Contact

Video Content

Introduction to C2-Ai

A two-minute overview of the globally unique retrospective audit system and how it identifies issues and root causes in the 90% of cost and clinical variation that is missed by today’s systems.

Prioritisation of Elective Waiting Lists/Referrals Management

Learn about how prioritising the elective waiting list in terms of clinical urgency of individual patients has, according to health system reporting, helped:
  • 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 on manual patient prioritisation, boosting efficiency and cost savings
  • Track clinical outcomes by Social Determinants of Health (SDoH), providing essential data for addressing health inequalities.

Waiting Well – Using detailed patient risk to target health support

C2-Ai helps identify patients on the elective waiting list for surgery with modifiable comorbidities.  Providing health coaching to ‘the right patients’, compared to similar patients, reduced the average length of stay by 2.6 days, save £1100 in bed costs alone, reduced overall complications by 65% and resulted in no hospital acquired pneumonia cases (a 10%+ risk in the cohort).
By adopting this approach, we can drive meaningful improvements in elective recovery, delivering patients with better outcomes and significant cost reductions.
 

Interested in learning more?

Explore our elective recovery solutions

Why C2-Ai is different

The sophisticated algorithms can see and help resolve  900% more issues that drive cost and patient harm.  This is across all of acute care with one system that requires zero integration and does not change clinical workflows. 

C2-Ai Observatory

System wide, horizon scanning with no clinical workflow changes, zero integration and yet unique insights from system down through specialities to patients across all of acute care.

Transforming healthcare with C2-Ai

How C2-Ai’s unique systems provide the bridge from today’s unsustainable pressures to significant transformation in the industry.

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

C2-Ai can 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 overall mortality and complication risks, as well as the risk for specific complications, at individual patient level.
That can be combined with the detailed understanding of patient outcome performance across acute care from country, regional, system down to individual patient results.  
This means C2-Ai can identify which specialties, sub-specialties etc. are performing well and which are performing poorly.  No hospital will be uniformly good or bad and the system updates the analytics monthly to support intelligent decision-making and referral.  
The result is that the 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.

 

How to deliver on the Health Foundation patient safety aims

…but also improve margins, move Medicare/Medicaid to break even and reduce pressure on hospitals.

 

Professor Rowan Pritchard Jones talks about the impact of C2-Ai’s risk stratification and prioritisation of the elective waiting list

 

 

Academic Health Science Network presentation on real impact of C2-Ai’s risk stratification and prioritisation system for elective recovery

 

 

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from - Youtube
Vimeo
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google
Spotify
Consent to display content from - Spotify
Sound Cloud
Consent to display content from - Sound