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Pandemic influenza is considered catastrophic to global health, with severe economic and social effects. Consequently, a strategy for the rapid deployment of essential medical supplies used for the prevention of influenza transmission and to alleviate public panic caused by the expected shortage of such supplies needs to be developed. Therefore, we employ integrated information concepts to develop a simulated influenza medical material supply system to facilitate a rapid response to such a crisis. Various scenarios are analyzed to estimate the appropriate inventory policy needed under different pandemic influenza outbreaks, and to establish a mechanism to evaluate the necessary stockpiles of medications and other requirements in the different phases of the pandemic. This study constructed a web-based decision support system framework prototype that displayed transparent data related to medical stockpiles in each district and integrated expert opinion about the best distribution of these supplies in the influenza pandemic scenarios. A data collection system was also designed to gather information through a daily VPN transmitted into one central repository for reporting and distribution purposes. This study provides timely and transparent medical supplies distribution information that can help decision makers to make the appropriate decisions under different pandemic influenza outbreaks, and also attempts to establish a mechanism of evaluating the stockpiles and requirements in the different phases of the pandemic.

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis” (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis. Electronic supplementary material The online version of this article (10.1007/s10916-020-1538-4) contains supplementary material, which is available to authorized users.

The increasing amount of data produced by various biomedical and healthcare systems has led to a need for methodologies related to knowledge data discovery. Data mining (DM) offers a set of powerful techniques that allow the identification and extraction of relevant information from medical datasets, thus enabling doctors and patients to greatly benefit from DM, particularly in the case of diseases with high mortality and morbidity rates, such as heart disease (HD). Nonetheless, the use of raw medical data implies several challenges, such as missing data, noise, redundancy and high dimensionality, which make the extraction of useful and relevant information difficult and challenging. Intensive research has, therefore, recently begun in order to prepare raw healthcare data before knowledge extraction. In any knowledge data discovery (KDD) process, data preparation is the step prior to DM that deals with data imperfectness in order to improve its quality so as to satisfy the requirements and improve the performances of DM techniques. The objective of this paper is to perform a systematic mapping study (SMS) on data preparation for KDD in cardiology so as to provide an overview of the quantity and type of research carried out in this respect. The SMS consisted of a set of 58 selected papers published in the period January 2000 and December 2017. The selected studies were analyzed according to six criteria: year and channel of publication, preparation task, medical task, DM objective, research type and empirical type. The results show that a high amount of data preparation research was carried out in order to improve the performance of DM-based decision support systems in cardiology. Researchers were mainly interested in the data reduction preparation task and particularly in feature selection. Moreover, the majority of the selected studies focused on classification for the diagnosis of HD. Two main research types were identified in the selected studies: solution proposal and evaluation research, and the most frequently used empirical type was that of historical-based evaluation.

A good primary health care is the base for a better healthcare system. Taking a good decision on time by the primary health care physician could have a huge repercussion. In order to ease the diagnosis task arise the Decision Support Systems (DSS), which offer counselling instead of refresh the medical knowledge, in a profession where it is still learning every day. The implementation of these systems in diseases which are a frequent cause of visit to the doctor like ophthalmologic pathologies are, which affect directly to our quality of life, takes more importance. This paper aims to develop OphthalDSS, a totally new mobile DSS for red eye diseases diagnosis. The main utilities that OphthalDSS offers will be a study guide for medical students and a clinical decision support system for primary care professionals. Other important goal of this paper is to show the user experience results after OphthalDSS being used by medical students of the University of Valladolid. For achieving the main purpose of this research work, a decision algorithm will be developed and implemented by an Android mobile application. Moreover, the Quality of Experience (QoE) has been evaluated by the students through the questions of a short inquiry. The app developed which implements the algorithm OphthalDSS is capable of diagnose more than 30 eye's anterior segment diseases. A total of 67 medical students have evaluated the QoE. The students find the diseases' information presented very valuable, the appearance is adequate, it is always available and they have ever found what they were looking for. Furthermore, the students think that their quality of life has not been improved using the app and they can do the same without using the OphthalDSS app. OphthalDSS is easy to use, which is capable of diagnose more than 30 ocular diseases in addition to be used as a DSS tool as an educational tool at the same time.

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

In healthcare organizations, clinical workflows are executed by interdisciplinary healthcare teams (IHTs) that operate in ways that are difficult to manage. Responding to a need to support such teams, we designed and developed the MET4 multi-agent system that allows IHTs to manage patients according to presentation-specific clinical workflows. In this paper, we describe a significant extension of the MET4 system that allows for supporting rich team dynamics (understood as team formation, management and task-practitioner allocation), including selection and maintenance of the most responsible physician and more complex rules of selecting practitioners for the workflow tasks. In order to develop this extension, we introduced three semantic components: (1) a revised ontology describing concepts and relations pertinent to IHTs, workflows, and managed patients, (2) a set of behavioral rules describing the team dynamics, and (3) an instance base that stores facts corresponding to instances of concepts from the ontology and to relations between these instances. The semantic components are represented in first-order logic and they can be automatically processed using theorem proving and model finding techniques. We employ these techniques to find models that correspond to specific decisions controlling the dynamics of IHT. In the paper, we present the design of extended MET4 with a special focus on the new semantic components. We then describe its proof-of-concept implementation using the WADE multi-agent platform and the Z3 solver (theorem prover/model finder). We illustrate the main ideas discussed in the paper with a clinical scenario of an IHT managing a patient with chronic kidney disease.

Automated medical diagnosis systems based on knowledge-oriented descriptions have gained momentum with the emergence of semantic descriptions. The objective of this paper is to propose a normalized design that solves some of the problems which have been detected by authors in previous tools. The authors bring together two different technologies to develop a new clinical decision support system: description logics aimed at developing inference systems to improve decision support for the prevention, treatment and management of illness and semantic technologies. Because of its new design, the system is capable of obtaining improved diagnostics compared with previous efforts. However, this evaluation is more focused in the computational performance, giving as result that description logics is a good solution with small data sets. In this paper, we provide a well-structured ontology for automated diagnosis in the medical field and a three-fold formalization based on Description Logics with the use of Semantic Web technologies This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project TRAZAMED (IPT 090000 2010 007). Publicado

This paper presents an overview of the state-of-the-art on decision support systems (DSS) in telecare. The main aspect examined is the use of smaller subsystems—components in an integrated DSS, with emphasis on two application areas: medical home unit monitoring and real-time prioritisation of the alerts generated by them, and drug interaction checking. The paper suggests the development of an integrated hybrid telecare DSS synthesizing most of the technologies reviewed. Implementation issues are also examined, with an emphasis on the international trend towards the development of platform-independent, component-based, distributed software.

The ability of Patient Dependency Systems to provide information for staffing decisions and budgetary development has been demonstrated. In addition, they have become powerful tools in modern hospital management. This growing interest in Patient Dependency Systems has renewed calls for their automation. As advances in Information Technology and in particular Knowledge-Based Engineering reach new heights, hospitals can no longer afford to ignore the potential benefits obtainable from developing and implementing Patient Dependency Knowledge-Based Systems. Experience has shown that the vast majority of decisions and rules used in the Patient Dependency method are too complex to capture in the form of a traditional programming language. Furthermore, the conventional Patient Dependency Information System automates the simple and rigid bookkeeping functions. On the other hand Knowledge-Based Systems automate complex decision making and judgmental processes and therefore are the appropriate technology for automating the Patient Dependency method. In this paper a new technique to automate Patient Dependency Systems using knowledge processing is presented. In this approach all Patient Dependency factors have been translated into a set of Decision Rules suitable for use in a Knowledge-Based System. The system is capable of providing the decision-maker with a number of scenarios and their possible outcomes. This paper also presents the development of Patient Dependency Knowledge-Based Systems, which can be used in allocating and evaluating resources and nursing staff in hospitals on the basis of patients' needs.

Reduction of health care costs is of paramount importance in our time. This paper is a part of the research which proposes an expert hospital decision support system for resource scheduling. The proposed system combines mathematical programming, knowledge base, and database technologies, and what is more, its friendly interface is suitable for any novice user. Operating rooms in hospitals represent big investments and must be utilized efficiently. In this paper, first a mathematical model similar to job shop scheduling models is developed. The model loads surgical cases to operating rooms by maximizing room utilization and minimizing overtime in a multiple operating room setting. Then a prototype expert system which replaces the expertise of the operations research analyst for the model, drives the modelbase, database, and manages the user dialog is developed. Finally, an overview of the sequencing procedures for operations within an operating room is also presented. © 1995 Plenum Publishing Corporation.

Health examinations can obtain relatively complete health information and thus are important for the personal and public health management. For clinicians, one of the most important works in the health examinations is to interpret the health examination results. Continuously interpreting numerous health examination results of healthcare receivers is tedious and error-prone. This paper proposes a clinical decision support system to assist solving above problems. In order to customize the clinical decision support system intuitively and flexibly, this paper also proposes the rule syntax to implement computer-interpretable logic for health examinations. It is our purpose in this paper to describe the methodology of the proposed clinical decision support system. The evaluation was performed by the implementation and execution of decision rules on health examination results and a survey on clinical decision support system users. It reveals the efficiency and user satisfaction of proposed clinical decision support system. Positive impact of clinical data interpretation is also noted.

Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.

This paper has three major goals: (1) Show by demonstration (in a currently implemented CPR) that for this case, the additional information entry required by providers to acquire the information for Clinical Decision Support (CDS) and Resource Auditing (RA) comes as a side effect of patient care documentation. (2) Use simple simulation to explore the practical issues of allowing users database access to this cross-patient information for CDS and RA. (3) Relate by argument the findings in 1 and 2 to the concept of mediated information systems architectures for CDS and RA. The primary finding of this paper is that a mediated architecture is the only practical way to offer distributed knowledge and data resources to the CPR user.

Group Decision Support Systems (GDSS) are defined and discussed. A GDSS model developed by the author is reviewed in depth for communication of the concepts of GDSS. The model's components are related to health care applications. Questions about unique requirements and level of sophistication in health care applications are explored. What are the differences? What is needed in GDSS software? How do implementation strategies differ? The purpose of this paper is to define and discuss the uniqueness and level of sophistication of GDSS applications in health care. The information requirements and level of information abstraction are the major forces considered in the design of specific medical GDSSs. Data for the GDSS and queries originate both internally and externally to the system. Raw data may be in image form and require extensive analysis by the decision makers for information to be extracted from the raw data. Efforts also are made to relate financial and medical data for better business decisions. This integration often has limited success. Additionally, financial data represent multiple sources and present concerns of validity and reliability. In medical diagnoses the knowledge bases are large and contain thousands of rules. Treatment planning and progress reporting rely on medical records that contain thousands of information items and that often require interpretation by an expert. These information attributes go beyond qualitative versus quantitative definitions and are the author's basis for the analysis presented in this paper.

Lung cancer is considered one of the deadliest diseases in the world. An early and accurate diagnosis aims to promote the detection and characterization of pulmonary nodules, which is of vital importance to increase the patients' survival rates. The mentioned characterization is done through a segmentation process, facing several challenges due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper tackles pulmonary nodule segmentation in computed tomography scans proposing three distinct methodologies. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the filter's support points, matching the border coordinates. The remaining approaches are Deep Learning based, using the U-Net and a novel network called SegU-Net to achieve the same goal. Their performance is compared, as this work aims to identify the most promising tool to improve nodule characterization. All methodologies used 2653 nodules from the LIDC database, achieving a Dice score of 0.663, 0.830, and 0.823 for the SBF, U-Net and SegU-Net respectively. This way, the U-Net based models yield more identical results to the ground truth reference annotated by specialists, thus being a more reliable approach for the proposed exercise. The novel network revealed similar scores to the U-Net, while at the same time reducing computational cost and improving memory efficiency. Consequently, such study may contribute to the possible implementation of this model in a decision support system, assisting the physicians in establishing a reliable diagnosis of lung pathologies based on this segmentation task.

This study aims to systematically review prior research on the evaluation and benchmarking of automated acute leukaemia classification tasks. The review depends on three reliable search engines: ScienceDirect, Web of Science and IEEE Xplore. A research taxonomy developed for the review considers a wide perspective for automated detection and classification of acute leukaemia research and reflects the usage trends in the evaluation criteria in this field. The developed taxonomy consists of three main research directions in this domain. The taxonomy involves two phases. The first phase includes all three research directions. The second one demonstrates all the criteria used for evaluating acute leukaemia classification. The final set of studies includes 83 investigations, most of which focused on enhancing the accuracy and performance of detection and classification through proposed methods or systems. Few efforts were made to undertake the evaluation issues. According to the final set of articles, three groups of articles represented the main research directions in this domain: 56 articles highlighted the proposed methods, 22 articles involved proposals for system development and 5 papers centred on evaluation and comparison. The other taxonomy side included 16 main and sub-evaluation and benchmarking criteria. This review highlights three serious issues in the evaluation and benchmarking of multiclass classification of acute leukaemia, namely, conflicting criteria, evaluation criteria and criteria importance. It also determines the weakness of benchmarking tools. To solve these issues, multicriteria decision-making (MCDM) analysis techniques were proposed as effective recommended solutions in the methodological aspect. This methodological aspect involves a proposed decision support system based on MCDM for evaluation and benchmarking to select suitable multiclass classification models for acute leukaemia. The said support system is examined and has three sequential phases. Phase One presents the identification procedure and process for establishing a decision matrix based on a crossover of evaluation criteria and acute leukaemia multiclass classification models. Phase Two describes the decision matrix development for the selection of acute leukaemia classification models based on the integrated Best and worst method (BWM) and VIKOR. Phase Three entails the validation of the proposed system.

We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.

Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests. We present a semantic-web based Referral Knowledge Modeling and Execution Framework to computerize referral protocols, clinical guidelines and assessment tools in order to develop a computerized e-Referral system that offers protocol-based decision support to streamline and standardize the referral process. We have developed a Spinal Problem E-Referral (SPER) system that computerizes the Spinal Condition Consultation Protocol (SCCP) mandated by the Halifax Infirmary Division of Neurosurgery (Halifax, Canada) for referrals for spine related conditions (such as back pain). The SPER system executes the ontologically modeled SCCP to determine (i) patient's triaging option as per severity assessments stipulated by SCCP; and (b) clinical recommendations as per the clinical guidelines incorporated within SCCP. In operation, the SPER system identifies the critical cases and triages them for specialist referral, whereas for non-critical cases SPER system provides clinical guideline based recommendations to help the primary care physician effectively manage the patient. The SPER system has undergone a pilot usability study and was deemed to be easy to use by physicians with potential to improve the referral process within the Division of Neurosurgery at QEII Health Science Center, Halifax, Canada.

Decision support systems (DSS) are increasingly demanded due that diagnosis is one of the main activities that physicians accomplish every day. This fact seems critical when primary care physicians deal with uncommon problems belonging to specialized areas. The main objective of this paper is the development and user evaluation of a mobile DSS for iOS named OphthalDSS. This app has as purpose helping in anterior segment ocular diseases' diagnosis, besides offering educative content about ophthalmic diseases to users. For the deployment of this work, firstly it has been used the Apple IDE, Xcode, to develop the OphthalDSS mobile application using Objective-C as programming language. The core of the decision support system implemented by OphthalDSS is a decision tree developed by expert ophthalmologists. In order to evaluate the Quality of Experience (QoE) of primary care physicians after having tried the OphthalDSS app, a written inquiry based on the Likert scale was used. A total of 50 physicians answered to it, after trying the app during 1 month in their medical consultation. OphthalDSS is capable of helping to make diagnoses of diseases related to the anterior segment of the eye. Other features of OphthalDSS are a guide of each disease and an educational section. A 70% of the physicians answered in the survey that OphthalDSS performs in the way that they expected, and a 95% assures their trust in the reliability of the clinical information. Moreover, a 75% of them think that the decision system has a proper performance. Most of the primary care physicians agree with that OphthalDSS does the function that they expected, it is a user-friendly and the contents and structure are adequate. We can conclude that OphthalDSS is a practical tool but physicians require extra content that makes it a really useful one.

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

An improved Computer Aided Clinical Decision Support System has been developed to classify the retinal images using Neural Network and presented in this paper. The Optic Disc Parameters, thickness of the blood vessels, main vessel, and branch vessel and vein diameter have been extracted. Various types of Neural Network have been used for classification. The percentage of False Acceptance Rate and False Rejection Rate of the SVM classifier is found less than other classifiers. The accuracy of the proposed system has been verified and found to be 97.47%.

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

An improved Computer Aided Clinical Decision Support System has been developed to classify the tumor and identify the stages of the cancer using neural network and presented in this paper. The texture and shape features have been extracted and the optimal feature set has been obtained using multiobjective genetic algorithm (MOGA). The multilayer back propagation neural network with Ant Colony Optimization and Particle Swarm Optimization has been used. The accuracy of the proposed system has been verified and found that the accuracy of 99.5% can be achieved. The proposed system can provide valuable information to the physicians in clinical pathology.

This paper aims to investigate the efficacy and feasibility of Template-based Electronic Medical Record System (TEMRS) and factors for its successful implementation. A TEMRS was designed and implemented in one core clinic of a Hong Kong professional multi-disciplinary medical services provider with four core clinics located in different parts of Hong Kong. Eight doctors participated in the study. Surveys and interviews were conducted to acquire the users' feedback and satisfaction level. The design, development, and the factors related to the success of the implementation of TEMRS were analyzed. In the study period, 3,032 cases were collected. The most encountered diagnosis were upper respiratory tract infection (50.59%), gastroenteritis (10.19%), dermatitis (5.87%), dyspepsia (5.28%) and rhinitis (4.82%). The system gained an overall satisfaction by the users and the most satisfied areas were rapid retrieving the necessary information of patient (75%) and fasten the diagnostic selection (75%). TEMRS is an enabling system which can reduce the user resistance in new technology with its flexibility. The consideration of cost, security, human, technical, data migration and standardization issues are essential in the implementation of the TEMRS and further research should be conducted to expand the TEMRS's implementation in health care system.

mWard is a project whose purpose is to enhance existing clinical and administrative decision support and to consider mobile computers, connected via wireless network, for bringing clinical information to the point of care. The mWard project allowed a limited number of users to test and evaluate a selected range of mobile-wireless infrastructure and mobile health care computing devices at the neuroscience ward at Southern Health's Monash Medical Centre, Victoria, Australia. Before the project commenced, the ward had two PC's which were used as terminals by all ward-based staff and numerous multi-disciplinary staff who visited the ward each day. The first stage of the research, outlined in this paper, evaluates a selected range of mobile-wireless infrastructure.

Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of diabetics and subjects having risk factors of diabetes. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and their performances in detection of diabetics were compared. The performance of the classification algorithms was illustrated on the Pima Indians diabetes data set. The present research demonstrated that the modified mixture of experts (MME) achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.

This paper explores the role of clinical decision support systems (CDSS) in facilitating communication between physicians, nurses, patients and family members. Thirty-three critical care unit nurses and physicians were interviewed regarding the APACHE III CDSS. This qualitative, descriptive study suggests that registered nurses and physicians are primarily motivated to use CDSS when this technology allows them to forecast the potential outcomes of decisions prior to actually making those decisions. These forecasts are used to advocate for care decisions with other disciplines, patients and their family members. Implications for professional practice and recommendations for future research are described.

Self- assessed general health status is a commonly-used survey technique since it can be used as a predictor for several public health risks such as mortality, deprivation, and fear of crime or poverty. Therefore, it is a useful alternative measure to help assessing the public health situation of a neighborhood or town, and can be utilized by authorities in many decision support situations related to public health, budget allocation and general policy-making, among others. It can be considered as spatial decision problems, since both data location and spatial relationships make a prominent impact during the decision making process. This paper utilizes a recently-developed spatial intelligent decision system, named, Spatial RIMER(+), to model the self-rated health estimation decision problem using real data in the areas of Northern Ireland, UK. The goal is to learn from past or partial observations on self-rated health status to predict its future or neighborhood behavior and reference it in the map. Three scenarios in line of this goal are discussed in details, i.e., estimation of unknown, downscaling, and predictions over time. They are used to demonstrate the flexibility and applicability of the spatial decision support system and their positive capabilities in terms of accuracy, efficiency and visualization.

In the current paper, we describe the challenges in measuring return on investment (ROI) and review published ROI studies on health IT. In addition, given the absence of a robust ROI literature base, we review the general benefits and potential costs of various health IT applications including electronic health records (EHRs), computerized physicians order entry (CPOE) systems, and clinical decision support systems (CDSS). We conclude that articles examining these benefits are much more common than studies examining ROI itself. This trend suggests the early stage of this knowledge base. Additional research utilizing broader perspectives and multidisciplinary techniques will be needed before a better understanding of ROI from health IT is achieved.

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.

The growing importance that mobile devices have in daily life has also reached health care and medicine. This is making the paradigm of health care change and the concept of mHealth or mobile health more relevant, whose main essence is the apps. This new reality makes it possible for doctors who are not specialist to have easy access to all the information generated in different corners of the world, making them potential keepers of that knowledge. However, the new daily information exceeds the limits of the human intellect, making Decision Support Systems (DSS) necessary for helping doctors to diagnose diseases and also help them to decide the attitude that has to be taken towards these diagnoses. These could improve the health care in remote areas and developing countries. All of this is even more important in diseases that are more prevalent in primary care and that directly affect the people's quality of life, this is the case in ophthalmological problems where in first patient care a specialist in ophthalmology is not involved. The goal of this paper is to analyse the state of the art of DSS in Ophthalmology. Many of them focused on diseases affecting the eye's posterior pole. For achieving the main purpose of this research work, a literature review and commercial apps analysis will be done. The used databases and systems will be IEEE Xplore, Web of Science (WoS), Scopus, and PubMed. The search is limited to articles published from 2000 until now. Later, different Mobile Decision Support System (MDSS) in Ophthalmology will be analyzed in the virtual stores for Android and iOS. 37 articles were selected according their thematic (posterior pole, anterior pole, Electronic Health Records (EHRs), cloud, data mining, algorithms and structures for DSS, and other) from a total of 600 found in the above cited databases. Very few mobile apps were found in the different stores. It can be concluded that almost all existing mobile apps are focused on the eye's posterior pole. Among them, the most intended are for diagnostic of diabetic retinopathy. The primary market niche of the commercial apps is the general physicians.

Clinical terminologies are considered a key technology for capturing clinical data in a precise and standardized manner, which is critical to accurately exchange information among different applications, medical records and decision support systems. An important step to promote the real use of clinical terminologies, such as SNOMED-CT, is to facilitate the process of finding mappings between local terms of medical records and concepts of terminologies. In this paper, we propose a mapping tool to discover text-to-concept mappings in SNOMED-CT. Name-based techniques were combined with a query expansion system to generate alternative search terms, and with a strategy to analyze and take advantage of the semantic relationships of the SNOMED-CT concepts. The developed tool was evaluated and compared to the search services provided by two SNOMED-CT browsers. Our tool automatically mapped clinical terms from a Spanish glossary of procedures in pathology with 88.0 % precision and 51.4 % recall, providing a substantial improvement of recall (28 % and 60 %) over other publicly accessible mapping services. The improvements reached by the mapping tool are encouraging. Our results demonstrate the feasibility of accurately mapping clinical glossaries to SNOMED-CT concepts, by means a combination of structural, query expansion and named-based techniques. We have shown that SNOMED-CT is a great source of knowledge to infer synonyms for the medical domain. Results show that an automated query expansion system overcomes the challenge of vocabulary mismatch partially.

Decision support systems (DSSs) which are able to automatically supervise and control physical exercise training of patients affected by chronic obstructive pulmonary disease (COPD) are regarded as a novel method to promote rehabilitation. The objective of our research work for this paper was to evaluate the feasibility of a rule-based DSS for autonomous bicycle ergometer training of COPD patients. Load control is based on real-time analysis of sensor parameters oxygen saturation and heart rate. Ten COPD patients have participated in a study, performing altogether 18 training sessions. On average, 7.4 rules were fired in each training session. Four sessions had to be stopped for different reasons. The average ergometer training load ranged between 31 and 47 W. The average percentage of heart rate in or lower than the intended target zone was 45.9 and 41.6 %, respectively. The average patient-perceived Borg value was 12.6?±?2.4. Patients reported a high satisfaction for the automatically controlled training. With the help of the DSS, patients may change their training place from a rehabilitation center to their own homes. More studies are needed to assess long-term clinical and motivational effects of the DSS in home environment.

Diabetes management by insulin administration is based on medical experts' experience, intuition, and expertise. As there is very little information in medical literature concerning practical aspects of this issue, medical experts adopt their own rules for insulin regimen specification and dose adjustment. This paper investigates the application of a neural network approach for the development of a prototype system for knowledge classification in this domain. The system will further facilitate decision making for diabetic patient management by insulin administration. In particular, a generating algorithm for learning arbitrary classification is employed. The factors participating in the decision making were among others diabetes type, patient age, current treatment, glucose profile, physical activity, food intake, and desirable blood glucose control. The resulting system was trained with 100 cases and tested on 100 patient cases. The system proved to be applicable to this particular problem, classifing correctly 92% of the testing cases.

Globally one in twelve people have the Hepatitis B or Hepatitis C virus. Diagnosis and treatment of this disease is guided by liver biopsies where a small amount of tissue is removed by a surgeon and examined by a pathologist to determine the fibrosis stage from F0 (no damage) to F4 (cirrhosis). Biopsies are costly and carry some risk for the patient. Non-invasive techniques for determining fibrosis stage have been developed and evaluated since 2003. Non-invasive methods have utilized serum markers, imaging test, and genetic studies. The accuracy of these non-invasive techniques has not achieved sufficient acceptance and so the invasive biopsy is still considered the gold standard. Clinical decision support systems (CDSS) use decision support system theory and technology to assist clinicians in the evaluation and treatment process. Using historical clinical data and the relationship processed by Artificial Intelligence (AI) techniques to aid physicians in their decision making process is the goal of CDSS. The CDSS provides a large number of medical support functions to help clinicians make the most reasonable diagnosis and choose the best treatment measures. This paper applies four artificial intelligence predictive techniques to publicly available data on 424 Hepatitis B and Hepatitis C patients. Demographic and standard serum markers are utilized to predict fibrosis stage and compare these predictions to known biopsy results. A final decision tree evaluation is applied to make a final prediction. We have also developed a publically available web application that can be used as a prototype for presenting AI predictive results in a CDSS environment based on these models. This technique along with others could mitigate the need for some liver biopsies in the more than 500 million Hepatitis B and C patients worldwide with additional validation and verification.

In this paper, SAMS, a novel health information system architecture for developing intelligent health information systems is proposed and also some strategies for developing such systems are discussed. The systems fulfilling this architecture will be able to store electronic health records of the patients using OWL ontologies, share patient records among different hospitals and provide physicians expertise to assist them in making decisions. The system is intelligent because it is rule-based, makes use of rule-based reasoning and has the ability to learn and evolve itself. The learning capability is provided by extracting rules from previously given decisions by the physicians and then adding the extracted rules to the system. The proposed system is novel and original in all of these aspects. As a case study, a system is implemented conforming to SAMS architecture for use by dentists in the dental domain. The use of the developed system is described with a scenario. For evaluation, the developed dental information system will be used and tried by a group of dentists. The development of this system proves the applicability of SAMS architecture. By getting decision support from a system derived from this architecture, the cognitive gap between experienced and inexperienced physicians can be compensated. Thus, patient satisfaction can be achieved, inexperienced physicians are supported in decision making and the personnel can improve their knowledge. A physician can diagnose a case, which he/she has never diagnosed before, using this system. With the help of this system, it will be possible to store general domain knowledge in this system and the personnel's need to medical guideline documents will be reduced.

This paper deals with the development of a human resource planning (HRP) model for hospital laboratory personnel. External and organizational factors impacting the demand and supply of clinical laboratory personnel in an urban academic health center are identified by a Delphi process. These factors are structured into a hierarchy for the application of the Analytic Hierarchy Process (AHP). Computer software, Expert Choice, was used in model hierarchy development and judgmental elicitation process. The model result can be applicable to eliciting the perceptions, insights, and understanding of health-care decision-makers for strategic human resource planning.

A clinical process is typically a mixture of various latent treatment patterns, implicitly indicating the likelihood of what clinical activities are essential/critical to the process. Discovering these hidden patterns is one of the most important components of clinical process analysis. What makes the pattern discovery problem complex is that these patterns are hidden in clinical processes, are composed of variable clinical activities, and often vary significantly between patient individuals. This paper employs Latent Dirichlet Allocation (LDA) to discover treatment patterns as a probabilistic combination of clinical activities. The probability distribution derived from LDA surmises the essential features of treatment patterns, and clinical processes can be accurately described by combining different classes of distributions. The presented approach has been implemented and evaluated via real-world data sets.

Visekriterijumsko kompromisno rangiranje (VIKOR) method is one of the commonly used multi criteria decision making (MCDM) methods for improving the quality of decision making. VIKOR has an advantage in providing a ranking procedure for positive attributes and negative attributes when it is used and examined in decision support. However, we noticed that this method may failed to support an objective result in medical field because most medical data have normal reference ranges (e.g., for normally distributed data: NRR ∈ [μ ± 1.96σ], this limitation shows a negative effect on the acceptance of it as an effective decision supporting method in medical decision making. This paper proposes an improved VIKOR method with enhanced accuracy (ea-VIKOR) to make it suitable for such data in medical field by introducing a new data normalization method taking the original distance to the normal reference range (ODNRR) into account. In addition, an experimental example was presented to demonstrate efficiency and feasibility of the ea-VIKOR method, the results demonstrate the ability of ea-VIKOR to deal with moderate data and support the decision making in healthcare care management. For this reason, the ea-VIKOR should be considered for use as a decision support tool for future study.

Within the health care industry many decision making approaches and tools are used. This paper explores a new tool, the Analytic Hierarchy Process (AHP), which permits both subjective and objective information to be considered in a decision. AHP has tremendous potential to solve both traditional and non-traditional health care problems. Its strength as a decision-making tool is its ability to combine both subjective and objective data. Application of AHP is discussed within the context of a Group Decision Support System (GDSS) model developed by Hatcher.30 The model is reviewed in limited detail, and readers are referred to the original article that defined and discussed the uniqueness and level of sophistication of GDSS applications in the health care industry. Health and medical delivery problems are discussed to highlight AHP requirements and the complexity of AHP applications. Health care applications are unique in that they lend themselves ideally to the use of computer data, image, voice, text, and multimedia concepts.

Computerized information systems, especially decision support systems, have become an increasingly important role in medical applications, particularly in those where important decision must be made effectively and reliably. But the possibility of using computers in medical decision making is limited by many difficulties, including the complexity of conventional computer languages, methodologies and tools. Thus a conceptual simple decision making model with the possibility of automating learning should be used. In this paper we introduce a cardiological knowledge-based system based on the decision tree approach supporting the mitral valve prolapse determination. Prolapse is defined as the displacement of a bodily part from its normal position. The term mitral valve prolaps (PMV), therefore, implies that the mitral leaflets are displaced relative to some structure, generally taken to be the mitral annulus. The implications of the PMV are the following: disturbed normal laminar blood flow, turbulence of the blood flow, injury of the chordae tendinae, the possibility of thrombus's composition, bacterial endocarditis, and finally hemodynamic changes defined as mitral insufficiency and mitral regurgitation. Uncertainty persists about how it should be diagnosed and about its clinical importance. It is our deep belief that the echocardiography enables properly trained experts armed with proper criteria to evaluate PMV almost 100%. But unfortunately, there are some problems concerned with the use of echocardiography. In that manner we have decided to start a research project aimed at finding new criteria and enabling the general practitioner to evaluate PMV using conventional methods and to select potential patients from the general population. To empower one to perform needed activities we have developed a computer tool called ROSE (computeRised prOlaps Syndrom dEtermination) based on algorithms of automatic learning. This tool supports the definition of new criteria and the selection of potential PMV-patients.

In the present paper, a fuzzy rule-based system (FRBS) is designed to serve as a decision support system for Coronary heart disease (CHD) diagnosis that not only considers the decision accuracy of the rules but also their transparency at the same time. To achieve the two above mentioned objectives, we apply a multi-objective genetic algorithm to optimize both the accuracy and transparency of the FRBS. In addition and to help assess the certainty and the importance of each rule by the physician, an extended format of fuzzy rules that incorporates the degree of decision certainty and importance or support of each rule at the consequent part of the rules is introduced. Furthermore, a new way for employing Ensemble Classifiers Strategy (ECS) method is proposed to enhance the classification ability of the FRBS. The results show that the generated rules are humanly understandable while their accuracy compared favorably with other benchmark classification methods. In addition, the produced FRBS is able to identify the uncertainty cases so that the physician can give a special consideration to deal with them and this will result in a better management of efforts and tasks. Furthermore, employing ECS has specifically improved the ability of FRBS to detect patients with CHD which is desirable feature for any CHD diagnosis system.