Scaling up Surgical Capacity in Kenya: The Kenya Hospital Assessment Tool (K-HAT).

INTRODUCTION: Although substantial progress has been achieved to bring surgical care to the forefront of global health discussions, a number of low-and middle-income countries are still in the process of developing a National Surgical, Obstetric, and Anesthesia Plan (NSOAP). This paper describes the initial step toward the development of the NSOAP through the creation of the Kenya National Hospital Assessment Tool (K-HAT). METHODS: A study protocol was developed by a multisectoral collaborative group that represented the pillars of surgical capacity development in Kenya. The K-HAT was adapted from two World Health Organization (WHO) tools: the Service Availability and Readiness Assessment tool and the Situational Analysis Tool. The survey tool was deployed on Open Data Kit, an open-source electronic encrypted database. This new locally adapted tool was pilot tested in three hospitals in Kenya and subsequently deployed in Level 4 facilities. RESULTS: Eighty-nine questions representing over 800 data points divided into six WHO Health Systems Strengthening sections comprised the K-HAT which was deployed to over 95% of Level 4 hospitals in Kenya. When compared to the WHO Service Availability and Readiness Assessment tool, the K-HAT collected more detailed information. The pilot test team reported that K-HAT was easy to administer, easily understood by the respondents, and that it took approximately 1 hour to collect data from each facility. CONCLUSIONS: The K-HAT collected comprehensive information that can be used to develop Kenya's NSOAP.

Addressing Current Deficits in Patient Education Materials Through Crowdsourcing.

BACKGROUND: Patient education materials are commonly reported to be difficult to understand. OBJECTIVES: We aimed to use crowdsourcing to improve patient education materials at our institution. METHODS: This was a department-wide quality improvement project to increase organizational health literacy. There are 6 phases of this pilot study: (1) evaluating preexisting patient education materials, (2) evaluating online patient education materials at the society (the American Society of Plastic Surgeon) and government level (Medline Plus), (3) redesigning our patient education material and reevaluating the education material, (4) crowdsourcing to evaluate understandability of the new patient education material, (5) data analysis, and (6) incorporating crowdsourcing suggestions to the patient education material. RESULTS: Breast-related patient education materials are not easy to read at the institution level, the society level, and the government level. Our new implant-based breast reconstruction patient education material is easy to read as demonstrated by the crowdsourcing evaluation. More than 90% of the participants reported our material is "very easy to understand" or "easy to understand." The crowdsourcing process took 1.5 days, with 700 workers responding to the survey. The total cost was $9. After incorporating participants' feedback into the finalized material, the readability of the material is at the recommended reading level. The material also had the recommended length (between 400 and 800 words). DISCUSSION: Our study demonstrated a pathway for clinicians to efficiently obtain a large amount of feedback to improve patient education materials. Crowdsourcing is an effective tool to improve organizational health literacy.

Post-mastectomy Breast Reconstruction With Gas vs Saline Tissue Expanders: Does the Fill Type Matter?

The most common reconstruction technique following mastectomy is a 2-stage technique that involves tissue expansion followed by definitive implant-based reconstruction (IBR). Tissue expanders (TEs) have classically used saline for initial fill; however, TEs with an initial gas fill (GTE)-including the CO2-based AeroForm (AirXpanders, San Francisco, CA) TE and TEs initially filled with atmospheric air-have been increasingly used in the past decade. We aimed to compare the outcomes in breast reconstruction for tissue expanders initially filled with saline vs gas. PubMed was queried for studies comparing gas- and saline-filled tissue expanders (STEs) used in IBR. A meta-analysis was performed on major postoperative outcomes and the required expansion and definitive reconstruction time. Eleven studies were selected and included in the analysis. No significant differences existed between tissue expansion with GTEs vs STEs for 11 of the 13 postoperative outcomes investigated. Out of the complications investigated, only the risk of infection/cellulitis/abscess formation was significantly lower in the GTE cohort (odds ratio 0.62; 95% CI, 0.47 to 0.82; P = .0009). The time to definitive reconstruction was also significantly lower in the GTE cohort (mean difference [MD], 45.85 days; 95% CI, -57.80 to -33.90; P < .00001). The total time to full expansion approached significance in the GTE cohort (MD, -20.33 days; 95% CI, -41.71 to 1.04; P = .06). A cost analysis considering TE cost and infection risk determined that GTE use saved a predicted $2055.34 in overall healthcare costs. Surgical outcomes for both fill types were predominantly similar; however, GTEs were associated with a significantly decreased risk of postoperative infection compared to saline-filled TEs. GTEs could also reduce healthcare expenditures and require less time until definitive reconstruction after placement.

Complication Profiles of Smooth vs Textured Tissue Expanders in Breast Reconstruction: A Systematic Review and Meta-Analysis.

BACKGROUND: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a type of non-Hodgkin lymphoma first linked with breast implants in 2011. The correlation between BIA-ALCL and textured devices has led to increased use of smooth devices. However, much of the data surrounding smooth and textured devices investigates breast implants specifically and not tissue expanders. OBJECTIVES: We performed a systematic review and a meta-analysis to compare surgical outcomes for smooth tissue expanders (STEs) and textured tissue expanders (TTEs). METHODS: A search was performed on PubMed, including articles from 2016 to 2023 (n = 419). Studies comparing TTEs and STEs and reported complications were included. A random-effects model was utilized for meta-analysis. RESULTS: A total of 5 articles met inclusion criteria, representing 1709 patients in the STE cohort and 1716 patients in the TTE cohort. The mean duration of tissue expansion with STEs was 221.25 days, while TTEs had a mean time of tissue expansion of 220.43 days.Our meta-analysis found no differences in all surgical outcomes except for explantation risk. STE use was associated with increased odds of explantation by over 50% compared to TTE use (odds ratio = 1.53; 95% CI = 1.15 to 2.02; P = .003). CONCLUSIONS: Overall, STEs and TTEs had similar complication profiles. However, STEs had 1.5 times higher odds of explantation. The incidence of BIA-ALCL is low, and only a single case of BIA-ALCL has been reported with TTEs. This indicates that TTEs are safe and may lower the risk of early complications requiring explantation. Further studies are warranted to further define the relationship between tissue expanders and BIA-ALCL.

Comparison of Patient Education Materials Generated by Chat Generative Pre-Trained Transformer Versus Experts: An Innovative Way to Increase Readability of Patient Education Materials.

INTRODUCTION: Improving patient education materials may improve patient outcomes. This study aims to explore the possibility of generating patient education materials with the assistance of a large language model, Chat Generative Pre-Trained Transformer (ChatGPT). In addition, we compare the accuracy and readability of ChatGPT-generated materials versus expert-generated materials. METHODS: Patient education materials in implant-based breast reconstruction were generated by experts and ChatGPT independently. Readability and accuracy of the materials are the main outcomes. Readability of the materials was compared using Flesch-Kincaid score. Accuracy of the materials generated by ChatGPT was evaluated by 2 independent reviewers. Content errors are categorized into information errors, statistical errors, and multiple errors (errors more than 2 types). RESULTS: The content generated by experts had higher readability. The Flesch-Kincaid score is at the 7.5 grade for expert-generated materials, whereas the content generated by ChatGPT is at the 10.5 grade (despite ChatGPT being asked to generate content at the seventh grade level). The accuracy of ChatGPT-generated content is 50%, with most errors being information errors. ChatGPT often provides information about breast reduction or breast augmentation, despite being asked specifically about breast reconstruction. Despite its limitation, ChatGPT significantly reduced the time required to generate patient education materials. Although it takes experts 1 month to generate patient education materials, ChatGPT generates materials within 30 minutes. CONCLUSIONS: ChatGPT can be a powerful starting tool to generate patient education materials. However, its readability and accuracy still require improvements.