Assessing the Attitudes and Perceptions Regarding the Use of Mobile Health Technologies for Living Kidney Donor Follow-Up: Survey Study

Investigating the Classification of Living Kidney Donation Experiences on Reddit and Understanding the Sensitivity of ChatGPT to Prompt Engineering: Content Analysis

BACKGROUND

Living kidney donation (LKD), where individuals donate one kidney while alive, plays a critical role in increasing the number of kidneys available for those experiencing kidney failure. Previous studies show that many generous people are interested in becoming living donors; however, a huge gap exists between the number of patients on the waiting list and the number of living donors yearly.

OBJECTIVE

To bridge this gap, we aimed to investigate how to identify potential living donors from discussions on public social media forums so that educational interventions could later be directed to them.

METHODS

Using Reddit forums as an example, this study described the classification of Reddit content shared about LKD into three classes: (1) present (presently dealing with LKD personally), (2) past (dealt with LKD personally in the past), and (3) other (LKD general comments). An evaluation was conducted comparing a fine-tuned distilled version of the Bidirectional Encoder Representations from Transformers (BERT) model with inference using GPT-3.5 (ChatGPT). To systematically evaluate ChatGPT's sensitivity to distinguishing between the 3 prompt categories, we used a comprehensive prompt engineering strategy encompassing a full factorial analysis in 48 runs. A novel prompt engineering approach, dialogue until classification consensus, was introduced to simulate a deliberation between 2 domain experts until a consensus on classification was achieved.

RESULTS

BERT and GPT-3.5 exhibited classification accuracies of approximately 75% and 78%, respectively. Recognizing the inherent ambiguity between classes, a post hoc analysis of incorrect predictions revealed sensible reasoning and acceptable errors in the predictive models. Considering these acceptable mismatched predictions, the accuracy improved to 89.3% for BERT and 90.7% for GPT-3.5.

CONCLUSIONS

Large language models, such as GPT-3.5, are highly capable of detecting and categorizing LKD-targeted content on social media forums. They are sensitive to instructions, and the introduced dialogue until classification consensus method exhibited superior performance over stand-alone reasoning, highlighting the merit in advancing prompt engineering methodologies. The models can produce appropriate contextual reasoning, even when final conclusions differ from their human counterparts.

Necesidades asistenciales en el estudio de personas candidatas a donantes de riñón

Objetivos: Describir las necesidades asistenciales del candidato a donante de riñón; sus características sociodemográficas y clínicas; y analizar los resultados desde una perspectiva de género.Material y Método: Estudio observacional descriptivo transversal. Se recogieron datos clínicos; sociodemográficos; y el número y tipo de visitas y pruebas realizadas durante el año 2020.Resultados: Se incluyeron 60 candidatos a donantes de riñón (n=37 mujeres; 61,67%) con una media de 51,98±14,50 años y una mediana de 2,5 [RIQ (0,69-5,29)] meses de estudio. 16 (26,67%) fueron aptos para la donación, correspondiendo al 14,16% de la actividad en Trasplante Renal (TR) del centro de referencia. Se requirieron 757 visitas (20,60% de la actividad) de las que 341 (45,05%) fueron visitas con la enfermera. Se requirieron 423 pruebas (19,60% de la actividad) durante el estudio. Se identificó una media de 1,87±1,35 factores de riesgo cardiovascular en la muestra analizada, siendo de 1,56±0,81 en los que finalmente fueron donantes. Más mujeres (n=12; 75%) que hombres (n=4; 25%) fueron finalmente donantes renales.Conclusiones: El estudio del candidato a donante de riñón es complejo e implica el doble de actividad que en el de los candidatos a receptores de trasplante renal. El proceso finaliza en donación en el 27% de los candidatos estudiados. La enfermera concentra el 45% de las visitas que se requieren. Es necesario explorar estrategias para optimizar el proceso de estudio. Hay diferencias de género en cuanto a la predisposición para estudiarse voluntariamente como candidata a donante renal.

Living Kidney Donation: Practical Considerations on Setting Up a Program

Living kidney donation represents the best treatment for end stage renal disease patients, with the potentiality to pre-emptively address kidney failure and significantly expand the organ pool. Unfortunately, there is still limited knowledge about this underutilized resource. The present review aims to describe the general principles for the establishment, organization, and oversight of a successful living kidney transplantation program, highlighting recommendation for good practice and the work up of donor selection, in view of potential short- and long-terms risks, as well as the additional value of kidney paired exchange programs. The need for donor registries is also discussed, as well as the importance of lifelong follow up.

Perspectives on implementing mobile health technology for living kidney donor follow-up: In-depth interviews with transplant providers

BACKGROUND

United States transplant centers are required to report follow-up data for living kidney donors for 2 years post-donation. However, living kidney donor (LKD) follow-up is often incomplete. Mobile health (mHealth) technologies could ease data collection burden but have not yet been explored in this context.

METHODS

We conducted semi-structured in-depth interviews with a convenience sample of 21 transplant providers and thought leaders about challenges in LKD follow-up, and the potential role of mHealth in overcoming these challenges.

RESULTS

Participants reported challenges conveying the importance of follow-up to LKDs, limited data from international/out-of-town LKDs, and inadequate staffing. They believed the 2-year requirement was insufficient, but expressed difficulty engaging LKDs for even this short time and inadequate resources for longer-term follow-up. Participants believed an mHealth system for post-donation follow-up could benefit LKDs (by simplifying communication/tasks and improving donor engagement) and transplant centers (by streamlining communication and decreasing workforce burden). Concerns included cost, learning curves, security/privacy, patient language/socioeconomic barriers, and older donor comfort with mHealth technology.

CONCLUSIONS

Transplant providers felt that mHealth technology could improve LKD follow-up and help centers meet reporting thresholds. However, designing a secure, easy to use, and cost-effective system remains challenging.

The Impact of the mKidney mHealth System on Live Donor Follow-Up Compliance: Protocol for a Randomized Controlled Trial

Background

Every year, more than 5500 healthy people in the United States donate a kidney for the medical benefit of another person. The Organ Procurement and Transplantation Network (OPTN) requires transplant hospitals to monitor living kidney donors (LKDs) for 2 years postdonation. However, the majority (115/202, 57%) of transplant hospitals in the United States continue to fail to meet nationally mandated requirements for LKD follow-up. A novel method for collecting LKD follow-up is needed to ease both the transplant hospital-level and patient-level burden. We built mKidney—a mobile health (mHealth) system designed specifically to facilitate the collection and reporting of OPTN-required LKD follow-up data. The mKidney mobile app was developed on the basis of input elicited from LKDs, transplant providers, and thought leaders.

Objective

The primary objective of this study is to evaluate the impact of the mKidney smartphone app on LKD follow-up rates.

Methods

We will conduct a two-arm randomized controlled trial (RCT) with LKDs who undergo LKD transplantation at Methodist Specialty and Transplant Hospital in San Antonio, Texas. Eligible participants will be recruited in-person by a study team member at their 1-week postdonation clinical visit and randomly assigned to the intervention or control arm (1:1). Participants in the intervention arm will receive the mHealth intervention (mKidney), and participants in the control arm will receive the current standard of follow-up care. Our primary outcome will be policy-defined complete (all components addressed) and timely (60 days before or after the expected visit date) submission of LKD follow-up data at required 6-month, 1-year, and 2-year visits. Our secondary outcome will be hospital-level compliance with OPTN reporting requirements at each visit. Data analysis will follow the intention-to-treat principle. Additionally, we will collect quantitative and qualitative process data regarding the implementation of the mKidney system.

Results

We began recruitment for this RCT in May 2018. We plan to enroll 400 LKDs over 2 years and follow participants for the 2-year mandated follow-up period.

Conclusions

This pilot RCT will evaluate the impact of the mKidney system on rates of LKD and hospital compliance with OPTN-mandated LKD follow-up at a large LKD transplant hospital. It will provide valuable information on strategies for implementing such a system in a clinical setting and inform effect sizes for future RCT sample size calculations.

International Registered Report Identifier (IRRID)

DERR1-10.2196/11000