Clinical Decision Support: Key Concepts and Frameworks
Definition and Scope
Clinical decision support (CDS) applies clinical, scientific, and administrative knowledge to data to produce knowledge-based assessments and interventions that improve decision-making and health outcomes.
Core representation: \text{Knowledge} + \text{Data} \rightarrow \text{Intervention}
CDS can be provider-facing, patient-facing, static or dynamic, and computer- or paper-based, but computer-based CDS is emphasized for speed and sustainability.
Rationale
Clinicians and patients often do not adhere perfectly to evidence-based care; knowledge is growing rapidly, especially with precision medicine.
CDS helps identify relevant knowledge, apply it to patient data, and generate interventions to improve care quality and outcomes.
Example of CDS (Mrs CD)
Illustrates multiple CDS modalities: static guidelines, patient-specific recommendations, order-customization, and data-driven dosing/dosing alerts.
Outcome: CDS can alert to issues (e.g., infection signs, glucose spikes) and support dosing decisions within the EHR.
CDS can be provider- and patient-facing, static and dynamic, and delivered via computer or paper, but computer-based CDS is the focus.
Key Concepts and Breadth
Core idea: knowledge + data -> intervention; components include:
Knowledge: sources are expert guidelines, literature, textbooks, regulations, etc.
Data: discrete observations (demographics, labs, exams, sensors, patient-reported data).
Output: actionable items (orders, alerts, displays, calculations).
Knowledge Representation (KR) and Knowledge Sharing enable CDS deployment; data representation and vocabularies enable data use across systems.
KR + data model + terminology standards are essential for reliable inference and interoperability.
Knowledge Acquisition (KA)
KA = process to identify and structure clinical, scientific, and administrative knowledge for computer use.
Involves SME (subject-matter expert) + knowledge engineer; outputs include marked-up knowledge, data element mappings, and data sources.
Tools: markup formats (e.g., GEM), IF-THEN logic, or procedural code; outputs can plug into EHRs or standalone CDS apps.
KA is cyclical due to evolving knowledge (knowledge maintenance).
Knowledge Representation (KR) and Knowledge Access
KR vs knowledge access:
KR: executable or interpretable knowledge formats that can be processed by the computer.
Knowledge access: linking users to knowledge on a centralized server (e.g., Infobutton).
KR standards enable knowledge sharing (transfer and reuse) but universal agreement is not achieved.
KR often expressed as IF-THEN rules (production/EAC rules). Example: IF condition THEN action.
Knowledge access (Infobutton) provides context-sensitive, non-interruptive access to knowledge via a UI button.
Inferencing Mechanisms
Inferencing applies knowledge to data to produce CDS outputs.
Procedural/production-rule forms: execute defined sequences or match-rule conflicts and execute true conclusions.
Forward chaining: data-driven, from facts to conclusions.
Backward chaining: goal-driven, from desired outcomes to required facts.
Infobutton-based inferencing focuses on narrative markup and context matching for display rather than full automation.
Knowledge-Based Interventions
Interventions are designed to be actionable with minimal disruption to decision-making.
Outputs can be:
Orders or order sets (often in CPOE).
Narrative recommendations with links to sources.
Data displays or intermediate calculations used in further inference.
Modes of action:
Synchronous CDS: real-time alerts and context displays.
Asynchronous CDS: non-real-time channels (fax/email).
Consultative vs. critique: CDS may propose actions or critique clinician decisions to prevent errors.
Standards and Frameworks
Standards enable knowledge representation, access, and data interoperability; four origins: formal standards bodies, government, vendors, consortia.
CDS-specific standards and infrastructure standards include:
KR standards: Arden Syntax, Clinical Quality Language (CQL), Guideline Elements Model (GEM), GLIF.
HL7 Infobutton, CDS Hooks, FHIR data model.
HQMF (historic), later evolved into CQL-based representations.
Infrastructure standards focus on vocabularies and data models:
Vocabularies: ICD, SNOMED CT, LOINC.
Data models: FHIR, OMOP CDM, CDISC SDTM.
Curly-braces problem: localization of data references in MLMs; addressed by better data models like FHIR.
CDS in Practice: Outputs and Implementation
CDS outputs vary from simple alerts to complex order sets and dynamic data displays.
Framing CDS within workflow and user needs is critical; misaligned CDS leads to alert fatigue and ineffectiveness.
Ten Commandments for CDS (Bates et al.):
Speed is everything.
Anticipate needs and deliver in real time.
Fit into the user’s workflow.
Small details (coded vs free text) matter.
Intervene before action is started when possible.
Easier to change direction than to stop.
Simple interventions work best.
Ask for information only when needed.
Monitor impact, gather feedback, respond.
Manage and maintain knowledge systems.
Five Rights framework for effective CDS:
Right information
Right person
Right format
Right channel
Right point in workflow
Building a CDS Program
CDS should be a means to an end, anchored in an overall program with organizational goals.
Governance: include clinicians, administrators, researchers, and patients; establish CDS committees to set objectives and oversee interventions.
Steps to implement: identify goals, select knowledge-based interventions, integrate with IT architecture, and establish a knowledge maintenance cycle.
Knowledge lifecycle: acquisition → computable form → implementation → measurement → feedback → maintenance.
Learning health system: periodic revisiting and updating of knowledge assets (2–3 year cycles are common).
Medico-legal and Regulatory Issues
CDS logs can be used in malpractice litigation; need to balance documentation with clinical reasoning.
Regulation: FDA asserts authority over certain high-risk software (e.g., implantable devices, radiotherapy software).
Lower-risk CDS with human oversight may fall outside stringent regulation; frameworks balance safety with innovation.
AMIA and related bodies propose governance frameworks for organization-level oversight and regulatory input based on risk.
Conclusion
CDS is the application of knowledge to data to yield interventions that improve decision-making and outcomes.
It encompasses a broad set of technologies: alerts, reminders, order sets, data displays, and more, integrated via standards and infrastructure.
A complete CDS program requires governance, knowledge maintenance, workflow integration, and ongoing measurement to realize benefits while managing risk.
Practical takeaway
For success, align CDS with clinical workflow, deliver actionable and timely guidance, and maintain an ongoing knowledge lifecycle within a learning health system.