David A Asch’s research while affiliated with University of Pennsylvania and other places

We introduce OrigamiPlot, an open-source R package and Shiny web application designed to enhance the visualization of multivariate data. This package implements the origami plot, a novel visualization technique proposed by Duan et al. in 2023, which improves upon traditional radar charts by ensuring that the area of the connected region is invariant to the ordering of attributes, addressing a key limitation of radar charts. The software facilitates multivariate decision-making by supporting comparisons across multiple objects and attributes, offering customizable features such as auxiliary axes and weighted attributes for enhanced clarity. Through the R package and user-friendly Shiny interface, researchers can efficiently create and customize plots without requiring extensive programming knowledge. Demonstrated using network meta-analysis as a real-world example, OrigamiPlot proves to be a versatile tool for visualizing multivariate data across various fields. This package opens new opportunities for simplifying decision-making processes with complex data.

Traditional approaches for evaluating the impact of scientific research – mainly scholarship (i.e., publications, presentations) and grant funding – fail to capture the full extent of contributions that come from larger scientific initiatives. The Translational Science Benefits Model (TSBM) was developed to support more comprehensive evaluations of scientific endeavors, especially research designed to translate scientific discoveries into innovations in clinical or public health practice and policy-level changes. Here, we present the domains of the TSBM, including how it was expanded by researchers within the Implementation Science Centers in Cancer Control (ISC3) program supported by the National Cancer Institute. Next, we describe five studies supported by the Penn ISC3, each focused on testing implementation strategies informed by behavioral economics to reduce key practice gaps in the context of cancer care and identify how each study yields broader impacts consistent with TSBM domains. These indicators include Capacity Building, Methods Development (within the Implementation Field) and Rapid Cycle Approaches , implementing Software Technologies , and improving Health Care Delivery and Health Care Accessibility . The examples highlighted here can help guide other similar scientific initiatives to conceive and measure broader scientific impact to fully articulate the translation and effects of their work at the population level.

The long-term complications of COVID-19, known as the post-acute sequelae of SARS-CoV-2 infection (PASC), significantly burden healthcare resources. Quantifying the demand for post-acute healthcare is essential for understanding patients’ needs and optimizing the allocation of valuable medical resources for disease management. Driven by this need, we developed a heterogeneous latent transfer learning framework (Latent-TL) to generate critical insights for individual health systems in a distributed research network. Latent-TL enhances learning in a specific health system by borrowing information from all other health systems in the network in a data-driven fashion. By identifying subpopulations with varying healthcare needs, our Latent-TL framework can provide more effective guidance for decision-making. Applying Latent-TL to electronic health record (EHR) data from eight health systems in PEDSnet, a national learning health system in the US, revealed four distinct patient subpopulations with heterogeneous post-acute healthcare demands following COVID-19 infections, varying across subpopulations and hospitals.

INTRODUCTION Supplementing routine screening mammography with breast MRI can significantly increase cancer detection among women with extremely dense breasts, but breast MRI is not widely utilized. At Penn Medicine in 2021, only 8% of women with extremely dense breasts completed supplemental breast MRI screening, with even lower completion among Black women (3%). METHODS We designed a pragmatic trial to evaluate whether sending “nudges” (messages informed by behavioral economics) promoting breast MRI to patients, clinicians, or both increases breast MRI utilization among women with extremely dense breasts. Given baseline inequities, we also sought to assess whether nudges could reduce racial differences in MRI screening rates. This trial is being performed in a state with newly mandated insurance coverage for supplemental MRI screening for patients with extremely dense breasts. Patients aged 40-74 with extremely dense breasts reported on non-actionable mammograms are identified and independently randomized to receive a patient nudge or not. Patient nudges are delivered via text message following confirmation of identify and interest. Prior to launching the trial, rapid cycle approaches (RCAs) tested a set of messages in a diverse patient sample to optimize and de-risk nudge content. Clinicians receive nudges embedded within the mammogram report or through electronic health record in-basket messages. The primary outcome of the trial is ordering and/or scheduling of breast MRI within 6 months of a mammogram. Enrollment numbers and preliminary data on the implementation of the trial has been collected. RESULTS In 8 months, 990 women with extremely dense breasts have been enrolled. The study population currently includes 618 White women, 170 Black women, and 202 women of other races. In total, 36% of women in the patient nudge arm opted to receive the nudge. Patient nudge reception was 35% for White patients and 38% for Black patients. Additionally, 18% of women in the patient nudge arm opted out of receiving the nudge, with 18% of White patients and 22% of Black patients declining the nudge. The provider nudge was successfully delivered 97% of the time. Overall, MRIs have been ordered for about 22% of patients across all study arms. When stratified by race, the order rate was 25% for White patients compared to 16% for Black patients. While follow-up periods have not been completed for many patients, MRIs have been scheduled for 13% of all patients. CONCLUSIONS Despite laws mandating insurance coverage of supplemental screening and emerging evidence of the benefits of supplemental breast MRI for women with extremely dense breasts, there is still inequity in utilization. This trial aims to determine whether nudges can help reduce this existing inequity. Our early results suggest that patient acceptance of text message nudges was similar for White and Black patients. We eagerly await full analyses to determine the efficacy of nudges in increasing uptake and in improving equity of MRI screening among women with extremely dense breasts. Citation Format: Elizabeth Mack, Daniel Blumenthal, Claudia Fernandez Perez, Rinad Beidas, Justin Bekelman, David A. Asch, Anna-Marika Bauer, Alison M. Buttenheim, Emily F. Conant, Abigail Doucette, Oluwadamilola M. Fayanju, Peter Gabriel, Carmen Guerra, Linda W. Nunes, Martina Plag, Katharine A. Rendle, Rachel C. Shelton, Lawrence Shulman, Sue Ware, Bernadette C. Wheeler, Paul Wileyto, Robert Schnoll, Anne Marie McCarthy. Design and interim review of a pragmatic stepped wedge cluster randomized clinical trial testing behavioral economic implementation strategies to increase supplemental breast MRI screening among patients with extremely dense breasts [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A136.

Background Healthcare worker (HCW) anxiety and depression worsened during the pandemic, prompting the expansion of digital mental health platforms as potential solutions offering online assessments, access to resources and counselling. The use of these digital engagement tools may reflect tendencies and trends for the mental health needs of HCWs. Objectives This retrospective, cross-sectional study investigated the association between the use of an online mental health platform within a large academic health system and measures of that system’s COVID-19 burden during the first 3 years of the pandemic. Methods The study investigated the use of Cobalt, an online mental health platform, comprising deidentified mental health assessments and utilisation metrics. Cobalt, serves as an online mental health resource broadly available to health system employees, offering online evidence-based tools, coaching, therapy options and asynchronous content (podcasts, articles, videos and more). The analyses use validated mental health assessments (Generalised Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9) and post-traumatic stress disorder (PTSD)) alongside publicly available COVID-19 data. Statistical analyses employed univariate linear regression with Stata SE Statistical Software. Results Between March 2020 and March 2023, 43 308 independent user sessions were created on Cobalt, a majority being anonymous sessions (72%, n=31 151). Mental health assessments, including PHQ-4, PHQ-9, GAD-7 and primary care-PTSD, totalled 9462 over the time period. Risk for self-harm was noted in 17.1% of PHQ-9 assessments. Additionally, 4418 appointments were scheduled with mental health counsellors and clinicians. No significant associations were identified between COVID-19 case burden and Cobalt utilisation or assessment scores. Conclusion Cobalt emerged as an important access point for assessing the collective mental health of the workforce, witnessing increased engagement over time. Notably, the study indicates the nuanced nature of HCW assessments of anxiety, depression and PTSD, with mental health scores reflecting moderate decreases in depression and anxiety but signalling potential increases in PTSD. Tailored resources are imperative, acknowledging varied mental health needs within the healthcare workforce. Ultimately, this investigation lays the groundwork for continued exploration of the impact and effectiveness of digital platforms in supporting HCW mental health.

Importance: Anxiety disorders are prevalent and undertreated among young adults. Digital mental health interventions for anxiety are promising but limited by a narrow range of therapeutic components and low user engagement. Objective: To investigate the efficacy of and engagement with Maya, a scalable, self-guided, comprehensive mobile cognitive behavioral therapy (CBT) intervention with embedded engagement features, comparing the effects of 3 incentive conditions. Design, setting, and participants: This randomized clinical trial recruited young adults aged 18 to 25 years with anxiety disorders through online advertisements and outpatient psychiatry clinics at Weill Cornell Medicine. Enrollment was between June 16, 2021, and November 11, 2022. Data analysis was performed from December 21, 2022, to June 14, 2024. Intervention: Participants received a 6-week program of the intervention and were randomized to 1 of 3 different text message-based incentive conditions (gain-framed, loss-framed, or gain-social support). Main outcomes and measures: The primary outcome was change in anxious symptoms from baseline to end of treatment, as measured by the Hamilton Anxiety Rating Scale (HAM-A). The Anxiety Sensitivity Index and the Leibowitz Social Anxiety Scale scores were secondary measures. Results: The sample consisted of 59 participants (mean [SD] age, 23.1 [1.9] years; 46 [78%] female; 22 [37%] Asian, 3 [5%] Black, 5 [8%] Hispanic or Latino, 1 [2%] American Indian or Alaska Native, 25 [42%] White, and 6 [10%] >1 race; 32 [54%] college-educated and 12 [20%] graduate or professional school-educated; mean [SD] baseline HAM-A score, 15.0 [6.5]). Anxiety, measured by HAM-A, decreased across conditions from baseline to end of the intervention (mean difference, -5.64; 95% CI, -7.23 to -4.05), and symptomatic improvement was maintained at the week 12 follow-up (baseline to follow-up mean difference, -5.67; 95% CI, -7.29 to -4.04). However, there was no evidence that change in anxiety differed by incentive condition (loss-framed vs gain-social support mean difference, -1.40; 95% CI, -4.72 to 1.93; gain-framed vs gain-social support mean difference, 1.38; 95% CI, -1.19 to 3.96). Secondary anxiety measures (Anxiety Sensitivity Index and Liebowitz Social Anxiety Scale scores) showed a similar pattern of improvement, with no evidence of differences between incentive conditions. Participants completed most of the 12 sessions (mean [SD], 10.8 [2.1]; 95% CI, 10.3-11.4), and User Mobile Application Rating Scale app quality ratings exceeded the published threshold for acceptability at all study visits. There was no evidence that either session completion or app quality ratings differed by incentive condition. Conclusions and relevance: In this randomized clinical trial of an app-based intervention for anxiety, the primary hypothesis that improvement in anxiety would be greatest in the condition using gain of points plus social incentives was not supported; however, the results suggest that a CBT application incorporating a full suite of CBT skills and embedded user engagement features was efficacious in improving symptoms in young adults with anxiety disorders. Given these findings, digital interventions represent a promising step toward wider dissemination of high-quality, evidence-based interventions. Trial registration: ClinicalTrials.gov Identifier: NCT05130281.

Background Text messaging has emerged as a popular strategy to engage patients after hospital discharge. Little is known about how patients use these programs and what types of needs are addressed through this approach. Objective The goal of this study was to describe the types and timing of postdischarge needs identified during a 30‐day automated texting program. Methods The program ran from January to August 2021 at a primary care practice in Philadelphia. In this mixed‐methods study, two reviewers conducted a directed content analysis of patient needs expressed during the program, categorizing them along a well‐known transitional care framework. We describe the frequency of need categories and their timing relative to discharge. Results A total of 405 individuals were enrolled; the mean (SD) age was 62.7 (16.2); 64.2% were female; 47.4% were Black; and 49.9% had Medicare insurance. Of this population, 178 (44.0%) expressed at least one need during the 30‐day program. The most frequent needs addressed were related to symptoms (26.8%), coordinating follow‐up care (20.4%), and medication issues (15.7%). The mean (SD) number of days from discharge to need was 10.8 (7.9); there were no significant differences in timing based on need category. Conclusions The needs identified via an automated texting program were concentrated in three areas relevant to primary care practice and within nursing scope of practice. This program can serve as a model for health systems looking to support transitions through an operationally efficient approach, and the findings of this analysis can inform future iterations of this type of program.

Importance Serious illness conversations (SICs) that elicit patients’ values, goals, and care preferences reduce anxiety and depression and improve quality of life, but occur infrequently for patients with cancer. Behavioral economic implementation strategies (nudges) directed at clinicians and/or patients may increase SIC completion. Objective To test the independent and combined effects of clinician and patient nudges on SIC completion. Design, Setting, and Participants A 2 × 2 factorial, cluster randomized trial was conducted from September 7, 2021, to March 11, 2022, at oncology clinics across 4 hospitals and 6 community sites within a large academic health system in Pennsylvania and New Jersey among 163 medical and gynecologic oncology clinicians and 4450 patients with cancer at high risk of mortality (≥10% risk of 180-day mortality). Interventions Clinician clusters and patients were independently randomized to receive usual care vs nudges, resulting in 4 arms: (1) active control, operating for 2 years prior to trial start, consisting of clinician text message reminders to complete SICs for patients at high mortality risk; (2) clinician nudge only, consisting of active control plus weekly peer comparisons of clinician-level SIC completion rates; (3) patient nudge only, consisting of active control plus a preclinic electronic communication designed to prime patients for SICs; and (4) combined clinician and patient nudges. Main Outcomes and Measures The primary outcome was a documented SIC in the electronic health record within 6 months of a participant’s first clinic visit after randomization. Analysis was performed on an intent-to-treat basis at the patient level. Results The study accrued 4450 patients (median age, 67 years [IQR, 59-75 years]; 2352 women [52.9%]) seen by 163 clinicians, randomized to active control (n = 1004), clinician nudge (n = 1179), patient nudge (n = 997), or combined nudges (n = 1270). Overall patient-level rates of 6-month SIC completion were 11.2% for the active control arm (112 of 1004), 11.5% for the clinician nudge arm (136 of 1179), 11.5% for the patient nudge arm (115 of 997), and 14.1% for the combined nudge arm (179 of 1270). Compared with active control, the combined nudges were associated with an increase in SIC rates (ratio of hazard ratios [rHR], 1.55 [95% CI, 1.00-2.40]; P = .049), whereas the clinician nudge (HR, 0.95 [95% CI, 0.64-1.41; P = .79) and patient nudge (HR, 0.99 [95% CI, 0.73-1.33]; P = .93) were not. Conclusions and Relevance In this cluster randomized trial, nudges combining clinician peer comparisons with patient priming questionnaires were associated with a marginal increase in documented SICs compared with an active control. Combining clinician- and patient-directed nudges may help to promote SICs in routine cancer care. Trial Registration ClinicalTrials.gov Identifier: NCT04867850