BACKGROUND: Strokes pose a substantial health burden, impacting 1 in 6 people globally. One-tenth of patients will endure a second, often more severe, stroke within a year. Alarmingly, a younger demographic is being affected due to recent lifestyle changes. As fine motor and cognitive issues arise, patient disability as well as the strain on caregivers and health care resources is exacerbated. Contemporary occupational therapy assesses manual dexterity and cognitive functions through object manipulation and pen-and-paper recordings. However, these assessments are typically isolated, which makes it challenging for therapists to comprehensively evaluate specific patient conditions. Furthermore, the reliance on one-on-one training and assessment approaches on manual documentation is inefficient and prone to transcription errors.
OBJECTIVE: This study examines the feasibility of using an interactive electronic pegboard for stroke rehabilitation in clinical settings.
METHODS: A total of 10 patients with a history of stroke and 10 healthy older individuals were recruited. With a limit of 10 minutes, both groups of participants underwent a series of challenges involving tasks related to manual operation, shape recognition, and color discrimination. All participants underwent the Box and Block Test and the Purdue Pegboard Test to assess manual dexterity, as well as an array of cognitive assessments, including the Trail Making Test and the Mini-Mental Status Examination, which served as a basis to quantify participants\' attention, executive functioning, and cognitive abilities.
RESULTS: The findings validate the potential application of an interactive electronic pegboard for stroke rehabilitation in clinical contexts. Significant statistical differences (P<.01) were observed across all assessed variables, including age, Box and Block Test results, Purdue Pegboard Test outcomes, Trail Making Test-A scores, and Mini-Mental Status Examination performance, between patients with a history of stroke and their healthy older counterparts. Functional and task testing, along with questionnaire interviews, revealed that patients with a history of stroke demonstrated prolonged completion times and slightly inferior performance. Nonetheless, most patients perceived the prototype as user-friendly and engaging. Thus, in the context of patient rehabilitation interventions or the evaluation of patient cognition, physical functioning, or manual dexterity assessments, the developed pegboard could potentially serve as a valuable tool for hand function, attention, and cognitive rehabilitation, thereby mitigating the burden on health care professionals.
CONCLUSIONS: Health care professionals can use digital electronic pegboards not only as a precise one-on-one training tool but also as a flexible system that can be configured for online or offline, single-player or multiplayer use. Through data analysis, a more informed examination of patients\' cognitive and functional issues can be conducted. Importantly, patient records will be fully retained throughout practices, exercises, or tests, and by leveraging the characteristics of big data, patients can receive the most accurate rehabilitation prescriptions, thereby assisting them in obtaining optimal care.

BACKGROUND: Despite longstanding efforts and calls for reform, Canada\'s incremental approach to healthcare changes has left the country lagging behind other OECD nations. Reform to the Canadian healthcare system is essential to develop a higher performing system. This study sought to gain a deeper understanding of the views of Canadian stakeholders on structural and process deficiencies and strategies to improve the Canadian healthcare system substantially and meaningfully.
METHODS: We conducted individual, ~ 45-minute, semi-structured virtual interviews from May 2022 to August 2022. Using existing contacts and snowball sampling, we targeted one man and one woman from five regions in Canada across four stakeholder groups: (1) public citizens; (2) healthcare leaders; (3) academics; and (4) political decision makers. Interviews centered on participants\' perceptions of the state of the current healthcare system, including areas where major improvements are required, and strategies to achieve suggested enhancements; Donabedian\'s Model (i.e., structure, process, outcomes) was the guiding conceptual framework. Interviews were audio-recorded, transcribed verbatim, and de-identified, and inductive thematic analysis was performed independently and in duplicate according to published methods.
RESULTS: The data from 31 interviews with 13 (41.9%) public citizens, 10 (32.3%) healthcare leaders, 4 (12.9%) academics, and 4 (12.9%) political decision makers resulted in three themes related to the structure of the healthcare system (1. system reactivity; 2. linkage with the Canadian identity; and 3. political and funding structures), three themes related to healthcare processes (1. staffing shortages; 2. inefficient care; and 3. inconsistent care), and three strategies to improve short- and long-term population health outcomes (1. delineating roles and revising incentives; 2. enhanced health literacy; 3. interdisciplinary and patient-centred care).
CONCLUSIONS: Canadians in our sample identified important structural and process limitations to the Canadian healthcare system. Meaningful reforms are needed and will require addressing the link between the Canadian identity and our healthcare system to facilitate effective development and implementation of strategies to improve population health outcomes.

BACKGROUND: Research evidence to inform primary care policy and practice is essential for building high-performing primary care systems. Nevertheless, research output relating to primary care remains low worldwide. This study describes the factors associated with the research productivity of primary care researchers.
METHODS: A qualitative, descriptive key informant study approach was used to conduct semi-structured interviews with twenty-three primary care researchers across Canada. Qualitative data were analyzed using reflexive thematic analysis.
RESULTS: Twenty-three primary care researchers participated in the study. An interplay of personal (psychological characteristics, gender, race, parenthood, education, spousal occupation, and support), professional (mentorship before appointment, national collaborations, type of research, career length), institutional (leadership, culture, resources, protected time, mentorship, type), and system (funding, systematic bias, environment, international collaborations, research data infrastructure) factors were perceived to be associated with research productivity. Research institutes and mentors facilitated collaborations, and mentors and type of research enabled funding success. Jurisdictions with fewer primary care researchers had more national collaborations but fewer funding opportunities. The combination of institutional, professional, and system factors were barriers to the research productivity of female and/or racialized researchers.
CONCLUSIONS: This study illuminates the intersecting and multifaceted influences on the research productivity of primary care researchers. By exploring individual, professional, institutional, and systemic factors, we underscore the pivotal role of diverse elements in shaping RP. Understanding these intricate influencers is imperative for tailored, evidence-based interventions and policies at the level of academic institutions and funding agencies to optimize resources, promote fair evaluation metrics, and cultivate inclusive environments conducive to diverse research pursuits within the PC discipline in Canada.

BACKGROUND: The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts.
RESULTS: To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration.
CONCLUSIONS: The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.

OBJECTIVE: Literature states a higher self-contamination rate among healthcare workers (HCWs) while doffing personal protective equipment (PPE). During the Covid-19 pandemic, onsite trained observers were not always available to monitor PPE compliance. The remote audio-visual doffing surveillance (RADS) system has the potential to overcome this limitation. We aimed to compare the efficacy of this real-time RADS system against the onsite buddy system for monitoring the doffing of PPE.
METHODS: This prospective, observational study was carried out at our tertiary care centre in northern India. 200 HCWs who cared for Covid-19 patients in the intensive care units/operation theatres were included. Group A included HCWs who performed doffing with the help of an onsite trained observer and group B included HCWs who performed doffing with the RADS system. An independent observer noted the error at any step using the CDC doffing checklist, in both groups. An online questionnaire to analyse the level of satisfaction post-doffing was also surveyed.
RESULTS: The proportion of errors committed during doffing was significantly lower in group B compared to group A with a low relative risk of 0.34 (95% CI 0.22-0.51) (p < 0.001) (Figure 1A,B). In both groups, there was no difference in HCWs feedback regarding the ease of the system and fear of committing an error. Though the perceived quality of monitoring was felt better with onsite buddy, the overall confidence rating of being safe after doffing was better with the RADS system.
CONCLUSIONS: Real-time RADS system may be more effective than the onsite buddy system for ensuring the safety of HCWs during doffing PPE. HCWs level of satisfaction related to the ease and anxiety with the monitoring systems were comparable. RADS system can reduce reliance on HCW resources and can integrate well into existing healthcare systems.

Tackling complex system challenges like creating healthy environments requires understanding priorities and structures affecting multiple actors. This qualitative study, involving 132 multi-sectoral stakeholders spanning the urban development decision-making system, explores how to influence healthier place-making. Using thematic analysis we develop themes around competing stakeholder priorities; structural \'rules\' and influential relationships; and justifying a focus on health, requiring greater clarity and consensus around definitions of \'healthy\' urban development. Building on the socio-ecological model we highlight how a multi-faceted approach is required for change at multiple levels in the complex system to target individual actor motivations, organisational priorities and structural \'rules\'.

Clinical practice guidelines are systematically developed statements intended to optimize patient care. However, a gapless implementation of guideline recommendations requires health care personnel not only to be aware of the recommendations and to support their content but also to recognize every situation in which they are applicable. To not miss situations in which recommendations should be applied, computerized clinical decision support can be provided through a system that allows an automated monitoring of adherence to clinical guideline recommendations in individual patients.
This study aims to collect and analyze the requirements for a system that allows the monitoring of adherence to evidence-based clinical guideline recommendations in individual patients and, based on these requirements, to design and implement a software prototype that integrates guideline recommendations with individual patient data, and to demonstrate the prototype\'s utility in treatment recommendations.
We performed a work process analysis with experienced intensive care clinicians to develop a conceptual model of how to support guideline adherence monitoring in clinical routine and identified which steps in the model could be supported electronically. We then identified the core requirements of a software system to support recommendation adherence monitoring in a consensus-based requirements analysis within the loosely structured focus group work of key stakeholders (clinicians, guideline developers, health data engineers, and software developers). On the basis of these requirements, we designed and implemented a modular system architecture. To demonstrate its utility, we applied the prototype to monitor adherence to a COVID-19 treatment recommendation using clinical data from a large European university hospital.
We designed a system that integrates guideline recommendations with real-time clinical data to evaluate individual guideline recommendation adherence and developed a functional prototype. The needs analysis with clinical staff resulted in a flowchart describing the work process of how adherence to recommendations should be monitored. Four core requirements were identified: the ability to decide whether a recommendation is applicable and implemented for a specific patient, the ability to integrate clinical data from different data formats and data structures, the ability to display raw patient data, and the use of a Fast Healthcare Interoperability Resources-based format for the representation of clinical practice guidelines to provide an interoperable, standards-based guideline recommendation exchange format.
Our system has advantages in terms of individual patient treatment and quality management in hospitals. However, further studies are needed to measure its impact on patient outcomes and evaluate its resource effectiveness in different clinical settings. We specified a modular software architecture that allows experts from different fields to work independently and focus on their area of expertise. We have released the source code of our system under an open-source license and invite for collaborative further development of the system.

BACKGROUND: Internet of Things (IoT) technology enables physiological measurements to be recorded at home from people living with dementia and monitored remotely. However, measurements from people with dementia in this context have not been previously studied. We report on the distribution of physiological measurements from 82 people with dementia over approximately 2 years.
OBJECTIVE: Our objective was to characterize the physiology of people with dementia when measured in the context of their own homes. We also wanted to explore the possible use of an alerts-based system for detecting health deterioration and discuss the potential applications and limitations of this kind of system.
METHODS: We performed a longitudinal community-based cohort study of people with dementia using \"Minder,\" our IoT remote monitoring platform. All people with dementia received a blood pressure machine for systolic and diastolic blood pressure, a pulse oximeter measuring oxygen saturation and heart rate, body weight scales, and a thermometer, and were asked to use each device once a day at any time. Timings, distributions, and abnormalities in measurements were examined, including the rate of significant abnormalities (\"alerts\") defined by various standardized criteria. We used our own study criteria for alerts and compared them with the National Early Warning Score 2 criteria.
RESULTS: A total of 82 people with dementia, with a mean age of 80.4 (SD 7.8) years, recorded 147,203 measurements over 958,000 participant-hours. The median percentage of days when any participant took any measurements (ie, any device) was 56.2% (IQR 33.2%-83.7%, range 2.3%-100%). Reassuringly, engagement of people with dementia with the system did not wane with time, reflected in there being no change in the weekly number of measurements with respect to time (1-sample t-test on slopes of linear fit, P=.45). A total of 45% of people with dementia met criteria for hypertension. People with dementia with α-synuclein-related dementia had lower systolic blood pressure; 30% had clinically significant weight loss. Depending on the criteria used, 3.03%-9.46% of measurements generated alerts, at 0.066-0.233 per day per person with dementia. We also report 4 case studies, highlighting the potential benefits and challenges of remote physiological monitoring in people with dementia. These include case studies of people with dementia developing acute infections and one of a person with dementia developing symptomatic bradycardia while taking donepezil.
CONCLUSIONS: We present findings from a study of the physiology of people with dementia recorded remotely on a large scale. People with dementia and their carers showed acceptable compliance throughout, supporting the feasibility of the system. Our findings inform the development of technologies, care pathways, and policies for IoT-based remote monitoring. We show how IoT-based monitoring could improve the management of acute and chronic comorbidities in this clinically vulnerable group. Future randomized trials are required to establish if a system like this has measurable long-term benefits on health and quality of life outcomes.

In the second stage of the Electronic Health Record Sharing System (eHRSS) development, a mobile app (eHealth app) was launched to further enhance collaborative care among the public sector, the private sector, the community, and the caregivers.
This study aims to investigate the factors associated with the downloading and utilization of the app, as well as the awareness, perception, and future improvement of the app.
We collected 2110 surveys; respondents were stratified into 3 groups according to their status of enrollment in the eHRSS. The primary outcome measure was the downloading and acceptance of the eHealth app. We collected the data on social economics factors, variables of the Technology Acceptance Model and Theory of Planned Behavior. Any factors identified as significant in the univariate analysis (P<.20) will be included in a subsequent multivariable regression analysis model. All P values ≤.05 will be considered statistically significant in multiple logistic regression analysis. The structural equation modeling was performed to identify interactions among the variables.
The respondents had an overall high satisfaction rate and a positive attitude toward continuing to adopt and recommend the app. However, the satisfaction rate among respondents who have downloaded but not adopted the app was relatively lower, and few of them perceived that the downloading and acceptance processes are difficult. A high proportion of current users expressed a positive attitude about continuing to adopt and recommend the app to friends, colleagues, and family members. The behavioral intention strongly predicted the acceptance of the eHealth app (β=.89; P<.001). Attitude (β=.30; P<.001) and perceived norm; β=.37; P<.001) played important roles in determining behavioral intention, which could predict the downloading and acceptance of the eHealth app (β=.14; P<.001).
Despite the high satisfaction rate among the respondents, privacy concerns and perceived difficulties in adopting the app were the major challenges of promoting eHealth. Further promotion could be made through doctors and publicity. For future improvement, comprehensive health records and tailored health information should be included.

BACKGROUND: Postoperative complications following cardiac surgery are common and represent a serious burden to health services and society. However, there is a lack of consensus among experts on what events should be considered as a \"complication\" and how to assess their severity.
OBJECTIVE: This study aimed to consult domain experts to pilot the development of a definition and classification system for complications following cardiac surgery with the goal to allow the progression of standardized clinical processes and systems in cardiac surgery.
METHODS: We conducted a Delphi study, which is a well-established method to reach expert consensus on complex topics. We sent 2 rounds of surveys to domain experts, including cardiac surgeons and anesthetists, to define and classify postoperative complications following cardiac surgery. The responses to open-ended questions were analyzed using a thematic analysis framework.
RESULTS: In total, 71 and 37 experts\' opinions were included in the analysis in Round 1 and Round 2 of the study, respectively. Cardiac anesthetists and cardiac critical care specialists took part in the study. Cardiac surgeons did not participate. Experts agreed that a classification for postoperative complications for cardiac surgery is useful, and consensus was reached for the generic definition of a postoperative complication in cardiac surgery. Consensus was also reached on classification of complications according to the following 4 levels: \"Mild,\" \"Moderate,\" \"Severe,\" and \"Death.\" Consensus was also reached on definitions for \"Mild\" and \"Severe\" categories of complications.
CONCLUSIONS: Domain experts agreed on the definition and classification of complications in cardiac surgery for \"Mild\" and \"Severe\" complications. The standardization of complication identification, recording, and reporting in cardiac surgery should help the development of quality benchmarks, clinical audit, care quality assessment, resource planning, risk management, communication, and research.