Explore the vast range of titles available.

To evaluate the impact of Artificial Intelligence Assisted Prevention and Management for Delirium (AI-AntiDelirium) on improving adherence to delirium guidelines among nurses in the intensive care unit (ICU). Between November 2022 and June 2023, A cluster randomized controlled trial was undertaken. A total of 38 nurses were enrolled in the interventional arm, whereas 42 nurses were recruited for the control arm in six ICUs across two hospitals in Beijing, comparing nurses’ adherence and cognitive load in units that use AI-AntiDelirium or the control group. The AI-AntiDelirium tailored delirium preventive or treated interventions to address patients’ specific risk factors. The adherence rate of delirium interventions was the primary endpoint. The other endpoints were adherence to risk factors assessment, ICU delirium assessment, and nurses’ cognitive load. The repeated measures analysis of variance was utilized to explore the influence of time, group, and time × group interaction on the repeated measurement variable (e.g., adherence, cognitive load). A cumulative total of 1040 nurse days were analyzed for this study. The adherence to delirium intervention of nurses in AI-AntiDelirium groups was higher than control units (75 % vs. 58 %, P < 0.01). When compared to control groups, AI-AntiDelirium was found to be significantly effective in both decreasing extraneous cognitive load (P < 0.01) and improving germane cognitive load (P < 0.01). This study supports the effectiveness of AI-AntiDelirium in enhancing nurses’ adherence to evidence-based, individualized delirium intervention and also reducing extraneous cognitive load. A nurse-led systemshould be applied by nursing administrators to improve compliance with nursing interventions among ICU nurses.

While numerous antimicrobial stewardship programs aim to decrease inappropriate antibiotic prescriptions, evidence of their positive impact is needed to optimize future interventions. This study aimed to evaluate 2 multifaceted antibiotic stewardship interventions for inappropriate systemic antibiotic prescription in primary care. An open-label, cluster-randomized controlled trial of 2501 general practitioners (GPs) working in western France was conducted from July 2019 to January 2021. Two interventions were studied: the standard intervention, consisting of a visit by a health insurance representative who gave prescription feedback and provided a leaflet for treating cystitis and tonsillitis; and a clinical decision support system (CDSS)-based intervention, consisting of a visit with prescription feedback and a CDSS demonstration on antibiotic prescribing. The control group received no intervention. Data on systemic antibiotic dispensing was obtained from the National Health Insurance System (Système National d'Information Inter-Régimes de l'Assurance Maladie) database. The overall antibiotic volume dispensed per GP at 12 months was compared between arms using a 2-level hierarchical analysis of covariance adjusted for annual antibiotic prescription volume at baseline. Overall, 2501 GPs were randomized (n=1099, 43.9% women). At 12 months, the mean volume of systemic antibiotics per GP decreased by 219.2 (SD 61.4; 95% CI -339.5 to -98.8; P<.001) defined daily doses in the CDSS-based visit group compared with the control group. The decrease in the mean volume of systemic antibiotics dispensed per GP was not significantly different between the standard visit group and the control group (-109.7, SD 62.4; 95% CI -232.0 to 12.5 defined daily doses; P=.08). A visit by a health insurance representative combining feedback and a CDSS demonstration resulted in a 4.4% (-219.2/4930) reduction in the total volume of systemic antibiotic prescriptions in 12 months. ClinicalTrials.gov NCT04028830; https://clinicaltrials.gov/study/NCT04028830.

Background The role of appropriate semantic qualifiers (SQs) in the effective use of a clinical decision support system (CDSS) is not yet fully understood. Previous studies have not investigated the input. This study aimed to investigate whether the appropriateness of SQs modified the impact of CDSS on diagnostic accuracy among medical students. Methods For this randomized controlled trial, a total of forty-two fifth-year medical students in a clinical clerkship at Chiba University Hospital were enrolled from May to December 2020. They were divided into the CDSS (CDSS use; 22 participants) and control groups (no CDSS use; 20 participants). Students were presented with ten expert-developed case vignettes asking for SQs and a diagnosis. Three appropriate SQs were established for each case vignette. The participants were awarded one point for each SQ that was consistent with the set SQs. Those with two or more points were considered to have provided appropriate SQs. The CDSS used was the Current Decision Support Ⓡ . We evaluated diagnostic accuracy and the appropriateness of SQ differences between the CDSS and control groups. Results Data from all 42 participants were analyzed. The CDSS and control groups provided 133 (60.5%; 220 answers) and 115 (57.5%; 200 answers) appropriate SQs, respectively. Among CDSS users, diagnostic accuracy was significantly higher with appropriate SQs compared to inappropriate SQs (χ 2 (1) = 4.97, p  = 0.026). With appropriate SQs, diagnostic accuracy was significantly higher in the CDSS group compared to the control group (χ 2 (1) = 1.16 × 10, p  < 0.001). With inappropriate SQs, there was no significant difference in diagnostic accuracy between the two groups (χ 2 (1) = 8.62 × 10 –2 , p  = 0.769). Conclusions Medical students may make more accurate diagnoses using the CDSS if appropriate SQs are set. Improving students’ ability to set appropriate SQs may improve the effectiveness of CDSS use. Trial registration This study was registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (Unique trial number: UMIN000042831).

We have conducted a pragmatic clinical trial aimed to assess whether an electrocardiogram (ECG)-based, artificial intelligence (AI)-powered clinical decision support tool enables early diagnosis of low ejection fraction (EF), a condition that is underdiagnosed but treatable. In this trial ( NCT04000087 ), 120 primary care teams from 45 clinics or hospitals were cluster-randomized to either the intervention arm (access to AI results; 181 clinicians) or the control arm (usual care; 177 clinicians). ECGs were obtained as part of routine care from a total of 22,641 adults ( N  = 11,573 intervention; N  = 11,068 control) without prior heart failure. The primary outcome was a new diagnosis of low EF (≤50%) within 90 days of the ECG. The trial met the prespecified primary endpoint, demonstrating that the intervention increased the diagnosis of low EF in the overall cohort (1.6% in the control arm versus 2.1% in the intervention arm, odds ratio (OR) 1.32 (1.01–1.61), P  = 0.007) and among those who were identified as having a high likelihood of low EF (that is, positive AI-ECG, 6% of the overall cohort) (14.5% in the control arm versus 19.5% in the intervention arm, OR 1.43 (1.08–1.91), P  = 0.01). In the overall cohort, echocardiogram utilization was similar between the two arms (18.2% control versus 19.2% intervention, P  = 0.17); for patients with positive AI-ECGs, more echocardiograms were obtained in the intervention compared to the control arm (38.1% control versus 49.6% intervention, P  < 0.001). These results indicate that use of an AI algorithm based on ECGs can enable the early diagnosis of low EF in patients in the setting of routine primary care. In a pragmatic, cluster-randomized clinical trial, use of an AI algorithm for interpretation of electrocardiograms in primary care practices increased the frequency at which impaired heart function was diagnosed.

Background Clinical decision support systems (CDSSs) may promote practitioner adherence to evidence-based guidelines. This study examined if the addition of a CDSS influenced practitioner delivery of a brief intervention with treatment-seeking smokers who were drinking above recommended alcohol consumption guidelines, compared with practitioners who do not receive a CDSS prompt. Methods This was a cluster randomized controlled trial conducted in primary health care clinics across Ontario, Canada, implementing the Smoking Treatment for Ontario Patients (STOP) smoking cessation program. Clinics randomized to the intervention group received a prompt when a patient reported consuming alcohol above the Canadian Cancer Society (CCS) guidelines; the control group did not receive computer alerts. The primary outcome was an offer of an appropriate educational alcohol resource, an alcohol reduction workbook for patients drinking above the CCS guidelines, and an abstinence workbook to patients scoring above 20 points in the AUDIT screening tool; the secondary outcome was patient acceptance of the resource. The tertiary outcome was patient abstinence from smoking, and alcohol consumption within CCS guidelines, at 6-month follow-up. Results were analyzed using a generalized estimation approach for fitting logistic regression using a population-averaged method. Results Two hundred and twenty-one clinics across Ontario were randomized for this study; 110 to the intervention arm and 111 to the control arm. From the 15,222 patients that enrolled in the smoking cessation program, 15,150 (99.6% of patients) were screened for alcohol use and 5715 patients were identified as drinking above the CCS guidelines. No statistically significant difference between groups was seen in practitioner offer of an educational alcohol resource to appropriate patients (OR = 1.19, 95% CI 0.88–1.64, p  = 0.261) or in patient abstinence from smoking and drinking within the CCS guidelines at 6-month follow-up (OR = 0.93, 95% CI 0.71–1.22, p  = 0.594). However, a significantly greater proportion of patients in the intervention group accepted the alcohol resource offered to them by their practitioner (OR = 1.48, 95% CI 1.01–2.16, p  = 0.045). Conclusion A CDSS may not increase the likelihood of practitioners offering an educational alcohol resource, though it may have influenced patients’ acceptance of the resource. Trial registration This trial is registered with ClinicalTrials.gov, number NCT03108144 , registered on April 11, 2017, “retrospectively registered”.

Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals 1 . This could be related to a lack of expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested whether frequent insulin dose adjustments guided by an automated artificial intelligence-based decision support system (AI-DSS) is as effective and safe as those guided by physicians in controlling glucose levels. ADVICE4U was a six-month, multicenter, multinational, parallel, randomized controlled, non-inferiority trial in 108 participants with type 1 diabetes, aged 10–21 years and using insulin pump therapy (ClinicalTrials.gov no. NCT03003806). Participants were randomized 1:1 to receive remote insulin dose adjustment every three weeks guided by either an AI-DSS, (AI-DSS arm, n  = 54) or by physicians (physician arm, n  = 54). The results for the primary efficacy measure—the percentage of time spent within the target glucose range (70–180 mg dl −1 (3.9–10.0 mmol l −1 ))—in the AI-DSS arm were statistically non-inferior to those in the physician arm (50.2 ± 11.1% versus 51.6 ± 11.3%, respectively, P  < 1 × 10 −7 ). The percentage of readings below 54 mg dl −1 (<3.0 mmol l −1 ) within the AI-DSS arm was statistically non-inferior to that in the physician arm (1.3 ± 1.4% versus 1.0 ± 0.9%, respectively, P  < 0.0001). Three severe adverse events related to diabetes (two severe hypoglycemia, one diabetic ketoacidosis) were reported in the physician arm and none in the AI-DSS arm. In conclusion, use of an automated decision support tool for optimizing insulin pump settings was non-inferior to intensive insulin titration provided by physicians from specialized academic diabetes centers. The randomized-controlled trial ADVICE4U demonstrates non-inferiority of an automated AI-based decision support system compared with advice from expert physicians for optimal insulin dosing in youths with type 1 diabetes.

The aim of this study was to assess the effect of a dynamic electronic cognitive aid with embedded clinical decision support (dCA) versus a static cognitive aid (sCA) tool. Anesthesia residents in clinical anesthesia years 2 and 3 were recruited to participate. Each subject was randomized to one of two groups and performed an identical simulated clinical scenario. The primary outcome was task checklist performance with a secondary outcome of performance using the Anesthesia Non-technical skills (ANTS) scoring system. 34 residents were recruited to participate in the study. 19 residents were randomized to the sCA group and 15 to the dCA group. Overall inter-rater agreement for total checklist, malignant hyperthermia, hyperkalemia and ventricular fibrillation was 98.9%, 97.8%, 99.5% and 99.5% respectively with similar Kappa coefficient. Inter-rater agreement for ANTS partial ratings, however, was only 53.5% with a similar Kappa of 0.15. Mean performance was statistically higher in the dCA group versus the sCA group for total check list performance (15.70 ± 1.93 vs 12.95 ± 2.16, p < 0.0001). The difference in performance between dCA and sCA is most notable in dose-dependent related checklist items (4.60 ± 1.3 vs 1.89 ± 1.23, p < 0.0001), while the performance score for dose-independent checklist items was similar between the two groups (p = 0.8908). ANTS ratings did not differ between groups. In conclusion, we evaluated the use of a sCA versus a dCA with embedded decision support in a simulated environment. The dCA group was found to perform more checklist items correctly.Clinical Trial Registration: Clinicaltrials.gov study #: NCT02440607.

Introduction A recent meta-analysis showed that weaning with SmartCare™ (Dräger, Lübeck, Germany) significantly decreased weaning time in critically ill patients. However, its utility compared with respiratory physiotherapist–protocolized weaning is still a matter of debate. We hypothesized that weaning with SmartCare™ would be as effective as respiratory physiotherapy–driven weaning in critically ill patients. Methods Adult critically ill patients mechanically ventilated for more than 24 hours in the adult intensive care unit of the Albert Einstein Hospital, São Paulo, Brazil, were randomly assigned to be weaned either by progressive discontinuation of pressure support ventilation (PSV) with SmartCare™. Demographic data, respiratory function parameters, level of PSV, tidal volume (VT), positive end-expiratory pressure (PEEP), inspired oxygen fraction (F i O 2 ), peripheral oxygen saturation (SpO 2 ), end-tidal carbon dioxide concentration (EtCO 2 ) and airway occlusion pressure at 0.1 second (P 0.1 ) were recorded at the beginning of the weaning process and before extubation. Mechanical ventilation time, weaning duration and rate of extubation failure were compared. Results Seventy patients were enrolled 35 in each group. There was no difference between the two groups concerning age, sex or diagnosis at study entry. There was no difference in maximal inspiratory pressure, maximal expiratory pressure, forced vital capacity or rapid shallow breathing index at the beginning of the weaning trial. PEEP, VT, F i O 2 , SpO 2 , respiratory rate, EtCO 2 and P 0.1 were similar between the two groups, but PSV was not (median: 8 vs. 10 cmH 2 O; p =0.007). When the patients were ready for extubation, PSV (8 vs. 5 cmH 2 O; p =0.015) and PEEP (8 vs. 5 cmH 2 O; p <0.001) were significantly higher in the respiratory physiotherapy–driven weaning group. Total duration of mechanical ventilation (3.5 [2.0–7.3] days vs. 4.1 [2.7-7.1] days; p =0.467) and extubation failure (2 vs. 2; p =1.00) were similar between the two groups. Weaning duration was shorter in the respiratory physiotherapy–driven weaning group (60 [50–80] minutes vs. 110 [80–130] minutes; p <0.001). Conclusion A respiratory physiotherapy–driven weaning protocol can decrease weaning time compared with an automatic system, as it takes into account individual weaning difficulties. Trial registration Clinicaltrials.gov Identifier: NCT02122016 . Date of Registration: 27 August 2013.

ObjectiveTo improve medication appropriateness and adherence in elderly patients with multimorbidity, we developed a complex intervention involving general practitioners (GPs) and their healthcare assistants (HCA). In accordance with the Medical Research Council guidance on developing and evaluating complex interventions, we prepared for the main study by testing the feasibility of the intervention and study design in a cluster randomised pilot study.Setting20 general practices in Hesse, Germany.Participants100 cognitively intact patients ≥65 years with ≥3 chronic conditions, ≥5 chronic prescriptions and capable of participating in telephone interviews; 94 patients completed the study.InterventionThe HCA conducted a checklist-based interview with patients on medication-related problems and reconciled their medications. Assisted by a computerised decision-support system (CDSS), the GPs discussed medication intake with patients and adjusted their medication regimens. The control group continued with usual care.Outcome measuresFeasibility of the intervention and required time were assessed for GPs, HCAs and patients using mixed methods (questionnaires, interviews and case vignettes after completion of the study). The feasibility of the study was assessed concerning success of achieving recruitment targets, balancing cluster sizes and minimising drop-out rates. Exploratory outcomes included the medication appropriateness index (MAI), quality of life, functional status and adherence-related measures. MAI was evaluated blinded to group assignment, and intra-rater/inter-rater reliability was assessed for a subsample of prescriptions.Results10 practices were randomised and analysed per group. GPs/HCAs were satisfied with the interventions despite the time required (35/45 min/patient). In case vignettes, GPs/HCAs needed help using the CDSS. The study made no patients feel uneasy. Intra-rater/inter-rater reliability for MAI was excellent. Inclusion criteria were challenging and potentially inadequate, and should therefore be adjusted. Outcome measures on pain, functionality and self-reported adherence were unfeasible due to frequent missing values, an incorrect manual or potentially invalid results.ConclusionsIntervention and trial design were feasible. The pilot study revealed important limitations that influenced the design and conduct of the main study, thus highlighting the value of piloting complex interventions.Trial registration numberISRCTN99691973; Results.

IntroductionRising burden of cardiovascular disease (CVD) and diabetes is a major challenge to the health system in India. Innovative approaches such as mobile phone technology (mHealth) for electronic decision support in delivering evidence-based and integrated care for hypertension, diabetes and comorbid depression have potential to transform the primary healthcare system.Methods and analysismWellcare trial is a multicentre, cluster randomised controlled trial evaluating the clinical and cost-effectiveness of a mHealth system and nurse managed care for people with hypertension and diabetes in rural India. mWellcare system is an Android-based mobile application designed to generate algorithm-based clinical management prompts for treating hypertension and diabetes and also capable of storing health records, sending alerts and reminders for follow-up and adherence to medication. We recruited a total of 3702 participants from 40 Community Health Centres (CHCs), with ≥90 at each of the CHCs in the intervention and control (enhanced care) arms. The primary outcome is the difference in mean change (from baseline to 1 year) in systolic blood pressure and glycated haemoglobin (HbA1c) between the two treatment arms. The secondary outcomes are difference in mean change from baseline to 1 year in fasting plasma glucose, total cholesterol, predicted 10-year risk of CVD, depression, smoking behaviour, body mass index and alcohol use between the two treatment arms and cost-effectiveness.Ethics and disseminationThe study has been approved by the institutional Ethics Committees at Public Health Foundation of India and the London School of Hygiene and Tropical Medicine. Findings will be disseminated widely through peer-reviewed publications, conference presentations and other mechanisms.Trial registrationmWellcare trial is registered with Clinicaltrial.gov (Registration number NCT02480062; Pre-results) and Clinical Trial Registry of India (Registration number CTRI/2016/02/006641). The current version of the protocol is Version 2 dated 19 October 2015 and the study sponsor is Public Health Foundation of India, Gurgaon, India (www.phfi.org).