Evaluation of a Novel System to Enhance Clinicians' Recognition of Preadmission Adverse Drug Reactions

Abstract

Background Often unrecognized by providers, adverse drug reactions (ADRs) diminish patients' quality of life, cause preventable admissions and emergency department visits, and increase health care costs.

Objective This article evaluates whether an automated system, the Adverse Drug Effect Recognizer (ADER), could assist clinicians in detecting and addressing inpatients' ongoing preadmission ADRs.

Methods ADER uses natural language processing to extract patients' medications, findings, and past diagnoses from admission notes. It compares excerpted information to a database of known medication adverse effects and promptly warns clinicians about potential ongoing ADRs and potential confounders via alerts placed in patients' electronic health records (EHRs). A 3-month intervention trial evaluated ADER's impact on antihypertensive medication ordering behaviors. At the time of patient admission, ADER warned providers on the Internal Medicine wards of Vanderbilt University Hospital about potential ongoing preadmission antihypertensive medication ADRs. A retrospective control group, comprised similar physicians from a period prior to the intervention, received no alerts. The evaluation compared ordering behaviors for each group to determine if preadmission medications changed during hospitalization or at discharge. The study also analyzed intervention group participants' survey responses and user comments.

Results ADER identified potential preadmission ADRs for 30% of both groups. Compared with controls, intervention providers more often withheld or discontinued suspected ADR-causing medications during the inpatient stay (p < 0.001). Intervention providers who responded to alert-related surveys held or discontinued suspected ADR-causing medications more often at discharge (p < 0.001).

Conclusion Results indicate that ADER helped physicians recognize ADRs and reduced ordering of suspected ADR-causing medications. In hospitals using EHRs, ADER-like systems could improve clinicians' recognition and elimination of ongoing ADRs.

Keywords

adverse drug events - natural language processing - clinical decision support systems

Authors' Contributions

All authors contributed to the study design. J.C.S. and R.A.M. wrote the study protocols, acquired IRB approval, and recruited participants. J.C.S. performed all software development, debugging, data acquisition, and data processing under the supervision of R.A.M. With the assistance of K.B.J., R.A.M., and technical staff (see Acknowledgments), J.C.S. integrated the ADER system into the EHR and maintained the system during the study. J.C.S. and Q.C. performed the statistical analysis. J.C.S. and R.A.M. interpreted the results and wrote the first drafts of the manuscript. Q.C., J.C.D., D.M.R., and K.B.J. provided critical comments, suggestions, and changes to the manuscript. All authors approved the final manuscript.

Protection of Human and Animal Subjects

This study was performed in compliance with all applicable ethical standards for medical research involving human subjects. The Vanderbilt University Institutional Review Board approved this study (IRB #141341).

• References

• 1 U.S. Department of Health and Human Services, Office of Disease Prevention and Health Promotion. National Action Plan for Adverse Drug Event Prevention. [Internet]. Washington, DC; 2014. Available at: https://health.gov/hcq/ade-action-plan.asp . Accessed September 24, 2017
• 2 CDC-Basic-Medication Safety Program. Available at: http://www.cdc.gov/medicationsafety/basics.html . Accessed October 3, 2017
• 3 Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 1998; 279 (15) 1200-1205
• 4 Pirmohamed M, James S, Meakin S. , et al. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 2004; 329 (7456): 15-19
• 5 Kongkaew C, Noyce PR, Ashcroft DM. Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies. Ann Pharmacother 2008; 42 (07) 1017-1025
• 6 Atiqi R, van Bommel E, Cleophas TJ, Zwinderman AH. Prevalence of iatrogenic admissions to the Departments of Medicine/Cardiology/ Pulmonology in a 1,250 bed general hospital. Int J Clin Pharmacol Ther 2010; 48 (08) 517-524
• 7 Hakkarainen KM, Gyllensten H, Jönsson AK, Andersson Sundell K, Petzold M, Hägg S. Prevalence, nature and potential preventability of adverse drug events - A population-based medical record study of 4970 adults. Br J Clin Pharmacol 2014; 78 (01) 170-183
• 8 International drug monitoring: the role of national centres. Report of a WHO meeting. World Health Organ Tech Rep Ser 1972; 498: 1-25
• 9 Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet 2000; 356 (9237): 1255-1259
• 10 Stausberg J. International prevalence of adverse drug events in hospitals: an analysis of routine data from England, Germany, and the USA. BMC Health Serv Res 2014; 14: 125
• 11 Pedrós C, Quintana B, Rebolledo M, Porta N, Vallano A, Arnau JM. Prevalence, risk factors and main features of adverse drug reactions leading to hospital admission. Eur J Clin Pharmacol 2014; 70 (03) 361-367
• 12 Nebeker JR, Barach P, Samore MH. Clarifying adverse drug events: a clinician's guide to terminology, documentation, and reporting. Ann Intern Med 2004; 140 (10) 795-801
• 13 Classen DC, Pestotnik SL, Evans RS, Lloyd JF, Burke JP. Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. JAMA 1997; 277 (04) 301-306
• 14 Reducing and Preventing Adverse Drug Events To Decrease Hospital Costs | Agency for Healthcare Research & Quality (AHRQ). Available at: http://www.ahrq.gov/research/findings/factsheets/errors-safety/aderia/index.html
• 15 Sultana J, Cutroneo P, Trifirò G. Clinical and economic burden of adverse drug reactions. J Pharmacol Pharmacother 2013; 4 (05) (Suppl. 01) S73-S77
• 16 Lahue BJ, Pyenson B, Iwasaki K, Blumen HE, Forray S, Rothschild JM. National burden of preventable adverse drug events associated with inpatient injectable medications: healthcare and medical professional liability costs. Am Health Drug Benefits 2012; 5 (07) 1-10
• 17 Handler SM, Altman RL, Perera S. , et al. A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting. J Am Med Inform Assoc 2007; 14 (04) 451-458
• 18 Murff HJ, Patel VL, Hripcsak G, Bates DW. Detecting adverse events for patient safety research: a review of current methodologies. J Biomed Inform 2003; 36 (1-2): 131-143
• 19 Classen DC, Pestotnik SL, Evans RS, Burke JP. Computerized surveillance of adverse drug events in hospital patients. JAMA 1991; 266 (20) 2847-2851
• 20 Evans RS, Pestotnik SL, Classen DC. , et al. Development of a computerized adverse drug event monitor. Proc Annu Symp Comput Appl Sic Med Care Symp Comput Appl Med Care; 1991:23–27
• 21 Evans RS, Pestotnik SL, Classen DC, Bass SB, Burke JP. Prevention of adverse drug events through computerized surveillance. Proc Annu Symp Comput Appl Sic Med Care Symp Comput Appl Med Care; 1992:437–441
• 22 Jha AK, Kuperman GJ, Teich JM. , et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc 1998; 5 (03) 305-314
• 23 Honigman B, Lee J, Rothschild J. , et al. Using computerized data to identify adverse drug events in outpatients. J Am Med Inform Assoc 2001; 8 (03) 254-266
• 24 Bates DW, Evans RS, Murff H, Stetson PD, Pizziferri L, Hripcsak G. Detecting adverse events using information technology. J Am Med Inform Assoc 2003; 10 (02) 115-128
• 25 Murff HJ, Forster AJ, Peterson JF, Fiskio JM, Heiman HL, Bates DW. Electronically screening discharge summaries for adverse medical events. J Am Med Inform Assoc 2003; 10 (04) 339-350
• 26 Raschke RA, Gollihare B, Wunderlich TA. , et al. A computer alert system to prevent injury from adverse drug events: development and evaluation in a community teaching hospital. JAMA 1998; 280 (15) 1317-1320
• 27 Peterson JF. Drug-lab triggers have potential to prevent adverse drug events in outpatients. J Am Med Inform Assoc 2002; 9 (90061): 39-40
• 28 Chen ES, Hripcsak G, Xu H, Markatou M, Friedman C. Automated acquisition of disease drug knowledge from biomedical and clinical documents: an initial study. J Am Med Inform Assoc 2008; 15 (01) 87-98
• 29 Wang X, Hripcsak G, Markatou M, Friedman C. Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study. J Am Med Inform Assoc 2009; 16 (03) 328-337
• 30 Wang X, Chase H, Markatou M, Hripcsak G, Friedman C. Selecting information in electronic health records for knowledge acquisition. J Biomed Inform 2010; 43 (04) 595-601
• 31 Wang X, Chase HS, Li J, Hripcsak G, Friedman C. Integrating heterogeneous knowledge sources to acquire executable drug-related knowledge. AMIA Annu Symp Proc AMIA Symp 2010;2010:852–856
• 32 Li Y, Salmasian H, Harpaz R, Chase H, Friedman C. Determining the Reasons for Medication Prescriptions in the EHR using Knowledge and Natural Language Processing. AMIA Annu Symp Proc AMIA Symp 2011;2011:768–776
• 33 LePendu P, Iyer SV, Bauer-Mehren A. , et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther 2013; 93 (06) 547-555
• 34 Harpaz R, Vilar S, Dumouchel W. , et al. Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions. J Am Med Inform Assoc 2013; 20 (03) 413-419
• 35 Harpaz R, DuMouchel W, Shah NH, Madigan D, Ryan P, Friedman C. Novel data-mining methodologies for adverse drug event discovery and analysis. Clin Pharmacol Ther 2012; 91 (06) 1010-1021
• 36 Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Sci Transl Med 2012; 4 (125) 125ra31
• 37 Harpaz R, Callahan A, Tamang S. , et al. Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf 2014; 37 (10) 777-790
• 38 Duke JD, Friedlin J. ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data. AMIA Annu Symp Proc AMIA Symp 2010;2010:177–181
• 39 Warrer P, Hansen EH, Juhl-Jensen L, Aagaard L. Using text-mining techniques in electronic patient records to identify ADRs from medicine use. Br J Clin Pharmacol 2012; 73 (05) 674-684
• 40 Denny JC, Miller RA, Johnson KB, Spickard III A. Development and evaluation of a clinical note section header terminology. AMIA Annu Symp Proc AMIA Symp 2008:156–160
• 41 Denny JC, Smithers JD, Miller RA, Spickard III A. “Understanding” medical school curriculum content using KnowledgeMap. J Am Med Inform Assoc 2003; 10 (04) 351-362
• 42 Denny JC, Peterson JF, Choma NN. , et al. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. J Am Med Inform Assoc 2010; 17 (04) 383-388
• 43 MetaMap - A Tool for Recognizing UMLS Concepts in Text. Available at: https://metamap.nlm.nih.gov/
• 44 Xu H, Stenner SP, Doan S, Johnson KB, Waitman LR, Denny JC. MedEx: a medication information extraction system for clinical narratives. J Am Med Inform Assoc 2010; 17 (01) 19-24
• 45 Peterson JF, Shi Y, Denny JC. , et al. Prevalence and Clinical Significance of Discrepancies within Three Computerized Pre-Admission Medication Lists. AMIA Annu Symp Proc AMIA Symp 2010; 2010:642–646
• 46 Unified Medical Language System (UMLS). Available at: https://www.nlm.nih.gov/research/umls/ . Accessed September 24, 2017
• 47 RxNorm Overview. Available at: https://www.nlm.nih.gov/research/umls/rxnorm/overview.html . Accessed September 24, 2017
• 48 SNOMED CT. Available at: https://www.nlm.nih.gov/healthit/snomedct/ . Accessed September 24, 2017
• 49 Chapman WW, Bridewell W, Hanbury P, Cooper GF, Buchanan BG. A simple algorithm for identifying negated findings and diseases in discharge summaries. J Biomed Inform 2001; 34 (05) 301-310
• 50 Smith JC, Denny JC, Chen Q. , et al. Lessons learned from developing a drug evidence base to support pharmacovigilance. Appl Clin Inform 2013; 4 (04) 596-617
• 51 Giuse DA. Supporting communication in an integrated patient record system. AMIA Annu Symp Proc AMIA Symp. 2003:1065
• 52 Phansalkar S, van der Sijs H, Tucker AD. , et al. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc 2013; 20 (03) 489-493
• 53 Dexheimer JW, Kirkendall ES, Kouril M. , et al. The effects of medication alerts on prescriber response in a pediatric hospital. Appl Clin Inform 2017; 8 (02) 491-501
• 54 Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians' decisions to override computerized drug alerts in primary care. Arch Intern Med 2003; 163 (21) 2625-2631
• 55 Cho I, Slight SP, Nanji KC. , et al. The effect of provider characteristics on the responses to medication-related decision support alerts. Int J Med Inform 2015; 84 (09) 630-639
• 56 McDonald CJ. Protocol-based computer reminders, the quality of care and the non-perfectability of man. N Engl J Med 1976; 295 (24) 1351-1355