Artificial intelligence (AI) is rapidly reshaping healthcare, but better technology alone doesn鈥檛 guarantee better care. Clinical excellence depends on how thoughtfully and responsibly AI is introduced, understood, and applied across care teams.
As part of our multi-part webinar series exploring the role of AI in modern care delivery, 小猪视频 continued the conversation with .
Moderated by 小猪视频 Senior Vice President of Public Policy Deborah Hoyt, the discussion featured 小猪视频 Executive Vice President of Professional Services Tammy Ross, MHA, BSN, RN, CCM; 小猪视频 Senior Consultant and Vice President of Clinical Services Arlene Maxim, RN, HCS-C, and 小猪视频 Senior Vice President of Client Experience Wendy Conlon, MSPT, who explored how clinicians and healthcare leaders can build essential AI literacy, overcome common adoption barriers, and use AI to strengthen clinical intelligence and operational efficiency in care at home settings.
AI Literacy and Education
Education is the foundation of AI literacy; and for many clinicians, it is the key to overcoming hesitation and fear around adoption.
鈥淧roviders know that there鈥檚 a great impact for personalization of care using AI,鈥� Ross said. 鈥淭hey鈥檙e still afraid to use it. But the gap isn鈥檛 really that clinicians don鈥檛 care about innovation; they do. AI literacy is really the bottleneck.鈥�
Ross noted that demographics play a meaningful role in this tension. With the average age of the clinical workforce around 52, and approximately 40% of clinicians over the age of 55, many care professionals are not digital natives. As a result, AI can trigger uncertainty or anxiety rather than excitement.
To address this, Ross emphasized the importance of first positioning AI as clinical decision support, not as a replacement for clinical judgment. When clinicians understand that humans remain in control and AI functions as an assistive partner, trust increases and adoption becomes more attainable. Data shared during the discussion validated that clinicians are significantly more willing to engage with AI when it is introduced within this supportive framework.
Ultimately, the panel reinforced that AI literacy is not built overnight. Creating understanding and confidence across care teams lays the foundation for meaningful long-term gains, including stronger documentation quality, and as AI-driven processes mature, fewer audits.
Educating Without Overwhelming
Maxim cautioned organizations against framing AI education as a large-scale technology transition.
鈥淚 think that one of the biggest mistakes that organizations are going to make, or could make, is that they鈥檙e going to treat this like an overall technology change, almost like they鈥檙e switching EMRs,鈥� she said. 鈥淭his is just going to be, as Tammy said, a clinical support tool. It鈥檚 not a complete change in technology at all.鈥�
Instead, she recommended incremental, practical education embedded into existing workflows. Short, focused microlearning modules, such as brief sessions on how AI can support IDG meetings or case conferences, enable clinicians to build familiarity without disrupting care delivery.
Maxim also recommended identifying a clinical champion with deep AI knowledge to serve as a peer resource during the transition. This kind of peer guidance helps clinicians use AI more confidently and consistently, reducing trial-and-error and accelerating efficiency gains, including meaningful time savings.
AI Adoption
While education builds confidence, sustainable AI adoption depends on the quality of the data and the discipline of the processes that support it. As Conlon emphasized, AI outcomes are only as reliable as the clinical foundation beneath them.
鈥淎I is only as strong as the clinical data feeding it,鈥� said Conlon. 鈥淎I does truly rely on structured, accurate, and timely clinical documentation, so we need to get there as an industry.鈥�
Beyond data quality, Conlon stressed that adoption requires intentional workflow design and close collaboration with clinical teams to ensure AI enhances, not complicates, care delivery.
鈥淲hen we test out these workflows for trust and accuracy, that involves understanding from the clinical team,鈥� Conlon said. 鈥淲here does the AI output occur? Who sees that AI output first and does that change the documentation behavior? We want to ensure there鈥檚 no duplication of work, no alert fatigue.鈥�
According to Conlon, combining high-quality data with thoughtful implementation doesn鈥檛 just support adoption; it actively strengthens operations.
鈥淚f we鈥檙e using high-quality clinical data plus that disciplined implementation strategy, we will refine our core clinical and operational processes, which is what drives that adoption,鈥� she said. 鈥淪o what I鈥檓 really saying is that AI drives process maturity, and if we accept that, we will see gains accordingly.鈥�
She further noted that AI can help organizations move away from workarounds built to compensate for technology limitations, replacing them with more streamlined, automated processes.
AI Pilot Programs
As organizations move from education to adoption and execution, the panel advised taking a measured, problem-first approach to AI implementation.
鈥淪tart with small pilot projects and maybe things that don鈥檛 impact direct patient care,鈥� Ross said. 鈥淏ut also start with a problem. You don鈥檛 just use AI because AI is there. What problem is it solving for the organization?鈥�
Ross noted that early pilots are most effective when frontline leaders are involved from the outset. Informal champions help ground experimentation in real clinical needs while creating a feedback loop that enables organizations to refine AI tools before broader deployment.
Just as important, she cautioned against waiting too long to begin. As AI becomes more deeply embedded in healthcare operations, delayed engagement only widens the learning curve. Pilot programs give clinicians space to provide feedback and help shape how AI is used, provided organizations listen and respond. Clear communication and transparency throughout this phase are critical to sustaining trust and momentum.
Partnering With Your EMR
Pilot success and long-term adoption, the panel agreed, ultimately depend on a strong technology foundation. Without the right EMR partner, even well-designed AI initiatives can falter.
鈥淔olks [who] are still on paper 鈥� are not going to be successful with AI,鈥� Maxim said. 鈥淭hey need to have an EMR partner, a partner that understands AI for efficient, safe, and effective care.鈥�
She emphasized that AI must be seamlessly integrated into existing clinical workflows, not layered on top of them. When AI is embedded directly into the EMR and introduced incrementally, it supports care delivery without adding cognitive or administrative burden.
鈥淢aking sure that AI is part of [clinicians鈥橾 workflow is going to be very important,鈥� Maxim said.
She also highlighted the importance of choosing technology partners with deep regulatory expertise, including alignment with Conditions of Participation, Medicare policy, and data security requirements. These considerations, she noted, should be part of the evaluation process from the start.
From the clinician鈥檚 perspective, this alignment is what makes adoption feel natural rather than forced. As Conlon explained, the most effective AI operates in the background, enhancing clinical work without demanding attention.
鈥淎doption will be so much greater for us as clinicians if the AI is embedded in our workflow and we鈥檙e not feeling it; it鈥檚 happening for us,鈥� Conlon said.
A Path Forward for Clinical Excellence
Across education, pilots, and technology partnerships, a consistent message emerged: achieving clinical excellence with AI is less about speed and more about intention.
When organizations invest in AI literacy, introduce tools thoughtfully, and design workflows that respect clinical realities, AI becomes a practical ally, strengthening care delivery, supporting clinicians, and helping healthcare organizations build sustainable, future-ready operations.
To listen to the full webinar, click . To learn more about how 小猪视频 is helping organizations put responsible AI into practice, explore our full on-demand AI webinar series here.