Risk-Adjusted Data from Sources Across the Continuum of Care Delivered Directly to Your EHR

SYNTHESIS

Risk-adjusted data from sources across the continuum of care delivered directly to your EHR

THE MDPortals synthesis engine unlocks the potential that comprehensive data capture provides

The data MDPortals extracts from a wide range of disparate sources comes in a variety of incompatible and unstructured formats. MDPortals synthesis engine prepares the ingested data for use with analysis tools and point-of-care guidance by transforming the raw inputs into a clean and reliable picture of a patients’ health.

The MDPortals synthesis engine utilizes OCR, advanced parsing techniques, Clinical Natural Language Processing (cNLP), proprietary logic and NPI matching to clean, de-duplicate and enrich all structured and unstructured inputs from data acquisition.

syntheSIZED DATA

STRUCTURED

Faxed records, free-text physician notes, scanned charts and other unstructured inputs are converted into machine-readable text and mapped to the appropriate section in the patient’s structured record.

Synthesized data

NORMALIZED

100+ normalization steps are applied to raw inputs to grade completeness & syntax;

Our code-mapping techniques leverage advanced cNLP terminology tools to recognize millions of permutations for clinical concepts;

The data is processed against over 30,000+ terminology mappings and clinical terms are into industry-standard classification, reference and billing terminologies for all major clinical domains (Allergies, Encounters, Medications, Immunizations, Payers, Problems, Procedures, Results & Vital Signs);

Support for all national standard vocabularies: CVX, CPT, HL7, ICD-9, ICD-10, LOINC, RxNorm, SNOMED, UCUM, NDC and UNII;

Mapping of SNOMED to ICD10 codes;

Inconsistent diagnosis, procedure, medication, and lab data from diverse systems are standardized into common, clinically validated terminology;

Context-sensitive information model produces more robust, consumable data than achieved with common parsing techniques.

Synthesized normalized

MATCHED, DE-DEDUPLICATED AND FILTERED

Duplicate records, redundancies and inconsistencies are eliminated;

Precise matching techniques ensures data extracted from multiple systems is consistent and complete.

MATCHED, DE-DEDUPLICATED AND FILTERED

PARSED AND CLASSIFIED

Multi-factor EHR identification with brand-specific extraction, parsing and classification;

Drug classification (NDF-RT) and ingredient decomposition for multi-ingredient drugs;

Problem classification (CCS);

Ontology and category assignment.

PARSED AND CLASSIFIED

ENRICHED

Infers missing medical concepts including:

Core ingredient decomposition for combination medications;

Medication dose form, dose strength, frequency, route and preconditions;

Laboratory interpretation and reference ranges;

Dates of services & type (ambulatory, emergency, inpatient);

Problem duration and resolution;

Error-fixing: automated correction of common vocabulary and syntax mistakes.

Addition of meta-data for streamlined analytics such as:

Laboratory result classification;

Allergy type classification;

Problem and diagnosis groupings;

Procedure groupings.

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Synthesis Enriched

UNIFIED

All inputs from data acquisition are unified into the Compendium C-CDA, an enhanced composite longitudinal record in C-CDA format.

Synthesis unified