Payments For Acute Myocardial Infarction Episodes Of Care By Hospital Interventional Capability

It is not known whether hospitals with percutaneous coronary intervention (PCI) capability provide more costly care than hospitals without PCI capability for patients admitted for acute myocardial infarction (AMI). The growing number of PCI-capable hospitals and higher rate of PCI use at technologically advanced hospitals may result in higher costs for episodes of care initiated at PCI hospitals. However, higher rates of transfers and post-acute care procedures may result in higher costs for episodes of care initiated at non-PCI hospitals. We identified all AMI admissions in 2008 among Medicare fee-for-service beneficiaries and classified hospitals as PCI- or non-PCI-capable based on hospitals\u27 2007 PCI performance. We added all payments from the time of admission through 30 days post-admission, including payments to hospitals other than the admitting hospital. We calculated and compared risk- standardized payment for PCI and non-PCI hospitals using 2-level hierarchical generalized linear models that adjust for patient demographics and clinical characteristics. PCI hospitals had a slightly higher mean 30-day risk-standardized payment than non-PCI hospitals ($20,340 v.$19,713, P\u3c0.001). Patients presenting to PCI hospitals had higher PCI rates (39.2% v. 13.2%, P\u3c0.001) and higher coronary artery bypass graft (CABG) rates (9.5% v. 4.4%, P\u3c0.001) during index AMI admissions, lower transfer rates (2.2% v. 25.4%, P\u3c0.001), and lower revascularization rates within 30 days (0.15% v. 0.27%, P\u3c0.0001) than those presenting to non- PCI hospitals. Despite higher PCI and CABG rates for patients who began their 30-day episode of care at PCI hospitals, PCI hospitals were only $627 more costly than non-PCI hospitals for the treatment of patients with AMI

3T Framework for AI Adoption in Human Resource Management: A Strategic Assessment Tool of Talent, Trust, and Technology

Artificial intelligence (AI) is steadily entering and transforming the management, work, and organizational ecosystems. We observe AI-based applications assisting employees in daily tasks, project management, decision-making, and collaboration. AI applications are increasingly assisting also Human Re-source Management (HRM) in undertaking time-critical tasks and managerial and administrative decision-making. However, more in-depth and comprehensive studies are needed to understand the specific factors affecting the full adoption of AI technology from a multi-level viewpoint and address the potential limitations of AI appropriation or its adverse outcomes in HRM.The purpose of this study is to investigate the conditions in which human talent may take advantage of the unique opportunities offered by AI. However, whereas previous studies were conducted on the individual perception of AI and technology readiness or adoption, an integrated approach aiming to combine talent management-related dimensions and managerial-related dimensions is still not avail-able. For this research gap, we build a strategic management assessment frame-work of the driving factors of Talent, Trust, and Technology (3T) in AI adoption in HRM. We investigate the impact of these trends on the human-related and technology ecosystems and provide an integrated analysis of individual micro (talent management) organizational macro (trust and technology) adoption of AI technology.The paper advances the current definition and understanding of individual human facilitators and impediments behind the ability to speed up the adoption of AI-based technology. The practical contribution can facilitate the human-centered and trustworthy design and adoption of AI