Costs and carbon emissions of virtual preoperative visits implementation
Bibliographic record
Abstract
The rise in carbon dioxide (CO2) in the atmosphere has contributed significantly to climate change, which has been called the greatest threat to human health.1, 2 The healthcare system accounts for 4.4% of all CO2 emissions worldwide, more than global aviation, with the United States contributing 27% of this effect.3, 4 Telemedicine has been adopted in various medical disciplines with the emergence of the SARS-CoV2 pandemic and has been shown to reduce costs.1, 5 Currently, data regarding the sustainability effect of preoperative visits in gynaecology are limited. We aimed to evaluate the economic and carbon emission effects of preoperative visit types, virtual versus in-person in the office, in a minimally invasive gynaecologic surgery practice. All women who underwent surgery with a Division of Minimally Invasive Gynecologic Surgery at a high-volume urban referral quaternary care centre from January 2016 to May 2023 were included. The Division of Minimally Invasive Gynecologic Surgery treats benign gynaecologic conditions only. Virtual consultations, preoperative and postoperative visits were implemented in March 2020, during the COVID-19 pandemic, and have continued to the present day. Prior to March 2020, all patients were seen in person, at our outpatient clinic. After March 2020, decision on the type of preoperative visit since the pandemic was according to clinic closures due to infection surges, patients' preferences and providers' permission, though most patients were recommended to be seen virtually for their initial consultation if an in-office procedure was not required. Patients who experienced both types of visits prior to surgery, virtual and in-person, were excluded. We analysed the costs associated with driving to office visits, driving times, distances and costs, CO2 emissions, as well as patient characteristics, surgical characteristics and complications defined according to the Clavien–Dindo classification. Driving distances, times and CO2 emissions were calculated based on patients' zip codes and their distance to the office (Data S1). Virtual and office visits were compared. The primary outcome was the quantification in driving costs, driving times and CO2 emissions for each group. A total of 1196 and 1751 women had preoperative virtual and office visits, respectively (Tables 1 and S1). Median age was lower in the group of virtual visits (37.0 vs. 40.0 years, p < 0.001). There was a higher proportion of stage IV endometriosis (16.4% vs. 7.8%, p < 0.001) and minimally invasive surgery (89.7% vs 77.7%, p < 0.001) in the virtual group compared to the office visit group. Complication proportions were similar in both groups (5.9% virtual vs. 6.3% office groups, p = 0.639, Table S2). Intraoperative complication proportion was significantly lower in the virtual visits' groups, but comparable after multivariable regression analysis (p = 0.262). Any complications remained comparable between groups after multivariable regression analysis (p = 0.733). The 1751 preoperative office visits resulted in a total of $29 381.78 driving costs, 22 899.61 kg of CO2 emission, 55 716.82 driving miles and 104 429.64 driving minutes/1740.49 h (Figure 1). Per patient, median driving cost was $16.78, CO2 emission was 13.08 kg, driving distance was 31.82 miles and driving time was 59.64 min. Those who had a preoperative virtual visit had more preoperative visits (median 1.00 for both groups, p < 0.001) and less postoperative visits (median 1.00 for both groups, p < 0.001) than women who had a preoperative office visit. The total postoperative office driving costs, CO2 emissions, driving distance and driving times were 11.3 times lower for the patients who had preoperative virtual visits and postoperative office visits (111/1196, 9.3%) than for the patients who had preoperative and postoperative office visits (1254/1751, 71.6%). Preoperative virtual visits resulted in significantly reduced driving costs, CO2 emissions and driving time compared to preoperative office visits, without impacting intraoperative and postoperative outcomes. The prevented carbon emission load for the virtual visit group is equivalent to the amount of carbon sequestered, for example, by 18.7 acres of forest in 1 year, or emitted by driving more than 40 K miles in an average car.6 Time saved by patients in the virtual visits extends beyond driving time only, as it may include time off work and parking time, two parameters that were not included in the analysis.1 Limitations of this study include its retrospective design, single-centre setting, inclusion of patients of relatively high socioeconomic status and calculation based on zip codes that may not be accurate.7 RM- conception, design, acquisition of data, analysis and interpretation of data, drafting the article, approval of the final version; KH- acquisition of data, critical revision of the article, approval of the final version; RS- acquisition of data, critical revision of the article, approval of the final version; GL- conception and design, analysis and interpretation of data, critical revision of the article, approval of the final version; MT- acquisition of data, critical revision of the article, approval of the final version; MS- acquisition of data, critical revision of the article, approval of the final version; KW- conception, design, acquisition of data, critical revision of the article, approval of the final version. None. No funding was received for this research. MT- consultant for Ethicon, Medtronic, Heracure Medical and Cooper Surgical; MS- consultant for Applied Medical and Intuitive Surgical; KW- consultant for Aqua Therapeutics, Hologic, Ethicon and Karl Storz; RM- consultant for Intuitive Surgical. All other authors report no conflicts of interest. The study received approval from the institutional review board at Cedars-Sinai Medical Center (#00001714, 8/28/2023). Cedars Sinai Medical Center, Los Angeles, CA, USA. The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Figure S1. Table S1. Table S2. Data S1. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".