Departmental and laboratory computing in two hospitals

This report describes the departmental and laboratory use of integrated, hospital-wide computing systems at Beth Israel and Brigham and Women's hospitals in Boston. The systems have an important role in the admitting, outpatient, and medical records departments; in the clinical departments (blood bank, cardiology, neurophysiology, pathology, radiology, and pharmacy); in the clinical laboratories; and at Brigham and Women's Hospital, in the financial departments. Information that is collected in the computers from these departments and laboratories is available for viewing by clinicians at terminals located throughout each hospital and is used in compiling charges for each patient's account. The programs are heavily used. During a one-week study period, 742 departmental and laboratory workers at Beth Israel Hospital filed or edited information in patients' computerized records 137,526 times. During the same week, 984 departmental and laboratory workers at Brigham and Women's Hospital filed or edited information 293,367 times. After the computing systems were introduced, the time required to collect unpaid bills decreased substantially at both hospitals.     

Robust heart rate variability analysis by generalized entropy minimization

Typical heart rate variability (HRV) times series are cluttered with outliers generated by measurement errors, artifacts and ectopic beats. Robust estimation is an important tool in HRV analysis, since it allows clinicians to detect arrhythmia and other anomalous patterns by reducing the impact of outliers. A robust estimator for a flexible class of time series models is proposed and its empirical performance in the context of HRV data analysis is studied. The methodology entails the minimization of a pseudo-likelihood criterion function based on a generalized measure of information. The resulting estimating functions are typically re-descending, which enable reliable detection of anomalous HRV patterns and stable estimates in the presence of outliers. The infinitesimal robustness and the stability properties of the new method are illustrated through numerical simulations and two case studies from the Massachusetts Institute of Technology and Boston’s Beth Israel Hospital data, an important benchmark data set in HRV analysis.     

A computer-based outpatient medical record for a teaching hospital.

We developed a computer-based outpatient medical record system to facilitate direct physician interaction with the clinical computing system at the Beth Israel Hospital in Boston. During the 2 years since the medical record system was installed, 20 staff physicians, 5 fellows, 64 residents, and 11 nurse practitioners have entered 15,121 active problems and 1996 inactive problems for 3524 patients, as well as 12,651 active medications and 1894 discontinued medications for 3430 patients. Another 20,321 items were entered on health-promotion and disease-prevention screening sheets, and with the help of automatic updating by the computer, an additional 21,897 entries on screening sheets were made for 8686 patients. Clinicians wrote 10.9 +/- 12.8 (mean +/- SD) words per problem when they were working at the computer, as compared with 4.3 +/- 2.5 words per problem when they were writing in the paper medical record. We conclude that physicians will readily enter data directly into a computing system when they are given appropriate tools, and that they consider the computer-based problem list to be a valuable improvement over its paper counterpart. Use of a computer-based medical record system has obvious benefits for data management and patient care.     

Secured Health Data Transmission Using Lagrange Interpolation and Artificial Neural Network

In recent decades, the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services. The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations. This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases, electrocardiogram (ECG) reports, and payment details. The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) resources over the private and public cloud. The stored data are categorized as significant and non-significant by Artificial Neural Networks (ANN). The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer. Upon receiving the request from a secondary user, the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user. Once the key matches the key in the hidden layer, the preserved information will be shared between the users. Due to the enhanced privacy preserving key generation, the proposed work prevents the tracking of keys by malicious users. The outcomes reveal that the introduced work provides improved success rate with reduced computational time.     

Modeling the diagnosis of stroke at two hospitals

A comparison of five major categories of stroke in 651 patients revealed significant differences in the frequencies of diagnoses at the Beth Israel and Massachusetts General hospitals in Boston, Mass. (P less than 0.001 by chi-square test). To analyze these differences, we modeled the diagnostic process at each hospital with a Bayesian procedure and performed a crossover study in which each patient was rediagnosed by the model from the opposite hospital. The results indicate that the differences in the frequency of lacune and subarachnoid hemorrhage were associated with the patient population, whereas the differences in the frequency of embolism and atherothrombosis were associated with the diagnostic process. There was a marked difference in the use of arteriograms on the two stroke services, but no difference in morbidity or mortality. The modeling procedure described can be used to compare clinical processes when the allocation of patients is thought to be biased.     

Optimal positioning of read/write heads in mirrored disks

This work is concerned with finding the expected-travel-distance-minimizing anticipatory positions of disk arms in mirrored disk systems. In such systems, data is duplicated across two or more disk drives. A ‘read’ request may choose to read from any copy, and thus do so from the disk whose arm is closest to the request location. Since a ‘write’ must update all copies, the response time for such a request will depend on the distance of the arm which is furthest away from the request's location. Some problems of optimally positioning emergency service units on a line and of positioning idle elevators can be viewed mathematically as a special case of the mirrored disks scenario in which there are ‘read’ requests only. We show that, for any request location distribution, if there are more write than read requests then both arms should be located as if read requests did not exist — both at the median of the distribution. For situations where most requests are of ‘read’ type, we derive necessary conditions for optimal locations.     

Information technology can improve healthcare quality

A big move meant big change for Emergency Department personnel at John Peter Smith Hospital, part of the Tarrant County Hospital District, Forth Worth, Texas. Implementing a patient tracking system has allowed ED managers and clinicians to assess outcomes, reengineer patient flow and investigate clinical issues, ultimately resulting in better, more timely care.     

Implementation of a computerized patient advice system using the HELP clinical information system

A COMputerized Patient Advice System (COMPAS) was designed to test the feasibility of using the HELP clinical information system to direct the respiratory therapy of intensive care (ICU) patients acutely ill with adult respiratory distress syndrome. A modified black-board control architecture allowed the application of knowledge in either a forward or a backward chaining mode. Expert clinicians recommended decision logic and actions for five different modes of ventilatory support. The clinical staff used COMPAS to manage the ICU ventilatory support of five patients for a total of 624 hr. During that time there were 407 decision-making opportunities. COMPAS automatically generated therapy suggestions 379 (93.1%) times and the clinical staff accepted COMPAS's recommendation in 320 (84.4%) of these cases. These results suggest that the ventilatory support of severely ill ICU patients can be managed by a clinical information system using a blackboard control architecture.     

An efficient new method for the detection of QRS in electrocardiogram

A simple and efficient new method for QRS detection in Electrocardiogram (ECG) is proposed in this paper. Initially data is preprocessed using two stage median filter for removing baseline drift. The second stage enhances the peaks of ECG wave components by using sixth power of signal. The next stage identifies the QRS complex by taking a variable window size. The detection sensitivity (Se) and positive predictivity (+P) of CSE (Common Standards for Quantitative Electrocardiography) measurement database, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) Arrhythmia database, European ST-T database and QT database are Se 99.51 & +P 99.69%, Se 99.21 & +P 99.34%, Se 99.53 & +P 99.72% and Se 99.87 & +P 99.95% respectively. These four standard databases used to perform QRS detection considered 368 cases, tested 1,006,168 beats and achieved overall average sensitivity 99.52% and positive predictivity 99.69%. The MIT/BIH Noise Stress Test Database also tested by proposed method.     

Understanding how primary care clinicians make sense of chronic pain

Chronic pain leads to reduced quality of life for patients, and strains health systems worldwide. In the US and some other countries, the complexities of caring for chronic pain are exacerbated by individual and public health risks associated with commonly used opioid analgesics. To help understand and improve pain care, this article uses the data frame theory of sensemaking to explore how primary care clinicians in the US manage their patients with chronic noncancer pain. We conducted Critical Decision Method interviews with ten primary care clinicians about 30 individual patients with chronic pain. In these interviews, we identified several patients, social/environmental, and clinician factors that influence the frames clinicians use to assess their patients and determine a pain management plan. Findings suggest significant ambiguity and uncertainty in clinical pain management decision making. Therefore, interventions to improve pain care might focus on supporting sensemaking in the context of clinical evidence rather than attempting to provide clinicians with decontextualized and/or algorithm-based decision rules. Interventions might focus on delivering convenient and easily interpreted patient and social/environmental information in the context of clinical practice guidelines.     

Fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias

This paper proposes using fractal QRS-complexes pattern recognition for imperative cardiac arrhythmias. A typical electrocardiogram (ECG) signal is comprised of P-wave, QRS-complex, and T-wave. Fractal dimension transformation (FDT) is employed to adjoin the QRS-complex from time-domain ECG signals, including the fractal features of supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. FDT with fractal dimension (FD) is addressed for constructing various symptomatic features, and can produce family functions and enhance features, making the difference between healthy and unhealthy subjects more significant. The probabilistic neural network (PNN) is proposed for recognizing the states of cardiac physiologic function. The proposed method is tested using the MIT–BIH (Massachusetts Institute of Technology–Beth Israel Hospital) arrhythmia database. Compared with other methods, the numerical experiments demonstrate greater efficiency and higher accuracy in recognizing ECG signals.     

Stability, availability, and response in network file service

A network file system called Multifile is described. It meets response, availability, and stability requirements as primitive functions. Multifile has a high degree of responsiveness because its component parts compete among themselves to service file requests; it has high availability because it maintains multiple copies of files; and it exhibits stable behavior over wise range of system parameters. The responsiveness of Multifile to read requests improves as the number of pages per request rises, implying that read ahead pages can profitably be cached at client sites. The throughput of Multifile improves as the request size increases and as the number of clients increases. As server load increases, the responsiveness of Multifile to read requests is stable in most configurations. The throughput of writes is unstable as the number of pages in the wire request rises, implying that write back pages should not be cached at client sites. The scale of events in file service is dominated by disk activity, so lost message exceptions do not occur frequently enough to affect file service; however, duplicate message exceptions are a factor in performance     

Effects of User Request Patterns on a Multimedia Delivery System

Because of their size, service times, and drain on server resources, multimedia objects require specialized replication systems in order to meet demand and ensure content availability. We present a novel method for creating replication systems where the replicated objects' sizes and/or per-object service times are large. Such replication systems are well-suited to delivering multimedia objects on the Internet. Assuming that user request patterns to the system are known, we show how to create replication systems that distribute read load to servers in proportion to their contribution to system capacity and experimentally show the positive load distribution properties of such systems. However, when user request patterns differ from what the system was designed for, system performance will be affected. Therefore, we also report on results that reveal (i) how server loads are affected and (ii) the impact two system design parameters (indicators of a system's load distribution qualities) have on server load when request patterns differ from that for which a system was designed.     

Block-based neural networks for personalized ECG signal classification.

This paper presents evolvable block-based neural networks (BbNNs) for personalized ECG heartbeat pattern classification. A BbNN consists of a 2-D array of modular component NNs with flexible structures and internal configurations that can be implemented using reconfigurable digital hardware such as field-programmable gate arrays (FPGAs). Signal flow between the blocks determines the internal configuration of a block as well as the overall structure of the BbNN. Network structure and the weights are optimized using local gradient-based search and evolutionary operators with the rates changing adaptively according to their effectiveness in the previous evolution period. Such adaptive operator rate update scheme ensures higher fitness on average compared to predetermined fixed operator rates. The Hermite transform coefficients and the time interval between two neighboring R-peaks of ECG signals are used as inputs to the BbNN. A BbNN optimized with the proposed evolutionary algorithm (EA) makes a personalized heartbeat pattern classifier that copes with changing operating environments caused by individual difference and time-varying characteristics of ECG signals. Simulation results using the Massachusetts Institute of Technology/Beth Israel Hospital (MIT-BIH) arrhythmia database demonstrate high average detection accuracies of ventricular ectopic beats (98.1%) and supraventricular ectopic beats (96.6%) patterns for heartbeat monitoring, being a significant improvement over previously reported electrocardiogram (ECG) classification results.     

一种基于请求大小的固态盘I／O调度算法

对于同类型的I／O请求,基于闪存固态盘的请求响应时间与请求大小基本呈线性比例关系,并且固态盘的读写性能具有非对称性。针对该特性,提出一种基于请求大小的固态盘I／O调度（SIOS）算法,从I／O请求平均响应时间的角度提高固态盘设备的I／O性能。根据读写性能的非对称性,对读写请求进行分组并且优先处理读请求。在此基础上首先处理等待队列中的小请求,从而减少队列中请求的平均等待时间。采用SLC和MLC2种类型的固态盘进行实验,在5种测试负载的驱动下与Linux系统中的3种调度算法进行比较,对于SLC固态盘,SIOS平均响应时间分别减少18．4％、25．8％、14．9％、14．5％和13．1％,而对于MLC固态盘,平均响应时间分别减少16．9％、24．4％、13．1％、13．0％和13．7％,结果表明,SIOS能有效减少I／O请求的平均响应时间,提高固态盘存储系统的I／O性能。     

A Neural Network Based Model for Prognosis of Early Breast Cancer

The prediction of clinical outcome of patients after breast cancer surgery plays an important role in medical tasks such as diagnosis and treatment planning. Survival estimations are currently performed by clinicians using non-numerical techniques. Artificial neural networks are shown to be a powerful tool for analyzing data sets where there are complicated nonlinear interactions between the input data and the information to be predicted. In this paper, a new estimation to set the maximum bound on prediction accuracy is presented, based on the approximation of the a posteriori probability of Bayes by feed-forward three-layer neural networks. This result is applied to different patients' follow-up time intervals, in order to obtain the best prediction accuracy for the correct classification probability of patient relapse after breast cancer surgery using clinical-pathological data (tumor size, patient age, menarchy age, etc.), which were obtained from the Medical Oncology Service of the Hospital Clinico Universitario of Malaga, Spain. Different network topologies and learning parameters are investigated to obtain the best prediction accuracy. The actual results show as, after training process, the final model is appropriate to make predictions about the relapse probability at different times of follow-up.     

读请求的空间与时间特征建模

为了改善并行应用程序和并行文件系统的I/O性能，对读请求的空间与时间特征进行建模。使用自相关函数分析和Haar小波变换来自动识别自回归、集成的移动平均的模型结构，通过该模型来预测读请求的时间特征；使用马尔可夫模型对读请求的空间特征进行建模、预测。该模型可以将自回归、集成的移动平均时间预测模型与马尔可夫空间预测模型结合，并自适应地预测什么时间、取哪些数据块、取多少数据块。     

eXiTCDSS: A framework for a workflow-based CBR for interventional Clinical Decision Support Systems and its application to TAVI

Clinical Decision Support System (CDSSs) should form an important part of the field of clinical knowledge management technologies through their capacity to support the clinical process and use of knowledge, including knowledge maintenance and continuous learning, from diagnosis and investigation through surgery, treatment and long-term care. The work presented shows a workflow-based CDSS designed to give case-specific assessment to clinicians during complex surgery or Minimally Invasive Surgery (MISs). Following a perioperative workflow, the designed software will use a Case-Based Reasoning (CBR) methodology to retrieve similar past cases from a case base to provide support at any particular point of the process. The graphical user interface allows easy navigation through the whole support progress, from the initial configuration steps to the final results organized as sets of experiments easily visualized in a user-friendly way. The eXiTCDSS tool is presented giving support to a recent complex minimally invasive surgery which is receiving growing attention lately, the Transcatheter Aortic Valve Implantation (TAVI). The results obtained are presented on a basis of a real TAVI case base of 82 patients operated at Rennes University Hospital.