Cheryl D. Stults

Background: Ambient artificial intelligence (AI) scribes have shown promise to aid ambulatory clinicians with the documentation burden, but little has been shown about the inpatient experience. Objective: Understand inpatient physician experiences before and after ambient AI scribes implementation. Methods: Quality improvement pilot evaluation using utilization data, pre‐post surveys, and electronic health record (EHR) metrics at a large healthcare organization in northern and central California. Ambient AI was implemented in acute care facilities in March 2025, with EHR data collected 2 months before and after implementation. We examined ambient AI usage, inpatient Time in Notes per day, NASA‐TLX cognitive load, the mini‐Z burnout question, and overall experience. McNemar, paired t ‐test, and paired Wilcoxon signed‐rank tests were used. Results: Fifty‐nine physicians were included: 36 (61%) male, 28 (48%) adult hospitalists, and 20 completed both pre‐ and post‐surveys. Monthly utilization remained relatively stable (4.2%–3.5% notes by ambient AI). Ambient AI was used to generate 12% (647/5350) of history and physical notes, 2% (518/245,921) of progress notes, and 6% (33/511) of consult notes, with overall utilization rate of 3% (1198/46,161) across all inpatient note types. Mean time in notes (SD) per day was unchanged, from 41.9 (25.59) to 41.6 (27.3) min ( p = .77). Physicians requested additional functionality to “pull forward” or add onto previously generated notes. Conclusion: Ambient AI was used most consistently for inpatient consult notes, although overall utilization was low. Future development tailored to inpatient workflows is needed to increase adoption as organizations begin to expand ambient AI in this new clinical setting.

Background: Trust building is essential for effective healthcare, including care delivered using digital health technologies (DHTs). This analysis aimed to develop an understanding of the mechanisms of trust building in relation to DHTs for diabetes using tracking and measuring tools. Methods: This was a multimethod qualitative analysis conducted in Germany, Spain, and the United States, using a behavioral science approach. Interviews were conducted with different groups of participants from June 2022 to August 2022. The interviews consisted of one-on-one interviews (90 min) with experts in the diabetes support system, online dyad interviews (120 min), and online homework (120 min) with adults with type 2 diabetes (T2D) with different mind states (struggling, juggling, and controlling) and their caregivers. Focus group discussions were conducted with healthcare professionals (HCPs; each 150 min), including general physicians, specialists, and nurses, representing a conservative or innovator persona. Results: Nine interviews were conducted with experts (n=9), 28 dyad interviews were conducted with people with T2D (n=38) and caregivers (n=18), and 15 focus group discussions were conducted with 59 HCPs. Building trust in DHTs appeared to be created differently from building trust in medications, and requires activating, motivating, and sustaining desired behavior change. DHT trust builds systematically in three levels: trust in individual’s own role in diabetes management (self-efficacy), trust in data as a tool to create empowerment, and trust in viability of sustained behavior change. Conclusion: People who had built trust on all three levels felt in control of their diabetes and were motivated and engaged in DHTs. Focus on trust building is important for digital healthcare launch strategies, particularly in user conversion from consideration phase to actual initiation of use of DHTs.

Patient-reported experience measures (PREMs) are widely used for quality improvement, benchmarking, and physician compensation, yet few large-scale studies have examined interventions to improve scores. We examine the association of an annual, mandatory, peer-led continuing professional development (CPD) program at a large, multispecialty group practice aimed at enhancing patient–clinician communication. This quality improvement study analyzed ambulatory pre-post patient experience surveys from January 2018 to October 2022 for top box ratings for four PREMs focusing on provider communication for “ explains ”, “ listens ” to them, “ overall provider rating ,” and would “ recommend ” the practice were modeled using generalized estimating equations, adjusting for patient and provider characteristics. Over 1 million surveys per year were analyzed and the likelihood of top-box response in all four domains increased post implementation in 2019- 2022 compared to 2018 with all adjusted odds ratios > 1.0 and p ≤ 0.001. Our findings suggest CPD programs may improve PREMs in a large healthcare system, across all communication-related domains. However, there could be limits and a plateau to how much a CPD intervention can improve PREMs.

Objective: Understand the qualitative impact of an ambient artificial intelligence (AI) documentation platform on clinicians’ experiences and workflows. Materials and Methods: A quality improvement (QI) qualitative study using semi-structured interviews after pilot implementation of an ambient AI documentation platform at a large healthcare organization in Northern and Central California. Pragmatic thematic analysis was used to code and analyze the interviews. Results: 100 clinicians were invited and 42 (42%) participated in an interview. 23 (54.8%) were males and 28 (66.7%) in primary care. Many respondents noted that ambient AI had decreased their cognitive burden by eliminating the need to remember as many specific visit details and saved time for other tasks. They also liked how ambient AI generated transcripts in multiple languages and then created an English-language progress note. However, clinicians also reported challenges, particularly missed or inaccurate information that required them to review the transcript/audio and edit the note. Additionally, many clinicians, particularly specialists, disliked the note formatting and the inability to customize the note template as this resulted in additional manual editing. Discussion: Results from these QI qualitative interviews suggest that ambient AI improved clinicians’ overall experience at work. Conclusion: While there are many similarities, there may be key differences in clinician experience between ambient AI documentation platforms depending on unique features of each. Future research is needed to understand the potential range of experiences based on type of ambient AI platform and if these findings continue with longer use and broader expansion of this new and evolving technology.

Importance: The increase of electronic health record (EHR) work negatively impacts clinician well-being. One potential solution is incorporating an ambient artificial intelligence (AI) documentation platform.Objective: To understand clinician experience before and after implementing ambient AI.Design, Setting, and Participants: This quality improvement study was a pilot evaluation with before and after survey and EHR metrics conducted at a large health care organization in Northern and Central California. Clinicians were purposively sampled to be representative of region and specialty. Ambient AI was implemented in April 2024 with EHR data from 3 months before and after implementation. Data were analyzed from May to September 2024.Exposure: Ambient AI access.Main Outcomes and Measures: Metrics of time were examined in notes per appointment, off-hour EHR activities (5:30 pm to 7:00 am on weekdays and nonscheduled weekends and holidays), documentation note length, progress note length, NASA Task Load Index (NASA-TLX) score, mini-Z burnout question, and overall experience. It was hypothesized that time in notes per appointment would decrease and clinical well-being would improve. Logistic regression and linear mixed-effect models were used.Results: Among 100 clinicians (53 male [53.0%]; mean [SD] age, 48.9 [11.0] years), 58 clinicians (58.0%) were in primary care and 92 clinicians had EHR metrics. Among 57 clinicians who completed both preimplementation and postimplementation surveys, there was a decrease in burnout from 24 clinicians (42.1%) to 20 clinicians (35.1%), although this was not a significant difference (P = .12). Mean (SD) NASA-TLX scores all decreased after using ambient AI: mental demand of note writing (12.2 [4.0] to 6.3 [3.7]), hurried or rushed pace (13.2 [4.0] to 6.4 [4.2]), and effort to accomplish note writing (12.5 [4.1] to 7.4 [4.3]) (all P < .001). Mean (SD) time in notes per appointment significantly decreased from 6.2 (4.0) to 5.3 (3.5) minutes (P < .001), with a bigger decrease for female vs male clinicians (8.1 [3.9] to 6.7 [3.6] minutes vs 4.7 [3.5] to 4.2 [3.1] minutes; P = .001). More primary care clinicians (33 of 38 clinicians [85.8%]) reported that ambient AI improved overall satisfaction at work compared with clinicians in medical (4 of 11 clinicians [36.4%]) and surgical (4 of 8 clinicians [50.0%]) subspecialties (P < .001). After adjusting for participant characteristics, model results suggested that mean scores for NASA-TLX decreased for mental demand (−6.12 [95% CI, −7.52 to −4.72]), hurried or rushed pace (−6.96 [95% CI, −8.42 to −5.50]), and effort to accomplish note writing (−5.57 [95% CI, −6.93 to −4.21]), while mean time in note taking decreased by less than 1 minute per appointment (0.91 minutes [95% CI, −1.20 to −0.62 minutes]) (all P < .001).Conclusions and Relevance: This study found that ambient AI was associated with improved overall experience and time in notes for clinicians but with varying outcomes by sex and specialty. Future research should investigate outcomes after widescale expansion of this rapidly evolving technology.

While telegenetic counseling has increased substantially since the start of the COVID-19 pandemic, previous studies reported concerns around building rapport, nonverbal communication, and the patient-counselor relationship. This qualitative evaluation elicited feedback from genetic counselors, referring clinicians, and patients from a single healthcare organization to understand the user-driven reasons for overall satisfaction and experience. We conducted 22 in-depth, semi-structured interviews with participants from all 3 groups between February 2022 and February 2023. Interview recordings were transcribed and analyzed using a pragmatic thematic approach. Participants across all groups felt the style and content of the genetic counseling visit lent itself perfectly to telegenetics specifically because of no physical exam component. Most patient and counselor participants expressed having the genetic counseling over phone or video had no impact on the patient-counselor relationship or the amount of trust and emotional connection they were able to achieve remotely. Preference for visit type can be influenced by in-person masking requirements impeding full facial expressions or expressing strong emotions over phone. All respondents expressed strong support for all modalities going forward. Counselors with broad experience across platforms should be the focus of future recruitment as should patient education around the nature of the genetic counseling visit and the accuracy of various testing options. Telegenetic programs should consider perspectives from all 3 groups to ensure that specific needs of each are addressed.

In Reply We thank Oke et al both for their appreciation of the article and for seeking additional clarification and providing suggestions for future

Background: Effective primary care necessitates follow-up actions by the patient beyond the visit. Prior research suggests room for improvement in patient adherence. Objective: This study sought to understand patients’ views on their primary care visits, the plans generated therein, and their self-reported adherence after 3 months. Methods: As part of a large multisite cluster randomized pragmatic trial in 3 health care organizations, patients completed 2 surveys—the first within 7 days after the index primary care visit and another 3 months later. For this analysis of secondary outcomes, we combined the results across all study participants to understand patient adherence to care plans. We recorded patient characteristics and survey responses. Cross-tabulation and chi-square statistics were used to examine bivariate associations, adjusting for multiple comparisons when appropriate. We used multivariable logistic regression to assess how patients’ intention to follow, agreement, and understanding of their plans impacted their plan adherence, allowing for differences in individual characteristics. Qualitative content analysis was conducted to characterize the patient’s self-reported plans and reasons for adhering (or not) to the plan 3 months later. Results: Of 2555 patients, most selected the top box option (9=definitely agree) that they felt they had a clear plan (n=2011, 78%), agreed with the plan (n=2049, 80%), and intended to follow the plan (n=2108, 83%) discussed with their provider at the primary care visit. The most common elements of the plans reported included reference to exercise (n=359, 14.1%), testing (laboratory, imaging, etc; n=328, 12.8%), diet (n=296, 11.6%), and initiation or adjustment of medications; (n=284, 11.1%). Patients who strongly agreed that they had a clear plan, agreed with the plan, and intended to follow the plan were all more likely to report plan completion 3 months later (P<.001) than those providing less positive ratings. Patients who reported plans related to following up with the primary care provider (P=.008) to initiate or adjust medications (P≤.001) and to have a specialist visit were more likely to report that they had completely followed the plan (P=.003). Adjusting for demographic variables, patients who indicated intent to follow their plan were more likely to follow-through 3 months later (P<.001). Patients’ reasons for completely following the plan were mainly that the plan was clear (n=1114, 69.5%), consistent with what mattered (n=1060, 66.1%), and they were determined to carry through with the plan (n=887, 53.3%). The most common reasons for not following the plan were lack of time (n=217, 22.8%), having decided to try a different approach (n=105, 11%), and the COVID-19 pandemic impacted the plan (n=105, 11%). Conclusions: Patients’ initial assessment of their plan as clear, their agreement with the plan, and their initial willingness to follow the plan were all strongly related to their self-reported completion of the plan 3 months later. Patients whose plans involved lifestyle changes were less likely to report that they had “completely” followed their plan. Trial Registration: ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/study/NCT03385512 International Registered Report Identifier (IRRID): RR2-10.2196/30431

Importance: Amblyopia can result in permanent vision loss if not properly treated before age 7 years. In 2017, the US Preventive Services Task Force recommended that vision screening should occur at least once in all children aged 3 to 5 years to detect amblyopia.Objective: To understand trends and factors associated with screening, referral, or diagnosis of amblyopia before and after photoscreening expansion across a relatively large health care system in late 2017.Design, Setting, and Participants: This is a retrospective cohort study of electronic health record data from patients with a well child care visit at approximately age 3 years (ages 2.75-3.25 years) in a relatively large, multispecialty group practice in Northern California and linked census data between 2015 and 2022. Data were extracted and analyzed from October 2022 through August 2023.Exposures: Patient sex, race and ethnicity, immunization records, previous well child care visits, and census-level median household income.Main Outcomes and Measures: Vision screening, pediatric ophthalmology referral, or amblyopia diagnosis, compared using adjusted odds ratios (AORs).Results: The study included 2015-2017 data from 23 246 patients aged 3 years with at least 1 well child care visit (11 206 [48.2%] female) compared with 2018-2022 postexpansion data from 34 281 patients (16 517 [48.2%] female). The screening rate increased from 5.7% (424 of 7505) in 2015 to 72.1% (4578 of 6354) in 2022. The referral rate increased from 17.0% (1279 of 7505) in 2015 to 23.6% (1836 of 7792) in 2018. The diagnosis rate was 2.7% (200 of 7505) in 2015, peaked at 3.4% (263 of 7792) in 2018, and decreased to 1.4% (88 of 6354) in 2022. Compared with White patients, patients who were Asian, Black, or Hispanic were less likely to be screened (Asian: AOR, 0.80; 95% CI, 0.72-0.88; Black: AOR, 0.71; 95% CI, 0.53-0.96; Hispanic: AOR, 0.88; 95% CI, 0.80-0.97). Compared with White patients, patients who were Asian or Hispanic were more likely to be referred (Asian: AOR, 1.49; 95% CI, 1.36-1.62; Hispanic: AOR, 1.32; 95% CI, 1.18-1.48) and were more likely to be diagnosed (Asian: AOR, 1.29; 95% CI, 1.07-1.56; Hispanic: AOR, 1.67; 95% CI, 1.33-2.11).Conclusions and Relevance: In this study, increased availability of photoscreeners was associated with an increase in overall rates of vision screening for children aged 3 years in a relatively large health care system. Given that US rates of visual impairment are predicted to increase, additional targeted interventions would be needed to address remaining disparities in amblyopia care along patient- and clinician-level factors.

BACKGROUND Effective primary care necessitates follow-up actions by the patient beyond the visit. Prior research suggests room for improvement in patient adherence. OBJECTIVE This study sought to understand patients’ views on their primary care visits, the plans generated therein, and their self-reported adherence after 3 months. METHODS As part of a large multisite cluster randomized pragmatic trial in 3 health care organizations, patients completed 2 surveys—the first within 7 days after the index primary care visit and another 3 months later. For this analysis of secondary outcomes, we combined the results across all study participants to understand patient adherence to care plans. We recorded patient characteristics and survey responses. Cross-tabulation and chi-square statistics were used to examine bivariate associations, adjusting for multiple comparisons when appropriate. We used multivariable logistic regression to assess how patients’ intention to follow, agreement, and understanding of their plans impacted their plan adherence, allowing for differences in individual characteristics. Qualitative content analysis was conducted to characterize the patient’s self-reported plans and reasons for adhering (or not) to the plan 3 months later. RESULTS Of 2555 patients, most selected the top box option (9=definitely agree) that they felt they had a clear plan (n=2011, 78%), agreed with the plan (n=2049, 80%), and intended to follow the plan (n=2108, 83%) discussed with their provider at the primary care visit. The most common elements of the plans reported included reference to exercise (n=359, 14.1%), testing (laboratory, imaging, etc; n=328, 12.8%), diet (n=296, 11.6%), and initiation or adjustment of medications; (n=284, 11.1%). Patients who strongly agreed that they had a clear plan, agreed with the plan, and intended to follow the plan were all more likely to report plan completion 3 months later (P<.001) than those providing less positive ratings. Patients who reported plans related to following up with the primary care provider (P=.008) to initiate or adjust medications (P≤.001) and to have a specialist visit were more likely to report that they had completely followed the plan (P=.003). Adjusting for demographic variables, patients who indicated intent to follow their plan were more likely to follow-through 3 months later (P<.001). Patients’ reasons for completely following the plan were mainly that the plan was clear (n=1114, 69.5%), consistent with what mattered (n=1060, 66.1%), and they were determined to carry through with the plan (n=887, 53.3%). The most common reasons for not following the plan were lack of time (n=217, 22.8%), having decided to try a different approach (n=105, 11%), and the COVID-19 pandemic impacted the plan (n=105, 11%). CONCLUSIONS Patients’ initial assessment of their plan as clear, their agreement with the plan, and their initial willingness to follow the plan were all strongly related to their self-reported completion of the plan 3 months later. Patients whose plans involved lifestyle changes were less likely to report that they had “completely” followed their plan. CLINICALTRIAL ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/study/NCT03385512 INTERNATIONAL REGISTERED REPORT RR2-10.2196/30431