Population-level management of Type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health

OBJECTIVE: To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review. RESEARCH DESIGN AND METHODS: We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review. RESULTS: The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 min per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6–16.9 pp) greater time-in-range (70–180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 months after T1D onset. CONCLUSIONS: An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range

Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard

Background Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. Objectives This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. Methods We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. Results The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. Conclusion A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.</p

Bounded analytic functions on the polydisc

In the paper 'Distinguished Varieties' Agler and McCarthy proved several connections between the theory of bounded analytic functions on the bidisc and 1-dimensional algebraic varieties that exit the bidisc through the distinguished boundary. In this paper we extend several of their results to the theory of bounded analytic functions on the polydisc. We give sufficient conditions for a rational inner function on the polydisc to be uniquely determined in the Schur class of the polydisc by it's values on a finite set of points. This follows from giving sufficient conditions for a Pick problem on the polydisc to have a unique solution. We demonstrate that our results can be though of as a generalization to the polydisc of the Schwarz Lemma and Pick's Theorem on the disc. We establish our results by studying the Pick problem with Hilbert function space technique

Health and healthcare disparities associated with the digital divide

The prioritization of telehealth in response to the COVID-19 pandemic may accelerate progress to virtual integrated care at scale. However, disparities in healthcare and health outcomes may widen if telehealth is not accessible to the underserved communities that are most in need. Providers faced with practical constraints have already defaulted into a two-tiered system of telehealth: technology-driven, integrated care for those with the requisite resources; and phone-based, ad-hoc care, possibly less effective, for those without. Insights based on data from federal organizations offer a starting point for primary care providers to understand common structural barriers to telehealth access, identify interventions they can implement to increase equity in their virtual care practice, and advocate for their patients.http://deepblue.lib.umich.edu/bitstream/2027.42/166083/1/Digital Divide and Health Disparities_01.27.2021JH.pdfDescription of Digital Divide and Health Disparities_01.27.2021JH.pdf : Main ArticleSEL