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Appl Clin Inform 2018; 09(02): 313-325
DOI: 10.1055/s-0038-1646963
Research Article
Schattauer GmbH Stuttgart

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

Joshua C. Smith
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Qingxia Chen
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
2   Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Joshua C. Denny
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
3   Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Dan M. Roden
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
3   Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
4   Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Kevin B. Johnson
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
5   Department of Pediatrics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
,
Randolph A. Miller
1   Department of Biomedical Informatics, Vanderbilt University Medical Center and Vanderbilt University School of Medicine, Nashville, Tennessee, United States
3   Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
6   School of Nursing, Vanderbilt University, Nashville, Tennessee, United States
› Author Affiliations Funding This research was supported in part by AHRQ Health Services Dissertation Grant R36HS023485, National Library of Medicine (NLM) Training Grant (T15LM007450), NLM R01LM007995 and R01LM010828. Resources provided by the Vanderbilt Institute for Clinical and Translational Research (VICTR), supported by CTSA award UL1TR000445 from the National Center for Advancing Translational Sciences, also benefitted the study. The AHRQ study section reviewed the preliminary study design as part of making its funding decision. Funding sources had no role in the design, implementation, or interpretation of this study or the decision to submit the manuscript for publication.
Further Information

Publication History

17 October 2017

22 March 2018

Publication Date:
09 May 2018 (online)

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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).


Supplementary Material

 

  • 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
    PubMedGoogle Scholar
  • 2 CDC-Basic-Medication Safety Program. Available at: http://www.cdc.gov/medicationsafety/basics.html . Accessed October 3, 2017
    PubMed
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 8 International drug monitoring: the role of national centres. Report of a WHO meeting. World Health Organ Tech Rep Ser 1972; 498: 1-25
    PubMedGoogle Scholar
  • 9 Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet 2000; 356 (9237): 1255-1259
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMed
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 19 Classen DC, Pestotnik SL, Evans RS, Burke JP. Computerized surveillance of adverse drug events in hospital patients. JAMA 1991; 266 (20) 2847-2851
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 33 LePendu P, Iyer SV, Bauer-Mehren A. , et al. Pharmacovigilance using clinical notes. Clin Pharmacol Ther 2013; 93 (06) 547-555
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 36 Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Sci Transl Med 2012; 4 (125) 125ra31
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 43 MetaMap - A Tool for Recognizing UMLS Concepts in Text. Available at: https://metamap.nlm.nih.gov/
    PubMed
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    PubMedGoogle Scholar
  • 46 Unified Medical Language System (UMLS). Available at: https://www.nlm.nih.gov/research/umls/ . Accessed September 24, 2017
    PubMed
  • 47 RxNorm Overview. Available at: https://www.nlm.nih.gov/research/umls/rxnorm/overview.html . Accessed September 24, 2017
    PubMed
  • 48 SNOMED CT. Available at: https://www.nlm.nih.gov/healthit/snomedct/ . Accessed September 24, 2017
    PubMed
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    Article in Thieme ConnectPubMedGoogle Scholar
  • 51 Giuse DA. Supporting communication in an integrated patient record system. AMIA Annu Symp Proc AMIA Symp. 2003:1065
    PubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    Article in Thieme ConnectPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar
  • 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
    CrossrefPubMedGoogle Scholar