Each one of us will encounter minor ailments throughout our lifetime, and unfortunately many of us will also encounter more serious and life threatening illnesses.
In each instance we will be prescribed medication and drug treatments that have passed through a complex and highly regulated 4-stage trial process. These could range from skin ointments and pain relief, to treatments for cancers and serious circulatory conditions.
However, the process of bringing medication and drug treatments to market is not only complex, it’s fraught with deep inefficiency beginning with the identification and acquisition of patients for medical trials. Notionally, only 20% of medications that begin a trial process actually recruit enough patient participants to complete a first trial. This means around 80% of pharmaceutical R&D fails at the first hurdle, in a market that is spending over $180B annually.
This trial participant bottleneck can condemn a drug’s future before it even demonstrates any adequacy at treating the target illness. And this directly results in fewer life-changing drugs coming to market, higher drug prices, and poorer healthcare outcomes globally.
“Because we are developing lots of new treatments and not enough people are volunteering to take part in clinical trials, the net result is that trials are taking a very long time and that’s not in the interest of anybody”, Prof Ian Frazer AC of Queensland University, and 2006 Australian of the Year
The root-cause for this failure is an old-world patient acquisition industry rooted in manual process, and an inability to efficiently match specific drug trial criteria to granular data to each specific patient’s condition at-scale. HealthMatch is changing this paradigm.
Manuri Gunawardena (CEO) began her career as a brain cancer researcher and saw the damaging effects of drug trial confusion first-hand. It was from this experience, and after partnering with Aaron Schlossberg, that they began their journey to change an industry with HealthMatch.
By applying machine learning and artificial intelligence to clinical data, HealthMatch aims to dramatically accelerate patient recruitment, and progress life-saving cures onto the market faster than existing practices allow.
At the pharmaceutical level, HealthMatch is sourcing the qualifying criteria for trials being run by the world’s largest pharmaceutical companies. As pharmaceutical companies don’t use standard criteria or provide information in a standard form, HealthMatch sources this data itself, and applies NLP (neuro-linguistic processing) to build a proprietary base-line standardised data set, with Ai being deployed to refine this data over time.
For clinicians and patients, there is a real struggle to identify and match patients to appropriate trials, particularly where the criteria relating to each trial is inconsistently articulated. HealthMatch uses a web/mobile based application to filter qualifying questions relating to each patients condition. The application matches clinician responses to its trial dataset, and confirms only the correct-match trials for that patient — thereby streamlining the identification of suitable trials and facilitating a patient’s application to those trials.
With Ai and software at the core, HealthMatch delivers a radically simple product to match patients with drug trials, backed by an execution approach to data that is deceptively complex to achieve and replicate. In doing so, HealthMatch is dramatically changing an industry that has essentially not moved beyond telephones and excel spreadsheets.
Our mission at Tempus Partners is to back great founders building unique companies, and we have led this seed investment in to HealthMatch because we believe in the team, and we believe the company has an opportunity to deliver a material change to healthcare globally, and more importantly to save lives.
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