Journal of Medical Systems

, Volume 36, Issue 6, pp 3533–3541 | Cite as

A High-Speed Drug Interaction Search System for Ease of Use in the Clinical Environment

  • Masahiro TakadaEmail author
  • Hiroshi Inada
  • Kazuo Nakazawa
  • Shoko Tani
  • Michiaki Iwata
  • Yoshihisa Sugimoto
  • Satoru Nagata
Original Paper


With the advancement of pharmaceutical development, drug interactions have become increasingly complex. As a result, a computer-based drug interaction search system is required to organize the whole of drug interaction data. To overcome problems faced with the existing systems, we developed a drug interaction search system using a hash table, which offers higher processing speeds and easier maintenance operations compared with relational databases (RDB). In order to compare the performance of our system and MySQL RDB in terms of search speed, drug interaction searches were repeated for all 45 possible combinations of two out of a group of 10 drugs for two cases: 5,604 and 56,040 drug interaction data. As the principal result, our system was able to process the search approximately 19 times faster than the system using the MySQL RDB. Our system also has several other merits such as that drug interaction data can be created in comma-separated value (CSV) format, thereby facilitating data maintenance. Although our system uses the well-known method of a hash table, it is expected to resolve problems common to existing systems and to be an effective system that enables the safe management of drugs.


Drug interaction search system Hash table High speed search processing CSV format RDB 


  1. 1.
    Diasio, R. B., Sorivudine and 5-fluorouracil; a clinically significant drug-drug interaction due to inhibition of dihydropyrimidine dehydrogenase. Br. J. Clin. Pharmacol. 46(1):1–4, 1998.CrossRefGoogle Scholar
  2. 2.
    Okuda, H., Ogura, K., Kato, A., Takubo, H., and Watabe, T., A possible mechanism of eighteen patient deaths caused by interactions of sorivudine, a new antiviral drug, with oral 5-fluorouracil prodrugs. J. Pharmacol. Exp. Ther. 287(2):791–799, 1998.Google Scholar
  3. 3.
    Rochon, P. A., Field, T. S., Bates, D. W., Lee, M., Gavendo, L., Erramuspe-Mainard, J., Judge, J., and Gurwitz, J. H., Clinical application of a computerized system for physician order entry with clinical decision support to prevent adverse drug events in long-term care. CMAJ 174(1):52–54, 2006.CrossRefGoogle Scholar
  4. 4.
    Novak, P. H., Ekins-Daukes, S., Simpson, C. R., Milne, R. M., Helms, P., and McLay, J. S., Acute drug prescribing to children on chronic antiepilepsy therapy and the potential for adverse drug interactions in primary care. Br. J. Clin. Pharmacol. 59(6):712–717, 2005. doi: 10.1111/j1365–2125.2004.02234.x.CrossRefGoogle Scholar
  5. 5.
    Dallenbach, M. F., Bovier, P. A., and Desmeules, J., Detecting drug interactions using personal digital assistants in an out-patient clinic. QJM 100(11):691–697, 2007. doi: 10.1093/qjmed/hcm088.CrossRefGoogle Scholar
  6. 6.
    Grams, R. R., Zhang, D., and Yue, B., A primary care application of an integrated computer-based pharmacy system. J Med Syst 20(6):413–422, 1996.CrossRefGoogle Scholar
  7. 7.
    Linnarsson, R., Drug interactions in primary health care. A retrospective database study and its implications for the design of a computerized decision support system. Scand J Prim Health Care 11(3):181–186, 1993.CrossRefGoogle Scholar
  8. 8.
    Riou, C., Pouliquen, B., and Le Beux, P., A computer-assisted drug prescription system: the model and its implementation in the ATM knowledge base. Methods Inf Med 38(1):25–30, 1999.Google Scholar
  9. 9.
    Seino, T., Olii, T., Sato, H., Sawada, Y., and Iga, T., Development and Evaluation of a Prescription-Inspecting Supporting System for Rational Use of Medications (I). YAKUGAKU ZASSHI 118(5):168–178, 1998 (in Japanese).Google Scholar
  10. 10.
    Feldstein, A., Simon, S. R., Schneider, J., Krall, M., Laferriere, D., Smith, D. H., Sittig, D. F., and Soumerai, S. B., How to design computerized alerts to safe prescribing practices. Jt Comm J Qual Saf 30(11):602–613, 2004.Google Scholar
  11. 11.
    Gaikwad, R., Sketris, I., Shepherd, M., and Duffy, J., Evaluation of accuracy of drug interaction alerts triggered by two electronic medical record systems in primary healthcare. Health Informatics J 13(3):163–177, 2007.CrossRefGoogle Scholar
  12. 12.
    Abarca, J., Malone, D. C., Skrepnek, G. H., Rehfeld, R. A., Murphy, J. E., Grizzle, A. J., Armstrong, E. P., Woosley, R. L., et al., Community pharmacy managers’ perception of computerized drug-drug interaction alerts. J. Am. Pharm. Assoc. 46(2):148–153, 2006.CrossRefGoogle Scholar
  13. 13.
    Abarca, J., Colon, L. R., Wang, V. S., Malone, D. C., Murphy, J. E., and Armstrong, E. P., Evaluation of the performance of drug-drug interaction screening software in community and hospital pharmacies. J Manag Care Pharm 12(5):383–389, 2006.Google Scholar
  14. 14.
    Barrons, R., Evaluation of personal digital assistant software for drug interactions. Am J Health Syst Pharm 61(4):380–385, 2004.Google Scholar
  15. 15.
    Higuchi, N., Ichikawa, N., Mine, T., Nakashima, M., Hirai, M., and Sasaki, H., Contribution to Medical Risk Management of Computerized Prescription Order Entry Systems: Improvement of Master Maintenance System Using a Commercially Available Order Entry Program. Jpn. J. Pharm. Health Care Sci 30(6):382–388, 2004 (in Japanese).CrossRefGoogle Scholar
  16. 16.
    Kawai, S., Kobayashi, M., Fukai, T., Ogino, O., Iseki, K., Kudo, T., Miyasaka, K., and Miyazaki, K., Prescription Ordering System at Hokkaido University Hospital (3): Introduction of Warning System for Hazardous Drug Interactions. Jpn J Hosp Pharm 22(2):189–197, 1996 (in Japanese).CrossRefGoogle Scholar
  17. 17.
    DeLorenze, G. N., Follansbee, S. F., Nguyen, D. P., Klein, D. B., Horberg, M., Quesenberry, C. P., Jr., et al., Medication error in the care of HIV/AIDS patients: electronic surveillance, confirmation, and adverse events. Med Care 43(9 Suppl):III63–III68, 2005.Google Scholar
  18. 18.
    Smith, W. D., Hatton, R. C., Fann, A. L., Baz, M. A., and Kaplan, B., Evaluation of drug interaction software to identify alerts for transplant medications. Ann. Pharmacother. 39(1):45–50, 2005. doi: 10.1345/aph.1E331.Google Scholar
  19. 19.
    Hayasaka, M., Aoyagi, K., Kimura, T., Makihara, T., and Makimura, M., Development of an Auto monitoring System for the Drug Interaction on Prescription and Injectable Drug Prescription. Jpn. J. Pharm. Health Care Sci 27(4):380–385, 2001 (in Japanese).CrossRefGoogle Scholar
  20. 20.
    Awaya, T., Ohtaki, K., Ishihara, M., Ono, T., Chiba, K., et al., Analysis of Contraindicated Combinations Using a Check System for Drug Interactions, Including those of Injections: Pharmacists Should Verify Drug Interactions in Patient Medication Histories. Jpn J Pharm. Health Care Sci 31(6):425–434, 2005 (in Japanese).CrossRefGoogle Scholar
  21. 21.
    Brilla, R., and Wartenberg, K. E., Introducing new technology: handheld computers and drug databases. A comparison between two residency programs. J Med Syst 28(1):57–61, 2004.CrossRefGoogle Scholar
  22. 22.
    Perkins, N. A., Murphy, J. E., Malone, D. C., and Armstrong, E. P., Performance of drug-drug interaction software for personal digital assistants. Ann. Pharmacother. 40(5):850–855, 2006. doi: 10.1345/aph.1G603.CrossRefGoogle Scholar
  23. 23.
    Kawanobe, M., Kataoka, S., and Sato, H., Development of a Drug Interaction Appraisal System for Individual Prescriptions Utilizing Web Access Functions Embedded in Mobile Phones. Jpn J Pharm. Health Care Sci 32(6):531–540, 2006 (in Japanese).CrossRefGoogle Scholar
  24. 24.
    Cormen, T. H., Leiserson, C. E., and Rivest, R. L., Hash tables. In: Introduction to Algorithms. 2nd ed. Massachusetts: The Mit Press:221–222, 2001.Google Scholar
  25. 25.
    Knuth, D. E., HASHING. In: The Art of Computer Programming Vol. 3, 2nd ed. California: Addison-Wesley:513–58, 1998.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Masahiro Takada
    • 1
    Email author
  • Hiroshi Inada
    • 2
  • Kazuo Nakazawa
    • 3
  • Shoko Tani
    • 3
  • Michiaki Iwata
    • 3
  • Yoshihisa Sugimoto
    • 1
  • Satoru Nagata
    • 1
  1. 1.Department of Medical Informatics and Biomedical EngineeringShiga University of Medical ScienceShigaJapan
  2. 2.Course of Healthcare Informatics, Graduate School of Applied InformaticsUniversity of HyogoKobeJapan
  3. 3.National Cerebral and Cardiovascular Center Research InstituteOsakaJapan

Personalised recommendations