Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices

Pratima Saravanan, Jessica Menold

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. Background: With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. Method: An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts (n = 20) and novices (n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants’ rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Results: Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. Conclusion: The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. Application: By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

Original languageEnglish (US)
JournalHuman Factors
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Applied Psychology
  • Behavioral Neuroscience

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