Early vs Single Match in the Transition to Residency: Analysis Using NRMP Data From 2014 to 2021.

Background: An Early Result Acceptance Program (ERAP) has been proposed for obstetrics and gynecology (OB/GYN) to address challenges in the transition to residency. However, there are no available data-driven analyses on the effects of ERAP on the residency transition. Objective: We used National Resident Matching Program (NRMP) data to simulate the outcomes of ERAP and compare those to what occurred in the Match historically. Methods: We simulated ERAP outcomes in OB/GYN, using the de-identified applicant and program rank order lists from 2014 to 2021, and compared them to the actual NRMP Match outcomes. We report outcomes and sensitivity analyses and consider likely behavioral adaptations. Results: Fourteen percent of applicants receive a less preferred match under ERAP, while only 8% of applicants receive a more preferred match. Less preferred matches disproportionately affect DOs and international medical graduates (IMGs) compared to US MD seniors. Forty-one percent of programs fill with more preferred sets of applicants, while 24% fill with less preferred sets of applicants. Twelve percent of applicants and 52% of programs are in mutually dissatisfied applicant-program pairs (a pair in which both prefer each other to the match each received). Seventy percent of applicants who receive less preferred matches are part of a mutually dissatisfied pair. In 75% of programs with more preferred outcomes, at least one assigned applicant is part of a mutually dissatisfied pair. Conclusions: In this simulation, ERAP fills most OB/GYN positions, but many applicants and programs receive less preferred matches, and disparities increase for DOs and IMGs. ERAP creates mutually dissatisfied applicant-program pairs and problems for mixed-specialty couples, which provides incentives for gamesmanship.

Equilibrium Allocations under Alternative Waitlist Designs: Evidence from Deceased Donor Kidneys Kidneys.

Waitlists are often used to ration scarce resources, but the trade-offs in designing these mechanisms depend on agents' preferences. We study equilibrium allocations under alternative designs for the deceased donor kidney waitlist. We model the decision to accept an organ or wait for a preferable one as an optimal stopping problem and estimate preferences using administrative data from the New York City area. Our estimates show that while some kidney types are desirable for all patients, there is substantial match-specific heterogeneity in values. We then develop methods to evaluate alternative mechanisms, comparing their effects on patient welfare to an equivalent change in donor supply. Past reforms to the kidney waitlist primarily resulted in redistribution, with similar welfare and organ discard rates to the benchmark first come first served mechanism. These mechanisms and other commonly studied theoretical benchmarks remain far from optimal. We design a mechanism that increases patient welfare by the equivalent of an 18.2 percent increase in donor supply.

Predicting and improving patient-level antibiotic adherence.

Low adherence to prescribed medications causes substantial health and economic burden. We analyzed primary data from electronic medical records of 250,000 random patients from Israel's Maccabi Healthcare services from 2007 to 2017 to predict whether a patient will purchase a prescribed antibiotic. We developed a decision model to evaluate whether an intervention to improve purchasing adherence is warranted for the patient, considering the cost of the intervention and the cost of non-adherence. The best performing prediction model achieved an average area under the receiver operating characteristic curve (AUC) of 0.684, with 82% accuracy in detecting individuals who had less than 50% chance of purchasing a prescribed drug. Using the decision model, an adherence intervention targeted to patients whose predicted purchasing probability is below a specified threshold can increase the number of prescriptions filled while generating significant savings compared to no intervention - on the order of 6.4% savings and 4.0% more prescriptions filled for our dataset. We conclude that analysis of large-scale patient data from electronic medical records can help predict the probability that a patient will purchase a prescribed antibiotic and can provide real-time predictions to physicians, who can then counsel the patient about medication importance. More broadly, in-depth analysis of patient-level data can help shape the next generation of personalized interventions.

The 6-year clinical outcomes for patients registered in a multiregional United States Kidney Paired Donation program - a retrospective study.

We examined what happened during a 6-year period to 1121 end-stage renal disease patients who registered with their willing/incompatible living donors for kidney exchanges with the Alliance for Paired Donation (APD). Of all patients, 65% were transplanted: 37% in kidney paired donation (APD-KPD, APD-other-KPD); 10% with compatible live donors (APD-LD); and 18% with deceased donors (APD-DD). The remaining patients were withdrawn (sick/died/others; 15%), or were still waiting (20%). For those patients with a cPRA 0-94%, 72% received a transplant. In contrast, only 49% of very highly sensitized (VHS; cPRA 95-100%) were transplanted. Of the VHS patients, 50% were transplanted by KPD/APD-LD while 50% benefited through prioritization of deceased donors in the modified kidney allocation system (KAS introduced in 2014). All APD transplanted groups had similar death-censored 4-year graft survivals as their relevant Organ Procurement and Transplantation Network (OPTN) groups. It is noteworthy that VHS graft and patient survival results were comparable to less sensitized and nonsensitized patients. All patients should be encouraged to search for compatible donors through different options. Expanding the donor pool through KPD and the new KAS of the OPTN increases the likelihood of transplantation for VHS patients.

Effect of match-run frequencies on the number of transplants and waiting times in kidney exchange.

Numerous kidney exchange (kidney paired donation [KPD]) registries in the United States have gradually shifted to high-frequency match-runs, raising the question of whether this harms the number of transplants. We conducted simulations using clinical data from 2 KPD registries-the Alliance for Paired Donation, which runs multihospital exchanges, and Methodist San Antonio, which runs single-center exchanges-to study how the frequency of match-runs impacts the number of transplants and the average waiting times. We simulate the options facing each of the 2 registries by repeated resampling from their historical pools of patient-donor pairs and nondirected donors, with arrival and departure rates corresponding to the historical data. We find that longer intervals between match-runs do not increase the total number of transplants, and that prioritizing highly sensitized patients is more effective than waiting longer between match-runs for transplanting highly sensitized patients. While we do not find that frequent match-runs result in fewer transplanted pairs, we do find that increasing arrival rates of new pairs improves both the fraction of transplanted pairs and waiting times.

Finding long chains in kidney exchange using the traveling salesman problem.

As of May 2014 there were more than 100,000 patients on the waiting list for a kidney transplant from a deceased donor. Although the preferred treatment is a kidney transplant, every year there are fewer donors than new patients, so the wait for a transplant continues to grow. To address this shortage, kidney paired donation (KPD) programs allow patients with living but biologically incompatible donors to exchange donors through cycles or chains initiated by altruistic (nondirected) donors, thereby increasing the supply of kidneys in the system. In many KPD programs a centralized algorithm determines which exchanges will take place to maximize the total number of transplants performed. This optimization problem has proven challenging both in theory, because it is NP-hard, and in practice, because the algorithms previously used were unable to optimally search over all long chains. We give two new algorithms that use integer programming to optimally solve this problem, one of which is inspired by the techniques used to solve the traveling salesman problem. These algorithms provide the tools needed to find optimal solutions in practice.