Case Study: BioSymetrics Identifies $5Million in Savings for Healthcare Network

By | Case Study, Value Based Care | No Comments

USE CASE: Value Based Care

CLIENT: Major UK based Healthcare network in partnership with Intacare.

OVERVIEW: The annual cost of radiotherapy is escalating year-on-year with little visibility of root cause and control.  Maintaining cost efficient healthcare for patients required an investigation of current code/claim and cost data.

GOAL: Identify and quantify potential cost savings of revising existing reimbursement mechanisms.

“Processing the data manually would have required many months of man hours.”

PROBLEM: Data was incongruous.  Each healthcare provider used different systems, taxonomies, codes and cost basis for mapping radiotherapy procedures when submitting claims.

  • 75,000+ claims
  • 1725+ unique narratives
  • 1000’s of individual codes and duplicate codes
  • Data types: text, numeric

SOLUTION: Intacare used Augusta Pre-Processing workflows to quickly normalize procedure and cost data collected from multiple sources. The team also created an automatable workflow to streamline future analysis and report generation.


  • Identified inefficiencies across 24,281 claims
  • 39% of total claims
  • Projected cost savings: $5Million


Pre-processing of data using Augusta workflows reduced the time to manage the data from multiple weeks to just hours.

Moreover, the workflows are now available within Augusta as standard packages, easily replicated for future projects or if additional data needs to be interrogated.

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Identifying and Addressing the Challenges in the Diagnosis of Sepsis

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Sepsis is the leading cause of death in the Intensive Care Unit, and it’s responsible for 1 in 3 hospital deaths. Each hour without treatment increases a patient’s risk of death by 4-8%. Thus, early detection of sepsis is crucial for improving survival.

With the inclusion of advanced data preprocessing and machine learning, our research has been able to better predict which patients will get sepsis during their hospital stay.

In our study, we sought to develop a robust sepsis prediction model using physiological data (vital signs and lab results) from the 2019 PhysioNet Challenge. In the first phase of our analysis, we trained a recurrent neural network using long short term memory (LSTM). While the LSTM parameters themselves can be optimized in well-understood ways to produce a more accurate classifier, the impact of pre-processing parameters on sepsis prediction performance remain largely unknown.

Now entering into our second phase, we are applying Augusta™ on a patient data set consisting of ~40,000 patients with the intent of more systematically considering the impact of upstream decisions made in processing the data before training a model.

Initial results look promising.

Bio International Convention – Media Advisory

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Bio International Convention

BioSymetrics to present at #Bio2019

BioSymetrics CSO Gabe Musso to Present on Artificial Intelligence and Machine Learning for Biomedical R&D Innovation at the 2019 BIO International Convention

PHILADELPHIA – (June 3, 2019) – Biomedical artificial intelligence (AI) company BioSymetrics will offer a presentation on the use of AI and machine learning for healthcare and biomedical R&D innovation at the 2019 BIO International Convention, which is being held June 3-6 at the Pennsylvania Convention Center in Philadelphia. The company’s Chief Scientific Office, Gabe Musso, will lead the presentation on Wednesday, June 5, at 11:15 am, in Theater 3. Read More