Section 1h: Cross-paradigm/Domain Analysis Models
HL7 CIMI Logical Model for Analysis: Analysis Normal Form (ANF), Release 1
Analysis Normal Form (ANF) is a logical model intended to represent a normalized view of aggregate clinical statements for analysis, research, clinical decision support, and other purposes. ANF can be used to represent any clinical statement irrespective of how the information was captured at its source The ANF Reference Model and methodology can be used in conjunction with other models intended to ensure that clinical information is structured and complete at the time of entry (e.g. CIMI models, ISO/TS 13972 Detailed Clinical Models) or exchanged among systems (e.g. HL7 CDA templates, HL7 V2 message profiles, FHIR profiles).
ANF statements are intended to safely and reliably support data analysis of aggregate clinical data. ANF may be used with data created using any standard or non-standard input form or exchange mechanism. ANF belongs to the CIMI family of logical models.
HL7 CIMI Logical Model for Analysis: Analysis Normal Form (ANF), Release 1 may also go by the following names or acronyms:
- Provides a reference model and rules and to create a predictable normal form for aggregate data sets across multiple systems
- Introduces the ability to compare normalized statements with ease and maintains the semantic integrity of the data.
- Simplifies the statement structure by using a small set of primitive types (e.g. float, varchar, boolean) and a sophisticated terminology.
- Provides a consistent and simple representation of data that can be used for data warehousing and data mining solutions.
- Enables analysis of a specific set of facts and dimensions, such as evaluation of outcomes associated with the use of a specific therapy, device, or medication.
ANF is a logical model intended to represent any type of clinical data using a complete yet simple normal form. It enables other software modules to reuse the information and derive new knowledge from it. For example, ANF implementation can help improve comprehensive data analysis to: (i) analyze the care that was delivered, (ii) find out what type of care leads to the best patient outcomes, and (iii) use rules and business triggers to automate clinical decision and workflow steps.
ANF could be used to design standards-based Application Programming Interfaces (APIs) optimized for a specific analysis purpose. ANF APIs may be implemented using FHIR resources, profiles, and extensions to access clinical decision support, clinical quality measures, and to support workflow automation by triggering reminders and clinical notifications. ANF also supports the design of database schemas to support aggregation of clinical statements. Furthermore, ANF can act as a foundation to big data analytics, data mining, business intelligence, healthcare quality programs, registries, etc. to provide consistent data structure and operable semantics that can be analyzed and aggregated for the benefit of individual patients, to evaluate an organization, or to establish new facts.
The users of ANF are developing implementation guides, solutions, and applications that require normalized clinical statements to establish that a clinically-relevant fact or situation was observed to exist or happened, or that a particular procedure was requested. They wish to ensure that this determination is reliable and performed in accordance with the principles of patient safety and high-reliability organizations. ANF may be used for clinical decision support, reimbursement, public health reporting, outcomes research, and other types of data analysis.
- Clinical Statement
RESPONSIBLE WORK GROUP
- Clinical Information Model
- Clinical and Public Health Laboratories
- Clinical Decision Support Systems Vendors
- EHR, PHR Vendors
- Emergency Services Providers
- Equipment Vendors
- Health Care IT Vendors
- Healthcare Institutions
- Immunization Registries
- Lab Vendors
- Local and State Departments of Health
- Quality Reporting Agencies
- Regulatory Agency
- Standards Development Organizations (SDOs)