10 research outputs found
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A web-based survey to assess perceptions of managed care organization representatives regarding the use of co-pay subsidy coupons for prescription drugs
textPharmaceutical manufacturersâ use of prescription brand-name drugs coupons and vouchers to subsidize patientsâ cost-sharing obligations such as co-pays has increased. The co-pays are used by managed care organizations (MCOs) to give their plan members an appreciation of drug costs as well as to offer incentives to use available equivalent generic alternatives due to lower co-pays. With higher tiered co-pays for brand-name drugs being offset by coupons, little is known about MCO representativesâ perceptions about use of co-pay subsidy coupons for brand-name prescription drugs.
The objective of this study was to assess health plan managers and pharmacy benefit managers (PBMs) perceptions on the use of prescription drug co-pay subsidy coupons. An online survey instrument was used to collect data. A convenience sample of 834 MCO representatives was selected from the Academy of Managed Care Pharmacy (AMCP) membership directory. A total of 122 surveys were returned of which 105 were usable surveys, giving a response rate of 13.7%. A five-point, 11-item Likert scale ranging from 1 through 5, (1 = âStrong Disagreeâ and 5 = âStrongly Agreeâ) was used to measure respondentsâperceptions. Some items referred to coupons used to get co-pay discounts repeatedly over a year (i.e., long-term
use coupons) while some items referred to coupons distributed for trial purposes (i.e., short-term use coupons). Of the 105 respondents, 42 (40%) âagreed,â while 58 (55.2%) âstrongly agreedâ that co-pay subsidy coupons encouraged non-preferred brand-name drugs over preferred brand-name
drugs. A total of 78 respondents (74.3%) reported that brand-name drug coupons undermined tiered formulary structure. Sixty respondents (57.1%) âstrongly agreedâ that short-term use coupons increased plan sponsorâs costs and 72 respondents (68.6%) âagreedâ that sponsor cost increased with long-term use coupons. A total of 42 (40%) reported to âstrongly agreeâ that short-term use coupons should be eliminated whereas 49 (46.7%) respondents reported âstrongly agreedâ that long-term use coupons should be eliminated.
In summary, MCO representatives believe that brand-name drug utilization is increasing due to prescription drug incentives such as coupons which undermines their formulary controls and in turn, increases health care costs.Pharmac
Object Transfer Point Estimation for Prompt Human to Robot Handovers
Handing over objects is the foundation of many human-robot interaction and collaboration tasks. In the scenario where a human is handing over an object to a robot, the human chooses where the object needs to be transferred. The robot needs to accurately predict this point of transfer to reach out proactively, instead of waiting for the final position to be presented.\nWe first conduct a human-to-robot handover motion study to analyze the effect of user height, arm length, position, orientation and robot gaze on the object transfer point. Our study presents new observations on the effect of robot's gaze on the point of object transfer. \n\nNext, we present an efficient method for predicting the Object Transfer Point (OTP), which synthesizes (1) an offline OTP calculated based on human preferences observed in the human-robot motion study with (2) a dynamic OTP predicted based on the observed human motion. Our proposed OTP predictor is implemented on a humanoid nursing robot and experimentally validated in human-robot handover tasks. Compared to using only static or dynamic OTP estimators, it has better accuracy at the earlier phase of handover (up to 45% of the handover motion) and can render fluent handovers with a reach-to-grasp response time (about 3.1 secs) close to natural human receiver's response. In addition, the OTP prediction accuracy is maintained across the robot's visible workspace by utilizing a user-adaptive reference frame
Surrogate Assisted Generation of Human-Robot Interaction Scenarios
As human-robot interaction (HRI) systems advance, so does the difficulty of
evaluating and understanding the strengths and limitations of these systems in
different environments and with different users. To this end, previous methods
have algorithmically generated diverse scenarios that reveal system failures in
a shared control teleoperation task. However, these methods require directly
evaluating generated scenarios by simulating robot policies and human actions.
The computational cost of these evaluations limits their applicability in more
complex domains. Thus, we propose augmenting scenario generation systems with
surrogate models that predict both human and robot behaviors. In the shared
control teleoperation domain and a more complex shared workspace collaboration
task, we show that surrogate assisted scenario generation efficiently
synthesizes diverse datasets of challenging scenarios. We demonstrate that
these failures are reproducible in real-world interactions.Comment: 18 pages; 9 figures; 2 table