The effect of patient age on discharge destination and complications after lumbar spinal fusion.

Age is an important patient characteristic that has been correlated with specific outcomes after lumbar spine surgery. We performed a retrospective cohort study to model the effect of age on discharge destination and complications after a 1-level or multi-level lumbar spine fusion surgery. The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was used to identify patients who underwent lumbar spinal fusion surgery from 2013 through 2017. Perioperative outcomes were compared across ages 18 to 90 using multivariable nonlinear logistic regressioncontrolling for preoperative characteristics. A total of 61,315 patients were analyzed, with patients over 70 having a higher risk of being discharged to an inpatient rehabilitation center and receiving an intraoperative or postoperative blood transfusion. However, the rates of the other complications and outcomes analyzed in this study were not significantly different as patients age. In conclusion, advanced-age affects the discharge destination after a one- or multi-level fusion and intraoperative/postoperative blood transfusion after a one-level fusion. However, age alone does not significantly affect the risk of the other complications and outcomes assessed in this study. This study will help guide preoperative discussion with advanced-aged patients who are considering a 1-level or multi-level lumbar spine fusion surgery.

More robust estimation of average treatment effects using kernel optimal matching in an observational study of spine surgical interventions.

Inverse probability of treatment weighting (IPTW), which has been used to estimate average treatment effects (ATE) using observational data, tenuously relies on the positivity assumption and the correct specification of the treatment assignment model, both of which are problematic assumptions in many observational studies. Various methods have been proposed to overcome these challenges, including truncation, covariate-balancing propensity scores, and stable balancing weights. Motivated by an observational study in spine surgery, in which positivity is violated and the true treatment assignment model is unknown, we present the use of optimal balancing by kernel optimal matching (KOM) to estimate ATE. By uniformly controlling the conditional mean squared error of a weighted estimator over a class of models, KOM simultaneously mitigates issues of possible misspecification of the treatment assignment model and is able to handle practical violations of the positivity assumption, as shown in our simulation study. Using data from a clinical registry, we apply KOM to compare two spine surgical interventions and demonstrate how the result matches the conclusions of clinical trials that IPTW estimates spuriously refute.

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning.

Empirical risk minimization (ERM) is the workhorse of machine learning, whether for classification and regression or for off-policy policy learning, but its model-agnostic guarantees can fail when we use adaptively collected data, such as the result of running a contextual bandit algorithm. We study a generic importance sampling weighted ERM algorithm for using adaptively collected data to minimize the average of a loss function over a hypothesis class and provide first-of-their-kind generalization guarantees and fast convergence rates. Our results are based on a new maximal inequality that carefully leverages the importance sampling structure to obtain rates with the good dependence on the exploration rate in the data. For regression, we provide fast rates that leverage the strong convexity of squared-error loss. For policy learning, we provide regret guarantees that close an open gap in the existing literature whenever exploration decays to zero, as is the case for bandit-collected data. An empirical investigation validates our theory.

Post-Contextual-Bandit Inference.

Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study participants and increase the chance of identifying good or even best policies. To support credible inference on novel interventions at the end of the study, nonetheless, we still want to construct valid confidence intervals on average treatment effects, subgroup effects, or value of new policies. The adaptive nature of the data collected by contextual bandit algorithms, however, makes this difficult: standard estimators are no longer asymptotically normally distributed and classic confidence intervals fail to provide correct coverage. While this has been addressed in non-contextual settings by using stabilized estimators, the contextual setting poses unique challenges that we tackle for the first time in this paper. We propose the Contextual Adaptive Doubly Robust (CADR) estimator, the first estimator for policy value that is asymptotically normal under contextual adaptive data collection. The main technical challenge in constructing CADR is designing adaptive and consistent conditional standard deviation estimators for stabilization. Extensive numerical experiments using 57 OpenML datasets demonstrate that confidence intervals based on CADR uniquely provide correct coverage.

Personalized Diabetes Management Using Electronic Medical Records.

OBJECTIVE: Current clinical guidelines for managing type 2 diabetes do not differentiate based on patient-specific factors. We present a data-driven algorithm for personalized diabetes management that improves health outcomes relative to the standard of care. RESEARCH DESIGN AND METHODS: We modeled outcomes under 13 pharmacological therapies based on electronic medical records from 1999 to 2014 for 10,806 patients with type 2 diabetes from Boston Medical Center. For each patient visit, we analyzed the range of outcomes under alternative care using a k-nearest neighbor approach. The neighbors were chosen to maximize similarity on individual patient characteristics and medical history that were most predictive of health outcomes. The recommendation algorithm prescribes the regimen with best predicted outcome if the expected improvement from switching regimens exceeds a threshold. We evaluated the effect of recommendations on matched patient outcomes from unseen data. RESULTS: Among the 48,140 patient visits in the test set, the algorithm's recommendation mirrored the observed standard of care in 68.2% of visits. For patient visits in which the algorithmic recommendation differed from the standard of care, the mean posttreatment glycated hemoglobin A1c (HbA1c) under the algorithm was lower than standard of care by 0.44 ± 0.03% (4.8 ± 0.3 mmol/mol) (P < 0.001), from 8.37% under the standard of care to 7.93% under our algorithm (68.0 to 63.2 mmol/mol). CONCLUSIONS: A personalized approach to diabetes management yielded substantial improvements in HbA1c outcomes relative to the standard of care. Our prototyped dashboard visualizing the recommendation algorithm can be used by providers to inform diabetes care and improve outcomes.

Scheduling, revenue management, and fairness in an academic-hospital radiology division.

RATIONALE AND OBJECTIVES: Physician staff of academic hospitals today practice in several geographic locations including their main hospital. This is referred to as the extended campus. With extended campuses expanding, the growing complexity of a single division's schedule means that a naive approach to scheduling compromises revenue. Moreover, it may provide an unfair allocation of individual revenue, desirable or burdensome assignments, and the extent to which the preferences of each individual are met. This has adverse consequences on incentivization and employee satisfaction and is simply against business policy. MATERIALS AND METHODS: We identify the daily scheduling of physicians in this context as an operational problem that incorporates scheduling, revenue management, and fairness. Noting previous success of operations research and optimization in each of these disciplines, we propose a simple unified optimization formulation of this scheduling problem using mixed-integer optimization. RESULTS: Through a study of implementing the approach at the Division of Angiography and Interventional Radiology at the Brigham and Women's Hospital, which is directed by one of the authors, we exemplify the flexibility of the model to adapt to specific applications, the tractability of solving the model in practical settings, and the significant impact of the approach, most notably in increasing revenue by 8.2% over previous operating revenue while adhering strictly to a codified fairness and objectivity. CONCLUSIONS: We found that the investment in implementing such a system is far outweighed by the large potential revenue increase and the other benefits outlined.