As powerful as AI can be in clinical environments, its success depends on understanding how it fits into–and enhances–existing workflows. Preparing for AI adoption means looking closely at the hospital workflows it will impact, from the ED to specialized care units, and ensuring that every department involved is considered. Below, we outline three essential steps to help healthcare facilities assess their workflows and set the stage for maximizing AI’s impact.
To ensure AI integration is seamless and effective, start by mapping the entire patient journey, from pre-arrival to discharge and, if applicable, follow-up care. AI can support various stages of care, but its implementation must enhance, not disrupt, patient management workflows. By taking this approach, hospitals can see how AI applications will interact with different touch points along the patient’s journey and contribute to improved care delivery.
For instance, consider an ED scenario where a patient with complex symptoms arrives. Without AI, an overburdened health system may face delays in diagnosis and communication, affecting timely care. With AI, however, imaging results can be expedited and clinicians can collaborate through a care coordination platform that quickly unites radiologists, ED physicians and other specialists in real time. This type of AI-driven workflow transformation enables better outcomes as patients could receive faster, more coordinated care.
Mark arrives in the ED with shortness of breath and a history of smoking. In a non-AI environment, it could take over an hour for his CT scans to be read, leading to delays in appropriate care. With AI, however, his scans are flagged for priority review, notifying the radiologist of a critical finding and alerting other relevant teams such as the ED, pulmonary and interventional radiology so that immediate, targeted care decisions can be made.
In this example, the AI system doesn’t just enhance Mark’s experience; it transforms workflows by ensuring timely, coordinated care at every touchpoint, reducing the risk of delayed treatment and improving overall patient management. When AI is strategically mapped to address critical workflow stages, it bridges communication gaps and can help speed up diagnostic and decision-making processes, helping facilities achieve timely, quality care as a standard.
As our CEO Elad Walach noted earlier this year, one of AI’s most profound impacts is in optimizing the patient journey–improving not only the individual patient experience but contributing to measurable outcomes that matter to healthcare facilities. For a 1,046-bed facility, studies showed a 23% reduction in ICU length of stay for PE patients, translating to a cost savings of $10,500 per mechanical thrombectomy patient.1
By aligning AI with specific workflows, facilities can realize both patient-centered and operational improvements, making the ROI of AI adoption not just financially compelling but essential for elevating care quality across the board.
When preparing to adopt AI, it’s essential to consider all user groups involved in patient care, from clinical teams to operational staff. Each of these groups play a role in the patient journey, and they all contribute to the workflow. For clinical teams, this might include understanding which staff members will interact directly with the AI system. But non-clinical users, such as operational and administrative teams, are equally essential to AI’s success; they ensure that resources are allocated efficiently and that the AI system aligns with compliance and data privacy protocols.
In healthcare, the principle of “minimum viable access” is crucial to maintain security, just as imaging safety operates on the principle of ALAR (as low as reasonably achievable). AI governance, therefore, should involve a streamlined decision-making process by an empowered group of stakeholders. By ensuring that the right people are involved in decision-making at the right times, organizations can avoid decision paralysis and conflicting interests.
To measure the impact of AI on clinical workflows, identifying relevant metrics is essential. Many hospitals already have existing metrics tied to treatment times and patient outcomes (like the AHA GWTG) . These benchmarks provide a starting point for evaluating AI’s success. Using both objective and subjective data helps capture trends and supports continuous improvement.
For instance, facilities can track metrics like time-to-diagnosis, ED length of stay, or specific treatment timelines that AI could potentially accelerate. Alongside these metrics, gather feedback from clinical staff to understand any workflow challenges or bottlenecks AI might introduce. This dual approach provides a holistic view of AI’s effectiveness, guiding facilities in refining its application and ensuring a smooth integration process.
Analytics are a powerful ally in assessing AI’s impact. Without them, an AI system’s value can be challenging to quantify and sustain. By tracking metrics that show clinical,operational and financial improvements, healthcare providers can make data-driven decisions that optimize Ai’s use and benefit both patients and staff.
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