Dec 09 2022
Patient-Centered Care

AI-Driven Clinical Care Guidelines Can Lead to Better Patient Outcomes

Artificial intelligence models must be integrated into providers’ current workflows for solutions to reach their full potential in point-of-care settings.

For years, the success of artificial intelligence in healthcare has been heavily debated. AI has been used successfully to gain insights into issues that impact the broader population, but its efficacy in helping to treat individual patients at the point of care has not been nearly as successful.

For the technology to reach its full potential in point-of-care settings, AI models must be integrated into providers’ current workflows. This can be achieved by converting text-based clinical guidelines into digitized and automated process models that deliver insights when providers most need them.

Process models offer a way to define a clinical-care pathway and incorporate AI into the workflow. The process model is responsible for defining activities and decision gates, while AI provides the clinician with real-time recommendations for which decisions and activities may lead to better outcomes.

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An Always-Learning Health System

In parallel, AI that asserts recommendations through the process models continues to learn. Each iteration becomes more sensitive and better tuned to the affected cohort.

When AI models assert insights directly to a process model, they can evolve in response to new clinical research without the need to change workflows to accommodate new iterations. Knowledge backlogs can be significantly reduced and replaced by an always-evolving, always-learning health system, which may define the future of healthcare.

The long-sought goal of value-based care may also become a real possibility. The healthcare industry has been excited about the prospects of value-based care for decades but has struggled with implementation due to the difficulties of measuring quality of care.

One of the positive side effects of AI-driven process models is their innate likeness to quality measures. A process model not only tracks current status but leaves a digital audit trail for the care that was delivered. Each step along that trail is measurable based on best practices and patient outcomes. In this way, the “value” in value-based care can be measured and used to influence payment plans.

RELATED: Find out how to mitigate nurse and doctor shortages with clinical automation.

Tips for Incorporating AI in Healthcare With Process Models

Converting clinical guidelines into process models does not have to be a monumental undertaking. The key is to start small by focusing on converting clinical care pathways that are already well defined and understood by the clinical staff.

Once those pathways are converted, the tasks and recommendations associated with best-practice clinical-care guidelines can begin appearing in the systems that healthcare practitioners use every day. These include email, electronic health records and other tools.

This agnostic approach to the user experience means AI insights can be consumed by clinicians without having to train them on a new user interface or add additional administrative burdens. Because these AI-driven process models influence the EHR, but are not owned by an EHR vendor, the IT staff responsible for deploying the models is not affected by the EHR vendor’s software lifecycle.

Once the process begins, providers can be well on their way to tapping the potential of AI in their healthcare settings.

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