"Be Really Careful about That": Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection.

Objective:  This study aimed to explore clinicians' perceptions and preferences of prototype intelligence augmentation (IA)-based visualization displays of in-hospital deterioration risk scores to inform future user interface design and implementation in clinical care.

Methods:  Prototype visualization displays incorporating an IA-based early warning score (EWS) for in-hospital deterioration were developed using cognitive theory and user-centered design principles. The displays featured variations of EWS and clinical data arranged in multipatient and single-patient views. Physician and nurse participants with at least 5 years of clinical experience were recruited to participate in semistructured qualitative interviews focused on understanding their experiences with IA and thoughts and preferences about the prototype displays. A thematic analysis was performed on these data.

Results:  Six themes were identified: (1) clinicians perceive IA as valuable with some caveats related to function and context; (2) individual differences among users influence preferences for customizability; (3) EWS are particularly useful for patient triage; (4) need for patient-centered contextual information to complement EWS; (5) perspectives related to understanding the EWS composition; and (6) design preferences that focus on clarity for interpretation of information.

Conclusion:  This study demonstrates clinicians' interest in and reservations about IA tools for clinical deterioration. The findings underscore the importance of understanding clinicians' cognitive needs and framing IA-generated tools as complementary to support them. A clinician focuses on high-level pattern matching information, and clinician's comments related to the power of consistency with typical views (e.g., this is "how I usually see things"), and questions regarding support of score interpretation (e.g., age of the data, questions about what the model "knows") suggest some of the challenges of IA implementation. The findings also identify design implications including the need for contextualizing the EWS for the patient's specific situation, incorporating trend information, and explaining the display purpose for clinical use.