Strong and Meaningful Use of Healthcare Information Systems (HIS)

Arkalgud Ramaprasad, Thant Syn

Abstract

The translation of science to practice to policy for meaningful use of healthcare information system (HIS) is embedded in a complex milieu of meaningful, meaningless, non-, and mis- use of the system by a variety of stakeholders seeking to manage the cost, quality, safety, and parity of healthcare. The problem of HIS use can be modeled as an ontology which encapsulates the core logic of use. The ontology includes the three components of translation, the four types of use, the key stakeholders, and the four basic outcomes. It is a comprehensive structured natural-language model which can be extended and refined. It is parsimonious and can be easily understood and interpreted by all the stakeholders. We argue that such a model is necessary to develop a roadmap for strengthening the meaningful use of HIS. In its absence meaningful use of HIS will be weak.

References

  1. Blumenthal, D. 2009. Stimulating the adoption of health information technology. New England Journal of Medicine, 360, 1477-1479.
  2. Callen, J. L., Westbrook, J. I., Georgiou, A. & Li, J. 2011. Failure to Follow-Up Test Results for Ambulatory Patients: A Systematic Review. Journal of General Internal Medicine, 27, 1334-1348.
  3. Classen, D. C., Phansalkar, S. & Bates, D. W. 2011. Critical drug-drug interactions for use in electronic health records systems with computerized physician order entry: review of leading approaches. Journal of Patient Safety, 7, 61-65.
  4. Crosson, J. C., Schueth, A. J., Isaacson, N. & Bell, D. S. 2012. Early adopters of electronic prescribing struggle to make meaningful use of formulary checks and medication history documentation. The Journal of the American Board of Family Medicine, 25, 24-32.
  5. Gaikwad, R., Sketris, I., Shepherd, M. & Duffy, J. 2007. Evaluation of accuracy of drug interaction alerts triggered by two electronic medical record systems in primary healthcare. Health informatics journal, 13, 163-177.
  6. Interagency Breast Cancer and Environmental Research Coordinating Committee 2013. Breast Cancer and the Environment: Prioritizing Prevention. http://www.niehs.nih.gov/about/assets/docs/ibcercc_fu ll.pdf.
  7. Phansalkar, S., Desai, A. A., Bell, D., Yoshida, E., Doole, J., Czochanski, M., Middleton, B. & Bates, D. W. 2012a. High-priority drug-drug interactions for use in electronic health records. Journal of the American Medical Informatics Association, 19, 735-743.
  8. Phansalkar, S., van der Sijs, H., Tucker, A. D., Desai, A. A., Bell, D. S., Teich, J. M., Middleton, B. & Bates, D. W. 2012b. Drug-drug interactions that should be noninterruptive in order to reduce alert fatigue in electronic health records. Journal of the American Medical Informatics Association.
  9. Platt, J. R. 1964. Strong inference. Science, 146, 347-353.
  10. Rahmner, P. B., Eiermann, B., Korkmaz, S., Gustafsson, L. L., Gruvén, M., Maxwell, S., Eichle, H.-G. & Vég, A. 2012. Physicians' reported needs of drug information at point of care in Sweden. British Journal of Clinical Pharmacology, 73, 115-125.
  11. Ramaprasad, A. & Thirumalai, M. 2012. Managing Population Health: An Ontological Framework (Poster). 2012 Summit on the Science of Eliminating Health Disparities. Washington DC, USA.
  12. Seidling, H. M., Phansalkar, S., Seger, D. L., Paterno, M. D., Shaykevich, S., Haefeli, W. E. & Bates, D. W. 2011. Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support. Journal of the American Medical Informatics Association, 18, 479-484.
  13. Smithburger, P. L., Buckley, M. S., Bejian, S., Burenheide, K. & Kane-Gill, S. L. 2011. A critical evaluation of clinical decision support for the detection of drug-drug interactions. Expert Opinion on Drug Safety, 10, 871-882.
  14. Spina, J. R., Glassman, P. A., Simon, B., Lanto, A., Lee, M., Cunningham, F. & Good, C. B. 2011. Potential Safety Gaps in Order Entry and Automated Drug Alerts: A Nationwide Survey of VA Physician SelfReported Practices With Computerized Order Entry. Medical Care, 49, 904-910.
  15. Takarabe, M., Shigemizu, D., Kotera, M., Goto, S. & Kanehisa, M. 2011. Network-Based Analysis and Characterization of Adverse Drug-Drug Interactions. Journal of chemical information and modeling, 51, 2977-2985.
Download


Paper Citation


in Harvard Style

Ramaprasad A. and Syn T. (2014). Strong and Meaningful Use of Healthcare Information Systems (HIS) . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014) ISBN 978-989-758-010-9, pages 381-386. DOI: 10.5220/0004870303810386


in Bibtex Style

@conference{healthinf14,
author={Arkalgud Ramaprasad and Thant Syn},
title={Strong and Meaningful Use of Healthcare Information Systems (HIS)},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)},
year={2014},
pages={381-386},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004870303810386},
isbn={978-989-758-010-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2014)
TI - Strong and Meaningful Use of Healthcare Information Systems (HIS)
SN - 978-989-758-010-9
AU - Ramaprasad A.
AU - Syn T.
PY - 2014
SP - 381
EP - 386
DO - 10.5220/0004870303810386