Willingness to use clinical scales for menopause management among general practitioners.

OBJECTIVES: The use of digital healthcare technologies to enhance healthcare delivery has seen significant growth. However, a notable a notable research gap exists in the application of clinical scales for menopause management by general practitioners (GPs). This study aims to investigate willingness of GPs to use specific menopausal scale tools in the care of females for menopause management. METHOD: An anonymous online survey was developed, which received responses from 348 French GPs in 2023. Multiple backward logistic regression was performed to identify the factors influencing the willingness to use a practical menopause management scale. RESULTS: In total, 87.93% of GPs are not familiar with the Greene Climacteric Scale and 90.52% are not familiar with the Menopause Quick 6 scale. In contrast, 90.52% would be interested in having access to such scales. The willingness to use a menopause management scale is associated with caring for menopausal females (odds ratio [OR] = 6.13, 95% confidence interval [CI] [2.08-18.08], p = 0.001), less experience (OR = 7.10, 95% CI [2.05-25.22], p = 0.002), the importance of health prevention in daily practice (comparing 'very important' to 'not', OR = 12.98, 95% CI [1.68-97.60], p = 0.004) and the use of a digital scale in daily practice for menopausal management (OR = 2.13, 95% CI [1.04-5.83], p = 0.014). CONCLUSION: Future research is essential in representative population to confirm these findings in menopause management.

Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review.

BACKGROUND: Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. OBJECTIVE: This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. METHODS: We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. RESULTS: A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). CONCLUSIONS: The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.

Assessment of Exoskeletons on Nurses' Quality of Work Life: A Pilot Study at Foch Hospital.

BACKGROUND: The prevention of occupational risks is part of the quality of work life and it is a component that improves the physical work environment. The purpose of the present study was to investigate how to maintain posture and to reduce pain and fatigue for nurses, with an exoskeleton adapted to the work at hospital. METHODS: The exoskeleton was used between 2022 to 2023 at Foch Hospital, France. Phase 1 consisted of the selection of the exoskeleton, and Phase 2 included the testing of the device by the nurses and a questionnaire to assess it. RESULTS: The "active" ATLAS model from JAPET, ensuring lumbar protection, was selected because it corresponds to all the specification criteria to tackle the nurses' unmet need. Among the 14 healthcare professionals, 86% were women; the age of the nurses was between 23 years old and 58 years old. The global median satisfaction score of the nurses relative to the use of the exoskeleton was 6/10. The median impact of the exoskeleton on nurses' fatigue was 7/10. CONCLUSIONS: The implementation of the exoskeleton received global positive qualitative feedback from the nurses concerning the improvement of posture and the reduction in fatigue and pain.

Are current clinical studies on artificial intelligence-based medical devices comprehensive enough to support a full health technology assessment? A systematic review.

INTRODUCTION: Artificial Intelligence-based Medical Devices (AI-based MDs) are experiencing exponential growth in healthcare. This study aimed to investigate whether current studies assessing AI contain the information required for health technology assessment (HTA) by HTA bodies. METHODS: We conducted a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to extract articles published between 2016 and 2021 related to the assessment of AI-based MDs. Data extraction focused on study characteristics, technology, algorithms, comparators, and results. AI quality assessment and HTA scores were calculated to evaluate whether the items present in the included studies were concordant with the HTA requirements. We performed a linear regression for the HTA and AI scores with the explanatory variables of the impact factor, publication date, and medical specialty. We conducted a univariate analysis of the HTA score and a multivariate analysis of the AI score with an alpha risk of 5 %. RESULTS: Of 5578 retrieved records, 56 were included. The mean AI quality assessment score was 67 %; 32 % of articles had an AI quality score ≥ 70 %, 50 % had a score between 50 % and 70 %, and 18 % had a score under 50 %. The highest quality scores were observed for the study design (82 %) and optimisation (69 %) categories, whereas the scores were lowest in the clinical practice category (23 %). The mean HTA score was 52 % for all seven domains. 100 % of the studies assessed clinical effectiveness, whereas only 9 % evaluated safety, and 20 % evaluated economic issues. There was a statistically significant relationship between the impact factor and the HTA and AI scores (both p = 0.046). DISCUSSION: Clinical studies on AI-based MDs have limitations and often lack adapted, robust, and complete evidence. High-quality datasets are also required because the output data can only be trusted if the inputs are reliable. The existing assessment frameworks are not specifically designed to assess AI-based MDs. From the perspective of regulatory authorities, we suggest that these frameworks should be adapted to assess the interpretability, explainability, cybersecurity, and safety of ongoing updates. From the perspective of HTA agencies, we highlight that transparency, professional and patient acceptance, ethical issues, and organizational changes are required for the implementation of these devices. Economic assessments of AI should rely on a robust methodology (business impact or health economic models) to provide decision-makers with more reliable evidence. CONCLUSION: Currently, AI studies are insufficient to cover HTA prerequisites. HTA processes also need to be adapted because they do not consider the important specificities of AI-based MDs. Specific HTA workflows and accurate assessment tools should be designed to standardise evaluations, generate reliable evidence, and create confidence.

Robot-Assisted Surgery vs Robotic Stereotactic Body Radiotherapy in Prostate Cancer: A Cost-Utility Analysis.

Prostate cancer is the most common men cancer in France. Continuous progress in oncology led to develop robot-assisted Radical Prostatectomies (rRP) and robot-assisted stereotactic body radiotherapy (rSBRT). The present study aims at comparing economic and clinical impacts of prostate cancer treatments performed either with rSBRT or rRP in France. A Markov model using TreeAge Pro software was chosen to calculate annual costs; utilities and transition probabilities of localized prostate cancer treatments. Patients were eligible for radiotherapy or surgery and the therapeutic decision was a robot-assisted intervention. Over a 10-year period, rSBRT yielded a significantly higher number of quality-adjusted life years than rRP (8.37 vs 6.85). In France, rSBRT seemed more expensive than rRP (€19,475 vs €18,968, respectively). From a societal perspective, rRP was more cost-saving (incremental cost effectiveness ratio = €332/QALY). The model was sensitive to variations of costs of the initial and recurrence state in one-way sensitivity analyses. Robot-assisted stereotactic body radiotherapy seems more cost-effective than Radical Prostatectomy in terms of QALY despite the slightly higher initial cost due to the use of radiotherapy. It would be interesting to conduct comparative quality of life studies in France over longer periods of time.

Single-use flexible bronchoscopes compared with reusable bronchoscopes: Positive organizational impact but a costly solution.

RATIONALE, AIMS, AND OBJECTIVES: There is at present no standard methodology to analyse the organizational impacts (OIs) of medical devices (MDs), and the field is still in its infancy. The aim of the present study was to assess, at a hospital level, the organizational and economic impacts of the introduction of a new MD, specifically the single-use flexible bronchoscope (FB). METHODS: Both the organizational and economic impacts of the single-use FB were evaluated in comparison with the reusable FB currently used as standard practice in our institution. First, process maps were created for both devices (reusable and single use). Based on the 12 types of OI defined by Roussel et al, interviews were conducted with all stakeholders, and the positive and negative aspects of the reusable and single-use processes were analysed. In a second step, microcosting analysis was conducted to determine the most economical balance in use of the 2 technologies. RESULTS: Process maps highlighted the complexity of the reusable device process when compared with the single-use device process. Among the 12 types of OI, the single-use FB process scored better than the reusable FB process in 75% of cases. With the "fleet" of 15 reusable FBs available in our institution, using single-use FBs would represent an extra cost of €154 per procedure. Single-use and reusable devices would have the same cost (€232 per procedure) with a theoretical annual activity of 328 bronchoscopies, which is much lower than our current activity (1644 procedures per year). CONCLUSIONS: Organizational impact should be considered when assessing MDs. We show in this study that from an organizational viewpoint, there are many advantages to using single-use bronchoscopes. However, in economic impact, it is more cost-effective for our institution, with more than 1500 bronchoscopies performed annually, to use reusable devices.