Digitally enabled perioperative pathways combine prehabilitation, education, electronic patient-reported outcome measures (ePROMs)/electronic patient-reported experience measures (ePREMs), and postdischarge telemonitoring. Patient-level evaluations of the Betty Coaching perioperative digital therapeutic have reported feasibility, ePROM adherence, recovery outcomes, and transitions from on-site to app-based prehabilitation and rehabilitation pathways [1-3]. Associations between personalized eHealth and recovery after major abdominal surgery were described in a multicenter randomized trial [4], and other independent studies described remote monitoring in oncology [5], the relationship between eHealth and patient participation [6], and smartphone app–supported postoperative monitoring after oncologic surgery [7]. Whether comparable patterns appear in French national administrative benchmarks is unknown. VISUCHIR (Visualisation de la Chirurgie)—the Agence Technique de l’Information sur l’Hospitalisation (ATIH) surgical observatory derived from Programme de Médicalisation des Systèmes d’Information and Classification Commune des Actes Médicaux (CCAM) data—benchmarks ambulatory organization and readmission vigilance [8-10]. Our narrow aim is to describe whether 2024 VISUCHIR system-level indicators differ at private departments adopting Betty Coaching versus national private-sector benchmarks, without causal inference.

Across the three groupings, the VISUCHIR-published SDD rates at the four early-adopter centers were higher than the national private-sector benchmarks, with descriptive absolute differences ranging from +5.9% to +11.6%. The mean inpatient length of stay was shorter at the early-adopter centers, and the VISUCHIR-reported 5-year evolution of the 30-day readmission indicator was directionally more favorable (Table 1).

In this descriptive, VISUCHIR-based ecological comparison, the four early-adopter departments using Betty Coaching had a favorable, unadjusted benchmarking profile in 2024, with higher SDD rates, shorter mean lengths of stay, and directionally more favorable readmission-related VISUCHIR indicators when compared to national private-sector benchmarks. These findings represent system-level administrative observations, not evidence that the digital therapeutic independently changed outcomes. Because this study is ecological, cross-sectional, nonrandomized, and unadjusted, its contribution is limited to identifying a hypothesis-generating, early-adopter signal across the three VISUCHIR surgical groupings.

These findings are consistent with, but do not validate, prior platform evaluations that reported feasibility, ePROM/ePREM adherence, and favorable recovery after urologic surgery [1-3]. They also align with broader independent evidence on personalized eHealth, smartphone-supported postoperative monitoring, and remote monitoring as components of perioperative care that may support recovery surveillance and patient-clinician coordination [4-7]. Our analysis extends this literature only at the administrative benchmarking level, by examining whether center-level implementation is accompanied by directionally coherent indicators.

Several limitations are central to interpretation. Early-adopter centers may already have been high-performing centers before implementation because of established ambulatory infrastructure, minimally invasive or robotic pathways, enhanced recovery after surgery protocols, discharge coordination, coding practices, or patient selection (“healthy adopter” bias). Moreover, the national private-sector benchmark includes the exposed centers; the comparison is therefore not a strict exposed-versus-unexposed contrast. Further, VISUCHIR provides aggregated public indicators rather than patient- or procedure-level data, precluding case-mix adjustment, clustering-aware modeling, center-level longitudinal analysis, and robust readmission inference; therefore, the absolute differences reported herein should be interpreted only as descriptive contrasts, not as adjusted effect estimates. The length of stay is available only as an aggregate mean, and the 5-year readmission-evolution indicator is a derived VISUCHIR vigilance metric, not a patient-level binomial outcome. Per-patient app uptake and adherence cannot be extracted from VISUCHIR, with individual exposure data for the same platform reported elsewhere [1-3].

These results support a cautious conclusion: early-adopter centers using Betty Coaching displayed a favorable, unadjusted VISUCHIR benchmarking profile in 2024, but causality and independent effectiveness remain unproven. Future work should use implementation timing; patient-level exposure and adherence; procedure-level case mixes; standardized readmission indices or observed-to-expected ratios; and quasi-experimental designs, such as interrupted time series, matched-center comparisons, mixed-effects modeling, or difference-in-differences methods.