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The objective of this scoping review was to survey the literature on the use of AI/ML applications in analyzing inpatient EHR data to identify bundles of care (groupings of interventions). If evidence suggested AI/ML models could determine bundles, the review aimed to explore whether implementing these interventions as bundles reduced practice pattern variance and positively impacted patient care outcomes for inpatients with T2DM. Six databases were searched for articles published from January 1, 2000, to January 1, 2024. Nine studies met criteria and were summarized by aims, outcome measures, clinical or practice implications, AI/ML model types, study variables, and AI/ML model outcomes. A variety of AI/ML models were used. Multiple data sources were leveraged to train the models, resulting in varying impacts on practice patterns and outcomes. Studies included aims across 4 thematic areas to address: therapeutic patterns of care, analysis of treatment pathways and their constraints, dashboard development for clinical decision support, and medication optimization and prescription pattern mining. Multiple disparate data sources (i.e., prescription payment data) were leveraged outside of those traditionally available within EHR databases. Notably missing was the use of holistic multidisciplinary data (i.e., nursing and ancillary) to train AI/ML models. AI/ML can assist in identifying the appropriateness of specific interventions to manage diabetic care and support adherence to efficacious treatment pathways if the appropriate data are incorporated into AI/ML design. Additional data sources beyond the EHR are needed to provide more complete data to develop AI/ML models that effectively discern meaningful clinical patterns. Further study is needed to better address nursing care using AI/ML to support effective inpatient diabetes management. Hospitals are experiencing a nursing shortage crisis that is expected to worsen over the next decade. Acute care settings, which manage the care of very complex patients, need innovations that lessen nurses' workload burden while ensuring safe patient care and outcomes. Thus, a pilot study was conducted to evaluate the feasibility of implementing a large-scale acute care telenurse program, where a hospital-employed telenurse would complete admission and discharge processes for hospitalized patients virtually. In 3 months, almost 9000 (67%) of patient admissions and discharges were conducted by an acute care telenurse, saving the bedside nurse an average of 45 minutes for each admission and discharge. Preliminary benefits to the program included more uninterrupted time with patients, more complete hospital admission and discharge documentation, and positive patient and nurse feedback about the program. No abstract available No abstract available Clinical practice nurses need adequate digital competence to use technologies appropriately at work. Questionnaires measuring clinical practice nurses' digital competence lack content validity because attitude is not included as a measure of digital competence. The aim of the current study was to identify items for an item pool of a questionnaire to measure clinical practice nurses' digital competence and to evaluate the content validity. A normative Delphi study was conducted, and the content validity index on item and scale levels was calculated. In each round, 21 to 24 panelists (medical informatics specialists, nurse informatics specialists, digital managers, and researchers) were asked to rate the items on a 4-point Likert scale ranging from “not relevant” to “very relevant.” Within three rounds, the panelists reached high consensus and rated 26 items of the initial 37 items as relevant. The average content validity index of 0.95 (SD, 0.07) demonstrates that the item pool showed high content validity. The final item pool included items to measure knowledge, skills, and attitude. The items included represent the international recommendations of core competences for clinical nursing. Future research should conduct psychometric testing for construct validity and internal consistency of the generated item pool. 
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