| Wang et al17
|
Front-end tools |
Benefits: process improvement, policy implementation, error prevention, decision support Domains: laboratory (process improvement), pharmacy (error prevention/decision support), Joint Commission (policy implementation) Classes: logically organize clinical rules by content type (eg, drug–drug interaction checking, automated orders, guided dosing) |
| Miller et al18
|
Back-end system capabilities |
Type of intervention (eg, optimal ordering, patient-specific decision support, optimal care, just-in-time (JIT) education) When in the workflow to introduce the intervention (eg, initiating a session, selecting an order) How disruptive the intervention should be (eg, incidental display, pop-up, complex protocol) |
| Garg et al1
|
Front-end tools (general) |
Systems for diagnosis Reminder systems for prevention Systems for disease management Systems for drug dosing and drug prescribing |
| Kawamoto et al2
|
Back-end system capabilities (general) |
General system features (eg, integration with charting, computerized physician order entry) Clinician–system interaction features (eg, automatic provision of CDS), provision at point-of-care, documentation of override reasons) Communication content features (eg, provision of a recommendation vs assessment, justification with reasoning and/or research evidence) Auxiliary features (eg, local user involvement in development, CDS provided to patients, periodic performance feedback) |
| Osheroff et al19
|
Back-end system capabilities |
Documentation forms/templates Relevant data display Order creation facilitators Time-based checking and protocol/pathway support Reference information and guidance Reactive alerts and reminders |
| Berlin et al20
|
Back-end system capabilities |
Context: setting, objectives, and other contextual factors (eg, clinical setting, clinical task) Knowledge and data source: sources of clinical knowledge (eg, guidelines) and patient data source (eg, electronic health record, direct entry) Decision support: type of inference being made and complexity of recommendations Information delivery: delivery format and mode Workflow: user of the system (eg, clinicians, patients), system–workflow integration |
| Wright et al16
21
|
Back-end system capabilities |
Triggers: events causing a CDS rule to be invoked (eg, prescribing a drug, ordering a laboratory test, entering a new problem on the problem list) Input data: data elements used by the rule to make interferences (eg, laboratory results, patient demographics, problem list) Interventions: possible actions a CDS tool can take (eg, send message, show guidance, log event) Offered choices: choices offered to the user (eg, cancel order, change order, override) |
