The Smart Hospital Maturity Model: Paving the Way for Healthcare's Digital Future

The Smart Hospital Maturity Model offers a framework to guide healthcare organisations through digital transformation. It defines six stages of smart hospital development, highlights trends like AI-driven revenue cycle management and data storage challenges, and stresses the importance of analytics maturity. By adopting SHMM, hospitals can systematically advance toward fully integrated, patient-centred smart care.

Key Points

1. The Smart Hospital Maturity Model provides a framework for healthcare digitalisation.
2. Hospitals progress through six stages toward full smart technology utilisation.
3. AI is transforming revenue cycle management, boosting efficiency and reducing errors.
4. Data storage demands are rising sharply, requiring strategic upgrades and planning.
5. Advanced analytics adoption is critical for personalised, efficient and predictive care.

Healthcare is undergoing a profound digital transformation, driven by the convergence of advanced technologies, data analytics and patient-centric care models. In an era of rapid technological advancement, hospitals strive to become more efficient, patient-focused and technologically integrated. To guide healthcare organisations through this evolution, the Smart Hospital Maturity Model (SHMM) provides a structured framework, helping them assess their current capabilities and plot a strategic course toward becoming fully "smart" hospitals.

Building the Foundation: Smart Care Technologies and Processes

Smart healthcare refers to the utilisation of advanced technologies, data analytics and connected systems to improve healthcare delivery efficiency and patient outcomes. A growing trend in healthcare is the increased adoption of smart care technologies and processes. These technologies include Internet of Things (IoT) medical devices, AI, big data analytics, mobile and virtual health applications, as well as remote and ambient patient monitoring and communication systems.

Key applications of smart care encompass a wide range of technologies and functionalities designed to enhance both clinical and operational performance. These include advanced analytics for extracting actionable insights from large datasets; real-time and ambient patient sensing and monitoring systems that continuously collect vital information; and automated diagnostics and clinical decision support tools that assist healthcare professionals in making timely and accurate decisions. Personalised medicine, leveraging genomic data and patient-specific information, enables tailored treatment strategies, while multiple application-integrated platforms ensure seamless connectivity and interoperability across different systems.

In practical terms, example implementations of these applications include patient sensors and wearable devices that monitor health indicators continuously, AI-powered tools that assist in clinical evaluations and automated medication management systems that ensure accurate dispensing and reduce human error. Real-time staff and patient identification technologies enhance security and operational flow, while real-time business functions such as insurance validation and materials management streamline administrative processes. Virtual care delivery through command centres integrates multiple applications to offer remote consultations, monitor patient status and manage resources efficiently, reflecting the growing emphasis on decentralised and patient-centred healthcare models.

The Six Stages of Smart Hospital Development

The SHMM, developed by the Alliance for Smart Healthcare Excellence with support from care.ai (now part of Stryker), evaluates healthcare providers across three dimensions: people, places and processes. Over 560 data points from an 89-question survey drive the scoring of the SHMM. Participants are placed in one of six stages, each reflecting a higher level of technological integration and operational sophistication:

• Leap 0 (Unassisted Processes): Basic IT infrastructure is in place, with limited digital integration and reliance on manual workflows.
• Leap 1 (Point Solutions): Hospitals use disjointed digital solutions to assist with specific tasks but lack a coordinated smart care strategy.
• Leap 2 (Functional Platforms): Integrated and interoperable networks emerge, with IoT devices like smart beds and connected medical equipment supporting care.
• Leap 3 (Integrated Systems): Cross-functional integration becomes prevalent, smart care strategies are scaled across the enterprise, and AI is introduced into operations.
• Leap 4 (Adaptive Practices): Business and clinical activities actively adapt to evolving situations, with AI supporting complex decision-making and autonomous technology pilots emerging.
• Leap 5 (Supervised Automation): Full realisation of a smart hospital, with interconnected systems, embedded AI for adaptive clinical and operational decision support and fully digitised patient experiences.

This structured approach helps institutions understand their current state and chart a path toward greater smart technology-assisted clinical and administrative operations.

As of early 2025, over 180 US hospitals have completed the survey. Early findings show larger hospitals (over 250 beds) have progressed farther on the smart hospital scale than smaller institutions. Scores are higher in the domain of Humans compared to Environments and Processes, reflecting a strong focus on electronic communications between patients and clinicians. Significant engagement of clinical staff in smart care strategy, selection and adoption is evident.

Key Trends and Insights from SHMM Survey Findings

There is a strong and growing focus on enhancing smart processes across healthcare organisations, particularly in for-profit systems where efficiencies and savings serve as key drivers of innovation. Respondents from the SHMM survey highlight the prioritisation of technologies aimed at addressing critical workforce challenges, including clinician burnout, job satisfaction and enabling flexible or alternative work arrangements. In response to these needs, ambient technologies—such as patient listening systems, real-time monitoring and voice-activated solutions—are seeing increased adoption. These innovations also support virtual nursing models, which can alleviate pressure on in-person clinical staff while maintaining high standards of patient care.

Another notable trend is the movement away from isolated point solutions toward integrated technology platforms that provide seamless, enterprise-wide functionality. While descriptive and diagnostic analytics remain the most commonly deployed forms of data analysis across healthcare settings, there is a clear emergence of predictive and prescriptive analytics, suggesting a maturing analytical capacity within the sector. AI implementation plans are widespread, with decision support systems being the most common application in clinical environments. Importantly, the SHMM recognises that digital transformation is not a strictly linear journey; healthcare organisations often advance in some domains while lagging in others, underscoring the complexity and multifaceted nature of becoming a truly "smart" hospital.

Transforming Healthcare Revenue Cycles with AI: A Strategic Role for SHMM

Healthcare revenue cycle management (RCM) is essential to the financial stability of healthcare systems. Covering the patient journey from appointment scheduling to final claim settlement, RCM involves processes where inefficiencies can lead to denials, delayed payments and financial strain.

Traditionally reliant on manual workflows, revenue cycle operations are now being transformed through artificial intelligence (AI). AI introduces automation that reduces errors and enhances efficiency, particularly in missed charge capture, predictive analytics for denial likelihood and real-time integrity audits. According to Smart Hospital Maturity Model (SHMM) survey findings, 45.5% of leading healthcare organisations already use AI to validate claims, moving beyond theory into practice.

Key AI applications in RCM include scanning patient records for missing charges, predicting claim denial risks and conducting real-time claim audits. These improvements reduce denials, accelerate payments and lower administrative burdens, ultimately improving patient experience and financial performance.

However, challenges such as data quality, regulatory compliance, workforce readiness and upfront costs remain. Here, the SHMM offers crucial strategic guidance, helping institutions assess digital capabilities, set goals, adopt AI incrementally and foster interdisciplinary collaboration.

Through the SHMM's structured framework, healthcare providers can confidently implement AI solutions, ensuring sustainable improvements in revenue cycle operations and supporting broader digital transformation. As AI's role expands into payment posting, appeals management and predictive modelling, the SHMM ensures hospitals evolve alongside the demands of a smart healthcare ecosystem.

Meeting the Growing Demand for Healthcare Data Storage

Healthcare organisations today manage massive volumes of data from EHRs, imaging files, real-time patient monitoring and telemedicine data. While 73% of HCOs believe they can adequately address inpatient data demands today, confidence drops significantly when projecting five years ahead, especially in residential care settings.

The rapid growth of healthcare data is driven by the digitisation of patient records, advances in medical imaging, remote monitoring, telehealth, precision medicine and increased use of AI and analytics. Each trend contributes significantly to the storage burden.

Inpatient care remains the most data-intensive setting, with ambulatory settings also generating large volumes of data. Residential care, driven by remote monitoring and wearable devices, is emerging as a major source of new data. However, only 36% of HCOs currently feel well-prepared for residential data demands.

Current infrastructure may fall short in five years due to ageing legacy systems, siloed data and interoperability gaps, regulatory pressures and data retention requirements, rise of edge and IoT devices and security and disaster recovery considerations.

The SHMM framework guides institutions in assessing current capacities, planning strategic upgrades, implementing phased improvements and maintaining continuous improvement cycles. Because data storage underpins advanced analytics, AI and transformative technologies, storage infrastructure improvements are critical.

Strategies for upgrading storage infrastructure include hybrid cloud adoption, data tiering and lifecycle management, edge computing for residential care, interoperability initiatives, enhanced security and encryption and AI and machine learning for proactive storage management.

Advancing Through the Analytics Maturity Curve

Advanced analytics is central to healthcare digital transformation. Healthcare analytics progresses from descriptive to diagnostic, predictive, prescriptive and generative analytics. Descriptive analytics summarise historical data; diagnostic analytics explore root causes. Predictive analytics forecast future outcomes; prescriptive analytics suggest optimal interventions. Generative analytics creates new content, suggestions, or solutions based on learned patterns.

While descriptive and diagnostic analytics are widely adopted (100% and 91%), predictive analytics usage drops to around 32% for clinical decisions, prescriptive analytics are used by less than half of the surveyed hospitals, and generative analytics adoption remains very low.

Barriers to advanced analytics adoption include data quality and silos, regulatory and compliance concerns, lack of skilled workforce, clinician trust and buy-in and financial constraints. The SHMM helps organisations set realistic goals, ensuring alignment between strategic objectives and analytics ambitions.

Unlocking the Future Potential with Advanced Analytics

Moving from descriptive to generative analytics requires incremental steps: starting with high-ROI projects, focusing on data governance, fostering multidisciplinary collaboration, ensuring ethical and regulatory readiness and continuing education and training.

Starting small with high-impact projects can quickly demonstrate value and drive enthusiasm. Investing in data governance improves model accuracy. Multidisciplinary collaboration builds trust and ensures relevance. Ethical and regulatory readiness maintains patient trust, and ongoing training cultivates a culture of data-driven decision-making.

As healthcare organisations climb the SHMM ladder, they gain greater confidence in using advanced analytics. Confidence eventually extends to clinical care, enabling providers to personalise treatments, streamline diagnostics and reduce human error. Unlocking the full potential of data transformation is critical for healthcare providers to position themselves as truly 'smart' hospitals.

Conclusion: Toward a Smart, Integrated Healthcare Future

The Smart Hospital Maturity Model offers healthcare leaders a comprehensive blueprint for navigating smart aspects of digital transformation. From implementing smart technologies and AI-driven RCM solutions to upgrading data storage and advancing through analytics maturity, the SHMM emphasises steady, strategic progress.

Healthcare’s shift toward connected care spanning inpatient, ambulatory and residential settings brings tremendous benefits but also substantial demands for robust data storage solutions. Although many organisations currently meet their immediate storage needs, the next five years promise a steep escalation in data volume.

By leveraging the SHMM framework, healthcare organisations can systematically assess readiness, implement strategic upgrades and confidently approach emerging data challenges. Combined with best practices like hybrid cloud adoption, interoperability standards and strong security protocols, healthcare providers can securely store, manage and use data at scale.

Proactively addressing challenges related to data quality, compliance, workforce readiness and financial investment unlocks the full potential of AI and advanced analytics. Doing so not only improves financial performance and reduces administrative burden, but also enhances the overall patient experience by ensuring timely, accurate and transparent billing.

Ultimately, through careful planning, trust-building and collaboration, healthcare providers can harness the full potential of data and technology to transform care delivery, ensuring operational excellence and patient-centred care in the years to come. The vision of a fully integrated, intelligent healthcare system is within reach for those willing to embrace the smart hospital journey.

Conflict of Interest

None