On-demand webinar

Optimize your Revenue Cycle, Workforce, and Data Quality with AI

The Academy's 2022 Executive Priorities Survey found that artificial intelligence (AI) applications are one of the top 3 areas Leading Health System executives believe they can improve upon this year. These results emphasize that LHS are increasingly recognizing AI as an opportunity to manage complex workforce and revenue cycle management challenges. In this webinar, a panel of finance, IT, and industry leaders will discuss key revenue cycle and auditing challenges that LHS are facing today and the top opportunities where AI adoption can unlock great efficiencies. 

Meet our speakers:

• Dr. Peter Bak, CIO at Humber River Hospital

• Jeff Jones, former CFO at Cone Health

• Mike Butler, former COO and President at Providence and Founder at Proponent

• Dr. Nicola Sahar, CEO at Semantic Health

Learning Goals:

  • Learn what challenges and opportunities health system leaders see for AI to transform the revenue cycle
  • Understand how health systems are adopting AI, including how one hospital is using AI to eradicate manual, random-sample auditing and automate the review of 100% of coded data
  • Evaluate short-term and long-term benefits of AI on revenue, cost, data quality improvement, and healthcare staffing shortages

Watch the webinar replay:

Watch the webinar replay:

About the speaker,

Dr. Nicola Sahar

Nicola Sahar, MD is the Co-Founder and Chief Executive Officer at Semantic Health. He leads a team of experienced medical coders, engineers, and machine learning researchers in transforming medical coding and auditing with machine learning. Before starting Semantic Health, Nicola was a clinical NLP researcher at the University of Toronto and a consultant at Bain & Company in their healthcare practice. He is passionate about making health data more actionable so that providers can improve the quality and cost of healthcare.

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Optimize your Revenue Cycle, Workforce, and Data Quality with AI

October 12, 2022 2:00 PM

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Learning Goals:

  • Learn what challenges and opportunities health system leaders see for AI to transform the revenue cycle
  • Understand how health systems are adopting AI, including how one hospital is using AI to eradicate manual, random-sample auditing and automate the review of 100% of coded data
  • Evaluate short-term and long-term benefits of AI on revenue, cost, data quality improvement, and healthcare staffing shortages

The Academy's 2022 Executive Priorities Survey found that artificial intelligence (AI) applications are one of the top 3 areas Leading Health System executives believe they can improve upon this year. These results emphasize that LHS are increasingly recognizing AI as an opportunity to manage complex workforce and revenue cycle management challenges. In this webinar, a panel of finance, IT, and industry leaders will discuss key revenue cycle and auditing challenges that LHS are facing today and the top opportunities where AI adoption can unlock great efficiencies. 

Meet our speakers:

• Dr. Peter Bak, CIO at Humber River Hospital

• Jeff Jones, former CFO at Cone Health

• Mike Butler, former COO and President at Providence and Founder at Proponent

• Dr. Nicola Sahar, CEO at Semantic Health

Watch Now

Improve Medical Coding, Data Quality Auditing, or Both!

Semantic Coder

✔️ AI software reviews patient charts as they are created

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✔️ Simple charts are automatically coded

✔️ Long and complex charts are significantly streamlined

Semantic Auditor

✔️ AI software audits coded charts to determine if assigned codes are supported by the clinical documentation

✔️ Auditors are directed to the exact location in the documentation requiring review

✔️ AI software is compatible with charts from your team or an external vendor

✔️ Insights can be used to spin up new data-driven, high-quality, and relevant clinical documentation improvement programs

Semantic Health Information Platform

✔️ Unlock the full benefits of both Semantic Coder and Semantic Auditor in one convenient and easy-to-use interface