Difference between revisions of "CDS"

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== History of decision support ==
 
== History of decision support ==
  
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''Main article: [[History of clinical decision support]]''
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* [[History of clinical decision support]]
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* [[The Evolution of Clinical Decision Support]]
  
 
== References ==
 
== References ==

Revision as of 13:37, 24 November 2012

Clinical decision support (CDS) refers broadly to providing clinicians or patients with clinical knowledge, intelligently filtered and presented at appropriate times. Clinical knowledge of interest could range from simple facts and relationships (such as an individual 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.

Importantly, the clinical knowledge should be the best available evidence. The knowledge should not be intrusive, distractive, extraneous or irrelevant. This requires sophisticated algorithms to determine which is the appropriate resource to be provided, when to present information, and how to do it appropriately.

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.

  • Better clinical decision-making leads to better practices.
  • Reduced medication errors
  • Promote preventive screening and use of evidence based recommendations
  • Improved cost-effectiveness
  • Increased patient convenience
  • Improved quality of healthcare delivery
  • Improved healthcare outcomes for patients and patient populations.

Role of electronic medical records

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.

CDS components

There are several key components of a good clinical decision support system. [1]

Types of Clinical Decision Support

This list was compiled by Osheroff and Slater. It was not intended to be completely inclusive. It is also not intended to be exclusive, in that some CDS technologies or implementations may have components that fit in to multiple type categories.

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.


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."

a. Order Sets

An order set is a group of related orders which a physician can place with a few keystrokes or mouse clicks. An order set allows users to issue prepackaged groups of orders that apply to a specified diagnosis or a particular period of time. Using order sets reduces both time spent entering orders and terminal usage. An order set may or may not contain medication orders as part of the set.

An example order set for Cardiac MRI order would include:

  • MRI order
  • Prescription to dispense IV contrast
  • Prescription for sedative during MRI
  • Order for renal function lab if none in EMR in last week
  • Order for transportation from hospital ward to radiology at time of MRI

b. 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.

c. "Single-order completers including consequent order"

These may be broken down in to Medication Safety Rules and Non-medication Safety Rules.

Medication safety rules and decision support

Non-medication safety rules

  • Diagnosis-Order RulesDrug and level. Postop order sets, disease specific order sets. Suggested dose. Suggested alternate medication for shortage or formulary. Guided dose algorithims for complex orders sucha s those required with insulin and heparin infusions in which nurses are given parameters with which to adjust dose on a regular basis.


4. Time-based checking and protocol/pathway support

5. Reference information and guidance

6. Reactive alerts and reminders

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

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

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 any new health IT system, there are some considerations and steps that should be followed to guarantee the system success; such as identifying the needs and functional requirements, deciding whether to purchase a commercial system or build the system, planning for encouraging physicians to use CDS, designing a system to evaluate how well the system has addressed the identified needs[1].

Clinical Decision Support overview

Success criteria estimates

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
  5. Individual impact
  6. Organizational impact.

Information Resources

History of decision support

References

  1. Slater, B. Osheroff, JA. Clinical Decision Support, in Electronic Health Records: A Guide for Clinicians and Administrators. American College of Physicians. 2008.
  2. Franklin, MJ, et al, Modifiable Templates Facilitate Customization of Physician Order Entry, [3]
  3. Sittig, DF, and Stead, WW, Computer-based Order Entry: The State of the Art, J Am Med Informatics Assoc., 1994;1:108-123. [4]
  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. [5]
  5. Southern Ohio Medical Center, [6]
  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

Updated by (Edward A W Dyer)

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