A Patient-Facing Dashboard to Promote Shingrix™ Vaccination in a Continuing Care Retirement Community: A Quality Improvement Project

BACKGROUND: Shingles is considered one of the most significant vaccine-preventable diseases of older adults based on its morbidity and public health burden, which increase drastically with age. Adult vaccine awareness and promotion programs are undervalued in the U.S.; in particular, educational programs targeting older adults are needed. Older adults have increasing rates of adoption of health information technology (HIT) to seek guidance and support for their medical needs. Leveraging HIT in the form of clinical dashboards is an option for providing reliable, safe and cost-effective vaccine education to older adults at high risk of vaccine-preventable disease. METHODS: The specific aims of this quality improvement project were to increase knowledge and uptake of recombinant zoster vaccine (Shingrix™) in older adults of a continuing retirement community (CCRC) through creation of a patient-facing clinical dashboard. The Four Pillars™ practice transformation program was used to guide implementation of the project including utilization of self-report surveys to determine baseline vaccination rates, perceptions of the dashboard and behavioral intention to receive future vaccination. The Patient Portal Acceptance Model (PPAM) was used as a theoretical framework to evaluate respondents’ perceptions of the dashboard across four domains: ease of use, usefulness, self-efficacy, and privacy/security. RESULTS: Respondents reported high levels of education and computer literacy. The majority reported using the internet for over 20 years and over 10 hours per week and 77.8% had used the internet to search for healthcare information within the past year. Baseline Shingrix™ vaccination levels in the CCRC were higher than national average but not at goal rates, and the majority of respondents eligible for vaccination did not plan to receive it. Respondents rated the dashboard moderately high on perceived ease of use, low on concerns about privacy/security, high on ability to use independently (self-efficacy), and low on perceived usefulness. DISCUSSION: The information provided by CCRC residents during development of this dashboard was valuable for elucidating motivators and barriers to HIT use in older adults, who largely view HIT as an adjunct to in-person interaction with a trusted provider. Improving older adults’ perceptions of HIT will be critical in the era of Covid-19, when many high-risk older adults are seeking alternatives to traditional provider visits. Respondents were willing and able to access and navigate the dashboard; however, shingles knowledge did not improve in this small sample. Improvements in the presentation of the material on the dashboard may improve perceptions of usefulness and comprehension of specialized clinical information. CONCLUSION: CCRC residents were receptive to receiving vaccine information via electronic dashboard and expressed interest in using this format as a source of other healthcare information. There is ample opportunity to expand patient-facing dashboards in the CCRC setting to provide a wide array of healthcare education for this population

Towards a tricorder: clinical, health economic, and ethical investigation of point-of-care artificial intelligence electrocardiogram for heart failure

Heart failure (HF) is an international public health priority and a focus of the NHS Long Term Plan. There is a particular need in primary care for screening and early detection of heart failure with reduced ejection fraction (HFrEF) – the most common and serious HF subtype, and the only one with an abundant evidence base for effective therapies. Digital health technologies (DHTs) integrating artificial intelligence (AI) could improve diagnosis of HFrEF. Specifically, through a convergence of DHTs and AI, a single-lead electrocardiogram (ECG) can be recorded by a smart stethoscope and interrogated by AI (AI-ECG) to potentially serve as a point-of-care HFrEF test. However, there are concerning evidence gaps for such DHTs applying AI; across intersecting clinical, health economic, and ethical considerations. My thesis therefore investigates hypotheses that AI-ECG is 1.) Reliable, accurate, unbiased, and can be patient self-administered, 2.) Of justifiable health economic impact for primary care deployment, and 3.) Appropriate across ethical domains for deployment as a tool for patient self-administered screening. The theoretical basis for this work is presented in the Introduction (Chapter 1). Chapter 2 describes the first large-scale, multi-centre independent external validation study of AI-ECG, prospectively recruiting 1,050 patients and highlighting impressive performance: area under the curve, sensitivity, and specificity up to 0·91 (95% confidence interval: 0·88–0·95), 91·9% (78·1–98·3), and 80·2% (75·5–84·3) respectively; and absence of bias by age, sex, and ethnicity. Performance was independent of operator, and usability of the tool extended to patients being able to self-examine. Chapter 3 presents a clinical and health economic outcomes analysis using a contemporary digital repository of 2.5 million NHS patient records. A propensity-matched cohort was derived using all patients diagnosed with HF from 2015-2020 (n = 34,208). Novel findings included the unacceptable reality that 70% of index HF diagnoses are made through hospitalisation; where index diagnosis through primary care conferred a medium-term survival advantage and long-term cost saving (£2,500 per patient). This underpins a health economic model for the deployment of AI-ECG across primary care. Chapter 4 approaches a normative ethical analysis focusing on equity, agency, data rights, and responsibility for safe, effective, and trustworthy implementation of an unprecedented at-home patient self-administered AI-ECG screening programme. I propose approaches to mitigating any potential harms, towards preserving and promoting trust, patient engagement, and public health. Collectively, this thesis marks novel work highlighting AI-ECG as tool with the potential to address major cardiovascular public health priorities. Scrutiny through complimentary clinical, health economic, and ethical considerations can directly serve patients and health systems by blueprinting best-practice for the evaluation and implementation of DHTs integrating AI – building the conviction needed to realise the full potential of such technologies.Open Acces

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Urban Health: A Practical Application for Clinical Based Learning

Urban Health: A Practical Application for Clinical Based Learning is an openly licensed, peer-reviewed textbook for clinical-based nursing educators covering barriers in urban health and their impact on patient health outcomes. The authors explore perspectives of urban communities, urban patients, and urban healthcare providers to offer insight into how healthcare providers can address disparities in urban healthcare, provide meaningful care with the lived experiences of urban patients in mind, and improve patient-provider communication by moving towards a more solution-driven, team-based care approach. Features include learning activities, exemplars, and case studies.https://digitalcommons.wayne.edu/oa_textbooks/1001/thumbnail.jp

Preventing Recurrent Falls In Elderly Home Bound Health Plan Members

Falls have become a major public health concern. As the population ages, the prevalence of falls among older adults continues to increase, as does mortality and morbidity. Evidence-based assessment and intervention tools are widely available, though in practice, providers experience challenges utilizing them. Homebound older adults are at particular risk since they have mobility and transportation barriers that prevent access to community fall screening and primary care sites. This project focused on the provision of fall assessment, interventions, and coordination of fall-prevention services for community-dwelling, homebound older adults covered by a managed health plan. A standardized fall assessment and intervention pilot program using evidenced based tools was successful in reducing recurrent falls in this population by 50%. Based on the data analysis, a multidisciplinary approach with tailored care plans to mitigate recurrent falls in an older adult homebound population proved beneficial to members. The project provides a model for scalable adoption by managed care plans that coordinate care for medically complex, homebound older adults. Providing an adoptable multidisciplinary fall management model for managed care plans that coordinate members care will address the multifactorial risks and causes of falls, and tailor appropriate interventions

Predicting the Risk of Falling with Artificial Intelligence

Predicting the Risk of Falling with Artificial Intelligence Abstract Background: Fall prevention is a huge patient safety concern among all healthcare organizations. The high prevalence of patient falls has grave consequences, including the cost of care, longer hospital stays, unintentional injuries, and decreased patient and staff satisfaction. Preventing a patient from falling is critical in maintaining a patient’s quality of life and averting the high cost of healthcare expenses. Local Problem: Two hospitals\u27 healthcare system saw a significant increase in inpatient falls. The fall rate is one of the nursing quality indicators, and fall reduction is a key performance indicator of high-quality patient care. Methods: This quality improvement evidence-based observational project compared the rate of fall (ROF) between the experimental and control unit. Pearson’s chi-square and Fisher’s exact test were used to analyze and compare results. Qualtrics surveys evaluated the nurses’ perception of AI, and results were analyzed using the Mann-Whitney Rank Sum test. Intervention. Implementing an artificial intelligence-assisted fall predictive analytics model that can timely and accurately predict fall risk can mitigate the increase in inpatient falls. Results: The pilot unit (Pearson’s chi-square = p pp\u3c0.001). Conclusions: AI-assisted automatic fall predictive risk assessment produced a significant reduction if the number of falls, the ROF, and the use of fall countermeasures. Further, nurses’ perception of AI improved after the introduction of FPAT and presentation