Around 300 questions could be asked of the patient; however, the system guides the nurse to ask 20 to 40 questions relevant to a particular patient. The progress note, organized in the SOAP format, is reviewed by the physician with the patient. The physician could also review the clinical data, weigh the suggestions from the system, and modify the Assessment or Plan sections. The Subjective and Objective sections could also be modified but rarely needed to be. Without the system a physician spent 21.35 min (+/- 0.95 sem, N=140) with the patient. With the system, the nurse spent 14.95 min (+/- 0.81 sem, N=27), and the physician spent 7.4 min (+/- 0.68 sem, N=27). Physician time was cut by about 66%. Using 1994 VA salaries for nurses and physicians, we have shown that the system reduced cost by about 40%.

We have compared the quality of the progress note generated by physicians to the computer generated note. Using a scoring system that divides the note data into essential and bonus categories, we found that the computer note quality was higher (95.5, +/-8.19 sd, N=12) compared to a physician's hand written note ( 85.2, +/-9.11 sd, N=24; p < 0.01).

Our informal assessment of the system is that it was well accepted by our physicians, nurses, and patients. Our physicians were willing to give up time on routine cases in exchange for more time on more difficult cases. Nurses liked the system because they could work at a higher level of expertise and spend more time with the patient. Patients seemed willing to accept the system even though they were waiting for two interviews (nurse and physician).

The system uses an object-oriented architecture and is divided into modules which contain both rules and data, and communicate with each other by passing conclusions. We organized the objects by physiological system. The system runs on a PC under Windows 3.11 and was constructed using ToolBook (Asymetric) for the user interface, Nexpert Object (Neuron Data) for the inferencing engine, and DBase III (Borland) for data storage. The system contained about 25 screens, 250 rules, and 300 data fields in about 30 files.

Doller, H. J., Hostetler, W. E., and Peterson, L. L.: Expert Systems Decrease the Cost While Increasing the Quality of Out Patient Clinical Encounters AMIA 1995 Spring Congress, Cambridge, MA, June 24-28, 1995.

Doller, H. J., Hostetler, W., Krishnamurthy, K., and Peterson, L.L.: Expert Systems: Cost Effective Patient Data Gathering Tools for the Electronic Medical Record. AAAI Spring Symposium, St Louis, May 9-15, 1993.

Hostetler, W.E. and Doller, H. J.: Epileptologists' Assistant: an Expert System for Epilepsy Clinic Improves Progress Note Quality While Decreasing Visit Cost, Epilepsia 35:(supp. 8) 45, 1994.

Hostetler, W., Doller, H. J., Krishnamurthy, K., and Peterson, L.L.: Epileptologist's Assistant: A Cost Effective Expert System for Clinical Medicine. First World Conference on Computational Medicine, Public Health and Biotechnology, Austin, Texas., April 24-26, 1994.

Hostetler, W., Krishnamurthy, K., Peterson, L.L., and Doller, H. J., The Physician's Interface to Epileptologist's Assistant - A Cost Effective Expert System, SCAMC 17:944, 1994.

Archive of AI systems in clinical practice previously administered by Enrico Coiera. Used with permission. Maintained and extended since 2001 by OpenClinical.