Digital Therapeutics for COPD Patient Self-Management: Needs Analysis and Design Study.

Timely management of Chronic Obstructive Pulmonary Disease (COPD) exacerbations can improve recovery and reduce the risk of hospitalization. Digital therapeutics are digital interventions, based on best evidence, designed to provide home-based, patient-centered and pervasive self-management support to patients. Digital therapeutics can be effectively used to offer personalized and explainable self-management and behaviour modification resources to patients to reduce the burden of COPD, especially the prevention of acute COPD exacerbations.

Validation of a flow-cytometry-based red blood cell antigen phenotyping method.

Background And Objectives: Current manual and automated phenotyping methods are based on visual detection of the antigen-antibody interaction. This approach has several limitations including the use of large volumes of patient and reagent red blood cells (RBCs) and antisera to produce a visually detectable reaction. We sought to determine whether the flow cytometry could be developed and validated to perform RBC phenotyping to enable a high-throughput method of phenotyping using comparatively miniscule reagent volumes via fluorescence-based detection of antibody binding.

Decision support for comorbid conditions via execution-time integration of clinical guidelines using transaction-based semantics and temporal planning.

In case of comorbidity, i.e., multiple medical conditions, Clinical Decision Support Systems (CDSS) should issue recommendations based on all relevant disease-related Clinical Practice Guidelines (CPG).

Using Interactive Visual Analytics to Optimize in Real-Time Blood Products Inventory at a Blood Bank.

Blood products and their derivatives are perishable commodities that require an efficient inventory management to ensure both a low wastage rate and a high product availability rate. To optimize blood product inventory, blood transfusion services need to reduce wastage by avoiding outdates and improve availability of different blood products. We used advance visualization techniques to design and develop a highly interactive real-time web-based dashboard to monitor the blood product inventory and the on-going blood unit transactions in near-real-time based on analysis of transactional data.

Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods.

In a digitally enabled healthcare setting, we posit that an individual's current location is pivotal for supporting many virtual care services-such as tailoring educational content towards an individual's current location, and, hence, current stage in an acute care process; improving activity recognition for supporting self-management in a home-based setting; and guiding individuals with cognitive decline through daily activities in their home. However, unobtrusively estimating an individual's indoor location in real-world care settings is still a challenging problem. Moreover, the needs of location-specific care interventions go beyond absolute coordinates and require the individual's discrete semantic location; i.

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study.

Background: Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium.

Intelligent health data analytics: A convergence of artificial intelligence and big data.

Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data.

Feasibility and usability of an ontology-based mobile intervention for patients with hypertension.

Background: Lifestyle changes and the adoption of healthy behaviours are well established recommendations for the management of hypertension-a risk factor for cardiovascular and kidney disease. Mobile health interventions offer unique advantages and novel approaches to helping individuals make and maintain such behaviour changes; however, current interventions often lack theoretical and scientific grounding.

Objective: The goal of this study is to effectively model the knowledge, concepts and relationships relevant to the management of a chronic illness like hypertension, and to implement this knowledge model within a mobile self-management application that can be used by patients.

Optimizing primary care management of atrial fibrillation: The rationale and methods of the Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) study.

The Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) is an investigator designed, prospective, randomized, un-blinded, cluster design clinical trial, conducted in the primary care setting of Nova Scotia, Canada. Its aim is to evaluate whether an electronic Clinical Decision Support System (CDSS) designed to assist both practitioners and patients with evidence-based management strategies for Atrial Fibrillation (AF) can improve process of care and outcomes in a cost-efficient manner as compared to usual AF care. At least 200 primary care providers are being recruited and randomized at the level of the practice to control (usual care) or intervention (eligible to access to CDSS) cohorts.

A Personalized Risk Stratification Platform for Population Lifetime Healthcare.

Chronic diseases are the leading cause of death worldwide. It is well understood that if modifiable risk factors are targeted, most chronic diseases can be prevented. Lifetime health is an emerging health paradigm that aims to assist individuals to achieve desired health targets, and avoid harmful lifecycle choices to mitigate the risk of chronic diseases.

Protocol-Driven Decision Support within e-Referral Systems to Streamline Patient Consultation, Triaging and Referrals from Primary Care to Specialist Clinics.

Patient referral is a protocol where the referring primary care physician refers the patient to a specialist for further treatment. The paper-based current referral process at times lead to communication and operational issues, resulting in either an unfulfilled referral request or an unnecessary referral request. Despite the availability of standardized referral protocols they are not readily applied because they are tedious and time-consuming, thus resulting in suboptimal referral requests.

Monitoring Activities Related to Medication Adherence in Ambient Assisted Living Environments.

A recent trend in healthcare is to motivate patients to self-manage their health conditions in home-based settings. Medication adherence is an important aspect in disease self-management since sub-optimal medication adherence by the patient can lead to serious healthcare costs and discomfort for the patient. In order to alleviate the limitations of self-reported medication adherence, we can use ambient assistive living (AAL) technologies in smart environments.

Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support.

Background: Capturing complete medical knowledge is challenging-often due to incomplete patient Electronic Health Records (EHR), but also because of valuable, tacit medical knowledge hidden away in physicians' experiences. To extend the coverage of incomplete medical knowledge-based systems beyond their deductive closure, and thus enhance their decision-support capabilities, we argue that innovative, multi-strategy reasoning approaches should be applied. In particular, mechanisms apply patterns from human thought processes, such as generalization, similarity and interpolation, based on attributional, hierarchical, and relational knowledge.

A Digital Health System to Assist Family Physicians to Safely Prescribe NOAC Medications.

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Generally, the therapeutic options for managing AF include the use of anticoagulant drugs that prevent the coagulation of blood. New Oral Anticoagulants (NOACs) are not optimally prescribed to patients, despite their efficacy.

INITIATE: An Intelligent Adaptive Alert Environment.

Exposure to a large volume of alerts generated by medical Alert Generating Systems (AGS) such as drug-drug interaction softwares or clinical decision support systems over-whelms users and causes alert fatigue in them. Some of alert fatigue effects are ignoring crucial alerts and longer response times. A common approach to avoid alert fatigue is to devise mechanisms in AGS to stop them from generating alerts that are deemed irrelevant.

The Shared Decision Making Frontier: a Feasibility and Usability Study for Managing Non-Critical Chronic Illness by Combining Behavioural & Decision Theory with Online Technology.

The objective of this study is to determine if shared decisions for managing non-critical chronic illness, made through an online biomedical technology intervention, us feasible and usable. The technology intervention incorporates behavioural and decision theories to increase patient engagement, and ultimately long term adherence to health behaviour change. We devised the iheart web intervention as a "proof of concept" in five phases.

A Mobile and Intelligent Patient Diary for Chronic Disease Self-Management.

By involving patients in their own long-term care, patient self-management approaches aim to increase self-sufficiency and reduce healthcare costs. For example, electronic patient diaries enable patients to collect health data autonomously, increasing self-reliance and reducing strain on health professionals. By deploying patient diaries on mobile platforms, health data collection can occur at any time and place, increasing the mobility of chronic patients who typically need to enter health data frequently.

Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines.

Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially web ontology language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL-based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution.

Shared decision making: using theories and technology to engage the patient in their health journey.

Shared decision making is considered the cornerstone of patient-centred care but transpires in only 10% of face-to-face consultative encounters. Technology interventions have rampantly sought to fill the shared decision making gap but fall short in patient engagement. Recent studies indicate that combining multiple approaches could lead to greater commitment towards achieving positive health outcomes.

Usability evaluation of family physicians' interaction with the Comorbidity Ontological Modeling and ExecuTion System (COMET).

Objective: To assess how well the COMET system meets the functional goals and usability needs of family physicians (FP) for the management of patients who have comorbid chronic heart failure (CHF) and atrial fibrillation (AF).

Design: Nonexperimental post-interaction Think Aloud Sessions and survey involving Likert scale questionnaires.

Participants: Licensed Family Physicians in Nova Scotia.

Computerizing clinical pathways: ontology-based modeling and execution.

Clinical Pathways (CP) stipulate an evidence-based patient care workflow for a specific disease within a localized setting. We present an ontology-based approach for computerizing CP so that they can be executed at the point-of-care. We present our CP modeling approach that features the integration of multiple localized CP to realize a unified disease-specific CP.

Ontology-based modeling of clinical practice guidelines: a clinical decision support system for breast cancer follow-up interventions at primary care settings.

Breast cancer follow-up care can be provided by family physicians after specialists complete the primary treatment. Cancer Care Nova Scotia has developed a breast cancer follow-up Clinical Practice Guideline (CPG) targeting family physicians. In this paper we present a project to computerize and deploy the said CPG in a Breast Cancer Follow-up Decision Support System (BCF-DSS) for use by family physicians in a primary care setting.