Welcome Message from Julio Avael
Thank you for visiting my research page. As a doctoral candidate at Temple University's Fox School of Business, I'm excited to share my work on artificial labor (AL) in healthcare with you.
In this space, you'll find my research materials, including my dissertation proposal, surveys, and collected data. I invite you to explore these resources and join me in examining the future of healthcare operations.
Julio Avael
Research & Innovation Leader
Julio Avael III is a senior executive with over 20 years of experience in healthcare, education, and media industries. He currently serves as a key executive at Leava Healthcare and the Florida Disceel Spinal Center, overseeing business development and multistate management.
Core Expertise
Growth and change management, crisis management, business development, finance, and operations management
Education
Doctoral candidate at Temple University's Fox School of Business, MS in Management from Barry University, and BA from Florida International University
Research Focus
Artificial labor (AL) in healthcare, including AI-powered diagnostics and robotic assistants to enhance operational efficiency and patient care
Avael has completed advanced certifications from Harvard University and Florida International University in Performance-Based Management, Corporate Governance, and Strategic Management.
As a thought leader, he regularly speaks on telehealth, mental health in school systems, and the future of employee training. His focus includes adapting learning programs to incorporate artificial intelligence.
His community service has earned recognition including the "Lifetime Volunteer Service Award" from former President Obama. He serves on the boards of the Rotary of Miami and the Non-Violence Foundation.
Why this research matters
Impact on Society
Investigating how AI transforms healthcare delivery systems, benefiting patients, providers, and communities.
Organized Labor Perspectives
Analyzing healthcare workers' viewpoints to ensure ethical AI implementation in medical settings.
Future Healthcare Outlook
Forecasting AI solutions for aging populations and chronic disease management.
Managerial Decision-Making
Studying hospital managers' AI adoption strategies and organizational change management.
Technological Implementation
Evaluating AI applications from diagnostics to administration, measuring efficiency and care quality improvements.
JulioAvael Research
Hospital Task Allocation Research Study
How Technology Adoption, Human Behavior, and Cost Efficiency Influence Hospital Task Allocation: A Resource-Based View and Technological Unemployment Perspective
Research Overview
This study examines the factors influencing hospital management's preference for allocating tasks to technology rather than human labor, focusing on three critical drivers:
1
Cost Efficiency
Analysis of economic factors driving technological implementation
2
Human Behavior Impact
Assessment of behavioral challenges in healthcare settings
3
Technology Adoption Patterns
Evaluation of implementation trends and acceptance factors
Research Committee
Principal Investigator
Julio Avael III
Committee Chair
Dr. C Anthony Di Benedetto
Department of Marketing
Committee Members
Dr. Min-Seok Pang
Department of Management Information Systems & Mentor
Dr. Sunil Wattal
Associate Dean - Research and Doctoral Programs
JulioAvael Research
Download the proposal outlining the research project.
Dissertation (TBA)
Access the full dissertation document.
Surveys
Survey One: Manager Preference of AI for Task Allocation
Download the survey instruments used in the research.
JulioAvael Research
Dissertation Defense Proposal
Welcome to my dissertation defense proposal session on technology adoption and task allocation in healthcare settings. This presentation will explore how hospitals make decisions about implementing new technologies and managing their workforce.
1
Introduction and Motivation
Overview of healthcare technology adoption challenges and research motivation based on 20+ years of industry experience.
2
Study I Discussion
Examination of factors influencing hospital management's technology adoption decisions, followed by Q&A session.
3
Study II Presentation
Analysis of technological unemployment implications in healthcare settings, followed by interactive discussion.
Each segment will include time for questions and collaborative discussion. Your insights and feedback are valuable to this research.
JulioAvael Research
Healthcare Technology Integration Research
Welcome to my research portal. As a healthcare operations management professional with over two decades of experience, I'm investigating the intersection of technology and healthcare operations.
Professional Background
With more than 20 years in healthcare operations management, I've held key leadership positions across various healthcare sectors including:
  • Community mental health facilities
  • Regenerative therapy centers
  • Pain management clinics
  • Healthcare investment projects
Research Focus
My current research examines the technological transformation in healthcare operations, specifically investigating:
  • Task allocation between human workers and technology
  • Operational efficiency improvements
  • Cost management strategies
  • Risk mitigation approaches
Key Research Motivators
Operational Challenges
Addressing critical issues including staff absenteeism, asset management, malpractice risk, and human error reduction through technological solutions.
Financial Pressures
Analyzing the impact of managed care, Medicare requirements, cost containment measures, and risk-sharing agreements on healthcare operations.
Industry Evolution
Investigating how healthcare organizations are adapting to technological change and making strategic decisions about task allocation.
JulioAvael Research
Healthcare Technology Integration Research Study
This research investigates the evolving relationship between technological solutions and human labor in modern healthcare organizations, focusing on task allocation and organizational preferences.
1
Primary Research Objective
To investigate healthcare organizations' preferences in task allocation between technological solutions and human labor, analyzing:
  • Decision-making factors in technology adoption
  • Cost-benefit considerations
  • Organizational efficiency metrics
2
Secondary Research Objective
To examine the permanence of technological transitions in healthcare settings, specifically:
  • Task transition patterns from human to technological solutions
  • Likelihood of reverting to human labor
  • Long-term implications for healthcare operations
This research aims to provide valuable insights into the strategic resource management of healthcare organizations and the role of artificial labor in maintaining organizational competitiveness.
JulioAvael Research
Understanding Healthcare Management & Technology: Foundational Concepts
A comprehensive exploration of the relationship between management, technology, and healthcare tasks in modern medical environments.
Management's Strategic Role
In healthcare organizations, management serves as the critical gatekeeper of productivity and efficiency. Their primary responsibilities include:
  • Strategic task allocation between human and technological resources
  • Resource optimization for maximum operational efficiency
  • Decision-making for organizational outcomes
Healthcare Task Framework
Healthcare tasks represent the fundamental work units across various medical functions (Autor, 2013), including:
  • Administrative operations and documentation
  • Advanced diagnostic imaging procedures
  • Continuous patient monitoring systems
  • Clinical decision-making processes
Technology's Dual Impact
Research by Onnasch et al. (2014) and Parasuraman & Riley (1997) reveals the complex relationship between technology and healthcare delivery:
  • Enhanced operational efficiency and accuracy
  • Potential risks of knowledge degradation
  • Concerns about system over-dependency
  • Impact on human skill retention
Labor Classification
According to Acemoglu and Restrepo (2018), modern healthcare labor is categorized into two distinct types:
  • Artificial Labor: Task management and execution through technological systems
  • Human Labor: Traditional task management by healthcare professionals
  • Understanding this distinction is crucial for optimal resource allocation
JulioAvael Research
Management Delegation & Trust in Modern Organizations
A comprehensive analysis of management delegation practices and the growing trust in technological solutions.
1
Understanding Management Delegation Patterns
Recent research has revealed several key factors that influence how managers approach task delegation in the workplace. Our analysis shows that management delegation decisions are deeply rooted in psychological and organizational factors.
  • Psychological barriers to delegation include uncertainty about team capabilities
  • Fear of project failure often prevents effective delegation
  • Personal recognition seeking can inhibit delegation practices
  • Trust levels significantly impact delegation decisions
2
The Technology Trust Factor
Our research indicates a growing preference for technological solutions in task management, supported by several key findings:
  • Enhanced efficiency and error reduction in automated processes
  • Consistent performance metrics in technological systems
  • Higher reliability compared to traditional human labor
  • Standardized output through automation
3
Research Sources
This analysis is based on comprehensive studies by leading researchers in management and organizational behavior:
  • Merritt & Ilgen (2008): Trust dynamics in management
  • Anderson (1992): Delegation patterns
  • Kerstholt et al. (2018): Technology integration
  • Additional insights from Jenks & Kelly (1985), Milewski & Lewis (1997), and Muir (1996)
JulioAvael Research
Artificial Intelligence Advancements in Healthcare: Research Findings
Our comprehensive analysis reveals significant AI integration across multiple healthcare domains, demonstrating enhanced efficiency and accuracy in previously human-operated tasks.
Medical Imaging & Diagnostics
Key Finding: AI-powered diagnostic tools, particularly Convolutional Neural Networks (CNNs), have revolutionized radiology by enabling:
  • Faster diagnostic processes
  • Improved accuracy in image interpretation
  • Enhanced detection of subtle abnormalities
Sources: Alyami, 2024; Kouser & Aggarwal, 2023; Javanmard, 2024; Ghayvat et al., 2023; Zhang, Joshi, & Hadi, 2024
Cardiology & Risk Assessment
Key Finding: Machine learning algorithms have transformed cardiovascular risk prediction through:
  • Enhanced precision in risk assessment
  • Real-time monitoring capabilities
  • Predictive analytics for patient outcomes
Sources: Gala et al., 2024; Triantafyllidis et al., 2022; Li et al., 2020; Shah et al., 2015; Narula et al., 2016
Healthcare Administration & Emergency Care
Administrative Improvements:
  • Automated EHR data extraction reducing manual entry time
  • Faster information retrieval systems
  • AI-enhanced triage systems reducing wait times
  • Improved emergency care coordination
Sources: Kaswan et al., 2021; Tong et al., 2023; Gellert et al., 2024; Kaur et al., 2023
Surgical Applications
Surgical Planning Advancements:
  • Reduced pre-operative planning time
  • Increased surgical precision
  • Enhanced outcome prediction
  • Improved surgical workflow optimization
Sources: Williams et al., 2021; Tariq et al., 2023; Larrain et al., 2024
JulioAvael Research
Technology's Permanent Task Ownership: A Research Analysis
Our research investigates how artificial intelligence and automation technologies tend to permanently retain ownership of tasks once they acquire them. This phenomenon has significant implications for workforce development and organizational planning.
1
Key Finding: Permanent Technological Dominance
Research demonstrates that when organizations transition tasks from human workers to technological solutions, these changes typically become permanent. This pattern has been observed across various industries and task types, suggesting a broader trend in technological adoption.
2
Technology Suppression Effect
Black and Boal (1994) found that technological implementation gradually diminishes human roles, making it increasingly difficult for human workers to re-engage with these tasks.
3
Technological Lock-In
W. Brian Arthur's (1983) research shows that early technology adoption creates a dominant position that persists even when potentially superior alternatives emerge later.
Impact on Workforce
These findings suggest organizations need to carefully consider the long-term implications of task automation, as these decisions often become irreversible.
Future Implications
Understanding this permanency is crucial for strategic planning and workforce development in an increasingly automated business environment.
JulioAvael Research
Exploring Critical Research Gaps in AI Labor Adoption
Our research identifies three major gaps in current understanding of artificial labor (AL) adoption in organizations:
1
Management's Technology Preference
Current research lacks comprehensive analysis of why and how management teams choose to assign organizational tasks to technology over human workers. This gap is particularly significant as organizations increasingly face decisions about task automation.
  • Decision-making processes in task allocation
  • Comparative analysis of human vs. technological solutions
  • Long-term implications of these choices
2
Preference Influencing Factors
There's limited understanding of the key factors that influence management's preference for artificial labor over human workers. Our research aims to identify and analyze these critical factors:
  • Cost-benefit considerations
  • Organizational culture impact
  • Industry-specific drivers
3
Permanent Technology Transition
We need to better understand the permanence of technology adoption in task management. Key questions include:
  • Reversibility of automation decisions
  • Long-term impacts on workforce planning
  • Sustainability of technology-first approaches
This research initiative aims to address these crucial gaps in our understanding of artificial labor adoption and its implications for organizational management in healthcare and beyond.
JulioAvael Research
Artificial vs Human Labor in Healthcare: Research Framework
Research Focus
This study examines organizations' preferences between artificial labor (AL) and human labor in healthcare settings, investigating both initial adoption patterns and long-term commitment.
Key Research Questions
  • Do organizations prefer assigning tasks to Artificial Labor over Human Labor?
  • Once implemented, how likely are organizations to revert from Artificial to Human Labor?
Hypothesis I: Decreasing Human Labor Preference
Primary Hypothesis: When organizations deploy Artificial Labor for tasks, their preference for using human labor decreases.
Theory: Resource-Based View Theory - strategic resources must maintain value, rarity, and be difficult to replicate (Penrose, 1959; Barney, 1991).
Healthcare Examples: Analytics platforms for diagnostics, patented AI-driven treatments, robotic surgery
Hypothesis II: Permanent Technology Adoption
Secondary Hypothesis: Organizations that adopt artificial labor practices are unlikely to revert to human labor.
Theory: Based on technological unemployment theory (Keynes, 2016) and the "Lock-In Effect" (Arthur, 1983).
Applications: Decision Support Systems, Service Robots, AI Diagnostics, Automated EHR
This research explores how healthcare organizations prioritize technology over human labor, focusing on long-term implications and decision-making patterns.
JulioAvael Research
Research Methodology & Survey Design
A comprehensive quantitative research study examining AI adoption in U.S. healthcare, involving 258 participants from managerial and executive positions.
Research Approach
Quantitative methodology utilizing structured survey data collection through Qualtrics platform, with quality assurance through Centiment's Audience Paneling services. Pre-testing conducted with 20 participants to optimize survey reliability and validity.
Target Audience & Demographics
Healthcare industry executives and managers across the U.S., classified using NAICS standards. Comprehensive demographic data collection including age, gender, education level, industry experience, company size, and operational scope.
Survey Structure
Multi-dimensional assessment using nominal categorical variables, ordinal scales for frequency and change measurement, and Likert agreement scales. Enhanced data validity through strategic attention and encouragement checks.
Key Research Areas

1

2

3

1
AI Adoption
Integration strategies, operational changes, and implementation commitment
2
Perception Analysis
Efficiency benefits, cost implications, and productivity metrics
3
Implementation Impact
Workforce dynamics, task transformation, and cultural adaptation
1
Implementation Details
Sample size of 258 participants with Qualtrics data collection and Centiment's Audience Paneling services for quality assurance
2
Quality Control
Soft launch with 20 participants, comprehensive demographic data collection, and mixed methodology using nominal categorical variables and ordinal scales
3
Focus Areas
AI adoption, implementation strategies, manager perceptions, and organizational culture impact assessment
JulioAvael Research
Healthcare Industry Demographics Study 2024
61.1%
AI Support
Favor AI implementation
46.6%
Decision Makers
Part of AI decision-making teams
16.2%
Primary Leaders
Primary decision-makers for AI
1
Workforce Demographics
Age & Gender Profile
• 82.5% are 35+ years old • Largest age group: 45-54 (30.7%) • Gender distribution: 76.1% female, 23.9% male
Education & Experience
• 33.7% hold Bachelor's degrees • 23.0% have Master's degrees • 65.7% have 11+ years industry experience • 35.0% with 20+ years expertise
2
Organizational Insights
Company Characteristics
• 34.3% are large enterprises (1000+ employees) • 57.3% operate in single state • 5.8% have nationwide presence • 4.5% operate internationally
Industry Distribution
• Hospitals: 27.0% • Specialty Care: 18.1% • Primary Care: 14.1% • Nursing Care: 8.9% • Home Healthcare: 6.3%
This comprehensive demographic study represents a highly experienced and well-educated group of healthcare professionals, primarily from large enterprises. The strong female representation and extensive industry experience provide valuable insights into the current state of healthcare management and AI adoption attitudes.
JulioAvael Research
Artificial Labor Adoption Study: Regression Analysis Findings
A comprehensive analysis of artificial labor adoption patterns in healthcare organizations
1
Methodological Approach
Initial factor analysis evolved into weighted least squares regression due to complex cross-loadings. Prioritized responses from hospital managers and supervisors for real-world applicability.
2
Statistical Findings
Model 1 (Adoption Pattern): R-Value: 0.331 | R²: 11.0% | p-value: 0.019 Model 2 (Reversion): R-Value: 0.316 | R²: 10.0% | p-value < 0.025
3
Key Hypotheses Results
I. Decreased Human Labor Preference • F-value = 5.912 • β = 0.331 (p=0.019)
II. Reduced Reversion Likelihood • F(1, 48) = 5.329 • β = 0.421 (p < 0.001)
Resource-Based View (RBV)
Analysis suggests a declining strategic value of Human Labor as Artificial Labor adoption increases, challenging traditional resource management perspectives.
Technological Unemployment Theory
Findings demonstrate a significant "lock-in" effect, where organizations show strong preference for maintaining technological solutions over reverting to human labor.
JulioAvael Research
Study One: Human Behavior & Artificial Labor Adoption Analysis
Key Research Findings
Our comprehensive analysis reveals significant insights into the adoption of Artificial Labor (AL) in workplace environments. The study focused on two primary aspects: behavioral challenges and cost efficiency considerations.
Statistical Highlights
Behavioral Impact
Behavioral issues account for 6% of variance in AL adoption (β = -0.237, p < .001)
Cost Efficiency
Cost efficiency shows strong correlation (β = .415, p < .001)
Reliability Factor
AL reliability explains 60% of adoption variance
Primary Adoption Drivers
Cost & Productivity
"The artificial labor attempt is to save money at all costs" - Survey Respondent
Attendance Issues
"Loss of hours from employees and constant tardiness caused AL adoption" - Survey Respondent
Workforce Reliability
"Could not find enough people to do specific repetitive jobs" - Survey Respondent
Detailed Analysis of Behavioral Factors
1
Employee Burnout
Time consideration and fatigue are significant factors
2
Productivity Issues
Distractions and phone usage affecting work completion
3
Remote Work Preferences
Increasing demand for flexible work arrangements
4
Cost Efficiency
Strategic value in reducing errors and labor costs
Cost Efficiency Analysis
  • Reduced labor costs and fewer errors are primary benefits
  • Faster task completion rates reported
  • Strategic advantage in repetitive task management
Behavioral Impact Assessment
  • Moderate impact on AL adoption decisions
  • Significant correlation with productivity metrics
  • Clear influence on management strategies
JulioAvael Research
Study II Proposal: Artificial Labor in Healthcare Settings
Research Overview
Building on Study I findings, this research investigates how cost efficiencies and human behavior impact managerial task allocation preferences in hospitals, specifically examining the shift toward Artificial Labor (AL) over Human Labor (HL).
Key Research Questions
• What negative human behaviors drive managerial preference for Artificial Labor over Human Labor in hospital settings?
• How does cost efficiency influence hospitals' decision to allocate tasks to artificial labor rather than human labor?
Theoretical Framework
This study employs the Resource-Based View (RBV) theory, examining AL as a strategic resource that must possess value, rarity, and be difficult to replicate or substitute (Penrose, 1959; Barney, 1991).
1
Human Behavior Findings
  • Disruptive behaviors undermine communication and patient care (Oliveira et al., 2016)
  • Higher rates of substance abuse among healthcare professionals (Oreskovich et al., 2012)
  • 67% of healthcare workers link disruptive behaviors to adverse events (Rosenstein & O'Daniel, 2008)
2
Cost Efficiency Impact
  • AI enhances administrative efficiency in billing and scheduling (Secinaro et al., 2021)
  • AI-powered diagnostics improve accuracy and reduce costs (Khanna et al., 2022)
  • Financial pressures drive cost-containment strategies (Bazzoli et al., 2004)
1
Hypothesis III
Negative human behaviors drive managerial task allocation preference for Artificial Labor over Human Labor in hospital settings.
2
Hypothesis IV
Cost efficiency is a key antecedent driving the preference for allocating tasks to artificial labor over human labor in hospital settings.
JulioAvael Research
Research Methodology: Healthcare AI Integration Study
A comprehensive quantitative research examining Artificial Labor (AL) adoption patterns in U.S. healthcare institutions, focusing on behavioral impacts and strategic implementation.
Research Parameters
1
Methodology: Quantitative research design with multi-component survey analysis
2
Sample Size: 264 U.S. hospital managers and decision-makers
3
Validation: 20-participant preliminary soft launch for survey optimization
4
Platform: Qualtrics implementation with Centiment's Audience Paneling recruitment
Survey Structure
1
1. Behavioral Impact Assessment
Comprehensive evaluation of 29 workplace behaviors influencing AL adoption, measured on a specialized 4-point scale for precise data collection
2
2. Qualitative Behavior Analysis
Open-ended response collection to identify additional behavioral factors affecting AL implementation in healthcare settings
3
3. Priority Behavior Ranking
Strategic prioritization of the five most impactful behaviors driving AL adoption decisions in healthcare environments
Research Impact
Healthcare Innovation
This research provides crucial insights into AL integration in healthcare settings, specifically addressing cost efficiencies and behavioral challenges faced by modern healthcare institutions.
Strategic Framework Integration
The study aligns AL adoption with the Resource-Based View (RBV) framework, demonstrating its long-term strategic value as a sustainable resource in healthcare operations management.
JulioAvael Research
Theoretical and Strategic Implications of Artificial Labor (AL)
Our research combines Technological Unemployment Theory and Resource-Based View (RBV) Theory to explain AL's role in task allocation, competitive advantage, and human labor displacement effects.
Key Theoretical Concepts
Task Superiority
AL demonstrates superior performance in repetitive or error-prone tasks
Lock-In Effect
Organizations show sustained reliance on AL post-implementation
Suppressing Effect
Reduced dependence on human labor in decision-making processes
Value Criteria
AL emerges as a valuable, rare, inimitable, and non-substitutable resource
Strategic Benefits
💰 Improved Cost Efficiencies
Reduces labor costs, mitigates HL challenges, streamlines operations, and enables more effective resource allocation.
🏥 Enhanced Patient Care
Enables faster, more accurate, and consistent task execution, improving diagnostic precision and patient outcomes.
📈 Financial Benefits
Reduces future labor costs, improves operational margins, and enhances organizational sustainability.
Labor Relations Impact
Union Dynamics & Scale
AL adoption affects 1.4M unionized healthcare workers (SEIU, NNU), significantly impacting negotiation scope.
Workforce Transformation
Automation reduces reliance on unionized roles, leading to workforce reductions or redefined jobs. Productivity gains historically bypass the bottom 80% of workers (Economic Policy Institute).
Adaptation Strategies
Unions are actively advocating for reskilling programs, severance provisions, and fair treatment policies to address technological transitions (American Progress, n.d.).
JulioAvael Research
Research Limitations & Future Directions
1
Industry-Specific Scope
Hospital-Centric Focus
Our findings primarily derive from hospital environments, which operate under unique conditions and regulations.
Limited Transferability
Results may not directly apply to industries such as:
  • Manufacturing sectors
  • Retail environments
  • Service-based industries
2
Methodological Constraints
Quantitative Emphasis
Our research relies heavily on numerical data and statistical analysis, potentially missing valuable qualitative insights.
Missing Perspectives
The study lacks insights from:
  • In-depth interviews
  • Focus group discussions
  • Case study observations
3
Organizational Factors
Union Consideration Gap
The study does not address crucial labor elements including:
  • Union influence on AL adoption
  • Collective bargaining impacts
  • Worker rights considerations
4
Ethical Dimensions
Unexplored Areas
Critical ethical considerations remain unaddressed:
  • Workforce displacement impact
  • Data privacy concerns
  • Algorithm bias implications
  • Long-term societal effects
These limitations present opportunities for future research to expand our understanding of AL adoption across different contexts and perspectives.
JulioAvael Research
Contact & Questions
Get in Touch
Have questions about my research? I welcome your inquiries and discussion.
  • Call: 786-444-5626, leave a message please
  • Office Hours: By appointment
Research Discussion
I'm happy to discuss methodology, findings, or potential collaborations.
  • Schedule a consultation
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Next Steps
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