Developing training tools for clinicians in LICs: Using hidden markov modeling to study the decision-making strategies of expert and novice prosthetists

Approximately, 40 million amputees reside in the rural parts of Low-Income Countries (LICs), and 95% of this population do not have proper access to prosthetic devices and rehabilitation services. A proper prosthetic prescription requires a clear understanding of the patient's ambulation, goals, cultural and societal norms, locally available prosthetic materials, etc., which can be accomplished only by a local prosthetist. However, due to the lack of prosthetic schools and training centers in LICs, the rural parts lack well-trained amputee care providers. Hence there is a need to educate the prosthetists and prosthetic technicians in the LIC, specifically in the rural regions. To accomplish this, the current research proposes a decisionsupport tool to aid decision-making during prescription and educate prosthetists. A controlled study was conducted with expert and novice prosthetists to compare effective decisionmaking strategies. Results suggest that experts leverage distinct decision-making strategies when prescribing prosthetic and orthotic devices; in comparison, novices exhibited less consistent patterns of decision-making tendencies. By modeling the decision-making strategies of expert prosthetists, this work lays the foundation to develop an automated decision support tool to support decision-making for prosthetists in LICs, improving overall amputee care.