The Rise of Smart Hospitals: AI Integration and Challenges in Healthcare

Council Post: Smart Hospitals: Combining Data Management, AI Tools And Compliance

Thanks to digital solutions, the healthcare industry has made immense progress over the last few decades. In a not-so-distant future, the state of things will be even more exciting. A smartwatch could detect an anomaly in a person's heart rate -- and in a matter of minutes, an AI-powered system at their hospital would analyze the data, cross-reference it with their medical history and schedule a consultation with a cardiologist. The promise of artificial intelligence for hospitals is indeed huge. However, as modern healthcare facilities transition to "smart" ones, how do we balance innovation with patient safety and compliance? Once prioritizing disease treatment, healthcare is now primarily focused on disease prevention. This requires constant monitoring of patients' health and effective data management, from EHR data to information gathered from wearables. Consequently, the volume of data is increasing rapidly, which leads to the adoption of various digital tools -- data management systems, AI, robotics, the IoT and many more -- by hospitals and clinics. All this has brought to life the concept of a smart hospital. Such facilities are currently on the rise. According to Research and Markets, in 2023, the smart hospitals market made up $57.53 billion. By 2030, it is expected to reach $187.20 billion. Statista figures also prove this exponential growth -- from $35.9 billion in 2021 to an impressive $83 billion in 2026. But what exactly are smart hospitals? While there's no universal definition, these facilities prioritize the digitization of information flows, seamless interoperability between systems and a future-proof technology infrastructure -- these are the key features McKinsey highlights. They are present in such institutions as Mayo Clinic, Cleveland Clinic, Massachusetts General Hospital and others that are topping the smart clinic rankings in 2024. According to Alena Burdzialouskaya, Head of Partnerships and Digital Health Solutions at Andersen, "The transition from a good hospital to a good smart hospital is a task of enormous complexity." She addressed this at a recent webinar we held in 2024. The infrastructure must be built and maintained, AI solutions and other digital tools must be implemented into clinical workflows smoothly, patient safety must be ensured, and hospital staff must be trained. All this is an uphill battle many healthcare systems still grapple with. At its core, a smart hospital runs on a centralized data platform ingesting, processing and analyzing information from countless sources -- EHRs, medical imaging, IoT devices and more. Machine learning models and AI applications then extract insights to guide care delivery. But integration is key. EMRs, hospital information systems, wearables and other data pipelines must adhere to interoperability standards. Such tech maturity demands the engagement of IT experts, that's why leading smart hospitals employ numerous tech personnel or have their own big innovation teams. For instance, one of Europe's largest university hospitals, an example mentioned in our webinar, employs approximately 1,200 specialists in administration and management. This is about 5.5% of the entire staff. Many of them are directly involved in making this hospital smart and maintaining the interconnected ecosystem. Of course, clinicians also contribute to building smart facilities with their hands-on knowledge and expertise, which ensures that AI products fully align with medical realities. External IT vendors, in turn, give hospitals access to specialized capabilities for remote monitoring, telemedicine and custom software development. As smart hospitals grow and introduce new solutions, they must handle complex regulations around data privacy, cybersecurity and patient safety. HIPAA compliance is a table stake, but healthcare facilities also need robust frameworks for obtaining explicit patient consent for using de-identified data to train AI models. Preventing unauthorized access and minimizing the risk of re-identification are vital. So is ensuring AI-generated clinical recommendations are unbiased and explainable. Clear guidelines must define liability when AI outputs contradict human clinician judgement. There are also compliance requirements for medical devices and wearables underpinning smart hospitals. Risk classifications, approval mechanisms and data integrity requirements get stricter as these tools increase in complexity and risk level. Biomedical labs also face stringent CLIA, CAP and quality control standards governing data handling and interoperability. But herein lies the core challenge -- existing regulatory frameworks aren't designed for the constant evolution of AI and machine learning algorithms. These self-learning models can change dramatically through continuous retraining cycles. So, ensuring patient safety while introducing new AI products is getting truly challenging. How can smart clinics achieve this? There are no easy answers, but in any case, this must be done through cross-disciplinary collaboration. Healthcare providers, tech companies, regulatory bodies and ethics boards must unite forces to introduce regulatory frameworks that consider AI's iterative nature. Transparency, ongoing monitoring and proactive risk management should be priorities. Smart hospitals can't afford missteps when developing and deploying intelligent medical solutions. Based on years of experience building clinical systems, Andersen's experts have developed a strategy for successful implementation of AI in a compliant medical device. It can also be used for other AI-related initiatives within smart hospitals: * Assemble cross-functional teams spanning healthcare, AI, regulatory and domain experts. * Conduct thorough requirements gathering anchored in clinical workflows. * Make regulatory compliance a nonnegotiable priority throughout the AI life cycle. * Maintain open communication with regulators, providers and ethics boards. * Integrate robust cybersecurity and data governance protocols. * Perform rigorous testing, validation and ongoing model monitoring. * Document comprehensively to facilitate audits. * Incorporate continuous end-user feedback. * Embed AI performance tracking into DevOps processes. * Invest in extensive clinician training on AI's impacts. By taking a systematic approach that prioritizes compliance, safety and human-centered design, healthcare facilities can unlock AI's potential while maintaining trust. AI's potential for smart clinics is staggering. However, implementing initiatives in this field requires a thoughtful approach and governance. There are still numerous technical, ethical and change management obstacles to overcome. But for health-tech leaders, the stakes have never been higher -- or the opportunity bigger -- to positively impact millions of lives through safe and responsible AI innovation. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

Council Post: Implementing AI In Healthcare Requires Overcoming These Five Challenges

Andrei Kasyanau, cofounder and CEO at Glorium Technologies. Startup advisor and an expert in health and real estate tech. A study published in 2024 revealed that artificial intelligence (AI) tools can analyze data from individual cells within tumors to forecast whether a patient's cancer will react to a particular drug. Another study by Google Health showed that their AI model could detect breast cancer from mammogram images with the accuracy of a trained radiologist. Their AI model reduced false positive and false negative rates and outperformed six expert radiologists. However, the researchers noted challenges in the clinical deployment of AI tools, citing issues like integrating it into existing radiology workflows and establishing regulatory approval processes. The case of AI in mammography is an excellent example of how implementing AI in healthcare can improve and streamline patient care, diagnostic procedures and more. However, it also shows that the path to successful AI implementation in healthcare is paved with obstacles. Let's explore the top five challenges hindering the widespread adoption of AI in the healthcare industry. One of the biggest stumbling blocks to real-world AI implementation has been a deceptively simple challenge: access to high-quality data. Experts predict that the CAGR of healthcare data will reach 36% by 2025. This massive increase in healthcare data poses great opportunities. At the same time, it introduces significant challenges for health tech companies adopting AI technology. The volume of data doesn't guarantee its quality and availability. Healthcare data is often fragmented across different systems, leading to inaccuracies and inconsistencies. These inaccuracies can adversely affect the performance and reliability of AI models, leading to flawed insights and recommendations. Addressing data quality and accessibility challenges lays the foundation for the transformative potential of AI in healthcare. Cleveland Clinic has been at the forefront of implementing AI in healthcare and overcoming data quality issues. By successfully implementing AI solutions to analyze patient flow and optimize scheduling, they've reduced wait times by 10%. Yet, achieving this level of AI integration requires significant investment in technological infrastructure, regulatory compliance, staff training and more. Organizations can mitigate these financial barriers by using trusted, experienced resources that reduce the cost of implementation and avoid wasteful steps. For example, the appropriate use of RAG architecture can significantly reduce runtime expenses and increase the performance and quality of the output. Additionally, companies often don't fully grasp the complexity of working with AI, seeing only the tip of the iceberg and missing the hidden costs. Significant additional investment is required to realize its potential. Beyond the executive level, AI implementation in healthcare affects patients and healthcare professionals in terms of human acceptance and trust issues. A 2023 study found that clinical staff may struggle to accept AI due to the need to learn new skills and take on more complex tasks. Experts focus on educational efforts and clear communication to address AI adoption concerns. Introducing AI systems that closely match current human activities -- like predictive analytics for patient monitoring and virtual assistants for scheduling -- demonstrates AI's reliability and direct benefits, which may enhance overall acceptance in healthcare. Without assessing AI systems' performance, successful implementation is unlikely. This involves examining their integration with existing processes and measuring their accuracy and reliability, user satisfaction and overall impact on business goals. As AI technologies become increasingly integrated into healthcare systems, executives and clinical leaders often lack frameworks for assessing AI's effectiveness and safety. This gap in experience can lead to challenges in determining how well AI solutions meet clinical goals, integrate with existing health IT systems, impact patient outcomes and affect the company's financial performance. This challenge underscores the need for comprehensive training programs, partnerships with AI experts and the development of standardized evaluation frameworks to build the necessary expertise in this rapidly evolving field. Last but not least, developing and deploying AI solutions in healthcare requires specialized technical expertise in machine learning, data science and software engineering. However, there's a shortage of skilled professionals with the necessary knowledge and experience to design, implement and maintain AI-driven systems in healthcare settings. More importantly, there's a gap in the skills needed to develop strategies for implementing AI from a business value perspective. The role of the chief AI officer (CAIO) has recently been rising to fill these gaps. Regardless, there's still a significant skills gap in the workforce regarding the understanding and management of AI technologies, according to a 2023 study by GlobalData. Before implementing AI, experts recommend building trust and confidence in AI across all healthcare workforce levels, including providers, IT staff, executives and administrators. Involving existing staff leverages their familiarity with operations, but incorporating external expertise -- such as hiring a CAIO or consulting with outsourcing firms -- can help navigate implementation challenges effectively. The integration of AI into healthcare is met with excitement and caution. Innovative AI applications generate industrywide enthusiasm but encounter challenges like data quality, regulatory hurdles and workforce skepticism. These issues often temper initial optimism, highlighting the complex path to adoption. Despite challenges, the increasing adoption of AI in healthcare suggests a transformative future. With the market expected to reach $188 billion by 2030, overcoming obstacles with innovation can significantly enhance healthcare quality, efficiency and accessibility, making AI a fundamental improvement rather than a fleeting trend. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?