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Case Studies

 

Transformative but not disruptive systems with zero integration needed 

metrics
%
of diagnoses
%
of patients tracked
changes to clinical workflows
million patient records processed
Countries providing patient data
Client countries
years of research

Some of our success stories

Results you can count on – even in the best hospitals

Since working with C2-Ai, we have delivered significant results across for profit, not for profit, public and private hospitals alike – in multiple countries.

Prioritisation of patients waiting for surgery/procedures

NHS reported results show:

  • 5 minutes saved per patient per triage (meaning up to 20,000 surgeon years of time saved potential across all of NHS)
  • Potential to save £3bn for NHS (opex alone)
  • 8% reduction in emergency admissions from those waiting for surgery
  • 125 bed-days saved per 1,000 patients
  • Improved patient outcomes leading to longer term savings through reduction in chronic conditions

C2-Ai systems deliver continuous improvement

Without us, hospital performance drifts. 

The hospital on the left worsened without our insights and root cause analysis.

On the right, the hospital feed to C2-Ai was turned off for a period.  Multiple metrics got worse.  It was only after restarting that performance returned to it’s exceptional levels.

Understanding the true picture

Hospital thinks there’s a problem at (A) but that it is  improving at (B)

Case-mix adjusted assessments of quality show hospital actually doing well at (A) but with a more challenging mix of patients.  At (B) there is a real  problem that is hidden in simple mortality rates.

Improving even excellent hospitals

This globally renowned hospital had great observed to expected complication rates (risk adjusted at individual patient level) for knee and hip surgery.  However, we have helped improve them further with O:E rates almost halved.

In parallel, our risk-adjusted excess bed-day calculations identify where patients have remained in hospital longer than expected and can be averaged across a cohort.  In this case, this showed hip surgery with an issue that once resolved, quickly saved bed-days and cost.

Illustrative example on Myocardial Infarction

We can assess large data sets and identify detailed cohorts.  The example illustrates outcome cohorts and drill down into greater detail across gender, SDoH, equity, country, region, site etc. (as appropriate given local data regulations).

Preventing avoidable conditions acquired in hospital

Smart triage of patients identifies their risk of hospital acquired Acute Kidney Injury and Pneumonia.  Care plans are suggested for those at higher risk, to prevent the conditions.  

Compared to the 6 and 8 days extra Length of Stay (LoS) those conditions lead to, the modifications result in lower opex costs and less care load on nursing staff.

Annualised Results – 13,000 bed-days freed, 500 excess deaths avoided and $9m direct costs saved

“I’ve been trialling the new C2-Ai App for AKI & HAP, both of which are phenomenal and work incredibly fast…delighted and excited as to how this tool can help us identify these patients early and put in place simple measures, which all have a significant impact”.

Sunjay Kanwar – Consultant General Surgeon at St Helens and Knowsley Teaching Hospital 

Improving operating theatre performance

 

Consultant-level investigation leading to quality and efficiency improvement 

Improving hospital performance

Identifying variation and changing practice 

Assuring corporate governance and safety

Continuous assurance for board and clinicians by building a picture of performance

Judging true hospital performance

Consultant-level investigation leading to quality and efficiency improvement 

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