Data Normalization

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“The complexity of modern medicine exceeds the inherent limitations of the unaided human mind” – David M. Eddy, MD, PhD

Clinical Data Normalization

Clinical data comes in all different forms even for the same piece of information. For example, age could be reported as 40 years for an adult, 18 months for a toddler or 3 days for an infant. Database normalization of clinical data fields in general fosters a design that allows for efficient storage avoiding duplication or repetition of data; data querying becomes easier. Without normalization, data can’t be used as a single a dataset.

Un-normalized Normalized (days) Normalized (months)
40 years 1436 47
18 months 543 18
3 days 3 0.1

The Need for Clinical Models

Detailed clinical models are the basis for retaining computable meaning when data is exchanged between heterogeneous computer systems. Detailed clinical models are also the basis for shared computable meaning when clinical data is referenced in decision support logic.

  • The need for the clinical models is dictated by what we want to accomplish as providers of health care
  • The best clinical care requires the use of computerized clinical decision support and automated data analysis
  • Clinical decision support and automated data analysis can only function against standard structured coded data
  • The detailed clinical models provide the standard structure and terminology needed for clinical decision support and automated data analysis

Data normalization & Clinical Models are at the heart of secondary use of clinical data. If the data is not comparable between sources, it can’t be aggregated into large datasets and used for example to reliably to answer research questions or survey populations from multiple health organizations.

Clinical Use Cases

  • Data sharing
  • Real time decision support
  • Sharing of decision logic
  • Direct assignment of billing codes
  • Bio-surveillance
  • Data analysis and reporting
    • Reportable diseases
    • HEDIS measurements
    • Quality improvements
    • Adverse drug events
  • Clinical research
    • Clinical trials
    • Continuous quality improvement

Real time, patient specific, decision support

  • Alerts
    • Potassium and digoxin
    • Coagulation clinic
  • Reminders
    • Mammography
    • Immunizations
  • Protocols
    • Ventilator weaning
    • ARDS protocol
    • Prophylactic use of antibiotics in surgery
  • Advising
    • Antibiotic assistant
  • Critiquing
    • Blood ordering
  • Interpretation
    • Blood gas interpretation
  • Management – purpose specific aggregation and presentation of data
    • DVT management
    • Diabetic report

What Needs to be Modeled?

All data in the patient’s EMR, including:

  • Allergies
  • Problem lists
  • Laboratory results
  • Medication and diagnostic orders
  • Medication administration
  • Physical exam and clinical measurements
  • Signs, symptoms, diagnoses
  • Clinical documents
  • Procedures
  • Family history, medical history and review of symptoms

How are Clinical Models used?

  • Data entry screens, flow sheets, reports, ad hoc queries
    • Basis for application access to clinical data
  • Computer-to-Computer Interfaces
    • Creation of maps from departmental/foreign system models to the standard database model
  • Core data storage services
    • Validation of data as it is stored in the database
  • Decision logic
    • Basis for referencing data in decision support logic
  • Does NOT dictate physical storage strategy

Introduction to Clinical Element Models (CEMs)

In order to represent detailed clinical data models, we have designed The Clinical Element Model (CEM). When we state “The Clinical Element Model” we are referring to the global modeling effort as a whole, or in other words, our approach to representing detailed clinical data models and the instances of data which conform to these models.


Clinical Element Model Introduction & Reference Manual
J.Coyle, Y.Heras, T.Oniki, S.Huff
November 14, 2008