A description of patient conditions should consist of the changes in and combination of clinical measures. Traditional data-processing method and classification algorithms might cause clinical information to disappear and reduce prediction performance. To improve the accuracy of clinical-outcome prediction by using multiple measurements, a new multiple-time-series data-processing algorithm with period merging is proposed. Clinical data from 83 hepatocellular carcinoma (HCC) patients were used in this research. Their clinical reports from a defined period were merged using the proposed merging algorithm, and statistical measures were also calculated. After data processing, Multiple Measurements Support Vector Machine (MMSVM) with radial basis function (RBF) kernels was used as a classification method to predict HCC recurrence. A Multiple Measurements Random Forest regression (MMRF) was also used as an additional evaluation/ classification method. To evaluate the data-merging algorithm, the performance of prediction using processed multiple measurements was compared to prediction using single measurements. The results of recurrence prediction by MMSVM with RBF using multiple measurements and a period of 120 days (accuracy 0.771, balanced accuracy 0.603) were optimal, and their superiority to the results obtained using single measurements was statistically significant (accuracy 0.626, balanced accuracy 0.459, P<0.01). In the cases of MMRF, the prediction results obtained after applying the proposed merging algorithm were also better than single-measurement results (P<0.05). The results show that the performance of HCC-recurrence prediction was significantly improved when the proposed data-processing algorithm was used, and that multiple measurements could be of greater value than single measurements in HCC-recurrence prediction.
Observing the pattern changes of inpatient fall and validating the Fall Prevention Tool Kit (FPTK) are essential for developing fall prevention strategies. However, the work requires the collection, calculation, and comparison of large amount of data. The information is often scattered in diverse information systems and lack of integration, which makes the work difficult and often neglected. This study demonstrates the development of an Interactive Data Repository System (IDRS) and uses it in the analysis of the pattern changes of inpatient fall within the institute, and validates efficiency of the FPTK across time. This study collected the incident data of year 2011 and compared it with the previous analysis in 2001. The result shows that reasons for patient fall had turned from physical disability to impaired conscious or cognition. The scoring result may be too sensitive in identifying patient falls. Patients with high scores needed to reinforce in functional strength.
Hospital selection is a complicated decision-making process. Although patients have expressed greater desire to participate in decision-makings of their healthcare, it can be problematic for them to accumulate large amount of information and using it for making an optimal choice in hospital selection. The aim of this research is to develop a decision engine for hospital selection (DEHS) to support patients while accessing healthcare resources. DEHS applied the analytic hierarchy process and the geographic information system to aggregate different decision factors and spatial information. The results were evaluated through investigating the consistency of the preferences that users inputted, the degree that the results match patient choices, the satisfactions of users, and the helpfulness of the results. Data were collected for 3 months. One hundred and four users visited DEHS and 85.5 % of them used DEHS more than once. Recommendations of the institutes (36 %) was ranked as the primary decision factor that most users concerned. Sixty-seven percent of the sessions searched for hospitals and 33 % for clinics. Eighty-eight percent of the results matched the choices of patients. Eighty-three percent of the users agreed that the suggested results were satisfactory, and 70 % agreed that the information were helpful. The DEHS provides the patients with simple measurements and individualized list of suggested medical institutes, and allows them to make decisions based on credible information and consults the experiences of others at the same time. The suggested results were considered satisfactory and helpful.
Recurrence of hepatocellular carcinoma (HCC) is an important issue despite effective treatments with tumor eradication. Identification of patients who are at high risk for recurrence may provide more efficacious screening and detection of tumor recurrence. The aim of this study was to develop recurrence predictive models for HCC patients who received radiofrequency ablation (RFA) treatment.
Explosive growth of next-generation sequencing data has resulted in ultra-large-scale data sets and ensuing computational problems. Cloud computing provides an on-demand and scalable environment for large-scale data analysis. Using a MapReduce framework, data and workload can be distributed via a network to computers in the cloud to substantially reduce computational latency. Hadoop/MapReduce has been successfully adopted in bioinformatics for genome assembly, mapping reads to genomes, and finding single nucleotide polymorphisms. Major cloud providers offer Hadoop cloud services to their users. However, it remains technically challenging to deploy a Hadoop cloud for those who prefer to run MapReduce programs in a cluster without built-in Hadoop/MapReduce.
Recently, Event-Related Potential (ERP) has being the most popular method in evaluating brain waves of schizophrenia patients. ERP is one of the electroencephalography (EEG), which is measured the change of brain waves after giving patients certain stimulations instead of resting state. However, with traditional statistical analysis method, both P50 and MMN showed significant difference between controls and patients but not in Gamma band. Gamma band is a 30-50 Hz auditory stimulation which had been suggested may be abnormal in schizophrenia patients. Our data are recruited from 5 schizophrenia patients and 5 controls in National Taiwan University Hospital have been tested with this platform. The results showed that detection rate is 88.24% and we also analyzed the importance of features, including Standard Deviation (SD) and Total Variation (TotalVar) in different stage of wavelet transform. Therefore, this proposed methodology could serve as a valuable clinical decision support for physiologists in evaluating schizophrenia.
Biomedical data analytic system has played an important role in doing the clinical diagnosis for several decades. Today, it is an emerging research area of analyzing these big data to make decision support for physicians. This paper presents a parallelized web-based tool with cloud computing service architecture to analyze the epilepsy. There are many modern analytic functions which are wavelet transform, genetic algorithm (GA), and support vector machine (SVM) cascaded in the system. To demonstrate the effectiveness of the system, it has been verified by two kinds of electroencephalography (EEG) data, which are short term EEG and long term EEG. The results reveal that our approach achieves the total classification accuracy higher than 90%. In addition, the entire training time accelerate about 4.66 times and prediction time is also meet requirements in real time.
Multiclass classification is an important technique to many complex bioinformatics problems. However, their performance is limited by the computation power. Based on the Apache Hadoop design framework, this study proposes a two layer architecture that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have reduced 86.55% features and raised accuracy from 97.53% to 98.03%. With a user-friendly web interface, the system provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of bioinformatics data.
To provide an efficient way for tracking patients condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports.
Self-management is an important skill for patients with diabetes, and it involves frequent monitoring of glucose levels and behavior modification. Techniques to enhance the behavior changes of diabetic patients have been developed, such as diabetes self-management education and telehealthcare. Although the patients are engaged in self-management activities, barriers to behavior changes remain and additional work is necessary to address the impact of electronic media and telehealthcare on patient self-care behaviors.
Pancreaticoduodenectomy (PD) is a major operation with high complication rate. Thereafter, patients may develop morbidity because of the complex reconstruction and loss of pancreatic parenchyma. A well-designed database is very important to address both the short-term and long-term outcomes after PD.
The classification of electroencephalography (EEG) signals is one of the most important methods for seizure detection. However, verification of an atypical epileptic seizure often can only be done through long-term EEG monitoring for 24 hours or longer. Hence, automatic EEG signal analysis for clinical screening is necessary for the diagnosis of epilepsy. We propose an EEG analysis system of seizure detection, based on a cascade of wavelet-approximate entropy for feature selection, Fisher scores for adaptive feature selection, and support vector machine for feature classification. Performance of the system was tested on open source data, and the overall accuracy reached 99.97%. We further tested the performance of the system on clinical EEG obtained from a clinical EEG laboratory and bedside EEG recordings. The results showed an overall accuracy of 98.73% for routine EEG, and 94.32% for bedside EEG, which verified the high performance and usefulness of such a cascade system for seizure detection. Also, the prediction model, trained by routine EEG, can be successfully generalized to bedside EEG of independent patients.
A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification.
Healthcare-associated infections (HAIs) are a major patient safety issue. These adverse events add to the burden of resource use, promote resistance to antibiotics, and contribute to patient deaths and disability. A rule-based HAI classification and surveillance system was developed for automatic integration, analysis, and interpretation of HAIs and related pathogens. Rule-based classification system was design and implement to facilitate healthcare-associated bloodstream infection (HABSI) surveillance. Electronic medical records from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of HABSI. The detailed information in each HABSI was presented systematically to support infection control personnel decision. The accuracy of HABSI classification was 0.94, and the square of the sample correlation coefficient was 0.99.
Disease management is a program which attempts to overcome the fragmentation of healthcare system and improve the quality of care. Many studies have proven the effectiveness of disease management. However, the case managers were spending the majority of time in documentation, coordinating the members of the care team. They need a tool to support them with daily practice and optimizing the inefficient workflow. Several discussions have indicated that information technology plays an important role in the era of disease management. Whereas applications have been developed, it is inefficient to develop information system for each disease management program individually. The aim of this research is to support the work of disease management, reform the inefficient workflow, and propose an architecture model that enhance on the reusability and time saving of information system development. The proposed architecture model had been successfully implemented into two disease management information system, and the result was evaluated through reusability analysis, time consumed analysis, pre- and post-implement workflow analysis, and user questionnaire survey. The reusability of the proposed model was high, less than half of the time was consumed, and the workflow had been improved. The overall user aspect is positive. The supportiveness during daily workflow is high. The system empowers the case managers with better information and leads to better decision making.
Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.
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.
With the initial completion of Human Genome Project, the post-genomic era is coming. Although the genome map of human has been decoded, the roles that each segment of sequences acts are not totally discovered. On the other hand, with the rapid expansion of sequence information, the issues of data compilation and data storage are increasingly important. In this paper, a "Human genome database system" is designed and implemented in National Taiwan University Hospital (NTUH). By accessing this system, the doctors can store and manage the experimental sequence data. The achievement of this system is that it integrates the modules of sequence alignment and data compression. By embedding with the NCBI alignment program-blastall , it automatically aligns the uploaded sequences and searches for the corresponding genomic positions. Besides, the system encodes the differences between sequences, effectively compresses them and decreases the demand of storage spaces by the compression ratio at 12.28. At the same time, it offers a variety of query methods. Users can quickly access the interesting data by inputting the keywords of specimen number, GI and sequence position, etc. The electronic health record (EHR) in Health Information System (HIS) of NTUH is also combined in this system and the doctors can utilize the valuable information to figure out the relation between the diseases and genes. With this system, a genetic personal healthcare environment will be established in the future.
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.
Medical resources are important and necessary in health care. Recently, the development of methods for improving the efficiency of medical resource utilization is an emerging problem. Despite evidence supporting the use of order sets in hospitals, only a small number of health information systems have successfully equipped physicians with analysis of complex order sequences from clinical pathway and clinical guideline. This paper presents a data-mining framework for transnational healthcare system to find alternative practices, including transfusion, pre-admission tests, and evaluation of liver diseases. However, individual countries vary with respect to geographical location, living habits, and culture, so disease risks and treatment methods also vary across countries. To realize the difference, a service-oriented architecture and cloud-computing technology are applied to analyze these medical data. The validity of the proposed system is demonstrated in including Taiwan and Mongolia, to ensure the feasibility of our approach.
The paper addresses Medical Hand Drawing Management System architecture and implementation. In the system, we developed four modules: hand drawing management module; patient medical records query module; hand drawing editing and upload module; hand drawing query module. The system adapts windows-based applications and encompasses web pages by ASP.NET hosting mechanism under web services platforms. The hand drawings implemented as files are stored in a FTP server. The file names with associated data, e.g. patient identification, drawing physician, access rights, etc. are reposited in a database. The modules can be conveniently embedded, integrated into any system. Therefore, the system possesses the hand drawing features to support daily medical operations, effectively improve healthcare qualities as well. Moreover, the system includes the printing capability to achieve a complete, computerized medical document process. In summary, the system allows web-based applications to facilitate the graphic processes for healthcare operations.
Today, many bio-signals such as Electroencephalography (EEG) are recorded in digital format. It is an emerging research area of analyzing these digital bio-signals to extract useful health information in biomedical engineering. In this paper, a bio-signal analyzing cloud computing architecture, called BACCA, is proposed. The system has been designed with the purpose of seamless integration into the National Taiwan University Health Information System. Based on the concept of. NET Service Oriented Architecture, the system integrates heterogeneous platforms, protocols, as well as applications. In this system, we add modern analytic functions such as approximated entropy and adaptive support vector machine (SVM). It is shown that the overall accuracy of EEG bio-signal analysis has increased to nearly 98% for different data sets, including open-source and clinical data sets.
The telehealth care system has been important in the healthcare world for several decades; however, Taiwan only began work on telehealth care this past year. This paper outlines the effectiveness of the telehealth care system developed by the National Taiwan University Hospital (NTUH). The usability of the integrated telehealth care system was analyzed through of heuristic evaluation and its usefulness. By using the heuristic evaluation form as developed by Nielsen, it is possible to examine the telehealth care system from the users perspective. In addition, in assessing the usefulness through lists of criteria, system developers can determine the pros and the cons of the database. Ultimately, the heuristic evaluation revealed several violations on the system, but are not prohibitive to the development of such as system. Similarly, evaluation of the usefulness comes out positive; despite the fact that the suggested changes proposed by the users can be said are the main weaknesses of the system. With some improvements, the telehealth care system can be used efficiently in NTUHs healthcare system.
Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.
The Clinical Document Architecture, introduced by Health Level Seven, is a XML-based standard intending to specify the encoding, structure, and semantics of clinical documents for exchange. Since the clinical document is in XML form, its authenticity and integrity could be guaranteed by the use of the XML signature published by W3C. While a clinical document wants to conceal some personal or private information, the document needs to be redacted. It makes the signed signature of the original clinical document not be verified. The redactable signature is thus proposed to enable verification for the redacted document. Only a little research does the implementation of the redactable signature, and there still not exists an appropriate scheme for the clinical document. This paper will investigate the existing web-technologies and find a compact and applicable model to implement a suitable redactable signature for the clinical document viewer.
Information security management for healthcare enterprises is complex as well as mission critical. Information technology requests from clinical users are of such urgency that the information office should do its best to achieve as many user requests as possible at a high service level using swift security policies. This research proposes the Agile Enterprise Regulation Architecture (AERA) of information security management for healthcare enterprises to implement as part of the electronic health record process. Survey outcomes and evidential experiences from a sample of medical center users proved that AERA encourages the information officials and enterprise administrators to overcome the challenges faced within an electronically equipped hospital.
The telecare medicine information system enables or supports health-care delivery services. In recent years, the increased availability of lower-cost telecommunications systems and custom made physiological monitoring devices for patients have made it possible to bring the advantages of telemedicine directly into the patients home. These systems are moving towards an environment where automated patient medical records and electronically interconnected telecare facilities are prevalent. A secure authentication scheme will thus be needed to safeguard data integrity, confidentiality, and availability. Many schemes based on cryptography have been proposed for the goals. However, much of the schemes are vulnerable to various attacks, and are neither efficient, nor user friendly. Specially, in terms of efficiency, some schemes need the exponential computation resulting in high time cost. Therefore, we propose a novel authentication scheme that is added the pre-computing idea within the communication process to avoid the time-consuming exponential computations. Finally, it is shown to be more secure and practical for telecare medicine environments.
This article illustrates a Web-based health information system that is comprised of specific information exchange standards related to health information for healthcare services in National Taiwan University Health System. Through multidisciplinary teamwork, medical and informatics experts collaborated and studied on system scope definition, standard selection challenges, system implementation barriers, system management outcomes, and further expandability of other systems. After user requirement analysis and prototyping, from 2005 to 2008, an online clinical decision support system with multiple functions of reminding and information push was implemented. It was to replace its original legacy systems and serve among the main hospital and three branches of 180-200 clinics and 7,500-8,000 patient visits per day. To evaluate the effectiveness of this system, user surveys were performed, which revealed that the average score of user satisfaction increased from 2.80 to 3.18 on a 4-point scale. Among the items, especially e-learning for training service, courtesy communications for system requests, and courtesy communications for system operations showed statistically significant improvement. From this study, the authors concluded that standardized information exchange technologies can be used to create a brand new enterprise value and steadily obtain more competitive advantages for a prestige healthcare system.
Discharge summary note is one of the essential clinical data in medical records, and it concisely capsules a patients status during hospitalization. In the article, we adopt web-based architecture in developing a new discharge summary system for the Healthcare Information System of National Taiwan University Hospital, to improve the traditional client/sever architecture. The article elaborates the design approaches and implementation illustrations in detail, including patients summary query and searching, model and phrase quoted, summary check list, major editing blocks as well as other functionalities. The system has been on-line and achieves successfully since October 2009.
With the rapid development of the Internet, digitization and electronic orientation are required in various applications of our daily life. For e-medicine, establishing Electronic patient records (EPRs) for all the patients has become the top issue during the last decade. Simultaneously, constructing an integrated EPR information system of all the patients is beneficial because it can provide medical institutions and the academia with most of the patients information in details for them to make correct decisions and clinical decisions, to maintain and analyze patients health. Also beneficial to doctors and scholars, the EPR system can give them record linkage for researches, payment audits, or other services bound to be developed and integrated into medicine. To tackle the illegal access and to prevent the information from theft during transmission over the insecure Internet, we propose a password-based user authentication scheme suitable for information integration.
Many existing healthcare information systems are composed of a number of heterogeneous systems and face the important issue of system scalability. This paper first describes the comprehensive healthcare information systems used in National Taiwan University Hospital (NTUH) and then presents a service-oriented architecture (SOA)-based healthcare information system (HIS) based on the service standard HL7. The proposed architecture focuses on system scalability, in terms of both hardware and software. Moreover, we describe how scalability is implemented in rightsizing, service groups, databases, and hardware scalability. Although SOA-based systems sometimes display poor performance, through a performance evaluation of our HIS based on SOA, the average response time for outpatient, inpatient, and emergency HL7Central systems are 0.035, 0.04, and 0.036 s, respectively. The outpatient, inpatient, and emergency WebUI average response times are 0.79, 1.25, and 0.82 s. The scalability of the rightsizing project and our evaluation results show that the SOA HIS we propose provides evidence that SOA can provide system scalability and sustainability in a highly demanding healthcare information system.
This paper illustrates how our development team has used some information technologies to let physicians obtain an instant abnormal laboratory result report for critical patient care services. We have implemented a healthcare message alerting system (HMAS) on a healthcare short message service (HSMS) engine and the distributed healthcare-oriented service environment (DiHOSE) in the National Taiwan University Hospital (NTUH). The HSMS engine has a general interface for all applications which could easily send any kind of alerting messages. Fundamentally, the DiHOSE uses HL7 standard formats to process the information exchange behaviors and can be flexibly extended for reasonable user requirements. The disease surveillance subsystem is an integral part of NTUH new hospital information system which is based on DiHOSE and the disease surveillance subsystem would send alerting messages through the HSMS engine. The latest cell phone message alerting subsystem, a case study, in NTUH proved that the DiHOSE could integrate the user required functions without much work. We concluded that both HSMS and DiHOSE can generalize and extend application demands efficiently.
The clinical symptoms of metabolic disorders during neonatal period are often not apparent. If not treated early, irreversible damages such as mental retardation may occur, even death. Therefore, practicing newborn screening is essential, imperative to prevent neonatal from these damages. In the paper, we establish a newborn screening model that utilizes Support Vector Machines (SVM) techniques and enhancements to evaluate, interpret the Methylmalonic Acidemia (MMA) metabolic disorders. The model encompasses the Feature Selections, Grid Search, Cross Validations as well as multi model Voting Mechanism. In the model, the predicting accuracy, sensitivity and specificity of MMA can be improved dramatically. The model will be able to apply to other metabolic diseases as well.
In this study, higher order statistics-based radial basis function network (RBF) was proposed for evoked potentials (EPs). EPs provide useful information on diagnosis of the nervous system. They are time-varying signals typically buried in ongoing EEG, and have to be extracted by special methods. RBF with least mean square (LMS) algorithm is an effective method to extract EPs. However, using LMS algorithm usually encounters gradient noise amplification problem, i.e., its performance is sensitive to the selection of step sizes and additional noise. Higher order statistics technique, which can effectively suppress Gaussian and symmetrically distributed non-Gaussian noises, was used to reduce gradient noise amplification problem on adaptation in this study. Simulations and human experiments were also carried out in this study.
The clinical symptoms of metabolic disorders are rarely apparent during the neonatal period, and if they are not treated earlier, irreversible damages, such as mental retardation or even death, may occur. Therefore, the practice of newborn screening is essential to prevent permanent disabilities in newborns. In the paper, we design, implement a newborn screening system using Support Vector Machine (SVM) classifications. By evaluating metabolic substances data collected from tandem mass spectrometry (MS/MS), we can interpret and determine whether a newborn has a metabolic disorder. In addition, National Taiwan University Hospital Information System (NTUHIS) has been developed and implemented to integrate heterogeneous platforms, protocols, databases as well as applications. To expedite adapting the diversities, we deploy Service-Oriented Architecture (SOA) concepts to the newborn screening system based on web services. The system can be embedded seamlessly into NTUHIS.
Health Level Seven (HL7) organization published the Clinical Document Architecture (CDA) for exchanging documents among heterogeneous systems and improving medical quality based on the design method in CDA. In practice, although the HL7 organization tried to make medical messages exchangeable, it is still hard to exchange medical messages. There are many issues when two hospitals want to exchange clinical documents, such as patient privacy, network security, budget, and the strategies of the hospital. In this article, we propose a method for the exchange and sharing of clinical documents in an offline model based on the CDA-the Portable CDA. This allows the physician to retrieve the patients medical record stored in a portal device, but not through the Internet in real time. The security and privacy of CDA data will also be considered.
In this paper, we established a newborn screening system under the HL7/Web Services frameworks. We rebuilt the NTUH Newborn Screening Laboratorys original standalone architecture, having various heterogeneous systems operating individually, and restructured it into a Service-Oriented Architecture (SOA), distributed platform for further integrity and enhancements of sample collections, testing, diagnoses, evaluations, treatments or follow-up services, screening database management, as well as collaboration, communication among hospitals; decision supports and improving screening accuracy over the Taiwan neonatal systems are also addressed. In addition, the new system not only integrates the newborn screening procedures among phlebotomy clinics, referral hospitals, as well as the newborn screening center in Taiwan, but also introduces new models of screening procedures for the associated, medical practitioners. Furthermore, it reduces the burden of manual operations, especially the reporting services, those were heavily dependent upon previously. The new system can accelerate the whole procedures effectively and efficiently. It improves the accuracy and the reliability of the screening by ensuring the quality control during the processing as well.
The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials.
Telehealthcare has been used to provide healthcare service, and information technology infrastructure appears to be essential while providing telehealthcare service. Insufficiencies have been identified, such as lack of integration, need of accommodation of diverse biometric sensors, and accessing diverse networks as different houses have varying facilities, which challenge the promotion of telehealthcare. This study designs an information technology framework to strengthen telehealthcare delivery.
To present the successful experiences of an integrated, collaborative, distributed, large-scale enterprise healthcare information system over a wired and wireless infrastructure in National Taiwan University Hospital (NTUH). In order to smoothly and sequentially transfer from the complex relations among the old (legacy) systems to the new-generation enterprise healthcare information system, we adopted the multitier framework based on service-oriented architecture to integrate the heterogeneous systems as well as to interoperate among many other components and multiple databases. We also present mechanisms of a logical layer reusability approach and data (message) exchange flow via Health Level 7 (HL7) middleware, DICOM standard, and the Integrating the Healthcare Enterprise workflow. The architecture and protocols of the NTUH enterprise healthcare information system, especially in the Inpatient Information System (IIS), are discussed in detail.
Today, in order to provide high-quality medical services and to extend resources and reduce costs, many large hospitals have adopted clinical guidelines as a structured way to manage medical activities. However, customization of clinical guidelines in order to treat a large number of patients is a major challenge. In this paper, we present a physician order category-based clinical guideline comparison system. The system uses a preprocessor software to convert the clinical guidelines from a Microsoft Word document into XML format, and it can also compare clinical guidelines over the conceptual view such as the physician order category. The system has already been used to compare the HCC surgical clinical guidelines of Taiwan and Mongolia-resulting in some differences being found, for which possible causes were discussed. Therefore, it can be seen that our research provides a practical and convenient way in which to compare clinical guidelines based on physician order category-thereby saving time and enabling physicians to quickly resolve discrepancies and make necessary adjustments to clinical guidelines.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.