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Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients' active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease. [ABSTRACT FROM AUTHOR]

Unlike the traditional medical system, telecare medicine information system (TMIS) ensures that patients can get health-care services via the Internet at home. Authenticated key agreement protocol is very important for protecting the security in TMIS. Recently scholars have proposed a lot of authenticated key agreement protocols. In 2016, Chiou et al. demonstrated that Chen et al.'s authentication scheme fails to provide user's anonymity and message authentication and then proposed an enhanced scheme (Chiou et al., J. Med. Syst. 40(4):1-15, 2006) to overcome these drawbacks. In this paper, we demonstrate that Chiou et al.'s scheme is defenseless against key compromise impersonation (KCI) attack and also fails to provide forward security. Moreover, we propose a novel authentication scheme namely ICASME to overcome the mentioned weaknesses in this paper. Security analyses show that ICASME achieves the forward security and KCI attack resistance. In addition, it is proved that the time taken to implement the ICASME is not intolerable compared to the original protocol. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Reducing child mortality with quality care is the prime-most concern of all nations. Thus in current IT era, our healthcare industry needs to focus on adapting information technology in healthcare services. Barring few preliminary attempts to digitalize basic hospital administrative and clinical functions, even today in India, child health and vaccination records are still maintained as paper-based records. Also, error in manually plotting the parameters in growth charts results in missed opportunities for early detection of growth disorders in children. To address these concerns, we present India's first hospital linked, affordable automated vaccination and real-time child's growth monitoring cloud based application- Integrated Child Health Record cloud (iCHRcloud). This application is based on HL7 protocol enabling integration with hospital's HIS/EMR system. It provides Java (Enterprise Service Bus and Hibernate) based web portal for doctors and mobile application for parents, enhancing doctor-parent engagement. It leverages highchart to automate chart preparation and provides access of data via Push Notification (GCM and APNS) to parents on iOS and Android mobile platforms. iCHRcloud has also been recognized as one of the best innovative solution in three nationwide challenges, 2016 in India. iCHRcloud offers a seamless, secure (256 bit HTTPS) and sustainable solution to reduce child mortality. Detail analysis on preliminary data of 16,490 child health records highlight the diversified need of various demographic regions. Thus, primary lesson would be to implement better validation strategies to fulfill the customize requisites of entire population. This paper presents first glimpse of data and power of the analytics in policy framework. [ABSTRACT FROM AUTHOR]

In confronting the sudden epidemic of COVID-19, China and other countries have been under great deal of pressure to block virus transmission and reduce death cases. Fangcang shelter hospital, which is converted from large-scale public venue, is proposed and proven to be an effective way for administering medical care and social isolation. This paper presents the practice in information technology support for a Fangcang shelter hospital in Wuhan, China. The experiences include the deployment strategy of IT infrastructure, the redesign of function modules in the hospital information system (HIS), equipment maintenance and medical staff training. The deployment strategy and HIS modules have ensured smoothness and efficiency of clinical work. The team established a quick response mechanism and adhered to the principle of nosocomial infection control. Deployment of network and modification of HIS was finished in the 48 hours before patient admittance. A repair hotline and remote support for equipment and software were available whenever medical workers met with any questions. No engineer ever entered the contaminated areas and no one was infected by the coronavirus during the hospital operation. Up to now, Fangcang shelter hospital is adopted by many regions around the world facing the collapse of their medical systems. This valuable experience in informatization construction and service in Wuhan may help participators involving in Fangcang shelter hospital get better information technology support, and find more practical interventions to fight the epidemic. [ABSTRACT FROM AUTHOR]

Healthcare systems are evolving towards a complex network of interconnected services due to the increasing costs and the increasing expectations for high service levels. It is evidenced in the literature the importance of implementing management techniques and sophisticated methods to improve the efficiency of healthcare systems, especially in emerging economies. This paper proposes an integrated collaboration model between two public hospitals to reach the reduction of weighted average lead time in outpatient internal medicine department. A strategic framework based on value stream mapping and collaborative practices has been developed in real case study settled in Colombia. [ABSTRACT FROM AUTHOR]

In China, day surgery has been promoted because its operation time and post-operative hospital stay are shorter than those of elective surgery. Day surgery can speed up the turnover of beds and operation rooms. Usually, the conditions of elective surgery patients are more complicated than those of day surgery patients. The development of the discipline, which means that the hospital has improved the skills of the doctors and the ability of doctors to cope with serious diseases and has increased the overall medical level of the hospital, requires surgeons to operate in some complicated elective surgeries. In the case of operating rooms and beds in short supply, there is a trade-off between the promotion of day surgery and the development of the discipline. Day surgery is relatively uncomplicated, but it requires more highly qualified surgeons. However, the development of the discipline requires surgeons to take on some complicated elective surgeris. Moreover, according to the notion of grading treatment, class-A tertiary hospitals are more suitable for patients with relatively complicated and serious conditions. In the emerging context of day surgery, highly qualified surgeons need to perform both day surgeries and elective surgeries. This paper studied how to control the admission of surgery patients. We take into account both day surgery promotion and discipline development in decision-making. A dynamic programming model was built for admission control, and a γ-adjust-threshold heuristic policy was proposed. We then compared the heuristic policy to three other policies through simulation. The results show that our heuristic policy outperforms the hospital's target policy. [ABSTRACT FROM AUTHOR]

Depression or Major Depressive Disorder (MDD) is a mental illness which negatively affects how a person thinks, acts or feels. MDD has become a major disease affecting millions of people presently. The diagnosis of depression is questionnaire based and is not based on any objective criteria. In this paper, feature extracted from EEG signal are used for the diagnosis of depression. Alpha, alpha1, alpha2, beta, delta and theta power and theta asymmetry was used as feature. Alpha1, alpha2 along with theta asymmetry was also used as a feature. Multi-Cluster Feature Selection (MCFS) was used for feature selection when feature combination was used. The classifiers used were Support Vector Machine (SVM), Logistic Regression (LR), Naïve-Bayesian (NB) and Decision Tree (DT). Alpha2 showed higher classification accuracy than alpha1 and alpha power in all applied classifier. From t-test it was found that there was a significant difference in the theta power of left and right hemisphere of normal subjects, but there was no significant difference in depression patients. Average theta asymmetry in normal subjects is higher than MDD patients but the difference in theta asymmetry in normal subjects and MDD patients is not significant. The combination of alpha2 and theta asymmetry showed the highest classification accuracy of 88.33% in SVM. [ABSTRACT FROM AUTHOR]

Intelligent agents and healthcare have been intimately linked in the last years. The intrinsic complexity and diversity of care can be tackled with the flexibility, dynamics and reliability of multi-agent systems. The purpose of this review is to show the feasibility of applying intelligent agents in the healthcare domain and use the findings to provide a discussion of current trends and devise future research directions. A review of the most recent literature (2009-2014) of applications of agents in healthcare is discussed, and two classifications considering the main goal of the health systems as well as the main actors involved have been investigated. This review shows that the number of published works exhibits a growing interest of researchers in this field in a wide range of applications. [ABSTRACT FROM AUTHOR]

Mobile Edge-Cloud Network is a new network structure after fog-cloud computing, where service and data computing are scattered in the most logical, nearby and efficient place. It provides better services than fog-cloud computing with better performance in reasonably low cost way and allows users to eliminate numerous limitations inherent in fog-cloud computing, although it inherits those security-privacy issues from fog-cloud computing. A novel privacy-preserving mutual authentication in TMIS for mobile Edge-Cloud architecture (abbreviated to NPMA) is constructed in this paper. NPMA scheme not only mitigates some weaknesses of fog-cloud computing, but has other advantages. First, NPMA scheme supports patients(edge-servers) anonymity and forward-backward untraceability (traceability, when needed), since their identities are hidden in two distinct dynamic anonyms and a static one and only the trusted center can recover their real identities, when needed. Second, each edge-server shares a secret value, which realizes authentication with extremely low computional cost in authentication phase. Finally, NPMA scheme is proven safely against passive and active attacks under elliptic curve computable Diffie-Hellman problem (ECDHP) assumption in random oracle model. Hence, it achieves the required security properties and outperforms prior approaches in terms of energy and computational costs. [ABSTRACT FROM AUTHOR]

In medical systems for patient's authentication, keeping biometric data secure is a general problem. Many studies have presented various ways of protecting biometric data especially finger vein biometric data. Thus, It is needs to find better ways of securing this data by applying the three principles of information security aforementioned, and creating a robust verification system with high levels of reliability, privacy and security. Moreover, it is very difficult to replace biometric information and any leakage of biometrics information leads to earnest risks for example replay attacks using the robbed biometric data. In this paper presented criticism and analysis to all attempts as revealed in the literature review and discussion the proposes a novel verification secure framework based confidentiality, integrity and availability (CIA) standard in triplex blockchain-particle swarm optimization (PSO)-advanced encryption standard (AES) techniques for medical systems patient's authentication. Three stages are performed on discussion. Firstly, proposes a new hybrid model pattern in order to increase the randomization based on radio frequency identification (RFID) and finger vein biometrics. To achieve this, proposed a new merge algorithm to combine the RFID features and finger vein features in one hybrid and random pattern. Secondly, how the propose verification secure framework are followed the CIA standard for telemedicine authentication by combination of AES encryption technique, blockchain and PSO in steganography technique based on proposed pattern model. Finally, discussed the validation and evaluation of the proposed verification secure framework. [ABSTRACT FROM AUTHOR]

Telecare medical information systems (TMIS), with the explosive growth of communication technology and physiological monitoring devices, are applied increasingly to enable and support healthcare delivery services. In order to safeguard patients' privacy and tackle the illegal access, authentication schemes for TMIS have been investigated and designed by many researchers. Many of them are promising for adoption in practice, nevertheless, they still have security flaws. In this paper, we propose a novel remote authentication scheme for TMIS using self-certified public keys, which is formally secure in the ID-mBJM model. Besides, the proposed scheme has better computational efficiency. Compared to the related schemes, our protocol is more practical for telemedicine system. [ABSTRACT FROM AUTHOR]

The use of Web portals has several benefits. They have been proven to enhance communication between patients and health care professionals, allow patients to play a more active role in their own treatment and self-management, increase self-efficiency. Doctors can access patients' medical data and give recommendations to the patients. Some evidence of positive outcomes has been reported including improved clinical indicators and reduced health service use, as well as enhanced feelings of security and increased satisfaction with health and social care services. However, a number of studies have demonstrated that these types of solutions often fail to be successfully implemented and adopted within routine healthcare. The goal of the study was to analyze attitude of tuberculosis patients and doctors and identify perceived opportunities and barriers to operate a Web portal. The perceptions of 30 tuberculosis patients and 18 doctors (10 general practitioners and 8 phthisiatricians) from Tomsk, Russia were collected through semi-structured interviews. The responses were analyzed using grounded theory and thematic analysis, in order to understand the participants' opinions and attitudes towards accepting tuberculosis web-portal in the treatment and rehabilitation process. We have recognized core topics that represented the participants' responses. These core topics indicated that most respondents mentioned "the potential of tuberculosis web-portal to establish a better communication between doctors and patients" and "the increased health status of the patients" as the most valuable outcomes of adopting the tool. Both doctors and patients also expressed their opinions on the usability of the solutions, with no significant concerns reported about its usability. The main barrier we identified was different expectations of doctors and patients: doctors expected that the portal would decrease the amount of paper work and will help them to quickly react in acute cases, however, the patients were disappointed that the portal did not allow them to be able to reach doctors on the 24/7 basis and have a consultation whenever they felt they needed it. Finally, our findings disclosed that participants appreciated the perceived clinical utility of the portal, which could further have an influence on their intention to use it. The findings from our reported study in the area of tuberculosis treatment and rehabilitation suggested that the participants generally accepted the introduction of web-portal for the purpose of reporting health status and getting recommendation from the doctors as an alternative to the traditional doctor visits.

Smart, interactive healthcare is necessary in the modern age. Several issues, such as accurate diagnosis, low-cost modeling, low-complexity design, seamless transmission, and sufficient storage, should be addressed while developing a complete healthcare framework. In this paper, we propose a patient state recognition system for the healthcare framework. We design the system in such a way that it provides good recognition accuracy, provides low-cost modeling, and is scalable. The system takes two main types of input, video and audio, which are captured in a multi-sensory environment. Speech and video input are processed separately during feature extraction and modeling; these two input modalities are merged at score level, where the scores are obtained from the models of different patients' states. For the experiments, 100 people were recruited to mimic a patient's states of normal, pain, and tensed. The experimental results show that the proposed system can achieve an average 98.2 % recognition accuracy.

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. [ABSTRACT FROM AUTHOR]

Many medical examinations involve acquisition of a large series of slice images for 3D reconstruction of the organ of interest. With the paperless hospital concept and telemedicine, there is very heavy utilization of limited electronic storage and transmission bandwidth. This paper proposes model-based compression to reduce the load on such resources, as well as aid diagnosis through the 3D reconstruction of the structures of interest, for images acquired by various modalities, such as MRI, Ultrasound, CT, PET etc. and stored in the DICOM file format. An example implementation for the biliary track in MRCP images is illustrated in the paper. Significant compression gains may be derived from the proposed method, and a suitable mixture of the models and raw images would enhance the patient medical history archives as the models may be stored in the DICOM file format used in most medical archiving systems. [ABSTRACT FROM AUTHOR]

This paper describes the development of statistical classifiers to help diagnose meningococcal meningitis, i.e. the most sever, infectious and deadliest type of this disease. The goal is to find a mechanism able to determine whether a patient has this type of meningitis from a set of symptoms that can be directly observed in the earliest stages of this pathology. Currently, in Brazil, a country that is heavily affected by meningitis, all suspected cases require immediate hospitalization and the beginning of a treatment with invasive tests and medicines. This procedure, therefore, entails expensive treatments unaffordable in less developed regions. For this purpose, we have gathered together a dataset of 22,602 records of suspected meningitis cases from the Brazilian state of Bahia. Seven classification techniques have been applied from input data of nine symptoms and other information about the patient such as age, sex and the area they live in, and a 10 cross-fold validation has been performed. Results show that the techniques applied are suitable for diagnosing the meningococcal meningitis. Several indexes, such as precision, recall or ROC area, have been computed to show the accuracy of the models. All of them provide good results, but the best corresponds to the J48 classifier with a precision of 0.942 and a ROC area over 0.95. These results indicate that our model can indeed help lead to a non-invasive and early diagnosis of this pathology. This is especially useful in less developed areas, where the epidemiologic risk is usually high and medical expenses, sometimes, unaffordable. [ABSTRACT FROM AUTHOR]

This paper presents an ensemble based classification proposal for predicting neurological outcome of severely traumatized patients. The study comprises both the whole group of patients and a subgroup containing those patients suffering traumatic brain injury (TBI). Data was gathered from patients hospitalized in the Intensive Care Unit (ICU) of the University Hospital in Salamanca. Predictive models were induced from both epidemiologic and clinical variables taken at the emergency room and along the stay in the ICU. The large number of variables leads to a low accuracy in the classifiers even when feature selection methods are used. In addition, the presence of a much larger number of instances of one of the classes in the subgroup of TBI patients produces a significantly lesser precision for the minority class. Usual ways of dealing with the last problem is to use undersampling and oversampling strategies, which can lead to the loss of valuable data and overfitting problems respectively. Our proposal for dealing with these problems is based in the use of ensemble multiclassifiers as well as in the use of an ensemble playing the role of base classifier in multiclassifiers. The proposed strategy gave the best values of the selected quality measures (accuracy, precision, sensitivity, specificity, F-measure and area under the Receiver Operator Characteristic curve) as well as the closest values of precision for the two classes under study in the case of the classification from imbalanced data. [ABSTRACT FROM AUTHOR]

In recent years, many countries have been trying to integrate electronic health data managed by each hospital to offer more efficient healthcare services. Since health data contain sensitive information of patients, there have been much research that present privacy preserving mechanisms. However, existing studies either require a patient to perform various steps to secure the data or restrict the patient to exerting control over the data. In this paper, we propose patient-controlled attribute-based encryption, which enables a patient (a data owner) to control access to the health data and reduces the operational burden for the patient, simultaneously. With our method, the patient has powerful control capability of his/her own health data in that he/she has the final say on the access with time limitation. In addition, our scheme provides emergency medical services which allow the emergency staffs to access the health data without the patient's permission only in the case of emergencies. We prove that our scheme is secure under cryptographic assumptions and analyze its efficiency from the patient's perspective. [ABSTRACT FROM AUTHOR]

Machine learning and data mining approaches are being successfully applied to different fields of life sciences for the past 20 years. Medicine is one of the most suitable application domains for these techniques since they help model diagnostic information based on causal and/or statistical data and therefore reveal hidden dependencies between symptoms and illnesses. In this paper we give a detailed overview of the recent machine learning research and its applications for predicting cognitive diseases, especially the Alzheimer's disease, mild cognitive impairment and the Parkinson's disease. We survey different state-of-the-art methodological approaches, data sources and public data, and provide their comparative analysis. We conclude by identifying the open problems within the field that include an early detection of the cognitive diseases and inclusion of machine learning tools into diagnostic practice and therapy planning. [ABSTRACT FROM AUTHOR]

As a significant part of the Internet of Things (IoT), Wireless Body Area Network (WBAN) has attract much attention in this years. In WBANs, sensors placed in or around the human body collect the sensitive data of the body and transmit it through an open wireless channel in which the messages may be intercepted, modified, etc. Recently, Wang et al. presented a new anonymous authentication scheme for WBANs and claimed that their scheme can solve the security problems in the previous schemes. Unfortunately, we demonstrate that their scheme cannot withstand impersonation attack. Either an adversary or a malicious legal client could impersonate another legal client to the application provider. In this paper, we give the detailed weakness analysis of Wang et al.'s scheme at first. Then we present a novel anonymous authentication scheme for WBANs and prove that it's secure under a random oracle model. At last, we demonstrate that our presented anonymous authentication scheme for WBANs is more suitable for practical application than Wang et al.'s scheme due to better security and performance. Compared with Wang et al.'s scheme, the computation cost of our scheme in WBANs has reduced by about 31.58 %. [ABSTRACT FROM AUTHOR]

Recent advances in medical treatment and emergency applications, the need of integrating wireless body area network (WBAN) with cloud computing can be motivated by providing useful and real time information about patients' health state to the doctors and emergency staffs. WBAN is a set of body sensors carried by the patient to collect and transmit numerous health items to medical clouds via wireless and public communication channels. Therefore, a cloud-assisted WBAN facilitates response in case of emergency which can save patients' lives. Since the patient's data is sensitive and private, it is important to provide strong security and protection on the patient's medical data over public and insecure communication channels. In this paper, we address the challenge of participant authentication in mobile emergency medical care systems for patients supervision and propose a secure cloud-assisted architecture for accessing and monitoring health items collected by WBAN. For ensuring a high level of security and providing a mutual authentication property, chaotic maps based authentication and key agreement mechanisms are designed according to the concept of Diffie-Hellman key exchange, which depends on the CMBDLP and CMBDHP problems. Security and performance analyses show how the proposed system guaranteed the patient privacy and the system confidentiality of sensitive medical data while preserving the low computation property in medical treatment and remote medical monitoring. [ABSTRACT FROM AUTHOR]

The E-health care systems employ IT infrastructure for maximizing health care resources utilization as well as providing flexible opportunities to the remote patient. Therefore, transmission of medical data over any public networks is necessary in health care system. Note that patient authentication including secure data transmission in e-health care system is critical issue. Although several user authentication schemes for accessing remote services are available, their security analysis show that none of them are free from relevant security attacks. We reviewed Das et al.'s scheme and demonstrated their scheme lacks proper protection against several security attacks such as user anonymity, off-line password guessing attack, smart card theft attack, user impersonation attack, server impersonation attack, session key discloser attack. In order to overcome the mentioned security pitfalls, this paper proposes an anonymity preserving remote patient authentication scheme usable in E-health care systems. We then validated the security of the proposed scheme using BAN logic that ensures secure mutual authentication and session key agreement. We also presented the experimental results of the proposed scheme using AVISPA software and the results ensure that our scheme is secure under OFMC and CL-AtSe models. Moreover, resilience of relevant security attacks has been proved through both formal and informal security analysis. The performance analysis and comparison with other schemes are also made, and it has been found that the proposed scheme overcomes the security drawbacks of the Das et al.'s scheme and additionally achieves extra security requirements. [ABSTRACT FROM AUTHOR]

As a result of the increased demand for improved life styles and the increment of senior citizens over the age of 65, new home care services are demanded. Simultaneously, the medical sector is increasingly becoming the new target of cybercriminals due the potential value of users' medical information. The use of biometrics seems an effective tool as a deterrent for many of such attacks. In this paper, we propose the use of electrocardiograms (ECGs) for the identification of individuals. For instance, for a telecare service, a user could be authenticated using the information extracted from her ECG signal. The majority of ECG-based biometrics systems extract information (fiducial features) from the characteristics points of an ECG wave. In this article, we propose the use of non-fiducial features via the Hadamard Transform (HT). We show how the use of highly compressed signals (only 24 coefficients of HT) is enough to unequivocally identify individuals with a high performance (classification accuracy of 0.97 and with identification system errors in the order of 10). [ABSTRACT FROM AUTHOR]

The development of personalized mobile monitoring applications is a complex work. Currently, the most of applications for patients monitoring through mobile devices, is not developed considering the particular characteristics of each patient, but these applications have been developed taking into account a general behavior depending on the diseases instead of the own patients. The diseases manifest different symptoms depending on the patient situation. Mary and John (hypothetic patients) have diabetes, but the same measurement of glucose for each one affects their health in a different way. This paper describes a framework that allows the development of mobile applications, personalized for each patient, in such a way that even if they have the same disease, the application will respond to the individual needs of each patient. [ABSTRACT FROM AUTHOR]

Telecare medicine information systems (TMIS) have been known as an effective mechanism to increase quality and security of healthcare services. In other to the protection of patient privacy, several authentication schemes have been proposed in TMIS, however, most of them have a security problems. Recently, Das proposed a secure and robust password-based remote user authentication scheme for the integrated EPR information system. However, in this paper, we show that his scheme have some security flaws. Then, we shall propose a secure authentication scheme to overcome their weaknesses. We prove the proposed scheme with random oracle and also use the BAN logic to prove the correctness of the proposed scheme. Furthermore, we simulate our scheme for the formal security analysis using the AVISPA (Automated Validation of Internet Security Protocols and Applications) tool. [ABSTRACT FROM AUTHOR]

Recently, Giri et al.'s proposed a RSA cryptosystem based remote user authentication scheme for telecare medical information system and claimed that the protocol is secure against all the relevant security attacks. However, we have scrutinized the Giri et al.'s protocol and pointed out that the protocol is not secure against off-line password guessing attack, privileged insider attack and also suffers from anonymity problem. Moreover, the extension of password guessing attack leads to more security weaknesses. Therefore, this protocol needs improvement in terms of security before implementing in real-life application. To fix the mentioned security pitfalls, this paper proposes an improved scheme over Giri et al.'s scheme, which preserves user anonymity property. We have then simulated the proposed protocol using widely-accepted AVISPA tool which ensures that the protocol is SAFE under OFMC and CL-AtSe models, that means the same protocol is secure against active and passive attacks including replay and man-in-the-middle attacks. The informal cryptanalysis has been also presented, which confirmed that the proposed protocol provides well security protection on the relevant security attacks. The performance analysis section compares the proposed protocol with other existing protocols in terms of security and it has been observed that the protocol provides more security and achieves additional functionalities such as user anonymity and session key verification. [ABSTRACT FROM AUTHOR]

Telecare medical information system (TMIS) makes an efficient and convenient connection between patient(s)/user(s) and doctor(s) over the insecure internet. Therefore, data security, privacy and user authentication are enormously important for accessing important medical data over insecure communication. Recently, many user authentication protocols for TMIS have been proposed in the literature and it has been observed that most of the protocols cannot achieve complete security requirements. In this paper, we have scrutinized two (Mishra et al., Xu et al.) remote user authentication protocols using smart card and explained that both the protocols are suffering against several security weaknesses. We have then presented three-factor user authentication and key agreement protocol usable for TMIS, which fix the security pitfalls of the above mentioned schemes. The informal cryptanalysis makes certain that the proposed protocol provides well security protection on the relevant security attacks. Furthermore, the simulator AVISPA tool confirms that the protocol is secure against active and passive attacks including replay and man-in-the-middle attacks. The security functionalities and performance comparison analysis confirm that our protocol not only provide strong protection on security attacks, but it also achieves better complexities along with efficient login and password change phase as well as session key verification property. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) based solutions are widely used for providing many healthcare applications include patient monitoring, object traceability, drug administration system and telecare medicine information system (TMIS) etc. In order to reduce malpractices and ensure patient privacy, in 2015, Srivastava et al. proposed a hash based RFID tag authentication protocol in TMIS. Their protocol uses lightweight hash operation and synchronized secret value shared between back-end server and tag, which is more secure and efficient than other related RFID authentication protocols. Unfortunately, in this paper, we demonstrate that Srivastava et al.'s tag authentication protocol has a serious security problem in that an adversary may use the stolen/lost reader to connect to the medical back-end server that store information associated with tagged objects and this privacy damage causing the adversary could reveal medical data obtained from stolen/lost readers in a malicious way. Therefore, we propose a secure and efficient RFID tag authentication protocol to overcome security flaws and improve the system efficiency. Compared with Srivastava et al.'s protocol, the proposed protocol not only inherits the advantages of Srivastava et al.'s authentication protocol for TMIS but also provides better security with high system efficiency. [ABSTRACT FROM AUTHOR]

Telecare medicine information system (TMIS) is widely used for providing a convenient and efficient communicating platform between patients at home and physicians at medical centers or home health care (HHC) organizations. To ensure patient privacy, in 2013, Hao et al. proposed a chaotic map based authentication scheme with user anonymity for TMIS. Later, Lee showed that Hao et al.'s scheme is in no provision for providing fairness in session key establishment and gave an efficient user authentication and key agreement scheme using smart cards, in which only few hashing and Chebyshev chaotic map operations are required. In addition, Jiang et al. discussed that Hao et al.'s scheme can not resist stolen smart card attack and they further presented an improved scheme which attempts to repair the security pitfalls found in Hao et al.'s scheme. In this paper, we found that both Lee's and Jiang et al.'s authentication schemes have a serious security problem in that a registered user's secret parameters may be intentionally exposed to many non-registered users and this problem causing the service misuse attack. Therefore, we propose a slight modification on Lee's scheme to prevent the shortcomings. Compared with previous schemes, our improved scheme not only inherits the advantages of Lee's and Jiang et al.'s authentication schemes for TMIS but also remedies the serious security weakness of not being able to withstand service misuse attack. [ABSTRACT FROM AUTHOR]

The article presents a case study on the implementation of electronic health records (EHR) by a large number of hospitals and physicians. The authors surveyed providers in a new medical center on several important areas including workflow, communication, patient satisfaction, documentation, and quality of care.

The article discusses a study which examined the factors causing long patient waiting time/clinic overtime in an outpatient clinic in Singapore. Topics covered include the development of a discrete simulation model to show how to improve the clinic performance, the appointment scheduling schemes proposed to address waiting time and the types of patients catered by the specialist outpatient clinic (SOC).

In this paper, a more effective use of Doppler techniques is presented for the purpose of diagnosing atherosclerosis in its early stages using the carotid artery Doppler signals. The power spectral density (PSD) graphics are obtained by applying the short-time Fourier transform (STFT)-Welch and the Eigenvector MUSIC methods to the discrete wavelet transform (DWT) of Doppler signals. The PSDs for the fourth approximation component (A4) of both methods estimated that the patients with atherosclerosis in its early phase had lower maximum frequency components. On the other hand, the healthy subjects had higher maximum frequency components. The area under the curve (AUC), which belongs to the receiver operating characteristic (ROC) curve for the frequency level of the maximum PSDs of the A4 approximation obtained from the STFT modeling, is computed as 0.97. The AUC for the MUSIC modeling is computed as 0.996. The AUC belonging to the ROC curve for the higher maximum frequency component is computed as 0.87. The AUC belonging to the ROC curve for the test parameter of the frequency level of the maximum PSDs derived from the MUSIC modeling is determined to be 0.882. The results of this study clearly demonstrate that it is possible to distinguish between the healthy people and the patients with atherosclerosis by using the frequency level of the maximum PSDs for the A4 approximation. Furthermore, it is concluded that the power of Eigenvector-MUSIC method in terms of the resolution of the high frequencies is better than that of the STFT methods. [ABSTRACT FROM AUTHOR]

In this paper, we introduced a computer aided detection (CAD) system to facilitate colonic polyp detection in computer tomography (CT) data using cellular neural network, genetic algorithm and three dimensional (3D) template matching with fuzzy rule based tresholding. The CAD system extracts colon region from CT images using cellular neural network (CNN) having A, B and I templates that are optimized by genetic algorithm in order to improve the segmentation performance. Then, the system performs a 3D template matching within four layers with three different cell of 8 × 8, 12 × 12 and 20 × 20 to detect polyps. The CAD system is evaluated with 1043 CT colonography images from 16 patients containing 15 marked polyps. All colon regions are segmented properly. The overall sensitivity of proposed CAD system is 100% with the level of 0.53 false positives (FPs) per slice and 11.75 FPs per patient for the 8 × 8 cell template. For the 12 × 12 cell templates, detection sensitivity is 100% at 0.494 FPs per slice and 8.75 FPs per patient and for the 20 × 20 cell templates, detection sensitivity is 86.66% with the level of 0.452 FPs per slice and 6.25 FPs per patient. [ABSTRACT FROM AUTHOR]

Competition among hospitals and managed care have forced hospital industry to be more efficient. With higher degrees of hospital competition and managed care penetration, hospitals have argued that the rate of increase in hospital cost is greater than the rate of increase in hospital revenue. By developing a payer-specific case mix index (CMI) for third-party patients, this paper examined the effect of hospital case mix on hospital cost and revenue for third-party patients in California using the hospital financial and utilization data covering 1986–1998. This study found that the coefficients for CMIs in the third-party hospital revenue model were greater than those in the hospital cost model until 1995. Since 1995, however, the coefficients for CMIs in the third-party hospital revenue model have been less than those in hospital cost models. Over time, the differences in coefficients for CMIs in hospital revenue and cost models for third-party patients have become smaller and smaller although those differences are statistically insignificant. [ABSTRACT FROM AUTHOR]

To safely use medications, professionals and consumers need usable and reliable methods to identify tablets patients are prescribed and taking. Currently, each manufacturer assigns its own identifying codes and symbols. Standardization of the system for identifying solid dosage forms is a goal that has been widely advocated, yet stubbornly resistant to progress. Physicians, pharmacists, and consumers attempting to identify pills must use various methods which have shortcomings in ease of use, availability, and accuracy. Arguments have been advanced, particularly by pharmaceutical manufacturers, that evidence of unworkability of the current system is not compelling, and costs of retooling current manufacturing processes could be prohibitive. These issues are currently being explored by a task force led by the U.S. Pharmacopeia Safe Medication Use, and Pharmaceutical Forms Dosage Expert Committees. This paper presents a fictitious case study of an elderly patient succumbing to digoxin overdose illustrating the dilemmas posed in the tablet-imprint debate. [ABSTRACT FROM AUTHOR]

Each year, untold deaths occur because of medical and medication errors in the United States. In generally, most people have the naïve perception that the health-care enterprise is a fail-safe system and as such do not take proactive measures to prevent potential medication errors. This paper is timely in light of the proliferation of medication use in outpatient settings and thus warrants the education of patients to take the responsibility in proper drug use. Patients and the health-care professionals must understand the need to see patients as part of the health-care team to ensure quality of care and decrease medication errors. A patient empowerment model to prevent medication error is therefore proposed. [ABSTRACT FROM AUTHOR]

Efficiency in hospital performance is becoming more and more important. Studies showed that diagnosis can considerably reduce the inefficiency, so one of the most important tasks in achieving greater hospital efficiency is to optimize the diagnostic process. For the best of the patient the diagnostic process has to be optimized regarding the number of the examinations and individualized in order to maximize accuracy, sensitivity and specificity. In addition the duration of the diagnostic process has to be minimized and the process has to be performed on the most reliable equipment. The main contribution of our paper is the introduction of the integrated computerized environment DIAPRO enabling the diagnostic process optimization. The DIAPRO is based on a single approach—evolutionary algorithms. [ABSTRACT FROM AUTHOR]

This paper relates a study of reliability of coding of surgical procedures in the domain of thoracic surgery. The reliability measured is inter-coder variability in form of agreement. Four classifications were used by four physicians on 100 patient cases. The classifications, having differing granularity and structure, were analyzed using a statistical method (kappa). These results are discussed and related to the differences between the classifications. One of the topics for discussion is how the granularity affects the degree of agreement, coupled to the usefulness of the classification. Also the concept of using formal methods for representing classifications is discussed, how this will affect how classifications are designed and used. [ABSTRACT FROM AUTHOR]

High usage rate in a surgical suite is extremely important in meeting the increasing demand for health care services and reducing costs to improve quality of care. In this paper a goal programming model which can produce schedules that best serve the needs of the hospital, i.e., by minimizing idle time and overtime, and increasing satisfaction of surgeons, patients, and staff, is described. The approach involves sorting the requests for a particular day on the basis of block restrictions, room utilization, surgeon preferences and intensive care capabilities. The model is tested using the data obtained during field studies at Dokuz Eylul University Hospital. The model is also tested for alternative achievement functions to examine the model's ability to satisfy abstract goals. [ABSTRACT FROM AUTHOR]

Forecasting medical resource requirements during combat operations is dependent upon reliable estimates of the anticipated patient load. Traditionally, mean patient load estimations are used to determine the medical resources and supplies. However, these approximations can underestimate the resources needed to treat casualties during certain peak periods of an operation. This paper discusses the use of percentile estimates in forecasting patient loads as an alternative to using measures of central tendency. To assist in providing alternative estimates of ground casualty rates, the SLICECAS model has been developed to compute the varying percentile estimates. Design and construction of the model is based on data and trends observed during previous combat operations. [ABSTRACT FROM AUTHOR]

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.

Evidence-based medicine often involves the identification of patients with similar conditions, which are often captured in ICD (International Classification of Diseases (World Health Organization 2013)) code sequences. With no satisfying prior solutions for matching ICD-10 code sequences, this paper presents a method which effectively captures the clinical similarity among routine patients who have multiple comorbidities and complex care needs. Our method leverages the recent progress in representation learning of individual ICD-10 codes, and it explicitly uses the sequential order of codes for matching. Empirical evaluation on a state-wide cancer data collection shows that our proposed method achieves significantly higher matching performance compared with state-of-the-art methods ignoring the sequential order. Our method better identifies similar patients in a number of clinical outcomes including readmission and mortality outlook. Although this paper focuses on ICD-10 diagnosis code sequences, our method can be adapted to work with other codified sequence data. [ABSTRACT FROM AUTHOR]

One of the most challenging decisions in resource allocations in hospitals is: how to allocate nursing duties on the basis of patients' needs? Patient Dependency Systems, in different forms, can be used to provide information for staffing decisions and budgetary developments. That is why Patient Dependency Systems are emerging as powerful tools in hospital management. It is anticipated that their use will grow, as hospitals everywhere come under pressure to reduce cost and improve the delivery and quality of health care to patients. Experience has shown that manual Patient Dependency Systems lack the ability to process and provide information fast enough to handle crisis situations. In addition, manual calculations are inefficient and are not free from human errors. However, the utilization of current advances in computing technology can overcome these disadvantages. Patient Dependency Systems are suitable for automation since their essence is too complex to handle manually. Furthermore, it is essential to automate the Patient Dependency Systems because of their critical role and their inherent complexity. In this paper, the automation of Patient Dependency Systems is presented. The development of Patient Dependency Automated Systems is shown to provide reliable and valid methods for evaluating the needs of patients in terms of the nursing effort required. [ABSTRACT FROM AUTHOR]

Hospitals are striving for funds and at the same time are required to improve the delivery and quality health care to patients. Naturally this strains hospital resources and requires sophistication in planning and in resource utilization. Nursing staff time remains one of the most important resources that require careful planning. Scheduling the time and duties of the nursing staff on the basis of patients' needs could improve the quality of care and ultimately leads to efficient utilization of hospital resources. This paper presents a model for allocating nursing staff duties using a Patient Dependency System. The model was developed using a time motion study conducted in a leading hospital. Resource utilization indexes were compared before and after the model was implemented. Results show significant increase in the efficiency of utilization of resources and improvement in the quality of the care. The model presented as a set of decision making rules suitable for use in a Patient Dependency Knowledge Based System. [ABSTRACT FROM AUTHOR]

The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system. Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background. This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont. The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas. In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions. [ABSTRACT FROM AUTHOR]

Emergency department (ED) triage scale determines the priority of patient care and foretells the prognosis. However, the information retrieved from the initial assessment is limited, hindering the risk identification accuracy of triage. Therefore, we sought to develop a 'dynamic' triage system as secondary screening, using artificial intelligence (AI) techniques to integrate information from initial assessment data and subsequent examinations. This retrospective cohort study included 134,112 ED visits with at least one electrocardiography (ECG) and chest X-ray (CXR) in a medical center from 2012 to 2022. Additionally, an independent community hospital provided 45,614 ED visits as an external validation set. We trained an eXtreme gradient boosting (XGB) model using initial assessment data to predict all-cause mortality in 7 days. Two deep learning models (DLMs) using ECG and CXR were trained to stratify mortality risks. The dynamic triage levels were based on output from the XGB-triage and DLMs from ECG and CXR. During the internal and external validation, the area under the receiver operating characteristic curve (AUC) of the XGB-triage model was >0.866; furthermore, the AUCs of DLMs using ECG and CXR were >0.862 and >0.886, respectively. The dynamic triage scale provided a higher C-index (0.914-0.920 vs. 0.827-0.843) than the original one and demonstrated better predictive ability for 5-year mortality, 30-day ED revisit, and 30-day discharge. The AI-based risk scale provides a more accurate and dynamic stratification of mortality risk in ED patients, particularly in identifying patients who tend to be overlooked due to atypical symptoms. [ABSTRACT FROM AUTHOR]