A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning.

ABSTRACT:

Objective

Identifying ethical concerns with ML applications to healthcare (ML-HCA) before problems arise is now a stated goal of ML design oversight groups and regulatory agencies. Lack of accepted standard methodology for ethical analysis, however, presents challenges. In this case study, we evaluate use of a stakeholder "values-collision" approach to identify consequential ethical challenges associated with an ML-HCA for advanced care planning (ACP). Identification of ethical challenges could guide revision and improvement of the ML-HCA.

Materials and methods

We conducted semistructured interviews of the designers, clinician-users, affiliated administrators, and patients, and inductive qualitative analysis of transcribed interviews using modified grounded theory.

Result

SUBMITTER: Cagliero D 

PROVIDER: S-EPMC10114055 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature