Is it possible to do both?
The organ procurement organizations (OPO) and transplant centers (TC) have been tasked with conflicting performance metrics. OPOs are driven for increased volume. TCs are driven for improved quality of care, improved patient outcomes, and reduced costs. OPO policies lead to the recovery of increased numbers of less than optimal organs (e.g. expanded criteria, older, and cardiac death donors). TC policies lead to more conservative practices and many more organ offers being declined. Many clinical studies have demonstrated the effectiveness of transplanting less than optimal organs in certain patients (cite: 1,2,3). Successful organ transplants require the right organ to the right recipient at the right time, requiring alignment among these organizations.
OPOs: increase volume.
TCs: improve patient outcomes while reducing cost.
The OPOs and TCs that work together effectively understand the conflicting performance metrics. When OPOs and TCs work together they can each achieve respective success, ultimately resulting in fulfillment of OPTN strategic goals. OPOs have more knowledge about the availability and quality of donated organs and where they are being transplanted. TCs have more knowledge about the specific needs of their waitlisted patients. To be more effective, OPOs must know about the waitlisted patients at their local TCs, i.e. understanding the needs of their customers. To be more effective, TCs must know about the availability and quality of the organs available to them, i.e. understanding the likelihood of getting a better offer for their patient in a timely manner. OPOs and TCs will adjust their practices if the proper knowledge is transferred effectively.
Health information technology (HIT) and artificial intelligence/machine learning (AI/ML) can rapidly improve this knowledge assembly and transfer. First, OPOs and TCs must process their respective data into actionable knowledge with respect to each performance structure. Second, the OPOs and TCs must communicate this knowledge in real-time allowing for dynamic modeling as the variables in the system change (e.g. waitlist, donor pool, policy, and advancement of medicine). HIT can assist with the accessibility, real-time, and data stream transferring needs. AI/ML can model many different variables simultaneously (big data) and dynamically update as new data become available. “The right data/knowledge to the right transplant professionals to inform the right decision about the right organ to the right patient at the right time” is possible with big data accessed in real-time with HIT and dynamically modeled with AI/ML. Thereby, making it possible to achieve the OPTN strategic goals while achieving OPOs and TCs successful performance metrics. Learn here:
- Axelrod DA, Schnitzler MA, Xiao H, et al. An economic assessment of contemporary kidney transplant practice. Am J Transplant. 2018;18:1168-1176. doi:10.1111/ajt.14702
- Massie AB, Luo X, Chow EKH, Alejo JL, Desai NM, Segev DL. Survival benefit of primary deceased donor transplantation with high-KDPI kidneys. Am J Transplant. 2014;14:2310-2316. doi:10.1111/ajt.12830
- Morales E, Gutiérrez E, Hernández A, et al. Preemptive kidney transplantation in elderly recipients with kidneys discarded of very old donors: A good alternative. Nefrol (English Ed. 2015;35(3):246-255. doi:10.1016/J.NEFROE.2015.07.003