The use of machine learning, predictive analytics and artificial intelligence has become mainstream in healthcare from patient care and clinical outcomes to billing and collections. The advantages of AI in healthcare has been extensively discussed in medical literature. Artificial intelligence is being used to predict outcomes and therefore effect treatment decisions. AI is also used in billing and revenue cycle to predict those areas needing special attention or care. Hospitals and providers accumulate massive amounts of data from patient care to billing and collection. By using past billing and claim data experiences in combination with the right business intelligence tools providers can improve collections and clean claims ratios in the revenue cycle. Further, denials can be more adequately managed by having knowledge on hand gleaned from past denial experience. The knowledge allows revenue cycle departments to take actions before claims are filed leading to a reduction in denials and an adjustment in claims revenue.
Artificial intelligence incorporates predictive analytics and machine learning which is driven by data and algorithms using a provider’s past data to predict future outcomes. The predictive ability of machine learning not only delivers results in patient care but also throughout administration and particularly in hospital coding and billing. For each case, there is an ample amount of data from past cases and past billings indicating trends in patient care that effect coding, billing and ultimately collections. By utilizing years of paid claims data, a hospital can isolate the cases needing special coding attention before billing. By using predictive analytics post-discharge, hospitals are able to cull the cases most likely to be denied or most likely to be under-coded and give them special attention before dropping the bill. Under-coding is the equivalent of not being paid for the services performed, and it leads to hospitals leaving significant revenue on the table. Vendors using predictive analytics, machine learning and artificial intelligence are leading the way towards optimizing hospital DRG revenue.
FairCode combines the domain expertise of experienced physicians with modern data science, artificial intelligence and analytics technologies to increase hospital revenue. FairCode bridges the gap between attending physicians and hospital coders. The result? Patient acuity and Case Mix Index are more accurately captured with the correct DRG; hospital reimbursement adjusts accordingly. DRG Validation and physician conducted medical chart reviews happen in real time, significantly impacting hospital quality rankings, case mix index (CMI) and bottom-line results. From clinical validationand chart reviews to payor denial defense, our physicians and data scientists are part of your team! Add us to your existing CDI initiatives, and see the difference. Outcomes are measurable and significant. FairCode averages greater than 4:1 Gross Return on Investment in client hospitals and has been delivering hospital revenue solutions for over 18 years.