Using machine learning to triage patients into risk categories

BSG helped a national renal care provider support their annual funder negotiations by generating a novel reimbursement model and a machine learning algorithm that triaged patients into high-level risk categories.

overview of the clients needs
  • Having lost market share to smaller providers, perceived to be cheaper, the Provider needed to justify its higher prices by demonstrating superior care
  • Differentiate the provider by enabling prediction of the level and type of care a new patient would need, based on historical demographic and clinical data of similar patients
  • Create a real-time dashboard, using hospital admission data, to track the frequency and duration of hospital admissions for the Provider’s patient vs. patients treated by other providers to quantify costs
  • Generate a scorecard to track and aggregate key performance indicators (KPIs) along the full patient journey
  • Develop a machine learning model to predict the level, duration and cost of care for new patients
  • Successfully demonstrated that, although more expensive in the short-term, the Provider’s care cost less in the long-term and resulted in fewer emergency admissions for acute renal failure
  • Enabled the Provider to quickly determine areas not meeting quality of care benchmarks and remediate
  • Manage patient and Medial Aid Scheme expectations around cost and frequency and duration of dialysis sessions, and provided significant cost-savings through a tailored patient experience
using machine learning to triage patients abridged

BSG used data to prove the Provider’s superior care and enable successful funder reimbursement negotiations

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