Introduction: Digital health software technologies (DHSTs) have the potential to shift models of care delivery and improve patient outcomes. Health-care students lack training to prepare them for the growing breadth and depth of DHSTs that they will need to identify, assess, and use in their future clinical careers. The implementation of this pilot digital health curriculum aimed to provide health-care trainees skills to identify, assess, and use DHSTs in their future clinical careers. Materials and Methods: From June to August of 2021, the authors implemented a 10 weeks pilot educational internship in partnership with the Accelerated Digital Clinical Ecosystem (ADviCE), a multi-institutional collaborative created by physicians within University of California, San Francisco Health Informatics. Interns completed a customized digital health didactic program developed by program faculty, including a speaker series with clinical informaticists in the field, journal club discussions, and intern-led presentations. In addition, interns were given real-world educational opportunities to directly interact with DHST vendors in the process of creating a DHST registry. Interns also completed a postcourse survey to procure feedback about the pilot program's strengths and weaknesses. Results: Interns learned from a didactic educational curriculum that included more than 10 h of live lectures, 3 supplemental readings, and structured interactions with 74 DHST vendors. Based on a survey to assess interns' satisfaction with the internship, interns ranked the expert speaker series the highest with an average standard deviation ranking on a 5-point Likert scale of 4.6 (0.57) and journal club discussions the lowest with a ranking of 4.31 (0.96). Interns also gave feedback on recommending the internship to future students with a ranking of 4.6 (0.65). Conclusion: This first internship led to the positively favored and successful execution of a remote educational experience exploring digital health in medicine, while also giving interns direct interaction with a broad array of digital health companies.

Objective: This study aimed to compare rehabilitation after lobectomy performed with a Da Vinci robot versus thoracoscopy. Materials and Methods: Patients who underwent lobectomy at the Changzhou First People's Hospital from November 2020 to May 2021 were analyzed retrospectively, including 182 patients in the Da Vinci robot group and 280 patients in the thoracoscopy group. Postoperative hospital stay and postoperative pain were compared between groups. Results: The hospitalization cost of the Da Vinci robot group was $ 10874.06 ± 1757.07, which was higher than that of the thoracoscopy group ($ 7500.96 ± 1704.83). The postoperative hospital stay was significantly shorter in the Da Vinci robot group (4.74 ± 2.104 days) than that in the thoracoscopy group (5.55 ± 2.669 days; t = −3.664, P < 0.001). No significant differences between groups were observed in postoperative intensive care unit admission time and postoperative chest tube indwelling time. The proportion of patients with no pain 2 h after the operation was significantly higher in the Da Vinci robot group than in the thoracoscopy group (26.9% vs. 11.8%; χ² = 17.639, P < 0.001). The proportion of patients with no pain during the first activity was significantly higher in the Da Vinci robot group than in the thoracoscopy group (54.9% vs. 39.6%; χ² = 12.109, P = 0.004). Conclusion: The total cost of Da Vinci robotic surgery was higher than that of thoracoscopy for lobectomy, but the postoperative recovery was better, and the pain was less severe.

Purpose: To clarify the diagnostic performance of reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) and compare prostate cancer (PCa) detection rates of rFOV DWI-targeted biopsy (rFOV DWI-TB) with systemic biopsy (SB). Materials and Methods: Ninety-eight consecutive patients with suspected PCa (mean prostate-specific antigen [PSA]: 17.85 ng/mL, range, 4–28 ng/mL) were prospectively enrolled in this study. All rFOV DWI data were carried out using PI-RADS V 2.0 assessment category. All patients underwent a 10-core SB and a further 2–4 cores of rFOV DWI-TB. The performance of rFOV DWI was analyzed, and the cancer detection rates between two methods were compared. Results: The sensitivity, specificity, positive predictive value, and negative predictive value for detecting PCa with rFOV DWI were 85.11%, 92.16%, 90.91%, and 87.04%, respectively. Area under curve for rFOV DWI was 0.886. In the digital rectal examination (DRE) normal and PSA ≥ 10 ng/mL subgroups, the PCa detection rates were statistically greater for rFOV DWI-TB than for SB (both P < 0.05). The mean Gleason score of cancers detected by rFOV DWI-TB was significantly higher than that detected by SB (P < 0.05). In addition, the detection rate for rFOV DWI-TB cores was significantly better than for SB cores (P < 0.001). Conclusion: RFOV DWI allowed for good diagnostic performance in patients suspected of PCa. It may be useful for clinically significant PCa detecting.