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In today’s rapidly evolving landscape of healthcare, healthcare provider organizations are grappling with a host of demanding conditions that challenge their ability to deliver high-quality, cost-effective care. Technology and more specifically, tools powered by artificial intelligence (AI), can help healthcare organizations respond to these conditions and position themselves for sustained success.
While AI has the potential to help healthcare organizations work better, smarter and faster, it is not intended to replace clinicians; it is intended to augment and support clinical decision making for speed to value. AI also has the potential to save the healthcare industry a significant amount of money. One study estimates that wide adoption of currently available AI technologies over the next five years could result in $200-$360 billion in annualized savings.
There are, however, notable concerns about AI in the healthcare setting related to bias, transparency, explainability, security and liability. Given AI’s many benefits, the development and utilization of AI tools in healthcare must be thoughtful and responsible to ensure the benefits are maximized and risks minimized.
Clinicians and other healthcare professionals are inundated with data on a daily basis, yet much of this information often fails to offer meaningful insights into healthcare operations and clinical care delivery. The overwhelming volume of data, often stemming from electronic health records (EHRs) and other administrative systems that aren’t interoperable, can create challenges in distilling what’s relevant and actionable.
Practical applications of AI can help alleviate data overload and the cognitive burden experienced by clinicians by converting the litany of data points into meaningful, real-time insights at the point of care – doing so with unparalleled efficiency and precision. Furthermore, AI can be leveraged to drive enhanced risk identification on patients, creating an improved care delivery model that tailors to the patient and their needs.
Supply chain disruptions, in times of crisis (e.g., the COVID-19 pandemic) and out, highlight the vulnerability of healthcare organizations to shortages of essential medical supplies and pharmaceuticals. Now more than ever, the right supply chain strategies are urgently needed to optimize limited resources and alleviate shortages to keep medical treatments and procedures on schedule and enable quality patient outcomes.
Predictive models driven by data shared between suppliers and providers combined with machine learning (ML) can help provide longitudinal visibility across the supply chain, which is needed to accurately manage forecasting and predict supply shortages. The most innovative predictive systems can pinpoint product shortages with greater than 90 percent accuracy and automatically recommend clinically approved equivalent products – saving staff precious time and minimizing supply chain disruptions for steady supply and patient care continuity.
Staffing shortages in healthcare continue to create challenges, and if today’s trends continue, one study projects more than 6.5 million U.S. healthcare professionals will permanently leave their positions by 2026, while only an estimated 1.9 million will step in to replace them – leaving a national industry shortage of more than 4 million estimated workers.
Workflow automation powered by AI alleviates manual intervention associated with lower-level repeatable tasks. This enables healthcare organizations to do more with less, allows staff to work at the “top of their license” and affords them more time to focus on direct patient care.
Time and effort associated with routine tasks such as prior authorization (PA) and coding and documentation result in significant manual intervention and cost to a healthcare organization and its patients. A survey conducted by the American Medical Association (AMA) revealed that 33 percent of participating physicians report PA-related delays have led to a serious adverse event for a patient in their care. The same survey also revealed that physicians and their staff spend an average of almost two business days (14 hours) each week completing PAs – and that 88 percent of physicians describe the burden associated with PA as ‘high’ or ‘extremely high.’
Point-of-order electronic prior authorization (ePA) submission with full management in the EHR can reduce administrative burden for areas such as diagnostic imaging and other procedures/medication requiring PA. This saves time and money associated with manual review process as well as speeds up the ability for patients to receive timely care impacting quality and overall experience.
AI, with its capacity to analyze vast amounts of data swiftly and accurately, is revolutionizing the healthcare landscape. From predictive analytics and clinical decision support to automated workflows supporting operational efficiencies, AI is enhancing healthcare delivery for clinicians and patients alike. The true potential of thriving in this exciting era lies not just in the technological capabilities but in the synergy between AI and the healthcare professionals using it.
Collaborative efforts, where AI augments the diagnostic acumen of clinicians and streamlines administrative tasks, lead to more effective, patient-centric care. Ethical considerations and other concerns (e.g., data privacy, security and transparent AI algorithms) are crucial elements for us all to think about as we navigate the successful implementation of AI in healthcare moving forward.
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