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BACKGROUND: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice. We implemented a CDSS to aid general practitioners (GPs) in treating patients with urinary tract infections (UTIs), which are a significant health burden worldwide. OBJECTIVE: This study aims to prospectively assess the impact of this CDSS on treatment success and change in antibiotic prescription behavior of the physician. In doing so, we hope to identify drivers and obstacles that positively impact the quality of health care practice with ML. METHODS: The CDSS was developed by Pacmed, Nivel, and Leiden University Medical Center (LUMC). The CDSS presents the expected outcomes of treatments, using interpretable decision trees as ML classifiers. Treatment success was defined as a subsequent period of 28 days during which no new antibiotic treatment for UTI was needed. In this prospective observational study, 36 primary care practices used the software for 4 months. Furthermore, 29 control practices were identified using propensity score-matching. All analyses were performed using electronic health records from the Nivel Primary Care Database. Patients for whom the software was used were identified in the Nivel database by sequential matching using CDSS use data. We compared the proportion of successful treatments before and during the study within the treatment arm. The same analysis was performed for the control practices and the patient subgroup the software was definitely used for. All analyses, including that of physicians' prescription behavior, were statistically tested using 2-sided z tests with an α level of .05. RESULTS: In the treatment practices, 4998 observations were included before and 3422 observations (of 2423 unique patients) were included during the implementation period. In the control practices, 5044 observations were included before and 3360 observations were included during the implementation period. The proportion of successful treatments increased significantly from 75% to 80% in treatment practices (z=5.47, P<.001). No significant difference was detected in control practices (76% before and 76% during the pilot, z=0.02; P=.98). Of the 2423 patients, we identified 734 (30.29%) in the CDSS use database in the Nivel database. For these patients, the proportion of successful treatments during the study was 83%-a statistically significant difference, with 75% of successful treatments before the study in the treatment practices (z=4.95; P<.001). CONCLUSIONS: The introduction of the CDSS as an intervention in the 36 treatment practices was associated with a statistically significant improvement in treatment success. We excluded temporal effects and validated the results with the subgroup analysis in patients for whom we were certain that the software was used. This study shows important strengths and points of attention for the development and implementation of an ML-based CDSS in clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT04408976; https://clinicaltrials.gov/ct2/show/NCT04408976.
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Working memory (WM)-the ability to keep information in mind for short periods of time-is linked to attention and inhibitory abilities, i.e., the capacity to ignore task-irrelevant information. These abilities have been associated with brain oscillations, especially parietal gamma and alpha bands, but it is yet unknown whether these oscillations also modulate attention and inhibitory abilities. To test this, we compared parietal gamma-transcranial alternating current stimulation (tACS) to alpha-tACS and to a non-stimulation condition (Sham) in 51 young participants. Stimulation was coupled with a WM task probing memory-based attention and inhibitory abilities by means of probabilistic retrospective cues, including informative (valid), uninformative (invalid) and neutral. Our results show that relative to alpha and sham stimulation, parietal gamma-tACS significantly increased working memory recall precision. Additional post hoc analyses also revealed strong individual variability before and following stimulation; low-baseline performers showed no significant changes in performance following both gamma and alpha-tACS relative to sham. In contrast, in high-baseline performers gamma- (but not alpha) tACS selectively and significantly improved misbinding-feature errors as well as memory precision, particularly in uninformative (invalid) cues which rely more strongly on attentional abilities. We concluded that parietal gamma oscillations, therefore, modulate working memory recall processes, although baseline performance may further influence the effect of stimulation.