SciForce Healthcare

HEALTHCARE

Dive deep into health data to get evidence-based decisions What can we solve for you?

Our unique team of data engineering-trained medical doctors and top-notch AI/ML researchers use the potential of computer vision and pattern recognition solutions for health monitoring, medical image analysis, vitals monitoring, and noninvasive diagnostics. Our journey began in the OHDSI community, where we have played a crucial role in the development of ontologies that now drive advancements in healthcare analytics and solutions.

Our service portfolio includes projects in healthcare data harmonization and analysis, the development of AI-enhanced digital health applications, and end-to-end medical software development. We prove that advances in AI, big data, and machine learning can provide new healthcare opportunities.

Our main use cases

Health data management and data harmonization

Health data management and data harmonization

Data integration brings together data from different sources like electronic health records (EHRs) and medical devices to create a complete view of patient information. We can analyze, clean and harmonize your healthcare data as well as align it with standard medical data models such as OMOP CDM.

Medical data ETL

Medical data ETL

Our team has extensive experience working on healthcare projects that utilize streaming ETL processes and we specialize in optimizing performance for these workflows. By leveraging ETL, healthcare organizations can gain insights into patient care, operational efficiency, financial performance, and other critical areas. Our expert team of data engineers can help you manage your data workflows seamlessly and efficiently.

Ontology development

Ontology development

Developing ontologies using common clinical data is a very important solution to recording healthcare patient history, implementing medical guidelines, and service accountability, and helping build stronger systems and higher interaction of information in healthcare. Our team was part of the OHDSI community from its early days, and have developed multiple ontologies for various use-cases.

Healthcare data science

Healthcare data science

Our team is highly experienced in conducting observational research within the OMOP CDM ecosystem, covering the mapping of source data, building concept sets and defined cohorts, characterizing populations using descriptive statistics, estimating and predicting population-level effects, and generating publishable evidence. Our full lifecycle support includes proposing design changes, implementing improvements, and concluding complete research at all stages using the developed enhancements. With our expertise in OMOP CDM, medical domain, and data science, we are equipped to guide and support you throughout the entire process of transforming raw data into completed research.

Medical data labeling

Medical data labeling

Mapping and labeling medical data is a serious challenge, since it requires both medical and engineering expertise. Our multi-disciplinary team includes both medical doctors and software engineers, thus we can quickly, accurately and efficiently prepare your data. We have years of experience in segmenting and labeling all anatomical structures using any type of medical image (e.g. MRI, CT etc.)

AI/ML models for medical tasks

AI/ML models for medical tasks

We have a team of CV and NLP researchers and engineers that have successfully built computer vision and machine learning algorithms for medical image processing and recognition. As well as the NLP models for claims and medical records processing. We can not only train and fine-tune your models, but also create an end-to-end CV-powered solution tailored for your task.

Observation studies support and supervision

Observation studies support and supervision

Our team has extensive expertise in working with healthcare data within the OMOP ecosystem, from initial conversion to generating evidence. Full lifecycle support from proposing design changes to concluding complete research at all stages using developed improvements.

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