CDS

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Clinical decision support (CDS) refers broadly to providing clinicians or patients with clinical knowledge, intelligently filtered and presented at appropriate times. [1] Clinical knowledge of interest could range from simple facts and relationships (such as an patient's vital signs, allergies and lab data) to relevant medical knowledge (such as best practices for managing patients with specific disease states, new clinical research, professional organizations' practice guidelines, expert opinion, systematic reviews, and other types of information.

History

Clinical decision support tools existed prior to development of electronic medical records (EMRs). They include expert consultation, practice guidelines carried in clinicians' pockets, patient cards used by nurses to track a patient's treatments, tables of important medical knowledge, and ICU patient flow sheets. Many of these CDS tools are still relevant, but integration of CDS with current EMRs presents an opportunity for the various types of decision support to be immediately available at the time of the decision-making. CDS can be more relevant, more accurate, and can facilitate and be integrated with clinical workflow.

For more on the history of CDS, see here and here.

CDS components

There are several key components of a good clinical decision support system.

  • Documentation tools
  • Clinician Checklists
  • Calculators
  • Reference Links

Documentation forms/templates

As mentioned above, these existed prior to EMRs in the form of structured documentation forms for conducting clinician assessments. Many of these have been supplanted by digital reproductions in EMR of the original paper documentation form.

Examples include:

  • nursing intake forms
  • physician "History & Physicals"
  • ER templates

Other tools that were artifacts of clinician workflow and existed prior to EMR implementation, now have the potential for added functionality when computerized, web-based, or automated. Added functionality includes dispersed access to the tool's information (ability for multiple users from multiple disciplines and geographic locations to share a single set of information), auto-population of accurate and current data from the clinical information system, linkages between tool task lists and CPOE, and improved order fulfillment efficiency.

Examples of these tools include:

  • Handoff tools (lists of patients with summations of clinical data used at time of a shift handoff between clinicians)
  • Rounding tools (summaries of data on a single patient, clinical task lists
  • ICU flowsheets for documenting and charting vital signs and hemodynamic data.

Alerts and reminders

Alerts are an important part of CDS.

Examples include:

  • Alert that appropriate cancer screening is due.
  • Drug allergy alert
  • Drug interaction alert
  • Underdose/overdose alerts based on renal or liver function, age, drug level
  • Result alerts to follow up with patient if a HBA1c was elevated patient needed to be retested in 3 months. [2]

Relevant data presentation

Examples of this include:

a) Patient specific data such as:

  • Display of relevant labs during medication CPOE such as patient's renal and liver function.
  • Display of other relevant patient data during CPOE such as patient's age (which may affect side affects and dosing) or conditions.

b) Population-specific data such as:

  • Retrospective filtering and aggregate reporting: disease registries and clinic population dashboards.
  • Microbiograms: tables of local bacterial flora and their sensitivity and susceptibility to various antibiotics

Order creation facilitators

Examples include: order sets, order menus, tools for complex ordering, and "single-order completers including consequent order."

Order Sets

An order set is a group of related orders which a physician or other licensed clinician can initiate with a few keystrokes or mouse clicks. An order set allows a user to quickly select one or more orders that apply to a specific diagnosis, clinical condition (such as shortness of breath or abdominal pain), treatment event (such as heart surgery), diagnostic test etc. Using order sets is intended to reduce both time spent in entering orders and errors of omission. They serve as a reminder of the tasks which may need to be accomplished in a particular patient in the same sense as a checklist and there is a great deal of overlap between checklists and order sets, both conceptually and in practice. An order set may contain medication orders, orders for diagnostic tests, orders for a clinician to carry out an action, and other types of orders, in any combination and essentially any number. It should be noted that increasing the number of orders in an order set is often counter-productive as this actually slows a clinician and increases cognitive load.

An example order set for a Cardiac MRI would include:

  • Order specifying the particular body part or organ to be imaged (in this case, the heart)
  • Order for renal function test (blood test) if there is no result for this test in the EMR in the last 6 weeks
  • Order to administer a sedative prior to the MRI
  • Order to administer contrast through an intravenous line (IV) during the exam
  • Order for transportation from hospital room to the MRI suite in the radiology department at time of MRI

Order Menus

An order menu is a group of related orders which are depicted onscreen together via an EMR's GUI so that an ordering clinician visualizes the breath and organization of the orders. An order menu allows CPOE/EMR developers to direct clinicians towards the most common or appropriate orders for a particular topic. Using order sets reduces time spent searching for the desired orders and provides a rudimentary level of knowledge and education. Order sets are commonly made up of medication orders, but non-medication orders may be included.

Examples of order menu content include:

  • anti-hypertensive medications arranged by class, by preference, by cost, or other means.
  • common pulmonary medications to treat COPD, asthma, embolisms, and chronic cough.


Interaction models

An interaction model is a set of rules for making clinical decisions. The rules are based on a large collection of medical knowledge and an accurate computer representation scheme.

Artificial intelligence

Artificial intelligence is a system that was developed by a team of system engineers and clinicians. The system would take some of the workload from medical teams by assisting the physicians with tasks like diagnosis & Therapy recommendations.

Business Intelligence and Data Warehousing

Validation and Verification of Clinical Decision Support

Sample Decision Support Content

Reviews

CDS Implementation

CDS should be designed to provide the right information to the right person in the right format through the right channel at the right time.

At the stage of planning for implementation of any new health IT system or their components, there are some considerations and steps that should be followed to maximize CDS system success:

  1. Needs Assessment: ensuring that identified clinical needs and functional requirements
  2. Assessing Organizational Readiness
        i)   Understanding prior physician and organizational experience with CDS
       ii)   Assessment of level of physician knowledge, perception, engagement, and willingness to change
      iii)   Aligned leadership with clear objectives
  1. CDS related factors
        i)    Deciding whether to purchasing a commercial system or build the system
       ii)    CDS usability: Will CDS increase physician workload? Can the level of intrusiveness of alerts be customized?
       iii)   Adequate planning for encouraging physicians to use CDS
       iv)    Appropriate training on using CDS
        v)    Mechanisms in  place to evaluate usage and effectiveness of the CDS

Alerts

Liability

Workflow

Usability

  • Evidence based content / Clinical content accuracy
  • Changing behavior (limited interaction by users, adherence to protocol)
  • Training and communication
  • System design limitations
  • Choosing the right metrics for reporting (Process / Clinical)
  • Potential breaks due to system upgrades

Clinical Decision Support Overview

CDS success measures

To estimate the success of the system we should look at the following points[3]:

  1. System quality.
  2. Information quality
  3. Usage
  4. User satisfaction (Process Outcome)
  5. Individual impact (Clinical Outcome)
  6. Organizational impact (Financial outcome).


Information Resources

CDS benefits

Results indicate the potential of CDS to improve the quality of care. These are good reasons for institutions to adopt CDS, but they should do so at their own pace and volition.

Promote use of evidence based recommendations

A stand-alone, disease-specific CDSS can improve concordance with established prescribing guidelines for a period measured in months.

Better clinical decision-making

Reduced medication errors

Improved cost-effectiveness

More research is needed to identify the cost-effectiveness of CDS as current research has found conflicting results of increased, decreased, or no change in cost of care. [3] [4]

Increased patient convenience

A Bayesian decision support tool for efficient dose individualization of warfarin in adults and children

Improved quality of healthcare delivery

"Smart Forms" in an Electronic Medical Record: documentation-based clinical decision support to improve disease management.

Improved healthcare outcomes for patients and patient populations

Current research has shown various systems associated with improved health outcomes but is still limited and requires more research. However, it has helped improved outcomes for chronic disease management particularly for individuals living with diabetes. [5] Family Health History is a leading predictor of disease risk. Clinical Decision Support can also be used to help healthcare providers fill in the family history gap [3]

Reviews

Related articles

Clinical Decision Support to Implement CYP2D6 Drug-Gene Interaction
Overrides of clinical decision support alerts in primary care clinics
Effect of computerized clinical decision support on the use and yield of CT pulmonary angiography in the emergency department
Clinical decision support in small community practice settings: a case study
Barriers and facilitators to the uptake of computerized clinical decision support systems in specialty hospitals: protocol for a qualitative cross-sectional study
Identifying Best Practices for Clinical Decision Support and Knowledge Management in the Field
Development and Implementation of Computerized Clinical Guidelines: Barriers and Solutions
Implementation Pearls from a New Guidebook on Improving Medication Use and Outcomes with Clinical Decision Support
Clinical decision support systems: A discussion of quality, safety and legal liability issues
Clinical decision support in electronic prescribing: recommendations and an action plan: report of the joint clinical decision support workgroup

References

  1. Slater, B. Osheroff, JA. Clinical Decision Support, in Electronic Health Records: A Guide for Clinicians and Administrators. American College of Physicians. 2008. http://books.google.com/books?hl=en&lr=&id=KtlUMwaZP98C
  2. The Impact of a Decision Support Tool Linked to an Electronic Medical Record on Glycemic Control in People with type 2 Diabetes.http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC3869133/
  3. Formative evaluation of clinician experience with integrated family history-based clinical decision support into clinical practice.http://clinfowiki.org/wiki/index.
  1. slater 2008
  2. Franklin, MJ, et al, Modifiable Templates Facilitate Customization of Physician Order Entry, [6]
  3. Sittig, DF, and Stead, WW, Computer-based Order Entry: The State of the Art, J Am Med Informatics Assoc., 1994;1:108-123. [7]
  4. Anderson, JG, et al, Physician Utilization of a hospital information system: a computer simulation model. Pric Annu Symp Compu Appl Med Care, IEEE, 1988;12:858-861. [8]
  5. Southern Ohio Medical Center, [9]
  6. Clinical Decision Support Systems :State of the Art AHRQ Publication No.09* 0069* EF June 2009
  7. Grand challenges in Clinical Decision Support Journal of Biomedical Informatics 41(2008) 387* 392
  8. Determinants of Success of Inpatient Clinical Information Systems: A Literature Review. M J van der Meijden, H J Tange, J Troost, et al. JAMIA 2003 10: 235* 243
  9. Improving Outcomes with Clinical Decision Support: An Implementer's Guide [Paperback]: Jerry Osheroff, Jonathan Teich, Donald Levick, Luis Saldana, Ferdinand Velasco, Dean Sittig, Kendall Rogers and Robert Jenders