HCV universal EHR prompt successfully increases screening, highlights potential disparities.

BACKGROUND & OBJECTIVES: Screening for hepatitis C virus is the first critical decision point for preventing morbidity and mortality from HCV cirrhosis and hepatocellular carcinoma and will ultimately contribute to global elimination of a curable disease. This study aims to portray the changes over time in HCV screening rates and the screened population characteristics following the 2020 implementation of an electronic health record (EHR) alert for universal screening in the outpatient setting in a large healthcare system in the US mid-Atlantic region. METHODS: Data was abstracted from the EHR on all outpatients from 1/1/2017 through 10/31/2021, including individual demographics and their HCV antibody (Ab) screening dates. For a limited period centered on the implementation of the HCV alert, mixed effects multivariable regression analyses were performed to compare the timeline and characteristics of those screened and un-screened. The final models included socio-demographic covariates of interest, time period (pre/post) and an interaction term between time period and sex. We also examined a model with time as a monthly variable to look at the potential impact of COVID-19 on screening for HCV. RESULTS: Absolute number of screens and screening rate increased by 103% and 62%, respectively, after adopting the universal EHR alert. Patients with Medicaid were more likely to be screened than private insurance (ORadj 1.10, 95% CI: 1.05, 1.15), while those with Medicare were less likely (ORadj 0.62, 95% CI: 0.62, 0.65); and Black (ORadj 1.59, 95% CI: 1.53, 1.64) race more than White. CONCLUSIONS: Implementation of universal EHR alerts could prove to be a critical next step in HCV elimination. Those with Medicare and Medicaid insurance were not screened proportionately to the national prevalence of HCV in these populations. Our findings support increased screening and re-testing efforts for those at high risk of HCV.

Using Community Detection Techniques to Identify Themes in COVID-19-Related Patient Safety Event Reports.

OBJECTIVES: The COVID-19 pandemic has transformed how healthcare is delivered to patients. As the pandemic progresses and healthcare systems continue to adapt, it is important to understand how these changes in care have changed patient care. This study aims to use community detection techniques to identify and facilitate analysis of themes in patient safety event (PSE) reports to better understand COVID-19 pandemic's impact on patient safety. With this approach, we also seek to understand how community detection techniques can be used to better identify themes and extract information from PSE reports. METHODS: We used community detection techniques to group 2082 PSE reports from January 1, 2020, to January 31, 2021, that mentioned COVID-19 into 65 communities. We then grouped these communities into 8 clinically relevant themes for analysis. RESULTS: We found the COVID-19 pandemic is associated with the following clinically relevant themes: (1) errors due to new and unknown COVID-19 protocols/workflows; (2) COVID-19 patients developing pressure ulcers; (3) unsuccessful/incomplete COVID-19 testing; (4) inadequate isolation of COVID-19 patients; (5) inappropriate/inadequate care for COVID-19 patients; (6) COVID-19 patient falls; (7) delays or errors communicating COVID-19 test results; and (8) COVID-19 patients developing venous thromboembolism. CONCLUSIONS: Our study begins the long process of understanding new challenges created by the pandemic and highlights how machine learning methods can be used to understand these and similar challenges. Using community detection techniques to analyze PSE reports and identify themes within them can help give healthcare systems the necessary information to improve patient safety and the quality of care they deliver.

Evaluation of a Text Message-Based COVID-19 Vaccine Outreach Program Among Older Patients: Cross-sectional Study.

BACKGROUND: COVID-19 vaccines are vital tools in the defense against infection and serious disease due to SARS-CoV-2. There are many challenges to implementing mass vaccination campaigns for large, diverse populations from crafting vaccine promotion messages to reaching individuals in a timely and effective manner. During this unprecedented period, with COVID-19 mass vaccination campaigns essential for protecting vulnerable patient populations and attaining herd immunity, health care systems were faced with the dual challenges of vaccine outreach and distribution. OBJECTIVE: The aim of this cross-sectional study was to assess the effectiveness of a COVID-19 vaccine text outreach approach for patients aged 65 years and older. Our goal was to determine whether this approach was successful in scheduling patients for COVID-19 vaccine appointments. METHODS: We developed SMS text messages using the Tavoca platform. These messages informed patients of their vaccine eligibility and allowed them to indicate their interest in scheduling an appointment via a specific method (email or phone) or indicate their lack of interest in the vaccine. We tracked the status of these messages and how patients responded. Messages were sent to patients aged 65 years and older (N=30,826) at a nonprofit health care system in Washington, DC. Data were collected and examined from January 14 to May 10, 2021. Data were analyzed using multivariate multinomial and binary logistic regression models in SAS (version 9.4; SAS Institute Inc). RESULTS: Approximately 57% of text messages were delivered to patients, but many messages received no response from patients (40%). Additionally, 42.1% (12,978/30,826) of messages were not delivered. Of the patients who expressed interest in the vaccine (2938/30,826, 9.5%), Black or African American patients preferred a phone call rather than an email for scheduling their appointment (odds ratio [OR] 1.69, 95% CI 1.29-2.21) compared to White patients. Patients aged 70-74 years were more likely to schedule an appointment (OR 1.38, 95% CI 1.01-1.89) than those aged 65-69 years, and Black or African American patients were more likely to schedule an appointment (OR 2.90, 95% CI 1.72-4.91) than White patients. CONCLUSIONS: This study provides insights into some advantages and challenges of using a text messaging vaccine outreach for patients aged 65 years and older. Lessons learned from this vaccine campaign underscore the importance of using multiple outreach methods and sharing of patient vaccination status between health systems, along with a patient-centered approach to address vaccine hesitancy and access issues.

Application of artificial intelligence in cataract management: current and future directions.

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.

Predicting the Severity of Symptoms of the COVID Stress Syndrome From Personality Traits: A Prospective Network Analysis.

Psychological stress reactions to the COVID-19 pandemic are complex and multifaceted. Research provides evidence of a COVID Stress Syndrome (CSS), consisting of (1) worry about the dangerousness of getting infected with SARSCoV2 and coming into contact with infected surfaces, (2) worry concerning the personal socioeconomic consequences of COVID-19, (3) xenophobic fears that SARSCOV2 is being spread by foreigners, (4) COVID-19-related traumatic stress symptoms (e.g., nightmares), and (5) compulsive checking and reassurance-seeking about COVID-19. Little is known about how these symptoms are related to vulnerability and protective personality factors. Based on data from 1,976 US and Canadian adults, we conducted a prospective network analysis in which personality factors were initially assessed at Time 1 and then symptoms of the CSS were assessed at Time 2, 2.5 months later. Results indicated that trait optimism and trait resilience were negatively associated with negative emotionality, suggesting a modulatory (inhibitory) influence. Negative emotionality was positively linked to the narrower traits of intolerance of uncertainty and health anxiety proneness. These narrower traits, in turn, were prospectively linked to symptoms of the CSS. Results suggest that the effects of broad personality traits (e.g., negative emotionality, trait resilience) on symptoms of the CSS were mediated by narrower traits such as the intolerance of uncertainty. Treatment implications are discussed.

A descriptive analysis of an on-demand telehealth approach for remote COVID-19 patient screening.

INTRODUCTION: COVID-19 requires methods for screening patients that adhere to physical distancing and other Centers for Disease Control and Prevention guidelines. There is little data on the use of on-demand telehealth to meet this need. METHODS: The functional performance of on-demand telehealth as a COVID-19 remote patient screening approach was conducted by analysing 9270 patient requests. RESULTS: Most on-demand telehealth requests (5712 of 9270 total requests; 61.6%) had a visit reason that was likely COVID-19 related. Of these, 79.1% (4518 of 5712) resulted in a completed encounter and 20.9% (1194 of 5712) resulted in left without being seen. Of the 4518 completed encounters, 19.1% were referred to an urgent care centre, emergency department or COVID-19 testing centre. The average completed encounter wait time was 26.5 min and the mean visit length was 8.8 min. For patients that completed an encounter 42.8% (1935 of 4518) stated they would have sought in-person care and 9.1% stated they would have done nothing if on-demand telehealth was unavailable. DISCUSSION: On-demand telehealth can serve as a low-barrier approach to screen patients for COVID-19. This approach can prevent patients from visiting healthcare facilities, which reduces physical contact and reduces healthcare worker use of personal protective equipment.