Journal of Medical Systems

Early and accurate diagnosis of Parkinson’s disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In this paper, a comprehensive computer model is presented for the diagnosis of PD based on motor, non-motor, and neuroimaging features using the recently-developed enhanced probabilistic neural network (EPNN). The model is tested for differentiating PD patients from those with scans without evidence of dopaminergic deficit (SWEDDs) using the Parkinson’s Progression Markers Initiative (PPMI) database, an observational, multi-center study designed to identify PD biomarkers for diagnosis and disease progression. The results are compared to four other commonly-used machine learning algorithms: the probabilistic neural network (PNN), support vector machine (SVM), k-nearest neighbors (k-NN) algorithm, and classification tree (CT). The EPNN had the highest classification accuracy at 92.5 % followed by the PNN (91.6 %), k-NN (90.8 %) and CT (90.2 %). The EPNN exhibited an accuracy of 98.6 % when classifying healthy control (HC) versus PD, higher than any previous studies.

Being the economic powerhouses of most large medical centers, operating rooms (ORs) require the highest levels of teamwork, communication, and efficiency in order to optimize patient safety and reduce hospital waste. A major component of OR waste comes from unused surgical instrumentation; instruments that are frequently prepared for procedures but are never touched by the surgical team still require a full reprocessing cycle at the conclusion of the case. Based on our own previous successes in the perioperative domain, in this work we detail an initiative that reduces surgical instrumentation waste of video-assisted thoracoscopic surgery (VATS) procedures by placing thoracotomy conversion instrumentation in a standby location and designing a specific instrument kit to be used solely for VATS cases. Our estimates suggest that this initiative will reduce at least 91,800 pounds of unnecessary surgical instrumentation from cycling through our ORs and reprocessing department annually, resulting in increased OR team communication without sacrificing the highest standard of patient safety.

This is a national survey of acute care hospitals. A random sample of 813 hospitals was selected. The purpose of the study was to measure the extent of information systems integration in the financial, medical, and administrative systems of the hospitals. The response rate is expected to be approximately 30%. This information will provide a benchmark for hospitals to determine their technology transfer position and to set goals.

Although the Canada federal government has invested over $3.1 billion developing health information technology (HIT), all 10 provinces still have their own separate HIT systems, which are non-interoperable, expensive, and inconsistent. After first reviewing how these systems operate, this paper analyzes the costs and savings of integrating the common billing, lab results, and diagnostic imaging (BLD) functions of these separate systems using free and open-source software and proposes a system for this, HermesAPI. Currently, 8 provincial governments representing over 95% of Canada’s population allow private companies to create their own electronic medical records (EMR) system and integrate with provincial BLD systems. This study found the cost to develop and maintain HermesAPI would be between CAD$610,000 to CAD$740,000, but would prevent CAD$120,000 per company per province in development costs for a total savings of $6.4 million. HermesAPI would lower barriers to entry for the HIT industry to increase competition, improve the quality of HIT products, and ultimately patient care. The proposed open-source approach of the HermesAPI is one option towards building a more interoperable, less expensive, and more consistent HIT system for Canada.

In the Medical Emergency System, any moment of delay in an emergency such as ambulance dispatch, ambulance diversion and clinical handover communication can significantly reduce a patient’s chance of survival. Without the disadvantage of centralized management, a new type of RFID application named 2G-RFID-Sys will be more efficient. It is suitable for the tagged ambulance dispatch management in a huge range. In this article, the prototype of 2G-RFID-Sys and the responsibility of each participant are refined. We take an example of applying the 2G-RFID-Sys to the Medical Emergency System, the traffic condition of the ambulance will be able to ensure.

Modern network technology yields new interface for telecare medicine information systems in short TMIS used for patient’s healthcare. This system is used to provide healthcare services to patients at their home. It can be observed, telecare medicine information systems generally suffer several attacks as information being transmitted over a public network. Therefore, various authentication and key agreement schemes are proposed for TMIS to ensure secure and authorized patients communication over given public network. However, most of the schemes fail to achieve essential attributes discussed in this article. Although the key attributes of security and efficiency should be achieved in a common framework. This paper proposes construction of an RSA based authentication scheme for authorized access to healthcare services and achieves desirable key attributes of authentication protocols. Proof of security against polynomial time adversary is given in the random oracle to justify the security of proposed scheme. Communication analysis and computation analysis of proposed scheme indicates that proposed scheme’s performance is comparable and having better security.

This article provides a conceptual model for benchmarking the use of clinical information systems within healthcare organizations. Additionally, it addresses the benefits of clinical information systems which include the reduction of errors, improvement in clinical decision-making and real time access to patient information. The literature suggests that clinical information systems provide financial benefits due to cost-savings from improved efficiency and reduction of errors. As a result, healthcare organizations should adopt such clinical information systems to improve quality of care and stay competitive in the marketplace. Our research clearly documents the increased adoption of electronic medical records in U.S. hospitals from 2005 to 2007. This is important because the electronic medical record provides an opportunity for integration of patient information and improvements in efficiency and quality of care across a wide range of patient populations. This was supported by recent federal initiatives such as the establishment of the Office of the National Coordinator of Health Information Technology (ONCHIT) to create an interoperable health information infrastructure. Potential barriers to the implementation of health information technology include cost, a lack of financial incentives for providers, and a need for interoperable systems. As a result, future government involvement and leadership may serve to accelerate widespread adoption of interoperable clinical information systems.

As a new concept that emerged in the middle of 1990’s, data mining can help researchers gain both novel and deep insights and can facilitate unprecedented understanding of large biomedical datasets. Data mining can uncover new biomedical and healthcare knowledge for clinical and administrative decision making as well as generate scientific hypotheses from large experimental data, clinical databases, and/or biomedical literature. This review first introduces data mining in general (e.g., the background, definition, and process of data mining), discusses the major differences between statistics and data mining and then speaks to the uniqueness of data mining in the biomedical and healthcare fields. A brief summarization of various data mining algorithms used for classification, clustering, and association as well as their respective advantages and drawbacks is also presented. Suggested guidelines on how to use data mining algorithms in each area of classification, clustering, and association are offered along with three examples of how data mining has been used in the healthcare industry. Given the successful application of data mining by health related organizations that has helped to predict health insurance fraud and under-diagnosed patients, and identify and classify at-risk people in terms of health with the goal of reducing healthcare cost, we introduce how data mining technologies (in each area of classification, clustering, and association) have been used for a multitude of purposes, including research in the biomedical and healthcare fields. A discussion of the technologies available to enable the prediction of healthcare costs (including length of hospital stay), disease diagnosis and prognosis, and the discovery of hidden biomedical and healthcare patterns from related databases is offered along with a discussion of the use of data mining to discover such relationships as those between health conditions and a disease, relationships among diseases, and relationships among drugs. The article concludes with a discussion of the problems that hamper the clinical use of data mining by health professionals.

Location-based services (LBS) and information security is a major concern in communication system.With the increasing popularity of location based services more attention is paid to preserve location information to protect the data. In order to protect and preserve the MANET and location based services, there are various existing location based anonymity protocols such as k-anonymity location based, but these protocols are more overhead due to the dynamic mobility nature of ad-hoc networks. In this paper we proposed an Adaptive Risk Prediction and Anonymous Secured Communication protocol to predict the risk before processing anonymous communication. The proposed protocol estimates the risk against adjacent nodes and estimates the vulnerability paths using hidden markov model and decision tree. The decision tree determines the evidence to identify the trusted paths. The anonymous communication message authentication scheme assigns the anonymous communication and organize the secured authentication scheme. We simulated the network by considering different attacks to determine the efficiency of Adaptive Risk Prediction and Anonymous Secured Communication using NS2 simulator.