These data suggest a dose-response association between apoB in young adults and the presence of midlife CAC independent of baseline traditional CVD risk factors.

Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly driven by the emergence of increasingly accurate machine learning models. However, the promise of AI delivering scalable and sustained value for patient care in the real world setting has yet to be realized. In order to safely and effectively bring AI into use in healthcare, there needs to be a concerted effort around not just the creation, but also the delivery of AI. This AI "delivery science" will require a broader set of tools, such as design thinking, process improvement, and implementation science, as well as a broader definition of what AI will look like in practice, which includes not just machine learning models and their predictions, but also the new systems for care delivery that they enable. The careful design, implementation, and evaluation of these AI enabled systems will be important in the effort to understand how AI can improve healthcare.

Serum RBP4 level is closely associated with impaired glucose regulation and is an independent risk factor for microalbuminuria.

Objective To analyze the impact of factors in healthcare delivery on the net benefit of triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month mortality. Materials and Methods We built a predictive model of 12-month mortality using electronic health record data and evaluated the impact of healthcare delivery factors on the net benefit of triggering an ACP workflow based on the models’ predictions. Factors included nonclinical reasons that make ACP inappropriate: limited capacity for ACP, inability to follow up due to patient discharge, and availability of an outpatient workflow to follow up on missed cases. We also quantified the relative benefits of increasing capacity for inpatient ACP versus outpatient ACP. Results Work capacity constraints and discharge timing can significantly reduce the net benefit of triggering the ACP workflow based on a model’s predictions. However, the reduction can be mitigated by creating an outpatient ACP workflow. Given limited resources to either add capacity for inpatient ACP versus developing outpatient ACP capability, the latter is likely to provide more benefit to patient care. Discussion The benefit of using a predictive model for identifying patients for interventions is highly dependent on the capacity to execute the workflow triggered by the model. We provide a framework for quantifying the impact of healthcare delivery factors and work capacity constraints on achieved benefit. Conclusion An analysis of the sensitivity of the net benefit realized by a predictive model triggered clinical workflow to various healthcare delivery factors is necessary for making predictive models useful in practice.

Introduction: Central line-associated bloodstream infections (CLABSIs) are the most common hospital-acquired infection in pediatric patients. High adherence to the CLABSI bundle mitigates CLABSIs. At our institution, there did not exist a hospital-wide system to measure bundle-adherence. We developed an electronic dashboard to monitor CLABSI bundle-adherence across the hospital and in real time. Methods: Institutional stakeholders and areas of opportunity were identified through interviews and data analyses. We created a data pipeline to pull adherence data from twice-daily bundle checks and populate a dashboard in the electronic health record. The dashboard was developed to allow visualization of overall and individual element bundle-adherence across units. Monthly dashboard accesses and element-level bundle-adherence were recorded, and the nursing staff’s feedback about the dashboard was obtained. Results: Following deployment in September 2018, the dashboard was primarily accessed by quality improvement, clinical effectiveness and analytics, and infection prevention and control. Quality improvement and infection prevention and control specialists presented dashboard data at improvement meetings to inform unit-level accountability initiatives. All-element adherence across the hospital increased from 25% in September 2018 to 44% in December 2019, and average adherence to each bundle element increased between 2018 and 2019. Conclusions: CLABSI bundle-adherence, overall and by element, increased across the hospital following the deployment of a real-time electronic data dashboard. The dashboard enabled population-level surveillance of CLABSI bundle-adherence that informed bundle accountability initiatives. Data transparency enabled by electronic dashboards promises to be a useful tool for infectious disease control.

Background Order sets are widely used tools in the electronic health record (EHR) for improving healthcare quality. However, there is limited insight into how well they facilitate clinician workflow. We assessed four indicators based on order set usage patterns in the EHR that reflect potential misalignment between order set design and clinician workflow needs. Methods We used data from the EHR on all orders of medication, laboratory, imaging and blood product items at an academic hospital and an itemset mining approach to extract orders that frequently co-occurred with order set use. We identified the following four indicators: infrequent ordering of order set items, rapid retraction of medication orders from order sets, additional a la carte ordering of items not included in order sets and a la carte ordering of items despite being listed in the order set. Results There was significant variability in workflow alignment across the 11 762 order set items used in the 77 421 inpatient encounters from 2014 to 2017. The median ordering rate was 4.1% (IQR 0.6%–18%) and median medication retraction rate was 4% (IQR 2%–10%). 143 (5%) medications were significantly less likely while 68 (3%) were significantly more likely to be retracted than if the same medication was ordered a la carte. 214 (39%) order sets were associated with least one additional item frequently ordered a la carte and 243 (45%) order sets contained at least one item that was instead more often ordered a la carte. Conclusion Order sets often do not align with what clinicians need at the point of care. Quantitative insights from EHRs may inform how order sets can be optimised to facilitate clinician workflow.

Purpose: Conventional volumetric modulated arc therapy (VMAT) discretizes the angular space into equally spaced control points during planning and then optimizes the apertures and weights of the control points. The aperture at an angle in between two control points is obtained through interpolation. This approach tacitly ignores the differential need for intensity modulation of different angles. As such, multiple arcs are often required, which may oversample some angle(s) and undersample others. The purpose of this work is to develop a segmentally boosted VMAT scheme to eliminate the need for multiple arcs in VMAT treatment with improved dose distribution and/or delivery efficiency. Methods: The essence of the new treatment scheme is how to identify the need of individual angles for intensity modulation and to provide the necessary beam intensity modulation for those beam angles that need it. We introduce a "demand metric" at each control point to decide which station or control points need intensity modulation. To boost the modulation at selected stations, additional segments are added in the vicinity of the selected stations. The added segments are then optimized together with the original set of station or control points as a whole. The authors apply the segmentally boosted planning technique to four previously treated clinical cases: two head and neck (HN) cases, one prostate case, and one liver case. The proposed planning technique is compared with conventional one-arc and two-arc VMAT. Results: The proposed segmentally boosted VMAT technique achieves better critical structure sparing than one-arc VMAT with similar or better target coverage in all four clinical cases. The segmentally boosted VMAT also outperforms two-arc VMAT for the two complicated HN cases, yet with ∼30% reduction in the machine monitor units (MUs) relative to two-arc VMAT, which leads to less leakage/scatter dose to the patient and can potentially translate into faster dose delivery. For the less challenging prostate and liver cases, similar critical structure sparing as the two-arc VMAT plans was obtained using the segmentally boosted VMAT. The benefit for the two simpler cases is the reduction of MUs and improvement of treatment delivery efficiency. Conclusions: Segmentally boosted VMAT achieves better dose conformality and/or reduced MUs through effective consideration of the need of individual beam angles for intensity modulation. Elimination of the need for multiple arcs in rotational arc therapy while improving the dose distribution should lead to improved workflow and treatment efficacy, thus may have significant implication to radiation oncology practice.

Aim: We assessed potential prognostic factors in pharynx squamous cell carcinoma (SCC) patients by quantitative morphological and intratumoural characteristics obtained by 18 F-fluorodeoxyglucose positron-emission tomography/computed tomography (FDG-PET/CT). Materials and Methods: We retrospectively analyzed the cases of 54 patients with pharynx SCC who underwent chemoradiation therapy. Using their FDG-PET data, we calculated the quantitative morphological and intratumoural characteristics of 14 parameters. The progression-free survival (PFS) and overall survival (OS) information was obtained from patient medical records. We performed univariate and multivariate analyses to assess the 14 quantitative parameters as well as the T-stage, N-stage and tumour location data for their relation to PFS and OS. When an independent predictor was suggested in the multivariate analysis, the parameter was further assessed by the Kaplan-Meier method. Results: In the assessment of PFS, the univariate and multivariate analyses indicated the following as independent predictors: the texture parameter of homogeneity, and the morphological parameter of sphericity. In the Kaplan-Meier analysis, the PFS rate was significantly improved in the patients who had both a higher value of homogeneity (p=0.01) and a higher value of sphericity (p=0.002). With the combined use of homogeneity and sphericity, we could divide the patients with different PFS rates more clearly. Conclusion: The quantitative parameters of homogeneity and sphericity obtained by FDG-PET can be useful for the prediction of the PFS of pharynx SCC patients, especially when used in combination.