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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.

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.

Sepsis continues to be a major contributor to morbidity and mortality among hospitalized patients globally, especially within intensive care and emergency departments, where rapid recognition is essential for improving survival through timely treatment. In recent years, machine learning approaches have gained attention for their ability to predict sepsis onset using routinely collected electronic health record data. This systematic review, conducted in accordance with PRISMA 2020 guidelines, synthesizes evidence from studies published between 2017 and 2025, focusing on model architectures, feature selection and engineering strategies, prediction time horizons, and validation methodologies. Searches across major biomedical and informatics databases identified 67 eligible studies. The included literature shows that logistic regression, ensemble tree-based algorithms, and deep learning models are most frequently applied for sepsis prediction tasks. However, the majority of studies rely on retrospective datasets with internal validation, while only a limited number incorporate prospective or real-world validation frameworks. Overall, although reported model performance is often strong in retrospective analyses, a consistent decline in accuracy is observed when models are evaluated in real clinical environments. These findings highlight that prospective validation and improved generalizability are still underdeveloped areas, underscoring the need for future research to emphasize real-time deployment and robust external validation before clinical integration.

Public health emergencies reveal critical weaknesses in healthcare supply chains, especially when PPE demand outpaces procurement and distribution capacity, making predictive analytics an important tool for forecasting demand and improving allocation during crises. This systematic review evaluates predictive analytics models for PPE demand forecasting and distribution optimization during public health emergencies, focusing on model types, data sources, validation approaches, performance metrics, equity considerations, and implementation readiness. Following PRISMA 2020 guidelines, searches were conducted in PubMed, Web of Science, Scopus, IEEE Xplore, and Google Scholar for studies published between 2017 and 2025, yielding 2,847 records, of which 35 met inclusion criteria. Included studies comprised time series and statistical models (34%), machine learning and hybrid approaches (29%), optimization methods (26%), and simulation or digital twin frameworks (11%), with limited evidence of real-world deployment. Overall, findings indicate that predictive analytics can enhance PPE supply chain resilience by improving demand forecasting, allocation decisions, and scenario testing, but widespread adoption is limited by poor data interoperability, insufficient prospective validation, weak equity integration, and limited operational integration into healthcare decision systems.

Federated and decentralized machine learning offer the potential to extract valuable healthcare insights from siloed data without requiring the centralization of sensitive patient records, addressing long-standing privacy and governance challenges. This critical review assesses federated learning in healthcare through three lenses: privacy-preserving technologies, incentive mechanisms, and regulatory compliance frameworks. It examines whether the claims in existing literature are substantiated by real-world evidence from healthcare settings. The review reveals considerable enthusiasm for federated learning but identifies gaps, including incomplete implementation of privacy technologies, theoretical incentive mechanisms, and regulatory compliance often assumed but not validated. Additionally, real-world deployments are limited in scale and duration. The review concludes that the gap between federated learning's theoretical potential and clinical application remains significant, with overstated privacy claims and a lack of established frameworks for incentives and compliance.

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.

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.

Healthcare workforce management faces persistent challenges, including staffing shortages, clinician burnout, skill misalignment, productivity pressure, and turnover. Machine learning has been proposed as a way to support earlier prediction, better allocation, and more responsive workforce decision-making. This systematic review synthesizes peer-reviewed evidence from 2017 to 2025 on machine learning applications in healthcare workforce analytics. The review focuses on staffing prediction, burnout detection, skill matching, productivity monitoring, and retention risk forecasting. A PRISMA 2020-aligned review approach was used to structure database searching, screening, eligibility assessment, and narrative synthesis. The review considered studies from PubMed, Scopus, IEEE Xplore, and Web of Science, with emphasis on workforce-relevant outcomes and healthcare operational settings. Staffing prediction and burnout detection were the most developed domains in the reviewed literature. Skill matching and productivity monitoring were less mature, while retention risk forecasting showed growing interest but limited implementation evidence. Machine learning offers useful tools for healthcare workforce analytics, but the evidence base remains uneven. Stronger prospective validation, fairness assessment, and implementation reporting are needed before these models can be considered mature workforce decision-support systems.

Many patients discharged from emergency departments require timely outpatient follow-up to complete diagnostic, therapeutic, or monitoring plans. When follow-up is delayed, unresolved symptoms, missed diagnoses, medication problems, and preventable return visits may occur. Current discharge workflows often rely on generic instructions and assume that patients can understand, schedule, and attend recommended care. Existing prediction approaches do not consistently combine unstructured discharge instructions, appointment access, portal engagement, social risk, and visit severity in a transparent way.This article proposes a transparent machine learning framework for predicting delayed follow-up after emergency department visits. The objective is to support patient-specific discharge planning by identifying both the likelihood of delay and the most actionable contributing barriers. The proposed model would combine structured emergency department and scheduling data with natural language processing features extracted from discharge instructions. A gradient-boosted tree model with SHAP-based explanations would provide patient-level and population-level interpretability. Conceptually, the model would identify patients at elevated risk of delayed follow-up and attribute that risk to factors such as unclear instructions, limited appointment availability, absent portal engagement, transportation barriers, or higher visit complexity. These explanations would be intended to guide targeted interventions rather than replace clinical judgment. A transparent model for delayed follow-up prediction could enable precision transitional care after emergency department discharge. By aligning predictions with actionable explanations, care teams could direct limited resources toward the specific barrier most likely to prevent timely follow-up.

Administrative tasks surrounding a clinical encounter include documentation, coding, billing, insurance verification, prior authorization, and care coordination. These tasks are unevenly distributed across encounters and can consume substantial clinical and operational capacity. Health systems often detect administrative overload only after coding backlogs, payer denials, unanswered messages, or staff overtime have already emerged. The absence of an encounter-level prediction tool limits the ability of practices to intervene before administrative work accumulates. This article proposes a machine learning model that predicts whether an encounter is likely to become a high-administrative-burden event. The model uses documentation complexity, billing requirements, insurance rules, care coordination needs, and provider workload indicators as core predictors. A gradient-boosted classification framework is conceptually specified using historical encounter, billing, scheduling, payer, and workload data. The model would generate an encounter-level burden risk score and provide interpretable feature-domain contributions to support operational decisions. Conceptually, the model could identify encounters likely to require additional coding review, prior authorization follow-up, payer documentation, or multidisciplinary coordination. The resulting risk score would support proactive staffing, pre-visit review, and workflow routing. A predictive model for high administrative burden encounters could help shift healthcare administration from reactive queue management to anticipatory operational planning. Such a model may support revenue integrity, reduce avoidable rework, and lessen administrative strain on clinicians and staff.