Decision modelling for economic evaluation of liver transplantation

Estimation of lifetime costs for patients receiving a transplant: the case of liver transplantation related to hepatitis B in Italy

Introduction

In Italy, post-liver transplant (LT) hepatitis B virus (HBV) reinfection prophylaxis is frequently based on a combined regimen of anti-HBV immunoglobulin (HBIG) and oral antivirals. However, little information is available at the national level on the cost of LT and the contribution of HBV prophylaxis. This study aimed to quantify the direct healthcare cost for adult patients undergoing LT for HBV-related disease over a lifetime horizon and from the perspective of a National Healthcare Service.

Methods

A pharmaco-economic model was implemented with a 4-tiered approach consisting of 1) preliminary literature research to define the research question; 2) pragmatic literature review to retrieve existing information and inform the model; 3) micro-simulated patient cycles; and 4) validation from a panel of national experts.

Results

The average lifetime healthcare cost of LT for HBV-related disease was €395,986. The greatest cost drivers were post-transplant end-stage renal failure (31.9% of the total), immunosuppression (20.6%), and acute transplant phase (15.8%). HBV reinfection prophylaxis with HBIG and antivirals accounted for 12.4% and 6.4% of the total cost, respectively; however, lifetime HBIG prophylaxis was only associated with a 6.6% increase (~€422 k). Various sensitivity analyses have shown that discount rates have the greatest impact on total costs.

Conclusion

This analysis showed that the burden of LT due to HBV is not only clinical but also economic.

Status of AI-Enabled Clinical Decision Support Systems Implementations in China

BACKGROUND

AI-enabled Clinical Decision Support Systems (AI + CDSSs) were heralded to contribute greatly to the advancement of health care services. There is an increased availability of monetary funds and technical expertise invested in projects and proposals targeting the building and implementation of such systems. Therefore, understanding the actual system implementation status in clinical practice is imperative.

OBJECTIVES

The aim of the study is to understand (1) the current situation of AI + CDSSs clinical implementations in Chinese hospitals and (2) concerns regarding AI + CDSSs current and future implementations.

METHODS

We investigated 160 tertiary hospitals from six provinces and province-level cities. Descriptive analysis, two-sided Fisher exact test, and Mann-Whitney U-test were utilized for analysis.

RESULTS

Thirty-eight of the surveyed hospitals (23.75%) had implemented AI + CDSSs. There were statistical differences on grade, scales, and medical volume between the two groups of hospitals (implemented vs. not-implemented AI + CDSSs, p <0.05). On the 5-point Likert scale, 81.58% (31/38) of respondents rated their overall satisfaction with the systems as "just neutral" to "satisfied." The three most common concerns were system functions improvement and integration into the clinical process, data quality and availability, and methodological bias.

CONCLUSION

While AI + CDSSs were not yet widespread in Chinese clinical settings, professionals recognize the potential benefits and challenges regarding in-hospital AI + CDSSs.

A systems approach to assessing complexity in health interventions: an effectiveness decay model for integrated community case management

Complexity is inherent to any system or program. This is especially true of integrated interventions, such as integrated community case management (iCCM). iCCM is a child health strategy designed to provide services through community health workers (CHWs) within hard-to-reach areas of low-and-middle-income countries (LMICs). It is comprised of many interlinked program components, processes and stakeholders. Elucidating the complexity of such programs is essential to designing interventions that respond to local contexts and successfully plan for sustainable integration. A pragmatic approach has yet to be developed that holistically assesses the many dimensions of iCCM or other integrated programs, their alignment with local systems, and how well they provide effective care. We propose an accessible systems approach to both measuring systems effectiveness and assessing its underlying complexity using a combination of systems thinking tools. We propose an effectiveness decay model for iCCM implementation to measure where patient loss occurs along the trajectory of care. The approach uses process mapping to examine critical bottlenecks of iCCM processes, their influence on effectiveness decay, and their integration into local systems; regression analysis and structural equation modeling to determine effects of key indicators on programmatic outcomes; and qualitative analysis with causal loop diagramming to assess stakeholder dynamics and their interactions within the iCCM program. An accurate assessment of the quality, effectiveness, and strength of community-based interventions relies on more than measuring core indicators and program outcomes; it requires an exploration of how its actors and core components interact as part of a system. Our approach produces an interactive iCCM effectiveness decay model to understand patient loss in context, examines key systems issues, and uses a range of systems thinking tools to assess the dynamic interactions that coalesce to produce observed program outcomes.