When artificial intelligence meets value based care

The saying in clinical documentation goes if you didn’t document it, you didn’t do it and if you didn’t do it, you can’t bill for it.

Physicians today face increased pressure to produce more accurate, complete and compliant clinical documentation while simultaneously maintaining their focus on patient care and satisfaction. However, without better clinical documentation processes and technology in place, physicians and hospitals risk receiving inaccurate quality scores and lower reimbursement rates.

The Centers for Medicare & Medicaid Services (CMS) continues to advance the level of reimbursement it ties to clinical performance. By 2018, CMS expects to base payment criteria for Medicare value-based purchasing programs wholly on clinical performance according to efficiency of care (25 percent), patient outcomes (25 percent), patient experience (25 percent) and safety (25 percent).

Bundled payment schemes also add another level of analysis, complexity and risk to executing a value based model at a profit. Doing so requires several skill sets, aided by AI, that are not as necessary in a fee for service environment. These opportunities are:

  1. Care coordination
  2. Risk stratification
  3. Identifying and focusing on patient centric team needs
  4. Managing risk
  5. Pricing risk
  6. Cost accounting specific episodes of care
  7. Quality standards, compliance and stratification
  8. Pricing a specific episode of care
  9. Optimizing profit margins for a specific surgical procedure or other intervention where there are gross geographic disparities in quality and costs, when patients and payers are willing to travel for value
  10. Meticulous documentation and coding

More data collection (from both patients and their telehealth practices) and more sharing of information (because of value-based health care) mean that there’s more information to sort through on a daily basis. More, in many cases, than humans could possibly review in their lifetime.

To help handle, sort, and utilize all this data, health centers are turning to artificial intelligence (AI) in droves, with 35 percent of health care institutions hoping to leverage AI within two years and 50 percent within the next five years. AI has already been adapted for health care in areas such as predictive analysis, population health management, clinical decision-making, and many other trend- or analytics-based solutions, and money spent on the technology is expected to reach nearly $8 billion by 2020.

AI has become the “Intel inside” of most value based care transactions. For most doctors, the less data and the more actionable information at the point of care we see, the better.


value based healthcare aiBy Arlen Meyers, MD, MBA is the President and CEO of the Society of Physician Entrepreneurs

Arlen Meyers is a professor emeritus of otolaryngology, dentistry, and engineering at the University of Colorado School of Medicine and the Colorado School of Public Health and President and CEO of the Society of Physician Entrepreneurs at www.sopenet.org . He has created several medical device and digital health companies. Most of them failed. His primary research centers around biomedical and health innovation and entrepreneurship and life science technology commercialization.

He consults for and speaks to companies, governments, colleges and universities around the world who need his expertise and contacts in the areas of bio entrepreneurship, bioscience, healthcare, healthcare IT, medical tourism — nationally and internationally, new product development, product design, and financing new ventures.

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