AI in healthcare: What business leaders need to know

Artificial intelligence may seem like a new, untested technology, but the reality is that AI is already integrated into our everyday lives. For instance, Siri, Amazon Alexa and Google Assistant use natural language processing and natural language understanding to analyze and respond to voice commands. Emails and text messages use NLP for predictive text and auto correct.

The rapid development of AI brings with it enormous concerns, especially regarding its applications in healthcare. However, AI is already transforming patient care in positive ways, for example, by making it easier for clinicians to diagnose and treat illness sooner, potentially reducing the need for costly specialized treatment or hospitalization.

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Chronic condition management and early detection

While clinical judgment by an actual human is still critical to ensuring patients receive the best possible care, AI can support clinicians and their decision-making by providing a more complete view of patient health.

For instance, radiologists are now using AI to more accurately analyze X-rays, MRIs, CT scans and mammograms. AI's sensitivity to distinguish slight changes from image to image can help detect chronic diseases earlier and more accurately. In one study, researchers found an AI system could predict diagnoses of Berger's kidney disease more accurately than trained nephrologists. In an attempt to slow the progression of kidney disease among veterans, such as Berger's disease, the Veterans Administration partnered with DeepMind, an AI research lab, to identify risk predictors for patient deterioration and alert clinicians early. DeepMind developed an AI model based on electronic health records from the Veterans Administration that identified 90% of all acute kidney injuries that required subsequent dialysis, with a lead time of up to 48 hours. 

Earlier intervention in the case of Berger's disease and other kidney conditions significantly impacts the economic burden of the disease, potentially saving plan sponsors between $276.80-$480.79 per member per month. 

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Automating administrative tasks

One of AI's greatest assets is its ability to quickly assess large volumes of data to optimize clinical and administrative time. Medical practices are utilizing AI-enabled technology to improve administrative efficiency and patient care. Automated documentation tools can reduce the time physicians spend on patient charting by 72%, which means physicians can spend more time treating and diagnosing plan members. AI can also integrate with electronic health records to pull relevant data, identify missing information and complete and submit prior authorization forms on behalf of providers.

Administrative expenses account for 15% to 25% of total healthcare expenditures. Reducing administrative overhead and claims errors, along with early diagnosis and treatment of chronic disease, can improve member outcomes and produce impressive cost savings for plan sponsors. AI has the potential to save $265 billion in overall healthcare costs by eliminating administrative overhead and documentation errors.

AI's ability to process vast quantities of data also benefits health plan administrators. Plan sponsors can implement AI tools that provide members with personalized treatment and support, identify health plans during enrollment that best fit specific member needs and determine additional benefits for members and their families. 

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Overcoming barriers to adoption

Despite its potential to reduce healthcare costs, improve patient outcomes and improve member experience, AI adoption is still slow. The initial investment required to implement AI can be high, and it includes the cost of the technology, staff training, system integration and maintenance of AI models, not to mention potential liability concerns. 

When considering utilizing AI for the purposes of improving efficiency and outcomes, organizations in the healthcare industry are: 

• Analyzing how AI solutions can support their population, and which modalities are likely to be (or have proven to be) successful
• Consulting with internal stakeholders from the beginning to identify potential challenges to adoption
• Evaluating potential cost savings and member outcomes
• Considering the quality and source of data used to train AI models
• Ensuring AI tools meet HIPAA requirements
  

AI in healthcare is no longer an idea of the future. It is here and already making significant improvements in patient outcomes. However, AI is dependent on data quality and clearly defined learning parameters to eliminate potential bias and make accurate predictions. Organizations must also weigh other risks associated with AI, such as informed consent issues that may arise if patients do not fully understand how their information is being used.