(HPC.Wire) Quantum Computing Inc. has announced a partnership with IPQ Analytics, LLC (IPQ), a life sciences and healthcare analytics innovator that provides a new breed of solutions for improved diagnostics and clinical trial outcomes. Through the partnership, IPQ will analyze real world data to generate novel temporally-defined disease models by combining its unique top-down knowledge graph representation of the patient journey with QCI’s quantum-powered community detection technology.
The ability to analyze complex networks that reflect biological processes and molecular interactions is critical for drug development. A key factor is determining the structure of these networks and detecting interconnected communities. Graph analytics can provide a powerful tool when examining patient symptoms and outcomes for medical analysis. Quantum computing has the potential to transcend limitations of existing systems and improve insight and results. QCI’s QGraph, a component of Qatalyst, empowers drug analysts to solve the most computationally expensive graph problems.
The partnership offers IPQ early access to the QDetect community-detection technology of QGraph. The resulting “next generation phenotypes” (NGP) will be used to “re-diagnose” patients in failed clinical trials to identify responsive patient subgroups; optimize payor reimbursement guidelines to reduce unnecessary testing and ineffective patient treatment; and to enhance clinical decision support for earlier, more accurate diagnoses and improved patient management.
The US has 12 million diagnostic errors per year resulting in 40,000 – 80,000 deaths, and at a cost of $750B to healthcare. Clinical trials cost an average of $1B, yet less than 15% of them succeed. Both misdiagnosis and missed-diagnoses contribute to these errors, reflecting the need for more effective, efficient, and accurate diagnoses. The innovative IPQ modeling approach, along with Qatalyst, has the potential to address these critical challenges for pharma (recovering failed clinical trials and improving trial design), for insurance/payors (improving reimbursement policies to reduce ineffective tests and treatments) and patients (improving diagnosis, treatment and outcomes.)