Background: Telemedicine expanded dramatically during the COVID-19 pandemic, transforming healthcare delivery worldwide. However, implementation faced challenges, and the impact on clinical outcomes, access, and quality remains under investigation. Objective: To systematically review the literature from 2020 to 2025 on telemedicine adoption, identifying key barriers and facilitators, and to evaluate clinical outcomes associated with telehealth use during this period. Methods: We followed PRISMA 2020 guidelines in conducting this review. Multiple databases were searched for studies on the implementation or evaluation of telemedicine/telehealth. Eligible studies included randomized trials and observational studies reporting telehealth-related outcomes, barriers, or facilitators. Two reviewers screened studies and extracted data on study characteristics, telemedicine interventions, barriers/facilitators, and clinical outcomes. Risk of bias was assessed using RoB2 for randomized controlled trials (RCTs) for qualitative or cross-sectional studies. Meta-analyses were performed where data were comparable, and qualitative synthesis was used to summarize barriers and facilitators. Results: Thirty-two studies (17 RCTs and 15 observational) were included. Telemedicine use surged in 2020 and remained elevated compared to baseline through August 2025. Reported barriers included insufficient broadband access, limited digital literacy, uncertain reimbursement policies, and workflow disruptions. Facilitators encompassed supportive policy waivers, the integration of telehealth into established care pathways, and strong acceptance from patients and providers. Clinical outcomes were generally comparable to in-person care. Telehealth enhanced chronic disease management (e.g., hypertension, diabetes) and decreased hospitalizations for heart failure, while ensuring safety in surgical follow-up and prenatal care. However, higher revisit rates were observed in some acute follow-up settings. Patient satisfaction consistently remained high, especially among rural and underserved populations reporting benefits, though disparities in digital access continued to exist. Conclusions: Telemedicine has become a sustainable component of healthcare, delivering clinical outcomes comparable to traditional care while offering convenience and resilience. Overcoming technology gaps, regulatory uncertainties, and equity issues is crucial for ongoing progress. Hybrid care models that combine telemedicine with in-person services, supported by strong policy frameworks, are recommended to maximize benefits and promote fair access in the post-pandemic era.

Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims. Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies. Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain.

Framed within the lens of systems theory and sociotechnical systems thinking, this systematic review examines telehealth as a complex adaptive system and dynamic health system shaped by the interactions between interconnected technological, social, and institutional components. Recognizing telehealth as part of a complex adaptive system, the review identifies how interdependent factors, such as digital literacy, connectivity, and policy, evolve and influence access to and the emergent properties of care. A systematic review was conducted following the PRISMA 2020 guidelines and PROSPERO registration (CRD420251103608), analyzing 42 peer-reviewed articles published between January 2020 and June 2025, identified through the MEDLINE, Web of Science, EBSCOhost, ACM Digital Library, PsycINFO, and Scopus databases. Key findings include sustained but reduced telehealth use after the pandemic peak, as well as a small yet statistically significant positive effect of telehealth interventions on cognitive emergent properties, defined here as measurable outcomes like memory, attention, executive function, and processing speed (SMD = 0.29; 95% CI [0.04, 0.54]) with very low heterogeneity (I² = 0%). Significant system components such as digital illiteracy, poor internet connectivity, and complex technology interfaces disproportionately affected economically disadvantaged, minority, and rural older adults. Practical strategies rooted in systems thinking include digital literacy programs, simplified interfaces, caregiver support, improved broadband infrastructure, hybrid healthcare models, and supportive policies. Future research should focus on evidence-based, system-level interventions across diverse settings to bridge the digital divide and promote equitable access to telehealth for older adults.

Background: The increasing prevalence of dementia and mild cognitive impairment (MCI) among the elderly population is a global health issue. Information and Communication Technology (ICT)-based interventions hold promises for maintaining cognition, but their viability is affected by several challenges. Objectives: This study aimed to significantly assess the effectiveness of ICT-based cognitive and memory aid technology for individuals with MCI or dementia, identify adoption drivers, and develop an implementation model to inform practice. Methods: A PRISMA-based systematic literature review, with the protocol registered in PROSPERO (CRD420251051515), was conducted using seven electronic databases published between January 2015 and January 2025 following the PECOS framework. Random effects models were used for meta-analysis, and risk of bias was assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklists. Results: A total of ten forms of ICT interventions that had proved effective to support older adults with MCI and dementia. Barriers to adoption included digital literacy differences, usability issues, privacy concerns, and the lack of caregiver support. Facilitators were individualized design, caregiver involvement, and culturally appropriate implementation. ICT-based interventions showed moderate improvements in cognitive outcomes (pooled Cohen’s d = 0.49, 95% CI: 0.14–1.03). A sensitivity analysis excluding high-risk studies yielded a comparable effect size (Cohen’s d = 0.50), indicating robust findings. However, trim-and-fill analysis suggested a slightly reduced corrected effect (Cohen’s d = 0.39, 95% CI: 0.28–0.49), reflecting potential small-study bias. Heterogeneity was moderate (I² = 46%) and increased to 55% after excluding high-risk studies. Subgroup analysis showed that tablet-based interventions tended to produce higher effect sizes. Conclusions: ICT-based interventions considerably enhance cognition status, autonomy, and social interaction in older adults with MCI and dementia. To ensure long-term scalability, future initiatives must prioritize user-centered design, caregiver education, equitable access to technology, accessible infrastructure and supportive policy frameworks.

Dementia is an increasing public health challenge, yet scalable methods for early risk detection using non-clinical data remain limited. This study develops and evaluates interpretable machine learning models to predict dementia risk among older adults using nationally representative longitudinal data. Data were sourced from the National Health and Aging Trends Study (NHATS, 2011–2022) and included 5984 community-dwelling U.S. adults aged 65 and older who were dementia-free at baseline. Dementia onset was identified using the validated NHATS classification algorithm based on cognitive assessments, proxy reports, and physician diagnoses. After data preprocessing and feature engineering, missing values in continuous variables were imputed with k-nearest neighbors, while categorical variables were handled via one-hot encoding and mode-based imputation. Five supervised machine learning algorithms were trained and evaluated through stratified cross-validation, using performance metrics that account for class imbalance. Among these models, XGBoost showed the strongest overall performance, achieving the highest classification accuracy (0.881 ± 0.004), the lowest Brier score (0.094 ± 0.002), and the highest ROC–AUC (0.823 ± 0.005), with RF showing comparable results. Explainable AI analyses with SHapley Additive exPlanations (SHAP) consistently identified digital technology use, outdoor activity frequency, and social network size as the most influential predictors across models. These findings indicate that interpretable machine learning based on non-clinical, modifiable behavioral and social factors can support scalable, prevention-focused dementia risk assessment and inform prevention-oriented strategies that promote digital inclusion and social engagement among older adults.

Purpose: Privacy-preserving artificial intelligence (AI) technologies increasingly support data-driven decision-making while safeguarding sensitive information. However, peer-reviewed evidence documenting governance integration and operational deployment remains limited. This study examines how the peer-reviewed academic literature documents the technical maturity, governance integration, and operational implementation of privacy-preserving AI systems relevant to enterprise information systems. Design/methodology/approach: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-compliant systematic literature review examined 22 peer-reviewed studies published from 2018 to May 2026. The review synthesized evidence on privacy–utility trade-offs, governance integration, fairness, operational readiness, and implementation maturity. We used Technology Readiness Levels (TRLs) and a proposed Translational Readiness Index (TRI) to classify readiness evidence reported in the included studies. Findings: Privacy-preserving AI systems demonstrated favorable privacy–utility trade-offs in the reviewed academic literature, with privacy budgets of e = 1–3 preserving approximately 85–96% of baseline predictive performance. Federated learning combined with differential privacy was the dominant implementation architecture. Most included studies demonstrated intermediate technical maturity (TRL 4–6), primarily through controlled evaluations and prototype development while none documented deployment within operational payer workflows. Studies also inconsistently documented governance integration, privacy accounting, interoperability, and fairness assessment. We characterize this imbalance in the published academic evidence as the proposed Enterprise AI Documentation Gap Hypothesis. The hypothesis concerns the distribution of evidence in peer-reviewed literature and should not be interpreted as evidence that healthcare organizations or insurers are unprepared for deployment. Research limitations/implications: This study is limited to reviewing scholarly papers published in the academic literature and did not systematically examine industry literature, company records, patent information, or practitioner views. Therefore, the results reflect the scholarly evidence base and do not reflect health organizations' or insurers' readiness for implementation. The absence of evidence in the literature on deployment does not imply that implementation does not occur outside scholarly journals. Future studies should cross-check scholarly evidence with industry and practitioner evidence on the same topic. Originality/value: The TRI offers an exploratory, multidimensional framework for organizing documented evidence across technology, privacy, governance, fairness, and operations. This study proposes the Enterprise AI Documentation Gap Hypothesis to describe the gap between the relative maturity of technical evidence and the lack of published evidence on governance and operations. These are conceptual contributions that require further empirical testing.