Clinician-Directed Intervention Software

• Clinician-directed intervention software generation is a framework empowering clinicians to define and oversee digital interventions tailored to complex clinical needs.
• It integrates methods like LLM-based code synthesis, formal model verification, and modular domain ontologies to guarantee safety, precision, and adaptability.
• Applications span rehabilitation, assistive technology, and critical care decision support, with iterative co-design and standardized metrics ensuring effective usability.

Clinician-directed intervention software generation describes frameworks, workflows, and systems in which clinicians—rather than purely technical developers or “black-box” AI—directly specify, configure, or synthesize clinical intervention software for deployment at the point of care. This paradigm ensures that software is grounded in clinical priorities, dynamically captures practitioner knowledge, and remains adaptable to evolving needs across domains such as electronic documentation, rehabilitation, decision support, assistive technology, behavior change, and simulation. Recent advances include formalized requirement-elicitation methodologies, LLM-based code synthesis, modular domain ontologies, and iterative co-design—all supporting safe, personalized, and effective translation of clinician input into functional software artifacts.

1. Collaborative Requirements Elicitation and the D&I Framework

A central challenge in clinician-directed software generation lies in systematically extracting and formalizing clinical requirements in complex, multi-stakeholder healthcare environments. The Dissemination & Implementation (D&I) Framework offers a transparent, repeatable method to transform clinician-voiced priorities into actionable requirements-elicitation guidelines (Márquez et al., 2021).

The D&I Framework comprises four stages:

1. Stakeholder Identification: Mapping every clinical role and their expectations, forming a stakeholder panel (clinicians plus IT facilitator).
2. Clinical-Priorities Workshop: Subgroup sessions, each led by IT, extract and rank a set $P$ of critical clinical priorities for the intervention.
3. Selection of Implementation & Dissemination Strategies: Interactive mapping of each $p \in P$ to one or more evidence-based strategies $s \in S$ (e.g., education, feedback), each color-coded by dissemination/implementation category.
4. Generation of Elicitation Guidelines: Given a catalog $C$ mapping each strategy $s$ to elicitation techniques $G(s)$, the derived checklist for each priority $p$ is computed as $G_p = \bigcup_{s \in S_p} G(s)$. This mapping forms the requirements-elicitation agenda.

The process operationalizes clinician input via one-to-one and group workshops, generating concrete artifacts—BPMN workflow models, conflict identification tables, social network probes—that are translated directly into requirements specifications. No opaque inference is applied: the system scaffolds the process, each step driven by clinician-assigned strategies.

SIGICAM, a bed management application developed using the D&I Framework, demonstrated statistically significant improvements in usability metrics (Health-ITUES global mean: 2.91 to 3.25; $t=-2.60$, $p=0.013$) across successive releases. Critical lessons include the necessity for formal stakeholder panels, evidence-based strategy catalogs, automated guideline mapping, repeated usability validation, and attention to unintended system consequences (Márquez et al., 2021).

2. Model-Driven and Formal Approaches

A different, but complementary, paradigm exploits formal clinical models—often finite state machines (FSM)—for synthesizing guidance and intervention software directly from structured best practice guidelines (Rahmaniheris et al., 2016).

In model-driven design, each clinical process (e.g., cardiac arrest protocols) is encoded as a domain-specific FSM:

• $M = (S, S_0, \Sigma, \delta, F)$ with states, initial states, event alphabets, transitions by guarded physiological predicates, and terminal states.
• Organ-centric decomposition: separate FSMs for cardiovascular, pulmonary, renal, or other major systems.
• Physician interaction models: parallel physician FSMs allow explicit “hold” or “override” operations, enabling systematic tracking of deviations from the algorithmic recommendations.

Safety and soundness are verified via translation to timed automata and formal model checking using UPPAAL, expressing properties such as deadlock-freedom, reachability, timely response, and physician-system convergence in CTL. After verification, executable code is automatically generated in C/Java from the FSMs, including all state logic, guard evaluation, event dispatch, and physician alerting protocols.

A full cardiac arrest system was constructed using this paradigm, with runtime integration into simulated EHR streams and physician control panels. Formal verification ensured that all safety and guideline consistency properties held prior to deployment, guaranteeing traceability from textual guideline to executable, physician-in-the-loop system (Rahmaniheris et al., 2016).

3. LLM-Based and Just-In-Time Software Synthesis

Recent systems enable direct translation of natural-language clinical prescriptions or configuration inputs into executable digital interventions at the point of care, using LLMs as code generators:

• Physical Rehabilitation Interventions: Licensed therapists author arbitrary, stepwise exercise instructions (“pick up the pink cube, hold 2 s...”), which are packaged with a domain-specific language (Scenic DSL), API documentation, and code-template examples. An LLM (Grok 3.0-beta) emits complete, monitorable AR applications (e.g., for Meta Quest), with one-to-one mapping from each clinical step to code blocks specifying monitoring predicates, descriptions, and timeouts (Kim et al., 23 Nov 2025).
  • Quantitative evaluation: 100% coverage of bespoke exercises (vs. 55% for fixed-template libraries), 99.78% fidelity in instruction delivery, and 88.4% monitoring accuracy.
  • All therapists rated the system as easy/very easy (mean 4.5/5), 90% judged it as safe as conventional worksheets, and adoption intent was 75%.
  • Remaining limitations were due to "off-the-shelf" monitoring models and lack of manual code review for hallucinations.
• Assistive Technology (AAC Authoring): A web application routes image uploads to a multimodal LLM (GPT-4o), which returns candidate “hotspot” labels for VSDs. Pre-service SLPs edit or accept labels and define bounding regions, thus constructing functional communication displays (Zastudil et al., 29 Jul 2025).
  • Time to create VSD reduced (median 77 s → 63.5 s), confidence improved, but there were trade-offs: 62% unedited AI-generated labels, rising homogeneity (measured by embedding similarity), and more irrelevant or “off-scene” labels.
  • Recommendations include cognitive guardrails, transparency, and user profiling to mitigate over-reliance.

These systems exemplify end-to-end clinician-led specification, automatic translation, and tangible software deployment, with strong support for personalization at the level of individual patients or devices.

4. Modular Domain Models and Parameterized Simulation

In procedural or simulation-intensive domains (e.g., minimally invasive cancer therapy), web-based modeling environments support clinician-driven intervention configuration without requiring direct code authoring (Weir et al., 2018). The Go-Smart system is structured around a clinical domain ontology encompassing:

• Context (organ, material)
• Modality (RFA, MWA, Cryo, IRE)
• Power generator, Needle, Protocol, Numerical model
• Combination (only valid sets of the above may be simulated)
• Parameter and Parameter Attribution

Clinicians select and configure interventions in a browser-based UI, with all options/parameters driven by the ontology and defaults established by protocol or manufacturer templates. On “simulate,” the system algorithmically traverses the parameter tree, instantiates a configuration XML (GSSA-XML), and dispatches it to open-source solver containers (Elmer, FEniCS, OpenFOAM). This enables plug-and-play extension of new devices, protocols, or models; every new component appears in the clinical UI and simulation configuration logic without bespoke programming.

In multi-center trials, Go-Smart achieved accuracy (mean DICE 0.66 for RFA, mean AAE 3.2 mm) and familiarity across diverse institutions. Its modular, clinician-controlled workflow supported immediate adoption of new modalities, protocols, and device models (Weir et al., 2018).

5. Clinician-Driven AI Intervention Dialogues and Decision Support

Behavioral interventions and critical care decision support increasingly employ modular, clinician-configurable AI systems:

• Persuasive Dialogue Systems: ChatCLIDS provides a robust, LLM-powered framework simulating behavior change counseling for closed-loop insulin system adoption (Yao et al., 31 Aug 2025). Clinicians configure each session with expert-validated patient profiles (including static clinical, demographic, and psychosocial fields, dynamic conversation history, and barrier ratings) and select or supervise persuasive strategy libraries. The system manages session states, infers or logs strategy efficacy, composes prompts and injects guidelines, and evaluates outcomes using metrics such as normalized persuasion rating and adaptation entropy.
  • Safeguards include modular abstraction layers for LLM invocation, strict encryption, audit logging, and human-in-the-loop review for all high-stakes recommendations.
• Critical Care CDS Systems: User-centered co-design produced an AI-driven ICU dashboard integrating EHR, wearable, video, and environmental sensor modalities, with risk models ($R_a$, $R_d$) recomputed every five minutes. The system employs top-factor attribution (SHAP), automatic mapping of risk drivers to “best practice” interventions, and interactive user interfaces allowing real-time clinician modification, deferral, or override of suggested actions (Davidson et al., 11 Mar 2025).
  • Iterative feedback refined alert thresholds (role-specific), suppression logic, and evidence-link interfaces, yielding acceptability across ICU roles and improved workflow fit.

6. Safety, Usability, and Best Practices

Across paradigms—requirements elicitation, formal modeling, LLM-based code generation, modular parameterization, and AI dialogue—several best practices have emerged:

• Anchor intervention logic in established clinical assessment/decision tools (e.g., VB-MAPP, OMOP CDM, implementation taxonomy).
• Maintain strict clinician-agency via human-in-the-loop review, modifiable interfaces, and override/justification capabilities.
• Rely on modular prompt engineering and domain ontologies, not hard-coded templates, for extensibility.
• Systematically log all inputs, modifications, and outcomes for traceable performance and continuous learning.
• Employ controlled evaluation (RCT, user-paper, panel review) with standardized, published metrics: usability surveys (Health-ITUES), outcome accuracy, instruction fidelity, monitoring sensitivity/specificity, DICE/AAE for simulation geometry, persuasion success rates, and more.
• Monitor for safety lapses, hallucinations, over-reliance, and unintended systemic effects (homogenization, alert fatigue, etc.), incorporating iterative feedback and human validation into all models (Márquez et al., 2021, Kim et al., 23 Nov 2025, Yao et al., 31 Aug 2025, Weir et al., 2018).

7. Future Directions

The trajectory of clinician-directed intervention software generation emphasizes:

• Expanded application of LLM-powered, natural-language-to-code pipelines enabling ultra-rapid, safe, point-of-care customization for diverse intervention domains.
• Tighter integration of formal verification and runtime instrumentation to guarantee correctness and transparency, especially in safety-critical and legally regulated workflows.
• Comprehensive, ontology-driven modularization ensuring compatibility across clinical specialties, devices, and intervention types, with seamless extension by researchers or manufacturers.
• Ongoing advances in explainable AI and user interfaces to mitigate over-reliance and ensure software diversity or personalization is preserved.
• Deployment of robust continuous-learning infrastructure, with clinician edits and usage data feeding back to improve models, prompts, and underlying logic, closing the loop between practitioner experience and software evolution (Yao et al., 31 Aug 2025, Fleming et al., 2023, Kim et al., 23 Nov 2025).

These technological and methodological advances collectively support a paradigm where clinicians function not as static end-users but as empowered directors of digital intervention pipelines, enabling precision, safety, and adaptability in clinical software practice.