Healthcare reimbursements in the US have been traditionally based upon a fee-for-service (FFS) scheme, providing incentives for high volume of care, rather than efficient care. The new healthcare legislation tests new payment models that remove such incentives, such as the bundled payment (BP) system. We consider a population of patients (beneficiaries). The provider may reject patients based on the patient's cost profile, and selects the treatment intensity based on a risk-averse utility function. Treatment may result in success or failure, where failure means that unforeseen complications require further care. Our interest is in analyzing the effect of different payment schemes on outcomes such as the presence and extent of patient selection, the treatment intensity, the provider's utility and financial risk, and the total system payoff. Our results confirm that FFS provides incentives for excessive treatment intensity and results in suboptimal system payoff. We show that BP could lead to suboptimal patient selection and treatment levels that may be lower or higher than desirable for the system, with a high level of financial risk for the provider. We also find that the performance of BP is extremely sensitive to the bundled payment value and to the provider's risk aversion.The performance of both BP and FFS degrades when the provider becomes more risk averse. We design two payment systems, hybrid payment and stop-loss mechanisms, that alleviate the shortcomings of FFS and BP and may induce system optimum decisions in a complementary manner.
We propose and analyze robust optimization models of an inventory management problem, where cumulative purchase, inventory, and shortage costs over n periods are minimized for correlated nonidentically distributed demand. We assume that the means and covariance matrix of stochastic demand are known; the distributions are not needed. We derive closed-form ordering quantities for both symmetric and asymmetric uncertainty sets, under capacitated inventory constraints, in both static and dynamic settings. The behaviors of our robust strategies differ qualitatively depending on the symmetry of the uncertainty set. For instance, considering our simplest static problem, (1) if the uncertainty set is symmetric, then there is positive ordering in all periods, whereas (2) if the set is asymmetric, then there is a set of periods in the middle of the planning horizon with zero orders. We also link the symmetry of the uncertainty set to the symmetry of the demand distribution. Finally, we present encouraging computational results where our solution compares favorably to previously studied, more complex robust solutions.
Background: Telemedicine use has expanded substantially in recent years. Studies evaluating the impact of telemedicine modalities on downstream office visits have demonstrated mixed results. Introduction: We evaluated insurance claims of a large commercial payer, Blue Cross Blue Shield of Michigan (BCBSM), to assess the frequency of follow-up visits following encounters initiated via telemedicine versus in-person. Materials and Methods: We used the BCBSM claim-level data set (2011)(2012)(2013)(2014)(2015)(2016)(2017) to assess encounters in the following places of service: hospital outpatient, doctor's office, patient's home, or psychiatric daycare facility. We identified the primary diagnostic category for 30-day episodes of care using clinical classifications software (CCS) and multilevel clinical classifications software (ML-CCS). Our intervention group consisted of episodes initiated via telemedicine; our control group consisted of episodes initiated in-person. Our primary outcome was the percentage of 30-day episodes with a related visit (encounters occurring within the same period and CCS categories) across CCS categories. Our secondary outcome was the mean related visit rate. Results: The final data set included 4,982,456 patients and 68,148,070 claims, of which 53,853 were telemedicine related. Many episodes did not have related visits (the mean related visit rate was 16%). Telemedicine visits had a higher frequency of related visits across all CCS categories. Discussion: Episodes of care initiated via telemedicine more frequently generate related visits within a 30-day period. This increased health care utilization could represent excessive care or could reflect expanded access to care. Conclusion: Further research should explore the cause of this increased utilization and potential unintended consequences.
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