UNASSIGNED: Connecting individuals to existing community resources is critical to addressing social needs and improving population health. While there is much ongoing informatics work embedding social needs screening and referrals into health care systems and their electronic health records, there has been less focus on the digital ecosystem and needs of community-based organizations (CBOs) providing or connecting individuals to these resources.
UNASSIGNED: We used human-centered design to develop a digital platform for CBOs, focused on identification of health and social resources and communication with their clients.
UNASSIGNED: Centered in the Develop phase of the design process, we conducted in-depth interviews in 2 phases with community-based organizational leadership and staff to create and iterate on the platform. We elicited and mapped participant feedback to theory-informed domains from the Technology Acceptance Model, such as Usefulness and Ease of Use, to build the final product and summarized all major design decisions as the platform development proceeded.
UNASSIGNED: Overall, we completed 22 interviews with 18 community-based organizational leadership and staff in 2 consecutive Develop phases. After coding of the interview transcripts, there were 4 major themes related to usability, relevance, and external factors impacting use. Specifically, CBOs expressed an interest in a customer relationship management software to manage their client interactions and communications, and they needed specific additional features to address the scope of their everyday work, namely (1) digital and SMS text messaging communication with clients and (2) easy ways to identify relevant community resources based on diverse client needs and various program eligibility criteria. Finally, clear implementation needs emerged, such as digital training and support for staff using new platforms. The final platform, titled \"Mapping to Enhance the Vitality of Engaged Neighborhoods (MAVEN),\" was completed in the Salesforce environment in 2022, and it included features and functions directly mapped to the design process.
UNASSIGNED: Engaging community organizations in user-centered design of a health and social resource platform was essential to tapping into their deep expertise in serving local communities and neighborhoods. Design methods informed by behavioral theory can be similarly employed in other informatics research. Moving forward, much more work will be necessary to support the implementation of platforms specific to CBOs\' needs, especially given the resources, training, and customization needed in these settings.

OBJECTIVE: We aimed to evaluate the feasibility of using ChatGPT as programming support for nursing PhD students conducting analyses using the All of Us Researcher Workbench.
METHODS: 9 students in a PhD-level nursing course were prospectively randomized into 2 groups who used ChatGPT for programming support on alternating assignments in the workbench. Students reported completion time, confidence, and qualitative reflections on barriers, resources used, and the learning process.
RESULTS: The median completion time was shorter for novices and certain assignments using ChatGPT. In qualitative reflections, students reported ChatGPT helped generate and troubleshoot code and facilitated learning but was occasionally inaccurate.
CONCLUSIONS: ChatGPT provided cognitive scaffolding that enabled students to move toward complex programming tasks using the All of Us Researcher Workbench but should be used in combination with other resources.
CONCLUSIONS: Our findings support the feasibility of using ChatGPT to help PhD nursing students use the All of Us Researcher Workbench to pursue novel research directions.

The advancement of technology and Artificial Intelligence applied health information systems demand high informatics competencies from nurses. To prepare nursing students to meet this demand, informatics courses are designed to increase informatics competencies. We offered an online informatics course to graduate students in a Nurse Educator program and assessed their informatics competency, including subdomains. Survey data were collected between Fall 2020 and Fall 2022 using an online Self-Assessment of Informatics Competency Scale for Health Professionals. We analyzed 109 responses and found that students were competent in overall informatics competency and the subdomains of \"basic computer skills\" and \"applied computer skills (clinical informatics).\" They were proficient in the \'role\' subdomain. However, students reported less competency in managing data and incorporating standard terminology into practice. These findings provide detailed insights of the current nursing students\' informatics competencies and can guide informatics faculty in improving their courses.

UNASSIGNED: Missed appointments can lead to treatment delays and adverse outcomes. Telemedicine may improve appointment completion because it addresses barriers to in-person visits, such as childcare and transportation. This study compared appointment completion for appointments using telemedicine versus in-person care in a large cohort of patients at an urban academic health sciences center.
UNASSIGNED: We conducted a retrospective cohort study of electronic health record data to determine whether telemedicine appointments have higher odds of completion compared to in-person care appointments, January 1, 2021, and April 30, 2023. The data were obtained from the University of South Florida (USF), a large academic health sciences center serving Tampa, FL, and surrounding communities. We implemented 1:1 propensity score matching based on age, gender, race, visit type, and Charlson Comorbidity Index (CCI).
UNASSIGNED: The matched cohort included 87 376 appointments, with diverse patient demographics. The percentage of completed telemedicine appointments exceeded that of completed in-person care appointments by 9.2 points (73.4% vs 64.2%, P < .001). The adjusted odds ratio for telemedicine versus in-person care in relation to appointment completion was 1.64 (95% CI, 1.59-1.69, P < .001), indicating that telemedicine appointments are associated with 64% higher odds of completion than in-person care appointments when controlling for other factors.
UNASSIGNED: This cohort study indicated that telemedicine appointments are more likely to be completed than in-person care appointments, regardless of demographics, comorbidity, payment type, or distance.
UNASSIGNED: Telemedicine appointments are more likely to be completed than in-person healthcare appointments.

BACKGROUND: Digital healthcare aims to deliver on the quadruple aim: enhance patient experiences, improve population health, reduce costs and improve provider experiences. Despite large investments, it is unclear how advancing digital health enables these healthcare aims.
OBJECTIVE: Our objectives were to: 1) measure the correlation between digital capability and health system outcomes mapped to the quadruple aim, and 2) measure the longitudinal impact of electronic medical record implementations upon health system outcomes.
METHODS: We undertook two studies: 1) Digital health correlational study investigating the association among healthcare system capability and healthcare aims, and 2) Digital hospital longitudinal study investigating outcomes pre and post electronic medical record implementation.
RESULTS: Digital health capability was associated with lower staff turnover. Digitising healthcare services was associated with decreased medication errors, decreased nosocomial infections, increased hospital activity, and a transient increase in staff leave.
CONCLUSIONS: These results suggest positive impacts on the population health and healthcare costs aim, minimal impacts on the provider experience aim and no observed impacts to the patient experience aim.
CONCLUSIONS: These findings should provide confidence to healthcare decision-makers investing in digital health.

A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications.
The aim of this adapted Delphi study was to collect researchers\' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care.
We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items.
The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data.
Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.

This chapter on the history of the DNA barcoding enterprise attempts to set the stage for the more scholarly contributions in this volume by addressing the following questions. How did the DNA barcoding enterprise begin? What were its goals, how did it develop, and to what degree are its goals being realized? We have taken a keen interest in the barcoding movement and its relationship to taxonomy, collections, and biodiversity informatics more broadly considered. This chapter integrates our two different perspectives on barcoding. DES was the Executive Secretary of the Consortium for the Barcode of Life from 2004 to 2017, with the mission to support the success of DNA barcoding without being directly involved in generating barcode data. RDMP viewed barcoding as an important entry into the landscape of biodiversity data, with many potential linkages to other components of that landscape. We also saw it as a critical step toward the era of international genomic research that was sure to follow. Like the Mercury Program that paved the way for lunar landings by the Apollo Program, we saw DNA barcoding as the proving grounds for the interdisciplinary and international cooperation that would be needed for success of whole-genome research.

BACKGROUND: Digital health and telemedicine are potentially important strategies to decrease health care\'s environmental impact and contribution to climate change by reducing transportation-related air pollution and greenhouse gas emissions. However, we currently lack robust national estimates of emissions savings attributable to telemedicine.
OBJECTIVE: This study aimed to (1) determine the travel distance between participants in US telemedicine sessions and (2) estimate the net reduction in carbon dioxide (CO2) emissions attributable to telemedicine in the United States, based on national observational data describing the geographical characteristics of telemedicine session participants.
METHODS: We conducted a retrospective observational study of telemedicine sessions in the United States between January 1, 2022, and February 21, 2023, on the doxy.me platform. Using Google Distance Matrix, we determined the median travel distance between participating providers and patients for a proportional sample of sessions. Further, based on the best available public data, we estimated the total annual emissions costs and savings attributable to telemedicine in the United States.
RESULTS: The median round trip travel distance between patients and providers was 49 (IQR 21-145) miles. The median CO2 emissions savings per telemedicine session was 20 (IQR 8-59) kg CO2). Accounting for the energy costs of telemedicine and US transportation patterns, among other factors, we estimate that the use of telemedicine in the United States during the years 2021-2022 resulted in approximate annual CO2 emissions savings of 1,443,800 metric tons.
CONCLUSIONS: These estimates of travel distance and telemedicine-associated CO2 emissions costs and savings, based on national data, indicate that telemedicine may be an important strategy in reducing the health care sector\'s carbon footprint.

OBJECTIVE: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab.
METHODS: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network.
METHODS: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion).
METHODS: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database\'s hazard ratio (HR) estimate into a single network-wide estimate.
METHODS: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure.
RESULTS: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60).
CONCLUSIONS: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents.
BACKGROUND: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

BACKGROUND: In many large health centers, patients face long appointment wait times and difficulties accessing care. Last-minute cancellations and patient no-shows leave unfilled slots in a clinician\'s schedule, exacerbating delays in care from poor access. The mismatch between the supply of outpatient appointments and patient demand has led health systems to adopt many tools and strategies to minimize appointment no-show rates and fill open slots left by patient cancellations.
OBJECTIVE: We evaluated an electronic health record (EHR)-based self-scheduling tool, Fast Pass, at a large academic medical center to understand the impacts of the tool on the ability to fill cancelled appointment slots, patient access to earlier appointments, and clinical revenue from visits that may otherwise have gone unscheduled.
METHODS: In this retrospective cohort study, we extracted Fast Pass appointment offers and scheduling data, including patient demographics, from the EHR between June 18, 2022, and March 9, 2023. We analyzed the outcomes of Fast Pass offers (accepted, declined, expired, and unavailable) and the outcomes of scheduled appointments resulting from accepted Fast Pass offers (completed, canceled, and no-show). We stratified outcomes based on appointment specialty. For each specialty, the patient service revenue from appointments filled by Fast Pass was calculated using the visit slots filled, the payer mix of the appointments, and the contribution margin by payer.
RESULTS: From June 18 to March 9, 2023, there were a total of 60,660 Fast Pass offers sent to patients for 21,978 available appointments. Of these offers, 6603 (11%) were accepted across all departments, and 5399 (8.9%) visits were completed. Patients were seen a median (IQR) of 14 (4-33) days sooner for their appointments. In a multivariate logistic regression model with primary outcome Fast Pass offer acceptance, patients who were aged 65 years or older (vs 20-40 years; P=.005 odds ratio [OR] 0.86, 95% CI 0.78-0.96), other ethnicity (vs White; P<.001, OR 0.84, 95% CI 0.77-0.91), primarily Chinese speakers (P<.001; OR 0.62, 95% CI 0.49-0.79), and other language speakers (vs English speakers; P=.001; OR 0.71, 95% CI 0.57-0.87) were less likely to accept an offer. Fast Pass added 2576 patient service hours to the clinical schedule, with a median (IQR) of 251 (216-322) hours per month. The estimated value of physician fees from these visits scheduled through 9 months of Fast Pass scheduling in professional fees at our institution was US $3 million.
CONCLUSIONS: Self-scheduling tools that provide patients with an opportunity to schedule into cancelled or unfilled appointment slots have the potential to improve patient access and efficiently capture additional revenue from filling unfilled slots. The demographics of the patients accepting these offers suggest that such digital tools may exacerbate inequities in access.