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Prioritisation of Elective Waiting Lists/Referrals Management

Learn about how prioritising the waiting list in terms of clinical urgency of individual patients has, according to health system reporting, helps:

  • Reduce emergency admissions by 8% from those on the waiting list
  • Save 125 bed-days per 1,000 patients
  • Save thousands of surgeon years of time spent manually prioritising the list

Get in touch if you’d like to hear more about this approach and how it can help with both elective waiting lists but also directing patients to the right location for their clinical risk.

The ‘Uber’ of healthcare referrals – matching clinical patient risk to the right location

We help payers evolve their network over time, whether that’s at hospital, specialty, sub-specialty or physician level – removing outliers and driving improvements in others.  These insights can also power a unique approach to intelligent referral management

We can understand the precise mortality and overall complication risks as well as the risk for specific complications at individual patient level.

That can be combined with the detailed understanding of performance across acute care from country, regional, system down to individual patient results.  Typically we find and can help resolve 900% more causes of cost and quality variation in hospitals, without disruption to clinical workflows.

This all means we can identify which specialties, sub-specialties etc. are performing well and which are performing poorly.  No hospital will be uniformly good or bad and we update the analytics monthly to support intelligent decision-making and referral.  

The result is that our analytics engine can deliver the decision-making to support intelligent referrals management – matching individual patient risk to the right location for their treatment.  And the referrals change as the patient’s risk and provider performance evolve.

This supports the right patient being referred to the right place, with the right outcome and the right cost.  


Precision population healthcare at scale

C2-Ai’s analytics platform is at the cutting edge of precision healthcare – assessing patients in detail but doing so at scale across populations of tens of millions of individuals.  

One way to use these unique analytics, is to look at hospital, regional or system level and consider the acuity or stage of disease progression of patients to understand problems and therefore potential actions upstream.  The simple objective of this is to identify how to stop people going into hospital in the first place.

The analytics support root cause analysis to help resolve the problems and reduce variation and cost.   When combined with health economic analysis, this can be extended to consider the impact of quality in whole of life care for patients, and so prioritise interventions and improvements at hospital level.

But we can also consider the impact of different scenarios.  In the same ways as before, the impact on patient outcomes and quality related costs to rectify complications can be derived.  The post-discharge impact on lifetime patient health costs can also be calculated.


This becomes even more interesting when compared to the base scenario.  The deltas on patient outcomes and cost can be derived within acute care and beyond.  The subsequent impact on hospital admission rates and acuities can also be derived.


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