We all constantly hear the terms "innovation" and "AI" (Artificial Intelligent) and expect science fiction to turn into reality in healthcare systems. But healthcare systems are lagging. Why?

Innovation has become a defining force in modern industries, driving efficiency, value, and outcomes. Yet, in healthcare systems which highly impact our health and lives, innovation does not gain sufficient momentum, particularly among health providers. While new technologies and practices emerge at an increasing pace, the rates at which healthcare systems and providers develop, adopt and utilize innovations remain slow. This lag in the adoption of innovation has been acknowledged in our studies. The underlying causes are multifaceted, complex, and deeply embedded in the culture and structure of healthcare delivery. In this article I am presenting common barriers to integrating innovations into health systems.

First, healthcare providers operate in a high-risk environment where every error or wrong decision may directly result in a loss of human lives. This justifiably ignites a culture of risk aversion, driven by the fear of causing harm which by bioethics can even outweigh potential benefits of adopting untested methods. In such an environment, "first, do no harm" becomes more than a guiding principle, it becomes a barrier to change. Many providers are hesitant to deviate from established protocols, even when new innovations promise better outcomes.

Moreover, within the hierarchical nature of healthcare organizations decision-making often rests in the hands of seniors or leaders who may be more invested in maintaining the status quo than experimenting with new ideas. This results in suppressing creative thinking and resisting rapid transformation.  Also, healthcare systems in many countries are fragmented. Hospitals, outpatient clinics, specialists, and payers often operate on different platforms with incompatible data systems. This lack of interoperability creates substantial barriers to innovation as innovative solutions often rely on data sharing, integration, and real-time communication among systems and their providers.

Further, since electronic health records are proprietary, implementing new technologies becomes complex and costly.  If well-designed tools cannot be embedded into the clinical workflows, even the best tool will be ineffective. Thus, without shared infrastructures, health providers face the burdens of customizing and configuring new technologies in piecemeal ways, often leading to burnout and disengagement rather than enthusiasm with innovative solutions.

In addition, as I previously wrote regarding value-based healthcare, traditional fee-for-service models reward volume rather than value, discouraging providers from adopting innovations that may reduce procedures that generate income for the health system.  For example, digital monitoring of fragile elders at their homes reduces hospital admissions, facilitating higher life quality and better patient outcomes, but may reduce the volume of hospitalized patients and bring financial harm to hospitals.

Similarly, providers are not always incentivized to adopt preventive solutions that do not show immediate returns. Innovations aimed at long-term health improvements (telemedicine, remote patient monitoring) may fail to gain traction. Thus, providers are in between paradigms: traditional reimbursement models on one hand and emerging value-based frameworks on the other. Until financial incentives are fully aligned with outcomes, I'm afraid that innovation will remain an uphill battle.

Moreover, providers are under constant pressure to deliver efficient, timely care. Providers juggle heavy caseloads, administrative burdens, making their time and attention a scarce resource. The adoption of any new tool or process must be justified against its impact on workflow and patient care. Digital or AI-driven tools may be viewed by hospitals and providers as disrupting their stressful reality requiring additional training, data entry, or altered routines. Even when new technology has proved effective, if may add to the cognitive load and is likely to be avoided rather than embraced. 

The challenge is therefore to seamlessly integrate innovations into existing workflows without adding friction that requires a deep collaboration between innovators and providers which is lacking in the product development process. Effective innovation requires co-design to bring together clinicians, administrators, patients, and technologists from the earliest stages of development. Without this collaborative approach, innovations are unlikely to solve real problems or gain user buy-in.  Furthermore, innovators may underestimate the complexity of clinical environments, leading to oversimplified solutions that do not scale. Health providers, in turn, may feel alienated or skeptical about tools they had no hand in shaping.

Beyond collaboration from the development phase, innovation requires financial and human capital. Many healthcare organizations operate under austerity, or thin margins and cannot afford to pilot new technologies or hire dedicated staff to operate the innovation. Even large health systems may prioritize capital expenditures for infrastructure or clinical equipment rather than digital or operational innovations.  Moreover, providers often lack dedicated teams for R&D, data science, or innovation strategy. Without these internal capabilities to evaluate, implement, and scale new ideas, promising solutions may never reach the frontlines of care.

Healthcare is justifiably one of the most heavily regulated sectors. Compliance with federal, state, and institutional policies can deter innovation by introducing layers of bureaucracy. Regulatory concerns around patient data, HIPAA compliance, and liability risks often slow down or block the adoption of new technologies. For instance, an AI-powered clinical decision support tool may be viewed with skepticism due to concerns over transparency, explainability, and malpractice liability. In other cases, the process of getting FDA clearance or undergoing internal compliance reviews can stretch for years, discouraging smaller players from entering the space altogether.  Such regulatory frameworks may not adapt quickly to new models of remote care, decentralized trials, or personalized medicine.

Evidence-based practice is the bedrock of modern medicine. Health providers are trained to rely on peer-reviewed studies, clinical trials, and guidelines before changing practice. However, many innovations, especially digital tools, AI driven tools and workflow enhancements lack robust clinical evidence at the time of launch. Conducting large-scale, randomized studies for every new app or algorithm is impractical, but without strong data, providers are unlikely to take the risk. Even when evidence exists, it may not be published in medical journals or may be seen as insufficiently generalizable. Innovators must therefore invest in real-world evidence generation, work with providers to gather meaningful outcome data and publish results. Until innovations prove their value in practical settings, skepticism will persist. Figures 1 and 2 present Barriers by levels.

Figure 1: System-Level Barriers

Figure 2. Provider-Level Barriers

To sum, the barriers to innovation among healthcare providers are not due to lack of talent or intent but rather stem from deeply rooted structural, cultural, and systemic factors that make change difficult. Providers are operating in complex, high-stakes environments where patient safety, regulatory compliance, and operational efficiency must all be balanced. For innovation to thrive in healthcare, stakeholders must align incentives, foster collaboration, invest in infrastructure, and design solutions that fit seamlessly into clinical practice. Equally important, innovators must develop a deep understanding of provider workflows, constraints, and motivations. Only by addressing these barriers will we be able to gradually unlock the full potential of healthcare innovation, in technology, in processes and in patient outcomes.

Additional Reading:

• Gabay, G., Ornoy, H., Gere, A., & Moskowitz, H. (2024, February). Personalizing Communication of Clinicians with Chronically Ill Elders in Digital Encounters—A Patient-Centered View. In Healthcare (Vol. 12, No. 4, p. 434). MDPI.

• Gabay, G., Ornoy, H., & Moskowitz, H. (2022). Patient-centered care in telemedicine–An experimental-design study. International Journal of Medical Informatics, 159, 104672.

• Gabay, G., Gere, A., Zemel, G., & Moskowitz, H. (2025, February). A Novel Strategy for Understanding What Patients Value Most in Informed Consent Before Surgery. In Healthcare (Vol. 13, No. 5, p. 534).

• Gabay, G., Tikva, S. S., & Kagan, I. (2025). Exploring EntrepreNursing: The influence of internal locus of control and organizational innovativeness on nurses' innovative behavior-A cross-sectional study. Applied Nursing Research, 81, 151900.

• Shafran-Tikva, S., Gabay, G., & Kagan, I. (2025, January). Transformative Insights into Community-Acquired Pressure Injuries Among the Elderly: A Big Data Analysis. In Healthcare (Vol. 13, No. 2, p. 153). Multidisciplinary Digital Publishing Institute.