Quality Measurement – Clinical Decision Support Harmonization

Proposal

Goal

• Leverage existing relevant work to establish modularized and harmonized standards that meet Quality-related business needs– Includes quality measurement and CDS

• Initial target timeframe: January 2014 ballot cycle– Work with TSC to extend ballot deadlines– Continue work past ballot submission

Proposed Approach

Foundational Components

Clinical data model Expression/logicDocument structureVocabularies Templates and meta-data

Quality Measurement-Specific Components

TBD, as needed

CDS-Specific Components

E.g., CDS usage context, such as CDS target = Spanish-speaking patient

Quality Measurement Standards

HQMF R2.X

CDS Standards

CDS Knowledge Artifact IG, DSS IG

Proposed Approach – DetailsObjective Jan. 2014 Ballot Strategy

(materials due in ~2 mo.)Long-Term Approach

Harmonize clinical data model, templates, and terminology for quality measurement and CDS

Modify vMR and templates as required to ensure encapsulation of QDM and QRDA semantics

1. Develop DMIM, templates, and “green” versions

2. Use FHIR and request enhancements

Separate out and harmonize expression logic

Separate out expression logic from HQMF

Develop common expression logic approach if possible

Develop common expression logic approach

Harmonize document specifications and implementation guides

Update existing specifications (HQMF, CDS KA IG, DSS IG ) to use common components

Update existing specifications to use common components

PSSs and Specifications (Jan. 2014)• vMR Logical Model update• vMR XML IG update• vMR Templates IG update• Expression Logic DAM and IG for Quality

Measurement & CDS• HQMF R2.X update• CDS Knowledge Artifact IG update• Decision Support Service IG update• Others updates as needed if QDM updated –

QRDA Cat. I/III, C-CDA

EXPRESSION LOGIC

The Foundation

• Clinical Quality is a data-centric problem– Quality measurement seeks to measure clinical indicators– Clinical decision support seeks to identify established patterns and

offer relevant guidance– Both these activities at their core involve set-based data processing– As such, they are most naturally and easily expressed in a data-

language• set-focused – deals with computation of sets of information (e.g.

encounters, medications, lab results, etc.)• expressive – express as naturally as possible the computation of

those sets (e.g. composable, reusable, flexible)• complete – provide mechanisms so that the computation can be

arbitrarily complex (e.g. computational, interval, set operations)

Operations

• Set Operations– Retrieve, Filter, Union, Intersect, Extend, Project, Join

• Computational– Comparison, Logical, String Manipulation, Arithmetic, etc.

• Interval– During, Between, Overlaps, Meets, etc.

• Aggregate– Count, Min, Max, First, Last, N

HQMF Operations

• Data Criteria– Retrieve– Filter w/ specific operations and attributes

• Temporally Related Information– Semi-join w/ temporal conditions

• Excerpt– Aggregate Computation

• Outbound Relationship– Semi-join w/out temporal conditions

• Grouper– Union and Intersection

• Population Criteria– Logical Operations (AND/OR/NAND/NOR/XOR)

• Measure Observations– Computation

An Approach to Harmonization

• Define the logical set of operations– Described above

• Define each of the HQMF constructs in terms of those operations– such that an existing HQMF document becomes a syntactic

short-hand for an equivalent underlying expression in terms of the foundational elements

• Note that the foundational elements are independent of any particular data model, but the syntactic short-hands need not be...

Data Criteria - Retrieval

Data Criteria – Value Filter

Data Criteria – Value Filter (cont)

Temporally Related Information

Excerpt

Outbound Relationship

• Same as temporally related information, but uses relationship definition as conditions instead of temporal operators

Grouper

Population Criteria

Measure Observation