OBJECTIVES: Patients value access to their medical reports on patient portals, but the terminology in those reports can cause confusion and anxiety. Can the artificial intelligence (AI) simplification of radiology reports into plain language improve patient comprehension? MATERIALS AND METHODS: Twenty original radiology reports (breast imaging, chest X-ray) were simplified into plain language using ChatGPT-4 using a customized prompt for each report type. For each report, clinicians created a gold standard of key findings and appropriate follow-up. In August 2024, a national sample of 2000 US adults reviewed 2 randomly assigned reports, 1 original and 1 AI-generated plain language. Participants answered questions focused on comprehension of key findings, follow-up, confidence, anxiety, and preferences. Comprehension and follow-up were compared to the gold standard. We compared patient accuracy for original vs AI-generated plain language reports. RESULTS: Participants (mean age 48 years) were 62.3% female. Compared to original reports, participants shown AI-generated plain language reports had higher accuracy in comprehension (68.0% vs 58.0%; marginal difference 10.8% [95% CI, 7.8%-13.8%]) and follow-up (64.5% vs 58.4%; marginal difference 6.8% [95% CI, 4.1%-9.4%]). Improvements were larger among participants aged >44 years and with less than college education. With plain language reports, participants reported higher confidence in their answers and lower anxiety. Despite these improvements, 60.0% of participants preferred the original report over the plain language version. DISCUSSION: Integrating AI simplification into patient portals may be helpful, but trust concerns remain. CONCLUSION: AI simplification improved patient comprehension and confidence. Further research is needed to address patient resistance to AI simplification.

OBJECTIVE: To determine user and electronic health records (EHR) integration requirements for a scalable remote symptom monitoring intervention for asthma patients and their providers. METHODS: Guided by the Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework, we conducted a user-centered design process involving English- and Spanish-speaking patients and providers affiliated with an academic medical center. We conducted a secondary analysis of interview transcripts from our prior study, new design sessions with patients and primary care providers (PCPs), and a survey of PCPs. We determined EHR integration requirements as part of the asthma app design and development process. RESULTS: Analysis of 26 transcripts (21 patients, 5 providers) from the prior study, 21 new design sessions (15 patients, 6 providers), and survey responses from 55 PCPs (71% of 78) identified requirements. Patient-facing requirements included: 1- or 5-item symptom questionnaires each week, depending on asthma control; option to request a callback; ability to enter notes, triggers, and peak flows; and tips pushed via the app prior to a clinic visit. PCP-facing requirements included a clinician-facing dashboard accessible from the EHR and an EHR inbox message preceding the visit. PCP preferences diverged regarding graphical presentations of patient-reported outcomes (PROs). Nurse-facing requirements included callback requests sent as an EHR inbox message. Requirements were consistent for English- and Spanish-speaking patients. EHR integration required use of custom application programming interfaces (APIs). CONCLUSION: Using the NASSS framework to guide our user-centered design process, we identified patient and provider requirements for scaling an EHR-integrated remote symptom monitoring intervention in primary care. These requirements met the needs of patients and providers. Additional standards for PRO displays and EHR inbox APIs are needed to facilitate spread.

OBJECTIVE: We quantify the use of clinical decision support (CDS) and the specific barriers reported by ambulatory clinics and examine whether CDS utilization and barriers differed based on clinics' affiliation with health systems, providing a benchmark for future empirical research and policies related to this topic. MATERIALS AND METHODS: Despite much discussion at the theoretic level, the existing literature provides little empirical understanding of barriers to using CDS in ambulatory care. We analyze data from 821 clinics in 117 medical groups, based on in Minnesota Community Measurement's annual Health Information Technology Survey (2014-2016). We examine clinics' use of 7 CDS tools, along with 7 barriers in 3 areas (resource, user acceptance, and technology). Employing linear probability models, we examine factors associated with CDS barriers. RESULTS: Clinics in health systems used more CDS tools than did clinics not in systems (24 percentage points higher in automated reminders), but they also reported more barriers related to resources and user acceptance (26 percentage points higher in barriers to implementation and 33 points higher in disruptive alarms). Barriers related to workflow redesign increased in clinics affiliated with health systems (33 points higher). Rural clinics were more likely to report barriers to training. CONCLUSIONS: CDS barriers related to resources and user acceptance remained substantial. Health systems, while being effective in promoting CDS tools, may need to provide further assistance to their affiliated ambulatory clinics to overcome barriers, especially the requirement to redesign workflow. Rural clinics may need more resources for training.

Despite the potential for electronic health records to help providers coordinate care, the current marketplace has failed to provide adequate solutions. Using a simple framework, we describe a vision of information technology capabilities that could substantially improve four care coordination activities: identifying collaborators, contacting collaborators, collaborating, and monitoring. Collaborators can include any individual clinician, caregiver, or provider organization involved in care for a given patient. This vision can be used to guide the development of care coordination tools and help policymakers track and promote their adoption.

In the March 2011 issue of the journal, Vest et al published one of the first empirical studies of clinicians' usage of a health information exchange (HIE).1 The setting for this study was the emergency department (ED). The ED is one important place where an HIE may have an impact on patient care because many emergency patients are unfamiliar to ED facilities and important clinical information is often missing.2 Surprisingly, Vest et al found that HIE usage was much lower for patients who were new to an ED facility compared with familiar patients. The authors suggest that ‘for the familiar patient, HIE might provide clinicians and organizations the necessary information to get and keep these patients out of the ED.’ However, they do not explain why the HIE was not used as often for the new patient, which the authors call the ‘poster child for justifying HIE in the ED setting.’ Why would clinicians use an HIE less often for new patients? I would like to suggest an explanation: the clinicians did not find the HIE helpful. The clinicians may have only accessed the HIE when they felt they had tried everything else but the patient kept returning to the ED. This interpretation is supported by the overall usage rate: the HIE was accessed in only 2.3% of all encounters, a rate which should be considered low if information is missing in approximately 32% of ED visits, as one study found.2 Furthermore, the authors observed a ‘degradation of (HIE) usage over time,’ which also suggests that some clinicians were not finding useful information and may have given up on the HIE altogether. If it is true that the ED clinicians did not generally find the HIE helpful, it would be important to understand why, which might be accomplished through qualitative means such as interviews. Findings from such a study might also be applicable to HIE usage in other care settings. As HIE is a new technology, it is important to apply both quantitative and qualitative methods to understand the factors that affect usage. Quantitative measures ‘may be useful, but they need to be grounded in qualitative data so that their meaning can be understood.’3 None. Not commissioned; internally peer reviewed.

BACKGROUND: The electronic exchange of health information among healthcare providers has the potential to produce enormous clinical benefits and financial savings, although realizing that potential will be challenging. The American Recovery and Reinvestment Act of 2009 will reward providers for 'meaningful use' of electronic health records, including participation in clinical data exchange, but the best ways to do so remain uncertain. METHODS: We analyzed patient visits in one community in which a high proportion of providers were using an electronic health record and participating in data exchange. Using claims data from one large private payer for individuals under age 65 years, we computed the number of visits to a provider which involved transitions in care from other providers as a percentage of total visits. We calculated this 'transition percentage' for individual providers and medical groups. RESULTS: On average, excluding radiology and pathology, approximately 51% of visits involved care transitions between individual providers in the community and 36%-41% involved transitions between medical groups. There was substantial variation in transition percentage across medical specialties, within specialties and across medical groups. Specialists tended to have higher transition percentages and smaller ranges within specialty than primary care physicians, who ranged from 32% to 95% (including transitions involving radiology and pathology). The transition percentages of pediatric practices were similar to those of adult primary care, except that many transitions occurred among pediatric physicians within a single medical group. CONCLUSIONS: Care transition patterns differed substantially by type of practice and should be considered in designing incentives to foster providers' meaningful use of health data exchange services.