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Predictive AI

&

Time-course Symptomatology

KumbacareRD:
rare disease detection in <2 minutes

 

KumbaCareRD

'making it easier for you to
make better decisions'

When a patients symptoms do not correspond to the anticipated… it might be a rare disease

 

But without professional support,  training or available time (refs1–3) this can result in a patient:

 

  • Spending a lot of time with different specialists for on average 5.5 years,        (often much longer) before actual   diagnosis (refs 4–6).

 

  • Generating 3–5x higher direct healthcare costs (ref 6).

 

  • Not receiving optimal care so as to plan  their life for themselves and their families (refs 6,7).

 

KumbacareRD, our Predictive AI solution , makes it easier for you, as a healthcare professional to make better decisions

Predictive AI

&

Time-course Symptomatology

in a point of care solution

  • To enable & empower healthcare professionals

  • To detect Patients living with a Rare Disease

  • To provide optimised care

Reduce

Time to clinical diagnosis

Pictudfsadfre 1.jpg

By upto
15 years

 

Healthcare costs & asset use

Pictudfsadfre 1.jpg

By upto
5–fold

 

Increase the quality of life for ALL your patients

An Indication Detection Support System (IDSS)​​​​

designed by Healthcare professionals for their peers to care for​​ patients living with rare diseases

in resource constrained locations

Please use the form below to request access to KumbacareRD

For any other information, including pricing, please reach out to us through the following

telephone: + 263 717 514 013

References

1 Irving G, et al. BMJ Open. 2017 Nov 8;7(10):e017902

2 Ramalle-Gómara E, et al. Orphanet J Rare Dis. 2020 Jan 17;15(1):18

3 Baynam, Gareth et al. The Lancet Global Health, Volume 12, Issue 7, July 2024 e1192 - e1199

4 Chung, Claudia C.Y. et al. The Lancet Regional Health – Western Pacific, Volume 34, 100711, 2024

5 Willmen T, et al.  BMC Health Serv Res. 2023 Aug 23;23(1):904. 

6 The Cost of Delayed Diagnosis in Rare Disease: A Health Economic Study. Sourced from https://everylifefoundation.org

7  Parikh R, et al . Proc (Bayl Univ Med Cent). 2016 Apr;29(2):212-3.

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