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.

Cost-Effectiveness of Dapagliflozin for Non-diabetic Chronic Kidney Disease.

BACKGROUND: In the USA, chronic kidney disease (CKD) affects 1 in 7 adults and costs $100 billion annually. The DAPA-CKD trial found dapagliflozin, a sodium glucose co-transporter 2 (SGLT2) inhibitor, to be effective in reducing CKD progression and mortality in patients with diabetic and non-diabetic CKD. Currently, SGLT2 inhibitors are not considered standard of care for patients with non-diabetic CKD. OBJECTIVE: Determine the cost-effectiveness of adding dapagliflozin to standard management of patients with non-diabetic CKD. DESIGN: Markov model with lifetime time horizon and US healthcare sector perspective. PATIENTS: Patients with non-diabetic CKD INTERVENTION: Dapagliflozin plus standard care versus standard care only. MAIN MEASURES: Quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs), all discounted at 3% annually; total incidence of kidney failure on kidney replacement therapy; average years on kidney replacement therapy. KEY RESULTS: Adding dapagliflozin to standard care improved life expectancy by 2 years, increased discounted QALYS (from 6.75 to 8.06), and reduced the total incidence of kidney failure on kidney replacement therapy (KRT) (from 17.4 to 11.0%) and average years on KRT (from 0.77 to 0.43) over the lifetime of the cohort. Dapagliflozin plus standard care was more effective than standard care alone while increasing lifetime costs (from $245,900 to $324,8900, or $60,000 per QALY gained). Results were robust to variations in assumptions about dapagliflozin's efficacy over time and by CKD stage, added costs of kidney replacement therapy, and expected population annual CKD progression rates and sensitive to the cost of dapagliflozin. The net 1-year budgetary implication of treating all US patients with non-diabetic CKD could be up to $21 billion. CONCLUSIONS: Dapagliflozin improved life expectancy and reduced progression of CKD, the proportion of patients requiring kidney replacement therapy, and time on kidney replacement therapy in patients with non-diabetic CKD. Use of dapagliflozin meets conventional criteria for cost-effectiveness.