Data Dissect has developed a Learning Healthcare System platform that utilises machine learning (Artificial Intelligence). The goal is to disrupt the Clinical Registry market by making quality clinical registries affordable and more user-friendly.
The Data Dissect LHS platform allows end-users (clinicians, institutions) to leverage and reduce resources and infrastructure constraints in the establishment of and hosting of CQRs.
The LHS proof of concept has been successfully tested in SA Health) and in its first year generated a 22% reduction in hospital stays for children with complicated appendicitis.
Data Dissect has developed advanced analytical software and processes for assembling, analysing and interpreting patient data. The building blocks of a Learning Healthcare System.
A Learning Healthcare System is a system in which science, informatics, behavioural incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience. Importantly, a Learning Healthcare System is a socio-technical system that explicitly uses technical and social approaches to learn and improve with every patient who is treated.
IMPROVING PATIENT OUTCOMES REDUCING COST OF CARE
Data Dissect will disrupt the clinical registry market by making quality clinical registries affordable. Patients will benefit by providing timely insights to clinicians for improving the quality of care and reducing the cost of care. The outcome will be a Learning Healthcare System.
The fragmented clinical registries in Australia does not support efficiencies in the Health Care System. Australia has an expensive (circa $130B) health care system with limited capacity for timely generation of evidence on the relative effectiveness, efficiency and safety of various interventions and treatments for clinicians.
This severe lack of insight exists in spite of investments of hundreds of millions of dollars by health services in electronic medical record systems which were supposed to make healthcare better, safer and cheaper
The main issue with all current ‘best of breed’ Electronic Medical Record (EMR) systems is that they have been designed for billing and documentation and not for data extraction and timely clinical treatment analysis. In summary EMR’s are;
• Very expensive for interface customisation
• Do not capture newer forms of health data
This has major impacts on the cost of running ‘Clinical Quality Registries’ – which today range from $1 million to $5 million per year to operate.
Data Dissect will be the preferential integrator for clinical quality registries into international health care information systems enabling health care end-users to systematically drive patient-centered improvements in the quality and value of health care and patient outcomes, across their national health care system.
We will disrupt, simplify and optimise a USD 2.5 Billion clinical registry software market and we will:
o Be the established leader in clinical quality patient registry intelligence
o Trusted by enterprises across the clinical quality patient registry spectrum
o Experts in data interoperability & analytical insights in clinical quality patient registries