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How AI can reduce burdens on clinicians

From automating tedious administrative tasks to streamlining diagnoses to fast-tracking treatment research to predicting risks and helping with health management, AI is delivering a host of new efficiencies in medicine.

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Sean Dow
Sean Dow
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For physicians, nurses, and administrators alike, times are tough. The number of patients and demands on staff in hospitals and clinics has been rising for years. And with the COVID-19 pandemic compounding care challenges and everyday stresses for clinicians, the years-long industry burnout crisis has only gotten worse. At the same time, wasted expenditures amount to a massive 30% globally.

Given the difficulty of the situation, any progress toward Quadruple Aim targets makes a difference. Although there is no panacea for industry ills, artificial intelligence (AI) is a strategic game changer for organizations that can effectively harness their data while meeting patient privacy and compliance requirements. That’s easier said than done, of course, but we’ll get to AI infrastructure shortly. First, let’s talk about the potential of AI in medicine.

Focusing on AI potential for Quadruple Aim gains

From automating tedious administrative tasks to streamlining diagnoses to fast-tracking treatment research to predicting risks and helping with health management, AI is delivering a host of new efficiencies in medicine. And the alignment between AI capabilities and Quadruple Aim objectives is promising.

Better outcomes

AI can help improve patient outcomes on multiple levels, starting with increased diagnostic accuracy based on deep learning and other methods. It also helps providers proactively and efficiently manage care through risk score predictions and tailored treatment protocol recommendations.

Improved clinician experience

With all that’s expected of doctors, any level of efficiency gain or offloaded task can lighten the mental load. AI offers significant and marginal improvements, starting with streamlining documentation and automating workflows. It can also perform accurate imaging reviews in mere seconds and help radiologists detect diseases such as lung cancer much earlier than with the naked eye.

Lower costs

Who loves repetitive, tedious, snowballing tasks? AI does. And that’s good news for administrators, physicians, and researchers alike. In the front and back office, AI could potentially automate up to 40% of administrative tasks, freeing staff to do more high-value work. Call scheduling and automatic rescheduling for appointments? You bet. But that’s only the beginning. Earlier, more effective disease interventions? Yep. Accelerated, lower-cost drug discovery and easier development of more targeted therapeutics for genetic diseases? Oh yes. The possibilities for controlling costs while improving outcomes are vast.

Improved patient experience

For patients, AI means better experiences and more personalized support. From streamlining appointment scheduling and rescheduling to improving care outcomes while automating personalized support for many needs, AI can make life easier and minimize time spent on the phone or searching for answers.

The prescription for AI success

The promise of AI to advance Quadruple Aim goals is exciting, but achieving maximum gains from AI isn’t easy. You have to make sure that your data is available in the right place at the right time while complying with strict regulations. So your data infrastructure really matters. The problem is that many AI architectures are unnecessarily complex, making them inefficient and difficult to scale. That’s why, when considering your AI strategy, it’s critical to factor three things into your planning.

  • Data volume. Accuracy and data volume go hand in hand. And your data volume compounds rapidly. The best-of-the-best machine learning and deep learning frameworks are key to efficiently training large AI models.
  • Data movement. Different AI applications need access to different types of structured and unstructured data from across the edge and throughout clinical applications and cloud environments. An ecosystem-spanning data pipeline with built-in data protection capabilities is key.
  • The need for speed. When lives are on the line and clinicians and nurses are scrambling to keep up, instant response times are essential for all kinds of applications, from remote monitoring devices to imaging to managing nursing assistants.

In the highly regulated healthcare industry, striking the right balance between data movement needs and compliance requirements is especially challenging. That’s why it’s so important to work with specialists with proven AI solutions and data management expertise to keep your initiatives on target.

Read the e-book, Your prescription for AI successto learn more about AI in medicine. And be sure to explore the portfolio of NetApp® solutions for AI.   

Sean Dow

Sean is the Market Strategist for Modern Analytics Solutions at NetApp. He joined the marketing team at NetApp in 2019 and has held roles on the Private Cloud and SAP solutions teams. Prior to NetApp, he spent 9 years in the financial industry, occupying various roles. Outside of work you’ll likely find him skiing, golfing, singing loudly in his car, or enjoying the Colorado mountains with his wife and dog.

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