With 15+ years of experience in the medical devices and healthcare domain and 1,000+ dedicated Medical & Healthcare engineers globally, QuEST empowers the healthcare and medical devices industry to capture new market opportunities quickly, support devices in operation, and sustain existing product lines. QuEST aims to build aim partnerships with technology companies to engineer products and to advance the ways people live. Being the Engineering Partner for 5 of the Top 10 Medical Device Companies, the QuEST approach aims to:
QuEST has an excellent track record of supporting development and substantiation of 40+ FDA approved medical device products that are currently in the market. They are also helping QuEST's medical device customers discover value-added business models in NPD & Sustenance (accelerate the programs and keep the R&D + Sustenance operational cost lower).
QuESTu2019s Certifications and Medical standards : ISO 13485 Certified | IEC 62304 | ISO 14971 | ISO 26262 |ISO 9001 | ISO 27001 |OHSAS 45001|IEC 62304
Concept & Feasibility, Design & Development, V&V, Regulatory
Design Transfer & Process Engineering, Manufacturing & Sourcing, Supply Chain
Post-Market Support, Continuity and End-Of-Life Engineering
A US based Medical Device leader wanted to launch the next generation of neonatal incubators in the market. Time to launch and limited capacity of the core R&D teams were two challenges QuEST helped mitigate. QuEST took the ownership of the sensor qualifications (early on the NPD, with many abstract elements) and the module ownership of the User Interface. Then they freed up the clientu2019s core R&D team to focus more on the domain/clinical centric aspects of the program, to accelerate the launch
A QuEST IP u2013 Computer Aided Diagnosis (CAD) is a major element in the Digital Healthcare paradigm, acting as assistance and second reader to the Radiologist. It automates and makes the diagnosis workflow productive, enabling faster diagnosis, and also lowers the diagnosis costs. The most important contribution of this is early detection of nodules is that it leads to a higher survival rate. This QuEST developed Deep Learning POC has achieved a better accuracy than conventional image processing methods to detect lung nodules. Weu2019ve achieved a sensitivity of 87% and 93% accuracy.
A North America based customer wanted to enable remote monitoring of vital parameters of ICU patients. This need is more pronounced in ICU care during the current Covid crisis. Integration of multiple clinical parameters and inputs from diverse monitoring systems on a real-time basis is a challenging technical ask. QuESTu2019s experience and medical device software capability helped the consumer to accelerate this NPD program.
The need to take multiple X-Rays to get the appropriate image is a problem in radiography. Improving the first time right was a challenge. QuESTu2019s 10 years of experience in working with Imaging Systems came handy in solving this, by analyzing the cause of a unusable exposure (like blurry image, poorly positioned patient, etc.) and improving the design. The major benefit was on patient safety. First Time Right means less X-ray exposure to both patient and the technicians.
Automating the clinical decision making was a major objective for this North America based med tech customer. Clinical parameters like ECG are complex to interpret and also time critical. QuEST was part of the R&D team which developed such ECG systems with inbuilt arrhythmia detection algorithms. This program essentially contributed to the early cardiac arrest predictions, and saved many lives.
This program tested the true mettle of QuESTu2019s engineering capability with regard to computational elements. QuEST engineers were able to optimize the MRI reconstruction algorithm performance from 2 minutes to 2 seconds. This meant that the patient stays a much shorter time inside the challenging environment of an MRI gantry.
This program tested the true mettle of QuESTu2019s engineering capability with regard to computational elements. QuEST engineers were able to optimize the MRI reconstruction algorithm performance from 2 minutes to 2 seconds. This meant that the patient stays a much shorter time inside the challenging environment of an MRI gantry.