Operating room procedure duration is central to scheduling efficiency, resource utilisation, staff coordination, and perioperative safety. Inaccurate forecasts can create idle capacity, overtime, delayed starts, cancellations, and avoidable strain across surgical services. Conventional estimates often rely on surgeon judgment, historical averages, or static regression models. These approaches do not fully account for evolving intraoperative conditions or the temporal structure of surgical progress once a case has begun. This article develops a conceptual temporal neural network for forecasting procedure duration before incision and updating the expected completion time during surgery. The model is designed to combine static case information with sequential intraoperative event logs. The proposed architecture uses procedure codes, surgeon-specific historical performance, anesthesia records, patient comorbidity profiles, and timestamped operative events. A recurrent neural network based on LSTM or GRU principles would update a remaining-time distribution as new events occur. Conceptually, the model could provide an initial duration estimate and then refine that estimate as the case progresses. For example, it would be expected to revise completion time upward when a laparoscopic case converts to an open procedure or when unexpected bleeding is recorded. A temporal duration-forecasting model could improve operating room coordination, reduce avoidable waiting, and support more responsive perioperative decision-making. Its value would depend on careful validation, workflow integration, and transparent communication to clinical teams.
Operating room time is a high-cost, clinically critical resource because each scheduled case depends on coordinated availability of surgeons, anesthesiologists, nurses, equipment, recovery beds, and downstream inpatient capacity. Inaccurate procedure-duration estimates can propagate through the surgical day, contributing to overtime, delayed starts, underused block time, avoidable cancellations, and staff fatigue. Prior work on operating room efficiency and surgical cost has shown that case-duration uncertainty is not merely a prediction problem but a system-level constraint on throughput and resource allocation [1-3]. For this reason, duration forecasting should be treated as a perioperative informatics problem embedded in operational decision-making rather than as a standalone statistical exercise.
Traditional duration estimates often rely on surgeon-entered values, historical averages, or linear modelling strategies that assume past case lengths can be summarised adequately before the procedure begins. Regression-based approaches have improved on simple averages, but they remain limited when procedure codes, patient condition, surgeon pace, and intraoperative deviations interact in nonlinear ways [4-6]. Scheduling methods that optimise staffing or block allocation may still inherit forecast error when the underlying case-length estimate is static and insensitive to the unfolding surgical course [7]. These limitations motivate models that can begin with preoperative information yet remain responsive after incision.
To clarify the conceptual distinction between traditional static estimation methods and the proposed dynamic framework, the structural comparison is presented in Table 1.
Table 1. Structural Comparison between Static and Temporal Approaches to Operating Room Duration Forecasting
Dimension | Conventional Static Models | Proposed Temporal Neural Network |
Prediction Timing | Preoperative only | Preoperative + continuous intraoperative updates |
Data Utilisation | Aggregated historical averages or regression inputs | Integrated static features + sequential intraoperative event streams |
Representation of Surgical Process | Fixed, non-evolving estimate | Dynamic, time-evolving representation of surgical progression |
Handling of Unexpected Events | Not explicitly incorporated | Explicitly integrated as high-impact sequential signals |
Model Structure | Linear regression or static ML models | Recurrent architecture (LSTM/GRU) with hidden state updates |
Personalisation | Limited or indirect | Explicit via surgeon-specific embeddings and historical patterns |
Uncertainty Quantification | Often absent or limited | Probabilistic output with prediction intervals |
Responsiveness During Surgery | None | Event-driven real-time forecast revision |
Workflow Integration | Scheduling-focused only | Integrated preoperative and intraoperative decision support |
Interpretability Context | Retrospective or static | Context-aware explanations linked to intraoperative events |
Machine learning has increasingly been applied to surgical case-duration prediction, including models that use clinical and nonclinical features to estimate operative time or total operating room use. Studies in surgical time prediction, robotic surgery, arthroplasty, and general operating room utilisation show that machine learning could capture richer feature interactions than rule-based or linear methods [8-11]. However, many of these approaches remain primarily preoperative or static, meaning that they do not fully exploit the information contained in intraoperative milestones, anesthesia timing, or evolving workflow events. A model for live duration forecasting should therefore integrate both baseline case features and the temporal sequence generated during the operation itself.
The central thesis of this Models/Deep Learning article is that a temporal neural network could forecast operating room procedure duration more adaptively by fusing static features with real-time intraoperative event streams. The static component would encode procedure codes, surgeon history, anesthesia plan, and comorbidity profile, while the temporal component would revise the forecast as operative milestones or adverse events are logged. Deep surgical workflow models, including recurrent, temporal convolutional, and attention-based architectures, demonstrate that surgical processes can be represented as ordered event or phase sequences rather than as fixed preoperative attributes alone. Such a framework would be expected to support both initial scheduling and intraoperative coordination through continuously updated estimates of remaining time.
Operating room scheduling requires matching uncertain surgical demand with constrained personnel, rooms, instruments, anesthesia resources, recovery capacity, and turnover processes. Poor duration prediction can create both under-utilisation, in which expensive rooms sit idle, and over-utilisation, in which later cases are delayed or pushed into overtime. Operations-oriented studies of workload planning and second-shift staffing indicate that schedule quality depends strongly on credible estimates of future case duration and day-of-surgery progression [2, 7]. Machine learning approaches to case-duration prediction and daily caseload forecasting therefore address a core operational problem rather than a narrow technical task [1, 12].
Surgical procedure codes provide a structured description of the planned intervention and can serve as a compact representation of expected operative work. Yet codes alone cannot capture anatomical difficulty, adhesions from prior operations, surgeon technique, equipment availability, or unexpected intraoperative findings. Case-duration models that use procedure descriptors have shown that coded procedural information can be valuable, but it must be combined with other perioperative variables to represent individual case complexity more fully [4, 8, 13]. Hierarchical grouping of rare procedures would therefore be important for a temporal neural network because the same coded procedure may have substantially different duration trajectories in different clinical contexts.
Individual surgeon pace, case selection, team familiarity, and procedural learning can influence operative time even when nominal procedure type is similar. Prior models of surgical duration and robot-assisted surgery suggest that case length is shaped by provider- and procedure-specific patterns that are not reducible to diagnosis or operation code alone [5, 6]. Anesthesia information also matters because induction, airway management, maintenance, emergence, and medication-related events affect total room occupation and may interact with surgical complexity [14, 15]. A duration model should therefore distinguish pure operative time from broader procedure-related and anesthesia-related intervals when generating forecasts for scheduling decisions.
Patient comorbidity can modify procedure duration by increasing technical difficulty, anesthesia complexity, positioning requirements, vascular access needs, or the likelihood of intraoperative instability. Models using preoperative patient factors for procedures such as total knee arthroplasty illustrate how patient characteristics can inform expectations about operative time and perioperative resource use [11]. General duration-prediction studies similarly support the inclusion of demographic, clinical, and procedural variables as joint predictors rather than treating the surgical procedure as the only determinant of time [8, 9]. For a temporal neural network, comorbidity profiles would provide the initial risk context against which later intraoperative events are interpreted.
Intraoperative event logs can encode milestones such as incision, laparoscopic access, specimen removal, closure, conversion to open surgery, bleeding events, device problems, or delayed counts. Surgical phase-recognition literature demonstrates that operative workflows contain temporal structure that can be learned from sequential data, including video-derived phases, workflow states, and action transitions [16-19]. Although event logs are less visually rich than surgical video, they are more directly available in many perioperative information systems and can support live updating of remaining-time predictions. A dynamic forecasting model should therefore treat each logged event as evidence that revises the expected completion trajectory rather than as a retrospective annotation only.
The proposed predictive pipeline begins before the procedure with static inputs that generate a baseline duration forecast for scheduling and preparation. As the case proceeds, timestamped intraoperative events are sequentially passed into the temporal component, which updates the hidden state and revises the remaining-time distribution. This design follows the broader shift from static surgical prediction toward models that represent operations as temporally ordered processes, as seen in recurrent and temporal convolutional workflow-recognition systems [17, 18, 20]. The intended output is not a definitive completion time but a continuously revisable forecast suitable for decision support.
The overall hierarchical structure of the proposed temporal neural network, including static feature encoding, sequential event integration, and dynamic forecast updating, is illustrated in Figure 1.
Figure 1. Hierarchical Architecture of a Temporal Neural Network for Dynamic Operating Room Procedure Duration Forecasting
Static inputs would include procedure codes, surgeon identifier, surgeon-specific historical speed metrics, anesthesia type, induction-related features, patient comorbidity vector, and relevant preoperative risk descriptors. Dynamic inputs would include timestamped intraoperative events such as instrument count, laparoscopic access, unexpected bleeding, specimen removal, conversion to open procedure, and closure-related milestones. Machine learning studies of operating room duration show the value of structured preoperative and perioperative variables, while workflow models show that sequential surgical information can be represented as an evolving signal [1, 8, 21, 22]. Combining these sources would allow the model to express both expected case complexity and observed progression.
The model should be real-time updatable, robust to missing or delayed intraoperative entries, personalised to surgeon-patient-procedure combinations, and calibrated to express uncertainty rather than only a point estimate. It should also separate preoperative scheduling utility from intraoperative control-desk utility, because the same forecast may be used differently before room entry, after incision, and near closure. Literature on surgical artificial intelligence emphasises that model outputs must be clinically interpretable, workflow-aware, and validated in realistic operating room conditions before use in decision support [23-25]. Accordingly, this architecture is framed as a conceptual model that should be evaluated prospectively rather than as a completed performance claim.
Preoperative features would be extracted from scheduling systems, surgical coding records, anesthesia documentation, and pre-anesthesia assessment. Procedure codes could be mapped to hierarchical categories, surgeon identifiers linked to moving historical summaries for similar cases, anesthesia plans encoded by airway and technique, and comorbidity measures represented through structured clinical profiles. Prior case-duration studies indicate that surgical time prediction benefits from combining procedure, provider, patient, and operational variables rather than relying on any single category alone [4, 5, 9, 11]. These features would form the baseline context from which the temporal model begins forecasting before intraoperative events are available.
The integration of heterogeneous perioperative data sources and their distinct functional roles within the temporal model are systematically outlined in Table 2.
Table 2. Integrated Feature Domains and Their Functional Roles in Temporal Operating Room Duration Forecasting
Feature Domain | Data Source | Representation Strategy | Functional Role in Model | Temporal Interaction |
Procedure Codes | Surgical scheduling systems | Hierarchical categorical embeddings | Encodes baseline procedural complexity | Provides initial context for all subsequent updates |
Surgeon-Specific Performance | Historical case databases | Learned embedding vectors with moving averages | Captures individual operative pace and variability | Modulates interpretation of intraoperative progress |
Anesthesia Records | Anesthesia information systems | Structured categorical + continuous variables | Represents perioperative workflow and physiological constraints | Influences both baseline estimate and late-stage progression |
Patient Comorbidity Profile | Preoperative assessment records | Multi-dimensional clinical feature vector | Defines baseline risk and procedural difficulty | Conditions response to intraoperative deviations |
Intraoperative Event Logs | Real-time OR documentation systems | Sequential timestamped event encoding | Provides dynamic evidence of surgical progression | Drives hidden state updates and forecast revision |
Temporal Features (Elapsed Time) | Derived from timestamps | Continuous normalized variables | Tracks progression relative to expected workflow | Enables detection of delays or accelerations |
Rare Event Signals | Event log anomalies | Sparse high-weight encoded features | Identifies deviations (e.g., bleeding, conversion) | Expands uncertainty and shifts duration trajectory |
Intraoperative event logs would be parsed as a sequence of discrete event types with associated timestamps aligned to the operating room clock. Each event would be represented by its type, elapsed time since case start, elapsed time since the previous event, and optionally its relationship to expected surgical phase. Work on surgical workflow recognition has shown that temporal ordering, phase transitions, and milestone progression are central to understanding operative processes, whether learned from video or structured activity streams [17, 19, 22, 26]. In this model, event logs would provide the online evidence needed to update the remaining-duration forecast as the procedure unfolds.
Feature alignment would require synchronising preoperative scheduling data, anesthesia records, patient assessments, and intraoperative event timestamps into a single case timeline. Categorical variables such as procedure group, surgeon, anesthesia type, and event type could be encoded as embeddings, while continuous variables such as elapsed time, body mass index, and historical duration summaries would be scaled consistently. Rare procedures could be grouped through hierarchical coding structures to reduce sparsity while preserving clinically meaningful distinctions [10, 13]. This approach would support generalisation across common and uncommon operations without treating every procedure code as an unrelated category.
The preoperative encoder would combine learned embeddings for categorical variables with continuous clinical and operational features in a feed-forward neural component. This component would generate an initial duration estimate and initialise the hidden state used by the recurrent portion of the model. Artificial neural network studies of surgical and anesthesia duration prediction provide a conceptual basis for nonlinear preoperative encoding, while operating room machine learning studies support the use of multi-source structured predictors [6, 14, 27]. The initial forecast would serve as the model’s best estimate before direct evidence of intraoperative progress is available.
The recurrent core would use LSTM or GRU logic to ingest intraoperative events in temporal order, updating the hidden representation each time a new event is logged. This design would allow earlier events to shape the interpretation of later milestones, which is important because the meaning of a delay depends on where the case is in its operative trajectory. Recurrent convolutional and temporal surgical workflow models demonstrate that surgical processes can be learned as ordered sequences with evolving state, while attention-regularised transformer approaches suggest that critical events may be weighted differently depending on context [17, 20, 28]. In the proposed model, the recurrent state would translate event history into a revised estimate of time remaining.
The output layer would produce a distribution of remaining time rather than only a single predicted endpoint, allowing the forecast to communicate uncertainty to scheduling teams. This could be implemented conceptually through quantile-based outputs or a parametric duration distribution, enabling prediction intervals for downstream operational decisions. Probabilistic forecasting work in surgical duration prediction supports the importance of uncertainty-aware estimates, while continuous real-time prediction research indicates that forecasts can be revised as the procedure advances [8, 21]. Such an output would be expected to help teams distinguish between cases that are predictably near completion and cases whose remaining duration remains uncertain.
The model would recompute the duration forecast whenever a new intraoperative milestone or event is entered into the operative record. Events such as incision, trocar placement, specimen extraction, closure start, or room-exit preparation would serve as temporal anchors indicating how far the case has progressed relative to expected workflow. Surgical phase-recognition models show that operative progress can be represented as a sequence of recognisable states, while real-time duration prediction work suggests that forecasts should be updated as the case unfolds rather than fixed at the start [18, 21, 29]. This event-driven approach would allow the forecast to reflect the current surgical phase instead of relying only on preoperative expectations.
Unexpected intraoperative events would be treated as high-information signals that may substantially alter the expected remaining duration. For example, conversion from laparoscopic to open surgery, unanticipated adhesions, major bleeding, device malfunction, or additional dissection could shift the hidden state toward a longer and more uncertain completion trajectory. Video-based and workflow-based surgical artificial intelligence studies show that models can recognise procedural deviations, safety-relevant states, and operative actions that may influence downstream progress [30-32]. Attention mechanisms could further help the model assign greater weight to rare but consequential events when revising the forecast [28].
Intraoperative event streams are likely to be irregular because not every milestone is documented immediately, and some entries may be charted retrospectively. The model should therefore represent elapsed time explicitly and maintain a provisional forecast when expected events are delayed or missing. Temporal convolutional and recurrent surgical workflow approaches are relevant because they are designed to reason over ordered but variably spaced procedural information [17, 20, 22]. A robust forecasting system would distinguish between true procedural delay and documentation latency while continuing to provide uncertainty-aware updates.
A surgeon embedding would allow the model to learn systematic differences in operative pace, case-selection patterns, team coordination, and sensitivity to complexity. This personalisation would not imply that faster or slower surgeons are better or worse, but rather that individual historical patterns can improve case-specific forecasting when used carefully. Studies of machine learning for case duration and robot-assisted procedures support the inclusion of surgeon- and procedure-level predictors, while research on robotic surgery diffusion suggests that technology adoption and surgeon familiarity can shape operative workflow over time [5, 6, 33]. The embedding should therefore be calibrated as a forecasting feature, not as a performance ranking tool.
For clinical acceptance, the model should provide human-readable explanations when it revises predicted completion time. A forecast update might state that remaining time increased because the case converted to open surgery, because closure had not begun when expected, or because anesthesia emergence preparation was delayed. Reviews of surgical artificial intelligence emphasise that transparency, workflow fit, and careful interpretation are essential when models are introduced into operating room environments [23-25]. Explanations should therefore accompany numerical forecasts so that clinicians can assess whether the update is plausible in the current clinical context.
The model could be integrated into a real-time dashboard showing current case phase, predicted end time, uncertainty range, and confidence status for each active room. Such a display would support decisions about calling the next patient, preparing instruments, coordinating anesthesia availability, and anticipating recovery-room demand. Operating room efficiency studies indicate that case-duration prediction has operational value only when linked to day-of-surgery coordination, staffing, and schedule management [1, 2, 12]. The dashboard should therefore present forecasts as actionable operational signals rather than as isolated machine-learning outputs.
Forecasts could be passed to scheduling and perioperative coordination systems to adjust expected start times for later cases and align turnover resources. If the model predicts a delayed finish, housekeeping, transport, anesthesia teams, and post-anesthesia care units could be alerted earlier; if completion appears imminent, the next case pathway could be accelerated. Studies on operating room cost, staffing, and delay factors show that prediction errors affect not only the current room but also the broader perioperative system [3, 7, 15]. Integration should therefore focus on safe coordination, avoiding disruptive overreaction to small forecast changes while responding to clinically meaningful shifts.
The model should be evaluated against surgeon estimates, historical averages, regression models, and static machine-learning baselines at preoperative and intraoperative checkpoints. Accuracy metrics could include mean absolute error, root mean squared error, and percentage error, but the evaluation should report them only in a formal empirical study rather than in this conceptual model article. Prior machine-learning and regression studies provide relevant comparator classes for assessing whether a temporal neural network adds value beyond static prediction [1, 4, 8, 9]. Evaluation should also examine whether forecast quality changes meaningfully after key events such as incision, midpoint milestones, and closure start.
Dynamic reliability should be assessed by determining whether uncertainty narrows appropriately as the procedure progresses and whether prediction intervals remain calibrated across case types. Distributional scoring approaches would be appropriate because a duration model that communicates uncertainty can be more useful than one that reports only a point estimate. Probabilistic surgical forecasting and real-time neural prediction studies support the importance of evaluating both the expected duration and the reliability of the forecast distribution [8, 21]. Reliability assessment should also examine whether rare events produce appropriately wider uncertainty rather than falsely precise estimates.
Operational evaluation should test whether dynamic forecasts would improve scheduling decisions, turnover coordination, and resource allocation compared with usual estimates. Simulation of surgical schedules could assess how updated predictions might affect under-utilisation, over-utilisation, overtime risk, delayed starts, and recovery-room coordination without claiming prospective benefit before deployment. Work on surgical scheduling, operating room economics, and daily caseload prediction provides a foundation for evaluating such system-level consequences [2, 3, 7, 12]. Because biomedical artificial intelligence benchmarks can be sensitive to design choices, operational evaluation should be interpreted cautiously and validated across realistic institutional workflows [34].
A central limitation is that many operating rooms do not maintain structured, timely, and standardised intraoperative event logs. Some events may be missing, entered late, or documented inconsistently across teams, which could make the temporal signal noisy and difficult to interpret. Surgical workflow research using video and structured phase data often relies on richer or more curated annotations than are available in routine perioperative information systems [19, 26, 29]. Therefore, the model’s usefulness would depend on the quality, completeness, and timeliness of local documentation practices.
A surgeon-specific model may learn institutional routines, staffing patterns, room practices, technology availability, and documentation habits that do not transfer cleanly to other hospitals. This risk is especially important when procedure mix, anesthesia workflow, robotic platform adoption, and perioperative staffing models differ across sites. Reviews of surgical artificial intelligence caution that models should not be assumed to generalise without external validation, workflow assessment, and careful recalibration [23-25]. A deployment strategy would therefore need to include site-specific monitoring and safeguards against encoding unfair or misleading provider-level interpretations.
A temporal neural network for operating room procedure-duration forecasting would combine preoperative case context with real-time evidence from the unfolding operation. It would begin with a baseline estimate and then revise expected remaining time as milestones, delays, and unexpected events are recorded.
The main strength of this approach is its ability to fuse procedure codes, surgeon-specific history, anesthesia information, patient comorbidity profiles, and intraoperative event streams into a single dynamic forecasting framework. By producing updated uncertainty estimates, the model could support more nuanced decisions than a fixed scheduled duration.
Important challenges remain before such a model could be used safely in live perioperative operations. These include incomplete event documentation, institutional variation, model calibration, user trust, alert fatigue, and integration with scheduling systems.
Prospective pilot studies in high-volume surgical suites would be needed to determine whether dynamic duration forecasts improve operating room efficiency and team acceptance. Such studies should evaluate not only predictive accuracy but also whether forecasts are understood, trusted, and used appropriately by perioperative teams.
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