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Objective: To investigate whether the use of the think-aloud method with propositional analysis could be helpful in the design of a Clinical Decision Support System (CDSS) providing guideline recommendations about long-term follow-up of childhood cancer survivors.Materials and Methods: The think-aloud method was used to gain insight into healthcare professionals' information processing while reviewing a paper-based guideline. A total of 13 healthcare professionals (6 physicians and 7 physician assistants) prepared 2 fictitious patient consults using the paper-based guideline. Propositional analysis was used to analyze verbal protocols of the think-aloud sessions. A prototype CDSS was developed and a usability study was performed, again with the think-aloud method.Results: The analysis revealed that the paper-based guideline did not support healthcare practitioners in finding patient-specific recommendations. An information processing model for retrieving recommendations was developed and used as input for the design of a CDSS prototype user interface. Usability analysis of the prototype CDSS showed that the navigational structure of the system fitted well with healthcare practitioners' daily practices.Conclusions: The think-aloud method combined with propositional analysis of healthcare practitioners' verbal utterances while they processed a paper-based guideline was useful in the design of a usable CDSS providing patient-specific guideline recommendations. [ABSTRACT FROM AUTHOR]

Previous reports show that a substantial proportion of (near) medical errors in the operating theatre is attributable to ineffective communication between healthcare professionals. Speaking up about observed medical errors is a safety behaviour which promotes effective communication between health care professionals, consequently optimising patient care by reducing medical error risk. Speaking up by healthcare professionals (e.g., nurses, residents) remains difficult to execute in practice despite increasing awareness of its importance. Therefore, this paper discourses a computational model concerning the mechanisms known from psychological, observational, and medical literature which underlie the speaking up behaviour of a health care professional. It also addresses how a doctor may respond to the communicated message. Through several scenarios we illustrate what pattern of factors causes a healthcare professional to speak up when witnessing a (near) medical error. We moreover demonstrate how introducing an observant agent can facilitate effective communication and help to ensure patient safety through speaking up when a nurse can not. In conclusion, the current paper introduces an adaptive computational model which predicts speaking up behaviour from the perspective of the speaker and receiver, with the addition of a virtual coach to further optimise patient safety when a patient could be in harm's way. [ABSTRACT FROM AUTHOR]

Digital remote care has emerged as a transformative approach in the delivery of healthcare services. This paper presents an empirical case study that investigates the utilization of digital remote care for postoperative rehabilitation in a relatively large-scale implementation in China. To gather the empirical data, we employed a mixed-methods approach including observations, document analysis, semi-structured interviews, and a participatory workshop. The findings obtained from these diverse sources were documented and analyzed through journey mapping, which provides a holistic view of the process of delivering digital remote care services. Drawing from the journey map, we gain insights into the specific problems faced by health professionals and use this knowledge to identify areas for improvement. Simultaneously, we recognize opportunities to enhance the overall digital remote care experience and facilitate a successful large-scale implementation. [ABSTRACT FROM AUTHOR]

The European health care system is moving toward personalised, distributed, and home-based services. This is made possible via new and improved connected medical devices (CMDs), and will benefit health care providers in terms of reduced cost and improved service. Patients will see improved quality of life in terms of reduced travel time and reduced stress via treatment at home or where they want it. However, for these benefits to be fully realised, the cybersecurity of CMDs needs to be ensured. This paper presents a brief survey of challenges to building security into CMDs, and introduces NEMECYS, an EU-funded project which will help practitioners to (a) comply with Medical Device (MD) regulations; (b) be able to apply proportionate MD cybersecurity, and (c) build in cybersecurity by design for both MDs and the connected scenarios they operate in. [ABSTRACT FROM AUTHOR]

Remote patient monitoring is a healthcare delivery model that uses technology to collect and transmit patient data from a remote location to healthcare providers for analysis and treatment. Remote patient monitoring systems rely on a network infrastructure to gather and transmit data from patients to healthcare providers through a network. While these systems become more prevalent, they may also become targets for cyberattacks. This paper deals with the development of a domain ontology to facilitate partial automation to improve the security posture of IoT networks used in remote patient monitoring. For this purpose, it captures the semantics of the concepts and properties of the main security aspects of IoT medical devices. This is complemented by a comprehensive ruleset, evaluated by using SPARQL queries, and automated reasoning over the aggregated knowledge. [ABSTRACT FROM AUTHOR]

Mobile companions are a subset of telemedicine and they have gained increasing attention to enhance healthcare services and communication between patients and care providers. These digital tools allow for the delivery of care services through a variety of channels, increasing access to care and convenience for patients. By gathering and analyzing data from patients, mobile companions also provide care providers with valuable insights into patient health, helping to improve care. Additionally, they can be integrated with electronic medical records, making it easier for healthcare providers to access patient information and track progress over time. The use of mobile companions in healthcare services has many advantages and it brings new opportunities to care providers. However, it also presents many challenges to care providers that aim to adopt it. This paper presents a study where is addresses the opportunities and challenges of multichannel mobile companions in healthcare services provided to patients. As a result of this study, a SWOT analysis is presented and discussed regarding the mobile companion in healthcare services. [ABSTRACT FROM AUTHOR]

The human vestibular system is adversely affected by the aging process. Recent evidence indicates that vestibular information and cognitive functions are related, suggesting that age-related vestibular loss may contribute to cognitive impairment. In this study, we aimed to investigate the effects of repetitive, home-based galvanic vestibular stimulation (GVS) on cognitive functions in healthy older adults. Twenty-one participants (age = 64.66 ± 2.97 years, 12 females) were randomly allocated to either a home-based GVS or an active control group. The GVS intervention lasted 20 min per session, five times a week, for two weeks (10 sessions). Cognitive functions were assessed before and after the intervention using the Stroop Test, Trail Making Test A&B, and Dual-Task (digit recall and paper-pencil tracking test). Our findings revealed a significant group-by-time interaction effect for the tracking accuracy (F (1,18) = 7.713, p = 0.012, η p 2 = 0.30), with only the home-based GVS group showing significant improvement (t = −2.544, p = 0.029). The proposed home-based GVS protocol offers a promising non-pharmacological avenue for enhancing visuospatial ability in healthy older adults. Further research is needed to investigate the effects of different GVS protocols on various cognitive functions, particularly in older individuals with different health conditions. • Home-based GVS is feasible for older adults. • Repetitive GVS improves visuospatial ability in healthy older adults. • GVS is a promising non-pharmacological tool for improving cognition. [ABSTRACT FROM AUTHOR]

There is a growing trend towards data mining applications in medicine. Different algorithms have been explored by medical practitioners in an attempt to assist their work; the diagnosis of breast cancer is one of those applications. Machine learning algorithms are of vital importance to many medical problems, they can help to diagnose a disease, to detect its causes, to predict the outcome of a treatment, etc. K-Nearest Neighbors algorithm (KNN) is one of the simplest algorithms; it is widely used in predictive analysis. To optimize its performance and to accelerate its process, this paper proposes a new solution to speed up KNN algorithm based on clustering and attributes filtering. It also includes another improvement based on reliability coefficients which insures a more accurate classification. Thus, the contributions of this paper are three-fold: (i) the clustering of class instances, (ii) the selection of most significant attributes, and (iii) the ponderation of similarities by reliability coefficients. Results of the proposed approach exceeded most known classification techniques with an average f-measure exceeding 94% on the considered breast-cancer Dataset. [ABSTRACT FROM AUTHOR]

Data visualization is key in the Big Data context, enabling different cognitive perspectives over large datasets. These visual perspectives can prompt relevant advantages with regard to healthcare records, because they may contribute to a faster, more understandable, and adequate way to capture patients' health history and overall condition, thus improving healthcare quality. Timelines are well-known visual artifacts that help healthcare professionals to visualize patients' electronic health records (EHR) over a time period. As data stored in EHR tend to quickly grow with each interaction between patient and healthcare system both number and size, traditional linear timelines are can increasingly become more difficult to manage visually, as they often span over different screens. Considering that a holistic analysis is desirable to provide proper and quality health services, data visualization should enable a seamless understanding of patients' health history and overall condition. When dealing with critical episodes - such as an emergency - where time is an important factor, this is even more decisive. Furthermore, traditional timelines do not support multidimensional data representation. This paper presents a new visual model of time-dependent EHR based on radial models. It is capable of simultaneously displaying several data categories that characterize patients' medical history, enabling medical professionals to be aware of different data categories over time in a single display, without the need to scroll between screens. [ABSTRACT FROM AUTHOR]

Background: Mobile phone based applications are considered by many as potentially useful for addressing challenges and improving the quality of data collection in developing countries. Yet very little evidence is available supporting or refuting the potential and widely perceived benefits on the use of electronic forms on smartphones for routine patient data collection by health workers at primary health care facilities.Methods: A facility based cross sectional study using a structured paper checklist was prepared to assess the completeness and accuracy of 408 electronic records completed and submitted to a central database server using electronic forms on smartphones by 25 health workers. The 408 electronic records were selected randomly out of a total of 1772 maternal health records submitted by the health workers to the central database over a period of six months. Descriptive frequencies and percentages of data completeness and error rates were calculated.Results: When compared to paper records, the use of electronic forms significantly improved data completeness by 209 (8%) entries. Of a total 2622 entries checked for completeness, 2602 (99.2%) electronic record entries were complete, while 2393 (91.3%) paper record entries were complete. A very small percentage of error rates, which was easily identifiable, occurred in both electronic and paper forms although the error rate in the electronic records was more than double that of paper records (2.8% vs. 1.1%). More than half of entry errors in the electronic records related to entering a text value.Conclusions: With minimal training, supervision, and no incentives, health care workers were able to use electronic forms for patient assessment and routine data collection appropriately and accurately with a very small error rate. Minimising the number of questions requiring text responses in electronic forms would be helpful in minimizing data errors. [ABSTRACT FROM AUTHOR]

• A comprehensive systematic review on healthcare recommender systems is provided. • The main ideas, methods, tools, advantages, disadvantages, evaluation parameters, and evaluation methods are discussed and summarized. • A scientific taxonomy of healthcare recommender systems is presented. • A detailed list of challenges, open issues, and future research directions is outlined. The maintenance of health through prevention, diagnosis, and treatment of illnesses, injuries, and other mental and physical impairments in people is called healthcare that is performed by professionals in health fields. All activities performed in providing primary, secondary, and tertiary care, as well as in public health, are components of healthcare systems that are required to analyze massive amounts of patients' data to gain insights and aid in disease prediction. The healthcare recommender system (HRS) is a growing and robust platform for healthcare services. The purpose of this paper is to identify , taxonomically classify , and compare current HRS researches in a systematic way. This study presents a systematic literature review (SLR) method for HRSs regarding 41 papers published between 2010 and 2021; the selected articles fall into five classes: collaborative-based, content-based, knowledge-based, context-based, and hybrid. Seventeen essential factors are identified to examine the selected HRSs, and each of the papers were studied to find advantages, disadvantages, evaluation types, and tools. In addition, we address a discussion of challenges, future directions, and open issues. This paper demonstrates that HRSs are still in their early stages of maturity, but they are developing. It identifies the need for a scalable, confidential, and reliable framework to aid in the improvement of both patients' and health professionals' trust in HRSs. This research reveals a lack of standardization of medical codes across various healthcare systems. In addition, this review identifies the need for novel patterns to address cold start problems. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak posed several challenges to healthcare professionals in treating patients: limiting the spread of infection; develop appropriate short-term strategies; and formulating long-term plans. Each of the affected countries had specific capacities before the outbreak began. This potential may have helped reduce the spread of the disease and should help reduce its impact. This paper uses the VMCM method to determine the capacity of health services in European countries. It allowed for a ranking of countries in terms of this potential. This allows the capacity of the health service to be related to the number of infected and deceased people. The purpose of this article is to examine the relationship of infections and mortality on COVID-19 to health care capacity. [ABSTRACT FROM AUTHOR]

Personal health records (PHR) offer significant benefit for patients, such as reducing medical cost and improving the quality of medical care. Majority of the current schemes lack provisions for tracking and revoking malicious doctors. The explicit access policies are prone to leaking patient private information. What is more, owning to the uneven distribution of medical supplies, shocking computational overhead during decryption is a burden that cannot be ignored for busy medical workers. This paper proposed a patient-centric medical service matching scheme that supports policy hiding, attribute matching, fine-grained access control, and user dynamic management. The scheme uses ciphertext policy-based attribute encryption (CP-ABE) to achieve fine-grained access control and supports policy hiding. It utilizes white-box tracking technology and binary tree structure to achieve malicious doctor tracking. Revocation information is ciphertext to achieve dynamic management of doctors. From the experimental results, it can be concluded that our protocol achieves both patient-centric security and performance advantages. • A secure patient-centric medical service matching scheme with policy hiding. • Achieve fine-grained access control and withdraw untrustworthy doctors. • Low computational and communication overhead. [ABSTRACT FROM AUTHOR]

The paper aims to present the C3HIS Ontology project, a web based solution for Covid-19 Crisis Health Care Information System. In the health care services, employee skills are a major resource and an essential part of everyday practice and a requirement for all health professions. We aim to prove how using individual profiles based on competencies can make a difference between life and death in times. As the performance assessment is driven by actors competencies we have to put human actors in the core of quality processes of health care services management in COVID-19 crisis. [ABSTRACT FROM AUTHOR]

Background: This paper identifies the best instruments for service providers to measure the quality of life (QoL) of children with a disability, with a focus on their alignment with the Convention on the Rights of Persons with a Disability (CRPD).Methods: This study reviewed systematic reviews to identify generic QoL instruments for children and adolescents, followed by an appraisal process using newly developed criteria. QoL instruments with a health status, functioning, and condition-specific focus were excluded.Results: Twenty generic QoL instruments for children were identified from existing systematic reviews to undergo further review. Only 2 of the 20 instruments were recommended for service providers to measure the QoL of children with a disability (KIDSCREEN and KINDL). Many pediatric QoL instruments (N = 9) focus on functioning and are not consistent with the CRPD, confounding a child's functioning with their feelings about their life. KIDSCREEN and KINDL have self-report and parent report versions, are applicable for childhood and adolescence, demonstrate adequate reliability and validity, involved children in their development, focus on wellbeing, are likely to be able to be completed by a child with a disability, and are low in cost.Conclusions: Many instruments focus on functioning rather than wellbeing and thus may not capture the QoL of children with a disability. A child's functional limitations may not be consistent with their feelings about life. Two instruments that assess wellbeing and meet the criteria important for service providers now require further testing to explore their usefulness and validity for children with varying abilities. [ABSTRACT FROM AUTHOR]

There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians' resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model's generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model's clinical impact; (6) internal validation of the model's performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems. [ABSTRACT FROM AUTHOR]

Objective: An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians' assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system.Materials and Methods: The natural language processing and machine learning-based Watson1 method models clinical thinking in identifying a patient's problem list using clinical notes and structured data. This pilot study assessed the Watson method and included 15 randomly selected, de-identified patient records from a large healthcare system that were each planned to be reviewed by at least two internal medicine physicians. The physicians created their own problem lists, and then evaluated the overall usefulness of their own problem lists (P), Watson generated problem lists (W), and the existing EHR problem lists (E) on a 10-point scale. The primary outcome was pairwise comparisons of P, W, and E.Results: Six out of the 10 invited physicians completed 27 assessments of P, W, and E, and in process evaluated 732 Watson generated problems and 444 problems in the EHR system. As expected, physicians rated their own lists, P, highest. However, W was rated higher than E. Among 89% of assessments, Watson identified at least one important problem that physicians missed.Conclusion: Cognitive computing systems like this Watson system hold the potential for accurate, problem-list-centered summarization of patient records, potentially leading to increased efficiency, better clinical decision support, and improved quality of patient care. [ABSTRACT FROM AUTHOR]

Telemedicine systems have been considered as a necessary measure to alleviate the shortfall in skilled medical specialists in developing countries. However, the obvious challenge is whether clinicians are willing to use this technological innovation, which has aided medical practice globally. One factor which has received little academic attention is the provision of suitable encouragement for clinicians to adopt telemedicine, in the form of rewards, motivation or incentives. A further consideration for telemedicine usage in developing countries, especially sub-Saharan Africa and Nigeria in particular, are to the severe shortage of available practising clinicians. The researchers therefore explore the need to positively reinforce the adoption of telemedicine amongst clinicians in Nigeria, and also offer a rationale for this using the UTAUT model. Data were collected using a structured paper-based questionnaire, with 252 physicians and nurses from six government hospitals in Ondo state, Nigeria. The study applied SmartPLS 2.0 for analysis to determine the relationship between six variables. Demographic moderating variables, age, gender and profession, were included. The results indicate that performance expectancy (p<0.05), effort expectancy (p<0.05), facilitating condition (p<0.01) and reinforcement factor (p<0.001) have significant effects on clinicians' behavioural intention to use telemedicine systems, as predicted using the extended UTAUT model. Our results showed that the use of telemedicine by clinicians in the Nigerian context is perceived as a dual responsibility which requires suitable reinforcement. In addition, performance expectancy, effort expectancy, facilitating condition and reinforcement determinants are influential factors in the use of telemedicine services for remote-patient clinical diagnosis and management by the Nigerian clinicians. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the usability of concept mapping to elicit the expectations of healthcare professionals regarding the implementation of a new electronic health record (EHR). These expectations need to be taken into account during the implementation process to maximize the chance of success of the EHR.Setting: Two university hospitals in Amsterdam, The Netherlands, in the preparation phase of jointly implementing a new EHR. During this study the hospitals had different methods of documenting patient information (legacy EHR vs. paper-based records).Method: Concept mapping was used to determine and classify the expectations of healthcare professionals regarding the implementation of a new EHR. A multidisciplinary group of 46 healthcare professionals from both university hospitals participated in this study. Expectations were elicited in focus groups, their relevance and feasibility were assessed through a web-questionnaire. Nonmetric multidimensional scaling and clustering methods were used to identify clusters of expectations.Results: We found nine clusters of expectations, each covering an important topic to enable the healthcare professionals to work properly with the new EHR once implemented: usability, data use and reuse, facility conditions, data registration, support, training, internal communication, patients, and collaboration. Average importance and feasibility of each of the clusters was high.Conclusion: Concept mapping is an effective method to find topics that, according to healthcare professionals, are important to consider during the implementation of a new EHR. The method helps to combine the input of a large group of stakeholders at limited efforts. [ABSTRACT FROM AUTHOR]

Summary Dr Thato Mosidi never expected to be diagnosed with tuberculosis (TB), despite widely prevalent exposure and very limited infection control measures. The life-threatening diagnosis of primary extensively drug-resistant TB (XDR-TB) came as an even greater shock. The inconvenient truth is that, rather than being protected, Dr Mosidi and thousands of her healthcare colleagues are at an increased risk of TB and especially drug-resistant TB. In this viewpoint paper we debunk the widely held false belief that healthcare workers are somehow immune to TB disease (TB-proof) and explore some of the key factors contributing to the pervasive stigmatization and subsequent non-disclosure of occupational TB. Our front-line workers are some of the first to suffer the consequences of a progressively more resistant and fatal TB epidemic, and urgent interventions are needed to ensure the safety and continued availability of these precious healthcare resources. These include the rapid development and scale-up of improved diagnostic and treatment options, strengthened infection control measures, and focused interventions to tackle stigma and discrimination in all its forms. We call our colleagues to action to protect themselves and those they care for. [ABSTRACT FROM AUTHOR]

Understanding patient opinions expressed towards healthcare services in online platforms could allow healthcare professionals to respond to address patients' concerns in a timely manner. Extracting patient opinion towards various aspects of health services is closely related to aspect-based sentiment analysis (ABSA) in which we need to identify both opinion targets and target-specific opinion expressions. The lack of aspect-level annotations however makes it difficult to build such an ABSA system. This paper proposes a joint learning framework for simultaneous unsupervised aspect extraction at the sentence level and supervised sentiment classification at the document level. It achieves 98.2% sentiment classification accuracy when tested on the reviews about healthcare services collected from Yelp, outperforming several strong baselines. Moreover, our model can extract coherent aspects and can automatically infer the distribution of aspects under different polarities without requiring aspect-level annotations for model learning. [ABSTRACT FROM AUTHOR]

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice. [ABSTRACT FROM AUTHOR]

In this paper we study the working conditions of Community Health Workers in performing house calls within low-income, violent communities in order to understand the challenges in delivering primary care in developing countries. We conducted field studies in two primary healthcare clinics and telephone surveys for 5 months in 2017 within systematic sample of 1690 community health workers based on clinics distributed along the health regions of the city of Rio de Janeiro, Brazil. A number of 759 interviews were completed, approximately 50% of the sample, 86% men and 14% women. Most participants are 30-39 years old (35%), followed by 27% of 40-49 years old participants. Results show that exposure to territorial violence and environmental or health-related diseases significantly affects CHWs. Moreover, CHWs have to develop a significant set of skills to cope with aspects of the territory, and those skills are not present in their training. As community health workers work on the sharp end of the healthcare system, responsible for outreaching, community education, counseling, and social support, our study presents contributions to government and management levels on working conditions inside communities, constraints in assistance, and difficulties in implementing primary care policies. [ABSTRACT FROM AUTHOR]