Understanding the Data

• Health Histories. This is data that is directly associated with the diagnoses and procedures history of an individual, or group of individuals. This data will generally include:
  • Claims Data
  • EMR / EHR Data
  • Pharmacy Data
  • Lab Data
  • Imaging Data
  • Other Clinical Data
  This data can be challenging to acquire and to work with; HIPAA and other regulations make it difficult to obtain, and each company will have its own set of 'use cases' that define what data can be acquired and what it can be used for. Often, to build a complete health history for a group of individuals, data from multiple vendors will be required, and associating the different vendors presents additional challenges.
• Industry Data. In addition to health histories and other data directly associated with patients in the healthcare system, there are many other data resources that are directly applicable to building analytics and business intelligence across medical data sets. Some of these include:

  CMS Databases Medicare Provider Utilization and Payment Data CMS Medicare and Medicaid Statistical Supplement Facility. Comparison Data CDC Data National Health and Nutrition Examination Survey (NHANES) Behavioral Risk Factor Surveillance System (BRFSS) National Notifiable Diseases Surveillance System (NNDSS) ClinicalTrials.gov , part of the National Library of Medicine. HealthData.gov

  NIH Data NIH Clinical Center Data National Database for Autism Research (NDAR) World Health Organization Global Health Observatory data repository WHO Global Health Expenditure Database HHS Health Indicators Warehouse National Database of Child Care Licensing Regulations FAIR Health healthcare claims database OptumLabs Data Warehouse American Hospital Directory National Plan & Provider Enumeration System (NPPES)

  While rich and relatively accessible, nearly all of these data sets exist 'on their own' and can be challenging to aggregate and integrate into a business model. Curation methods, de-duplication strategies, general accuracy, allowed uses, licensing, and other factors all add complexity to an already complicated landscape. Hagimo can help you navigate these challenges.
• Support Data. All of the disparate processes and systems that comprise the U.S. (and global) health system are necessarily complex, with many different entities curating different portions of the whole. In order to render actionable business intelligence based on these resources, it's necessary to refer to numerous external data resources to bring context to any analytics. Some of this data includes:

  International Classification of Diseases (ICD): Used worldwide for morbidity and mortality statistics, insurance billing, and health management. ICD codes represent diagnoses and health conditions. Current Procedural Terminology (CPT): A set of codes, descriptors, and guidelines developed by the American Medical Association. CPT codes describe medical, surgical, and diagnostic services and are used for billing and documentation. Healthcare Common Procedure Coding System (HCPCS): Includes Level I (CPT codes) and Level II codes, which cover healthcare services and products not included in CPT, such as ambulance services, durable medical equipment, and certain drugs and medicines. National Drug Codes (NDC): A unique, three-segment number for each medication listed under the Drug Listing Act of 1972. It identifies the labeler, product, and trade package size. Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT): A comprehensive, multilingual clinical healthcare terminology. It provides a standard way to represent clinical phrases captured by healthcare professionals. Logical Observation Identifiers Names and Codes (LOINC): Used to identify health measurements, observations, and documents. LOINC codes standardize the identification of medical test observations. RxNorm: Provides normalized names for clinical drugs and links its names to many of the drug vocabularies commonly used in pharmacy management and drug interaction software. Unified Medical Language System (UMLS): A set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. Medical Subject Headings (MeSH): A comprehensive controlled vocabulary for the purpose of indexing journal articles and books in the life sciences. MeSH provides a consistent way to retrieve information that may use different terminology for the same concepts. Diagnostic and Statistical Manual of Mental Disorders (DSM): Used for psychiatric diagnosis, it classifies mental disorders with associated criteria designed to facilitate more reliable diagnoses of these disorders. Global Medical Device Nomenclature (GMDN): A standardized naming system for medical devices used for patient diagnosis, prevention, monitoring, treatment, or alleviation of disease or injury. Orphanet Rare Disease Ontology (ORDO): A structured vocabulary for rare diseases, capturing information including synonyms, definitions, and relationships to other diseases, genes, and proteins.

  These data sets are critical for building deep analytics and accurate business intelligence. They generally have licensing and use restrictions, in addition to variable pricing and availability. In many cases, different versions of these data sets must be properly matched with the versions and time periods associated with medical history data in order to render accurate analytics. Hagimo can help you select the right data sets to work with your health history data and other information assets.
  
  

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  Hagimo has been working exclusively with healthcare data for over 20 years. It's a complex landscape, but the power and insights to be gained from it are immense. Let us work with you to put this wealth of information to work for your company.