The tech industry is continuing to advance their AI tools, such as large language models (LLMs) and beyond, in leaps and bounds. These tools are beginning to have major impacts in many areas, such as education, the workplace, and healthcare. The healthcare field is still beset with many challenges, such as staffing issues, rising costs, and medical errors. AI can potentially offer solutions for many of these issues. AI tools are poised to become an essential component of health system performance, with the potential to transform healthcare delivery, improve the financial health of hospitals, and enhance patient outcomes. Despite this potential, adoption of AI in healthcare has been uneven and haphazard, with some systems at the forefront and others lagging behind. In this article, healthcare leaders will have the opportunity to learn best practices for how to implement AI advancements without compromising outcomes, safety, and other core aspects of their mission.
Defining and Measuring Healthcare Quality in the Digital Age
The Agency for Healthcare Research and Quality identifies six domains of healthcare quality:
- Safety
- Timeliness
- Effectiveness
- Efficiency
- Equitable
- Patient-centered
All of these characteristics are also centered around access to care, an area where, with responsible and organized implementation, AI stands positioned to deliver potential improvements. Management of chronic illness (which is becoming an ever more vital aspect of treatment as the population ages), referrals to specialty care, and more efficient scheduling are executive priorities that AI can help hospitals and health systems meet.
Healthcare quality also plays a role in hospitals and medical centers affiliated with research and academia. A strong reputation for quality care helps draw in top talent. Ultimately, healthcare quality should be integrated as a central component of hospital business strategy. Improvements in quality have been shown to have significant impacts on financial success through reduced readmissions, reputation enhancements, and value-based models of care. Federal programs such as the Hospital Readmissions Reduction Program (HRRP) and the Hospital Value-Based Purchasing Program (VBP) tie reimbursement directly to these outcomes.
Another important consideration in the area of healthcare quality is the importance of robust data analytics. You can’t improve what you don’t measure. Strong data analytics is foundational for any successful AI implementation by helping recognize quality gaps, tracking return on investment (ROI), and monitoring progress. Ultimately, AI models are only as effective as the data they are trained on.
How AI Fits into Modern Care Delivery
Perhaps the “low-hanging fruit” appeal of AI adoption is operational efficiency. One example in the healthcare context is predictive staffing. AI has the potential to be used to forecast patient census and acuity in order to optimize the scheduling of nurses and other frontline staff, helping reduce employee burnout and cutting down on overtime costs. With over 138,000 nurses having left the workforce since 2022 due to stress, retirement, and burnout, such AI-driven staffing tools are critical for maintaining care quality and safety. They could help mitigate staffing losses by improving conditions and enhancing employee capacity. AI automation could also play a role in streamlining revenue cycle management, handling medical coding and prior authorization. This could help reduce claim denials and overall administrative burden.
However, AI also has great potential in clinical augmentation by enhancing (but not replacing) physician expertise. The latest ultrasound imaging devices come programmed with AI algorithms that allow clinicians to administer care with enhanced precision and efficiency, boosting patient outcomes and overall satisfaction. Predictive analytics could also play a role in this area too, helping to identify patients at high risk of complications, such as readmission to the hospital and sepsis.
Another area where AI has become increasingly widespread is in patient-facing tools. AI-powered chatbots can help provide patients with 24/7 access to critical information, such as symptom checking and appointment scheduling. This can reduce staffing burden and call center volume while improving engagement. AI also has potential for use in analyzing patient data in order to recommend tailored, personalized treatment plans and lifestyle modifications.
A Timeline for System-Wide AI Integration
While AI adoption is continuing at a rapid pace, experts believe that comprehensive integration of AI, when it becomes a ubiquitous component of modern health systems across the country, will take place over the next two to five years. This adoption process could take place in three phases:
- Phase 1: Foundational Adoption (1-2 years)
- Focus: Administration and operational tasks, with a clear ROI.
- Examples: Revenue cycle management, intelligent scheduling, supply chain optimization
- Goal: Demonstrate value, build internal capabilities, and secure buy-in.
- Phase 2: Clinical Augmentation (2-5 years)
- Focus: Introduction and implementation of validated, FDA-cleared AI tools directly into clinical workflows.
- Examples: Ongoing deployment of AI-powered ultrasound devices in specialist fields, such as neuraxial anesthesia, radiology and pathology for diagnostic support. Use of predictive models for EMR dashboards to identify at-risk patients.
- Goal: Augment clinician decision-making and improve quality metrics, such as readmission rates and sepsis mortality.
- Phase 3: Transformational Implementation (5 years or more)
- Focus: Deep integration of AI resources throughout health system and patient care operations.
- Examples: Population health management powered by predictive analytics, personalized medicine based on lifestyle and genome data, and “smart hospital” operations.
- Goal: Complete the shift from reactive to proactive, predictive care delivery.
Responsible AI Implementation
Implementing AI technology in a way that minimizes disruption and preserves or enhances healthcare quality requires a concerted, strategic effort from healthcare leadership. To ensure an effective transition, they will need to consider four different areas that will have to be made a top priority:
- Equity
- Will this tool reduce disparities or does it risk perpetuating them?
- Action: Consider implementing mandatory algorithmic bias audits both before and following deployment. Make sure that training data represents the diversity of your patient population.
- Ethics
- How do we establish accountability for recommendations made with AI, and how can we explain its decisions to patients and staff?
- Establish clear policies on transparency, informed patient consent for AI use
- Efficiency
- Does this tool solve a real issue and merge seamlessly into current workflows?
- Action: Seek involvement of frontline clinicians in the design and selection process in order to verify usability and minimize new burdens for staff.
- Patient Safety
- How can we monitor AI tools for performance degradation or unintended consequences?
- Establish a clinical governance structure for AI with human oversight for high-risk decision-making.
The integration of AI is a process that has already begun in hospitals and health systems across the country. While the transition will inevitably come with challenges, the adoption of AI tools will be a strategic necessity with the potential to enhance healthcare quality, safety, and operational viability.
Leading in this process will require healthcare executives to step into an active role. Vital steps will include establishment of a committee for AI governance for each health system, investment in data and analytics infrastructure, emphasizing pilot projects in high-impact departments, and fostering a culture of innovation centered around care quality and patient safety. Organizations that step into this transformative moment will help define the future of healthcare.
Patient satisfaction builds reputations.