Ambulatory antibiotic prescription rates for acute respiratory infection rebound two years after the start of the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, acute respiratory infection (ARI) antibiotic prescribing in ambulatory care markedly decreased. It is unclear if antibiotic prescription rates will remain lowered. METHODS: We used trend analyses of antibiotics prescribed during and after the first wave of COVID-19 to determine whether ARI antibiotic prescribing rates in ambulatory care have remained suppressed compared to pre-COVID-19 levels. Retrospective data was used from patients with ARI or UTI diagnosis code(s) for their encounter from 298 primary care and 66 urgent care practices within four academic health systems in New York, Wisconsin, and Utah between January 2017 and June 2022. The primary measures included antibiotic prescriptions per 100 non-COVID ARI encounters, encounter volume, prescribing trends, and change from expected trend. RESULTS: At baseline, during and after the first wave, the overall ARI antibiotic prescribing rates were 54.7, 38.5, and 54.7 prescriptions per 100 encounters, respectively. ARI antibiotic prescription rates saw a statistically significant decline after COVID-19 onset (step change -15.2, 95% CI: -19.6 to -4.8). During the first wave, encounter volume decreased 29.4% and, after the first wave, remained decreased by 188%. After the first wave, ARI antibiotic prescription rates were no longer significantly suppressed from baseline (step change 0.01, 95% CI: -6.3 to 6.2). There was no significant difference between UTI antibiotic prescription rates at baseline versus the end of the observation period. CONCLUSIONS: The decline in ARI antibiotic prescribing observed after the onset of COVID-19 was temporary, not mirrored in UTI antibiotic prescribing, and does not represent a long-term change in clinician prescribing behaviors. During a period of heightened awareness of a viral cause of ARI, a substantial and clinically meaningful decrease in clinician antibiotic prescribing was observed. Future efforts in antibiotic stewardship may benefit from continued study of factors leading to this reduction and rebound in prescribing rates.

Barriers to Implementing Registered Nurse-Driven Clinical Decision Support for Antibiotic Stewardship: Retrospective Case Study.

BACKGROUND: Up to 50% of antibiotic prescriptions for upper respiratory infections (URIs) are inappropriate. Clinical decision support (CDS) systems to mitigate unnecessary antibiotic prescriptions have been implemented into electronic health records, but their use by providers has been limited. OBJECTIVE: As a delegation protocol, we adapted a validated electronic health record-integrated clinical prediction rule (iCPR) CDS-based intervention for registered nurses (RNs), consisting of triage to identify patients with low-acuity URI followed by CDS-guided RN visits. It was implemented in February 2022 as a randomized controlled stepped-wedge trial in 43 primary and urgent care practices within 4 academic health systems in New York, Wisconsin, and Utah. While issues were pragmatically addressed as they arose, a systematic assessment of the barriers to implementation is needed to better understand and address these barriers. METHODS: We performed a retrospective case study, collecting quantitative and qualitative data regarding clinical workflows and triage-template use from expert interviews, study surveys, routine check-ins with practice personnel, and chart reviews over the first year of implementation of the iCPR intervention. Guided by the updated CFIR (Consolidated Framework for Implementation Research), we characterized the initial barriers to implementing a URI iCPR intervention for RNs in ambulatory care. CFIR constructs were coded as missing, neutral, weak, or strong implementation factors. RESULTS: Barriers were identified within all implementation domains. The strongest barriers were found in the outer setting, with those factors trickling down to impact the inner setting. Local conditions driven by COVID-19 served as one of the strongest barriers, impacting attitudes among practice staff and ultimately contributing to a work infrastructure characterized by staff changes, RN shortages and turnover, and competing responsibilities. Policies and laws regarding scope of practice of RNs varied by state and institutional application of those laws, with some allowing more clinical autonomy for RNs. This necessitated different study procedures at each study site to meet practice requirements, increasing innovation complexity. Similarly, institutional policies led to varying levels of compatibility with existing triage, rooming, and documentation workflows. These workflow conflicts were compounded by limited available resources, as well as an implementation climate of optional participation, few participation incentives, and thus low relative priority compared to other clinical duties. CONCLUSIONS: Both between and within health care systems, significant variability existed in workflows for patient intake and triage. Even in a relatively straightforward clinical workflow, workflow and cultural differences appreciably impacted intervention adoption. Takeaways from this study can be applied to other RN delegation protocol implementations of new and innovative CDS tools within existing workflows to support integration and improve uptake. When implementing a system-wide clinical care intervention, considerations must be made for variability in culture and workflows at the state, health system, practice, and individual levels. TRIAL REGISTRATION: ClinicalTrials.gov NCT04255303; https://clinicaltrials.gov/ct2/show/NCT04255303.

Leveraging Generative AI Tools to Support the Development of Digital Solutions in Health Care Research: Case Study.

BACKGROUND: Generative artificial intelligence has the potential to revolutionize health technology product development by improving coding quality, efficiency, documentation, quality assessment and review, and troubleshooting. OBJECTIVE: This paper explores the application of a commercially available generative artificial intelligence tool (ChatGPT) to the development of a digital health behavior change intervention designed to support patient engagement in a commercial digital diabetes prevention program. METHODS: We examined the capacity, advantages, and limitations of ChatGPT to support digital product idea conceptualization, intervention content development, and the software engineering process, including software requirement generation, software design, and code production. In total, 11 evaluators, each with at least 10 years of experience in fields of study ranging from medicine and implementation science to computer science, participated in the output review process (ChatGPT vs human-generated output). All had familiarity or prior exposure to the original personalized automatic messaging system intervention. The evaluators rated the ChatGPT-produced outputs in terms of understandability, usability, novelty, relevance, completeness, and efficiency. RESULTS: Most metrics received positive scores. We identified that ChatGPT can (1) support developers to achieve high-quality products faster and (2) facilitate nontechnical communication and system understanding between technical and nontechnical team members around the development goal of rapid and easy-to-build computational solutions for medical technologies. CONCLUSIONS: ChatGPT can serve as a usable facilitator for researchers engaging in the software development life cycle, from product conceptualization to feature identification and user story development to code generation. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500.

Effectiveness of an Integrated Engagement Support System to Facilitate Patient Use of Digital Diabetes Prevention Programs: Protocol for a Randomized Controlled Trial.

BACKGROUND: Digital diabetes prevention programs (dDPPs) are effective behavior change tools to prevent disease progression in patients at risk for diabetes. At present, these programs are poorly integrated into existing health information technology infrastructure and clinical workflows, resulting in barriers to provider-level knowledge of, interaction with, and support of patients who use dDPPs. Tools that can facilitate patient-provider interaction around dDPPs may contribute to improved patient engagement and adherence to these programs and improved health outcomes. OBJECTIVE: This study aims to use a rigorous, user-centered design (UCD) methodology to develop a theory-driven system that supports patient engagement with dDPPs and their primary care providers with their care. METHODS: This study will be conducted in 3 phases. In phase 1, we will use systematic UCD, Agile software development, and qualitative research methods to identify key user (patients, providers, clinical staff, digital health technologists, and content experts) requirements, constraints, and prioritization of high-impact features to design, develop, and refine a viable intervention prototype for the engagement system. In phase 2, we will conduct a single-arm feasibility pilot of the engagement system among patients with prediabetes and their primary care providers. In phase 3, we will conduct a 2-arm randomized controlled trial using the engagement system. Primary outcomes will be weight, BMI, and A1c at 6 and 12 months. Secondary outcomes will be patient engagement (use and activity) in the dDPP. The mediator variables (self-efficacy, digital health literacy, and patient-provider relationship) will be measured. RESULTS: The project was initiated in 2018 and funded in September 2019. Enrollment and data collection for phase 1 began in September 2019 under an Institutional Review Board quality improvement waiver granted in July 2019. As of December 2020, 27 patients have been enrolled and first results are expected to be submitted for publication in early 2021. The study received Institutional Review Board approval for phases 2 and 3 in December 2020, and phase 2 enrollment is expected to begin in early 2021. CONCLUSIONS: Our findings will provide guidance for the design and development of technology to integrate dDPP platforms into existing clinical workflows. This will facilitate patient engagement in digital behavior change interventions and provider engagement in patients' use of dDPPs. Integrated clinical tools that can facilitate patient-provider interaction around dDPPs may contribute to improved patient adherence to these programs and improved health outcomes by addressing barriers faced by both patients and providers. Further evaluation with pilot testing and a clinical trial will assess the effectiveness and implementation of these tools. TRIAL REGISTRATION: ClinicalTrials.gov NCT04049500; https://clinicaltrials.gov/ct2/show/NCT04049500. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/26750.

Smartphone-Delivered Progressive Muscle Relaxation for the Treatment of Migraine in Primary Care: A Randomized Controlled Trial.

OBJECTIVE: Scalable, accessible forms of behavioral therapy for migraine prevention are needed. We assessed the feasibility and acceptability of progressive muscle relaxation (PMR) delivered by a smartphone application (app) in the Primary Care setting. METHODS: This pilot study was a non-blinded, randomized, parallel-arm controlled trial of adults with migraine and 4+ headache days/month. Eligible participants spoke English and owned a smartphone. All participants were given the RELAXaHEAD app which includes an electronic headache diary. Participants were randomized to receive 1 of the 2 versions of the app-one with PMR and the other without PMR. The primary outcomes were measures of feasibility (adherence to the intervention and diary entries during the 90-day interval) and acceptability (satisfaction levels). We conducted exploratory analyses to determine whether there was a change in Migraine Disability Assessment Scale (MIDAS) scores or a change in headache days. RESULTS: Of 139 participants (77 PMR, 62 control), 116 (83%) were female, mean age was 41.7 ± 12.8 years. Most patients 108/139 (78%) had moderate-severe disability. Using a 1-5 Likert scale, participants found the app easy to use (mean 4.2 ± 0.7) and stated that they would be happy to engage in the PMR intervention again (mean 4.3 ± 0.6). For the first 6 weeks, participants practiced PMR 2-4 days/week. Mean per session duration was 11.1 ± 8.3 minutes. Relative to the diary-only group, the PMR group showed a greater non-significant decline in mean MIDAS scores (-8.7 vs -22.7, P = .100) corresponding to a small-moderate mean effect size (Cohen's d = 0.38). CONCLUSION: Smartphone-delivered PMR may be an acceptable, accessible form of therapy for migraine. Mean effects show a small-moderate mean effect size in disability scores.

Telemedicine Video Visits for patients receiving palliative care: A qualitative study.

In this needs assessment, gathered patient perceptions on how telemedicine video visits might influence their care. Patients in this study (n = 13) were all diagnosed with end-stage cancer and were receiving palliative care at an urban academic medical center. Interview themes addressed: 1. impact on patient's health management, 2. user experience, 3. technical issues and 4. cost and time. Ultimately, despite concerns over truncated physical exams and prescription limits, the majority of patients favored having the opportunity for telemedicine video visits, felt that the doctor-patient relationship would not suffer, had confidence in their or their surrogate's technical abilities to navigate the video visit, had privacy concerns on par with other technologies, had few cost concerns, and believed a video alternative to an in-person visit might increase access, save time as well as increase comfort and safety by avoiding a trip to the office. These results suggest potential for acceptance of video-based telemedicine by an urban population of oncology patients receiving palliative care.

Identification of an ideal adjuvant for receptor-binding domain-based subunit vaccines against Middle East respiratory syndrome coronavirus.

Middle East respiratory syndrome (MERS), an emerging infectious disease caused by MERS coronavirus (MERS-CoV), has garnered worldwide attention as a consequence of its continuous spread and pandemic potential, making the development of effective vaccines a high priority. We previously demonstrated that residues 377-588 of MERS-CoV spike (S) protein receptor-binding domain (RBD) is a very promising MERS subunit vaccine candidate, capable of inducing potent neutralization antibody responses. In this study, we sought to identify an adjuvant that optimally enhanced the immunogenicity of S377-588 protein fused with Fc of human IgG (S377-588-Fc). Specifically, we compared several commercially available adjuvants, including Freund's adjuvant, aluminum, Monophosphoryl lipid A, Montanide ISA51 and MF59 with regard to their capacity to enhance the immunogenicity of this subunit vaccine. In the absence of adjuvant, S377-588-Fc alone induced readily detectable neutralizing antibody and T-cell responses in immunized mice. However, incorporating an adjuvant improved its immunogenicity. Particularly, among the aforementioned adjuvants evaluated, MF59 is the most potent as judged by its superior ability to induce the highest titers of IgG, IgG1 and IgG2a subtypes, and neutralizing antibodies. The addition of MF59 significantly augmented the immunogenicity of S377-588-Fc to induce strong IgG and neutralizing antibody responses as well as protection against MERS-CoV infection in mice, suggesting that MF59 is an optimal adjuvant for MERS-CoV RBD-based subunit vaccines.