BACKGROUND:Delays in lung cancer diagnosis and treatment remain a persistent challenge in community health systems, where fragmented referral pathways and limited coordination may impede timely care. In response to observed delays in diagnostic workup, a community health system implemented a centralized Suspicion of Cancer (SOC) clinic designed to streamline evaluation of patients with suspected malignancy. METHODS:We conducted a descriptive observational study of consecutive patients referred to a community-based Suspicion of Cancer Clinic between January 2022 to March 2024. The SOC model incorporates centralized referral triage, rapid imaging review, multidisciplinary coordination, and leadership by a dedicated internal medicine physician with experience as an oncology hospitalist. All referred patients with diagnosed primary lung malignancy were included (n = 77), consistent with an intention-to-treat approach. Time intervals were measured in calendar days across key pathway segments, including referral-to-consult, referral-to-diagnosis, and referral-to-treatment. Interval distributions were summarized using means, medians, interquartile ranges (IQR), standard deviations, and ranges to characterize variability and identify potential delays. RESULTS:Among 286 patients referred to the SOC clinic for suspicion of lung malignancy during the study period, 77 were diagnosed with primary lung cancer and included in the analysis. The mean referral-to-consult interval was 5.5 calendar days with a median of 6 (3-7) calendar days. The mean referral-to-diagnosis time was 26.2 calendar days with a median of 22 (16-29.75) calendar days. The study group's mean referral-to-treatment was 76.5 calendar days with the median value being 72 (IQR, 53.25-96.25). Demographic and staging data revealed a diverse, community-based population with representation across insurance types and geographic classifications. CONCLUSIONS:The community-based diagnostic system at the Suspicion of Cancer Clinic achieved a median referral-to-diagnosis interval shorter than the NHS Faster Diagnosis Standard while identifying referral-to-treatment intervals that warrant further evaluation. These findings describe that structured, multidisciplinary diagnostic pathways can function effectively outside of academic or nationally integrated systems. The structural components of this model, including centralized triage and dedicated internal medicine leadership, provide a feasible and potentially replicable framework for community health systems seeking to achieve coordination and timeliness in cancer diagnosis.
Pregnancy of unknown location (PUL) describes a clinical condition in which a patient has a positive pregnancy test, but no visible pregnancy on ultrasound. Patients with PUL must be tracked carefully until the final diagnosis is confirmed, to exclude an ectopic pregnancy which can lead to morbidity and mortality for the patient if unrecognized. The aim of this quality improvement study was to improve follow-up care and communication for patients diagnosed with PUL. Our study was conducted from October of 2019 to January of 2021. Our cohort consisted of patients diagnosed with PUL who were followed until a final diagnosis was established. We used Plan-Do-Study-Act (PDSA) cycles to improve patient follow-up and patient-to-provider bidirectional communication. Our main metrics were the proportion of patients returning at 48-h for their blood draw, rate of 5-day delay in care, and number of patients lost to follow-up (those who did not follow up within 2 weeks of their anticipated follow up). PDSA cycles were compared using run charts. Following implementation through PDSA of a text-based follow-up program for patients with pregnancy of unknown location, the proportion of patients who were lost to follow-up decreased from 25% to 12.5%, as did the proportion of patients experiencing delays of 5 days or more during follow-up, from 41% at baseline to 33% at the time of the last PDSA cycle. However, our lost to follow-up rate reduction was not maintained in our final cycle and rose again to 30%.
BACKGROUND:To overcome barriers to the implementation of effective interventions, it is important to understand the factors which may influence stakeholders' implementation decisions. We aimed to identify these factors and quantify their relative importance in the decision-making process. Stakeholders included those wishing to implement an intervention (e.g., health service researchers and providers), and those funding implementation efforts (e.g., administrators and policymakers). DESIGN:We utilized discrete choice experiment methodology to explore several factors relevant to implementation decisions. The survey instrument was developed through a systematic review and expert consultation process. Conditional and mixed logit models were used to capture observed and unobserved preference heterogeneity. We tested the robustness of our results with subgroup and sensitivity analyses. RESULTS:We received 131 survey responses. There was a strong preference for intervention factors that 1) met stakeholders' needs, 2) had excellent levels of evidence, and 3) demonstrated strong organizational readiness. Respondents showed a preference for the mid-level cost ($150,000) over the lowest cost level ($100,000) within the experimental cost attribute (relative to an annual budget of $5 million). This suggests that respondents may prioritize the other three factors over the cost of implementation. These results remained relatively consistent across professional subgroups and sensitivity analyses. CONCLUSIONS:Meeting stakeholders' needs, levels of evidence, and organizational readiness had a large influence on implementation decisions. These factors were ranked of higher importance than implementation costs, administrative processes, and the presence of local champions within an organization. Designing research programs that support these influential factors will encourage successful implementation and ensure optimal healthcare delivery.
BACKGROUND:Heart failure (HF) affects 1-3% of adults globally with high hospital re-admission rates and healthcare costs. Research aims to improve the discharge process to reduce post-discharge complications. OBJECTIVES:This study compares the quality, readability, and health literacy requirements of artificial intelligence (AI) generated discharge instructions with commonly accessed patient education materials. METHODS:A blinded survey of 15 cardiovascular disease fellows evaluated HF discharge instructions generated from WebMD, Lexicomp®, and ChatGPT-4o. Quality was assessed using a modified DISCERN criteria and the Global Quality Scale (GQS) while readability was analyzed using the Flesch Reading Ease Score (FRES), Flesch-Kincaid Reading Grade Level (FKGL), and the Gunning-Fog Index (GFI). Statistical analysis identified the highest quality patient-centered discharge materials and reading level requirements. RESULTS:Quality metrics showed no statistical differences. However, ChatGPT-4o was rated as "good" while WebMD and Lexicomp® were rated as "excellent." Importantly, expert reviewers identified no clinical inaccuracies in the AI-generated text. Readability analysis found ChatGPT-4o-generated instructions required college-level literacy, significantly higher than WebMD and Lexicomp® (8th-9th grade). Qualitative comparison revealed that WebMD and Lexicomp® utilized direct, behavioral commands, whereas AI-generated text relied on abstract cognitive tasks, contributing to the higher literacy burden. CONCLUSION:While all sources provided high-quality information, AI-generated materials required significantly higher literacy levels. This study highlights the need for improved readability in patient education materials. Future study should analyze prompt engineering strategies that prioritize concrete examples over abstract concepts, ensuring that high-quality AI tools are accessible to patients with lower health literacy.
