An accurate medication history is an essential part of patient assessment and can have vital impact on the person's care. However, manually-acquired histories are prone to inaccuracies. The problem is especially serious in emergency care and in disaster situations due to the lack of time, overloaded staff and special patient conditions (e.g. comatose or confused patients, unaccompanied minors or elderly patients). This study is partially funded by the Bethesda Hospitals Emergency Preparedness Partnership. It focuses on patients attended by the Emergency Department (ED) of a regional hospital (Suburban Hospital, Bethesda, MD) and evaluates the potential added value of prescription history information from Surescripts, a consortium of major pharmacy benefit managers, pharmacies and healthcare providers. We established a secure electronic connection between the hospital and Surescripts so that prescription-filling reports could be retrieved from Surescripts in real-time, on presentation of four pieces of patient identifying information (name, date of birth, gender and zip code) gleaned from HL7 messages generated by the electronic registration system of the hospital. For three months we collected in parallel the Surescripts information as well as the medication history manually acquired by the ED nurse. We also retrieved demographic, administrative (e.g. class of insurance, mode of arrival) and clinical (e.g. vital signs, Glasgow coma score) information from the hospitals database. All the information was de-identified before being sent to NLM for analysis. This research only involved de-identified data collected for routine care purposes. We conducted the data analysis retrospectively;thus, there was no intervention or patient contact. The ED medications were manually typed in and their names were more variable. Surescripts data used more standard names. To make the two sources comparable, we mapped all drugs to their standard names in RxNorm, the U.S. standard reference drug terminology. Mapping was done largely by automatic text matching algorithms followed by manual review of the unmapped items. About two-thirds of all ED patients were registered in the Surescripts database, and for about half of all patients Surescripts returned some medication history information. There is substantial overlap between the ED medication history and Surescripts data. Neither source of data is found to be complete, but Surescripts does add substantial information to the medication histories collected by the ED personnel. We also classified the missing drugs according to their potential impact on patient care. A significant proportion of missing drugs in the ED medication history were considered critical or important in patient care (e.g. anticoagulants). Since the Surescripts information is mainly derived from insurance-related sources, the availability of information varies with patient demographics and insurance coverage, among other factors. In order to assess the generalizability of the results of our study population, we have built a prediction model for the availability of Surescripts data based on patient characteristics. Our model shows that English-speaking Caucasian patients who are either on commercial insurance or Medicare are more likely to be found in the Surescripts database. Since the data collection period ended, healthcare providers at the Suburban Hospital ED have been receiving Surescripts information as easily-readable printed summaries that include graphs. Suburban Hospital surveyed providers and results indicated that the users of the Surescripts summaries find them very useful, particularly in patients who cannot remember the names of the drugs they are taking. Since the Surescripts information contains not only the drugs that the patient is currently taking but also the full history of prescriptions being filled in the past year, providers also found the summaries very useful in spotting problematic behaviors (e.g. narcotic drugs abuse, poor drug compliance). This research shows the advantages and potential value of utilizing an external source of medication information in direct patient care. We hope to heighten the awareness of, and demand for, this information resource. Furthermore, these results may persuade policy makers to change existing rules and regulations to make it easier to share electronic prescription data, and thus enhance the the completeness and accuracy of patient prescription information.

Project Start
Project End
Budget Start
Budget End
Support Year
4
Fiscal Year
2011
Total Cost
$268,611
Indirect Cost
Name
National Library of Medicine
Department
Type
DUNS #
City
State
Country
Zip Code
Drawz, Paul E; Archdeacon, Patrick; McDonald, Clement J et al. (2015) CKD as a Model for Improving Chronic Disease Care through Electronic Health Records. Clin J Am Soc Nephrol 10:1488-99
Demner-Fushman, Dina; Kohli, Marc D; Rosenman, Marc B et al. (2015) Preparing a collection of radiology examinations for distribution and retrieval. J Am Med Inform Assoc :
Ayvaz, Serkan; Horn, John; Hassanzadeh, Oktie et al. (2015) Toward a complete dataset of drug-drug interaction information from publicly available sources. J Biomed Inform 55:206-17
Abhyankar, Swapna; Demner-Fushman, Dina; Callaghan, Fiona M et al. (2014) Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis. J Am Med Inform Assoc 21:801-7
Pan, Xuequn; Cimino, James J (2014) Locating relevant patient information in electronic health record data using representations of clinical concepts and database structures. AMIA Annu Symp Proc 2014:969-75
Winnenburg, Rainer; Bodenreider, Olivier (2014) A framework for assessing the consistency of drug classes across sources. J Biomed Semantics 5:30
Bodenreider, Olivier; Rodriguez, Laritza M (2014) Analyzing U.S. prescription lists with RxNorm and the ATC/DDD Index. AMIA Annu Symp Proc 2014:297-306
Fung, Kin Wah; Kayaalp, Mehmet; Callaghan, Fiona et al. (2013) Comparison of electronic pharmacy prescription records with manually collected medication histories in an emergency department. Ann Emerg Med 62:205-11
Fung, Kin Wah; McDonald, Clement; Srinivasan, Suresh (2010) The UMLS-CORE project: a study of the problem list terminologies used in large healthcare institutions. J Am Med Inform Assoc 17:675-80
Demner-Fushman, Dina; Chapman, Wendy W; McDonald, Clement J (2009) What can natural language processing do for clinical decision support? J Biomed Inform 42:760-72

Showing the most recent 10 out of 16 publications