AI can aid clinicians in the delivery of standardization of care

As the science of healthcare continues to evolve, so do the tools that support it – today, that tool is artificial intelligence (AI). With this evolution comes increased capabilities that further raise expectations among patients and providers alike. One of these expectations is the standardization of care, an adherence that is often challenged due to the myriad of clinicians, locations and data that are part of a patient’s journey; and this was before the continued spread of Covid-19 which limited staffing and further integrated the use of telehealth services. Propitiously though, current AI tools can offer solutions to the barriers in delivering the standardization of care, such as disparate clinical workflows and lack of adherence to standard-of-care guidelines.

Administrative inaccuracies

One simple, but significant, benefit is AI’s ability to automate mundane, repetitive tasks in the workflow processes through comprehensive management systems. This not only saves time and cost, but greatly increases capacity to help reduce physician burnout that can arise from repetitive tasks, end-to-end processes and complex workflows. As clinicians are stretched during what feels like an endless pandemic, 55% of frontline healthcare workers are reporting burnout, which, data suggests, has contributed to poor patient satisfaction and medical errors, the third leading cause of death in the U.S.  However, today’s health technology can automate clinical workflows in acute care and enable physicians to deliver the highest quality of care in the moments that matter most.

Disparate communications

Hospitals are also currently consolidating into larger systems, involving numerous locations. Pooling resources, researchers and experts from leading facilities can significantly improve patient outcomes as each location may operate according to different financial, operational or clinical standards. This jeopardizes everything from treatment time to standardization of care, threatening the original intent to benefit the patient.

The stakes are even higher during critical care situations, where literal minutes can make the difference. With automated processes and clinical AI applications, frontline decision-makers are empowered with quick, confident and accurate decision support.  They are now able to deliver consistent and repeatable clinical results across multiple locations and among several teams, supporting the original goal of improving patient care and reducing risk of error.

Decreased decision capacity

In addition to automating and standardizing clinical workflows to deliver care when it matters most, AI can tease out the most useful predictive data from the overflow of massive data sets now available. With such predictive modeling, clinicians – and the healthcare industry as a whole – are able to transition from descriptive and diagnostic to predictive and prescriptive.

While the goal will never be to replace a clinician with “Dr. AI,” there is a growing body of evidence that supports how AI can augment rather than replace clinicians. For instance, many patients in acute settings have negative findings, which diverts clinician attention from those needing immediate attention, or patients are often treated on a first-in, first-out basis, timely care to patients needing urgent, aggressive attention. Having tools that can rapidly and accurately provide clinicians with better data and intel allows for better decision making and, ultimately, leads to better patient outcomes.

We have an incredible opportunity to further streamline processes and workflows like never before. By incorporating practices driven by analytics and emerging technology, healthcare systems can meet the goal of delivering consistent, quality care.

Photo: Natali_Mis, Getty Images

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