Redesigning healthcare



Healthcare systems worldwide are struggling to deliver clinical excellence to ageing populations with increasing morbidity. Traditionally, increasing amounts of revenue has been thrown at healthcare rather than creating cost-effective solutions.


Countless patients suffer unnecessarily due to late diagnosis, poor management and avoidable complications, which results in inappropriate utilisation of medical resources and the inability to manage those most in need.


Healthcare needs to be redesigned to identify the underlying reasons for these complications and prevent them by using data analysis of the combined experience of millions of patients, artificial intelligence to create and validate new cost-effective pathways, and implementation of these pathways at organisational and patient levels to achieve true clinical improvements.


Traditional pathways are developed by clinical experts using evidence-based publications. These tend to be pharmaceutical-based solutions that look at utilitarian outcomes based on selected cohorts of patients that are rarely validated in a real world setting.  


The result: over a billion prescriptions being issued each year by the NHS, many of which put vulnerable patients at further risk.


Real world medicine is only loosely based on these clinical trials and detailed analysis reveals that many patients respond better to alternative treatments or struggle with traditional pathways for a wide variety of reasons. There is remarkable apathy in collecting this data and identifying the true reasons for poor patient outcomes and low patient medication adherence.


Detailed data analysis confirms that patient adherence to medication regimes is less than 50%, which may be partially attributable to the levels of inappropriate prescribing. Detailed evaluation reveals 12% of these regimes to be putting patients at unnecessary risk due to the complexities of multiple conflicting conditions and the inability of traditional pathways to embrace both polypharmacy and multiple morbidities.


The current way of demonstrating the efficacy and value of medicines is fundamentally flawed and this is perpetuated by the way healthcare systems utilise them. The end result is a lack of patient engagement resulting in poor clinical outcomes.



Da Vinci - Intelligent population level data analysis realising true patient-centred care. 


Data Collection: clinical systems, patients, demographics, lifestyle apps, monitoring devices and outcome data.

Analysis: automated algorithms run against large databases to identify patients at risk.

Validation: automated pathway testing, enabling true validation of established medical practice at a patient level.

INtelligence: automated identification of refined pathways and prospective simulation based on real world data. This is the beginning of applied artificial intelligence within healthcare.

Clinical Improvements: automated standardised analysis of the resulting Da Vinci pathways  to identify their true value to personalised care. 

 Identification: of genuine improvements and further refinement of the pathways.


Da Vinci effortlessly uses 'big data' to apply refined care at an individual patient level.



Even the most experienced and skilled doctor cannot determine the full impact of their decisions. They are limited by their own experience and capacity.


Da Vinci gathers data from a limitless population, effortlessly running billions of algorithms to effectively understand the impact of every clinical decision on a patient’s health. This dynamic analysis operates to refine each patient Da Vinci pathway, actively gathering patient feedback through automated questionnaires when more data is required. This data collection, analysis, refinement, validation and feedback is on-going, creating a never-ending virtuous cycle of health optimisation.