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Original Investigation |

Discrepancies in the Use of Medications:  Their Extent and Predictors in an Outpatient Practice FREE

Susanna E. Bedell, MD; Samer Jabbour, MD, MPH; Robert Goldberg, PhD; Helene Glaser, RN; Susan Gobble, MBA; Yinong Young-Xu, MA; Thomas B. Graboys, MD; Shmuel Ravid, MD
[+] Author Affiliations

From Lown Cardiovascular Center, Brookline, Mass (Drs Bedell, Jabbour, Graboys, and Ravid, Ms Glaser, and Mr Young-Xu); The Department of Medicine, Harvard Medical School (Drs Bedell, Jabbour, Graboys, and Ravid), and the Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Medical School (Dr Goldberg), Boston; and the Department of Medicine, Memorial Health Services, Long Beach, Calif (Ms Gobble).


Arch Intern Med. 2000;160(14):2129-2134. doi:10.1001/archinte.160.14.2129.
Text Size: A A A
Published online

Background  Misuse of medications is a major cause of morbidity and mortality. Few studies have examined the frequency of, and factors associated with, discrepancies between what doctors prescribe and what patients take in actual practice.

Patients and Methods  Patients' medication bottles and their reported use of medications were compared with physicians' records of outpatients seen between November 1997 and February 1998 in a private practice affiliated with an academic medical center in Boston, Mass. Three hundred twelve patients from the practices of 5 cardiologists and 2 internists who were returning for their routine follow-up visits were included.

Main Outcome Measure  The presence of discrepancies based on comparing medication bottles with medical records.

Results  Discrepancies were present in 239 patients (76%). The 545 discrepancies in these patients were the result of patients taking medications that were not recorded (n = 278 [51%]); patients not taking a recorded medication (n = 158 [29%]); and differences in dosage (n = 109 [20%]). Overall, discrepancies were randomly distributed among different drugs and discrepancy types with no discernible pattern. On multivariate analysis, patient age and number of recorded medications were the 2 most significant predictors of medication discrepancy.

Conclusions  Discrepancies among recorded and reported medications were common and involved all classes of medications, including cardiac and prescription drugs. Older age and polypharmacy were the most significant correlates of discrepancy. The pervasiveness of discrepancies can have significant health care implications, and action is urgently needed to address their causes. Such action would likely have a positive impact on patient care.

IN THIS ERA of polypharmacy, extensive literature has documented the growing problem of adverse drug reactions, misuse of medications, and significant cost implications of drug-related morbidity and mortality.16 While these problems affect all segments of society, they are especially prevalent among the elderly, a group that is especially vunerable because it comprises individuals who often have multiple medical conditions and therefore need multiple medications.5,79 Errors and noncompliance in the use of medications involve all types of drugs, including those that may be lifesaving, such as cardiac medications, and the resultant morbidity and mortality can be significant.1015 Understanding the magnitude and cause of medication misuse is essential to devising adequate strategies to control this problem. Understanding medication misuse is especially important in the outpatient setting, where there is opportunity to address associated risk factors. Currently, more is known about adherence to medications and less about discrepancy.16 The present study was carried out in an outpatient practice setting to assess the magnitude of the discrepancies between what drugs are documented in the medical record and the medications that patients actually take, to identify the types of discrepancies, and to examine factors associated with such discrepancies.

STUDY SETTING

The practice setting was the physician offices of 5 board-certified cardiologists and 2 board-certified internists, all of whom were affiliated with the same academic medical center. All but 1 physician had practiced for more than 15 years. In general, physicians saw their patients on an annual or as-necessary basis. On average, they spent 1 hour with a new patient and 30 minutes with established patients. Medication changes by the primary care or covering physician were documented in the medical record. A cardiovascular fellow responded to patient calls after office hours and was instructed to document in the charts any recommended changes in the use of medications.

DATA COLLECTION

The medical record of each patient contained a list of the patient's current medications, which was shared by all health professionals involved in the patient's care, both in the office and in the hospital. This list was reviewed and updated at each office visit and became part of the medical note dictated on the day of the patient visit. It was also updated whenever prescriptions were renewed or added outside the office visit. This has always been the established process in our practice. The expectation was that the drug list would contain information about the use of over-the-counter medications. An assistant to the 2 internists, but not the cardiologists, at times verified the list of medications with the patient at the time of the office visit.

Information was abstracted from the medical record about the patient's sex, age, number of medications currently prescribed, person(s) responsible for the administration and supervision of the medications, whether other physicians participated in the patient's care, number of years the patient had been with the physician office, and date of the patient's last office visit.

Between November 1, 1997, and February 28, 1998, all patients scheduled for a visit with one of the physicians in the practice were called by a research assistant on the day before their appointment. Each day, the patients of a different physician, assigned randomly, were interviewed so that there would be an equal opportunity to sample patients from all physicians in the practice. Patients were asked to bring all their drugs (prescription and over-the-counter) and medicated creams to the office visit. Random samples of patients were selected from the practices of all physicians.

The research assistant (H.G. or S.G.) specifically asked patients to confirm that the medication bottles they brought with them accurately reflected the name, dosage, and timing of the drugs taken at home. She noted the labels on the medication bottles but recorded the patients' comments about what medications they actually took to confirm the instructions on these labels. She compared this information with the list of medications recorded in the chart. She also determined whether the patient took any additional medications that were not on the medication list and whether the patient was responsible for taking his/her own medications. At the end of the structured interview, open-ended questions were asked to evaluate factors determining medication usage and to elicit patients' concerns and comments about their medications.

DATA ANALYSIS

We defined medication discrepancy as the difference between the list of medications in the medical record (referred to as recorded medications) and what a patient actually took based on medication bottles and on self-reports to the trained research assistant (referred to as reported medications). We categorized medications into 5 groups: (1) over-the-counter medications, including vitamins/minerals, acetaminophen, decongestants, and gastrointestinal remedies such as antacids or histamine2 blockers; (2) anti-inflammatory medications, including aspirin; (3) psychoactive medications, including sleeping pills, antidepressants, and anxiolytics; (4) cardiac medications; and (5) other prescription drugs. Because of the importance of cardiac medications in our large cardiology practice, we further subdivided these medications into 8 groups: (1) β-blockers; (2) calcium channel blockers; (3) nitrates (nitroglycerine and long-acting nitrates); (4) angiotensin-converting enzyme inhibitors; (5) lipid-lowering drugs; (6) diuretics; (7) warfarin sodium (Coumadin); and (8) others, including antihypertensive agents (other than those noted in categories 1, 2, 4, and 6).

For each medication group, we determined whether there was a discrepancy in the type or dosage of medication. We noted whether the disparity occurred because the patient was taking a medication not listed on the medical record or because he/she was not taking a documented medication.

STATISTICAL ANALYSIS

We used t tests for 2-group comparisons of continuous variables, and χ2 analysis or the Fisher exact test of statistical significance for comparisons of proportions. To identify multivariate adjusted predictors of medication discrepancy, we conducted 2 sets of analyses using different definitions of discrepancy. The first regression model, which we used to examine factors associated with medication discrepancy, included discrepancies related to over-the-counter medications and dosage differences, while the second analysis excluded these discrepancies. The rationale behind these modeling approaches was to remove the effects of discrepancies of lesser clinical significance. We used logistic regression to identify factors associated with discrepancy, while controlling for potentially confounding variables. Univariate associations of independent covariates, such as age and sex, with medication discrepancy were initially determined. Clinically relevant 2-way interactions were examined after the initial data categorization. Clinically relevant or statistically significant variables, including interaction terms, were entered into the final regression models. The most predictive and parsimonious models were selected. Hosmer-Lemeshow goodness-of-fit testing was performed on selected regression models, and likelihood ratio testing was performed to compare different models. We used a significance level of .05. All P values were 2-sided.

The study sample included 312 white patients; 48% were men and the mean age was 62 years. Table 1 describes the clinical characteristics of the patient sample. We stratified the sample according to the specialty of the responsible physician in our office because the patient populations seen by internists and cardiologists might be different.

Table Graphic Jump LocationTable 1. Demographic Data by Physician Specialty*

Patients seen by internists were significantly younger. There was a nonsignificant trend toward an increased number of recorded medications in internal medicine patients when compared with cardiology patients (6.2 vs 5.5, P = .07). This trend was most likely the result of greater use of nonprescription drugs. The majority of patients had established long-term relationships with their physicians. Most patients were responsible for administering their own medications, and the majority had other physicians who also participated in their care.

MEDICATION DISCREPANCIES

In 239 (76%) of the 312 patients, a total of 545 medication discrepancies were identified. Table 2 summarizes patient characteristics according to the presence of discrepancy.

Table Graphic Jump LocationTable 2. Patient Characteristics According to Presence of Medication Discrepancy

Medication discrepancy occurred equally among men and women. Overall, patients with discrepancy were significantly older. The percentage of discrepancies in different age groups was as follows: younger than 40 years, 47%; 40 through 49 years, 85%; 50 through 59 years, 73%; and 60 years and older, 82%. Patients who had other physicians participate in their care were more likely to have discrepancies. This is not surprising, as these patients were significantly older (mean age, 64 vs 49 years) and took more medications (mean number of medications, 6.0 vs 3.9) (P<.001 for both). Similarly, patients cared for by cardiologists were more likely to have medication discrepancies, as they too were significantly older (mean age, 67 vs 53 years; P<.001). Patients with discrepancies had a longer association with our practice and a greater number of medications listed on their medical records.

Most discrepancies, 278 (51%), were attributable to patients taking medications that were not recorded. The rest of the discrepancies were attributable to patients not taking a recorded medication (29%) or to differences in dosage (20%). The distribution of discrepancies according to medication type is shown in Table 3. While over-the-counter medications were the single largest category, 61% of discrepancies involved prescription medications.

Table Graphic Jump LocationTable 3. Detailed Medication Discrepancies by Drug Type and by Subspecialty

Discrepancies in cardiac medications according to subcategory and discrepancy type were noted (Table 4). Inconsistencies for nitrates were the most frequent, followed by diuretics, angiotensin-converting enzyme inhibitors, and β-blockers. Patients were as likely to have a discrepancy owing to a difference in dosage, taking an unrecorded medication, or not taking a recorded medication. Overall, discrepancies were randomly distributed among different drugs and discrepancy types, with no discernible pattern noted.

Table Graphic Jump LocationTable 4. Cardiac Medication Discrepancies by Type
PREDICTORS OF DISCREPANCY

We determined the association between any medication discrepancy and demographic and clinical variables previously examined (Table 5). In univariate analyses, the following covariates were associated with the presence of any discrepancy: patient age, physician specialty, participation of another physician in patient care, years with the physician, and the number of recorded medications. On multivariate analysis, patient age and the number of recorded medications were the 2 most significant predictors of discrepancy. As expected, we found a significant interaction between the number of medications on the medical record and patient age, with older patients taking more medications. We included this interaction term in the regression models because we thought that it was of clinical significance and had an impact on the assessment of the effect of other variables. There was evidence for a nonsignificant trend toward increased discrepancy when the patient was female, when the managing physician was a cardiologist rather than an internist, and when another physician participated in patient care.

Table Graphic Jump LocationTable 5. Crude and Multivariate Predictors of Medication Discrepancy*

Finally, we examined factors associated with medication discrepancy, excluding over-the-counter medications and dosage discrepancies, separately and jointly (Table 5). Age and number of recorded medications remained significant predictors of discrepancy even though the magnitude of effect for each of these variables changed. Participation of another physician was the most significant predictor of this type of discrepancy.

PATIENTS' COMMENTS

Comments from patients were informal, rather than quantified, but we did elicit meaningful responses about their expressed concerns. We categorized patients' feedback about their medications into the following 4 areas:

  • Desire for more information. Patients wanted more details from their physicians about how the drug they were prescribed would help their symptoms or how it would interact with other medications.

  • Concerns about adverse effects. Adverse effects that were important to patients were often vague, such as "feeling blah" or "feeling not myself." Some patients worried that the dose of the medication they took was "too much." Specific complaints most often focused on loss of libido or concern about liver toxicity.

  • Obstacles from convenience or cost. Convenience in taking medications and filling prescriptions was more important to our patients than medication costs. Even patients on a twice-daily medication regimen asked to substitute it for a once-a-day medication. Patients wanted more medications with each prescription to avoid the delay or inconvenience of frequent refills. Some wondered whether splitting a stronger pill would offer them savings.

  • Influence of multiple physicians. The majority of patients' comments focused on the problems of having multiple physicians involved in their care. Many patients complained about lack of ready access to subspecialists and that their primary care physician made medication changes without consulting a specialist. One patient, for example, said he had been "easing off" all his medications because his primary care physician said that it would be fine to do so as long as he "felt all right." Medication lists were often modified after the patients were discharged from the hospital, and patients were not always aware of the need to inform their physician in our office of changes that were made in their regimen outside the practice.

This study demonstrates that there is considerable discrepancy between recorded and reported medications in the majority of cases in our academic outpatient private practice. The discrepancies include all medications, prescription and nonprescription, and were of different types, including discrepancies in dosages, not taking recorded medications, and taking nonrecorded medications. One third of the discrepancies involved over-the-counter drugs or herbal therapies. Miscommunication about herbal therapies is relatively common because patients often self-prescribe without consulting or informing their physicians. Adverse effects from such therapies are not necessarily trivial.1719

The majority of discrepancies occurred with prescription drugs and a full quarter with cardiac drugs. These findings are especially striking because medical therapy has been the foundation of medical care in our practice and the basis of successful outcomes.20 While the extent of medication discrepancies in our study was higher than in previous reports, this difference likely reflected the meticulous effort given to correct identification of medications taken and the uniqueness of our study in using the patients' medication bottles rather than patients' diaries, computer printouts, or pharmacy records to verify the presence of discrepancy.21,22 Consistent with the report of Monson and Bond,23 we found that the more drugs a patient takes, the more likely that there will be a discrepancy.21

The existing literature on medication use and misuse has primarily focused on one aspect of discrepancy, namely patient compliance, which assesses the failure of patients to adhere to prescribed medications. Our study highlights the larger picture of discrepancy and extends the previous work of Wagner and Hogan22 demonstrating that what medications a patient takes does not depend on volition alone. Other factors, such as miscommunication among physicians or between physicians and patients, can play an important role, as suggested by other reports.21,24 Our patients were sometimes following another physician's orders, frequently outside our office practice, when they failed to take prescribed medications or took additional nonrecorded drugs. The differences between the definitions of noncompliance and discrepancy notwithstanding, existing data on high rates of noncompliance are consistent with the present findings.2529 A unique aspect of our study was that we took into consideration the patients' perspectives; similar to previous reports about medication compliance, we observed that concerns about the convenience of taking medications, filling prescriptions, and adverse effects were most important for our patients.

Another method to determine the medications patients actually take is to evaluate computerized drug databases, such as pharmacy records.23 While this method has the advantage of improving efficiency in larger studies and can serve as a surrogate for pill counting, it may be impractical to use in an outpatient setting where drugs are obtained from multiple sources. On the other hand, self-reporting of drug intake may be subject to recollection bias but provides the clinician with ready access to important information if meticulously performed.

FACTORS ASSOCIATED WITH MEDICATION DISCREPANCY

Older age and a higher number of recorded medications were strongly associated with medication discrepancies. Consistent with a previous report, our findings demonstrated that discrepancy was as likely to involve prescription and potentially toxic medications as over-the-counter medications.21 Our finding that older age is associated with medication discrepancy seems plausible clinically, but, to our knowledge, prior studies have not evaluated the influence of age on discrepancy.21,24,30 In fact, within groups of patients older than 65 years, older age is associated with better compliance with antihypertensive therapy and with treatment of congestive heart failure.26,31 Reports on compliance or likelihood of adverse drug reactions have not found an association with advancing age.7,3234 We found nonsignificant trends toward increased discrepancy in cases involving female patients, in cases involving patients managed by cardiologists as compared with internists, and in cases in which there was participation of another physician in patient care. A long-term patient-physician relationship did not diminish the likelihood of medication discrepancy.

We expected a lower number of discrepancies, considering that our patient population was well educated and of high socioeconomic status. To our knowledge, there is no information in the published literature that indicates how socioeconomic status may affect medication discrepancy, and the relationship may be more complicated than we expected.

STUDY LIMITATIONS

There are several limitations to the present observational study. First, while the study sample was representative of our practice as a whole, our results might not be generalizable to other clinical settings, to patients enrolled in health maintenance organizations, or to patients of low to moderate socioeconomic status. Second, our data did not allow us to separate discrepancies that were caused by improper practice in our office from those resulting from a lack of communication from an outside physician or those resulting from patients acting independently. Third, we did not collect information on comorbid illnesses and thereby did not assess their influence on discrepancy. Fourth, we may have found fewer discrepancies had we assessed medications in the medical record after a physician visit rather than before. However, the discrepancies we identified reflected the realities of day-to-day use of the medical record in patient care. Also, if the charts were to be reviewed after the patient visit, physicians aware of the study may have attempted to take a more accurate medication history, thereby introducing bias. Finally, this study did not assess the impact of medication discrepancy on patients' outcomes. However, studies on noncompliance have clearly documented the association between medication misuse and adverse health outcomes, in both the outpatient and the inpatient settings.4,3539 This medication misuse may have a major impact on outcomes for many illnesses, including cardiovascular disease.1113,15 For example, discontinuing certain cardiac medications, such as β-blockers, may be detrimental to patients with coronary disease, triggering potentially life-threatening arrhythmias or myocardial infarction.13

RECOMMENDATIONS

There are several possible solutions to improve medication accuracy. Our findings suggest that a compulsive, specific, and systematic review of the patient's medication bottles should become a standard element in the patient's care. Although this system may seem time consuming and cumbersome, it is unlikely to outweigh the cost of medication misuse for patients with chronic or comorbid illnesses. It will ensure accuracy and identify any change in medications, whether initiated by an independent or noncompliant patient or by a physician who fails to communicate his/her adjustment of medications. Critical review of the medication list should emphasize the simplest, most parsimonious prescribing regimen.40 Communication among primary care physicians and subspecialists, such as cardiologists, clearly needs to improve to achieve greater accuracy in medication use and instruction.

Other proposals that have been developed in the past to enhance medication accuracy include a telecommunications system for monitoring drugs,41,42 continuous electronic monitoring of medication containers,43 follow-up by pharmacists,44 computerized prescribing,45 "one-write" noncarbon prescription forms,46 or use of a standardized drug questionnaire.47 We hope to initiate a program of pharmacy bar code labeling into our practice so that drugs can be mechanically recorded at the time of each visit and printed up for the physician to review. This system allows physicians to track changes in medications initiated by other physicians or during hospitalizations. Patients' input should be carefully sought before adopting any solution to ensure feasibility and relevance to patients' preferences.

Discrepancies among recorded and reported medications were common in our study; they occurred in 75% of the patients. Discrepancies involved all classes of medications, including cardiac and prescription drugs. Older age and an increasing number of prescribed drugs were the most significant correlates of discrepancy. The pervasiveness of medication discrepancy may have significant health care implications that deserve further study. Action is needed to address the variety of causes that may have an impact on discrepancy. Such action would likely have a positive impact on patient care, patient-physician relationships, and long-term outcomes.

Accepted for publication January 11, 2000.

This study was supported in part by the Lown Cardiovascular Research Foundation, Brookline, Mass, and by the Grimshaw-Gudewicz Charitable Foundation, Fall River, Mass.

Reprints: Susanna E. Bedell, MD, Lown Cardiovascular Center, 21 Longwood Ave, Brookline, MA 02446 (e-mail: Bambil@tiac.net).

Brennan  TALeape  LLLaird  N  et al.  Incidence of adverse events and negligence in hospitalized patients: results from the Harvard Medical Practice Study I. N Engl J Med. 1991;324370- 376
Bates  DWCullen  DLaird  N  et al.  Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA. 1995;27429- 34
Lesar  TSBriceland  LStein  D Factors related to errors in medication prescribing. JAMA. 1997;277312- 317
Classen  DCPestotnik  SLEvans  RSLloyd  JFBurke  JP Adverse drug events in hospitalized patients: excess length of stay, extra costs, and attributable mortality. JAMA. 1997;277301- 306
Colley  CALucas  LM Polypharmacy: the cure becomes the disease. J Gen Intern Med. 1993;8278- 283
Johnson  JABootman  LJ Drug-related morbidity and mortality: a cost-of-illness model. Arch Intern Med. 1995;1551949- 1956
Gurwitz  JHAvorn  J The ambiguous relation between aging and adverse drug reactions. Ann Intern Med. 1991;114956- 966
Monette  JGurwitz  JHAvorn  J Epidemiology of adverse drug events in the nursing home setting. Drugs Aging. 1995;7203- 211
Classen  DCPestotnik  SLEvens  RSBurke  JP Computerized surveillance of adverse drug events in hospital patients. JAMA. 1991;2662847- 2851
Coronary Drug Project Research Group, Influence of adherence to treatment and response of cholesterol on mortality in the Coronary Drug Project. N Engl J Med. 1980;3031038- 1041
Gallagher  EJViscoli  CMHorwitz  RI The relationship of treatment adherence to the risk of death after myocardial infarction in women. JAMA. 1993;270742- 744
Horwitz  RIViscoli  CMBerkman  L  et al.  Treatment adherence and risk of death after a myocardial infarction. Lancet. 1990;336542- 545
Psaty  BMKoepsell  TDWagner  EHLoGerfo  JPInui  TS The relative risk of incident coronary heart disease associated with recently stopping the use of β-blockers. JAMA. 1990;2631653- 1657
Miller  NH Compliance with treatment regimens in chronic asymptomatic diseases. Am J Med. 1997;10243- 49
McDermott  MMSchmitt  BWallner  E Impact of medication nonadherence on coronary heart disease outcomes. Arch Intern Med. 1997;1571921- 1929
Gurwitz  JHYeomans  SMGlynn  RJLewis  BELevin  RMAvorn  J Patient noncompliance in the managed care setting: the case of medical therapy for glaucoma. Med Care. 1998;36357- 369
Pillans  PI Toxicity of herbal products. N Z Med J. 1995;108469- 471
McRae  S Elevated serum digoxin levels in a patient taking digoxin and Siberian ginseng. CMAJ. 1996;155293- 295
Not Available, Ginkgo biloba for dementia. Med Lett Drugs Ther. 1998;4063- 64
Graboys  TBBlatt  CMRavid  S Optimal medical therapy reduces referrals for invasive cardiovascular procedures. Am Coll Cardiol Curr J Rev. January/February1997;81- 84
Straka  RJFish  JTBenson  SRSuh  JT Patient self-reporting of compliance does not correspond with electronic monitoring: an evaluation using isosorbide dinitrate as a model drug. Pharmacotherapy. 1997;17126- 132
Wagner  MMHogan  WR The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc. 1996;3234- 244
Monson  RABond  CA The accuracy of the medical record as an index of outpatient drug therapy. JAMA. 1978;2402182- 2184
Cramer  JAMattson  RHPrevey  MLScheyer  RDOullette  VL How often is medication taken as prescribed? JAMA. 1989;2613273- 3277
Monane  MBohn  RLGurwitz  JHGlynn  RJAvorn  J Noncompliance with congestive heart failure therapy in the elderly. Arch Intern Med. 1994;154433- 437
Rudd  PTul  VBrown  KDavidson  SMBostwick  GJ Hypertension continuation adherence: natural history and role as an indicator condition. Arch Intern Med. 1979;139545- 549
Skaer  TLSclar  DARobison  LM  et al.  Effect of pharmaceutical formulation for antihypertensive therapy on health service utilization. Clin Ther. 1993;15715- 725
Dekker  FWDieleman  FEKaptein  AAMulder  JD Compliance with pulmonary medication in general practice. Eur Respir J. 1993;6886- 890
Price  DCooke  JSingleton  SFeely  M Doctors' unawareness of the drugs their patients are taking: a major cause of overprescribing? Br Med J (Clin Red Ed). 1986;29299- 100
Monane  MBohn  RLGurwitz  JHGlynn  RJLevin  RAvorn  J Compliance with antihypertensive therapy among elderly Medicaid enrollees: the roles of age, gender, and race. Am J Public Health. 1996;861805- 1808
Horwitz  RIHorwitz  SM Adherence to treatment and health outcomes. Arch Intern Med. 1993;1531863- 1868
Col  NFanale  JEKronholm  P The role of medication noncompliance and adverse drug reactions in hospitalizations of the elderly. Arch Intern Med. 1990;150841- 845
Murphy  JCoster  G Issues in patient compliance. Drugs. 1997;54797- 800
Friedman  GDCollen  MFHarris  LEVan Brunt  EEDavis  LS Experience in monitoring drug reactions in outpatients: The Kaiser-Permanente Drug Monitoring System. JAMA. 1971;217567- 572
Steel  KGertman  PMCrescenzi  CAnderson  J Iatrogenic illness on a general medical service at a university hospital. N Engl J Med. 1981;304638- 642
Shapiro  SSlone  DLewis  GPJick  H Fatal drug reactions among medical inpatients. JAMA. 1971;216467- 472
Lazarou  JPomeranz  BHCorey  PN Incidence of adverse drug reactions in hospitalized patients. JAMA. 1998;2791200- 1205
Bootman  JLHarrison  DLCox  E The health care cost of drug-related morbidity and mortality in nursing facilities. Arch Intern Med. 1997;1572089- 2096
Bedell  SEDeitz  DCLeeman  DDelbanco  TL Incidence and characteristics of preventable iatrogenic cardiac arrests. JAMA. 1991;2652815- 2820
Sanson-Fisher  RWClover  K Compliance in the treatment of hypertension: a need for action. Am J Hypertens. 1995;882S- 88S
Friedman  RHKazis  LEJette  A  et al.  A telecommunications system for monitoring and counseling patients with hypertension: impact on medication adherence and blood pressure control. Am J Hypertens. 1996;9285- 292
Wasson  JGaudette  CWhaley  F  et al.  Telephone care as a substitute for routine clinic follow-up. JAMA. 1992;2671788- 1793
Kruse  WKoch-Gwinner  PNikolaus  TOster  PSchlierf  GWeber  E Measurement of drug compliance by continuous electronic monitoring: a pilot study in elderly patients discharged from hospital. J Am Geriatr Soc. 1992;401151- 1155
Muirhead  G Consenting adults. Drug Top. 1996;14056
Schiff  GDRucker  D Computerized prescribing: building the electronic infrastructure for better medication usage. JAMA. 1998;2791024- 1030
Miller  LGMatson  CCRogers  JC Improving prescription documentation in the ambulatory setting. Fam Pract Res J. 1992;12421- 429
Colvin  R Prescription Drug Abuse: The Hidden Epidemic.  Omaha, Neb Addicus Books Inc1998;21- 25

Figures

Tables

Table Graphic Jump LocationTable 1. Demographic Data by Physician Specialty*
Table Graphic Jump LocationTable 2. Patient Characteristics According to Presence of Medication Discrepancy
Table Graphic Jump LocationTable 3. Detailed Medication Discrepancies by Drug Type and by Subspecialty
Table Graphic Jump LocationTable 4. Cardiac Medication Discrepancies by Type
Table Graphic Jump LocationTable 5. Crude and Multivariate Predictors of Medication Discrepancy*

References

Brennan  TALeape  LLLaird  N  et al.  Incidence of adverse events and negligence in hospitalized patients: results from the Harvard Medical Practice Study I. N Engl J Med. 1991;324370- 376
Bates  DWCullen  DLaird  N  et al.  Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA. 1995;27429- 34
Lesar  TSBriceland  LStein  D Factors related to errors in medication prescribing. JAMA. 1997;277312- 317
Classen  DCPestotnik  SLEvans  RSLloyd  JFBurke  JP Adverse drug events in hospitalized patients: excess length of stay, extra costs, and attributable mortality. JAMA. 1997;277301- 306
Colley  CALucas  LM Polypharmacy: the cure becomes the disease. J Gen Intern Med. 1993;8278- 283
Johnson  JABootman  LJ Drug-related morbidity and mortality: a cost-of-illness model. Arch Intern Med. 1995;1551949- 1956
Gurwitz  JHAvorn  J The ambiguous relation between aging and adverse drug reactions. Ann Intern Med. 1991;114956- 966
Monette  JGurwitz  JHAvorn  J Epidemiology of adverse drug events in the nursing home setting. Drugs Aging. 1995;7203- 211
Classen  DCPestotnik  SLEvens  RSBurke  JP Computerized surveillance of adverse drug events in hospital patients. JAMA. 1991;2662847- 2851
Coronary Drug Project Research Group, Influence of adherence to treatment and response of cholesterol on mortality in the Coronary Drug Project. N Engl J Med. 1980;3031038- 1041
Gallagher  EJViscoli  CMHorwitz  RI The relationship of treatment adherence to the risk of death after myocardial infarction in women. JAMA. 1993;270742- 744
Horwitz  RIViscoli  CMBerkman  L  et al.  Treatment adherence and risk of death after a myocardial infarction. Lancet. 1990;336542- 545
Psaty  BMKoepsell  TDWagner  EHLoGerfo  JPInui  TS The relative risk of incident coronary heart disease associated with recently stopping the use of β-blockers. JAMA. 1990;2631653- 1657
Miller  NH Compliance with treatment regimens in chronic asymptomatic diseases. Am J Med. 1997;10243- 49
McDermott  MMSchmitt  BWallner  E Impact of medication nonadherence on coronary heart disease outcomes. Arch Intern Med. 1997;1571921- 1929
Gurwitz  JHYeomans  SMGlynn  RJLewis  BELevin  RMAvorn  J Patient noncompliance in the managed care setting: the case of medical therapy for glaucoma. Med Care. 1998;36357- 369
Pillans  PI Toxicity of herbal products. N Z Med J. 1995;108469- 471
McRae  S Elevated serum digoxin levels in a patient taking digoxin and Siberian ginseng. CMAJ. 1996;155293- 295
Not Available, Ginkgo biloba for dementia. Med Lett Drugs Ther. 1998;4063- 64
Graboys  TBBlatt  CMRavid  S Optimal medical therapy reduces referrals for invasive cardiovascular procedures. Am Coll Cardiol Curr J Rev. January/February1997;81- 84
Straka  RJFish  JTBenson  SRSuh  JT Patient self-reporting of compliance does not correspond with electronic monitoring: an evaluation using isosorbide dinitrate as a model drug. Pharmacotherapy. 1997;17126- 132
Wagner  MMHogan  WR The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc. 1996;3234- 244
Monson  RABond  CA The accuracy of the medical record as an index of outpatient drug therapy. JAMA. 1978;2402182- 2184
Cramer  JAMattson  RHPrevey  MLScheyer  RDOullette  VL How often is medication taken as prescribed? JAMA. 1989;2613273- 3277
Monane  MBohn  RLGurwitz  JHGlynn  RJAvorn  J Noncompliance with congestive heart failure therapy in the elderly. Arch Intern Med. 1994;154433- 437
Rudd  PTul  VBrown  KDavidson  SMBostwick  GJ Hypertension continuation adherence: natural history and role as an indicator condition. Arch Intern Med. 1979;139545- 549
Skaer  TLSclar  DARobison  LM  et al.  Effect of pharmaceutical formulation for antihypertensive therapy on health service utilization. Clin Ther. 1993;15715- 725
Dekker  FWDieleman  FEKaptein  AAMulder  JD Compliance with pulmonary medication in general practice. Eur Respir J. 1993;6886- 890
Price  DCooke  JSingleton  SFeely  M Doctors' unawareness of the drugs their patients are taking: a major cause of overprescribing? Br Med J (Clin Red Ed). 1986;29299- 100
Monane  MBohn  RLGurwitz  JHGlynn  RJLevin  RAvorn  J Compliance with antihypertensive therapy among elderly Medicaid enrollees: the roles of age, gender, and race. Am J Public Health. 1996;861805- 1808
Horwitz  RIHorwitz  SM Adherence to treatment and health outcomes. Arch Intern Med. 1993;1531863- 1868
Col  NFanale  JEKronholm  P The role of medication noncompliance and adverse drug reactions in hospitalizations of the elderly. Arch Intern Med. 1990;150841- 845
Murphy  JCoster  G Issues in patient compliance. Drugs. 1997;54797- 800
Friedman  GDCollen  MFHarris  LEVan Brunt  EEDavis  LS Experience in monitoring drug reactions in outpatients: The Kaiser-Permanente Drug Monitoring System. JAMA. 1971;217567- 572
Steel  KGertman  PMCrescenzi  CAnderson  J Iatrogenic illness on a general medical service at a university hospital. N Engl J Med. 1981;304638- 642
Shapiro  SSlone  DLewis  GPJick  H Fatal drug reactions among medical inpatients. JAMA. 1971;216467- 472
Lazarou  JPomeranz  BHCorey  PN Incidence of adverse drug reactions in hospitalized patients. JAMA. 1998;2791200- 1205
Bootman  JLHarrison  DLCox  E The health care cost of drug-related morbidity and mortality in nursing facilities. Arch Intern Med. 1997;1572089- 2096
Bedell  SEDeitz  DCLeeman  DDelbanco  TL Incidence and characteristics of preventable iatrogenic cardiac arrests. JAMA. 1991;2652815- 2820
Sanson-Fisher  RWClover  K Compliance in the treatment of hypertension: a need for action. Am J Hypertens. 1995;882S- 88S
Friedman  RHKazis  LEJette  A  et al.  A telecommunications system for monitoring and counseling patients with hypertension: impact on medication adherence and blood pressure control. Am J Hypertens. 1996;9285- 292
Wasson  JGaudette  CWhaley  F  et al.  Telephone care as a substitute for routine clinic follow-up. JAMA. 1992;2671788- 1793
Kruse  WKoch-Gwinner  PNikolaus  TOster  PSchlierf  GWeber  E Measurement of drug compliance by continuous electronic monitoring: a pilot study in elderly patients discharged from hospital. J Am Geriatr Soc. 1992;401151- 1155
Muirhead  G Consenting adults. Drug Top. 1996;14056
Schiff  GDRucker  D Computerized prescribing: building the electronic infrastructure for better medication usage. JAMA. 1998;2791024- 1030
Miller  LGMatson  CCRogers  JC Improving prescription documentation in the ambulatory setting. Fam Pract Res J. 1992;12421- 429
Colvin  R Prescription Drug Abuse: The Hidden Epidemic.  Omaha, Neb Addicus Books Inc1998;21- 25

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