Decision Support for Healthcare Processes and Services
Permanent URI for this collectionhttps://hdl.handle.net/10125/112475
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Item type: Item , Optimizing Mental Health Referral Workflows: A Framework for Trust-Critical Decision Points(2026-01-06) Das, JishnuMental health referral systems exhibit significant workflow failures, with rejection rates varying from 33% nationally to 70-90% among individual practitioners. Through systematic literature review and semi-structured interviews with nine Norwegian general practitioners, we identified five trust-critical decision points where uncertainty triggers defensive behaviors: severity assessment, confidence determination, rejection response, pathway selection, and information handoff. We developed a multi-criteria optimization framework proposing uncertainty visualization, predictive analytics, and adaptive documentation interventions. Based on comparable implementations achieving 20-40% efficiency gains, our framework addresses the gap between system metrics and practitioner reality, reconceptualizing trust as a dynamic workflow factor.Item type: Item , Is AI Agreement Reassuring? It Depends on What Patients Believe About AI(2026-01-06) Chen, Cheng; Sun, Yuan; Hua, Peixin; Cheng, YangPeople often consult online information when choosing a doctor prior to their first visit. As AI increasingly supports medical decision-making, we examined how incorporating an AI agreement cue in a doctor's online profile influences patients’ perceptions of credibility and their intention to visit. In a user study (N = 415), participants reviewed a doctor’s profile indicating 90% diagnostic agreement with either AI decision support systems or human experts. The results show that neither the AI nor the expert agreement cue significantly influences credibility perceptions or visit intention. However, among participants with low or moderate beliefs in the positive machine heuristic, or low beliefs in the negative machine heuristic, the AI agreement cue reduces perceived competence, trustworthiness, and intention to visit. These findings highlight how beliefs about AI shape user responses to the AI agreement cue and offer implications for effective self-presentation strategies for healthcare professionals collaborating with AI.Item type: Item , Benchmarking the Nurse Re-Rostering Problem: A Dataset and Instance Generator for evaluating Algorithms that handle short-term scheduling Disruptions(2026-01-06) Veenaas, Britta; Buchwitz, BenjaminThe Nurse Re-Rostering Problem (NRRP) involves short-term schedule adjustments in response to disruptions such as staff absences or sudden changes in demand. Unlike the well-studied Nurse Rostering Problem, the NRRP receive relatively little attention-both in terms of algorithm development and in terms of the availability of standardized benchmark datasets. Existing benchmarks primarily focus on initial rostering. To address this gap, we propose a configurable benchmark instance generator specifically designed for the NRRP, along with two benchmark datasets covering planning horizons of 14 and 28 days and varying levels of complexity. The generator enables researchers to simulate realistic disruption scenarios with adjustable complexity, while the datasets support reproducible and standardized evaluation of re-rostering algorithms. This benchmark dataset facilitates fair comparisons and drives progress in algorithmic research for the NRRP.Item type: Item , Introduction to the Minitrack on Decision Support for Healthcare Processes and Services(2026-01-06) Reuter-Oppermann, Melanie; Walker, Cameron; Furian, Nikolaus