Interaction model

Clinical decision support (CDS) are rules in user interaction model for making clinical decisions. The rules are based on a large collection of medical knowledge and an accurate computer representation scheme.

Introduction

Informaticians have been developing knowledge-based clinical decision support systems for over 30 years with many notable successes [1]

Nature of the medical decision

The key item that must be considered is the nature of the medical decision being made. Many medical decisions are based on numerous simple and widely agreed upon, rules that all clinicians know but have difficulty bringing to bear with 100% accuracy.

Examples of such decisions might include

• Does this infant need an MMR vaccination today?
• Do these particular arterial blood gas values represent a metabolic or respiratory acidosis?
• Has the patient's sodium value fallen more than 25% over the last 12 hours?

These determinations are best implemented as interpretation or monitoring systems.

no clear cut solutions

Other decisions are fraught with complicated risk assessments and competing alternatives; they have no clear-cut "best" solutions. Such decisions are best implemented as critiquing systems. The consultation mode, on the other hand, has not met with much success in the clinical realm for the simple reason that clinicians are reluctant to spend extended periods of time entering data into a computer in order to receive advice. Finding the appropriate user interaction model is one of the most important, but often overlooked, tasks.

Teaching models

Any of the above mentioned interaction models can be enhanced by offering a "teaching mode" to the user. Such a mode would allow the system to "explain" its reasoning to the clinician. In a landmark article, Teach and Shortliffe stated that the ability of a system to "explain" its reasoning was one of the key factors in clinician acceptance of decision support systems [Teach, 1981]. Since that time many systems have been successfully deployed without this capability, although system developers are still encouraged to provide it when possible. Many developers skirt this issue by citing a scientific journal article or displaying the actual rules (along with the patient's data values) the system used to reach the conclusion.

Interpretation

Interpretation systems present information to clinicians passively. These systems can create relatively simple, yet nicely formatted clinical laboratory reports and graphs, or sophisticated interpretations of such things as electrocardiograms (EKGs) [Klingeman, 1967]. Interpretation systems work best when interfaced directly to the data generated by laboratory instruments that produce numeric data. In addition, there should be a well understood physiologic model in existence which unambiguously interprets the data. These systems have found their greatest clinical utility in areas such as arterial blood gas interpretation [Gardner, 1975], spirometry [Ostler, 1984], and automated PAP smear analysis [Wilbur, 1998], to name just a few.

1. Klingeman J, Pipberger HV. Computer classifications of electrocardiograms. Comput Biomed Res 1967 Mar;1(1):1-17
2. Gardner RM, Cannon GH, Morris AH, Olsen KR, Price WG: Computerized blood gas interpretation and reporting system. IEEE Computer 1975; 8:39-45.
3. Ostler DV, Gardner RM, Crapo RO A computer system for analysis and transmission of spirometry waveforms using volume sampling. Comput Biomed Res 1984 Jun;17(3):229-40.
4. Wilbur DC, Prey MU, Miller WM, Pawlick GF, Colgan TJ. The AutoPap system for primary screening in cervical cytology. Comparing the results of a prospective, intended-use study with routine manual practice. Acta Cytol 1998 Jan-Feb;42(1):214-20

References

1. Reisman Y. [2] Computer-based clinical decision aids]. A review of methods and assessment of systems. Med Inf (Lond) 1996 Jul-Sep;21(3):179-97
2. Teach RL, Shortliffe EH. An analysis of physician attitudes regarding computer-based clinical consultation systems. Comput Biomed Res 1981 Dec;14(6):542-58