Background Management of contacts to medical communication centers relies heavily on clinical judgment, contextual understanding, and communication skills. Decision support systems, intended to complement medical expertise, may, due to their rigidity, impede effective caller interaction and may, together with the obligatory documentation of calls, contribute to a workflow that draws attention away from the communication. Recommender systems have demonstrated potential in supporting decision-making across various domains by nudging individuals toward better choices without undermining autonomy. We built a prototype that combined artificial intelligence–based question recommendations with structured documentation (hereafter: the prototype) and conducted a feasibility study to test its influence on operators’ performance. Objective This study aimed to examine whether the prototype influenced the operators’ performance during telephone triage. We hypothesized that the prototype would affect medical quality without affecting communication quality. Methods A quasi-experimental pre- and posttest feasibility study was conducted in a simulated setting. Twenty-five operators were voluntarily recruited from 5 Norwegian medical communication centers, in which 22 operators contributed to both the pretest (before the prototype) and the posttest (with the prototype). The operators handled the same 15 medical cases presented by simulated callers, with a 5-month interval between the 2 sessions. The question recommender was trained on other data and then fine-tuned on the 15 scenarios used. Audio recordings of the calls were rated using the tool Assessment of Quality in Telephone Triage. Pre- and posttest values were compared, with overall medical and communication quality as the primary outcomes. Secondary outcomes included specific items related to medical content and communication, accuracy of triage, patient safety, call duration, and efficiency. Results A total of 320 paired calls were analyzed. Overall medical quality improved significantly with use of the prototype, from a mean of 6.83 points pretest to 7.16 points posttest rated on a 10-point scale (difference 0.34, 95% CI 0.11-0.57; P=.004). The effect size was small (Cohen dz=0.16). No significant change was observed in overall communication quality, with a mean of 7.06 points pretest and 6.97 points posttest (difference –0.09 points, 95% CI –0.28 to 0.10; P=.35). A significant decrease from pre‑ to posttest was observed in the specific items “Collects information about the patient’s location” (P<.001) and “Ensures that the triage decision is understandable and feasible” (P=.002). None of the remaining secondary outcomes showed significant changes. Conclusions The prototype yielded a modest improvement in medical quality within the scenario‑based test environment. Although overall communication quality remained unchanged, aspects of the interaction were negatively affected. Artificial intelligence–based question recommendations combined with structured documentation may serve as useful functionalities within a decision support system, but each functionality requires further testing and development before such technology can be implemented in the triage of unselected, real‑world calls.
Fotland et al. (Thu,) studied this question.