Data analysis, informed by artificial intelligence, can help reduce claims denials and minimize downcoding.

Artificial intelligence and machine learning are expected to play a part.

The global health care analytics market is expected to quadruple between 2020 and 2030, growing at a compound annual growth rate (CAGR) of 21.5%. In fact, the artificial intelligence in health care market is valued at $15.4 billion globally, and is expected to expand at a CAGR of 37.5% between 2023 and 2030.

A good portion of this growth is fueled by the development of artificial intelligence (AI) and machine learning (ML) applications that use complex datasets to innovate solutions for relevant health care issues.

d2i sees several opportunities to use AI and ML to create solutions to critical issues that health care organizations are facing, specifically across three domains: resource optimization, improved patient safety and outcomes, and AI-driven coding.

Resource Optimization

The health care workforce is in a period of tremendous flux, with dire shortages in some facilities. These shortages exacerbate the long-standing issue of matching health care staffing demand to capacity. Emergency departments provide a perfect example of this problem because arrival patterns and acuity mix are variable and have become even more volatile.

While leveraging the power of AI with curated datasets and multivariate analytics, facilities can use on-demand telemedicine physicians as part of a hybrid care model that load-balances resources across facilities.

Machine learning also can be used to forecast demand and optimize staff scheduling. Supported by clean, reliable data, such an approach not only improves patient care, but also leads to decreased costs and less provider burnout.

Improved Patient Safety and Outcomes

AI can use volumes of past data and analysis to identify patients who are at high risk for unfavorable outcomes due to a multitude of factors, including comorbidities, return visits, and length of stay. Clinical data gathered during the encounter — such as vital signs and test results –can then be used with algorithms designed to gauge risk in order to influence treatment decisions and follow-up based care.

Patients benefit from this type of technology, but the benefit to physicians is tangible as well. Medical malpractice insurance is costlier than ever, and proactive approaches using AI can help support clinicians and reduce professional risk. Not only is this good medicine, but demonstrating that such a program exists can help organizations negotiate better malpractice insurance rates.

AI-Driven Coding

The health care revenue cycle, from patient registration to the final bill payment, is complicated and labor-intensive. Coding, billing, claims handling, and collections all involve costs that bite into the bottom line. At a time when organizations are reporting slim margins and ever-increasing expenses, technology that can improve revenue cycle productivity is a smart investment.

AI applications can effectively improve the accuracy and efficiency of coding processes, providing several benefits, including:

  • Reduction of claims denials due to errors. Claims denials delay payments by an average of 28 days, hindering cash flow.
  • Minimization of downcoding. This increases reimbursement, and also improves risk adjustment for value-based payment programs.
  • Use of the right data to support E&M levels and medical decision-making (MDM).

For health care to realize the power of AI and ML in health care, the data itself must be impeccable. Only the cleanest data going into the algorithm will be effective. d2i understands health care data and has a track record of producing amazing insights from both structured and unstructured data sets in large systems.

Ready to head toward the future of transformative health care analytics? d2i is here to help you. Contact our team for an overview of our products and a free demo.