CDS

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Clinical decision support (CDS) refers broadly to providing clinicians or patients with clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. Clinical knowledge of interest could range from simple facts and relationships to best practices for managing patients with specific disease states, new medical knowledge from clinical research and other types of information.

Overview

For an overview of the process that healthcare organizations can use to begin, or improve, a clinical decision support (CDS) initiative interested parties can follow the guidelines described in Improving Outcomes with Clinical Decision Support: An Implementer's Guide [1] to measurably improve key healthcare outcomes such as the quality, safety, and cost-effectiveness of care delivery.

Interaction models for clinical decision support

Order set

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. One of the main impetuses for order sets comes from the need to improve user acceptance of computer-based physician order entry, by decreasing the time physicians require to enter orders. Using order sets reduces both time spent entering orders and terminal usage. [2][3] [4]

Personal order sets

Individuals may create their own personal order sets by a variety of methods, and some are even available on the internet. In addition, some institutions have developed modifiable templates that allow physicians to customize their own order sets. [5] Mostly, however, order sets are developed by a group of physicians or a department with a particular clinical focus. They then come up with a set of diagnostic and treatment options that encompass current best practices. This latter approach results in a more limited number of order sets, and is easier to manage. [6] [7]

Information Resources

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

Medication-related Clinical Decision Support

Basic Medication-Related Decision Support

Advanced Medication-Related Decision Support

Advanced Checking of Drug-Disease Interactions and Contraindications

Adverse Drug Events

Non-Medication-Based Safety Rules

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].

Benefits and roles of CDS

  • They can alter clinical decision making and actions towards better practices.*
  • Reduce the medication errors.
  • Promote preventive screening and use of evidence based recommendations.
  • Cost reduction and increased patient convenience.

The overall 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. We should all remember that simple human processes and innovations provide large opportunities for improvement, especially when thoughtfully harmonized with robust technological solutions; so always "Do CDS with users not to them".

Challenges and considerations

  • Improve the human* computer interface.
  • Disseminate best practices in CDS design, development, and implementation.
  • Summarize patient* level information.
  • Prioritize and filter recommendations to the user.
  • Create an architecture for sharing executable CDS modules and services.
  • Combine recommendations for patients with co* morbidities.
  • Create internet* accessible clinical decision support repositories.
  • Use free text information to drive clinical decision support.
  • Mine large clinical database to create new CDS.

Those are important points that are critical for achievement of the potential of CDS and improve the quality, safety, and efficiency of healthcare[2].

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.

References

  1. Clinical Decision Support Systems :State of the Art AHRQ Publication No.09* 0069* EF June 2009
  2. Grand challenges in Clinical Decision Support Journal of Biomedical Informatics 41(2008) 387* 392
  3. 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

References

  1. Franklin, MJ, et al, Modifiable Templates Facilitate Customization of Physician Order Entry, [9]
  2. Sittig, DF, and Stead, WW, Computer-based Order Entry: The State of the Art, J Am Med Informatics Assoc., 1994;1:108-123. [10]
  3. 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. [11]
  4. Southern Ohio Medical Center, [12]
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