Explore the vast range of titles available.

Artificial intelligence (AI)-enabled devices are increasingly used in health care. However, there has been limited research on patients' informational preferences, including which elements of AI device labeling enhance patient understanding, trust, and acceptance. Clear and effective patient-facing communication is essential to address patient concerns and support informed decision-making regarding AI-enabled care. We evaluated 3 aims using simulated AI device labels in a cardiovascular context. First, we identified key information elements that influence patient trust and acceptance of an AI device. Second, we examined how these effects varied based on patient characteristics. Third, we explored how patients evaluated informational content of AI labels and their perceived effectiveness of the AI labels in informing decision-making about the use of AI device, building trust in the device, and shaping their intention to use it in their health care. We recruited 340 US patients from ResearchMatch.org to participate in a web-based survey that contained 2 experiments. In the discrete choice experiment, participants indicated preferences in terms of trust and acceptance regarding 16 pairs of simulated AI device labels that varied across 8 types of information needs identified in our previous qualitative work. In the single profile factorial experiment, participants evaluated 4 randomly assigned label prototypes regarding the label's legibility, comprehensibility, information overload, credibility, and perceived effectiveness in informing about the AI device, as well as participants' trust in the AI device and intention to use the device in their health care. Data were analyzed using mixed effects binary or ordinal logistic regression. The discrete choice experiment showed that information about regulatory approval, high device performance, provider oversight, and AI's value added to usual care significantly increased the likelihood of patient trust by 14.1%-19.3% and acceptance by 13.3%-17.9%. Subgroup analyses revealed variations based on patient characteristics such as familiarity with AI, health literacy, and recency of last medical checkup. The single profile factorial experiment showed that patients reported good label comprehension, and that information about provider oversight, regulatory approval, device performance, and AI's added value improved perceived credibility and effectiveness of the AI label (odds ratio [OR] range: 1.35-2.05), reduced doubts in the AI device (OR range: 0.61-0.77), and increased trust and intention to use the AI device (OR range: 1.47-1.73). However, information about data privacy and safety management protocols was less influential. Patients value information about an AI device's performance, provider oversight, regulatory status, and added value during decision-making. Providing transparent, easily understandable information about these aspects is critical to support patient determinations of trust and acceptance of AI-enabled health care. Information elements' impact on patient trust and acceptance varies by patient characteristics, highlighting the need for a tailored approach to address the concerns of diverse patient groups about AI in health care.

Patient-reported outcome measure (PROM) surveillance and collaborative care improve cancer symptom control. However, human resource requirements constrain their implementation and reach. Electronic health record (EHR) facilitation reduces resource needs and might allow population-level scaling. We aimed to assess the effect of EHR facilitation of PROM-directed collaborative care on clinical and health services outcomes. E2C2 was a cohort cluster-randomised, unblinded, stepped-wedge, pragmatic trial, in which we randomly assigned 15 clusters of medical oncology and haematology clinics in the USA sharing a common EHR, Epic, to five sequences to compare an intervention of remotely delivered electronic PROM (ePROM) symptom surveillance and EHR-facilitated collaborative care (ECC) management with a usual care (UC) control of ePROM surveillance alone. Sequences transitioned from the UC control to the ECC intervention state at 8-month intervals. All adult (aged ≥18 years) patients who received medical oncology or haematology care in a US multi-state health system were enrolled. All cancer stages, cancer types, and treatment phases were included, except for patients enrolled in hospice or with acute leukaemia. SPPADE symptoms (sleep interference, pain, impaired physical function, anxiety, depression, and energy deficit or fatigue) were assessed with 0–10-point numerical rating scales linked to clinical encounters. The prespecified co-primary outcomes were all post-baseline SPPADE scores, and clinically actionable scores (≥4/10), among participants who completed at least two ePROMs. The outcomes were assessed using multivariate regression of cluster-period mean SPPADE symptom scores against intervention exposure, baseline SPPADE scores, fixed-cluster, and secular time effects. The trial is registered at ClinicalTrials.gov (NCT03892967) and is now closed to recruitment. From March 28, 2019, to Jan 31, 2023, 50 207 patients were enrolled and administered ePROMs in association with oncology or haematology visits. In the analytical cohort of 24 874 participants, 10 390 (42%) were assigned ePROMS in both the ECC and UC periods. Of the 19 084 [77%] in the ECC group, 11 138 (58%) were female, 7946 (42%) were male, and 18189 (95%) were White; and of the 16 180 (65%) in the UC group, 9621 (60%) were female, 6559 (40%) were male, and 15468 (96%) were White. 21 153 (85%) participants reported one or more clinically actionable symptoms (defined as SPPADE score ≥4/10). In multivariate analyses, mean population joint SPPADE symptom burden favoured ECC periods (p=0·0055) with adjusted mean differences of –0·12 (95% CI –0·19 to –0·05) for anxiety, –0·08 (–0·15 to –0·01) for depression, –0·06 (–0·16 to 0·03) for fatigue, –0·04 (–0·14 to 0·07) for pain, 0·03 (–0·07 to 0·14) for physical function, and –0·07 (–0·16 to 0·02) for sleep. ECC benefit was also noted following actionable scores (p<0·0001) with adjusted mean differences of –0·10 (–0·17 to –0·03) for anxiety, –0·09 (–0·16 to –0·02) for depression, –0·09 (–0·18 to –0·01) for fatigue, 0·04 (–0·05 to 0·12) for pain, 0·07 (–0·03 to 0·17) for physical function, and –0·02 (–0·10 to 0·07) for sleep. Centralised EHR-facilitated, symptom surveillance and collaborative care management are more beneficial than symptom surveillance alone in reducing the population burden of SPPADE symptoms in oncology patients. US National Institutes of Health.

Background As part of a pragmatic cluster-randomized trial for patients with cancer, we recruited representatives from each care setting and trained them to be practice facilitators (“Symptom Sages”). This mixed methods study evaluated barriers and facilitators individuals encountered in this role. Methods Symptom Sages were invited to complete a brief web-based survey and semi-structured interview, developed using constructs from the Consolidated Framework for Implementation Research (CFIR) 2.0. Survey responses were summarized descriptively. Two researchers coded interview transcripts to CFIR 2.0 constructs, reviewed coded text, and created memos to identify themes. Final themes were agreed on via discussion. Results Between August and November 2023, 12 practice facilitators participated (11 completed surveys and 7 completed interviews). Interviews revealed the following themes: 1) training and knowledge acquisition for the facilitator role were sufficient but time was not, 2) mixed perceptions of the intervention and inability to modify the intervention impacted role motivation, and 3) there were challenges motivating colleagues and getting organizational support. Survey results revealed similar findings. Conclusion Training alone is insufficient for practice facilitators’ success in the absence of allocated protected time. Additional implementation strategies may be needed to support adoption in oncology settings. Trial registration This trial was registered at ClinicalTrials.gov, NCT03892967, on March 25, 2019, https://clinicaltrials.gov/study/NCT03892967 .

As health systems incorporate artificial intelligence (AI) into various aspects of patient care, there is growing interest in understanding how to ensure transparent and trustworthy implementation. However, little attention has been given to what information patients need about these technologies to promote transparency of their use. We conducted three asynchronous online focus groups with 42 patients across the United States discussing perspectives on their information needs for trust and uptake of AI, focusing on its use in cardiovascular care. Data were analyzed using a rapid content analysis approach. Our results suggest that patients have a set of core information needs, including specific information factors pertaining to the AI tool, oversight, and healthcare experience, that are relevant to calibrating trust as well as perspectives concerning information delivery, disclosure, consent, and physician AI use. Identifying patient information needs is a critical starting point for calibrating trust in healthcare AI systems and designing strategies for information delivery. These findings highlight the importance of patient-centered engagement when developing AI model documentation and communicating and provisioning information about these technologies in clinical encounters.

Background Peri-operative pain management guidelines recommend multimodal strategies, but education on non-pharmacological pain care (NPPC) is lacking. The present study explored patients’ experiences participating in the Healing After Surgery (HAS) initiative as part of a pragmatic clinical trial, designed to provide patients with peri-operative education and support for using NPPC. Methods We sought to interview two patients from each of the 31 surgical practices participating in the trial. Interviews were semi-structured, audio-recorded, and transcribed verbatim. We used a rapid analytic approach to summarize interview transcripts. Summaries were uploaded to NVivo and two researchers independently reviewed queries and produced analytic memos with key themes organized according to the Theoretical Framework of Acceptability’s seven constructs. Results We analyzed interview transcripts for 71 patients. Findings revealed that patients (1) generally liked the HAS initiative (affective attitude), (2) it aligned with patients’ beliefs about wellness techniques and concerns about opioids (ethicality), and (3) many patients had experience using NPPC (self-efficacy). However, (4) care team education and provision of NPPC was inconsistent (intervention coherence), (5) patients varied on their thoughts about the effectiveness of NPPC and the role of NPPC and medication (perceived effectiveness), (6) some patients found resources repetitive and encountered logistical challenges engaging with resources or using NPPC (burden), and (7) patients cited completing tasks ahead of surgery and competing demands post-operatively (e.g., caring for a spouse or young children) as barriers (opportunity costs). An additional theme, the invisible and individualistic nature of pain, also emerged. Conclusions Peri-operative initiatives that educate patients about NPPC may be well-received and remind patients of “wellness techniques” and alert them to their role in pain management. Familiarity with NPPC may contribute to patients’ self-efficacy using these techniques. However, some patients may require additional support to feel comfortable using NPPC in a post-operative context. Emphasizing the care team’s role of directing patients to existing educational recourse or interactive supportive resources may be a low burden way of providing this support. Adding a health coach role to the intervention may also be an option for providing extra support without increasing care teams’ workload. Trial registration This study was registered on ClinicalTrials.gov, NCT05166356, https://clinicaltrials.gov/study/NCT05166356?term=%20NCT05166356&rank=1 Patient enrollment began on 3/01/2022.

Objective: Given limited critical care resources and an aging population, early interventions to prevent critical illness are vital. In this work, we measured post-implementation outcomes after introducing a novel electronic scoring system (Elders Risk Assessment—ERA) and a risk-factor checklist, Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), to detect older patients at high risk of critical illness in a primary care setting. Methods: The study was conducted at a family medicine clinic in Kasson, MN. The ADAPT-ITT framework was used to modify the CERTAIN checklist for primary care during 2 co-design workshops involving interdisciplinary clinicians, held in April 2023. The ERA score and modified CERTAIN checklist were implemented between May and July 2023 and identify and assess all patients age ≥60 years at risk of critical illness during their primary care visits. Implementation outcomes were evaluated at the end of the study via an anonymous survey and EHR data extraction. Results: Fourteen clinicians participated in 2 co-design workshops. A total of 19 clinicians participated in a post-pilot survey. All survey items were rated on a 5-point Likert type scale. Mean acceptability of the ERA score and checklist was rated 3.35 (SD = 0.75) and 3.09 (SD = 0.64), respectively. Appropriateness had a mean rating of 3.38 (SD = 0.82) for the ERA score and 3.19 (SD = 0.59) for the checklist. Mean feasibility was rated 3.38(SD = 0.85) and 2.92 (SD = 0.76) for the ERA score and checklist, respectively. The adoption rate was 50% (19/38) among clinicians, but the reach was low at 17% (49/289) of eligible patients. Conclusions: This pilot study evaluated the implementation of an intervention that introduced the ERA score and CERTAIN checklist into a primary care practice. Results indicate moderate acceptability, appropriateness, and feasibility of the ERA score, and similar ratings for the checklist, with slightly lower feasibility. While checklist adoption was moderate, reach was limited, indicating inconsistent use. Recommendations: We plan to use the open-ended resurvey responses to further modify the CERTAIN-FM checklist and implementation process. The ADAPT-ITT framework is a useful model for adapting the checklist to meet the primary care clinician needs.

Best practice guidelines recommend educating surgical patients about non-pharmacological pain care (NPPC) techniques that can be used in addition to pain medication for perioperative pain management, given the risks for opioid misuse following surgery. As part of the parent non-pharmacologic options in postoperative hospital-based and rehabilitation pain management (NOHARM) clinical trial, we implemented the Healing After Surgery initiative, which leveraged the Epic electronic health record (EHR) to provide patients with education on NPPC techniques perioperatively. We disseminated educational materials directly to patients via the EHR patient portal and prompted patients to select the techniques they were most interested in using, which auto-populated the EHR so that their care team could view their preferences. We also built clinical decision support elements in the EHR to prompt and support inpatient nurses in providing patients with education and reinforcement for using their preferred NPPC techniques. Print materials, a website, a DVD, videos on hospital televisions, a toll-free number, and Zoom-based group calls provided additional education on NPPC techniques. This study evaluated nurses' perceptions of barriers and facilitators to implementing the EHR-based Healing After Surgery initiative. We invited inpatient nursing leaders and bedside nurses to participate in a semistructured interview. Inpatient nursing leaders were invited to complete a brief survey that asked them to rate their agreement with 7 items using a numeric rating scale (1=not at all, 10=a great deal). Interview findings from 29 nurses revealed: (1) nurses gravitated towards providing NPPC techniques they were familiar with, (2) the initiative was patient-centric with opportunities to better engage patients, and (3) nurses experienced challenges implementing and prioritizing the intervention in the inpatient setting due to competing demands in a pandemic and postpandemic environment. Interviews revealed mixed effectiveness of implementation strategies. We received survey responses from 47 nursing leaders who indicated that their staff knew about the Healing After Surgery initiative (mean=7.53, SD=1.77) and what they were expected to do (mean=7, SD=1.88). They thought the Healing After Surgery initiative supported patients' pain management needs (mean=6.76, SD=2.24), endorsed it as a priority (mean=7.02, SD=2.56), and encouraged staff to support it (mean=5.98, SD=2.78). They indicated staff experienced some burden supporting the initiative (mean=3.93, SD=2.47), but supported some variation of the initiative continuing once the parent trial ended (mean=7.72, SD=2.62). Nurses understood the intervention's benefit but struggled to implement unfamiliar NPPC techniques and prioritize the initiative due to other clinical demands. Additional implementation strategies may be needed to better engage patients and facilitate intervention delivery.

Purpose Symptoms can negatively impact quality of life for patients with a history of cancer. Digital, electronic health record (EHR)-integrated approaches to routine symptom monitoring accompanied by evidence-based interventions for symptom management have been explored as a scalable way to improve symptom management, particularly between clinic visits. However, little research has evaluated barriers and facilitators to implementing these approaches in real-world settings, particularly during the pre-implementation phase. Pre-implementation assessment is critical for informing the selection and sequencing of implementation strategies and intervention adaptation. Thus, this study sought to understand pre-implementation perceptions of a remote cancer symptom monitoring and management intervention that uses electronic patient-reported outcome measures for symptom assessment. Methods We interviewed 20 clinical and administrative stakeholders from 4 geographic regions within an academic medical center and its affiliated health system during the months prior to initiation of a stepped-wedge, cluster randomized pragmatic trial. Transcripts were coded using the Consolidated Framework for Implementation Research [CFIR] 2.0. Two study team members reviewed coded transcripts to understand how determinants were relevant in the pre-implementation phase of the trial and prepared analytic memos to identify themes. Results Findings are summarized in four themes: (1) ability of the intervention to meet patient needs [recipient characteristics], (2) designing with care team needs in mind [innovation design and adaptability], (3) fit of the intervention with existing practice workflows [compatibility], and (4) engaging care teams early [engaging deliverers]. Conclusion Attention to these aspects when planning intervention protocols can promote intervention compatibility with patients, providers, and practices thereby increasing implementation success.

The study and application of transition metal hydrides (TMHs) has been an active area of chemical research since the early 1960s 1 , for energy storage, through the reduction of protons to generate hydrogen 2 , 3 , and for organic synthesis, for the functionalization of unsaturated C–C, C–O and C–N bonds 4 , 5 . In the former instance, electrochemical means for driving such reactivity has been common place since the 1950s 6 but the use of stoichiometric exogenous organic- and metal-based reductants to harness the power of TMHs in synthetic chemistry remains the norm. In particular, cobalt-based TMHs have found widespread use for the derivatization of olefins and alkynes in complex molecule construction, often by a net hydrogen atom transfer (HAT) 7 . Here we show how an electrocatalytic approach inspired by decades of energy storage research can be made use of in the context of modern organic synthesis. This strategy not only offers benefits in terms of sustainability and efficiency but also enables enhanced chemoselectivity and distinct, tunable reactivity. Ten different reaction manifolds across dozens of substrates are exemplified, along with detailed mechanistic insights into this scalable electrochemical entry into Co–H generation that takes place through a low-valent intermediate. A perspective is given on how an electrocatalytic approach, inspired by decades of energy storage studies, can be used in the context of efficient cobalt-hydride generation with a variety of applications in modern organic synthesis.