Research Article

Computer system for assisting with clinical interpretation of tumour marker data.

BMJ 1992; 305 doi: https://doi.org/10.1136/bmj.305.6857.804 (Published 03 October 1992) Cite this as: BMJ 1992;305:804
  1. M. S. Leaning,
  2. S. Gallivan,
  3. E. S. Newlands,
  4. J. Dent,
  5. M. Brampton,
  6. D. B. Smith,
  7. K. D. Bagshawe
  1. Department of Statistical Science, University College, London.

    Abstract

    OBJECTIVE--To design and evaluate a computer advisory system for the treatment of gestational trophoblastic tumour. DESIGN--A comparison of clinicians' treatment decisions with those of the computer system. Two datasets were used: one to calibrate the system and one to independently evaluate it. SETTING--Department of medical oncology. PATIENTS--Computerised records of 290 patients with low risk gestational trophoblastic tumour for whom the advisory system could predict the adequacy of treatment. The calibration set comprised patients admitted during 1979-86(227) and the test set patients during 1986-89(63). MAIN OUTCOME MEASURES--The system's accuracy in predicting need to change treatment compared with clinicians' actions. The mean time faster that the system was in predicting the need to change treatment. RESULTS--On the calibration dataset the system was 94% (164/174) accurate in predicting patients whose treatment was adequate, recommending change when none occurred in only 10 (6%) patients. In patients whose treatment was changed the system recommended change earlier than clinicians in 39/53 cases (74%), with a mean time advantage of 14.9 (SE 2.02) days. On the test dataset the system had an accuracy of 91% (31/34) in predicting treatment adequacy and a false positive rate of 9% (3/34). The system recommended change earlier than clinicians in 22/29 cases (76%), with a mean time advantage of 12.5 (2.22) days. CONCLUSIONS--The computer advisory system could improve patient management by reducing the time spent receiving ineffective treatment. This has implications for both patient time and clinical costs.