Valérie D’Acremont’s research while affiliated with Policlinique Médicale Universitaire Lausanne and other places

Digital clinical decision support algorithms (CDSAs) that guide healthcare workers during consultations can enhance adherence to guidelines and the resulting quality of care. However, this improvement depends on the accuracy of inputs (symptoms and signs) entered by healthcare workers into the digital tool, which relies mainly on their clinical skills, that are often limited, especially in resource-constrained primary care settings. This study aimed to identify and characterize potential clinical skill gaps based on CDSA data patterns and clinical observations. We retrospectively analyzed data from 20,204 pediatric consultations conducted using an IMCI-based CDSA in 16 primary health centers in Rwanda. We focused on clinical signs with numerical values: temperature, mid-upper arm circumference (MUAC), weight, height, z-scores (MUAC for age, weight for age, and weight for height), heart rate, respiratory rate and blood oxygen saturation. Statistical summary measures (frequency of skipped measurements, frequent plausible and implausible values) and their variation in individual health centers compared to the overall average were used to identify 10 health centers with irregular data patterns signaling potential clinical skill gaps. We subsequently observed 188 consultations in these health centers and interviewed healthcare workers to understand potential error causes. Observations indicated basic measurements not being assessed correctly in most children; weight (70%), MUAC (69%), temperature (67%), height (54%). These measures were predominantly conducted by minimally trained non-clinical staff in the registration area. More complex measures, done mostly by healthcare workers in the consultation room, were often skipped: respiratory rate (43%), heart rate (37%), blood oxygen saturation (33%). This was linked to underestimating the importance of these signs in child management, especially in the context of high patient loads typical at primary care level. Addressing clinical skill gaps through in-person training, eLearning and regular personalized mentoring tailored to specific health center needs is imperative to improve quality of care and enhance the benefits of CDSAs.

Digital clinical decision support tools have contributed to improved quality of care at primary care level health facilities. However, data from real-world randomized trials are lacking. We conducted a cluster randomized, open-label trial in Tanzania evaluating the use of a digital clinical decision support algorithm (CDSA), enhanced by point-of-care tests, training and mentorship, compared with usual care, among sick children 2 to 59 months old presenting to primary care facilities for an acute illness in Tanzania (ClinicalTrials.gov NCT05144763). The primary outcome was the mean proportion of 14 major Integrated Management of Childhood Illness (IMCI) symptoms and signs assessed by clinicians. Secondary outcomes included antibiotic prescription, counseling provided, and the appropriateness of antimalarial and antibiotic prescriptions. A total of 450 consultations were observed in 9 intervention and 9 control health facilities. The mean proportion of major symptoms and signs assessed in intervention health facilities was 46.4% (range 7.7% to 91.7%) compared to 26.3% (range 0% to 66.7%) in control health facilities, an adjusted difference of 15.1% (95% confidence interval [CI] 4.8% to 25.4%). Only weight, height, and pallor were assessed statistically more often when using the digital CDSA compared to controls. Observed antibiotic prescription was 37.3% in intervention facilities, and 76.4% in control facilities (adjusted risk ratio 0.5; 95% CI 0.4 to 0.7; p<0.001). Appropriate antibiotic prescription was 81.9% in intervention facilities and 51.4% in control facilities (adjusted risk ratio 1.5; 95% CI 1.2 to 1.8; p = 0.003). The implementation of a digital CDSA improved the mean proportion of IMCI symptoms and signs assessed in consultations with sick children, however most symptoms and signs were assessed infrequently. Nonetheless, antibiotics were prescribed less often, and more appropriately. Innovative approaches to overcome barriers related to clinicians’ motivation and work environment are needed.

Background Despite the proven positive effects of clinical decision support systems (CDSSs) on general practitioners’ (GPs’) performance and patient management, their adoption remains slow. Several factors have been proposed to explain GPs' reluctance to adopt these tools. This study hypothesizes that the influence of CDSSs on patient-physician interactions could be a determining factor. To explore this hypothesis, we utilized the FeverTravelApp, designed to assist GPs in managing patients presenting with fever after returning from the tropics. A case–control study was conducted, observing and analyzing fourteen consultations between seven physicians and three simulated patients. Each physician conducted consultations both with and without the FeverTravelApp. The consultations were video-recorded and analyzed using a custom analysis grid based on three existing tools. Simulated patients completed the Communication Assessment Tool (CAT) after each consultation, and each physician participated in a semistructured interview following the use of the app. Results The use of the FeverTravelApp influenced multiple aspects of the consultation, particularly communication. Both patient and GP speaking times decreased, while active silence (no one talking while the GP actively performed a task) increased. GPs focused more on the app, which reduced direct patient interaction. However, this influence seemed to bother GPs more than simulated patients, who rated their GPs equally whether the app was used or not. This could be because patients felt better understood when GPs asked fewer but more specific questions related to travel medicine, thus effectively addressing their concerns. Conclusions This study supports the hypothesis that CDSSs influence consultation dynamics, which may contribute to their slow adoption. It is essential to involve clinicians early in the development of CDSSs to adapt them to clinical workflows and ensure system interoperability. Additionally, tools that allow clinicians to follow the entire clinical reasoning process, such as decision trees, are needed. Further research is necessary to confirm these findings in real patient settings and to develop CDSSs that meet both patients’ and GPs’ expectations.

Measuring the health impact of an epidemic using appropriate indicators is necessarily complex. Mortality does not sum up all the issues, but at least it seems to be an objective indicator. There are, however, a number of different mortality indicators, which do not all convey the same message. During the Covid-19 epidemic in Switzerland, the mortality rate rose by 10.2% in 2020, while life expectancy fell by "only" 0.8%, or 8.3 months, a decline described as "modest" or "complete freefall" depending on when it was published. In reality, the population living in Switzerland in 2020 lost an average of "only" 2.4 days, as the epidemic did not last their entire lives. The use of such an indicator, in comparison with losses due to other factors, would enable us to better estimate the real impact of an epidemic.