Improving Prescribing Patterns for the Elderly Through an Online Drug Utilization Review Intervention a System Linking the Physician, Pharmacist, and Computer

Improving Prescribing Patterns for the Elderly Through an Online Drug Utilization Review Intervention A System Linking the Physician, Pharmacist, and Computer

Mark Monane, MD, MS; Dipika M. Matthias, MBA; Becky A. Nagle, PharmD; Miriam A. Kelly, PhD, MEd

Context.—Pharmacotherapy is among the most powerful interventions to INDIVIDUALS aged 65 years and improve health outcomes in the elderly. However, since some medications are less older constitute 12% of the US popula- appropriate for older patients, systems approaches to improving pharmacy care tion; however, they consume approxi- 1 may be an effective way to reduce inappropriate medication use. mately 30% of prescribed medications. Objective.—To determine whether a computerized drug utilization review (DUR) Olderpatientsarepronetoadversedrug events (ADEs) because use of multiple database linked to a telepharmacy intervention can improve suboptimal medication medications regardless of age increases use in the elderly. the chance of ADEs2,3 and age-related Design.—Population-based cohort design, April 1, 1996, through March 31, physiologic changes alter the pharmaco- 1997. kinetic and pharmacodynamic proper- Setting.—Ambulatory care. ties of many drugs.4 The incidence of Patients.—A total of 23 269 patients aged 65 years and older throughout the ADEs in the elderly varies from 5% to United States receiving prescription drug benefits from a large pharmaceutical 35%, depending on the method used to 5 benefits manager during a 12-month period. define and measure the event. Adverse Intervention.—Evaluation of provider prescribing through a computerized drug events may result in the need for additional medications, disability, de- online DUR database using explicit criteria to identify potentially inappropriate drug creased quality of life and functioning, use in the elderly. Computer alerts triggered telephone calls to physicians by phar- hospitalization, or death.5 macists with training in geriatrics, whereby principles of geriatric pharmacology Some medications are particularly were discussed along with therapeutic substitution options. prone to precipitating ADEs. Beers et Main Outcome Measures.—Contact rate with physicians and change rate to al6 used a Delphi survey with a panel of suggested drug regimen. experts in geriatrics to develop explicit Results.—A total of 43 007 alerts were triggered. From a total of 43 007 criteria to identify medications that telepharmacy calls generated by the alerts, we were able to reach 19 368 should be avoided in older patients. The physicians regarding 24 266 alerts (56%). Rate of change to a more appropriate recommendation focused on drugs that therapeutic agent was 24% (5860), but ranged from 40% for long half-life benzo- should be avoided, excessive dosing, and excessive duration of treatment. A sub- diazepines to 2% to 7% for drugs that theoretically were contraindicated by patients’ set of these criteria was applied to the self-reported history. Except for rate of change of ␤-blockers in patients with chronic community-dwelling elderly in the Na- obstructive pulmonary disease, all rates of change were significantly greater than tional Medical Expenditure Survey in the expected baseline 2% rate of change. 1987, which showed that nearly 25% of Conclusions.—Using a system integrating computers, pharmacists, and physicians, our large-scale intervention improved prescribing patterns and quality of care and thus provides a population-based approach to advance geriatric From the Departments of Medical Affairs (Drs Monane, Nagle, and Kelly) and Health and Utilization clinical pharmacology. Future research should focus on the demonstration of Management (Ms Matthias), Merck-Medco Managed improved health outcomes resulting from improved prescribing choices for the Care, LLC, Montvale, NJ. elderly. Reprints: Mark Monane, MD, MS, Merck-Medco Managed Care, LLC, 100 Summit Ave, Montvale, NJ JAMA. 1998;280:1249-1252 07645 (e-mail: [email protected]).

Downloaded From: https://jamanetwork.com/ on 09/28/2021 Type of Drug Utilization Review (DUR) Alert by Therapeutic Model and DUR Change Rate than 65 years. Drug-disease criteria de-

Alerts, DUR Alert fined drugs that should not be used in an Description No.* Change Rate, %† older patient in the presence of a specific Drug-age interaction (n = 19 362) condition that could be aggravated by Long elimination half-life benzodiazepine hypnotics (flurazepam) 1679 40‡ the drug. The disease history informa- Antidiabetic (chlorpropamide) 728 33‡ tion was self-reported by the patient Short-acting barbiturates (pentobarbital, secobarbital) 44 25‡ during enrollment in the Partners for Long elimination half-life benzodiazepine anxiolytics 11 344 24‡ Healthy Aging Program,௡ a health man- (chlordiazepoxide, clorazepate, diazepam, quazepam) agement program for the elderly de- Anxiolytics (meprobamate) 835 23‡ signed and implemented by MMMC. Cardiovascular (methyldopa) 1300 22‡ Thesesenior-specificDURcriteriawere Anticholinergic antidepressants (amitriptyline, doxepin) 2856 17‡ then computerized and coded by the Na- Narcotic analgesic (meperidine, pentazocine) 576 19‡ tional Drug Code to identify prescrip- Maximum daily dose exceeded (n = 4532) tions requiring intervention. Intermediate- or short-acting benzodiazepine 4532 25‡ Pharmacist training for the DUR pro- (alprazolam, lorazepam, oxazepam, temazepam, triazolam) gram was conducted by a team of geri- Drug-disease interaction (n = 372) Any nonsteroidal anti-inflammatory drug and peptic ulcer disease 238 7‡ atric pharmacy experts at all of the 13 Any ␤-adrenergic receptor antagonist and 134 2 MMMC mail-service pharmacies. These chronic obstructive pulmonary disease pharmacists were instructed in both the pharmacy science around the DUR *Alert refers to specific computer-based DUR criteria with a recommended change in prescribing as categorized by drug-age (inappropriate use due to age of patient); maximum daily dose (prescribed level outside of therapeutic alerts, as well as communication theory dose); drug-disease (possible adverse reaction based on known disease state). to conduct telephone one-to-one educa- †DUR alert change rate is indicated by the percentage of events in which physicians were contacted and 11 recommended action was taken (change rate = [number of accepted recommendations/number of recommenda- tional outreach with physicians. If a tions] ϫ100). potentially unsafe medication was re- ‡PϽ.001 vs no change. quested, the computer sent the pharmacist a warning. The pharmacist subse- quently attempted to call the physician the elderly take at least 1 medication proximately 51 million Americans. Dur- to discuss the alert, possible therapeutic that should be avoided.7 ing the study period from April 1, 1996, alternatives, and applicable withdrawal Inappropriate prescribing in the el- through March 31, 1997, 2.3 million pa- recommendations.Theinterventionout- derly is often attributed to the lack of tients aged 65 years and older filled at comes included the following: (1) a dis- training in geriatrics in medical and least 1 prescription through an MMMC continuation or change in therapy, (2) no pharmacy education.8 An effective way mail-service pharmacy. In general, pa- changeintherapy,or(3)considerationof to overcome this problem may be tients use mail service more frequently a change in therapy at the next patient throughaconcurrentdrugutilizationre- to obtain maintenance medications for visit. Both the physician and the patient view (DUR) program. This type of uti- chronic diseases. We report on all pa- received an explanatory confirmation lization management system is designed tients targeted through our computer- letter in the mail if the original prescrip- to send a warning to pharmacists when ized DUR system with an actionable tion was changed. The prescription or potentially inappropriate drugs are pre- alert (alert triggering a conversation be- changes were then filled and dispensed scribed.9,10 tween physician and pharmacist) com- The warning provides an op- to the patient through the MMMC mail- pleted in the 1-year surveillance period portunity to educate the pharmacist and service channel. All relevant data per- (N = 23 269). physician through a discussion about the taining to the intervention were re- safety and effectiveness of a targeted corded electronically in MMMC DUR medication before it is dispensed. Study Intervention files. The purpose of this study was to An independent medical advisory evaluateaprogramdesignedtodecrease board, established by MMMC, consist- Statistical Analysis the use of potentially inappropriate ing of geriatric specialists in pharmacy, Frequency distributions as well as uni- medications among the elderly through medicine, and nursing adopted the cri- variate and bivariate statistics were com- a computer-based DUR intervention. teria of Beers et al6 to identify the most puted to measure use of the targeted medi- The intervention included the rationale dangerous drugs for the elderly from a cations and the number of physician for the alert, therapeutic alternatives, safety perspective. The MMMC Depart- contacts. The DUR change rate was de- and withdrawal protocols if necessary, ment of Medical Affairs developed an termined as a function of the number of and presented an opportunity to change integrated DUR education and inter- interventions completed during the thepotentiallyinappropriatemedication ventionprogramcenteredaroundacom- 1-year period of surveillance. Specifi- before it was dispensed. Within this puterized online database aimed at de- cally, the DUR change rate was equal to quality-of-care intervention, we mea- creasing the use of these potentially un- the percentage of events in which calls to sured the change rate to a more appro- safe drugs. physicians were completed and recom- priate medication in this population of These senior-specific criteria covered mended action was taken (DUR change elderly persons. 3 DUR categories: drug-age, maximum rate = [number of accepted recommenda- METHODS dailydose,anddrug-disease(Table).The tions/number of recommendations]ϫ100). drug-age category defined drugs with The overall change rate was calculated for Study Population pharmacokinetic, pharmacodynamic, or the 3 drug classes and separately for drugs All subjects were at least 65 years of ADE profiles known to be harmful in the within the 3 groups. We also used z tests age and receiving prescription benefits elderly and for which safer alternatives to determine the significance of these from Merck-Medco Managed Care, LLC exist. Maximum daily dose alerts were DUR change rates from 2%, a level re- (MMMC), a prescription benefits man- limited to the short-acting benzodiaz- ported in another comprehensive sum- ager that provides medications through epines, for which specific dosing recom- mary article to be the baseline rate of retail and mail pharmacy services for ap- mendations exist for individuals older change in prescribing over time.12,13

Downloaded From: https://jamanetwork.com/ on 09/28/2021 RESULTS There was marked variability in the prescribed by the physicians, which of- change rate for specific DUR rules ten is not the case.16,17 This evaluation A total of 43 007 alerts among 23 269 within the 3 categories as described may underestimate the extent of the elderly patients were triggered during above. The drug-age alert resulted in problem and overestimate the potential the study period across the 3 DUR cat- change rates of 17% to 40%: within this benefit of the intervention because it egories. The median age of the study category, the long elimination half-life was based on mail-service prescriptions population was 72 years (25%-75% inter- benzodiazepine hypnotics had the high- and did not include retail pharmacies. quartile range, 67-77 years); 24% were est rate of change at 40%. The maximum Additionally, because the response rate 80 years or older. Women constituted daily dose category included only 1 class to the health questionnaire among pa- 62% of the population; 35% of patients of drugs, the intermediate- or short-act- tients was only 25% and we had only lived in the South, 31% in the Midwest, ing benzodiazepines, and resulted in a limited clinical data beyond patients’ and 24% in the Northeast. Patients re- 25%rateofsuccess.Therewere2classes self-report diagnoses and prescription ceived a median number of 8 unique pre- ofdrugsincludedinthedrug-diseasecat- medication profile, this study may have scriptions during the study period. Ap- egory, with success rates ranging from underestimated the number of medica- proximately 25% of the patients com- 2% to 7%. The reasons physicians gave tion alerts. Nonetheless, tracking of pleted medical history information, re- for not changing were as follows: agreed prescribing patterns represents an in- porting an average of 6 comorbidities. with intervention but it was not appli- termediate outcome that can be easily The most common reported comorbid cable to the patient (55%; 10 123/18 406), monitored in a large database and facili- conditions included the following: (1) hy- disagreed with intervention (41%; 7546/ tates the identification and measure- pertension, (2) osteoarthritis, (3) hyper- 18 406), agreed with intervention but it ment of an important indicator of quality cholesterolemia, (4) peptic ulcer disease, was inconvenient for the patient (2%; of care. and (5) angina. 368/18 406), agreed with the interven- As reported earlier, when estimating The contact rate for reaching the tar- tion but the patient did not (1%; 184/ the impact on the quality of prescribing, geted physicians of these patients was 56% 18 406), or the physician terminated the one should not compare the overall ef- (24 266/43 007 alerts; average interven- call without giving an explanation (1%; fect size of 24% with a theoretical 100% tion time, 15 minutes), decreasing the 184/18 406). change rate, but rather against the 2% number of actionable alerts to 24 266. A baseline rate of change that occurs in total of 19 368 physicians were contacted COMMENT physicians’ prescribing over time.13 Fur- for the actionable alerts (average, 1.25 This large-scale study examined a thermore, the drug alerts chosen for this alerts per physician). Most physicians computerized online DUR database de- computer-based intervention represent were male (93%), between the ages of 40 signed to help reduce inappropriate pre- probable quality-of-care indicators and and 60 years (66%); 40% were located in scribing and improve quality of care in may be more effective studying a popu- the South. Approximately 1 actionable an elderly population. More than 43 000 lation than applying them in a case-by- alert was generated per patient (24 266 prescriptions in more than 23 000 pa- case setting. Also, the drug criteria used alerts per 23 269 patients). The overall tients were evaluated in this study. The here are likely to represent the most DUR change rate, defined as the percent- telepharmacy intervention yielded a clinically relevant issues in geriatric age of accepted recommendations di- contact rate with physicians greater pharmacy, thus small changes may have vided by the total number of recommen- than 50%, and the content of each inter- a major impact on clinical and economic dations, was 24% (N = 5860/24 266). vention call focused on quality-of-care outcomes.18,19 The validity of these drug Fifteen percent (3599/24 266) of alerts re- messages based on best practices as de- criteria is based on the best available sulted in immediate change to a thera- termined by the scientific literature and medical and pharmacy literature, as es- peutic alternative, and 9% (2261/24 266) re- clinical guidelines. Messages linking the tablished by consensus panels of experts sulted in a physician’s indication to review computer, pharmacist, and physician led or other methods to develop clinical pro- the therapeutic alternative at the pa- to improved quality of care with nearly a tocols.20 tient’s next visit. For the purpose of this quarter of all DUR alerts accepted by The physician contact rate of 56% was analysis, we present the DUR change rate physicians,butvariedfrom40%forlong- lessthanoptimalforimprovingtheover- as the sum of these 2 results above, since acting benzodiazepines to 2% for use of all care of older patients. While some more than 90% of these patients did not ␤-adrenergicreceptorantagonistagents physician feedback suggested an un- receive the targeted medication within the in patients with chronic obstructive pul- availability or unwillingness to partici- next 6 months. monary disease. Efforts to provide pate in our DUR intervention, other The data were first analyzed by type alerts to physicians at the point of care physicians stated their appreciation for of DUR alert (Table). The intervention are thought to provide ever greater this patient-specific information. Fur- had the greatest impact on alerts in the changesinprescribingandrepresentthe thermore,ifweconsideredallalertsgen- drug-age category (N = 19 362), result- next frontier for DUR intervention. erated and called on (N = 43 007) vs the ing in a 24% rate of change. Although the The use of prescription claims data of- actionable alerts when a physician was number of alerts was lower for the maxi- fers major advantages in drug surveil- successfully contacted (N = 24 266), the mum daily dose category (N = 4532), this lance, including the ability to document overall change rate drops from 24% to intervention resulted in a similar 25% all health service use without recall bias 14%. We believe the actionable rate is rate of change. Interventions involving or incomplete drug history. Yet the limi- the more appropriate figure to use as it the drug-disease category (N = 372) re- tationsofclaims-basedinformationmust estimates the population exposed to the sulted in a 5% rate of success. The over- be recognized.14,15 This study used infor- intervention. The ability to merge com- all DUR change rate compared with an mation on medications that were actu- puter online databases, pharmacist in- expected change rate of 2% was signifi- ally prescribed and dispensed and does tervention, and physician involvement cantatthePϽ.001level.Allchangerates notincludenonprescriptiondruguse.No will continue to be a major hurdle given were significant except for the ␤-adren- information is recorded on use of medi- resource constraints as discussed above. ergic receptor antagonist-chronic ob- cation prescribed and not dispensed in Yet within the current environment of structive pulmonary disease drug-dis- this analysis. In addition, we assumed cost and resource containment, our ease category. that patients took the medications as change rate may represent the best that

Downloaded From: https://jamanetwork.com/ on 09/28/2021 can be achieved, as well as a useful ba- sicians who stated they would “change ing overall health status, functional sta- rometer for physician feedback concern- medication later,” more than 90% of the tus, and quality of life.21,22 Third, an as- ing our intervention. targeted patients did not receive the in- sessment of the cost-effectiveness of the Despite these limitations, the results appropriate medication in the following interventionisneeded.Fourth,thetech- of this study suggest that such an inter- 6 months. While we did not recontact the nology of communication must be fur- vention based on computer-generated physicians, these data support the phy- ther examined, such as fax and Internet DUR linked to systems that enhance sicians’ initial decisions in response to approaches. Finally, the ideal scenario communication between pharmacists the DUR intervention. involves an intervention at the point of and physicians can be successful and can What is the future of such an interven- care, when the physician and patient can improve prescribing of drug therapy for tion program? The need for intervention discuss therapeutic options in the most large patient populations. Our change models targeted toward drugs with less logical place, the office setting. rates of 15% (immediate changes) and than optimal risk-benefit profiles in the The authors wish to thank Coleen Sullivan and Ed 9% (consider change later) are further elderly patient is evident, especially to Burleigh for data management; Zhongyun Zhao, evidence of the ability of our program to reduce drug-drug interactions. Second, PhD, for statistical support; David Bender and both track prescriptions and intervene the use of these drug-based quality in- Camelot Ives for program support; Larry Hirsch, on inappropriate medications before the dicatorsrepresentsanintermediateout- Steve Boccuzzi, Kevin Colgan, and Robert Epstein for their editorial assistance; and Kelly Serritella, medication reaches the patient. Fur- come as mentioned above, with the final Catrina Vella, and Lauren Giovanniello for their as- thermore, in a follow-up analysis of phy- end points that should be measured be- sistance in preparing this article.

References 1. Baum C, Kennedy DL, Forbes MB, Jones JK. 8. General Accounting Office. Prescription Drugs prevalence using drug consumption data. Am J Epi- Drug use in the United States in 1981. JAMA. 1984; in the Elderly. Washington, DC: General Account- demiol. 1995;141:782-787. 251:1293-1297. ing Office; 1995. Document GAO/HEHS-95-152. 16. Stewart RB, Caranasos GJ. Medication compli- 2. Gurwitz JH, Avorn J. The ambiguous relation 9. Lipton LL, Bird JA. Drug utilization review in ance in the elderly. Med Clin North Am. 1989;73: between aging and adverse drug reactions. Ann In- ambulatory settings: state of the science and direc- 1551-1563. tern Med. 1991;114:956-966. tions for outcomes research. Med Care. 1993;31: 17. Monane M, Bohn RL, Gurwitz JH, Glynn RJ, Levin 3. Stewart RB, Cooper JW. Polypharmacy in the 1069-1082. R, Avorn J. Compliance with antihypertensive therapy aged: practical solutions. Drugs Aging. 1994;6:449- 10. Classen DC, Pestotnik SL, Evans RS, Burke JP. among elderly Medicaid enrollees: the roles of age, gen- 461. Computerized surveillance of adverse drug events in der, and race. Am J Public Health. 1996;86:1805-1809. 4. Lindley CM, Tully MP, Paramsothy V, Tallis RC. hospital patients. JAMA. 1991;266:2847-2851. 18. Beers MH, Ouslander JG, Fingold SF, et al. Inappropriate medication is a major cause of ad- 11. Soumerai SB, Avorn JA. Principles of education Inappropriatemedicationprescribinginskillednurs- verse reactions in elderly patients. Age Aging. 1992; outreach: academic detailing to improve clinical de- ing facilities. Arch Intern Med. 1992;117:684-689. 21:294-300. cision making. JAMA. 1990;263:549-556. 19. Stuck A, Beers MH, Steiner A, et al. Inappro- 5. Hanlon JT, Schmader KE, Lewis JK. Adverse 12. Rosner B. Hypothesis testing: categorical data. priate medication use in the ambulatory elderly. drug reactions. In: Delafuente JC, Stewart RB, eds. In: Fundamentals of Biostatistics. Boston, Mass: Arch Intern Med. 1994;154:2195-2200. Therapeutics in the Elderly. Cincinnati, Ohio: Har- PWS Publishers; 1986:302-367 20. Beers MH. Explicit criteria for determining po- vey Whitney Books; 1995:212-224. 13. Soumerai SB, Avorn J. Economic and policy tentially inappropriate medication use by the elder- 6. Beers MH, Ouslander JG, Rollinger I, Brooks J, analysis of university based drug detailing. Med ly. Arch Intern Med. 1997;157:1531-1536. Reuben D, Beck JC. Explicit criteria for determin- Care. 1986;24:313-331. 21. Soumerai SB, Lipton HL. Computer-based ing inappropriate medication use in nursing homes. 14. Fisher ES, Whaley FS, Krushat WM, et al. The drug-utilization review: risk, benefit, or boon- Arch Intern Med. 1991;151:1825-1832. accuracy of Medicare’s hospital claims data: doggle? N Engl J Med. 1995;332:1641-1643. 7. Wilcox SM, Himmelstein DU, Woolhander S. In- progress has been made, but problems remain. Am 22. Groves RE. Therapeutic drug-use review for appropriate prescribing for the community-dwell- J Public Health. 1992;82:243-248. the Florida Medicaid program. Am J Hosp Pharm. ing elderly. JAMA. 1994;272:292-296. 15. Sartor F, Walckiers D. Estimate of disease 1985;42:316-319.

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