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How AI fits in the healthcare puzzle – four things to know

January 29, 2019
Artificial Intelligence
By Kevin Ruiz and Jimmy Solis

U.S. healthcare operators, regardless of size or specialty, face increasing pressure to reduce costs while maximizing revenue and cash flow in an already complicated system. Outdated controls, disparate IT systems and complex processes for patient interaction, coding, billing and reimbursement make it difficult to identify and implement change. The increased scrutiny on patient outcomes, safety and satisfaction, along with increasing labor, medical supply and drug costs only make the task of running a successful healthcare business more challenging.

At the intersection of information technology, healthcare operations and business performance improvement, artificial intelligence has emerged as a production-level, highly-customizable solution that has driven increased profitability at the largest healthcare providers and payors. Advances in AI tools and technology have made these robust solutions increasingly accessible to early-life cycle healthcare startups, PE and VC-held growth operators and other middle market healthcare organizations.

What does AI mean for healthcare organizations across the U.S.? How can organizations take practical steps to assess, implement and leverage AI solutions? Here are four things every healthcare organization should know as they consider introducing AI into their IT environment.

1. What is artificial intelligence?
The term artificial intelligence broadly refers to a wide array of capabilities along a spectrum of complexity. This spectrum goes from the familiar, such as expert systems, decision support and predictive analytics tools to advanced automation solutions, such as machine learning, natural language processing and robot process automation (RPA). The truly autonomous, sentient AI, arguably, does not exist – as far as we know. The spectrum of innovation and application is ever-growing and constantly debated.

Most of us are already familiar with some version of “AI”. We are comfortable receiving AI guidance on our shopping decisions, health, entertainment or even romantic partners. To accomplish this feat, systems rely on relatively straightforward predictive analytics: recognizing patterns and making assumptions based on large data sets, consumption activities and behaviors. Getting the “system” to detect patterns and understand the relevant signal within the noise requires a great deal of guidance, rules-based logic, a large data set to train the system and manual exception processing. All of this activity – all of these rules and selected inputs – are developed, built and maintained by humans.

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