Digital dermatopathology implementation: Experience at a multisite academic institution.

BACKGROUND: Technology has revolutionized not only direct patient care but also diagnostic care processes. This study evaluates the transition from glass-slide microscopy to digital pathology (DP) at a multisite academic institution, using mixed methods to understand user perceptions of digitization and key productivity metrics of practice change. METHODS: Participants included dermatopathologists, pathology reporting specialists, and clinicians. Electronic surveys and individual or group interviews included questions related to technology comfort, trust in DP, and rationale for DP adoption. Case volumes and turnaround times were abstracted from the electronic health record from Qtr 4 2020 to Qtr 1 2023 (inclusive). Data were analyzed descriptively, while interviews were analyzed using methods of content analysis. RESULTS: Thirty-four staff completed surveys and 22 participated in an interview. Case volumes and diagnostic turnaround time did not differ across the institution during or after implementation timelines (p = 0.084; p = 0.133, respectively). 82.5% (28/34) of staff agreed that DP improved the sign-out experience, with accessibility, ergonomics, and annotation features described as key factors. Clinicians reported positive perspectives of DP impact on patient safety and interdisciplinary collaboration. CONCLUSIONS: Our study demonstrates that DP has a high acceptance rate, does not adversely impact productivity, and may improve patient safety and care collaboration.

Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review.

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.

Building Capacity for Pragmatic Trials of Digital Technology in Primary Care.

Frontline primary care teams face important challenges in seeking to transform the quality of care delivered to patients and to reduce clerical burden for clinicians. Digital technologies using artificial intelligence hold substantial promise to aid in this transformation. Both pragmatic clinical trials and implementation science are key tools to successfully introduce, evaluate, and sustain innovations in real-world primary care practices. Previous articles in this thematic series have provided an in-depth overview of pragmatic trials and implementation science. This paper demonstrates and provides a framework for how these concepts, together with digital transformation, can be used to solve many of the challenges facing primary care. This framework is conceived as the collaboration of frontline primary care teams with innovators in academic institutions and industry through pragmatic trials and implementation science.

Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care.

OBJECTIVE: To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting patients with low EF. METHODS: Clinicians in 48 practice sites of a US Midwest health system were cluster-randomized by the care team to usual care or to receive a notification that suggested ordering an echocardiogram in patients flagged as potentially having low EF based on an AI-ECG algorithm. Enrollment was between June 26, 2019, and July 30, 2019; participation concluded on March 31, 2020. This report is focused on those clinicians randomized to receive the notification of the AI-ECG algorithm. At the patient level, data were analyzed for the proportion of patients with positive AI-ECG results. Adoption was defined as the clinician order of an echocardiogram after prompted by the alert. RESULTS: A total of 165 clinicians and 11,573 patients were included in this analysis. Among patients with positive AI-ECG, high adopters (n=41) were twice as likely to diagnose patients with low EF (33.9%) vs low adopters, n=124, (16.9%); odds ratio, 1.62; 95% CI, 1.21 to 2.17). High adopters were more often advanced practice providers (eg, nurse practitioners and physician assistants) vs physicians, Family Medicine vs Internal Medicine specialty, and tended to have less complex patients. CONCLUSION: Clinicians who most frequently followed the recommendations of an AI tool were twice as likely to diagnose low EF. Those clinicians with less complex patients were more likely to be high adopters. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT04000087.

A Standardized Positive Physical Approach to Improve Oral Hygiene for Nursing Home Residents With Alzheimer's Disease and Related Dementias.

Residents with Alzheimer's disease and related dementias (ADRD) in nursing homes (NHs) rely on direct care workers (DCWs) to assist with activities of daily living, such as oral hygiene. The current quality improvement project was implemented to evaluate the effectiveness of teaching a standardized positive physical approach to oral hygiene completion for patients with ADRD residing in a NH. A pre-/postintervention evaluation incorporating a video presentation coupled with a hands-on simulation experience showed a statistically significant improvement in DCWs' overall Sense of Competency in Dementia score, as well as all subcategories of the Sense of Competence in Dementia Care Staff survey. In addition, residents' day shift oral hygiene care completion rates increased monthly pre- to postintervention. NHs should consider implementing training that includes hands-on experiences to equip DCWs with the knowledge and skill needed to improve oral hygiene among residents with ADRD. [Journal of Gerontological Nursing, 48(11), 15-20.].

Sociotechnical Intervention for Improved Delivery of Preventive Cardiovascular Care to Rural Communities: Participatory Design Approach.

BACKGROUND: Clinical practice guidelines recommend antiplatelet and statin therapies as well as blood pressure control and tobacco cessation for secondary prevention in patients with established atherosclerotic cardiovascular diseases (ASCVDs). However, these strategies for risk modification are underused, especially in rural communities. Moreover, resources to support the delivery of preventive care to rural patients are fewer than those for their urban counterparts. Transformative interventions for the delivery of tailored preventive cardiovascular care to rural patients are needed. OBJECTIVE: A multidisciplinary team developed a rural-specific, team-based model of care intervention assisted by clinical decision support (CDS) technology using participatory design in a sociotechnical conceptual framework. The model of care intervention included redesigned workflows and a novel CDS technology for the coordination and delivery of guideline recommendations by primary care teams in a rural clinic. METHODS: The design of the model of care intervention comprised 3 phases: problem identification, experimentation, and testing. Input from team members (n=35) required 150 hours, including observations of clinical encounters, provider workshops, and interviews with patients and health care professionals. The intervention was prototyped, iteratively refined, and tested with user feedback. In a 3-month pilot trial, 369 patients with ASCVDs were randomized into the control or intervention arm. RESULTS: New workflows and a novel CDS tool were created to identify patients with ASCVDs who had gaps in preventive care and assign the right care team member for delivery of tailored recommendations. During the pilot, the intervention prototype was iteratively refined and tested. The pilot demonstrated feasibility for successful implementation of the sociotechnical intervention as the proportion of patients who had encounters with advanced practice providers (nurse practitioners and physician assistants), pharmacists, or tobacco cessation coaches for the delivery of guideline recommendations in the intervention arm was greater than that in the control arm. CONCLUSIONS: Participatory design and a sociotechnical conceptual framework enabled the development of a rural-specific, team-based model of care intervention assisted by CDS technology for the transformation of preventive health care delivery for ASCVDs.

Association of Patient and System-Level Factors With Social Determinants of Health Screening.

BACKGROUND: Health systems are increasingly recognizing the importance of collecting social determinants of health (SDoH) data. However, gaps remain in our understanding of facilitators or barriers to collection. To address these gaps, we evaluated a real-world implementation of a SDoH screening tool. METHODS: We conducted a retrospective analysis of the implementation of the SDoH screening tool at Mayo Clinic in 2019. The outcomes are: (1) completion of screening and (2) the modality used (MyChart: filled out on patient portal; WelcomeTablet: filled out by patient on a PC-tablet; EpicCare: data obtained directly by provider and entered in chart). We conducted logistic regression for completion and multinomial logistic regression for modality. The factors of interest included race and ethnicity, use of an interpreter, and whether the visit was for primary care. RESULTS: Overall, 58.7% (293,668/499,931) of screenings were completed. Patients using interpreters and racial/ethnic minorities were less likely to complete the screening. Primary care visits were associated with an increase in completion compared with specialty care visits. Patients who used an interpreter, racial and ethnic minorities, and primary care visits were all associated with greater WelcomeTablet and lower MyChart use. CONCLUSION: Patient and system-level factors were associated with completion and modality. The lower completion and greater WelcomeTablet use among patients who use interpreters and racial and ethnic minorities points to the need to improve screening in these groups and that the availability of the WelcomeTablet may have prevented greater differences. The higher completion in primary care visits may mean more outreach is needed for specialists.

A framework for examining patient attitudes regarding applications of artificial intelligence in healthcare.

Background: While use of artificial intelligence (AI) in healthcare is increasing, little is known about how patients view healthcare AI. Characterizing patient attitudes and beliefs about healthcare AI and the factors that lead to these attitudes can help ensure patient values are in close alignment with the implementation of these new technologies. Methods: We conducted 15 focus groups with adult patients who had a recent primary care visit at a large academic health center. Using modified grounded theory, focus-group data was analyzed for themes related to the formation of attitudes and beliefs about healthcare AI. Results: When evaluating AI in healthcare, we found that patients draw on a variety of factors to contextualize these new technologies including previous experiences of illness, interactions with health systems and established health technologies, comfort with other information technology, and other personal experiences. We found that these experiences informed normative and cultural beliefs about the values and goals of healthcare technologies that patients applied when engaging with AI. The results of this study form the basis for a theoretical framework for understanding patient orientation to applications of AI in healthcare, highlighting a number of specific social, health, and technological experiences that will likely shape patient opinions about future healthcare AI applications. Conclusions: Understanding the basis of patient attitudes and beliefs about healthcare AI is a crucial first step in effective patient engagement and education. The theoretical framework we present provides a foundation for future studies examining patient opinions about applications of AI in healthcare.

A hybrid method of healthcare delivery research and human-centered design to develop technology-enabled support for caregivers of hematopoietic stem cell transplant recipients.

Health information technology (HIT) is a widely recognized strategy to encourage cancer patients and caregivers to participate in healthcare delivery in a sustainable and cost-effective way. In the context of autologous hematopoietic cell transplant (HSCT), HIT-enabled tools have the potential to effectively engage, educate, support, and optimize outcomes of patients and caregivers in the outpatient setting. This study sought to leverage human-centered design to develop a high-fidelity prototype of a HIT-enabled psychoeducational tool for HSCT caregivers. Phase 1 focuses on breadth and depth of information gathering through a systematic review and semi-structured interviews to determine optimal tool use. Phase 2 engages in human-centered design synthesis and visualization methods to identify key opportunities for the HIT design. Phase 3 employs human-centered design evaluation, engaging caregivers to respond to low-fidelity concepts and scenarios to help co-design an optimal tool for HSCT. This study outlines a hybrid method of healthcare delivery research and human-centered design to develop technology-enabled support for HSCT caregivers. Herein, we present a design methodology for developing a prototype of HIT-enabled psychoeducational tool which can be leveraged to develop future eHealth innovations to optimize HSCT.

Provider Perspectives on Artificial Intelligence-Guided Screening for Low Ejection Fraction in Primary Care: Qualitative Study.

BACKGROUND: The promise of artificial intelligence (AI) to transform health care is threatened by a tangle of challenges that emerge as new AI tools are introduced into clinical practice. AI tools with high accuracy, especially those that detect asymptomatic cases, may be hindered by barriers to adoption. Understanding provider needs and concerns is critical to inform implementation strategies that improve provider buy-in and adoption of AI tools in medicine. OBJECTIVE: This study aimed to describe provider perspectives on the adoption of an AI-enabled screening tool in primary care to inform effective integration and sustained use. METHODS: A qualitative study was conducted between December 2019 and February 2020 as part of a pragmatic randomized controlled trial at a large academic medical center in the United States. In all, 29 primary care providers were purposively sampled using a positive deviance approach for participation in semistructured focus groups after their use of the AI tool in the randomized controlled trial was complete. Focus group data were analyzed using a grounded theory approach; iterative analysis was conducted to identify codes and themes, which were synthesized into findings. RESULTS: Our findings revealed that providers understood the purpose and functionality of the AI tool and saw potential value for more accurate and faster diagnoses. However, successful adoption into routine patient care requires the smooth integration of the tool with clinical decision-making and existing workflow to address provider needs and preferences during implementation. To fulfill the AI tool's promise of clinical value, providers identified areas for improvement including integration with clinical decision-making, cost-effectiveness and resource allocation, provider training, workflow integration, care pathway coordination, and provider-patient communication. CONCLUSIONS: The implementation of AI-enabled tools in medicine can benefit from sensitivity to the nuanced context of care and provider needs to enable the useful adoption of AI tools at the point of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04000087; https://clinicaltrials.gov/ct2/show/NCT04000087.

Patient apprehensions about the use of artificial intelligence in healthcare.

While there is significant enthusiasm in the medical community about the use of artificial intelligence (AI) technologies in healthcare, few research studies have sought to assess patient perspectives on these technologies. We conducted 15 focus groups examining patient views of diverse applications of AI in healthcare. Our results indicate that patients have multiple concerns, including concerns related to the safety of AI, threats to patient choice, potential increases in healthcare costs, data-source bias, and data security. We also found that patient acceptance of AI is contingent on mitigating these possible harms. Our results highlight an array of patient concerns that may limit enthusiasm for applications of AI in healthcare. Proactively addressing these concerns is critical for the flourishing of ethical innovation and ensuring the long-term success of AI applications in healthcare.

Supportive Accountability and Mobile App Use in a Tobacco Control Intervention Targeting Low-Income Minority Mothers Who Smoke: Observational Study.

BACKGROUND: Smartphone mobile apps are frequently used in standalone or multimodal smoking cessation interventions. However, factors that impede or improve app usage are poorly understood. OBJECTIVE: This study used the supportive accountability model to investigate factors that influence app usage in the context of a trial designed to reduce maternal smoking in low-income and predominantly minority communities. METHODS: We conducted a secondary analysis of data (N=181) from a randomized controlled trial that included a smoking cessation app (QuitPal-m). Supportive accountability was measured by the number of times a participant was advised by their cessation counselor to use QuitPal-m. Participants reported app use helpfulness and barriers. Investigators tracked reported phone and technical problems that impeded app use. RESULTS: Most participants rated the app as very helpful (103/155, 66.5%), but daily use declined rapidly over time. App use was positively related to the level of perceived app helpfulness (P=.02) and education (P=.002) and inversely related to perceived barriers (P=.003), phone technical problems (P<.001), and cigarettes smoked per day at the end of treatment (P<.001). Participants used the app a greater proportion of the days following app advice than those preceding app advice (0.45 versus 0.34; P<.001). The positive relation between counselor app advice and app usage 24 hours after receiving advice was stronger among smokers with no plan to quit than in those planning to quit (P=.03), independent of education and phone or app problems. CONCLUSIONS: Findings show the utility of supportive accountability for increasing smoking cessation app use in a predominantly low-income, minority population, particularly if quit motivation is low. Results also highlight the importance of addressing personal and phone/technical barriers in addition to adding supportive accountability. TRIAL REGISTRATION: ClinicalTrials.gov NCT02602288; https://clinicaltrials.gov/ct2/show/NCT02602288.

Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial.

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults (N = 11,573 intervention; N = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01-1.61), P = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08-1.91), P = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care.

Changes in social relationships during an initial "stay-at-home" phase of the COVID-19 pandemic: A longitudinal survey study in the U.S.

RATIONALE: Severe acute respiratory syndrome Coronavirus 2 (SARS CoV-2), the virus that causes COVID-19, and consequent social distancing directives have been observed to negatively impact social relationships but the impact of these changes on the quality of social relationships at a population level has not been explored. OBJECTIVE: To evaluate changes in social relationships in a U.S. population sample during a time of social distancing. METHODS: We deployed a matched, longitudinal survey design of the National Institutes of Health Adult Social Relationship Scales to assess the social aspects of emotional support, instrumental support, friendship, loneliness, perceived hostility, and perceived rejection from a time without social distancing (February 2018) to a time where social distancing directives were active (May 2020). Changes in social relationships were compared using paired t-tests, and generalized linear regression models were constructed to identify subpopulations experiencing differential changes in each subdomain of social relationships during social distancing. RESULTS: Within our sample population, individuals experienced an increased sense of emotional support, instrumental support, and loneliness, and decreased feelings of friendship and perceived hostility during a period of social distancing. Individuals with low self-rated health experienced a decreased sense of emotional support, and females experienced increased feelings of loneliness compared with males. CONCLUSIONS: Social distancing measurably impacts social relationships and may have a disproportionate impact on females and individuals with lower self-rated health. If novel emergent infectious diseases become more commonplace, social interventions may be needed to mitigate the potential adverse impact of social distancing on social relationships.

Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE).

The article details the materials that will be used in a clinical trial - ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial [1]. It includes a clinician-facing action recommendation report that will translate an artificial intelligence algorithm to routine practice and an alert when a positive screening result is found. This report was developed using a user-centered approach via an iterative process with input from multiple physician groups. Such data can be reused and adapted to translate other artificial intelligence algorithms. This article also includes data collection forms we developed for the clinical trial aiming to evaluate the artificial intelligence algorithm. Such materials can be adapted for other clinical trials.

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial.

BACKGROUND: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment. OBJECTIVES: To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices. DESIGN: The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize >100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. SUMMARY: This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms.

Transforming the Early Drug Development Paradigm at the National Cancer Institute: The Formation of NCI's Experimental Therapeutics Clinical Trials Network (ETCTN).

Early drug development for cancer requires broad collaboration and skilled clinical investigators to enable enrollment of patients whose tumors have defined molecular profiles. To respond to these challenges, the National Cancer Institute (NCI) transformed its 60-year-old early-phase drug development program in 2014 into the Experimental Therapeutics Clinical Trials Network (ETCTN). The ETCTN is a consolidated, national network of 40+ academic institutions responsible for conducting more than 100 early-phase clinical trials. It promotes team science coordinated among basic, translational, and clinical investigators, emphasizing the inclusion of early career trialists. This perspective provides a brief overview of the ETCTN, summarizes its successes and challenges over its first grant funding cycle, and discusses the program's future directions. Measures indicated strong connectivity across the institutions, significant increases in investigator approval of the ETCTN scientific portfolio from years 1 to 4, and substantial research activity over 5 years, with 334 letters of intent submitted, 102 trials activated, and 3,570 patients accrued. The ETCTN's successful adoption relied heavily on the inclusion of senior investigators who have long-standing interactions with the NCI and a willingness to participate in a team science approach and to mentor early career investigators. In addition, NCI invested substantial resources in a centralized infrastructure to conduct trials and to support the inclusion of biomarkers in its studies. The ETCTN provides evidence that a collaborative national clinical trial network for early drug development is feasible and can address the demands of precision medicine approaches to oncologic clinical trials.

OB Nest: Reimagining Low-Risk Prenatal Care.

Using a human-centered design method, our team sought to envision a new model of care for women experiencing low-risk pregnancy. This model, called OB Nest, aimed to demedicalize the experience of pregnancy by providing a supportive and empowering experience that fits within patients' daily lives. To explore this topic, we invited women to use self-monitoring tools, a text-based smartphone application to communicate with their care team, and moderated online communities to connect with other pregnant women. Through observations of tool use and patient- and care team-provided feedback, we found that self-measurement and access to a fetal heart monitor provided women with confidence and joy in the progress of their pregnancies while shifting their position to being an active participant in their care. The smartphone application gave women direct access to their care team, provided continuity, and removed hurdles in establishing communication. The online community platform was a space where women in the same obstetric clinic could share nonmedical questions and advice with one another. This created a sense of community, leveraged the knowledge of women, and provided a venue beyond the clinic visit for information exchange. These findings were integrated into the design of the Mayo Clinic OB Nest model. This model redistributes care based on the individual needs of patients by providing self-measurement tools and continuous flexible access to their care team. By enabling women to meaningfully participate in their care, there is potential for cost savings and improved patient satisfaction.

Health literacy and usability of clinical trial search engines.

Several web-based search engines have been developed to assist individuals to find clinical trials for which they may be interested in volunteering. However, these search engines may be difficult for individuals with low health and computer literacy to navigate. The authors present findings from a usability evaluation of clinical trial search tools with 41 participants across the health and computer literacy spectrum. The study consisted of 3 parts: (a) a usability study of an existing web-based clinical trial search tool; (b) a usability study of a keyword-based clinical trial search tool; and (c) an exploratory study investigating users' information needs when deciding among 2 or more candidate clinical trials. From the first 2 studies, the authors found that users with low health literacy have difficulty forming queries using keywords and have significantly more difficulty using a standard web-based clinical trial search tool compared with users with adequate health literacy. From the third study, the authors identified the search factors most important to individuals searching for clinical trials and how these varied by health literacy level.