Healthcare | Starschema

Healthcare

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The global healthcare industry has embraced technology, partially thanks to unification initiatives like HL7. This created a vast amount of assessable clinical data that can be used to gain insights into disease, treatments and other factors that can lead to better health outcomes and cost efficient services. Much of this however remains unrealized. Starschema’s data scientists have both the clinical understanding and data science expertise to design and develop systems, visualizations, and workflows that unlock the value hidden in clinical data.

Healthcare

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.

Healthcare

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).

Healthcare

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.

Healthcare

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.

Healthcare

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.

Technologies

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.

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