Context awareness in health care: a review

Research on workflow recognition for liver rupture repair surgery

Liver rupture repair surgery serves as one tool to treat liver rupture, especially beneficial for cases of mild liver rupture hemorrhage. Liver rupture can catalyze critical conditions such as hemorrhage and shock. Surgical workflow recognition in liver rupture repair surgery videos presents a significant task aimed at reducing surgical mistakes and enhancing the quality of surgeries conducted by surgeons. A liver rupture repair simulation surgical dataset is proposed in this paper which consists of 45 videos collaboratively completed by nine surgeons. Furthermore, an end-to-end SA-RLNet, a self attention-based recurrent convolutional neural network, is introduced in this paper. The self-attention mechanism is used to automatically identify the importance of input features in various instances and associate the relationships between input features. The accuracy of the surgical phase classification of the SA-RLNet approach is 90.6%. The present study demonstrates that the SA-RLNet approach shows strong generalization capabilities on the dataset. SA-RLNet has proved to be advantageous in capturing subtle variations between surgical phases. The application of surgical workflow recognition has promising feasibility in liver rupture repair surgery.

Nonlinear regression of remaining surgery duration from videos via Bayesian LSTM-based deep negative correlation learning

In this paper, we address the problem of estimating remaining surgery duration (RSD) from surgical video frames. We propose a Bayesian long short-term memory (LSTM) network-based Deep Negative Correlation Learning approach called BD-Net for accurate regression of RSD prediction as well as estimation of prediction uncertainty. Our method aims to extract discriminative visual features from surgical video frames and model the temporal dependencies among frames to improve the RSD prediction accuracy. To this end, we propose to train an ensemble of Bayesian LSTMs on top of a backbone network by the way of deep negative correlation learning (DNCL). More specifically, we deeply learn a pool of decorrelated Bayesian regressors with sound generalization capabilities through managing their intrinsic diversities. BD-Net is simple and efficient. After training, it can produce both RSD prediction and uncertainty estimation in a single inference run. We demonstrate the efficacy of BD-Net on publicly available datasets of two different types of surgeries: one containing 101 cataract microscopic surgeries with short durations and the other containing 80 cholecystectomy laparoscopic surgeries with relatively longer durations. Experimental results on both datasets demonstrate that the proposed BD-Net achieves better results than the state-of-the-art (SOTA) methods. A reference implementation of our method can be found at: https://github.com/jywu511/BD-Net.

Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts

Context awareness is a field in pervasive computing, which has begun to impact medical systems via an increasing number of healthcare applications that are starting to use context awareness. The present work seeks to determine which contexts are important for medical applications and which domains of context-aware applications exist in healthcare. A systematic scoping review of context-aware medical systems currently used by patients or healthcare providers (inclusion criteria) was conducted between April 2021 and June 2023. A search strategy was designed and applied to Pub Med, EBSCO, IEEE Explore, Wiley, Science Direct, Springer Link, and ACM, articles from the databases were then filtered based on their abstract, and relevant articles were screened using a questionnaire applied to their full texts prior to data extraction. Applications were grouped into context-aware healthcare application domains based on past reviews and screening results. A total of 25 articles passed all screening levels and underwent data extraction. The most common contexts used were user location (8 out of 25 studies), demographic information (6 out of 25 studies), movement status/activity level (7 out of 25 studies), time of day (5 out of 25 studies), phone usage patterns (5 out of 25 studies), lab/vitals (7 out of 25 studies), and patient history data (8 out of 23 studies). Through a systematic review process, the current study determined the key contexts within context-aware healthcare applications that have reached healthcare providers and patients. The present work has illuminated many of the early successful context-aware healthcare applications. Additionally, the primary contexts leveraged by these systems have been identified, allowing future systems to focus on prioritizing the integration of these key contexts.

Building a COVID-Safe Navigation App Using a Meta-Model Based Context Server

Building context-aware applications is an already widely researched topic. It is our belief that context awareness has the potential to supplement the Internet of Things, when a suitable methodology including supporting tools will ease the development of context-aware applications. We believe that a meta-model based approach can be key to achieving this goal. In this paper, we present our meta-model based methodology, which allows us to define and build application-specific context models and the integration of sensor data without any programming. We describe how that methodology is applied with the implementation of a relatively simple context-aware COVID-safe navigation app. The outcome showed that programmers with no experience in context-awareness were able to understand the concepts easily and were able to effectively use it after receiving a short training. Therefore, context-awareness is able to be implemented within a short amount of time. We conclude that this can also be the case for the development of other context-aware applications, which have the same context-awareness characteristics. We have also identified further optimization potential, which we will discuss at the conclusion of this article.

Uncharted Waters of Machine and Deep Learning for Surgical Phase Recognition in Neurosurgery

Recent years have witnessed artificial intelligence (AI) make meteoric leaps in both medicine and surgery, bridging the gap between the capabilities of humans and machines. Digitization of operating rooms and the creation of massive quantities of data have paved the way for machine learning and computer vision applications in surgery. Surgical phase recognition (SPR) is a newly emerging technology that uses data derived from operative videos to train machine and deep learning algorithms to identify the phases of surgery. Advancement of this technology will be key in establishing context-aware surgical systems in the future. By automatically recognizing and evaluating the current surgical scenario, these intelligent systems are able to provide intraoperative decision support, improve operating room efficiency, assess surgical skills, and aid in surgical training and education. Still in its infancy, SPR has been mainly studied in laparoscopic surgeries, with a contrasting stark lack of research within neurosurgery. Given the high-tech and rapidly advancing nature of neurosurgery, we believe SPR has a tremendous untapped potential in this field. Herein, we present an overview of the SPR technology, its potential applications in neurosurgery, and the challenges that lie ahead.

ACCU3RATE: A mobile health application rating scale based on user reviews

Background

Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being.

Objective

This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU³RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings.

Method

Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score.

Results and conclusions

ACCU³RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU³RATE, matches more closely to the rating performed by experts.

Semi-supervised learning with progressive unlabeled data excavation for label-efficient surgical workflow recognition

Surgical workflow recognition is a fundamental task in computer-assisted surgery and a key component of various applications in operating rooms. Existing deep learning models have achieved promising results for surgical workflow recognition, heavily relying on a large amount of annotated videos. However, obtaining annotation is time-consuming and requires the domain knowledge of surgeons. In this paper, we propose a novel two-stage Semi-Supervised Learning method for label-efficient Surgical workflow recognition, named as SurgSSL. Our proposed SurgSSL progressively leverages the inherent knowledge held in the unlabeled data to a larger extent: from implicit unlabeled data excavation via motion knowledge excavation, to explicit unlabeled data excavation via pre-knowledge pseudo labeling. Specifically, we first propose a novel intra-sequence Visual and Temporal Dynamic Consistency (VTDC) scheme for implicit excavation. It enforces prediction consistency of the same data under perturbations in both spatial and temporal spaces, encouraging model to capture rich motion knowledge. We further perform explicit excavation by optimizing the model towards our pre-knowledge pseudo label. It is naturally generated by the VTDC regularized model with prior knowledge of unlabeled data encoded, and demonstrates superior reliability for model supervision compared with the label generated by existing methods. We extensively evaluate our method on two public surgical datasets of Cholec80 and M2CAI challenge dataset. Our method surpasses the state-of-the-art semi-supervised methods by a large margin, e.g., improving 10.5% Accuracy under the severest annotation regime of M2CAI dataset. Using only 50% labeled videos on Cholec80, our approach achieves competitive performance compared with full-data training method.

Multi-task temporal convolutional networks for joint recognition of surgical phases and steps in gastric bypass procedures

PURPOSE

Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted surgeries. Prior works have focused on recognizing either coarse activities, such as phases, or fine-grained activities, such as gestures. This work aims at jointly recognizing two complementary levels of granularity directly from videos, namely phases and steps.

METHODS

We introduce two correlated surgical activities, phases and steps, for the laparoscopic gastric bypass procedure. We propose a multi-task multi-stage temporal convolutional network (MTMS-TCN) along with a multi-task convolutional neural network (CNN) training setup to jointly predict the phases and steps and benefit from their complementarity to better evaluate the execution of the procedure. We evaluate the proposed method on a large video dataset consisting of 40 surgical procedures (Bypass40).

RESULTS

We present experimental results from several baseline models for both phase and step recognition on the Bypass40. The proposed MTMS-TCN method outperforms single-task methods in both phase and step recognition by 1-2% in accuracy, precision and recall. Furthermore, for step recognition, MTMS-TCN achieves a superior performance of 3-6% compared to LSTM-based models on all metrics.

CONCLUSION

In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on a gastric bypass dataset with multi-level annotations. The proposed method shows that the joint modeling of phases and steps is beneficial to improve the overall recognition of each type of activity.

The essentials of nursing leadership: A systematic review of factors and educational interventions influencing nursing leadership

BACKGROUND

Nursing leadership plays a vital role in shaping outcomes for healthcare organizations, personnel and patients. With much of the leadership workforce set to retire in the near future, identifying factors that positively contribute to the development of leadership in nurses is of utmost importance.

OBJECTIVES

To identify determining factors of nursing leadership, and the effectiveness of interventions to enhance leadership in nurses.

DESIGN

We conducted a systematic review, including a total of nine electronic databases.

DATA SOURCES

Databases included: Medline, Academic Search Premier, Embase, PsychInfo, Sociological Abstracts, ABI, CINAHL, ERIC, and Cochrane.

REVIEW METHODS

Studies were included if they quantitatively examined factors contributing to nursing leadership or educational interventions implemented with the intention of developing leadership practices in nurses. Two research team members independently reviewed each article to determine inclusion. All included studies underwent quality assessment, data extraction and content analysis.

RESULTS

49,502 titles/abstracts were screened resulting in 100 included manuscripts reporting on 93 studies (n=44 correlational studies and n=49 intervention studies). One hundred and five factors examined in correlational studies were categorized into 5 groups experience and education, individuals' traits and characteristics, relationship with work, role in the practice setting, and organizational context. Correlational studies revealed mixed results with some studies finding positive correlations and other non-significant relationships with leadership. Participation in leadership interventions had a positive impact on the development of a variety of leadership styles in 44 of 49 intervention studies, with relational leadership styles being the most common target of interventions.

CONCLUSIONS

The findings of this review make it clear that targeted educational interventions are an effective method of leadership development in nurses. However, due to equivocal results reported in many included studies and heterogeneity of leadership measurement tools, few conclusions can be drawn regarding which specific nurse characteristics and organizational factors most effectively contribute to the development of nursing leadership. Contextual and confounding factors that may mediate the relationships between nursing characteristics, development of leadership and enhancement of leadership development programs also require further examination. Targeted development of nursing leadership will help ensure that nurses of the future are well equipped to tackle the challenges of a burdened health-care system.

LRTD: long-range temporal dependency based active learning for surgical workflow recognition

PURPOSE

Automatic surgical workflow recognition in video is an essentially fundamental yet challenging problem for developing computer-assisted and robotic-assisted surgery. Existing approaches with deep learning have achieved remarkable performance on analysis of surgical videos, however, heavily relying on large-scale labelled datasets. Unfortunately, the annotation is not often available in abundance, because it requires the domain knowledge of surgeons. Even for experts, it is very tedious and time-consuming to do a sufficient amount of annotations.

METHODS

In this paper, we propose a novel active learning method for cost-effective surgical video analysis. Specifically, we propose a non-local recurrent convolutional network, which introduces non-local block to capture the long-range temporal dependency (LRTD) among continuous frames. We then formulate an intra-clip dependency score to represent the overall dependency within this clip. By ranking scores among clips in unlabelled data pool, we select the clips with weak dependencies to annotate, which indicates the most informative ones to better benefit network training.

RESULTS

We validate our approach on a large surgical video dataset (Cholec80) by performing surgical workflow recognition task. By using our LRTD based selection strategy, we can outperform other state-of-the-art active learning methods who only consider neighbor-frame information. Using only up to 50% of samples, our approach can exceed the performance of full-data training.

CONCLUSION

By modeling the intra-clip dependency, our LRTD based strategy shows stronger capability to select informative video clips for annotation compared with other active learning methods, through the evaluation on a popular public surgical dataset. The results also show the promising potential of our framework for reducing annotation workload in the clinical practice.

Midwives perceptions: Birth unit environment and the implementation of best intrapartum care practices

PROBLEM

Midwives related avoidable factors causing maternal morbidity and mortality rates continue to occur despite the existing intrapartum care-related evidence-based practice guidelines and continuing staff development initiatives.

RESEARCH QUESTION

What are your perceptions regarding a birth unit environment that supports the implementation of best intrapartum care practices.

OBJECTIVE

To explore and describe midwives' perceptions about the birth environment that supports the implementation of best intrapartum care practices.

METHOD

A qualitative design that is explorative, descriptive, and contextual in nature using a descriptive phenomenology approach.

SETTING

A public hospital birth unit in the Gauteng Province in South Africa.

POPULATION AND SAMPLE

The population comprised of 56 permanently employed female registered midwives. A purposive sampling method was used to select 26 participants who met the selection criteria, these participants were willing to participate in the study and to sign the consent form. Data collection process involved three focus group interviews using semi-structured interviews. A qualitative data analysis method was used to analyse data. Trustworthiness was ensured and ethical considerations were adhered to.

FINDINGS

Three main themes emerged namely, interpersonal skills, improved staff development, and adequate resources.

DISCUSSION

Conducive birth environment is crucial to childbirth outcomes. Midwives' constant introspection is essential in fulfilling their obligation to render competent and ethical intrapartum care.

CONCLUSION

Midwives identified perceived birth environment barriers affecting their implementation of best intrapartum care practices. Adoption of a comprehensive approach to address the birth unit environment-related factors is suggested to support midwives in their endeavour to provide the best care to women during childbirth.

Health Information Systems in the Digital Health Ecosystem-Problems and Solutions for Ethics, Trust and Privacy

Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject's perspective, there is frequently no guarantee and therefore no trust that PHI is processed ethically in Digital Health Ecosystems. This results in new ethical, privacy and trust challenges to be solved. The authors' objective is to find a combination of ethical principles, privacy and trust models, together enabling design, implementation of DHIS acting ethically, being trustworthy, and supporting the user's privacy needs. Research published in journals, conference proceedings, and standards documents is analyzed from the viewpoint of ethics, privacy and trust. In that context, systems theory and systems engineering approaches together with heuristic analysis are deployed. The ethical model proposed is a combination of consequentialism, professional medical ethics and utilitarianism. Privacy enforcement can be facilitated by defining it as health information specific contextual intellectual property right, where a service user can express their own privacy needs using computer-understandable policies. Thereby, privacy as a dynamic, indeterminate concept, and computational trust, deploys linguistic values and fuzzy mathematics. The proposed solution, combining ethical principles, privacy as intellectual property and computational trust models, shows a new way to achieve ethically acceptable, trustworthy and privacy-enabling DHIS and Digital Health Ecosystems.

"Energy is a Finite Resource": Designing Technology to Support Individuals across Fluctuating Symptoms of Depression

While the HCI field increasingly examines how digital tools can support individuals in managing mental health conditions, it remains unclear how these tools can accommodate these conditions' temporal aspects. Based on weekly interviews with five individuals with depression, conducted over six weeks, this study identifies design opportunities and challenges related to extending technology-based support across fluctuating symptoms. Our findings suggest that participants perceive events and contexts in daily life to have marked impact on their symptoms. Results also illustrate that ebbs and flows in symptoms profoundly affect how individuals practice depression self-management. While digital tools often aim to reach individuals while they feel depressed, we suggest they should also engage individuals when they are less symptomatic, leveraging their energy and motivation to build habits, establish plans and goals, and generate and organize content to prepare for symptom onset.

Multi-task recurrent convolutional network with correlation loss for surgical video analysis

Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis as well as very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is typically well-defined, most previous methods tackled them separately, without making full use of their relatedness. In this paper, we present a novel method by developing a multi-task recurrent convolutional network with correlation loss (MTRCNet-CL) to exploit their relatedness to simultaneously boost the performance of both tasks. Specifically, our proposed MTRCNet-CL model has an end-to-end architecture with two branches, which share earlier feature encoders to extract general visual features while holding respective higher layers targeting for specific tasks. Given that temporal information is crucial for phase recognition, long-short term memory (LSTM) is explored to model the sequential dependencies in the phase recognition branch. More importantly, a novel and effective correlation loss is designed to model the relatedness between tool presence and phase identification of each video frame, by minimizing the divergence of predictions from the two branches. Mutually leveraging both low-level feature sharing and high-level prediction correlating, our MTRCNet-CL method can encourage the interactions between the two tasks to a large extent, and hence can bring about benefits to each other. Extensive experiments on a large surgical video dataset (Cholec80) demonstrate outstanding performance of our proposed method, consistently exceeding the state-of-the-art methods by a large margin, e.g., 89.1% v.s. 81.0% for the mAP in tool presence detection and 87.4% v.s. 84.5% for F1 score in phase recognition.

Medication adherence: A method for designing context-aware reminders

OBJECTIVES

Several interventions have been proposed to improve medication adherence including those using reminders. Context-aware reminders can be effective because they operate when the dose is due, has not been taken, and is still safe to take. Although very promising, we find that there is no method to design context-aware reminders. To address these, we focus on proposing a method to design context-aware reminders.

METHODS

We conducted a systematic review of context-aware reminders for medication adherence using PRISMA approach. The analysis of literature leads to several interesting observations including the need for a method to design context-aware reminders. In this study, we present Method to Design Context-Aware Reminders (MDCAR) that can also meet special requirements. We used domain experts reasoning to evaluate the designed Context-Aware Reminders for Medication Adherence (CARS-MA). Further, we used analytical model to evaluate reliability, side effects, and cost of intervention.

RESULTS

This is the first paper that addresses "how to" design context-aware reminders. The proposed design method can lead to range of context-aware reminders including CARS-MA. The verification, validation, and evaluation of CARS-MA indicate that the context-aware reminders perform better than simple reminders in improving medication adherence.

CONCLUSIONS

The proposed method for context-aware reminders will help healthcare professionals and researchers to implement and select a suitable intervention to improve medication adherence. Further, it can lead to decision support systems for patients, healthcare professionals, researchers and policy makers for medication adherence. The design method can be extended for complex scenarios of multiple medications, persistent-reminders, and composite interventions.

How organizations implement new practices in dynamic context: role of deliberate learning and dynamic capabilities development in health care units

Purpose

Organizations learn semi-automatically through experience or consciously through deliberate learning efforts. As there seems to be a “black-box” in the possible linkages between deliberate learning and new practice implementation, this paper aims to develop and test a process model, linking deliberate learning and new practice implementation through complementary competencies of task and environmental flexibility.

Design/methodology/approach

As part of a field study, health-care improvement program (to transfer the improvement training program for new practice implementation) of 186 HCUs was used for testing our hypothesis. In addition to descriptive statistics, multiple hierarchical regressions and bootstrapping were used to test the study hypotheses.

Findings

Findings suggest that deliberate learning is positively and significantly related with new practice implementation, and dynamic capabilities in the form of task and environmental flexibility mediates this relationship.

Research limitations/implications

The present study makes theoretical and practical contributions by linking literature from new practice, organizational learning and dynamic capabilities; and by delving into the deliberate learning activities undertaken by health-care units.

Originality/value

Organizational learning in health care has almost become inevitable today due to the ever-changing dynamics of the industry. Barring handful of studies, the current state of literature is almost entirely tilted towards experience-based learning and deliberate learning is not well studied. To address this gap, the study aims to develop and test a process model linking development of dynamic capabilities with deliberate learning and new practice implementation. Further, findings of this study will help organizations and managers to understand and thereby effectively manage new practice implementation process through the use of deliberate activities.

Context-Aware Systems for Chronic Disease Patients: Scoping Review

Background

Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient work.

Objective

This study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures.

Methods

A total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy. Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients’ health-related activities, and reported the evaluation of the systems by the users. Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted.

Results

The database search retrieved 1478 citations; 6 papers were included, all published from 2009 onwards. The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users; there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system. In the included studies, context was captured using sensors or self-reports, sometimes involving both. Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback. A total of 3 studies examined the impact of interventions on health-related measures, showing positive results.

Conclusions

The use of context-aware systems to support patient work is an emerging area of research. RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.

Ontology-Based Model to Support Ubiquitous Healthcare Systems for COPD Patients

Over the past 30 years, information technology has gradually transformed the way health care is provisioned for patients. Chronic Obstructive Pulmonary Disease (COPD) is an incurable malady that threatens the lives of millions around the world. The huge amount of medical information in terms of complex interdependence between progression of health problems and various other factors makes the representation of data more challenging. This study investigated how formal semantic standards could be used for building an ontology knowledge repository to provide ubiquitous healthcare and medical recommendations for COPD patient to reduce preventable harm. The novel contribution of the suggested framework resides in the patient-centered monitoring approach, as we work to create dynamic adaptive protection services according to the current context of patient. This work executes a sequential modular approach consisting of patient, disease, location, devices, activities, environment and services to deliver personalized real-time medical care for COPD patients. The main benefits of this project are: (1) adhering to dynamic safe boundaries for the vital signs, which may vary depending on multiple factors; (2) assessing environmental risk factors; and (3) evaluating the patient’s daily activities through scheduled events to avoid potentially dangerous situations. This solution implements an interrelated set of ontologies with a logical base of Semantic Web Rule Language (SWRL) rules derived from the medical guidelines and expert pneumologists to handle all contextual situations.

The digital scribe

Current generation electronic health records suffer a number of problems that make them inefficient and associated with poor clinical satisfaction. Digital scribes or intelligent documentation support systems, take advantage of advances in speech recognition, natural language processing and artificial intelligence, to automate the clinical documentation task currently conducted by humans. Whilst in their infancy, digital scribes are likely to evolve through three broad stages. Human led systems task clinicians with creating documentation, but provide tools to make the task simpler and more effective, for example with dictation support, semantic checking and templates. Mixed-initiative systems are delegated part of the documentation task, converting the conversations in a clinical encounter into summaries suitable for the electronic record. Computer-led systems are delegated full control of documentation and only request human interaction when exceptions are encountered. Intelligent clinical environments permit such augmented clinical encounters to occur in a fully digitised space where the environment becomes the computer. Data from clinical instruments can be automatically transmitted, interpreted using AI and entered directly into the record. Digital scribes raise many issues for clinical practice, including new patient safety risks. Automation bias may see clinicians automatically accept scribe documents without checking. The electronic record also shifts from a human created summary of events to potentially a full audio, video and sensor record of the clinical encounter. Digital scribes promisingly offer a gateway into the clinical workflow for more advanced support for diagnostic, prognostic and therapeutic tasks.

SmartMedicist: a context-aware system for recommending an alternative medicine

Purpose

This study aims to discuss a context-aware system, SmartMedicist, which can recommend an alternative medicine from a set of available medicines present at a patient’s home for an unavailable medicine. The system is applied to the chronic disease patients only. The system requires only a smartphone, and provides a reminder to the patient to take medicine at appropriate times and to procure medicines from drug store. The system discusses the output method for the physically challenged patient. Although there are existing systems that can remind a patient for taking medicines, the authors are not aware of any such system that has the capability to recommend an alternative medicine for the prescribed medicine.

Design/methodology/approach

The study developed a pharmacology knowledge base that consists of a representation of a set of diseases, according to family, type and medicines, in a k-ary tree. An alternative medicine is recommended based on the set of available medicines and knowledge base.

Findings

We considered four diseases: Hypertension, Gastritis, Alzheimer’s disease, and Parkinson; and performed several experiments for each disease for the different number of available medicines. The execution time to find an alternative medicine (if any) in each case is around four seconds.

Originality/value

The proposed system is cost effective and affordable for most families in India. Although the proposed system is not a substitute of a doctor, this system will enhance the safety golden period for a patient to consult a doctor in the emergency exhaustion of the prescribed medicines.

SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network

We propose an analysis of surgical videos that is based on a novel recurrent convolutional network (SV-RCNet), specifically for automatic workflow recognition from surgical videos online, which is a key component for developing the context-aware computer-assisted intervention systems. Different from previous methods which harness visual and temporal information separately, the proposed SV-RCNet seamlessly integrates a convolutional neural network (CNN) and a recurrent neural network (RNN) to form a novel recurrent convolutional architecture in order to take full advantages of the complementary information of visual and temporal features learned from surgical videos. We effectively train the SV-RCNet in an end-to-end manner so that the visual representations and sequential dynamics can be jointly optimized in the learning process. In order to produce more discriminative spatio-temporal features, we exploit a deep residual network (ResNet) and a long short term memory (LSTM) network, to extract visual features and temporal dependencies, respectively, and integrate them into the SV-RCNet. Moreover, based on the phase transition-sensitive predictions from the SV-RCNet, we propose a simple yet effective inference scheme, namely the prior knowledge inference (PKI), by leveraging the natural characteristic of surgical video. Such a strategy further improves the consistency of results and largely boosts the recognition performance. Extensive experiments have been conducted with the MICCAI 2016 Modeling and Monitoring of Computer Assisted Interventions Workflow Challenge dataset and Cholec80 dataset to validate SV-RCNet. Our approach not only achieves superior performance on these two datasets but also outperforms the state-of-the-art methods by a significant margin.

Protocol for the mixed-methods process and context evaluation of the TB & Tobacco randomised controlled trial in Bangladesh and Pakistan: a hybrid effectiveness-implementation study

INTRODUCTION

Tuberculosis (TB) remains a significant public health problem in South Asia. Tobacco use increases the risks of TB infection and TB progression. The TB& Tobacco placebo-controlled randomised trial aims to (1) assess the effectiveness of the tobacco cessation medication cytisine versus placebo when combined with behavioural support and (2) implement tobacco cessation medication and behavioural support as part of general TB care in Bangladesh and Pakistan. This paper summarises the process and context evaluation protocol embedded in the effectiveness-implementation hybrid design.

METHODS AND ANALYSIS

We are conducting a mixed-methods process and context evaluation informed by an intervention logic model that draws on the UK Medical Research Council's Process Evaluation Guidance. Our approach includes quantitative and qualitative data collection on context, recruitment, reach, dose delivered, dose received and fidelity. Quantitative data include patient characteristics, reach of recruitment among eligible patients, routine trial data on dose delivered and dose received, and a COM-B ('capability', 'opportunity', 'motivation' and 'behaviour') questionnaire filled in by participating health workers. Qualitative data include semistructured interviews with TB health workers and patients, and with policy-makers at district and central levels in each country. Interviews will be analysed using the framework approach. The behavioural intervention delivery is audio recorded and assessed using a predefined fidelity coding index based on behavioural change technique taxonomy.

ETHICS AND DISSEMINATION

The study complies with the guidelines of the Declaration of Helsinki. Ethics approval for the study and process evaluation was granted by the University of Leeds (qualitative components), University of York (trial data and fidelity assessment), Bangladesh Medical Research Council and Bangladesh Drug Administration (trial data and qualitative components) and Pakistan Medical Research Council (trial data and qualitative components). Results of this research will be disseminated through reports to stakeholders and peer-reviewed publications and conference presentations.

TRIAL REGISTRATION NUMBER

ISRCTN43811467; Pre-results.

Exploring the Notion of Context in Medical Data

Scientific and technological knowledge and skills are becoming crucial for most data analysis activities. Two rather distinct, but at the same time collaborating, domains are the ones of computer science and medicine; the former offers significant aid towards a more efficient understanding of the latter's research trends. Still, the process of meaningfully analyzing and understanding medical information and data is a tedious one, bound to several challenges. One of them is the efficient utilization of contextual information in the process leading to optimized, context-aware data analysis results. Nowadays, researchers are provided with tools and opportunities to analytically study medical data, but at the same time significant and rather complex computational challenges are yet to be tackled, among others due to the humanistic nature and increased rate of new content and information production imposed by related hardware and applications. So, the ultimate goal of this position paper is to provide interested parties an overview of major contextual information types to be identified within the medical data processing framework.

Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

In the last three decades, researchers have examined extensively how context-aware systems can assist people, specifically those suffering from incurable diseases, to help them cope with their medical illness. Over the years, a huge number of studies on Chronic Obstructive Pulmonary Disease (COPD) have been published. However, how to derive relevant attributes and early detection of COPD exacerbations remains a challenge. In this research work, we will use an efficient algorithm to select relevant attributes where there is no proper approach in this domain. Such algorithm predicts exacerbations with high accuracy by adding discretization process, and organizes the pertinent attributes in priority order based on their impact to facilitate the emergency medical treatment. In this paper, we propose an extension of our existing Helper Context-Aware Engine System (HCES) for COPD. This project uses Bayesian network algorithm to depict the dependency between the COPD symptoms (attributes) in order to overcome the insufficiency and the independency hypothesis of naïve Bayesian. In addition, the dependency in Bayesian network is realized using TAN algorithm rather than consulting pneumologists. All these combined algorithms (discretization, selection, dependency, and the ordering of the relevant attributes) constitute an effective prediction model, comparing to effective ones. Moreover, an investigation and comparison of different scenarios of these algorithms are also done to verify which sequence of steps of prediction model gives more accurate results. Finally, we designed and validated a computer-aided support application to integrate different steps of this model. The findings of our system HCES has shown promising results using Area Under Receiver Operating Characteristic (AUC = 81.5%).

Surgical process modeling

Due to the rapidly evolving medical, technological, and technical possibilities, surgical procedures are becoming more and more complex. On the one hand, this offers an increasing number of advantages for patients, such as enhanced patient safety, minimal invasive interventions, and less medical malpractices. On the other hand, it also heightens pressure on surgeons and other clinical staff and has brought about a new policy in hospitals, which must rely on a great number of economic, social, psychological, qualitative, practical, and technological resources. As a result, medical disciplines, such as surgery, are slowly merging with technical disciplines. However, this synergy is not yet fully matured. The current information and communication technology in hospitals cannot manage the clinical and operational sequence adequately. The consequences are breaches in the surgical workflow, extensions in procedure times, and media disruptions. Furthermore, the data accrued in operating rooms (ORs) by surgeons and systems are not sufficiently implemented. A flood of information, “big data”, is available from information systems. That might be deployed in the context of Medicine 4.0 to facilitate the surgical treatment. However, it is unused due to infrastructure breaches or communication errors. Surgical process models (SPMs) alleviate these problems. They can be defined as simplified, formal, or semiformal representations of a network of surgery-related activities, reflecting a predefined subset of interest. They can employ different means of generation, languages, and data acquisition strategies. They can represent surgical interventions with high resolution, offering qualifiable and quantifiable information on the course of the intervention on the level of single, minute, surgical work-steps. The basic idea is to gather information concerning the surgical intervention and its activities, such as performance time, surgical instrument used, trajectories, movements, or intervention phases. These data can be gathered by means of workflow recordings. These recordings are abstracted to represent an individual surgical process as a model and are an essential requirement to enable Medicine 4.0 in the OR. Further abstraction can be generated by merging individual process models to form generic SPMs to increase the validity for a larger number of patients. Furthermore, these models can be applied in a wide variety of use-cases. In this regard, the term “modeling” can be used to support either one or more of the following tasks: “to describe”, “to understand”, “to explain”, to optimize”, “to learn”, “to teach”, or “to automate”. Possible use-cases are requirements analyses, evaluating surgical assist systems, generating surgeon-specific training-recommendation, creating workflow management systems for ORs, and comparing different surgical strategies. The presented chapter will give an introduction into this challenging topic, presenting different methods to generate SPMs from the workflow in the OR, as well as various use-cases, and state-of-the-art research in this field. Although many examples in the article are given according to SPMs that were computed based on observations, the same approaches can be easily applied to SPMs that were measured automatically and mined from big data.

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

Personal Health Records: A Systematic Literature Review

Background

Information and communication technology (ICT) has transformed the health care field worldwide. One of the main drivers of this change is the electronic health record (EHR). However, there are still open issues and challenges because the EHR usually reflects the partial view of a health care provider without the ability for patients to control or interact with their data. Furthermore, with the growth of mobile and ubiquitous computing, the number of records regarding personal health is increasing exponentially. This movement has been characterized as the Internet of Things (IoT), including the widespread development of wearable computing technology and assorted types of health-related sensors. This leads to the need for an integrated method of storing health-related data, defined as the personal health record (PHR), which could be used by health care providers and patients. This approach could combine EHRs with data gathered from sensors or other wearable computing devices. This unified view of patients’ health could be shared with providers, who may not only use previous health-related records but also expand them with data resulting from their interactions. Another PHR advantage is that patients can interact with their health data, making decisions that may positively affect their health.

Objective

This work aimed to explore the recent literature related to PHRs by defining the taxonomy and identifying challenges and open questions. In addition, this study specifically sought to identify data types, standards, profiles, goals, methods, functions, and architecture with regard to PHRs.

Methods

The method to achieve these objectives consists of using the systematic literature review approach, which is guided by research questions using the population, intervention, comparison, outcome, and context (PICOC) criteria.

Results

As a result, we reviewed more than 5000 scientific studies published in the last 10 years, selected the most significant approaches, and thoroughly surveyed the health care field related to PHRs. We developed an updated taxonomy and identified challenges, open questions, and current data types, related standards, main profiles, input strategies, goals, functions, and architectures of the PHR.

Conclusions

All of these results contribute to the achievement of a significant degree of coverage regarding the technology related to PHRs.

IoT Contextual Factors on Healthcare

With the emergence of the Internet of Things, new services in healthcare will be available and existing systems will be integrated in the IoT framework, providing automated medical supervision and efficient medical treatment. Context awareness plays a critical role in realizing the vision of the IoT, providing rich contextual information that can help the system act more efficiently. Since context in healthcare has its unique characteristics, it is necessary to define an appropriate context aware framework for healthcare IoT applications. We identify this context as perceived in healthcare applications and describe the context aware procedures. We also present an architecture that connects the sensors that measure biometric data with the sensory networks of the environment and the various IoT middleware that reside in the geographical area. Finally, we discuss the challenges for the realization of this vision.

Towards a conceptual framework of OSH risk management in smart working environments based on smart PPE, ambient intelligence and the Internet of Things technologies

Recent developments in domains of ambient intelligence (AmI), Internet of Things, cyber-physical systems (CPS), ubiquitous/pervasive computing, etc., have led to numerous attempts to apply ICT solutions in the occupational safety and health (OSH) area. A literature review reveals a wide range of examples of smart materials, smart personal protective equipment and other AmI applications that have been developed to improve workers' safety and health. Because the use of these solutions modifies work methods, increases complexity of production processes and introduces high dynamism into thus created smart working environments (SWE), a new conceptual framework for dynamic OSH management in SWE is called for. A proposed framework is based on a new paradigm of OSH risk management consisting of real-time risk assessment and the capacity to monitor the risk level of each worker individually. A rationale for context-based reasoning in SWE and a respective model of the SWE-dedicated CPS are also proposed.

On Curating Multimodal Sensory Data for Health and Wellness Platforms

In recent years, the focus of healthcare and wellness technologies has shown a significant shift towards personal vital signs devices. The technology has evolved from smartphone-based wellness applications to fitness bands and smartwatches. The novelty of these devices is the accumulation of activity data as their users go about their daily life routine. However, these implementations are device specific and lack the ability to incorporate multimodal data sources. Data accumulated in their usage does not offer rich contextual information that is adequate for providing a holistic view of a user’s lifelog. As a result, making decisions and generating recommendations based on this data are single dimensional. In this paper, we present our Data Curation Framework (DCF) which is device independent and accumulates a user’s sensory data from multimodal data sources in real time. DCF curates the context of this accumulated data over the user’s lifelog. DCF provides rule-based anomaly detection over this context-rich lifelog in real time. To provide computation and persistence over the large volume of sensory data, DCF utilizes the distributed and ubiquitous environment of the cloud platform. DCF has been evaluated for its performance, correctness, ability to detect complex anomalies, and management support for a large volume of sensory data.

HuMOVE: a low-invasive wearable monitoring platform in sexual medicine

OBJECTIVE

To investigate an accelerometer-based wearable system, named Human Movement (HuMOVE) platform, designed to enable quantitative and continuous measurement of sexual performance with minimal invasiveness and inconvenience for users.

MATERIALS AND METHODS

Design, implementation, and development of HuMOVE, a wearable platform equipped with an accelerometer sensor for monitoring inertial parameters for sexual performance assessment and diagnosis, were performed. The system enables quantitative measurement of movement parameters during sexual intercourse, meeting the requirements of wearability, data storage, sampling rate, and interfacing methods, which are fundamental for human sexual intercourse performance analysis. HuMOVE was validated through characterization using a controlled experimental test bench and evaluated in a human model during simulated sexual intercourse conditions.

RESULTS

HuMOVE demonstrated to be a robust and quantitative monitoring platform and a reliable candidate for sexual performance evaluation and diagnosis. Characterization analysis on the controlled experimental test bench demonstrated an accurate correlation between the HuMOVE system and data from a reference displacement sensor. Experimental tests in the human model during simulated intercourse conditions confirmed the accuracy of the sexual performance evaluation platform and the effectiveness of the selected and derived parameters. The obtained outcomes also established the project expectations in terms of usability and comfort, evidenced by the questionnaires that highlighted the low invasiveness and acceptance of the device.

CONCLUSION

To the best of our knowledge, HuMOVE platform is the first device for human sexual performance analysis compatible with sexual intercourse; the system has the potential to be a helpful tool for physicians to accurately classify sexual disorders, such as premature or delayed ejaculation.

Adoption of clinical decision support in multimorbidity: a systematic review

Background

Patients with multiple conditions have complex needs and are increasing in number as populations age. This multimorbidity is one of the greatest challenges facing health care. Having more than 1 condition generates (1) interactions between pathologies, (2) duplication of tests, (3) difficulties in adhering to often conflicting clinical practice guidelines, (4) obstacles in the continuity of care, (5) confusing self-management information, and (6) medication errors. In this context, clinical decision support (CDS) systems need to be able to handle realistic complexity and minimize iatrogenic risks.

Objective

The aim of this review was to identify to what extent CDS is adopted in multimorbidity.

Methods

This review followed PRISMA guidance and adopted a multidisciplinary approach. Scopus and PubMed searches were performed by combining terms from 3 different thesauri containing synonyms for (1) multimorbidity and comorbidity, (2) polypharmacy, and (3) CDS. The relevant articles were identified by examining the titles and abstracts. The full text of selected/relevant articles was analyzed in-depth. For articles appropriate for this review, data were collected on clinical tasks, diseases, decision maker, methods, data input context, user interface considerations, and evaluation of effectiveness.

Results

A total of 50 articles were selected for the full in-depth analysis and 20 studies were included in the final review. Medication (n=10) and clinical guidance (n=8) were the predominant clinical tasks. Four studies focused on merging concurrent clinical practice guidelines. A total of 17 articles reported their CDS systems were knowledge-based. Most articles reviewed considered patients’ clinical records (n=19), clinical practice guidelines (n=12), and clinicians’ knowledge (n=10) as contextual input data. The most frequent diseases mentioned were cardiovascular (n=9) and diabetes mellitus (n=5). In all, 12 articles mentioned generalist doctor(s) as the decision maker(s). For articles reviewed, there were no studies referring to the active involvement of the patient in the decision-making process or to patient self-management. None of the articles reviewed adopted mobile technologies. There were no rigorous evaluations of usability or effectiveness of the CDS systems reported.

Conclusions

This review shows that multimorbidity is underinvestigated in the informatics of supporting clinical decisions. CDS interventions that systematize clinical practice guidelines without considering the interactions of different conditions and care processes may lead to unhelpful or harmful clinical actions. To improve patient safety in multimorbidity, there is a need for more evidence about how both conditions and care processes interact. The data needed to build this evidence base exist in many electronic health record systems and are underused.

Ambient-aware continuous care through semantic context dissemination

Background

The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e.g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate.

The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data.

Methods

The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability.

Results

A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered.

Conclusions

The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results.

Exploiting personal web servers for mobile context-aware applications

There is an increasing trend in moving desktop applications to web browsers, even when the web server is running on the same desktop machine. In this paper, we go further in this direction and show how to combine a web server, a web application framework (enhanced to support desktop-like Model–View–Controller interaction) and a context-aware architecture to develop web-based mobile context-aware applications. By using this approach we take advantage of the well-established web paradigm to design the graphical user interfaces (GUIs) and the inherent ability of the web to mash up applications with external components (such as Google Maps). On top of that, since the web server runs on the device itself, the application can access local resources (such as disk space or sensing devices, which are indispensable for context-aware systems) avoiding the sandbox model of the web browsers. To illustrate our approach we show how a mobile hypermedia system has been built on top of our platform.

Privacy-related context information for ubiquitous health

Background

Ubiquitous health has been defined as a dynamic network of interconnected systems. A system is composed of one or more information systems, their stakeholders, and the environment. These systems offer health services to individuals and thus implement ubiquitous computing. Privacy is the key challenge for ubiquitous health because of autonomous processing, rich contextual metadata, lack of predefined trust among participants, and the business objectives. Additionally, regulations and policies of stakeholders may be unknown to the individual. Context-sensitive privacy policies are needed to regulate information processing.

Objective

Our goal was to analyze privacy-related context information and to define the corresponding components and their properties that support privacy management in ubiquitous health. These properties should describe the privacy issues of information processing. With components and their properties, individuals can define context-aware privacy policies and set their privacy preferences that can change in different information-processing situations.

Methods

Scenarios and user stories are used to analyze typical activities in ubiquitous health to identify main actors, goals, tasks, and stakeholders. Context arises from an activity and, therefore, we can determine different situations, services, and systems to identify properties for privacy-related context information in information-processing situations.

Results

Privacy-related context information components are situation, environment, individual, information technology system, service, and stakeholder. Combining our analyses and previously identified characteristics of ubiquitous health, more detailed properties for the components are defined. Properties define explicitly what context information for different components is needed to create context-aware privacy policies that can control, limit, and constrain information processing. With properties, we can define, for example, how data can be processed or how components are regulated or in what kind of environment data can be processed.

Conclusions

This study added to the vision of ubiquitous health by analyzing information processing from the viewpoint of an individual’s privacy. We learned that health and wellness-related activities may happen in several environments and situations with multiple stakeholders, services, and systems. We have provided new knowledge regarding privacy-related context information and corresponding components by analyzing typical activities in ubiquitous health. With the identified components and their properties, individuals can define their personal preferences on information processing based on situational information, and privacy services can capture privacy-related context of the information-processing situation.

IAServ: an intelligent home care web services platform in a cloud for aging-in-place

As the elderly population has been rapidly expanding and the core tax-paying population has been shrinking, the need for adequate elderly health and housing services continues to grow while the resources to provide such services are becoming increasingly scarce. Thus, increasing the efficiency of the delivery of healthcare services through the use of modern technology is a pressing issue. The seamless integration of such enabling technologies as ontology, intelligent agents, web services, and cloud computing is transforming healthcare from hospital-based treatments to home-based self-care and preventive care. A ubiquitous healthcare platform based on this technological integration, which synergizes service providers with patients’ needs to be developed to provide personalized healthcare services at the right time, in the right place, and the right manner. This paper presents the development and overall architecture of IAServ (the Intelligent Aging-in-place Home care Web Services Platform) to provide personalized healthcare service ubiquitously in a cloud computing setting to support the most desirable and cost-efficient method of care for the aged-aging in place. The IAServ is expected to offer intelligent, pervasive, accurate and contextually-aware personal care services. Architecturally the implemented IAServ leverages web services and cloud computing to provide economic, scalable, and robust healthcare services over the Internet.

Implementing a Pervasive Real-Time Intelligent System for Tracking Critical Events with Intensive Care Patients

Nowadays, it is increasingly important to utilize intelligent systems to support the decision making process (DMP) in challenging areas such as Intensive Medicine. In Intensive Care Units (ICU), some of the biggest challenges relate both to the number and the different types of available data sources. Even though in such a setting the values for some variables are easy to collect, data collection is still performed manually in particular instances. In order to improve the DMP in ICU, a Pervasive Intelligent Decision Support System, called INTCare was deployed in the ICU of Centro Hospitalar do Porto in Portugal. This system altered the way information is collected and presented. Moreover, the tracking system deployed as a specific module of INTCare – Electronic Nursing Record (ENR) is made accessible anywhere and anytime. The system allows for the calculation of the critical events regarding five variables that are typically monitored in an ICU. Specifically, the INTCare tracking system characterizes a grid that shows the events by type and duration, empowers a warning system to alert the doctors and promotes intuitive graphics that allow care providers to follow the patient care journey. User acceptance was measured through a questionnaire designed in accordance with the Technology Acceptance Model (TAM) and results of implementing the INTCare tracking system, and its interface are reported.

Communication spaces

Background and objective

Annotations to physical workspaces such as signs and notes are ubiquitous. When densely annotated, work areas become communication spaces. This study aims to characterize the types and purpose of such annotations.

Methods

A qualitative observational study was undertaken in two wards and the radiology department of a 440-bed metropolitan teaching hospital. Images were purposefully sampled; 39 were analyzed after excluding inferior images.

Results

Annotation functions included signaling identity, location, capability, status, availability, and operation. They encoded data, rules or procedural descriptions. Most aggregated into groups that either created a workflow by referencing each other, supported a common workflow without reference to each other, or were heterogeneous, referring to many workflows. Higher-level assemblies of such groupings were also observed.

Discussion

Annotations make visible the gap between work done and the capability of a space to support work. Annotations are repairs of an environment, improving fitness for purpose, fixing inadequacy in design, or meeting emergent needs. Annotations thus record the missing information needed to undertake tasks, typically added post-implemented. Measuring annotation levels post-implementation could help assess the fit of technology to task. Physical and digital spaces could meet broader user needs by formally supporting user customization, ‘programming through annotation’. Augmented reality systems could also directly support annotation, addressing existing information gaps, and enhancing work with context sensitive annotation.

Conclusions

Communication spaces offer a model of how work unfolds. Annotations make visible local adaptation that makes technology fit for purpose post-implementation and suggest an important role for annotatable information systems and digital augmentation of the physical environment.

A platform for the development of patient applications in the domain of personalized health

Personalized health (p-health) systems can contribute significantly to the sustainability of healthcare systems, though their feasibility is yet to be proven. One of the problems related to their development is the lack of well-established development tools for this domain. As the p-health paradigm is focused on patient self-management, big challenges arise around the design and implementation of patient systems. This paper presents a reference platform created for the development of these applications, and shows the advantages of its adoption in a complex project dealing with cardio-vascular diseases.

Energy-efficient context classification with dynamic sensor control

Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naïve Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved.