Page 25 - GHES-3-2

Global Health Economics and
Sustainability
Retrospective analysis of dialysis and kidney transplant

This survey has a score range of 0–100, with higher scores transplant programs, even when they are more costly
indicating better QoL. Clinically, this survey can guide upfront but garner more significant savings over time.
treatments by incorporating patient-reported outcomes, These insights can also support the justification of seeking
providing better patient monitoring of symptoms and supplementary funds or investment to ensure the efficient
therapy outcomes. Ultimately, it helps to correlate these utilization of resources (Sarhan et al., 2021).
factors, leading to improved patient QoL (Maglakelidze
et al., 2011). 8.2. Clinical significance
The clinical significance of this analysis lies in its potential
7.2. QoL analysis of kidney transplant patients
to improve patient outcomes by providing a comparative
Unlike the survey for dialysis patients, the questionnaire analysis of the QoL between dialysis and transplant patients.
for kidney transplant patients includes various categories, For instance, if a study indicates that transplant patients
such as physical health, mental health, financial condition, experience significantly better QoL, this could justify
daily life experience, and medication change. The scoring prioritizing transplants to enhance patient satisfaction and
typically ranges from 1 to 5, with higher scores indicating long-term health outcomes (Van Mil et al., 2024).
better QoL. The total score, when summed, reflects the The findings by Van Mil et al. (2024) can improve patient
overall well-being in that area (Maglakelidze et al., 2011).
care by providing evidence-based recommendations. For
7.3. Inclusion criteria example, if one establishes that kidney transplants offer far
greater QoL compared to dialysis, clinicians may be more
The inclusion criteria were as follows: (i) adult participants likely to recommend transplantation earlier in the process
diagnosed with hypertension and diabetes mellitus who of treatment (Van Mil et al., 2024).
had undergone kidney transplants or dialysis at least
6 months – 1 year prior; and (ii) participants with a stable 9. Future advancements in the field of the
medical condition.
analysis of the QoL of patients
7.4. Exclusion criteria QoL assessment is a relatively new field in patient care,
The exclusion criteria were as follows: (i) individuals who more so for patients suffering from chronic diseases or
have received kidney transplants before starting dialysis to undergoing long-term treatment processes. In this regard,
maintain homogeneity within the dialysis group; and/or future improvements are most likely to emanate from
(ii) those who are unwilling to participate or have recently technological advances, more holistic and patient-centered
undergone major surgeries or complications that might assessment tools, and growing interest in personalized
affect their current QoL or cost-effectiveness metrics. medicine, some of which are discussed in this section.

8. Significance of the analysis of dialysis 9.1. Digital health and wearable technology
and kidney transplants Monitoring physical and psychological health is anticipated

8.1. Impact on the hospital sector to be conducted by wearable devices and mobile health
apps, where the patient’s wearable continuously records data
8.1.1. Policy formulation on various QoL metrics. These assessments are expected
This analysis is instrumental in convincing hospitals to to be timelier and more accurate (Swan, 2012). Health
make policies that emphasize kidney transplant programs, practitioners can also make more informed decisions
as they not only boost patient success but also prove to be regarding patient care by integrating wearable technology
cost-effective. Achieving this requires establishing more data into electronic health records, providing a more
transplant centers and enhancing pre- and post-transplant comprehensive view of the patient’s health (Swan, 2012).
care systems to better serve a growing number of patients
(Sarhan et al., 2021). 9.2. Utilization of artificial intelligence (AI) and
machine learning
8.1.2. Financial planning The use of AI and machine learning algorithms can predict
Cost analysis between dialysis and transplantation can potential QoL outcomes based on a patient’s prior records,
better support hospital financial planning by allocating selected treatment plan, and real-time data; proactive
budgets to treatments that have the biggest effect on patient clinical and scientific treatments can also be included
outcomes and long-term cost-effectiveness. This approach to enhance QoL prediction (Cohen et al., 2021). Other
also helps in supporting long-term financial planning by implications of natural language processing in the deep
focusing on sustainable investments, such as expanding analysis of patient-reported outcomes arise from various

Volume 3 Issue 2 (2025) 17 https://doi.org/10.36922/ghes.4639