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Missed medication doses in long-term care facilities compromise resident safety and arise from intersecting medication, resident, staffing, route, and workload factors. These risks are especially important where residents have complex regimens and high care dependency. Current medication safety approaches in long-term care are often retrospective, audit-based, or broadly applied across all residents. They do not forecast which specific resident–medication pass combinations are most vulnerable before administration occurs. The objective is to describe a predictive model that could estimate the probability of a missed medication dose for each resident–medication pass combination. The model would use medication complexity, resident dependency, staff availability, administration route, and shift-level workload indicators. A supervised classification approach could be trained using electronic medication administration records, staffing rosters, resident assessment data, and medication order characteristics. The model would output a dose-level missed-dose risk score before the relevant medication pass. Conceptually, the model would identify high-risk medication–resident–shift triples and provide an interpretable explanation of dominant risk contributors. For example, the system could flag a non-oral high-risk medication scheduled during a low-staffed morning medication round. Such a model could support proactive prevention by directing nursing attention toward the most vulnerable doses before they are missed. It could also inform shift-level workload planning and safer medication pass organization.

Failure to follow up after abnormal screening results is a persistent ambulatory safety problem. Because the diagnostic process often spans patient notification, scheduling, primary care review, and reminder outreach, delays may emerge gradually before they become visible in registry reports. Existing care gap reports commonly classify results as closed or open after a defined time window. This retrospective framing limits the ability to detect patients who are currently moving toward delayed diagnostic resolution. The objective of this article is to describe a predictive model that estimates the probability of delayed diagnostic follow-up after an abnormal screening result. The proposed model integrates patient engagement signals, scheduling activity, clinician workload, result severity, and reminder history. The model would use a gradient-boosted classification framework trained on historical abnormal results and longitudinal care-process features. Inputs would be extracted from patient portals, scheduling systems, primary care workload records, laboratory and radiology result metadata, and reminder logs. Conceptually, the model would generate a daily updated risk score for each unreconciled abnormal result. It would also identify the dominant drivers of risk, such as unread portal messages, repeated appointment cancellations, limited primary care availability, high-severity findings, or escalating reminder activity. A predictive approach could shift delayed follow-up management from retrospective audit to proactive prioritization. By identifying patients at greatest risk before the diagnostic window closes, care teams could better allocate outreach and navigation resources.

Healthcare quality improvement increasingly relies on routinely collected data to identify preventable harm, missed care opportunities, adverse outcomes, and variation in performance. Artificial intelligence predictive models may support earlier detection of quality risks and enable more proactive monitoring than retrospective audits alone. This systematic review examined artificial intelligence predictive models for healthcare quality improvement from 2017 to 2024. The review focused on patient safety events, care gaps, adverse clinical outcomes, and performance monitoring systems. A PRISMA 2020-compliant search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore for peer-reviewed English-language studies published from 2017 through 2024. Dual screening, structured data extraction, risk-of-bias assessment, and narrative synthesis were used. The evidence base showed growing use of machine learning for pressure injuries, sepsis, readmission, mortality, ICU transfer, and continuous monitoring. However, most studies remained retrospective model-development or validation studies, while fewer described deployment within formal quality improvement workflows. Technical progress in predictive modelling for quality improvement is substantial, but evidence of sustained improvement in care processes, safety outcomes, or organisational performance remains limited. Stronger prospective evaluation and clearer integration with improvement methods are needed.

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.

Diagnostic services are central to clinical decision-making because imaging, laboratory testing, and cardiology diagnostics often determine the next step in diagnosis, treatment, or referral. Bottlenecks in these services can delay care pathways and increase wait times when demand rises faster than available capacity. Current forecasting approaches in diagnostic departments are often reactive and based on historical averages, recent appointment counts, or manual manager judgment. Such approaches may miss upstream signals such as referral surges, seasonal disease activity, and physician ordering behavior. This article proposes a predictive model for forecasting diagnostic service demand by integrating ambulatory referral volume, seasonal disease trends, physician ordering patterns, equipment availability, and historical appointment backlogs. The model is intended to support short- and medium-term capacity planning across diagnostic services. The proposed approach uses a supervised time-series forecasting framework, such as gradient boosting with temporal features or a recurrent neural architecture, trained on historical diagnostic order and scheduling data. Inputs would be engineered from referral streams, diagnostic ordering records, seasonal indicators, equipment schedules, and backlog measures. Conceptually, the model would generate daily or weekly demand forecasts for each diagnostic modality and service line. Forecasts would include uncertainty bounds and operational alerts when projected demand is expected to exceed available appointment capacity. The proposed predictive model could enable proactive capacity management in diagnostic departments. By anticipating demand before backlogs become severe, the model could support improved scheduling, better equipment utilization, and reduced patient waiting times.