07/17/2024
While an intelligent machine-centric future used to exist for entertainment only (remember The Terminator?), we now find that AI is a reality that has the power to transform our lives. AI’s journey began in the 1950s with the Turing Test and evolved into an integral part of daily life by 2022. Fast forward to 2024, and AI’s applications—from virtual assistants to predictive analytics—enhance productivity and improve lives.
Healthcare has experienced a perfect storm of challenges and innovations—overwhelming patient loads and clinician burnout, personalized medicine and patient care, and data-driven decision-making software—that have fueled an eagerness to explore the potential of AI. The latest breakthroughs in AI are primed to satisfy the growing appetite for more efficient patient care and advanced medical solutions, but how do you implement them across your health systems?
The Communication Challenge of AI
Healthcare is facing a world that is asking for a double-shot of, “AI, now please!” Patients, providers, policymakers, and power players are clamoring for this tech superhero to swoop in and create a world where scheduling appointments is a breeze, diagnoses are laser-focused, treatment plans are personalized.
The plot twist is that the loudest folks in favor of this revolution are oftentimes the most hesitant to trust it. They are concerned about biased data leading to unfair decisions, AI being a black box incapable of reaching practical conclusions, and yes, The Terminator-induced fears and anxieties about job displacement and the new algorithmic uprising.
Healthcare professionals, including individuals who both desire AI and are skeptical of its ramifications, are typically driven by the same value-based intention focused on the benefits for patients and themselves. In order to effectively communicate to this audience about AI, potential solutions must address real-world needs, not just technical feats. Additionally, there’s a need to manage expectations with transparency to build trust.
A Guide to Artificial Intelligence in Healthcare
Implementing AI in a Clinical Setting
Installing a new paging system? Rolling out a new software to collect support requests? Replacing the computers as a nurse workstation? An organization will likely use a standard technology implementation framework to execute these types of operational and administrative changes. And, with every key player in healthcare interested in what it means to bring AI into their organization or daily functions, it is key to remember that delivering an AI solution will closely resemble other types of technology implementations.
Consider leveraging modern use cases of AI in the clinical setting, seen below, to launch your organization’s AI engagement. Keep in mind that AI should always have human interaction in the clinical setting and never act autonomously.
- Medical Imaging and Diagnostic Assistance
- Personalized Treatment Planning Suggestions
- Patient Engagement and Education
- Remote Consultations
- Scheduling Optimization
- Data Analysis
Ensuring Data Transparency
Process, People, and Technology—These are the foundational considerations for any transformational change being made to an organization. But with AI, there is a critical fourth consideration that needs to be included in any type of implementation: Data.
With the eruption of AI, people are creating and training models left and right despite the lack of formal regulations or compliance standards for data sourcing. If an organization is implementing AI in a clinical setting with direct patient impact, it will be imperative to validate a mature model trained on accurate data. The repercussions of an immature model, improper data, or using AI without human interaction are everyone’s biggest fear, primarily because when applied in a clinical setting, AI could truly change someone’s life through hallucinations or biases in the model-based bad data. To mitigate this risk, an organization will have to invest in a solid company that has a reputable algorithm and clean, accurate data to use.
Aligning AI Implementations with Proper Governance
So patients, providers, policymakers, and power players are all vying to have the big breakthrough of transformative AI. How can they get past having an idea of a use case and translate that into actual execution? An organization should consider a third-party partner to come in at the start to help build an AI Center of Excellence from the ground up. An AI CoE team can aid health systems with:
- Translating use cases into business cases
- Evaluating process implications
- Guiding vendor selections
- Taking an organization from implementation through success measurement
- Executing successful change management
- Providing ongoing AI education
If you’re interested in learning more about how your healthcare organization can approach the application of AI, fill out the form below to connect to a Sendero consultant.
