Pairing of people and technology key to successful AI integration in healthcare quality


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Artificial intelligence (AI) is currently one of the hottest trends in business. And for good reason. AI has the potential to increase profitability an average of 38% and bring an economic boost of $14 trillion across 16 industries by 2035, a recent analysis suggests. This includes healthcare.

There is no shortage of possibilities for AI-supported solutions in healthcare. They range from improving doctor-patient communications to recognizing disease in diagnostic imaging. With its immense data-reporting demands, healthcare quality nears the top of this list. In fact, implementing value-based care is a concern keeping many U.S. health system CEOs up at night.


In recent years, many hospitals struggled to submit complete electronic clinical quality measures (eCQMs) reports on time. A Joint Commission survey conducted in conjunction with two leading hospital associations found that 78% of hospitals were not ready for the 2017 eCQM reporting period. Around the same time, CMS released findings from an inpatient quality reporting validation pilot program evaluation highlighting workflow issues and processing procedures as critical barriers to successful quality reporting. This teaches us that even the most promising information technologies will only reach their full potential when paired with the proper clinical expertise and ongoing support.

Natural Language Processing (NLP) is a type of AI technology that enables a computer or software to understand human language and information patterns natively - versus 'structuring' data in a way that makes it possible for the software to consume. An obvious use for NLP is leveraging it for quality data reporting - since up to 80% of data in EHRs can be unstructured information, such as physicians' notes and additional comments. This unstructured data tends to hold rich clinical insights. NLP has the power to analyze entire EHR databases, and can be trained to hone in on clinical data and documentation practices that are specific to each hospital or group of clinicians.

When properly deployed, NLP in healthcare quality will set the stage for numerous benefits, including:

  • Speedier and more-accurate reporting, which can help hospitals work toward achieving maximum reimbursement and minimal payment penalties.
  • Saved time and resources that can be redirected to take on additional clinical data registries and other quality improvement programs.
  • Minimal disruption to staff. In other words, clinicians do not have to drastically change how they enter information into EHRs and instead can focus on delivering and improving care.
  • Fewer staff hours needed for manual data abstraction.
  • Improved patient-reported outcomes and satisfaction due to adherence to quality measures.


Source: Becker's Hospital Review (View full article)

Posted by Dan Corcoran on March 9, 2018 06:37 AM

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