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BACKGROUND In-hospital dermatological care has shifted from dedicated dermatology wards to consultation services, and some consulted patients may require postdischarge follow-up in outpatient dermatology. Safe and timely care transitions from inpatient-to-outpatient specialty care are critical for patient health, but communication around these transitions can be disjointed, and workflows can be complex. OBJECTIVE In this 3-phase quality improvement effort, we developed and evaluated an intervention that leveraged an electronic health record (EHR) feature, known as SmartPhrase, to enable a new workflow to improve transitions from inpatient care to outpatient dermatology. METHODS Phase 1 (February-March 2021) included interviews with patients and process mapping with key stakeholders to identify gaps and inform an intervention: a SmartPhrase table and associated workflow to promote collection of patient information needed for scheduling follow-up and closed-loop communication between dermatology and scheduling teams. In phase 2 (April-May 2021), semistructured interviews—with dermatologists (n=5), dermatology residents (n=5), and schedulers (n=6)—identified pain points and refinements. In phase 3, the intervention was evaluated by triangulating data from these interviews with measured changes in scheduling efficiency, visit completion, and messaging volume preimplementation (January-February 2021) and postimplementation (April-May 2021). RESULTS Preintervention pain points included unclear workflow for care transitions, limited patient input in follow-up planning, multiple messaging channels (eg, EHR based, email, and phone messages), and time-inefficient patient tracking. The intervention addressed most pain points; interviewees reported the intervention was easy to adopt and improved scheduling efficiency, workload, and patient involvement. More visits were completed within the desired timeframe of 14 days after discharge during the postimplementation period (21/47, 45%) than the preimplementation period (28/41, 68%; P=.03). The messaging workload also decreased from 88 scheduling-related messages sent for 25 patients before implementation to 30 messages for 8 patients after implementation. CONCLUSIONS Inpatient-to-outpatient specialty care transitions are complex and involve multiple stakeholders, thus requiring multifaceted solutions. With deliberate evaluation, broad stakeholder input, and iteration, we designed and implemented a successful solution using a standard EHR feature, SmartPhrase, integrated into a standardized workflow to improve the timeliness of posthospital specialty care and reduce workload.

BACKGROUND Both clinicians and patients have increasingly turned to telemedicine to improve access to care, even in physical exam dependent specialties like dermatology. However, little is known whether teledermatology supports effective and timely transitions from inpatient to outpatient care, a common care coordination gap. OBJECTIVE Using mixed methods, we sought to evaluate how teledermatology use affected clinic capacity, scheduling efficiency, and timeliness of follow-up care for patients transitioning from inpatient to outpatient dermatology care. METHODS Patient-level encounter scheduling data were used to compare the number and proportion of patients who were scheduled and received in-clinic or video dermatology follow-up within 90 and 14 days post-discharge across three phases: (1) June-September 2019 (pre-teledermatology), (2) June-September 2020 (early teledermatology), (3) February-May 2021 (sustained teledermatology). Time from discharge to scheduling and completion of patient’s follow-up visit for each care modality was also compared. Dermatology clinicians and schedulers were also interviewed between April and May 2021 to assess their perceptions on teledermatology for post-discharge patients. RESULTS The clinic’s capacity to provide follow-up to patients transitioning from inpatient care increased by 36% when teledermatology services were introduced; more patients completed follow-up within 90 days post-discharge during early (n=101) and sustained (n=100) teledermatology use than at baseline (n=74). During early teledermatology use, 61% (n=62) of follow-ups were conducted via video. This decreased significantly to 47% (n=47) the following year when COVID-19-related restrictions started to lift (P = .04) indicating more targeted but still substantial use. The proportion of patients who followed up within the recommended 14 days post-discharge did not differ significantly between video and in-clinic visits during the early (53% vs. 38%; P = .15) or sustained (51% vs. 49%; P = .29) teledermatology periods. Interviewees agreed that teledermatology will continue to be offered. Most considered post-discharge follow-up patients to be ideal candidates for teledermatology since they had had a recent in-person assessment and might have difficulty attending in-clinic visits due to competing health priorities. Some reported patients needing technological support. Ultimately, most agreed that the choice of follow-up care modality should be the patient’s. CONCLUSIONS Teledermatology could be an important tool in maintaining accessible, flexible, and convenient care for recently discharged patients needing follow-up care. Teledermatology increased clinic capacity, even during a pandemic, although timeliness of care transitions were not improved. Ultimately, the care modality should be determined in communication with patients to incorporate their and their family/caregivers’ preferences.

Introduction: A small subset of T cells from patients treated with MART-1 engineered adoptive T cell transfer (ACT) were found to co-express Yamanaka transcription factors SOX2, OCT3/4, and NANOG (TSON). Identifying these T cells may have potential use in future T cell based immunotherapy. Methods: Flow (FC) and mass cytometry (MC) were used to evaluate T cells with induced pluripotent stem cell (iPS)-like markers. Peripheral mononuclear cells (PBMC) were collected from 3 melanoma patients (MP) treated with ACT and 1 healthy donor (HD0). Bone marrow (BM) samples were collected from patients with Non-Hodgkin's lymphoma (NHL) and multiple myeloma (MM). T cell populations were then screened for stem cell markers: SOX2 OCT3/4 NANOG TRA-1-81 SSEA4 TRA-1-60 CD3+ CD34-. Naïve T cells (CD45RO- CD62L+) from HD0 were exposed to IL-7/IL-15/IL-21 with or without pan-AKT inhibitor (AKTi). These cells were analyzed after 7 days for TSON expression and the following surface markers: CD27 CD28 CD45RO CD45RA CD57 CD62L CD95 CD122 CD127 CCR4 CCR6 CCR7. PBMC collections from 5 healthy donors (HD) were expanded with or without IL-7/IL-15/IL-21 exposure in G-Rex bioreactors for 7 days. TSON expression was analyzed using MC and characterized for the already mentioned markers as well as CXCR3 and Ki67. Lastly, expanded CD95+ cells were single-cell sorted for TSON and their RNA was extracted using the FRISCR method for single-cell RNA sequencing (scRNA-seq). The iPS cell line H1 was included as a positive control. Results: T cells across all 3 MP had a small percentage of TSON expression (0.0002%, 0.0001%, 0.0001%, respectively). This small cohort of T cells co-expressed TRA-1-81, SSEA4, and TRA-1-60 (0.05%, 0.05%, 0.8%, respectively). TSON expression for HD0 was 0.0000013%, while co-expression of TRA-1-81, SSEA4, and TRA-1-60 was undetectable. BM samples NHL and MM also showed TSON expression (0.18% vs 0.06%). Naïve T cells treated with IL-7/IL-15/IL-21 had a significantly higher percentage of TSON after 7 days of exposure compared to T cells treated with IL-7/IL-15/IL-21/AKTi (0.008% vs 0.001%, respectively). Furthermore TSON cell subsets in both treatment populations displayed a phenotype similar to T memory stem cells (TMSC) and central memory T cells (TCM) (CD45RA+ CD27+ CD28+ CD62L+ CCR7+ CD95+ CD57- CD45RO+). Concerning PBMCs from the 5 HD, the proliferation marker Ki67 was displayed on 3 non-stimulated HD and all stimulated HD with a range of median intensities [10, 67]. The presence of CD62L CD27 CD95 CCR7 CD28 CD45RO in both non-stimulated and stimulated cell populations was similar to TCM phenotype. Validation of scRNA-seq is still in process. Early results show that OCT3, NANOG, and cMYC are detected in both TSON and H1 while SOX2 could only be detected in H1. Conclusion: Our data show evidence of T cells with natural TSON expression. However, additional studies are needed to further understand the role of TSON in immunotherapy. Citation Format: Michael Cerniglia, Helena Escuin-Ordinas, Caroline Porter, Maria Alexandrovna Aleshin, Gardenia Cheung-Lau, Zoran Galic, Inbal Abraham-Davidi, Daniel Sanghoon Shin, Orit Rosenblatt-Rosen, Antoni Ribas, Begoña Comin-Anduix. Phenotypic characteristics of T cells co-expressing SOX2, OCT3/4, and NANOG [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1551.