Development and pilot feasibility study of a health information technology tool to calculate mortality risk for patients with asymptomatic carotid stenosis: the Carotid Risk Assessment Tool (CARAT)

Application of Smartphone Technologies in Disease Monitoring: A Systematic Review

Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients.

Interdisciplinary collaboration between nursing and engineering in health care: A scoping review

BACKGROUND

Due to the rapid advancements in precision medicine and artificial intelligence, interdisciplinary collaborations between nursing and engineering have emerged. Although engineering is vital in solving complex nursing problems and advancing healthcare, the collaboration between the two fields has not been fully elucidated.

OBJECTIVES

To identify the study areas of interdisciplinary collaboration between nursing and engineering in health care, particularly focusing on the role of nurses in the collaboration.

METHODS

In this study, a scoping review using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews was performed. A comprehensive search for published literature was conducted using the PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, Embase, Web of Science, ScienceDirect, Institute of Electrical and Electronics Engineers Digital Library, and Association for Computing Machinery Digital Library from inception to November 22, 2020. Data screening and extraction were performed independently by two reviewers. Any discrepancies in results were resolved through discussions or in consultation with a third reviewer. Data were analyzed by descriptive statistics and content analysis. Results were visualized in an interdisciplinary collaboration model.

RESULTS

We identified 6,752 studies through the literature search, and 60 studies met the inclusion criteria. The study areas of interdisciplinary collaboration concentrated on patient safety (n = 18), symptom monitoring and health management (n = 18), information system and nursing human resource management (n = 16), health education (n = 5), and nurse-patient communication (n = 3). The roles of nurses in the interdisciplinary collaboration were divided into four themes: requirement analyst (n = 21), designer (n = 22), tester(n = 37) and evaluator (n = 49). Based on these results, an interdisciplinary collaboration model was constructed.

CONCLUSIONS

Interdisciplinary collaborations between nursing and engineering promote nursing innovation and practice. However, these collaborations are still emerging and in the early stages. In the future, nurses should be more involved in the early stages of solving healthcare problems, particularly in the requirement analysis and designing phases. Furthermore, there is an urgent need to develop interprofessional education, strengthen nursing connections with the healthcare engineering industry, and provide more platforms and resources to bring nursing and engineering disciplines together.

Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review

Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.

A mixed-methods process evaluation of Family Navigation implementation for autism spectrum disorder

There is growing interest in Family Navigation as an approach to improving access to care for children with autism spectrum disorder, yet little data exist on the implementation of Family Navigation. The aim of this study was to identify potential failures in implementing Family Navigation for children with autism spectrum disorder, using a failure modes and effects analysis. This mixed-methods study was set within a randomized controlled trial testing the effectiveness of Family Navigation in reducing the time from screening to diagnosis and treatment for autism spectrum disorder across three states. Using standard failure modes and effects analysis methodology, experts in Family Navigation for autism spectrum disorder (n = 9) rated potential failures in implementation on a 10-point scale in three categories: likelihood of the failure occurring, likelihood of not detecting the failure, and severity of failure. Ratings were then used to create a risk priority number for each failure. The failure modes and effects analysis detected five areas for potential "high priority" failures in implementation: (1) setting up community-based services, (2) initial family meeting, (3) training, (4) fidelity monitoring, and (5) attending testing appointments. Reasons for failure included families not receptive, scheduling, and insufficient training time. The process with the highest risk profile was "setting up community-based services." Failure in "attending testing appointment" was rated as the most severe potential failure. A number of potential failures in Family Navigation implementation-along with strategies for mitigation-were identified. These data can guide those working to implement Family Navigation for children with autism spectrum disorder.

Utilization of Workflow Process Maps to Analyze Gaps in Critical Event Notification at a Large, Urban Hospital

Stroke care is a time-sensitive workflow involving multiple specialties acting in unison, often relying on one-way paging systems to alert care providers. The goal of this study was to map and quantitatively evaluate such a system and address communication gaps with system improvements. A workflow process map of the stroke notification system at a large, urban hospital was created via observation and interviews with hospital staff. We recorded pager communication regarding 45 patients in the emergency department (ED), neuroradiology reading room (NRR), and a clinician residence (CR), categorizing transmissions as successful or unsuccessful (dropped or unintelligible). Data analysis and consultation with information technology staff and the vendor informed a quality intervention-replacing one paging antenna and adding another. Data from a 1-month post-intervention period was collected. Error rates before and after were compared using a chi-squared test. Seventy-five pages regarding 45 patients were recorded pre-intervention; 88 pages regarding 86 patients were recorded post-intervention. Initial transmission error rates in the ED, NRR, and CR were 40.0, 22.7, and 12.0 %. Post-intervention, error rates were 5.1, 18.8, and 1.1 %, a statistically significant improvement in the ED (p < 0.0001) and CR (p = 0.004) but not NRR (p = 0.208). This intervention resulted in measureable improvement in pager communication to the ED and CR. While results in the NRR were not significant, this intervention bolsters the utility of workflow process maps. The workflow process map effectively defined communication failure parameters, allowing for systematic testing and intervention to improve communication in essential clinical locations.

Development and feasibility of a personalized, interactive risk calculator for knee osteoarthritis

Background

The incidence of knee osteoarthritis (OA) is rising. While several risk factors have been associated with the development of knee OA, this information is not readily accessible to those at risk for osteoarthritis. Risk calculators have been developed for several prevalent chronic conditions but not for OA. Using published evidence on established risk factors, we developed an interactive, personalized knee OA risk calculator (OA Risk C) and conducted a pilot study to evaluate its acceptability and feasibility.

Methods

We used the Osteoarthritis Policy (OAPol) Model, a validated, state-transition simulation of the natural history and management of OA, to generate data for OA Risk C. Risk estimates for calculator users were based on a set of demographic and clinical factors (age, sex, race/ethnicity, obesity) and select risk factors (family history of knee OA, occupational exposure, and history of knee injury). OA Risk C presents personalized risk of knee OA in several ways to maximize understanding among a wide range of users. We conducted a study of 45 subjects in a primary care setting to establish the feasibility and acceptability of the OA risk calculator. Pilot study participants were asked several questions regarding ease of use, clarity of presentation, and clarity of the graphical representation of their risk. These questions used a five-level agreement scale ranging from strongly disagree to strongly agree.

Results

OA Risk C depicts information about users’ risk of symptomatic knee OA in 5 year intervals. Study participants estimated their lifetime risk at 38 %, while their actual lifetime risk, as estimated by OA Risk C, was 25 %. Eighty-four percent of pilot study participants reported that OA Risk C was easy to understand, and 89 % agreed that the graphs depicting their risk were clear and comprehensible.

Conclusions

We have developed a personalized, computer-based OA risk calculator that is easy to use. OA Risk C may be utilized to estimate individuals’ knee OA risk and to deliver educational and behavioral interventions focused on osteoarthritis risk reduction.