causaLens logocausaLens logo alternate

Causal AI for Healthcare Providers

The opportunities are in place for healthcare providers to massively reduce costs while improving the quality of care with AI. However, correlation-based machine learning systems are black boxes that neither staff nor patients can trust. Causal AI in healthcare reliably augments human decision-making.

Challenges facing healthcare providers we work with

  1. Increasingly high patient expectations
  2. Pressure to reduce costs without compromising the quality of care
  3. Forecasting and navigating public health crises
  4. AI systems that are not trusted in deployment


Optimizing patient flow improves the quality of care, facilitating a smoother patient journey with measurably better clinical outcomes, while simultaneously reducing healthcare costs by as much as 4%. The best practice is to leverage big data to forecast patient demand, but today’s machine learning systems can’t anticipate abnormal demand surges or take account of healthcare interventions.

causaLens produces reliable, robust patient demand estimates, and can evaluate the impact of staff reallocation and hospital re-engineering on patient flow. Models built with Causal AI are transparent, enabling healthcare providers to easily explain interventions to all stakeholders.

Supply chain optimization is a priority for all participants in the healthcare ecosystem, and can have a significant impact on costs, clinical outcomes and the patient experience. However, current machine learning approaches fail to make reliable demand predictions during health crises and disruption, precisely when they are needed most.

causaLens enables more agile epidemiological forecasting that adapts three-times faster than standard technologies to crises. Causal AI can also evaluate hypothetical “what if” scenarios, to de-risk the supply chain and manage uncertainty.

Healthcare fraud accounts for approximately 2% of healthcare spending in Europe, and 10-20% of health insurance claims involve some element of fraud. Huge data volume and extreme class imbalance between genuine and fraudulent claims make it infeasible for humans to manually detect fraud. While AI is needed, current fraud detection systems generate large numbers of false positives. Causal AI facilitates human-machine collaboration, combining expert insight into the drivers of fraud with reliable causal modelling. 

Hospital readmissions needlessly drive up healthcare costs for governments and insurers, and often also lead to worse patient outcomes. causaLens can detect the causal drivers of readmissions, enabling healthcare providers to preemptively address root causes and minimise unnecessary costs.