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Data Quality Solutions for Oncology and Precision Medicine

ACCELERATING OUR ABILITY TO OUTSMART CANCER

Having high quality data to support precision medicine and oncology care is critical to advancing cancer research and treatment across the globe.

The United States expenditure on cancer care exceeded $112.5 billion in 20181, and the need for oncology care is projected to increase by over 50% by the year 20402. To provide the best available care, clinicians and researchers must:

Capture

high-resolution patient data for research and clinical analysis

Utilize

up-to-date terminologies as the basis for data quality initiatives

Aggregate

patient data for improved insights

Adhere

to rapidly evolving guidelines

Extract

meaningful information from unstructured patient data

Capturing High-Resolution Patient Data

Using Symedical’s Advanced Terminology Normalization Engine, generate the high-quality clinico-genomic data sets that are required to support the broader goals of using real-world evidence for precision medicine and oncology.

Data is King: Set against the promise of the [precision medicine] field is the acute awareness among industry experts, researchers and clinicians that, above all, precision medicine relies on data that is high in quantity, quality and diversity.

– Life Sciences Intellectual Property Review

Symedical enables normalization and ontological reasoning for complex clinical data.

Utilize up-to-date terminologies to support data quality initiatives

Acquire, maintain, and manage up-to-date standard and local terminologies, ontologies, and value sets in one enterprise platform, including: premier standard oncology terminologies, including NCI Thesaurus, mCODE, SNOMED, ICD, and ClinVar.

Aggregating patient data for improved insights

Data from cohorts of similar patients must be shared, aggregated, and the nuances of both clinical and genomic findings need to be captured. Element Set Manager enables efficient authoring and maintenance of value sets used to classify, categorize, aggregate and understand patient conditions, labs, medications, and other critical data used to treat cancer.

Utilize up-to-date terminologies to support data quality initiatives

Acquire, maintain, and manage up-to-date standard and local terminologies, ontologies, and value sets in one enterprise platform, including: premier standard oncology terminologies, including NCI Thesaurus, mCODE, SNOMED, ICD, and ClinVar.

Aggregating patient data for improved insights

Data from cohorts of similar patients must be shared, aggregated, and the nuances of both clinical and genomic findings need to be captured. Element Set Manager enables efficient authoring and maintenance of value sets used to classify, categorize, aggregate and understand patient conditions, labs, medications, and other critical data used to treat cancer.

Symedical enables normalization and ontological reasoning for complex clinical data.

Extracting meaningful information from unstructured data

SIFT Tumor Suite for Symedical helps organizations unleash structured oncology insights using targeted clinical natural language processing (NLP) and precision-engineered, pattern-based matching across terminologies.

Symedical enables extraction of relevant coded clinical concepts from free text.

Ensuring guideline ADHERENCE for best practices in care

The Advanced Clinical Awareness Suite can integrate into guideline adherence solutions, tumor board preparation applications, as well as solutions that target cohorts of patients. It can also be used to achieve customized, rules-based clinical reasoning and to enrich data so that artificial intelligence and machine learning can perform at their highest potential.

Interested in learning more?

Download the Solution Overview here:

Sources

1 “Total Expenditures In Millions By Condition, United States, 1996-2018,” MEPS Summary Tables: “Total expenditures ($)”, Agency for Healthcare Research and Quality, accessed October 13, 2021, https://datatools.ahrq.gov/meps-hc?type=tab&tab=mepshcmc

2 Brooke E. Wilson, Susannah Jacob, Mei Ling Yap, Jaques Ferlay, Freddie Bray, Michael B. Barton, “Estimates Of Global Chemotherapy Demands And Corresponding Physician Workforce Requirements For 2018 And 2040: A Population-Based Study,” Lancet 20, no. 6 (June 2019): 769-780, published online May 8, 2019, https://doi.org/10.1016/S1470-2045(19)30163-9.