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The integration of artificial intelligence (AI) into healthcare has enhanced data-driven decision-making, but missing data remains a major barrier to reliable model performance. This narrative review synthesizes literature on missing data in clinical machine learning, focusing on modeling decisions, common pitfalls, and emerging reporting standards within AI-enabled healthcare systems.Missing data in healthcare arises from sources such as electronic health records (EHRs), wearable devices, and clinical trials, and may follow mechanisms including missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). Addressing these gaps requires appropriate imputation strategies, from statistical methods like multiple imputation to advanced deep learning approaches such as generative adversarial networks (GANs), each carrying implications for bias and model generalizability.This review highlights key challenges, including underreporting of missingness, insufficient sensitivity analyses, and neglect of imputation uncertainty. It also examines evolving reporting standards that emphasize transparency in missing data handling. By synthesizing cross-study evidence, the review proposes a systems-level framework for integrating missing data management into AI governance, supporting more reliable, transparent, and equitable healthcare analytics.

Cardiovascular disease remains the leading global cause of death, emphasizing the need for improved risk stratification beyond traditional tools such as Framingham, ASCVD, QRISK, and SCORE, which show limitations in diverse modern populations. Machine learning methods applied to electronic health records can enhance prediction by capturing complex, high-dimensional, and nonlinear relationships. This systematic review (2017–2022) evaluated machine learning models for cardiovascular risk prediction using EHR data, focusing on discrimination (AUROC, AUPRC), calibration, external validation, and reporting quality including TRIPOD adherence. A PRISMA-compliant search identified peer-reviewed studies applying machine learning to EHR-based cardiovascular risk prediction. Risk of bias was assessed using PROBAST, and narrative synthesis was conducted due to heterogeneity. Twenty-nine studies were included. XGBoost, random forest, and neural networks were the most common models and generally outperformed logistic regression and traditional risk scores in discrimination. However, calibration was infrequently reported, and external validation was limited, often showing reduced performance. Machine learning models demonstrate improved predictive discrimination over conventional risk scores, but limited calibration assessment and weak external validation constrain clinical applicability. Stronger validation frameworks are needed for clinical translation.

Emergency department crowding is a persistent global healthcare challenge linked to longer wait times, increased patients leaving without being seen, worse clinical outcomes, and staff burnout. It also contributes to ambulance diversion and inefficient resource use, worsening hospital operational strain. This systematic review evaluates machine learning models for predicting ED crowding and optimizing patient flow, focusing on input features (e.g., arrival rates, acuity, bed availability) and reported operational outcomes such as waiting times and ambulance delays. A PRISMA-compliant review was conducted across PubMed, Embase, IEEE Xplore, and Scopus. Included studies applied machine learning to ED crowding or patient flow prediction and reported operational or crowding outcomes. Due to heterogeneity, a narrative synthesis was used, and risk of bias was assessed using an adapted tool. Thirty-two studies met inclusion criteria, using classification, regression, time-series, and deep learning models. Common predictors included arrival patterns, occupancy, and bed availability. While predictive performance was generally high, few studies evaluated real-world operational impacts, and most remained retrospective. Although machine learning models demonstrate strong predictive accuracy for ED crowding, evidence of real-world operational benefits remains limited. A clear gap exists between prediction and implementation into clinical workflow and decision-making. Future research should focus on translating predictions into measurable improvements in ED performance.

Patient no-shows in outpatient clinics (5%–30% across specialties) disrupt scheduling efficiency, increase wait times, and strain healthcare resources. To address this, healthcare systems are increasingly applying machine learning (ML) for predictive scheduling support. This systematic review synthesizes ML approaches for predicting outpatient no-shows, focusing on model types, feature usage, and reported operational deployment outcomes, with emphasis on translation into clinical scheduling practice. A PRISMA-compliant search of PubMed, Embase, IEEE Xplore, Scopus, and Web of Science identified studies using ML for no-show prediction in outpatient settings. Data on models, features, performance, and implementation were extracted. Risk of bias was assessed using an adapted PROBAST tool. Thirty-two studies were included. Logistic regression, random forest, and XGBoost were the most commonly used models. Historical attendance data was the dominant predictive feature. Fewer than 20% of studies reported real-world implementation, and reported intervention outcomes (e.g., overbooking, reminders) were inconsistent. While ML models show strong predictive performance, real-world deployment and evidence of operational impact remain limited. This gap highlights the need to prioritize implementation-focused research to translate predictive accuracy into measurable improvements in clinic efficiency and access.

Hypertension affects about 1.4 billion adults globally and is a major modifiable risk factor for cardiovascular disease. Although several first-line antihypertensive drug classes exist, randomized controlled trials typically report only average treatment effects (ATEs), which mask important variability in individual patient responses. As a result, clinical guidelines often assume a homogeneous patient population, leading to trial-and-error prescribing, delayed blood pressure control, and avoidable adverse effects. I argue that causal forest models combined with double machine learning (DML) enable reliable estimation of heterogeneous treatment effects (HTEs) from observational electronic health record data. These methods can approximate randomized trial validity while capturing clinically meaningful variation in treatment response across patients. Compared with traditional approaches, they are computationally feasible and better suited for individualized treatment assessment. Therefore, comparative effectiveness research in hypertension should move beyond ATE-focused analyses toward routine HTE estimation using causal machine learning. This shift would support more precise, data-driven prescribing and improve patient outcomes.

Postoperative complications including SSI (2–20%), VTE (1–5%), and respiratory failure (1–8%) significantly increase morbidity, mortality, length of stay, and readmissions. This systematic review assessed machine learning models predicting these outcomes, their performance, external validation, and clinical deployment. A PRISMA-based search (2017–2024) identified 32 eligible studies. Models such as random forest and XGBoost showed AUROC ranges of 0.70–0.85 for SSI, 0.75–0.90 for VTE (outperforming Caprini scores), and 0.75–0.88 for respiratory failure. However, fewer than 20% of studies included external validation and less than 5% reported clinical deployment. Overall, while machine learning models show strong retrospective performance, limited validation and minimal real-world implementation remain major barriers to clinical translation.

Suicidality and depression are major global health burdens, with over 700,000 suicide deaths annually and ~280 million people affected by major depressive disorder. Early risk prediction could support prevention, but traditional methods show limited accuracy. This PRISMA-compliant systematic review evaluated machine learning models for predicting suicidality and depression across electronic health records, social media, and wearable sensor data, focusing on performance, unimodal vs multimodal approaches, and ethical reporting. Searches of PubMed, PsycINFO, IEEE Xplore, arXiv, and ACM Digital Library identified eligible studies. EHR-based models showed AUROC 0.70–0.85 for suicide attempt prediction, social media models 0.70–0.80 for suicidal ideation, and wearable sensor models lower performance (0.65–0.75). Multimodal approaches improved performance by 5–10% over unimodal models. However, fewer than 20% of studies reported ethical considerations such as privacy, bias, or deployment safeguards. Overall, machine learning shows moderate-to-good predictive performance, with multimodal models performing best, but ethical reporting remains critically insufficient for clinical translation.

Hospital length-of-stay is a central operational metric for inpatient capacity planning, discharge coordination, and resource allocation. Accurate prediction remains difficult because patient trajectories are heterogeneous, nonlinear, and shaped by evolving clinical events during admission. Traditional statistical models often have limited flexibility for high-dimensional and sequential electronic health record data. Across the literature, there is no settled consensus regarding optimal model architecture, feature representation, validation design, or clinical implementation strategy. This systematic review synthesizes machine learning approaches for hospital length-of-stay prediction published from 2017 to 2022. It focuses on EHR feature types, model architectures, validation methods, interpretability strategies, and reported operational outcomes. A structured review of peer-reviewed literature was conducted using targeted search strings related to machine learning, deep learning, electronic health records, discharge prediction, and hospital length-of-stay. The review included studies across emergency, inpatient, surgical, pediatric, cardiovascular, and intensive care settings. The literature suggests that gradient boosting, random forest, ensemble learning, and recurrent neural networks are common approaches for LOS prediction. However, external validation remains uncommon, prediction horizons vary widely, and operational implementation outcomes are reported less consistently than model development results. Future research should prioritize external validation, prospective implementation studies, standardized outcome definitions, and transparent reporting of workflow barriers. Shared benchmarking datasets and multi-center validation consortia would strengthen comparability across LOS prediction studies.

Machine learning is increasingly used in hospital operations, revenue cycle management, and quality monitoring. These administrative applications require transparency because their outputs can influence access, resource allocation, financial decisions, and accountability. This systematic review examined explainable machine learning methods applied to operational decision support, revenue cycle analytics, and quality monitoring in healthcare administration. The review focused on the type, depth, and use of transparency methods rather than predictive performance. A PRISMA 2020–compliant search strategy was applied to PubMed, Scopus, IEEE Xplore, and Web of Science for studies published between 2017 and 2022. Screening, extraction, and narrative synthesis focused on administrative domain, model type, explanation method, stakeholder use, and risk of bias. SHAP and LIME were the most frequently discussed post-hoc explanation approaches, while feature importance, partial dependence, rule-based models, and attention mechanisms appeared in smaller subsets of the literature. Operational decision support showed the strongest explainability uptake, whereas revenue cycle analytics and administrative quality monitoring remained less developed. Explainability in healthcare administration remains uneven and often superficial. The largest gap is not the availability of explanation tools, but the limited evidence that explanations improve managerial decisions, accountability, fairness, or auditability.

Care coordination failures include missed referrals, lost follow-ups, fragmented communication, and incomplete transitions between primary and specialty care. These failures can delay diagnosis, weaken continuity, and increase avoidable utilisation. Existing detection approaches often depend on manual review, retrospective audits, or simple rule-based flags. Such approaches are poorly suited to capture the relational complexity of patient, provider, referral, messaging, and encounter networks. This article develops a conceptual graph-based machine learning model for predicting care coordination failures. The model represents patient–provider referral networks enriched with follow-up status, patient message activity, specialty access delays, and provider communication patterns. The proposed approach uses a heterogeneous graph neural network in which patients and providers are nodes. Referral, encounter, and messaging relationships are represented as edges, while node and edge features encode follow-up adherence, message frequency, wait-time signals, and communication context. Conceptually, the model would identify high-risk referral edges that combine delayed access, incomplete follow-up, weak messaging activity, or limited provider communication. These predictions would support coordinator review before a referral becomes a documented care gap. A graph-based model could shift care coordination from reactive tracking toward predictive prevention. By identifying fragile referral relationships early, it could support more timely outreach and safer continuity of care.

Laboratory alert fatigue erodes the effectiveness of clinical decision support by making repeated abnormal result notifications less likely to prompt timely clinical attention. It is often recognised only after providers begin delaying, overriding, or ignoring alerts. Current alert reduction strategies are commonly based on broad thresholds or blanket suppression rules. These approaches do not explain which providers, specialties, alert types, or repeated result patterns are most susceptible to fatigue. This article proposes an interpretable machine learning model that could predict whether a provider will exhibit fatigued behaviour toward a specific laboratory alert. The model is intended to support transparent, provider-aware alert redesign rather than opaque automation. The proposed framework would use historical alert logs with features capturing alert frequency, clinical severity, provider specialty, repeated abnormal results, response time history, and override behaviour. A regularised logistic regression or gradient-boosted tree model with SHAP explanations would provide both prediction and interpretability. Conceptually, the model would generate a fatigue risk score for each provider–alert pair. It would also attribute the score to specific drivers, such as repeated low-severity results, accumulated alert burden, or recent override patterns. An interpretable fatigue prediction model could enable personalised alert suppression, escalation, or redesign before a clinically important laboratory result is missed. Such a system would support safer, more adaptive clinical decision support.

Blood products are critical hospital resources with demand shaped by elective surgery, trauma, oncology treatment, and ongoing transfusion dependence. Volatility in daily use can create simultaneous risks of shortage and expiry-related wastage. Current inventory management often relies on par-level reordering and manual review of limited indicators. Such approaches may not anticipate daily demand shifts arising from multiple clinical drivers at the same time. This article develops a conceptual predictive model for forecasting daily blood product demand in tertiary hospitals. The model integrates surgical schedules, trauma admission patterns, transfusion history, oncology treatment plans, and inventory depletion data. The proposed approach uses time-series regression or gradient-boosted tree modelling trained on historical transfusion and hospital operations data. The model would output expected demand by blood product type for the next 24 hours. Conceptually, the model would provide a daily product-specific demand forecast with uncertainty bounds. It could flag days of expected high use driven by complex surgical lists, trauma activity, or planned oncology transfusion support. The model could support proactive, data-driven blood inventory management. It may help reduce emergency ordering, improve preparedness, and limit avoidable wastage.

Efficient hospital resource allocation is a persistent operational challenge because demand for beds, staff, equipment, diagnostics, and patient movement changes rapidly. Predictive analytics offers a way to anticipate demand and support more proactive operational decisions. This systematic review synthesised machine learning models applied to hospital resource allocation from 2017 to 2023. The review focused on bed management, staffing demand, equipment use, diagnostic capacity, and patient throughput. A PRISMA 2020-compliant review process was used, including structured searches of PubMed, Scopus, IEEE Xplore, and Web of Science. Screening, extraction, risk-of-bias assessment, and narrative synthesis were conducted to compare model targets, data sources, validation approaches, and implementation maturity. The evidence base was dominated by retrospective studies of patient throughput, emergency department admission prediction, bed demand, and length of stay. Staffing, equipment, and diagnostic capacity forecasting were less frequently represented, while integrated multi-resource command centre models remained uncommon. Machine learning for hospital resource allocation has become technically advanced, but operational translation remains uneven. Most models remained retrospective or locally validated, with limited evidence of prospective deployment, workflow integration, or measurable operational impact.

Revenue cycle inefficiencies, including claim denials, coding errors, delayed reimbursement, and prior authorization workload, impose substantial administrative and financial burdens on healthcare organizations. Machine learning has been proposed as a decision-support approach for improving prediction, automation, and workflow prioritization in these areas. This systematic review examined peer-reviewed and closely related scholarly literature from 2017 to 2023 on machine learning for healthcare revenue cycle analytics. The review focused on claim denial prediction, coding automation, payment delay forecasting, and prior authorization support. A PRISMA 2020-compliant review process was used, including structured database searching, dual screening, and domain-based narrative synthesis. Risk of bias was assessed using criteria adapted from PROBAST-AI, with attention to temporal validation, data leakage, and implementation relevance. The literature showed the greatest maturity in automated clinical coding and emerging but narrower evidence for claim denial prediction. Evidence for payment delay forecasting and prior authorization support was more limited, with few studies describing prospective implementation or measured operational impact. Machine learning shows promise for improving revenue cycle decision support, but most evidence remains retrospective and technically oriented. Deployment is constrained by data fragmentation, explainability requirements, workflow integration, and regulatory caution.

A small fraction of hospital episodes accounts for a disproportionate share of inpatient spending. Early recognition of these episodes remains difficult when risk assessment depends mainly on static admission information. More adaptive prediction is needed to support clinical and financial planning during hospitalization. Existing cost prediction models often emphasize claims, diagnoses, or broad utilization histories while underusing the dynamic signals that emerge during the inpatient stay. Pharmacy utilization, procedure sequencing, length-of-stay progression, and intensive care transfers may reveal escalating resource intensity before the final cost is known. Failure to integrate these modalities limits early identification of high-cost episodes. This article proposes a multimodal deep learning framework for predicting whether a hospitalization could become a high-cost outlier. The model is designed to combine pharmacy utilization, procedure sequences, length-of-stay trends, intensive care transfer events, and administrative claims data. The intended use is dynamic risk estimation early and repeatedly during the episode. The conceptual model uses separate modality-specific encoders for static claims features, temporal procedure events, pharmacy utilization patterns, length-of-stay trajectories, and intensive care transfer indicators. These representations are fused into a shared episode-level embedding trained with a cost-sensitive objective. The framework is intended for evaluation in historical and silent prospective deployment settings without assuming immediate clinical intervention effects. Conceptually, the model would output an updated probability that an active hospitalization will exceed a high-cost threshold. This probability would change as new medication orders, procedures, length-of-stay milestones, and intensive care transfers occur. The output could support utilization review, case management, pharmacy stewardship, and financial counseling workflows. A multimodal deep learning model for high-cost hospital episode prediction could help health systems identify emerging cost outliers before discharge. By combining static claims information with dynamic inpatient trajectories, such a model could support earlier resource allocation and more coordinated care planning. Its value should be assessed through careful validation, calibration, workflow integration, and prospective impact evaluation.

Clinical pathways are designed to standardize inpatient care for common conditions while allowing clinically justified individualization. Deviations from these pathways are frequent and may reflect either appropriate adaptation to patient complexity or potentially harmful departure from evidence-informed practice. Current deviation detection often depends on retrospective audit, static compliance rules, or aggregate dashboards. These approaches can miss subtle temporal drift in care delivery and rarely explain why a specific patient trajectory diverged from the expected pathway. This article proposes an interpretable machine learning model for detecting clinical pathway deviations in hospitalized patients. The model focuses on order sequences, vital sign trends, laboratory monitoring frequency, and provider decision patterns as dynamic indicators of care-process variation. Conceptually, the model would compare each patient’s evolving care trajectory with learned expected pathways using sequence-comparison and outlier-detection logic. SHAP-based or attention-informed explanations would identify the specific features responsible for a deviation flag, such as delayed monitoring, omitted follow-up testing, or unusual ordering behavior. The proposed model could detect when a patient’s care trajectory diverges from an expected pathway and provide a transparent rationale for review. For example, it could flag a missing repeat troponin, a delayed antibiotic escalation, or a laboratory monitoring pattern inconsistent with the patient’s clinical state. An interpretable pathway-deviation model could shift quality monitoring from manual, sample-based review toward continuous and transparent pathway surveillance. Such a system would support real-time clinical awareness, structured audit, and organizational learning.

Emergency admissions frequently wait for inpatient bed placement when hospital capacity, infection control needs, staffing limitations, and specialty-bed requirements collide. These delays can prolong emergency department boarding and disrupt hospital-wide patient flow. Current bed management is often reactive, relying on bed coordinators, charge nurses, manual communication, and local escalation routines. Without a prospective warning system, teams may recognize an impending placement delay only after the admission queue has already stalled. The objective is to develop a machine learning model that predicts, at the time of admission decision, whether a patient is likely to experience delayed placement beyond a defined operational threshold. The model would use bed assignment logs, isolation requirements, unit census, nurse staffing levels, and specialty service availability as core predictors. A supervised classification model based on gradient-boosted trees would be trained on historical emergency admissions and linked operational data. The model would generate a placement delay risk score that can be refreshed as bed status, staffing, and unit conditions change. Conceptually, the model would identify admissions at elevated risk for delayed placement and attribute risk to operational constraints such as limited isolation rooms, high census, low staffing, or unavailable specialty beds. These explanations would give bed managers lead time to intervene before the delay becomes entrenched. This predictive model could shift hospital bed management from a reactive queue-based process to proactive, data-driven placement coordination. It would support earlier escalation, more targeted resource allocation, and improved alignment between emergency admissions and inpatient capacity.

Duplicate patient records are a pervasive problem in electronic health records, endangering patient safety and inflating healthcare costs. In EHR-driven health systems, identity fragmentation can separate medications, allergies, diagnoses, laboratory results, and prior encounters across more than one record. Traditional probabilistic linkage relies on manual tuning of weights for demographic attributes and cannot fully exploit temporal address changes, insurance identifiers, or clinical patterns. These limitations become especially important when names are misspelled, addresses change, identifiers are missing, or patients receive care across multiple facilities. This article develops a conceptual model that combines probabilistic record linkage with machine learning to detect duplicate patient records. The model uses demographic similarity, address variation, encounter history, insurance identifiers, and clinical pattern matching as complementary evidence streams. The proposed model first applies probabilistic blocking to generate candidate record pairs, then uses a deep learning classifier, such as a Siamese network, to score pairs based on static and dynamic features. The output is a duplicate probability that can support automated ranking, human review, and master patient index maintenance. Conceptually, the model would be expected to improve duplicate detection compared with purely probabilistic linkage when demographic data are incomplete or unstable. Its main advantage is that it can anchor linkage decisions in more stable encounter patterns, longitudinal clinical trajectories, and repeated institutional contact signals. Such a hybrid model could enable a more accurate and self-maintaining master patient index while reducing the burden of manual record merging. It would also support safer registration workflows by identifying probable duplicates before identity fragmentation affects care delivery.

Hospital patient flow depends on timely transfer decisions, accurate discharge planning, and reliable post-discharge coordination. Delays in placement, premature discharge, missed follow-up, and unrecognized post-discharge risk can increase avoidable utilization and compromise continuity of care. This systematic review examined machine learning models for predicting placement delay, discharge readiness, follow-up completion, and post-discharge risk from 2017 to 2024. The objective was to synthesize model purposes, data sources, validation practices, implementation maturity, and practical relevance for care transitions. A PRISMA 2020-compliant systematic review was conducted using PubMed, Scopus, IEEE Xplore, and Web of Science. Dual screening, structured data extraction, and narrative synthesis were used because heterogeneity in model objectives, predictors, settings, and outcomes prevented meta-analysis. The evidence base was largest for post-discharge readmission risk and discharge readiness prediction, while placement delay and follow-up completion received less focused attention. Prospective validation, workflow integration, and outcome-based implementation studies were uncommon across all four domains. Machine learning offers promising tools to support proactive discharge planning and transitional care, but the evidence remains dominated by retrospective model development. Translation into routine practice requires prospective testing, interpretability, equity assessment, and integration into multidisciplinary workflows.

Appointment cancellations undermine clinic efficiency, disrupt continuity of care, and reduce access for patients waiting for limited appointment slots. Many cancellations may be preventable when risk is recognized early enough for staff to intervene with reminders, rescheduling support, transportation assistance, or telemedicine conversion. Existing appointment-risk models often emphasize historical attendance and demographic information while underusing scheduling notes, patient communication history, weather conditions, and transportation barriers. They also frequently provide risk scores without patient-specific explanations that staff can translate into meaningful outreach. This article proposes an explainable machine learning framework for predicting preventable appointment cancellations before the appointment occurs. The framework is designed to identify not only which appointments may be at risk, but also why the cancellation risk is elevated. The proposed framework uses a gradient-boosted classification model trained on structured scheduling variables, prior attendance behavior, communication history, weather-linked features, transportation indicators, and natural language processing outputs from scheduling notes. SHAP-based explanation layers would decompose each prediction into interpretable drivers that can be reviewed by scheduling staff, clinic managers, and governance teams. Conceptually, the framework would output a cancellation risk score together with a natural-language explanation of the dominant drivers. These outputs could support targeted interventions such as reminder escalation, proactive rescheduling, transportation support, or conversion to a virtual visit when appropriate. An explainable framework for preventable appointment cancellation prediction could shift patient access management from reactive backfilling toward proactive retention. By combining heterogeneous access signals with transparent attribution, clinics could better align outreach resources with patient-specific barriers.

Prior authorization delays impede timely patient care and contribute to administrative pressure across clinical and revenue cycle workflows. These delays can affect scheduling, medication access, procedural planning, and patient confidence in the care process. Current authorization management tools are largely reactive and often focus on tracking request status after submission. They rarely predict which requests are likely to experience approval delays or explain the operational, clinical, or payer-specific reasons behind those delays. This article proposes an interpretable machine learning model for predicting the likelihood of prior authorization approval delays. The model is designed to provide transparent, request-level explanations that can guide pre-submission correction and authorization preparation. The proposed framework uses a gradient-boosted tree model trained conceptually on historical authorization requests. Inputs include payer-specific rules, clinical documentation features, procedure type, medical necessity indicators, and historical approval timelines, with SHAP used to attribute predicted delay risk to individual features. Conceptually, the model would output a delay probability and an explanation of the dominant drivers of that prediction. These drivers could include incomplete documentation, mismatch with payer medical necessity criteria, procedure categories associated with additional review, or payer-procedure combinations with historically slow turnaround. An interpretable prior authorization delay model could support earlier correction of incomplete requests, reduce administrative waste, and improve patient access. By aligning predictive analytics with transparent explanations, the framework could make authorization preparation more proactive and accountable.

Hospital housekeeping demand is closely tied to bed turnover, discharge timing, isolation precautions, and unit-level patient movement. Delays in environmental services completion can slow bed availability and create downstream pressure on emergency departments, inpatient units, and procedural areas. Environmental services staffing is often managed through fixed shift patterns, current occupancy views, and reactive dispatch queues. These approaches may not anticipate cleaning surges caused by clustered discharges, isolation rooms, or changing census patterns. This manuscript proposes a predictive model for forecasting hospital housekeeping demand by combining discharge predictions, room turnover history, isolation status, environmental cleaning requirements, and unit-level census signals. The goal is to estimate the number, type, and timing of cleaning tasks needed across hospital units. The proposed model would use historical environmental services logs, admission-discharge-transfer data, bed management data, discharge prediction outputs, and infection-control status indicators. A supervised regression or time-series architecture could generate hourly unit-level demand forecasts for routine, terminal, and enhanced cleaning tasks. Conceptually, the model would be expected to identify upcoming cleaning pressure before it appears on the live dispatch board. For example, it could anticipate an afternoon surge in terminal cleans when several predicted discharges coincide with isolation rooms and high unit census. A forecasting model for housekeeping demand could support proactive environmental services staffing, reduce avoidable bed turnaround delays, and improve hospital throughput. Its value would depend on careful integration with existing bed management systems and prospective evaluation in operational settings.