How are hospitals using artificial intelligence in strategic decision making? —a scoping review

Artificial intelligence (AI) is a useful tool for clinical decision-making in hospitals, and for strategic decision-making in other industries. This scoping review provides a comprehensive review of the potential for AI to improve strategic decision-making in hospitals by exploring current applications of AI in this area. Peer-reviewed publications and conference presentations associated with AI for strategic decision-making were identified in Health Administration, Computer Science and Business and Management databases to answer the research question; how are hospitals using AI in strategic decision-making? The review found 19 published AI applications for hospital strategic decision-making. The applications used a variety of knowledge-based, probabilistic reasoning and data-driven AI, that generally followed the course of AI maturity. They focused on specific decisions, with none providing a comprehensive framework for strategic decision-making drawing on existing enterprise- or system-wide data. There was little evidence of evaluation of the AI applications, with no cost-benefit evaluation. The scoping review suggests the need for substantial improvement in the understanding of AI and its application among hospital decision-makers leading to greater organisational maturity. This would suggest that journals and researchers require evaluative and economic research and that training to improve understanding of AI be provided for board members, managers and clinicians

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

Impact of the Acute Surgical Unit on a Local and Global Scale

Introduction Traditionally, general surgical departments allocated their staff to elective operative and outpatient commitments, with emergency general surgical (EGS) patients managed ad-hoc. An acute surgical unit (ASU) model was pioneered in 1996 and spread globally. However, uptake remains slow, in part due to clinical equipoise. This thesis aims to address key gaps in the literature, to support hospitals considering establishing an ASU and EGS policymakers. Methods Locally, three retrospective studies were performed at the Lyell McEwin Health Service. For patients with appendicitis or cholecystitis, these compared cohorts ≤2.5 years pre/post ASU introduction. Primary outcomes were length of stay, time to theatre, after-hours operating rates, rates of cholecystectomy on index admission and rates of appropriate communication and management of incidental pathology (appendicitis patients only). A fourth study prospectively assessed patient reported outcomes within the Royal Adelaide Hospital ASU. Primary outcomes were factors associated with patient satisfaction on multivariate analysis. Nationally, two studies reported the results of a cross-sectional assessment of the general surgery departments in all medium-large sized Australian public hospitals. Primary outcomes were the spectrum of EGS models in use, staff satisfaction and operative exposure. Globally, two systematic reviews were performed. The first identified ASU-type dedicated models of care for emergency patients in urology. The primary outcome was the spectrum of models. The second collated for meta-analysis general surgery studies comparing the Traditional and ASU models. Primary outcomes were length of stay, cost and rates of after-hours operating and complications. Results Locally, single centre retrospective studies of 319–1,214 patients found that establishing an ASU was associated with reduced time to theatre and rates of after-hours operating, and superior rates of cholecystectomy on index admission. Length of stay was reduced for patients with cholecystitis but not appendicitis. For presumed-appendicitis patients with incidental pathology, rates of communication or appropriate management were unchanged. Nationally, the cross-sectional study enrolled 119/120 eligible hospitals. Sixty-four (54%) hospitals reported using an ASU or hybrid EGS model. Compared with the Traditional structure, hybrid or ASU models were associated with greater surgeon and registrar satisfaction. Registrar-perceived operating exposure was unaffected by EGS model. Globally, the first systematic review identified seven centres implementing a variety of dedicated models for emergency urological patients. The second review enrolled 77 publications representing 150,981 unique EGS patients from thirteen nations. Compared with the Traditional model, ASU introduction was associated with reductions in length of stay and rates of after-hours operating and complications. Financial assessments found the ASU to deliver equivalence or cost savings. Conclusion Compared with the Traditional structure, the ASU model delivers superior outcomes. The ASU model should be promoted in health policy to benefit patients, staff and health budgets. Further improvements may involve ASU wards as centres of education and excellence, linked contractual obligation and increased funding for general surgeons to deliver EGS care and greater inter-hospital coordination. Future research includes cost analyses, quality improvement initiatives measured by patient reported outcomes and assessment of ASU model utility in other surgical specialties and in low-income countries.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

2023-2024 Catalog

The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

An integrated decision analytic framework of machine learning with multi-criteria decision making for patient prioritization in elective surgeries

Objectif: De nombreux centres de santé à travers le monde utilisent des critères d'évaluation des préférences cliniques (CPAC) pour donner la priorité aux patients pour accéder aux chirurgies électives. Le processus de priorisation clinique du patient utilise à cette fin les caractéristiques du patient et se compose généralement de critères cliniques, d'expériences de patients précédemment hospitalisés et de commentaires sur les réseaux sociaux. Le but de la hiérarchisation des patients est de déterminer un ordre précis pour les patients et de déterminer combien chaque patient bénéficiera de la chirurgie. En d'autres termes, la hiérarchisation des patients est un type de problème de prise de décision qui détermine l'ordre de ceux qui ont le plus bénéficié de la chirurgie. Cette étude vise à développer une méthodologie hybride en intégrant des algorithmes d'apprentissage automatique et des techniques de prise de décision multicritères (MCDM) afin de développer un nouveau modèle de priorisation des patients. L'hypothèse principale est de valider le fait que l'intégration d'algorithmes d'apprentissage automatique et d'outils MCDM est capable de mieux prioriser les patients en chirurgie élective et pourrait conduire à une plus grande précision. Méthode: Cette étude vise à développer une méthodologie hybride en intégrant des algorithmes d'apprentissage automatique et des techniques de prise de décision multicritères (MCDM) afin de développer un modèle précis de priorisation des patients. Dans un premier temps, une revue de la littérature sera effectuée dans différentes bases de données pour identifier les méthodes récemment développées ainsi que les facteurs de risque / attributs les plus courants dans la hiérarchisation des patients. Ensuite, en utilisant différentes méthodes MCDM telles que la pondération additive simple (SAW), le processus de hiérarchie analytique (AHP) et VIKOR, l'étiquette appropriée pour chaque patient sera déterminée. Dans la troisième étape, plusieurs algorithmes d'apprentissage automatique seront appliqués pour deux raisons: d'abord la sélection des caractéristiques parmi les caractéristiques communes identifiées dans la littérature et ensuite pour prédire les classes de patients initialement déterminés. Enfin, les mesures détaillées des performances de prédiction des algorithmes pour chaque méthode seront déterminées. Résultats: Les résultats montrent que l'approche proposée a atteint une précision de priorisation assez élevée(~70 %). Cette précision a été obtenue sur la base des données de 300 patients et elle pourrait être considérablement améliorée si nous avions accès à plus de données réelles à l'avenir. À notre connaissance, cette étude présente la première et la plus importante du genre à combiner efficacement les méthodes MCDM avec des algorithmes d'apprentissage automatique dans le problème de priorisation des patients en chirurgie élective.Objective: Many healthcare centers worldwide use Clinical Preference Assessment criteria (CPAC) to prioritize patients for accessing elective surgeries [44]. The patient's clinical prioritization process uses patient characteristics for this purpose and usually consists of clinical criteria, experiences of patients who have been previously hospitalized, and comments on social media. The sense of patient prioritization is to determine an accurate ordering for patients and how much each patient will benefit from the surgery. This research intends to build a hybrid approach for creating a new patient prioritizing model by combining machine learning algorithms with multi-criteria decision-making (MCDM) methodologies. The central hypothesis is to validate that the integration of machine learning algorithms and MCDM tools can better prioritize elective surgery patients and lead to higher accuracy. Method: As a first step, a literature review was performed in different databases to identify the recently developed methods and the most common criteria in patient prioritization. Then, using various MCDM methods, including simple additive weighting (SAW), analytical hierarchy process (AHP), and VIKOR, the appropriate label for each patient was determined. As the third step, several machine learning algorithms were applied to predict each patient's classes. Finally, we established the algorithms' precise prediction performance metrics for each approach. Results: The results show that the proposed approach has achieved relatively high prioritization accuracy (~70%). This accuracy has been obtained based on the data from 300 patients, and it could be significantly improved if we have access to more accurate data in the future. To the best of our knowledge, this research is the first of its type to demonstrate the effectiveness of combining MCDM methodologies with machine learning algorithms in patient prioritization problems in elective surgery

2022-2023 Catalog

The 2022-2023 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

Graduate Catalogue 2020-2021

https://scholarship.shu.edu/graduate_catalogues/1018/thumbnail.jp

Graduate Catalogue 2021-2022

https://scholarship.shu.edu/graduate_catalogues/1021/thumbnail.jp

GVSU Undergraduate and Graduate Catalog, 2021-2022

Grand Valley State University 2021-2022 undergraduate and graduate course catalog. Course catalogs are published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1096/thumbnail.jp