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Clinical Data Analytics

Insights revealed through the analysis and visualization of clinical data can make the healthcare process more efficient, prevent waste and abuse and improve delivery of care. By unifying the clinical data repository and applying advanced natural language processing (NLP) techniques to extract meaning from free-text fields and code them to entity schemata like ICD–10, SNOMED-CT or MeSH for research purposes, the hidden value in clinical data can be unlocked while maintaining patient privacy, data security and HIPAA compliance.


Clinical AI development and validation

With the interconnectedness of clinical processes also comes an increasing need to analyze diagnostic imaging and other clinical data (e.g. EEG, ECG, ECHO). We develop algorithms that facilitate quality assurance of clinical processes, ensuring image quality and diagnostic suitability at point of acquisition while reducing costs and patient inconvenience arising from repeat examinations. Starschema works with academic and clinical centers in a range of clinical AI projects, including a major project aimed at time-of-scan detection and prevention of complex artifacts in magnetic resonance imaging (MRI).


Prescription, care optimization and quality metrics

Polypharmacy presents not only higher costs but also an increased risk of drug-drug interactions. Through prescription review and optimization, machine learning enabled models can assess co-prescription risk, recommend alternative options and reduce both the patient’s medication burden and care effectiveness. By integrating guidelines, best practices and performance measures like HEDIS into the clinical data processing workflow, care quality can be constantly measured, analysed and improved.


Patient pathway tracking, guidance and network leakage prevention

ER overuse costs US health insurers $38bn a year. Clinical analytics can ensure that patients do not slip through the cracks and adequately followed up with. This reduces the risk of complications by efficiently and effectively directing patients to the most appropriate services. Through integration with chatbots, a privacy-sensitive service can assist patients in determining the best course of action for his or her ailment and prevent ER overuse by directing non-emergency patients to alternative healthcare facilities. In the same way, network leakage – the use of out-of-network visits – can be reduced.


Personalized medicine, genomics and population-driven healthcare

By leveraging population health data and other available indicators — including genomics — and applying Artificial intelligence (AI), our data science team create solutions that empower point-of-care providers with real-time information that reveal the most appropriate course of care and reduce risks for the patient. Cutting edge solutions can ingest clinical guidelines, recommendations and best practices while correlating it with clinical outcomes recorded within the system and the patients data. This provides physicians with tools to make smarter decisions and patients with care tailored to her or his needs.


FHIR - is a entry of Starschema Ltd.
Python - is a entry of Starschema Ltd.
Tableau - is a entry of Starschema Ltd.
Pytorch - is a entry of Starschema Ltd.
Tensorflow - is a entry of Starschema Ltd.
Innovative Medical R&D Insights Using Machine Learning with Gedeon Richter

Gedeon Richter, a multinational pharmaceutical and biotechnology company, leveraged Starschema's data science expertise to jointly develop an ML-based methodology to quantify the properties of the mitochondrial network within neurons to enable more effective analysis of medications for various neurological diseases.

Starschema HealthLake

Healthcare Information Technology (HIT) is an indispensable part of managing and delivering healthcare services but patient data handling is highly regulated, presenting a challenge to practitioners. Learn how Starschema HealthLake can ensure compliance with the U.S. Health Insurance Portability and Accountability Act (HIPAA) while streamlining analytics.

Predictive Maintenance in Pharmaceuticals

Producing high-value, high-volume pharmaceuticals on a 24/7 operational schedule means that breakdowns are time-consuming, interrupt production and are often extremely costly. Fortunately, data scientists and manufacturing specialists can examine production lines to determine what data can be used to predict future failures and how to best collect it.