Physician Intent Trajectories in Clinical Care

• Physician intent trajectories are temporal sequences that capture evolving clinical decision-making by modeling latent reasoning and treatment choices.
• Methodologies like POMDPs, sequence labeling, and reinforcement learning enable precise analysis of intent transitions in diverse patient care settings.
• Applications in diagnostic imaging, clinical dialogue, and precision medicine enhance treatment optimization and support dynamic clinical decision-making.

Physician intent trajectories are temporal sequences that reflect the evolving decision-making patterns and internal goals of clinicians in patient care processes. This concept encompasses models of how a physician’s intent, assessment, or plan transitions as a function of patient health dynamics, clinical context, and new evidence, with manifestations in domains including longitudinal care, dialogue systems, diagnostic imaging, prescription recommendations, and more. Empirical studies and methodological advances address physician intent trajectories via formal latent-state modeling, sequence analysis, reinforcement learning, contrastive representation learning, trajectory-based treatment inference, and comprehensive annotation in clinical interaction datasets.

1. Conceptualization and Formal Modeling of Physician Intent Trajectories

A key challenge in reconstructing physician intent trajectories is the underdetermined nature of clinical decision making—where actions are observable but the underlying reasoning or intent is latent. Latent-state models for precision medicine (Xu et al., 2020) address this by embedding patient observations and treatment histories within a partially observable Markov decision process (POMDP). In this framework, the latent process $M(t) \in \{1, \ldots, K\}$ evolves according to a Markov process, while at each clinical visit $T^j$, the observable measurement $X^j$ and selected treatment $A^j$ are registered. By estimating posterior distributions of latent health states (filtered probabilities $B^j_\ell = P\{M(T^j) = \ell \mid H^j\}$ given observed history $H^j$) and summarizing as $S^j = (B^j, X^j)$, one can recast the original irregular, non-aligned patient trajectories into a homogeneous Markov decision process. Assuming that physician intent modulates treatment selection conditional on this filtered latent state, the intent trajectory is operationalized by the sequence $(S^j, A^j)$, which can be formally analyzed for intervention analysis, regime optimization, or intent inference.

Trajectory analysis can extend to explicit network and graph representations, as in the construction of multilayer comorbidity networks (Dervić et al., 2023), where temporal links between diagnostic states track the evolution and divergence of disease, serving as externalized proxies for changes in clinical assessment and therapeutic intent.

2. Methodologies for Capturing and Analyzing Intent Dynamics

Methodologies for intent trajectory discovery range from generative state-space models and clustering over longitudinal patient data (Aguiar et al., 2020), to supervised and semi-supervised intent classification in doctor–patient dialogue (Röhr et al., 26 Aug 2025), reinforcement learning (RL)-based inference of treatment policies (Jeong et al., 11 Nov 2024), and contrastive learning frameworks for disentangling coexisting medical intents in sequential prescription data (Moghaddam et al., 13 Aug 2024).

• POMDP and MDP Conversion: By mapping partially observed histories into latent-state posterior summaries, one can borrow statistical strength across heterogeneous, irregular histories, making estimation of intent-driven treatment regimes feasible on real-world data intractable for classical Q-learning or inverse probability weighting approaches (Xu et al., 2020).
• Elastic Principal Graphs and Clinical Pseudotime: Semi-supervised learning approaches model patient cohorts as trajectories on a principal tree, with branching (bifurcation) points corresponding to critical intent transitions—e.g., the adoption of aggressive interventions as prognosis worsens (Golovenkin et al., 2020).
• Sequence Labeling in Dialogue Data: Annotated datasets using fine-grained intent taxonomies (extending the SOAP—Subjective, Objective, Assessment, Plan—framework) enable the measurement of intent transitions in physician–patient conversations. Hierarchical intent prediction and summarization facilitates both the analysis of common dialogue trajectories and improves downstream clinical note generation (Röhr et al., 26 Aug 2025).
• Inverse Reinforcement Learning and Imitation Learning: Techniques such as behavioral cloning, IRL, and GAN-based imitation learning allow the recovery of expert physician policies from observed state-action sequences in ICU care, enabling comparison of intent trajectories across sub-populations and eras of clinical guidelines (Jeong et al., 11 Nov 2024).

3. Extraction and Application in Diverse Clinical Contexts

The operationalization of physician intent trajectories is domain specific.

• Chronic Mental Illness and Precision Medicine: Latent-state–dependent autoregressive and Markov transition models allow estimation of optimal, intent-aligned treatment regimes in mood disorder management. These regimes, when projected onto decision tree representations, facilitate interpretability and retrospective comparison with actual physician decision-making (Xu et al., 2020).
• Clinical Dialogue and Intent-taxonomy Datasets: Large-scale annotation of doctor–patient dialogues supports intent trajectory analysis by stratifying dialogue turns across fine-grained subjective, objective, assessment, and plan-based intents. Modeling reveals that physicians tend to iteratively gather symptoms (subjective phase), transition to objective assessment (exams, labs), refine diagnostic hypotheses, and finally advance to treatment planning, with observed challenges for current models in tracking transitions between phases (Röhr et al., 26 Aug 2025).
• Diagnostic Imaging: In radiology, transformer-based architectures process spatiotemporal sequences of eye fixations to infer "gaze intent trajectories," mapping how radiologists search for specific findings and adapt their scrutiny to diagnostic objectives or uncertainties. Intention-defined gaze datasets (systematic, opportunistic, or hybrid exploration) support nuanced trajectory modeling for interactive assistance and training (Pham et al., 16 Jul 2025).
• Robot-Assisted Surgery: Multi-modal monitoring (EEG, ECG, EMG, eye tracking) integrated via deep learning enables continuous fusion and interpretation of surgical intents under varying cognitive workload, providing real-time adaptation and safety enhancements in both training and operational settings (Sharma et al., 3 Aug 2025).
• Prescription Recommendation: Multi-level transformers with contrastive loss ensure that each attention head captures a distinct temporal intent ("intent path"). This architectural design allows drug recommendation systems to explain and disaggregate the multiple co-existing physician intents underlying sequential prescriptions, aligning interpretability and predictive performance (Moghaddam et al., 13 Aug 2024).

4. Quantitative Evaluation, Outcomes, and Clinical Impact

Empirical results indicate that intent trajectory modeling yields significant improvements in treatment optimization, risk stratification, and decision support.

• In longitudinal treatment regime estimation (Xu et al., 2020), POMDP-based estimators—leveraging latent states—achieve higher cumulative utility and lower variance than conventional approaches, with regimes aligning with known, intent-driven clinical practice (e.g., avoiding antidepressant monotherapy in bipolar disorder).
• In dialogue systems, intent filtering using fine-tuned classifiers yields substantial improvements in clinical note summarization, reducing noise by focusing on medically relevant utterances and increasing the salience of the differential diagnosis process (Röhr et al., 26 Aug 2025).
• Reinforcement learning frameworks with iterative trajectory inspection have exposed critical biases (e.g., preference for high vasopressor doses or premature discharge in sepsis management), demonstrating the role of clinician-driven review in correcting intent trajectory learning and aligning RL policies with clinical plausibility (Ji et al., 2020).
• Longitudinal variable importance and multi-modal trajectory modeling inform both outcome prediction and dynamic adjustment of physician priorities (e.g., focusing on suicide risk factors whose predictive utility remains stable across visits) (Williamson et al., 2023).

5. Limitations, Open Problems, and Future Directions

Although physician intent trajectory modeling has advanced significantly, acknowledged challenges include:

• Transition Detection: Even state-of-the-art NLP and sequence models struggle to accurately identify transitions between key phases of intent (e.g., from symptom gathering to assessment), indicating a need for models that better capture the temporal dependencies and context of intent shifts (Röhr et al., 26 Aug 2025).
• Interpretability: While latent-state and contrastive transformer models can encapsulate complex patterns of clinical decision-making, direct mapping between learned representations and explicit clinician reasoning or guideline adherence remains constrained.
• Data Limitations: Heterogeneous and irregularly sampled patient histories, class imbalance in intent labels, and limited access to rich multi-modal physician process data limit the generalizability and granularity of recovered intent trajectories.
• Intent Fusion and Personalization: Few methods explicitly account for physician-specific factors (risk tolerance, expertise) or the fusion of multiple simultaneous intents (e.g., comorbidity management, competing diagnostic hypotheses), though latent mixture models and inclusion of covariates are plausible extensions (Xu et al., 2020).
• Causal Inference and Counterfactuals: Modeling deviations in intent trajectories across demographic groups or eras (before and after clinical guideline introductions) enables the paper of care disparities, but robust causal inference remains methodologically and ethically complex (Jeong et al., 11 Nov 2024).

A plausible implication is that future research will increasingly focus on integrating explicit models of physician cognition, multi-agent interaction, and decision context, alongside end-to-end sequence modeling of both structured data and dialogue, to further refine the extraction and application of physician intent trajectories.

6. Implications for Clinical Systems and Decision Support

Knowledge and explicit modeling of physician intent trajectories underpin the design and deployment of advanced clinical decision support systems:

• Dynamic Treatment Recommendation: Integration of longitudinal patient trajectories and intent modeling into individualized treatment rules enhances the ability to exploit subtle differences in outcomes over time, supporting refined intervention decisions in domains such as depression management (Yao et al., 2022, Yao et al., 16 May 2024).
• Proactive Monitoring and Early Warning: Positioning patients on disease progression trees (using pseudotime or network trajectories) gives clinicians actionable early warnings, supporting both risk stratification and tailored preventative interventions (Golovenkin et al., 2020, Dervić et al., 2023).
• Closed-Loop Clinical Workflow: Systems such as TrajVis (Li et al., 16 Jan 2024) and multi-modal surgical assist frameworks (Sharma et al., 3 Aug 2025) offer real-time visualization and feedback of patient and physician intent trajectories, bridging the gap between complex AI/ML models and clinician interpretability, with proven impact on simulation-based learning, chronic disease management, and safety.

Understanding and operationalizing physician intent trajectories is thus central not only for retrospective clinical insight and error analysis but also as a blueprint for the next generation of context-aware diagnostic, therapeutic, and documentation support tools in precision medicine.