Author names: Tara Leah Klassen, Mohammad Alothman, Xinming Li, Jonathan Windram, and Hamid Zaman

1. INTRODUCTION

Healthcare systems around the world are experiencing sustained expenditure growth, raising concerns about long-term affordability and system sustainability. According to 2025 reports by the Canadian Institute of Health Information (CIHI), total health spending in 2025 was projected to reach $399 billion, accounting for 12.7% of GDP(CIHI, 2025a; CIHI, 2025b). Moreover, health spending in Canada grew by 7.4% in 2023 and 6.1% in 2024, which outpace broader economic expansion, indicating that health care is consuming an increasing share of national resources (CIHI, 2025a). Figure 1 presents the provincial health expense per capita for selected Canadian provinces, which shows steady upward trend over the last 20 years. On the other hand, healthcare expenses per capita in the United States is the highest among comparable OECD countries (Telesford et al., 2026). However, despite this high level of investment, improvements in health outcomes and access have not consistently matched spending increases, highlighting inefficiencies in the system (Kuehn, 2021).

Figure 1: Provincial Health Spending per Capita (CAD) for Selected Provinces

(Data Source: CIHI, Statistics Canada)

These trends underscore sustained medical inflation, which is defined as the rate at which healthcare costs increase over time. In Canada, expenditure growth is primarily driven by population growth, aging, and general inflation, along with increasing service utilization (CIHI, 2025a). Rapid population growth in recent years has further strained the system, limiting real per capita spending gains despite nominal increases (CIHI, 2025b). In contrast, the U.S. experience illustrates a different dimension of medical inflation: price escalation. Evidence shows that higher spending in the United States is largely attributable to higher prices for services, pharmaceuticals, provider wages, and administrative costs rather than increased utilization (John Hopkins Bloomberg School, 2019).

The persistence of medical inflation presents a fundamental sustainability challenge. When health expenditures grow faster than GDP, governments face increasing fiscal pressure, and health systems risk diverting resources from other public priorities. Moreover, simply increasing funding does not guarantee improved system performance, as evidenced by international comparisons where high-spending systems do not consistently achieve better outcomes (Kuehn, 2021).

Innovation in health care is therefore critical to bending the cost curve while maintaining or improving the quality of care. Key areas of opportunity include the digitization of care delivery, the transition from hospital-centric models to community-based and preventive care, and the deployment of advanced medical technologies. Recent advancements in areas such as artificial intelligence, robotics, and telehealth platforms have the potential to optimize clinical workflows, reduce administrative burdens, and improve access to care—particularly in high-cost systems where inefficiencies are prevalent (Anderson et al., 2019). At the same time, shifting care to lower-cost settings and emphasizing prevention can mitigate demand pressures associated with aging populations and chronic disease (CIHI, 2025a).

Within this innovation landscape, engineering disciplines can play a pivotal role in shaping sustainable health care systems by contributing to the design and optimization of health infrastructure and enabling more efficient use of capital and operational resources. Opportunities are also emerging for multi-disciplinary collaborations to drive advancements in medical technologies, including minimally invasive procedures and remote monitoring devices, which reduce reliance on resource-intensive acute care environments. This paper presents some emerging opportunities where engineering solutions can be leveraged to address healthcare challenges and to advance innovation, and also some related research initiatives at the University of Alberta.

2. RECENT HEALTHCARE INNOVATION AROUND THE WORLD 

Healthcare systems worldwide are facing growing pressures from aging populations, increasing prevalence of chronic disease, workforce shortages, rising patient expectations, and sustained expenditure growth (WHO, 2021; OECD, 2023; CIHI, 2025a). At the same time, advances in artificial intelligence (AI), robotics, digital health, advanced diagnostics, data analytics, automation, and connected devices are creating unprecedented opportunities to improve healthcare delivery, operational efficiency, and system sustainability (Topol, 2019; Hashimoto et al., 2018; Ahsan & Siddique, 2022). Increasingly, healthcare innovation is evolving beyond individual technologies toward integrated systems that connect patients, providers, infrastructure, devices, and data through digitally enabled ecosystems.

This transformation reflects the emergence of Healthcare 4.0, a paradigm adapted from Industry 4.0 principles that incorporates cyber-physical systems, Internet of Medical Things (IoMT) technologies, cloud computing, AI, digital twins, advanced analytics, and automation to create intelligent, interconnected healthcare environments (Ahsan & Siddique, 2022; WHO, 2021). These technologies are enabling healthcare organizations to move from reactive models of care toward predictive, personalized, and data-driven systems capable of supporting continuous improvement across clinical, operational, and infrastructure domains.

For engineers, this transition creates significant opportunities beyond traditional healthcare infrastructure projects. Engineering expertise is increasingly required to support intelligent facility design, digital infrastructure, systems integration, advanced sensing technologies, predictive modelling, automation, cybersecurity, sustainability, and medical device innovation. The following sections highlight key global innovation trends and emerging opportunities that are reshaping healthcare delivery.

2.1 Artificial Intelligence and Data-Driven Healthcare

Artificial intelligence has emerged as one of the most transformative healthcare innovations of the past decade. Advances in machine learning, computer vision, natural language processing, and generative AI are enabling healthcare organizations to derive meaningful insights from increasingly large and complex datasets (Jiang et al., 2017; Topol, 2019). AI applications are now reported across radiology, pathology, cardiology, oncology, genomics, and surgery, where algorithms support disease detection, image interpretation, risk prediction, and clinical decision-making (Topol, 2019; Hashimoto et al., 2018).

Current applications include:

• Clinical decision support systems
• Automated medical image interpretation
• Predictive analytics and risk stratification
• Natural language processing and clinical documentation
• Operational forecasting and resource optimization
• Generative AI-assisted knowledge management

Machine learning models have demonstrated potential to identify patients at risk of deterioration, hospital readmission, adverse events, and postoperative complications, enabling earlier intervention and more efficient allocation of healthcare resources (Jiang et al., 2017; Topol, 2019). Beyond clinical applications, AI is increasingly being used to optimize scheduling, staffing, patient flow, capacity planning, and supply chain operations (OECD, 2023).

Recent advances in foundation models and generative AI have further accelerated interest in healthcare applications. Emerging systems can summarize clinical documentation, assist with information retrieval, support administrative workflows, and facilitate communication among healthcare teams. While these technologies offer considerable promise, challenges related to data quality, bias, transparency, governance, privacy, and regulatory oversight remain important considerations for implementation (Topol, 2019; WHO, 2021).

2.2 Digital Health, Virtual Care, and Connected Systems

Digital health technologies have expanded rapidly worldwide, particularly following the COVID-19 pandemic, which accelerated adoption of virtual care and remote monitoring models (Keesara et al., 2020; WHO, 2021). Digital health encompasses a broad range of technologies that facilitate healthcare delivery, communication, monitoring, and decision-making beyond traditional clinical settings.

Current innovations include:

• Virtual care and telehealth platforms
• Remote patient monitoring systems
• Wearable and implantable sensors
• Mobile health applications
• Hospital-at-home programs
• Internet of Medical Things (IoMT) ecosystems

These technologies enable healthcare services to extend beyond hospitals and clinics, supporting more patient-centered models of care. Remote monitoring platforms can continuously collect physiologic information from patients in community settings, facilitating earlier detection of deterioration and reducing unnecessary hospital utilization (WHO, 2021). Virtual care programs have demonstrated benefits in improving access, particularly for rural and remote populations, while supporting chronic disease management and specialist consultation services (OECD, 2023).

The expansion of connected healthcare systems presents substantial engineering opportunities related to telecommunications, interoperability, cloud computing, cybersecurity, and digital infrastructure design. As the number of connected medical devices continues to increase, healthcare organizations must ensure reliable communication, secure data exchange, and resilience across increasingly complex digital ecosystems (WHO, 2021).

2.3 Precision Medicine and Advanced Diagnostics

Advances in genomics, molecular biology, computational science, and biomedical engineering are driving the transition toward precision medicine, which seeks to tailor prevention, diagnosis, and treatment strategies to individual patient characteristics (Collins & Varmus, 2015; Ashley, 2016). Rapid reductions in the cost of genomic sequencing, combined with advances in bioinformatics and computational analytics, have expanded the clinical applicability of personalized medicine across oncology, cardiology, rare diseases, and pharmacogenomics (Ashley, 2016). Simultaneously, advances in molecular diagnostics, biomarker detection, and liquid biopsy technologies are enabling earlier and more precise disease detection.

Key innovations include:

• Genomic and molecular diagnostics
• Precision oncology and targeted therapies
• Liquid biopsy technologies
• AI-assisted diagnostic platforms
• Point-of-care testing systems
• Advanced biomarker detection technologies

Beyond genomics, innovations in imaging, biosensing, and portable diagnostic technologies are bringing advanced diagnostic capabilities closer to the patient. Handheld ultrasound devices, wearable sensors, and point-of-care testing platforms are enabling rapid diagnosis in both hospital and community settings (WHO, 2021).

Engineering contributions are central to these developments and include laboratory automation, sensor development, computational biology, software engineering, advanced instrumentation design, and large-scale data management. Future advances will increasingly depend on the integration of biological, clinical, and operational data to support personalized care pathways.

2.4 Intelligent Healthcare Infrastructure and Digital Twins

Healthcare facilities are becoming increasingly sophisticated environments where physical infrastructure, digital technologies, medical devices, and operational systems must function as integrated ecosystems. This evolution has led to growing interest in digital twins, simulation modelling, and intelligent infrastructure platforms capable of supporting real-time monitoring and operational optimization (Pellegrino et al., 2025; Shen et al., 2024).

Key innovations include:

• Digital twin platforms
• Smart hospitals and intelligent buildings
• Real-time asset monitoring systems
• Predictive maintenance technologies
• Advanced building automation systems
• Integrated facility management platforms

Digital twins are virtual representations of physical systems that continuously update using real-world data. In healthcare, digital twins are being explored to model patient pathways, clinical operations, infrastructure utilization, workforce planning, and facility performance (Pellegrino et al., 2025). Recent reviews suggest significant potential for digital twins to support precision medicine, operational optimization, and predictive decision-making, although challenges related to interoperability, validation, and governance remain substantial (Shen et al., 2024). These developments align closely with engineering disciplines focused on systems integration, simulation modelling, operations research, and infrastructure resilience.

2.5 Horizon Scan: Perioperative System Transformation and Surgical Innovation

Surgical care represents one of the most technologically advanced and operationally complex domains within healthcare. Historically, innovation focused primarily on individual procedures or devices. Increasingly, however, surgical services are being conceptualized as integrated perioperative systems that connect patients, clinicians, devices, infrastructure, sterile processing, supply chains, analytics platforms, and operational workflows (ORNAC, 2025; Accreditation Canada, 2019).

Growing procedural demand, workforce shortages, fiscal constraints, and sustainability concerns are driving efforts to improve efficiency, safety, and access through system-level innovation (CIHI, 2025a; CIHI, 2025b). Professional and regulatory standards increasingly emphasize coordinated perioperative care, infection prevention, workflow optimization, quality monitoring, and continuous improvement across the entire surgical pathway (AORN, 2025a; AORN, 2025b; ORNAC, 2025; CSA, 2016).

2.6 Minimally Invasive and Image-Guided Surgery

Minimally invasive surgery has transformed clinical practice by reducing tissue trauma, shortening recovery times, and improving patient outcomes compared with conventional open procedures (Ljungqvist et al., 2017; Kehlet, 2018). Advances in instrumentation, imaging, navigation systems, and enhanced recovery protocols have enabled increasingly complex procedures to be performed using less invasive approaches.

Key innovations include:

• Laparoscopic surgery
• Advanced endoscopic procedures
• Natural orifice surgery (NOTES)
• Percutaneous image-guided interventions
• Enhanced Recovery After Surgery (ERAS) programs

Systematic reviews consistently demonstrate reductions in length of stay, postoperative complications, pain, and opioid utilization associated with minimally invasive techniques and ERAS pathways (Ljungqvist et al., 2017; Mahmudov et al., 2025; Smith et al., 2020).

2.7 Robotic Surgery and Telerobotics

Robotic-assisted surgery has evolved into a platform technology that combines advanced visualization, precision instrumentation, digital navigation, and increasingly AI-supported guidance systems (Hashimoto et al., 2018; Lanfranco et al., 2004).

Key innovations include:

• Robotic-assisted surgical systems
• Digital navigation technologies
• Augmented and mixed reality visualization
• Image-guided robotic surgery
• Telerobotic surgical platforms

Recent advances in telecommunications infrastructure have enabled increasing interest in remote and telerobotic surgery. Systematic reviews demonstrate technical feasibility and potential to improve access to specialized care in geographically dispersed regions, although challenges related to latency, cybersecurity, regulation, and infrastructure requirements remain significant (Ding et al., 2025; Yang et al., 2026).

2.8 Integrated Operating Rooms and Procedural Environments

Operating rooms are increasingly evolving into digitally integrated procedural environments that combine imaging, communications, information systems, and clinical technologies within a unified workspace (Steris, 2018; Springer Review, 2021).

Key innovations include:

• Hybrid operating rooms
• Integrated digital operating rooms
• Real-time imaging systems
• Centralized device management platforms
• Multidisciplinary procedural suites

These environments support image-guided interventions, improve workflow continuity, reduce patient transfers, and facilitate multidisciplinary collaboration. However, they also introduce significant infrastructure requirements related to power distribution, ventilation, networking, interoperability, and lifecycle management.

2.9 Digital Supply Chains and Sterile Processing

Perioperative supply chains and sterile processing departments are increasingly being transformed through automation, analytics, and digital traceability technologies (AORN, 2025a; CSA, 2016).

Key innovations include:

• RFID-enabled inventory tracking
• Instrument lifecycle management systems
• Automated sterilization monitoring
• Predictive inventory management
• Real-time asset tracking platforms

Studies have demonstrated substantial inefficiencies in surgical inventory systems, with many instruments rarely utilized despite requiring repeated reprocessing and storage (Eussen et al., 2025; Onuh et al., 2024). Integration of inventory, scheduling, and analytics systems presents opportunities to improve efficiency, reduce waste, and enhance surgical readiness.

2.10 Perioperative Analytics and Operational Orchestration

Operating rooms are among the most resource-intensive environments within healthcare systems. Advances in analytics, simulation modelling, and digital twin technologies are enabling increasingly sophisticated approaches to operational management.

Key innovations include:

• Real-time operational dashboards
• Predictive scheduling systems
• Capacity management platforms
• Workforce optimization tools
• Digital twin simulation environments

These technologies support improved utilization, reduced delays, enhanced throughput, and more effective allocation of resources. For engineers, perioperative operations represent a growing opportunity for the application of systems engineering, operations research, machine learning, and simulation methodologies.

2.11 Sustainability and Circular Healthcare Systems

Sustainability is emerging as a major priority across healthcare systems globally. Healthcare is estimated to contribute approximately 4–5% of global greenhouse gas emissions while consuming significant energy, materials, and natural resources (MacNeill et al., 2017; Health Care Without Harm, 2023). Operating rooms are among the most resource-intensive environments within hospitals and generate substantial waste through energy consumption, disposable supplies, and complex supply chains (MacNeill et al., 2017; Savio et al., 2026).

Key innovations include:

• Energy-efficient healthcare facility design
• Sustainable procurement programs
• Device reprocessing and reuse initiatives
• Circular supply chain models
• Lifecycle-based infrastructure planning

Increasingly, healthcare organizations are adopting decarbonization strategies, waste reduction initiatives, and circular economy principles that align environmental stewardship with operational efficiency (Health Care Without Harm, 2023; NHS England, 2022). Future engineering efforts will play a critical role in developing standardized methodologies for lifecycle assessment, environmental measurement, sustainable infrastructure design, and resource optimization.

3. OCCUPATIONAL ERGONOMICS RESEARCH AT THE UNIVERSITY OF ALBERTA 

This section presents an example of healthcare-related research ongoing at the Occupational Ergonomic Research Lab in the Mechanical Engineering Department at the University of Alberta.

Work-related musculoskeletal disorders (WMSDs) are highly prevalent in sonography, a predominantly women profession, due to prolonged ultrasound probe handling, repetitive movements, sustained awkward postures, and the gripping and pushing forces required during scanning (Coffin, 2014). At the Mazankowski Heart Institute (Edmonton, Alberta), 292 workdays were lost over the last three years because of these types of injuries. In a 2022 national survey of Sonography Canada members (Clarke, 2022), 83% reported work-related muscle and joint problems, and 69% said they scan while in pain; their top concerns were injuries, rising productivity demands, and burnout. These injuries also affect patient care by contributing to pain, fatigue, sick leave, staffing shortages, delayed appointments, and reduced service capacity.

Echocardiography is especially important because timely, high-quality cardiac imaging supports diagnosis and ongoing management for many patients across the lifespan. However, most previous research on sonographer injury has relied on self-report surveys and interviews (Fukumura, 2024; Evans, 2009; Russo, 2002). Although informative, these approaches do not provide objective, continuous measures of physical and cognitive workload during real clinical care. Existing studies have also not established an automated, objective method for posture-based ergonomic assessment in echocardiography practice (Moon, 2019).

The primary research question is: Can automated quantification of the physical and cognitive workload experienced by sonographers help reduce work-related injuries and symptoms while maintaining clinically feasible image acquisition?

And, the central hypothesis is that AI- and computer vision-based automated quantification of the physical and cognitive demands of sonography, particularly echocardiography, can help reduce WMSDs and inform the design of ergonomic interventions. Because this research focuses on injury prevention and safer work design in a women-dominated profession, it is directly relevant to improving women's health outcomes. It is also expected to improve care delivery by supporting workforce sustainability and throughput capacity.

The objectives are to:

1. Build a multimodal dataset of cardiac sonographer workload collected from real echocardiography examinations;
2. Quantify physical and cognitive workload using objective measures;
3. Develop models to estimate probe grip and push forces from muscle signals;
4. Develop an automated approach for posture-based ergonomic assessment; and
5. Build a digital-twin model of the echocardiography work environment to test safer workflows, room layouts, and staffing scenarios.

3.1 Methodology

This is a mixed-methods, real-world study conducted in the echocardiography laboratories of the Mazankowski Alberta Heart Institute, where experienced sonographers and trainees will perform echocardiography, targeting more than 50 patient examinations, including pregnant women.

During clinical exams, sonographers will wear non-invasive sensors to measure upper-extremity muscle activity, body motion, hand grip and push forces, visual and cognitive workload indicators, and physiological demand. Synchronized videos will support computer-vision-based posture assessment. We will also collect standardized and customized questionnaires, including demographics and work factors, the Nordic Musculoskeletal Questionnaire, and NASA-TLX.

3.2 Expected Outcomes and Significance

This project is expected to generate five main outcomes:

1. The first curated multimodal dataset of sonographer workload in echocardiography;
2. Automated ergonomic assessment tools using wearable sensors aided with AI and computer vision;
3. Validated methods to estimate probe forces from muscle signals; and
4. A digital-twin model capable of testing safer room layouts, task flows, and implementation scenarios before operational changes are made.

The significance of this work is both scientific and practical. Scientifically, it will advance methods in biomechanics, ergonomics, and sensing. Clinically, it will generate evidence to guide safer design and implementation of equipment, room layout, and workflow planning. Most importantly, it directly addresses women's health by targeting injury prevention, pain reduction, and safer working conditions in a predominantly women healthcare workforce. By improving sonographers' well-being and retention, this research also has the potential to strengthen service capacity and support more timely echocardiography access for patients and families.

4. CONCLUSIONS

Healthcare innovation is increasingly characterized by convergence among artificial intelligence, advanced diagnostics, robotics, digital health, automation, sustainability, and systems engineering (WHO, 2021; OECD, 2023). Future healthcare environments are expected to function as interconnected cyber-physical ecosystems where patients, clinicians, devices, infrastructure, and digital platforms continuously exchange information to support adaptive and data-driven care delivery (Ahsan & Siddique, 2022).

Achieving these benefits will require collaboration among clinicians, engineers, computer scientists, architects, policymakers, health system leaders, and industry partners. As healthcare systems continue to evolve, engineering disciplines will play a central role in developing resilient, sustainable, and technology-enabled models of care capable of improving outcomes while addressing future healthcare challenges.

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