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Leveraging Technology to Combat the Dangers of Multimedication Use


Multimedication use and the sequela of medication-related problems are leading causes of morbidity and mortality in the United States and have a major economic impact on our society. Managed care providers should take solace knowing that a substantial proportion of medication-related problems are predictable and, therefore, potentially avoidable.

This article was written by Kevin T. Bain, PharmD, MPH, BCPS, BCGP, CPH, FASCP, senior vice president of research and development at Tabula Rasa HealthCare.

Polypharmacy, the use of multiple medications, is common in managed care, with approximately 40% of adults age 65 years or older taking at least 5 prescription medications and 20% taking 10 or more.1,2 The prevalence of polypharmacy is even higher if over-the-counter medications, supplements, and vitamins, which are commonly underreported, are included in the total. Polypharmacy is by far the strongest risk factor for medication-related problems, such as nonadherence, use of potentially inappropriate and unnecessary medications, drug—drug interactions (DDIs), and adverse drug events (ADEs). Using Tabula Rasa HealthCare (TRHC)’s proprietary software, it has been estimated that the probability of at least 1 clinically relevant DDI is 50% in patients taking 5 to 9 medications, 80% with 10 to 14 medications, 90% with 15 to 19 medications, and 100% with 20 or more medications.3 Age-related changes in how a drug affects the body (pharmacodynamics) and how the body affects a drug (pharmacokinetics), coupled with polypharmacy, put older adults at even greater risk for ADEs. One study found that compared with patients taking fewer than 5 medications, patients taking 9 or more medications had 4 times the rate of ADEs.4

Indeed, multimedication use and the sequela of medication-related problems are leading causes of morbidity and mortality in the United States and have a major economic impact on our society. An estimated 35% of emergency department visits for ADEs occurred among adults aged 65 years or older in 2013-2014, compared with an estimated 25% in 2005-2006,5 and more than one-third of these visits required hospitalization.6 For every dollar spent on prescription medications, more than $1—approximately $1.50—in healthcare resources is consumed treating problems associated with those medications.7,8 The costs of medication-related morbidity and mortality exceed $200 billion annually.9 Of more concern than the economic costs are the costs associated with human life. For the last 10 years, medication-related problems have consistently ranked as the fourth-leading cause of death in the United States, accounting for approximately 150,000 deaths per annum.10,11

Despite these alarming data, the story is not all doom and gloom. On the bright side, managed care providers should take solace knowing that a substantial proportion—upwards of 50%—of medication-related problems are predictable and, therefore, potentially avoidable. Yet, simply knowing that multimedication use is common and egregious does not provide us with solutions. This is largely where the current healthcare system is jammed. We need to break through the roadblock with strategies that support our ability to manage the complexity that arises when older adults, and other vulnerable patients, take multiple medications as well as the reasonableness of the medication regimen as a whole. Our experience at TRHC indicates that medication risk mitigation (MRM) strategies that leverage technology are necessary to reduce medication-related problems and improve patient outcomes.12,13

Medication Risk Mitigation Technology

Strategies to mitigate medication-related problems in clinical practice lag behind the initiatives taken during the drug approval process to predict and confirm these problems.14 An exemplar case is drug interactions.

Knowledge on potential DDIs has primarily been translated to clinicians through use of “drug alert software” programs. These programs have been integrated into electronic medical records and pharmacy systems for the past 30 to 40 years, as part of standard care. Yet, the problem of drug interactions, and associated ADEs, has grown and become increasingly more complicated. Although the commonly used drug alert software programs are capable of identifying DDIs that have been reported in the medical literature and alerting clinicians to their severity, this strategy is not likely to be the primary solution to the problem, because it is fraught with limitations.

  • Many drug interactions reported in the medical literature are based on findings in healthy volunteers or case reports, and often the outcomes have not been confirmed in clinical studies or practice.14 In many cases, it may be inappropriate to extrapolate these findings to patients in real-world practice, particularly those with multimorbidity using multiple medications.
  • Similarly, the drug alert software programs that are commonly used in practice analyze DDIs based on pairwise interactions (ie, 1 drug interacting with another drug). In the real world, though, patients frequently use multiple medications that are substrates of a given cytochrome P450 isoenzyme or receive an inhibitor and/or inducer of the same isoenzyme.14 Applying the traditional pairwise interaction strategy to patients with complex medication regimens and polypharmacy is, at minimum, obsolete and, more often, very inappropriate.
  • Furthermore, as a consequence of the high frequency of multiple 1-to-1 drug pair alerts, which generate numerous, often incoherent, warnings—often of low clinical relevance—prescribers and pharmacists tend to override or ignore the majority of drug alert warnings. Substantial research has shown that the limited scope of these alerts and the phenomenon known as “alert fatigue” markedly reduce the impact of drug alert software programs on reducing medication-related problems.15,16 Perhaps the most outlandish example of this consequence is a recent investigation by the Chicago Tribune on December 15, 2016.17 In this investigation, reporters found that most pharmacies miss half of dangerous drug interactions, and pharmacists do not routinely act on DDIs, even when alerted by software programs.

Not surprisingly, prescribers’ and pharmacists’ ability to recognize and act on drug interactions in patients using multiple medications is limited, if not lacking. These shortcomings in clinical practice have fostered the need to evaluate multiple potentially interacting medications simultaneously. Indeed, a more thorough understanding of drug pharmacodynamics and pharmacokinetics has led to new strategies to try to predict and prevent clinically relevant DDIs. Another scientific advancement is the recognized need to identify the impact of pharmacogenetic polymorphisms on DDIs.

Technology can serve as the equalizing force in combating the dangers of multimedication use that plague our nation. This strategy includes the use of a proprietary clinical decision support system (CDSS) that provides rapid access to drug pharmacology data and risk algorithms to allow a clinician to formulate meaningful decisions and recommendations about a patient’s entire medication regimen. We also recognize that CDSS are never a substitute for the expert judgment of clinicians. Thus, education and training of clinicians is not only indispensable but also must be ongoing to keep pace with necessary updates to the CDSS.

The danger of multimedication use is 1 of the most sustainable epidemics that our country has ever faced. Within the next 10 years, the baby boomer generation will more than double, exponentially increasing medication utilization and the costs associated with medication-related problems. Disruptive innovations in technology, such as CDSS that simultaneously assess multiple drug interactions from a patient’s entire medication regimen, are needed to combat these dangers. We must act now to deploy these technologies, or we will pay dearly in costs and lives.

1. Qato DM, Wilder J, Schumm LP, Gillet V, Alexander GC. Changes in prescription and over-the-counter medication and dietary supplement use among older adults in the United States, 2005 vs 2011. JAMA Intern Med. 2016;176(4):473-482.

2. Hajjar ER, Hanlon JT, Sloane RJ, et al. Unnecessary drug use in frail older people at hospital discharge. J Am Geriatr Soc. 2005;53(9):1518-1523.

3. Doan J, Zakrzewski-Jakubiak H, Roy J, Turgeon J, Tannenbaum C. Prevalence and risk of potential cytochrome P450-mediated drug-drug interactions in older hospitalized patients with polypharmacy. Ann Pharmacother. 2013;47(3):324-332.

4. Onder G, Petrovic M, Tangiisuran B, et al. Development and validation of a score to assess risk of adverse drug reactions among in-hospital patients 65 years or older: the GerontoNet ADR risk score. Arch Intern Med. 2010;170(13):1142-1148.

5. Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ, Budnitz DS. US emergency department visits for outpatient adverse drug events, 2013-2014. JAMA. 2016;316(20):2115-2125.

6. Budnitz DS, Lovegrove MC, Shehab N, Richards CL. Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365(21):2002-2012.

7. Bootman JL, Harrison DL, Cox E. The health care cost of drug-related morbidity and mortality in nursing facilities. Arch Intern Med. 1997;157(18):2089-2096.

8. Johnson JA, Bootman JL. Drug-related morbidity and mortality and the economic impact of pharmaceutical care. Am J Health Syst Pharm. 1997;54(5):554-558.

9. Ernst FR, Grizzle AJ. Drug-related morbidity and mortality: updating the cost-of-illness model. J Am Pharm Assoc (Wash). 2001;41(2):192-199.

10. 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.

11. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004;291(10):1238-1245.

12. Bain KT, Knowlton CH, Turgeon J. Medication risk mitigation: coordinating and collaborating with health care systems, universities, and researchers to facilitate the design and execution of practice-based research. Clin Geriatr Med. 2017;33(2):257-281.

13. Turgeon J, Michaud V. Clinical decision support systems: great promises for better management of patients' drug therapy. Expert Opin Drug Metab Toxicol. 2016;12(9):993-995.

14. Tannenbaum C, Sheehan NL. Understanding and preventing drug-drug and drug-gene interactions. Expert Rev Clin Pharmacol. 2014;7(4):533-544.

15. Gurwitz JH, Field TS, Rochon P, et al. Effect of computerized provider order entry with clinical decision support on adverse drug events in the long-term care setting. J Am Geriatr Soc. 2008;56(12):2225-2233.

16. Roblek T, Vaupotic T, Mrhar A, Lainscak M. Drug-drug interaction software in clinical practice: a systematic review. Eur J Clin Pharmacol. 2015;71(2):131-142.

17. Roe S, Long R, King K. Pharmacies miss half of dangerous drug combinations. Chicago Tribune. December 15, 2016. http://www.chicagotribune.com/news/watchdog/druginteractions/ct-drug-interactions-pharmacy-met-20161214-story.html. Accessed August 20, 2018.


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