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IMPORTANCE Rehospitalization after major surgery is common and represents a significant cost to the health care system. Little is known regarding the causes of these readmissions and the degree to which they may be preventable.OBJECTIVE To evaluate the degree to which readmissions after major surgery are potentially preventable. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study used a weighted sample of 1 937 354 patients from the 2017 National Readmissions Database to evaluate all adult inpatient hospitalizations for 1 of 7 common major surgical procedures. Statistical analysis was performed from January 14 to November 30, 2020. MAIN OUTCOMES AND MEASURESThe study calculated 90-day readmission rates as well as rates of readmissions that were considered potentially preventable. Potentially preventable readmissions (PPRs) were defined as those with a primary diagnosis code for superficial surgical site infection, acute kidney injury, aspiration pneumonitis, or any of the Agency for Healthcare Research and Quality-defined ambulatory care sensitive conditions. Multivariable logistic regression was used to identify factors associated with PPRs. RESULTSA total weighted sample of 1 937 354 patients (1 048 046 women [54.1%]; mean age, 66.1 years [95% CI, 66.0-66.3 years]) underwent surgical procedures; 164 755 (8.5%) experienced a readmission within 90 days. Potentially preventable readmissions accounted for 29 321 (17.8%) of all 90-day readmissions, for an estimated total cost to the US health care system of approximately $296million. The most common reasons for PPRs were congestive heart failure exacerbation (34.6%), pneumonia (12.0%), and acute kidney injury (22.5%). In a multivariable model of adults aged 18 to 64 years, patients with public health insurance (Medicare or Medicaid) had more than twice the odds of PPR compared with those with private insurance (adjusted odds ratio, 2.09; 95% CI, 1.94-2.25).Among patients aged 65 years or older, patients with private insured had 18% lower odds of PPR compared with patients with Medicare as the primary payer (adjusted odds ratio, 0.82; 95% CI, 0.74-0.90). CONCLUSIONS AND RELEVANCEThis study suggests that nearly 1 in 5 readmissions after surgery are potentially preventable and account for nearly $300 million in costs. In addition to better inpatient care, improved access to ambulatory care may represent an opportunity to reduce costly readmissions among surgical patients.

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Background COVID-19 has mandated rapid adoption of telehealth for surgical care. However, many surgical providers may be unfamiliar with telehealth. This study evaluates the perspectives of surgical providers practicing telehealth care during COVID-19 to help identify targets for surgical telehealth optimization. Materials and Methods At a single tertiary care center with telehealth capabilities, all department of surgery providers (attending surgeons, residents, fellows, and advanced practice providers) were emailed a voluntary survey focused on telehealth during the pandemic. Descriptive statistics and Mann-Whitney U analyses were performed as appropriate on responses. Text responses were thematically coded to identify key concepts. Results The completion rate was 41.3% (145/351). Providers reported increased telehealth usage relative to the pandemic (p<0.001). Of respondents, 80% (116/145) had no formal telehealth training. Providers estimated that new patient video visits required less time than traditional visits (p=0.001). Satisfaction was high for several aspects of video visits. Comparatively lower satisfaction scores were reported for the ability to perform physical exams (sensitive and non-sensitive) and to break bad news. The largest barriers to effective video visits were limited physical exams (55.6%; 45/81) and lack of provider or patient internet access/equipment/connection (34.6%; 28/81). Other barriers included ineffective communication and difficulty with fostering rapport. Concerns regarding video-to-telephone visit conversion were loss of physical exam/verbal cues (34.3%; 24/70), less personal interactions (18.6%; 13/70), and reduced efficiency (18.6%; 13/70). Conclusions Telehealth remains a new experience for surgical providers despite its expansion. Optimization strategies should target technology barriers and include specialized virtual exam and communication training.

BACKGROUND: With the growing use of telehealth, understanding factors affecting patient follow-up in traditional and telehealth settings is important. Few data exist examining the use of telehealth compared with traditional settings. Bridging this gap is critical to optimizing telehealth use and reducing barriers. STUDY DESIGN: This is a retrospective cohort study of return and postoperative (electronic video [eClinic] and traditional) visits from January 2018 to March 2020 at single tertiary care center. There were 12,359 unique first-encounter patients with 903 eClinic and 11,456 traditional visits; 11,547 patients completed visits, while 812 patients did not show up. Multivariable logistic regression modeling was performed to identify factors associated with no-show. County-level mapping was used to identify patterns in no-show rates. RESULTS: Patients from the eClinic had twice the odds of no-show compared with those from a traditional clinic (p < 0.001). Age was inversely proportional to odds of no-show, with each additional decade associated with a 16% decrease in these odds (p < 0.001). African-American patients had greater odds of no-show compared to Caucasian patients (odds ratio [OR] 2.47; 95% CI 1.95e3.13, p < 0.001). Marital statuses of single and legal separation were associated with higher odds of no-show compared with married marital status (p < 0.001 and p ¼ 0.04, respectively). Minimally invasive and endocrine surgery clinics had lower odds of no-show compared with acute care surgery clinic (p < 0.001 for both). County-level no-show rates demonstrate similar patterns between clinic settings. CONCLUSIONS: Several factors are associated with increased odds of no-show, including the visit being in eClinic. County-level analysis suggests no-show variation is not dependent on geographic location. Understanding these patterns allows for prospective identification of barriers and development of interventions to optimize access and patient care.