recommendations

Abstract: Current electronic health record (EHR) systems facilitate the storage, retrieval, persistence, and sharing of patient data. However, the way physicians interact with EHRs has not changed much. More specifically, support for temporal analysis of a large number of EHRs has been lacking. A number of information visualization techniques have been proposed to alleviate this problem. Unfortunately, due to their limited application to a single case study, the results are often difficult to generalize across medical scenarios. We present the usage data of Lifelines2 (Wang et al. 2008), our information visualization system, and user comments, both collected over eight different medical case studies. We generalize our experience into a visual analytics process model for multiple EHRs. Based on our analysis, we make seven design recommendations to information visualization tools to explore EHR systems.

Diagnosis of Diabetic Retinopathy: Automatic Extraction of Optic Disc and Exudates from Retinal Images using Marker-controlled Watershed Transformation

Abstract: Due to increasing number of diabetic retinopathy cases, ophthalmologists are experiencing serious problem to automatically extract the features from the retinal images. Optic disc (OD), exudates, and cotton wool spots are the main features of fundus images which are used for diagnosing eye diseases, such as diabetic retinopathy and glaucoma. In this paper, a new algorithm for the extraction of these bright objects from fundus images based on marker-controlled watershed segmentation is presented. The proposed algorithm makes use of average filtering and contrast adjustment as preprocessing steps. The concept of the markers is used to modify the gradient before the watershed transformation is applied. The performance of the proposed algorithm is evaluated using the test images of STARE and DRIVE databases. It is shown that the proposed method can yield an average sensitivity value of about 95%, which is comparable to those obtained by the known methods.

Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification

Abstract: The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed forward network (MLFFN) trained with back propagation algorithm. Important parameters are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis using the MIT-BIH arrhythmia database has shown an average classification accuracy of 97.54% with FCM clustered MLFFN and 99.58% with FCM clustered PNN. Fuzzy clustering improves the classification speed as well. The result reveals the capability of the FCM clustered PNN in the computer-aided diagnosis of ECG abnormalities.

Functional and Mechanical Evaluation of Nerve Stretch Injury

Abstract: Peripheral nerves undergo tensile loading in common physiological conditions, but stretch can also induce nerve pathology, impairing electrophysiological conduction. The level of strain nerves can tolerate and the functional deficits which result from exceeding this threshold are not thoroughly understood. To examine these phenomena, a novel system for tensile electrophysiology was created using a grease gap-recording chamber paired with a computerized micromanipulator and load cell. Guinea pig sciatic nerves were stretched beyond their maximum physiologic length to examine the effects of tension on signal conduction. Mechanical and electrophysiological data such as load, position, compound action potential amplitude, and signal latency were recorded in real-time. While 5% strain did not affect conduction, further elongation decreased amplitude approximately linearly with strain. These experiments verify the findings of prior studies into nerve stretch, and demonstrate the utility of this apparatus for investigating the mechanical and electrophysiological properties of nerves undergoing strain.

Detecting Sleep Apnea by Heart Rate Variability Analysis: Assessing the Validity of Databases and Algorithms

Abstract: Obstructive sleep apnea (OSA) is a serious disorder caused by intermittent airway obstruction which may have dangerous impact on daily living activities. Heart rate variability (HRV) analysis could be used for diagnosing OSA, since this disease affects HRV during sleep. In order to validate different algorithms developed for detecting OSA employing HRV analysis, several public or proprietary data collections have been employed for different research groups. However, for validation purposes, it is obvious and evident the lack of a common standard database, worldwide recognized and accepted by the scientific community. In this paper, different algorithms employing HRV analysis were applied over diverse public and proprietary databases for detecting OSA, and the outcomes were validated in terms of a statistical analysis. Results indicate that the use of a specific database may strongly affect the performance of the algorithms, due to differences in methodologies of processing. Our results suggest that researchers must strongly take into consideration the database used when quoting their results, since selected cases are highly database dependent and would bias conclusions.

Significant Cancer Prevention Factor Extraction: An Association Rule Discovery Approach

Abstract: Cancer is increasing the total number of unexpected deaths around the world. Until now, cancer research could not significantly contribute to a proper solution for the cancer patient, and as a result, the high death rate is uncontrolled. The present research aim is to extract the significant prevention factors for particular types of cancer. To find out the prevention factors, we first constructed a prevention factor data set with an extensive literature review on bladder, breast, cervical, lung, prostate and skin cancer. We subsequently employed three association rule mining algorithms, Apriori, Predictive apriori and Tertius algorithms in order to discover most of the significant prevention factors against these specific types of cancer. Experimental results illustrate that Apriori is the most useful association rule-mining algorithm to be used in the discovery of prevention factors.

Employing post-DEA Cross-evaluation and Cluster Analysis in a Sample of Greek NHS Hospitals

Abstract: To increase Data Envelopment Analysis (DEA) discrimination of efficient Decision Making Units (DMUs), by complementing "self-evaluated" efficiencies with "peer-evaluated" cross-efficiencies and, based on these results, to classify the DMUs using cluster analysis. Healthcare, which is deprived of such studies, was chosen as the study area. The sample consisted of 27 small- to medium-sized (70---500 beds) NHS general hospitals distributed throughout Greece, in areas where they are the sole NHS representatives. DEA was performed on 2005 data collected from the Ministry of Health and the General Secretariat of the National Statistical Service. Three inputs -hospital beds, physicians and other health professionals- and three outputs -case-mix adjusted hospitalized cases, surgeries and outpatient visits- were included in input-oriented, constant-returns-to-scale (CRS) and variable-returns-to-scale (VRS) models. In a second stage (post-DEA), aggressive and benevolent cross-efficiency formulations and clustering were employed, to validate (or not) the initial DEA scores. The "maverick index" was used to sort the peer-appraised hospitals. All analyses were performed using custom-made software. Ten benchmark hospitals were identified by DEA, but using the aggressive and benevolent formulations showed that two and four of them respectively were at the lower end of the maverick index list. On the other hand, only one 100% efficient (self-appraised) hospital was at the higher end of the list, using either formulation. Cluster analysis produced a hierarchical "tree" structure which dichotomized the hospitals in accordance to the cross-evaluation results, and provided insight on the two-dimensional path to improving efficiency. This is, to our awareness, the first study in the healthcare domain to employ both of these post-DEA techniques (cross efficiency and clustering) at the hospital (i.e. micro) level. The potential benefit for decision-makers is the capability to examine high and low "all-round" performers and maverick hospitals more closely, and identify and address problems typically overlooked by first-stage DEA.

A Lightweight Security Scheme for Wireless Body Area Networks: Design, Energy Evaluation and Proposed Microprocessor Design

Abstract: In order for wireless body area networks to meet widespread adoption, a number of security implications must be explored to promote and maintain fundamental medical ethical principles and social expectations. As a result, integration of security functionality to sensor nodes is required. Integrating security functionality to a wireless sensor node increases the size of the stored software program in program memory, the required time that the sensor's microprocessor needs to process the data and the wireless network traffic which is exchanged among sensors. This security overhead has dominant impact on the energy dissipation which is strongly related to the lifetime of the sensor, a critical aspect in wireless sensor network (WSN) technology. Strict definition of the security functionality, complete hardware model (microprocessor and radio), WBAN topology and the structure of the medium access control (MAC) frame are required for an accurate estimation of the energy that security introduces into the WBAN. In this work, we define a lightweight security scheme for WBAN, we estimate the additional energy consumption that the security scheme introduces to WBAN based on commercial available off-the-shelf hardware components (microprocessor and radio), the network topology and the MAC frame. Furthermore, we propose a new microcontroller design in order to reduce the energy consumption of the system. Experimental results and comparisons with other works are given.

Measuring Hospital Efficiency with Data Envelopment Analysis: Nonsubstitutable vs. Substitutable Inputs and Outputs

Abstract: There is a conflict between Data Envelopment Analysis (DEA) theory's requirement that inputs (outputs) be substitutable, and the ubiquitous use of nonsubstitutable inputs and outputs in DEA applications to hospitals. This paper develops efficiency indicators valid for nonsubstitutable variables. Then, using a sample of 87 community hospitals, it compares the new measures' efficiency estimates with those of conventional DEA measures. DEA substantially overestimated the hospitals' efficiency on the average, and reported many inefficient hospitals to be efficient. Further, it greatly overestimated the efficiency of some hospitals but only slightly overestimated the efficiency of others, thus making any comparisons among hospitals questionable. These results suggest that conventional DEA models should not be used to estimate the efficiency of hospitals unless there is empirical evidence that the inputs (outputs) are substitutable. If inputs (outputs) are not substitutes, efficiency indicators valid for nonsubstitutability should be employed, or, before applying DEA, the nonsubstitutable variables should be combined using an appropriate weighting scheme or statistical methodology.

Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities

Abstract: The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.

Data Consistency in a Voluntary Medical Incident Reporting System

Abstract: Voluntary medical incident reporting systems are a valuable source for studying adverse events and near misses. Unfortunately, such systems usually contain a large amount of incomplete and inaccurate reports which negatively affect their utility for medical error research. To investigate the reporting quality and propose solutions towards quality voluntary reports, we employed a content analysis method to examine one-year voluntary medical incident reports of a University Hospital. Results indicate that there is a large amount of inconsistent records within the reports. About 25% of the reports were labeled as "miscellaneous" and "other". Through an in-depth analysis, those "miscellaneous" and "other" were substituted by their real incident types or error descriptions. Analysis shows that the pre-defined reporting categories serve well in general for the voluntary reporting need. In some cases, human factors play a key role in selecting accurate categories since reporters lack time or information to complete the report. We suggest that a human-centered, ontology based system design for voluntary reporting is feasible. Such a design could help improve the completeness and accuracy, and interoperability among national and international standards.

A Novel Medium Access Control Protocol with Low Delay and Traffic Adaptivity for Wireless Body Area Networks

Abstract: IEEE 802.15.4 technology provides one solution for low-rate short range communications. Based on the integrated superframe structure of IEEE 802.15.4, a novel low-delay traffic-adaptive medium access control (LDTA-MAC) protocol for wireless body area networks (WBANs) is proposed in the paper. In LDTA-MAC, the guaranteed time slots (GTSs) are allocated dynamically according to the traffic load. At the same time, the active portion of superframe is kept to be a reasonable duration to decrease the energy consumption of the network devices. Moreover, for the successful GTS requests, the related data packets are transmitted in the current superframe instead of waiting more time to reduce the average packet delay. Simulations are conducted to evaluate the network performance and verify our protocol design. Comparing with IEEE 802.15.4, the results reveal LDTA-MAC accommodates more devices access to the network and reduces the packet delay obviously without the cost of more energy consumption.