|Year : 2017 | Volume
| Issue : 1 | Page : 78-82
Medication discrepancies and potentially inadequate prescriptions in elderly adults with polypharmacy in ambulatory care
Juan Víctor Ariel Franco1, Sergio Adrián Terrasa2, Karin Silvana Kopitowski3
1 Research Area, Family and Community Medicine Division, Hospital Italiano de Buenos Aires, Buenos Aires; Department of Public Health, Instituto Universitario Hospital Italiano and Research Department, Hospital Italiano de Buenos Aires, Buenos Aires; Department of Research, Instituto Universitario Hospital Italiano, Buenos Aires; Department of Toxicology and Pharmacology, Universidad de, Buenos Aires, Argentina
2 Research Area, Family and Community Medicine Division, Hospital Italiano de Buenos Aires, Buenos Aires; Department of Public Health, Instituto Universitario Hospital Italiano and Research Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
3 Research Area, Family and Community Medicine Division, Hospital Italiano de Buenos Aires, Buenos Aires; Department of Research, Instituto Universitario Hospital Italiano, Buenos Aires, Argentina
|Date of Web Publication||18-Sep-2017|
Juan Víctor Ariel Franco
Family and Community Medicine Division, Hospital Italiano de Buenos Aires, Buenos Aires
Source of Support: None, Conflict of Interest: None
Objectives: The objective of this study is to describe the frequency and type of medication discrepancies (MD) through medication reconciliation and to describe the frequency of potentially inadequate prescription (PIP) medications using screening tool of older persons' prescriptions criteria. Design: Cross-sectional comparison of electronic medical record (EMR) medication lists and patient's self-report of their comprehensive medication histories obtained through telephone interviews. Inclusion criteria: Elderly individuals (>65 years old) with more than ten medications recorded in their EMR, who had not been hospitalized in the past year and were not under domiciliary care, affiliated to a private community hospital. Outcome Measures: The primary outcomes were the proportion of patients with MD and PIP. Secondary outcomes were the proportion of types of discrepancies and PIP. We analyzed possible associations between these variables and other demographic and clinical variables. Results: Out of 214 randomly selected individuals, 150 accepted to participate (70%). The mean number of medications referred to be consumed by patients was 9.1 (95% confidence interval [CI] =8.6–9.6), and the mean number of prescribed medications in their EMR was 13.9 (95% CI = 13.3–14.5). Ninety-nine percent had at least one discrepancy (total 1252 discrepancies); 46% consumed at least one prescription not documented in their EMR and 93% did not consume at least one of the prescriptions documented in their EMR. In 77% of the patients, a PIP was detected (total 186), 87% of them were at least within one of the following categories: Prolonged used of benzodiazepines or proton pump inhibitors and the use of aspirin for the primary prevention of cardiovascular disease. Conclusions: There was a high prevalence of MD and PIP within the community of elderly adults affiliated to a Private University Hospital. Future interventions should be aimed at reducing the number of PIP to prevent adverse drug events and improve EMR accuracy by lowering medications discrepancies.
Keywords: Medication discrepancies, medication reconciliation, polypharmacy, potentially inadequate prescriptions, screening tool of older persons' prescriptions criteria
|How to cite this article:|
Franco JV, Terrasa SA, Kopitowski KS. Medication discrepancies and potentially inadequate prescriptions in elderly adults with polypharmacy in ambulatory care. J Family Med Prim Care 2017;6:78-82
|How to cite this URL:|
Franco JV, Terrasa SA, Kopitowski KS. Medication discrepancies and potentially inadequate prescriptions in elderly adults with polypharmacy in ambulatory care. J Family Med Prim Care [serial online] 2017 [cited 2020 Jul 4];6:78-82. Available from: http://www.jfmpc.com/text.asp?2017/6/1/78/214962
| Introduction|| |
Approximately, one-third of patients over 60-year-old consume daily between 5 and 9 medications, and 12% consume ten or more. The risk of adverse drug events (ADE) increase significantly when the number of consumed medications is 5 or higher. Potentially inadequate prescriptions (PIPs) are those whose benefits are generally outweighed by their potential risks of ADE throughout inadequate dosing or duration of treatment, dangerous interactions or poor clinical effectiveness. PIP can also include the nonprescription of drugs with a significant clinical benefit. Medication discrepancies (MD) are those detected through medication reconciliation (MR). MR is a formal process for creating the most complete and accurate list possible of a patient's current medications and comparing the list to those in the patient record or medication orders. MR was the #8 Patient Safety Goals by the Joint Commission in 2005, and then it was suspended and reformulated within #3 Goal “Improving the safety of using medication.” MD are common in all clinical settings, ranging from 70% to 100%,, and about one-third are linked to potential harm. It is unclear whether interventions aimed at reducing MD prevents ADE or other harms since most of the research has been focused to inpatient settings and transitions of care. In one outpatient setting study, MR reduced the number of MD from 89% to 66%, though most of them were in minor discrepancies. Another study in our country used screening tool of older persons' prescriptions (STOPP) and Beers  criteria and found that approximately one-third of comorbid elderly adults had PIP.
This study aims to describe the frequency and type of both MD and PIP in a population of elderly adults with polypharmacy in a Private Academic Community Hospital of Buenos Aires, Argentina.
| Methods|| |
Study design and population
We performed a cross-sectional study. Random sequence generation was used to identify eligible participants from our hospital database (May 31, 2014) of elderly adults (65 year-old or older) with ten or more active prescriptions in their electronic medical records (EMR). An exclusion criterion was hospital admission or domiciliary care within the last 12 months.
We defined as primary outcomes the proportion of patients with MD and PIP. We defined as secondary outcomes the proportion of types of discrepancies and PIP. We also analyzed the association between the number of MD and PIP and other demographic and clinical variables.
A family physician ( first author initials, removed for blinding) called patients from the sample and invited them to participate in a telephone interview using a protocolized oral consent. If the patient accepted, then a form [Appendix I] [Additional file 1] was used to collect basic demographic characteristics (age, education, marital status) and the complete list of medications currently consumed by the patients (P-LIST). Each patient was called three times at a different time and day before catalogued as “nonrespondent.”
The P-LIST was then compared with the list present in the EMR (EMR LIST), and MD was consigned and classified [Table 1]. PIP was detected using STOPP criteria applied to the P-LIST. Since these criteria require in some cases clinical information when necessary the physician consulted the EMR or asked directly to the patient for information.
|Table 1: Basic demographic characteristics of those who accepted (n=150) to participate and those who did not (refused or were nonrespondent, n=64)|
Click here to view
Sample size and statistical analysis
Sample size calculation was based on an estimated proportion of MD of 75% and a semi-amplitude confidence interval (CI) of 7%. From previous experience in our institution, we estimated a response rate of approximately 50%. Therefore, a randomized sample of 214 patients was needed to achieve 150 individually completed telephone interviews.
We calculated summary statistic measurements using STATA 13 (StataCorp, College Station, Texas, USA) software. We used Chi-square test and two-sample t-test for dichotomous and continuous hypothesis testing respectively. Measures of associations were tested using regression models. We defined an alpha level of P = 0.05.
This study protocol and its oral consent form were approved by our Hospital's Research Ethics Committee.
| Results|| |
Out of the 214 randomly selected individual, 150 accepted to participate with a response rate of 70%. Twenty-eight declined the interview, and 36 were nonrespondent. There were no differences in sex, age, and number of prescriptions in the EMR between those who accepted and those who refused or were “nonrespondent” [Table 2].
|Table 2: Other demographic characteristics of those who accepted (n=150) and their 95% confidence intervals|
Click here to view
The majority of interviewees were women, and half of them were widows. The mean age was 78 years old. The mean number of medication referred to be consumed by patients was 9.1 (95% CI = 8.6–9.6), and the mean number of prescribed medications in their EMR was 13.9 (95% CI = 13.3–14.5). [Table 3] shows the additional characteristics.
|Table 3: Proportions of discrepancies found by type (95% confidence intervals)|
Click here to view
When comparing P-LIST with EMR LIST, a total of 1252 discrepancies were found. Ninety-nine percent of patients had at least one discrepancy. The most frequent discrepancy was that in which the patient was not consuming a prescribed medication in the EMR (93%, 95% CI = 88%–97%), and in a minority of patients (5%, 95% CI = 2%–9%) they were consuming a duplicated prescription (e.g. two types of benzodiazepines simultaneously). Other clinically relevant discrepancies are described in [Table 1]. The mean number of discrepancies per patient was 8.34 (95% CI = 7.65–9.04).
When performing linear regression analysis, we found that the number of prescribed medication was strongly associated with the number of MD even after adjusting by sex, age, household constitution, and marriage status [Figure 1]. For each additional prescription in the EMR, an additional mean of 0.9 MD could be found. No other variables were associated with the number of MD.
|Figure 1: Number of medication discrepancies and number of prescriptions|
Click here to view
Potentially inadequate prescriptions
Using STOPP criteria, 186 PIP were detected in 77% of patients (95% CI = 70%–83%). The mean number of PIP per patient was 1.24 (95% CI = 1.09–1.39). The number of PIP per patients is described in [Table 4].
|Table 4: Proportion of patients and number of potentially inadequate prescriptions|
Click here to view
Up to 87% of PIP involved three STOPP criteria: The prolonged use (>1 month) of benzodiazepines, the use of proton pump inhibitors for a period longer than 8 weeks and the use of aspirin for the primary prevention of cardiovascular disease [Table 5].
| Discussion|| |
MD was common between their prescriptions in their EMR, and the medication referred to be consumed by patients during their telephone interview. This is consistent with previous findings by Milone et al. where up to 98.5% of patients with 10 or more prescriptions had discrepancies when MR was performed by a pharmacist in a family medicine clinic. In that study, the most frequent source of discrepancies was “patient no longer taking medication” (54.1%) followed by “current medication not on list” with an average of 6.6 discrepancies per patient. In [Table 6], other experiences in MR are described. Possible factors associated with the high number of medication no longer taken by the patients are: The inadequate prescription of medication for acute conditions, the inadequate cancellation of old prescriptions when new treatments are indicated, conflicting prescriptions between multiple providers, low adherence, and insufficient stock.
|Table 6: Medication reconciliation and detection of medication discrepancies|
Click here to view
A large proportion of patients with PIPs was found in our study sample. Regueiro et al. found a lower proportion in a similar population in our country (21.3%), however, the most frequently found STOPP criteria were similar: Prolonged use of proton pump inhibitors, potentially inadequate use of aspirin and benzodiazepines. In a systematic review of studies using STOPP criteria to detect PIP, a wide range of prevalence of PIP was found (21%–79%), but the most commonly encountered were also the three most frequently found in our study.
There are several limitations in our study. The use of telephone interviews could have selected a population of elderly adults, nevertheless there was a high response rate and the demographic characteristics of responders were similar to those who did not. The recall could be a source of bias, especially in patients trying to remember long list of prescriptions or when medication taken by the patient and not registered in EMR could not be recalled. Our data collection method adapted from Stewart and Lynch  and Ekedahl et al. was not validated in our population, but was compatible with our current medical practice of comprehensive MR and review.
| Conclusions|| |
There is a high prevalence of MD and PIP within the community of elderly adults in ambulatory care affiliated to a Private University Hospital. Future interventions should be aimed at reducing the number of PIP to prevent ADEs and improve EMR accuracy by lowering medications discrepancies. Research in this area should also focus on the effect of these interventions in the incidence of adverse drug reactions.
Financial support and sponsorship
Hospital Italiano de Buenos Aires – Fundación MF.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Johnell K, Klarin I. The relationship between number of drugs and potential drug-drug interactions in the elderly: A study of over 600,000 elderly patients from the Swedish prescribed drug register. Drug Saf 2007;30:911-8.
Delgado Silveira E, Muñoz García M, Montero Errasquin B, Sánchez Castellano C, Gallagher PF, Cruz-Jentoft AJ. Inappropriate prescription in older patients: The STOPP/START criteria. Rev Esp Geriatr Gerontol 2009;44:273-9.
Milone AS, Philbrick AM, Harris IM, Fallert CJ. Medication reconciliation by clinical pharmacists in an outpatient family medicine clinic. J Am Pharm Assoc 2014;54:181-7.
Stewart AL, Lynch KJ. Identifying discrepancies in electronic medical records through pharmacist medication reconciliation. J Am Pharm Assoc 2012;52:59-66.
Mueller SK, Sponsler KC, Kripalani S, Schnipper JL. Hospital-based medication reconciliation practices: A systematic review. Arch Intern Med 2012;172:1057-69.
Porcelli PJ, Waitman LR, Brown SH. A review of medication reconciliation issues and experiences with clinical staff and information systems. Appl Clin Inform 2010;1:442-61.
American Geriatrics Society Beers Criteria Update Expert Panel for potentially inappropriate medication use in older adults. J Am Geriatr Soc 2012;60:616-31.
Regueiro M, Mendy N, Cañás M, Farina HO, Nagel P. Use of medication in non-institutionalized elderly adults. Rev Peru Med Exp Salud Pública 2011;28:643-7.
Orrico KB. Sources and types of discrepancies between electronic medical records and actual outpatient medication use. J Manag Care Pharm 2008;14:626-31.
Bedell SE, Jabbour S, Goldberg R, Glaser H, Gobble S, Young-Xu Y, et al.
Discrepancies in the use of medications: Their extent and predictors in an outpatient practice. Arch Intern Med 2000;160:2129-34.
Ernst ME, Brown GL, Klepser TB, Kelly MW. Medication discrepancies in an outpatient electronic medical record. Am J Health Syst Pharm 2001;58:2072-5.
Johnson CM, Marcy TR, Harrison DL, Young RE, Stevens EL, Shadid J. Medication reconciliation in a community pharmacy setting. J Am Pharm Assoc 2010;50:523-6.
Ekedahl A, Brosius H, Jönsson J, Karlsson H, Yngvesson M. Discrepancies between the electronic medical record, the prescriptions in the Swedish national prescription repository and the current medication reported by patients. Pharmacoepidemiol Drug Saf 2011;20:1177-83.
Coletti DJ, Stephanou H, Mazzola N, Conigliaro J, Gottridge J, Kane JM, et al.
Patterns and predictors of medication discrepancies in primary care. J Eval Clin Pract 2015;21:831-9.
Patel CH, Zimmerman KM, Fonda JR, Linsky A. Medication complexity, medication number, and their relationships to medication discrepancies. Ann Pharmacother 2016;50:534-40.
Hill-Taylor B, Sketris I, Hayden J, Byrne S, O'Sullivan D, Christie R. Application of the STOPP/START criteria: A systematic review of the prevalence of potentially inappropriate prescribing in older adults, and evidence of clinical, humanistic and economic impact. J Clin Pharm Ther 2013;38:360-72.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]
|This article has been cited by|
||A multifactorial intervention to lower potentially inappropriate medication use in older adults in Argentina
| ||Marcelo Schapira,Pablo Calabró,Manuel Montero-Odasso,Abdelhady Osman,María Elena Guajardo,Bernardo Martínez,Javier Pollán,Luis Cámera,Miguel Sassano,Gastón Perman |
| ||Aging Clinical and Experimental Research. 2020; |
|[Pubmed] | [DOI]|
||Medication Reconciliation and Patient Safety in Trauma: Applicability of Existing Strategies
| ||Jonathan H. DeAntonio,Stefan W. Leichtle,Sarah Hobgood,Laura Boomer,Michel Aboutanos,Martin J. Mangino,Dayanjan S. Wijesinghe,Sudha Jayaraman |
| ||Journal of Surgical Research. 2019; |
|[Pubmed] | [DOI]|
||Factors, influencing medication errors in prehospital care
| ||Nikolai Ramadanov,Roman Klein,Urs Schumann,Abner Daniel Valdez Aguilar,Wilhelm Behringer |
| ||Medicine. 2019; 98(49): e18200 |
|[Pubmed] | [DOI]|
||The Challenges of Medication Reconciliation for the Medical Home
| ||Amanda S. Mixon,Sunil Kripalani |
| ||The Joint Commission Journal on Quality and Patient Safety. 2019; |
|[Pubmed] | [DOI]|