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- PMC8387129
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Appl Clin Inform. 2021 Aug; 12(4): 845–855.
Published online 2021 Aug 25. doi:10.1055/s-0041-1735182
PMCID: PMC8387129
PMID: 34433219
David Aluga,1 Lawrence A. Nnyanzi,1 Nicola King,2 Elvis A. Okolie,1 and Peter Raby1
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Associated Data
- Supplementary Materials
Abstract
Background Electronic prescriptions are often created and delivered electronically to the pharmacy while paper-based/handwritten prescriptions may be delivered to the pharmacy by the patients. These differences in the mode of creation and transmission of the two types of prescription could influence the rate at which outpatients fill new prescriptions of previously untried medications.
Objectives This study aimed to evaluate literatures to determine the impact of electronic prescribing compared with paper-based/handwritten prescribing on primary medication adherence in an outpatient setting.
Methods The keywords and phrases “outpatients,” “e-prescriptions,” “paper-based prescriptions,” and “primary medication adherence” were combined with their relevant synonyms and medical subject headings. A comprehensive literature search was conducted on EMBASE, CINAHL, and MEDLINE databases, and Google Scholar. The results of the search were screened and selected using predefined inclusion and exclusion criteria. The Critical Appraisal Skills Program (CASP) was used for quality appraisal of included studies. Data relevant to the objective of the review were extracted and analyzed through narrative synthesis.
Results A total of 10 original studies were included in the final review, including 1 prospective randomized study and 9 observational studies. Nine of the 10 studies were performed in the United States. Four of the studies indicated that electronic prescribing significantly increases initial medication adherence, while four of the studies suggested the opposite. The remaining two studies found no significant difference in primary medication adherence between the two methods of prescribing. The variations in the studies did not allow the hom*ogeneity required for meta-analysis to be achieved.
Conclusion The conflicting findings relating to the efficacy of primary medication adherence across both systems demonstrate the need for a standardized measure of medication adherence. This would help further determine the respective benefits of both approaches. Future research should also be conducted in different countries to give a more accurate representation of adherence.
Keywords: electronic prescribing, electronic health records and systems, paper-based prescriptions, primary medication adherence, ambulatory care/primary care
Background and Significance
Nonadherence to prescribed medication is a significant concern to both public health1and health care systems by inhibiting the effectiveness of pharmacotherapy, and increasing the overall cost of disease management.2The scale of the problem is highlighted by Hubbard3who argue that interventions targeted at improving medication adherence alone would have more benefit than any improvement in specific disease treatment. Some of the risks attributed to nonadherence to medications include serious relapses, adverse drug events, drug resistance, longer hospitalizations and readmissions, increased costs of treatment, and drug toxicity.24Furthermore, nonadherence is found to be higher in patients with chronic disease2which has repercussions for the treatment and management of such conditions. Similarly higher nonadherence rates were exhibited among populations living in low- and middle-income countries when compared with those in high-income countries.2This is of concern in consideration of the already limited funding available to the respective health services of these countries.
Primary medication nonadherence occurs when a new prescription is written for a patient but the patient neither fills the prescription nor obtains a suitable alternative.5Nonadherence to medication can either be intentional or unintentional. Intentional nonadherence occurs when a patient actively decides not to use the medication or follow the treatment recommendations.6This is often a product of a rational decision-making process in which the patient weighs the risks and benefits of the medication.7The patient's belief and knowledge are important factors in this decision process,78910and it could be vital for the health care provider to communicate with the patient to explore the subjective norms that can affect the patient decision not to adhere to the treatment regimen.6Unintentional nonadherence happens due to unplanned behavior such as forgetfulness and lack of understanding of the drug regimen.78910It is a passive process that is associated with the complexity of the medication regimen (polypharmacy) and the memory of the patient.69Interventions aimed at addressing unintentional nonadherence should be targeted at simplifying the drug regimen, reminding patients to take their medications, and assisting patients to incorporate medication taking into their daily routine.6
Prescription errors resulting from illegible writing significantly contribute to the preventable errors, and it is suggested that electronic prescribing can assist in minimizing this.1112131415Further benefits of electronic prescribing also include improvement in pharmacy efficiency, promotion of formulary compliance by prescribers, and decrease in adverse drug reactions.14Essentially, electronic prescriptions can enhance prescription quality and provide for better pharmacovigilance.16However, the electronic prescription systems themselves can introduce a new type of medication error as a result of issues associated with the initial adoption of the system, untrained users, overriding of alerts, and poor interface functionality.171819
There are generally two types of electronic prescription systems used in an outpatient setting; the standalone systems can be used only for prescribing and integrated systems which are part of the electronic health record systems.20These electronic prescribing systems can contain various support systems such as clinical decision support, formulary, and safety alert. Integrated systems were found to provide better incremental benefits than standalone systems with regard to both drug safety and efficiency.20Electronic prescription systems containing clinical decision support can significantly reduce prescription drug cost due to a shift in prescribing practice away from high cost therapies and brand name medications.21The integration of generic substitution decision support with electronic prescribing systems could lead to a significant and sustained increase in outpatient generic (lower price) against brand names (high price) e-prescribing across different specialties.22Generic prescribing was found to reduce the patient's copayment which in turn enhances adherence to prescribed medications.2324This would mean considerable financial savings for both the patient and insurer as more prescriptions are written electronically. Enhanced connectivity and integration in the health system through electronic prescribing and electronic medical records (EMR) might improve the rate of primary medication adherence.25
The majority of published secondary studies comparing the effects of electronic and paper-based prescriptions focus on parameters such as prescribing and medication errors,19time spent prescribing, drug safety,2627and the cost of prescription and compliance to formulary by prescribers.28There is no secondary study, to our knowledge, that compares the two methods of prescribing based on their impact on primary medication adherence. This systematic review aims to determine the effectiveness of electronic versus paper-based prescribing on primary medication adherence among outpatients.
Methods
This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA)29and Synthesis without Meta-analysis (SWiM)30reporting guidelines, and the Critical Appraisal Skills Program (CASP)31risk of bias assessments. It was also registered at the PROSPERO (CRD42020186776). The focused question for this review was “what is the impact of electronic prescribing (I) compared with paper-based prescribing (C) on the primary medication adherence (O) of outpatients (P)?” The keywords and phrases identified from the population, intervention, comparator, and outcome (PICO) components of the question, along with relevant synonyms and Medical Subject Headings (MeSH), were combined using appropriate Boolean operators and advanced search techniques (seeSupplementary Appendices A–D, available in the online version). A detailed and comprehensive search was conducted on EMBASE, MEDLINE, and CINAHL databases, and Google Scholar from inception through March 4, 2021. The search strategy also included the reading of reference lists, searching of gray literature, and contacting of key authors of published studies. D.A. and N.K. formulated the search strategy and conducted the search.
Inclusion and Exclusion Criteria
The complete inclusion and exclusion criteria are given inTable 1. This systematic review considered both experimental and observational quantitative primary studies on outpatients. Primary studies using qualitative methods and publications, such as master's thesis, press release, and conference abstracts, were excluded. Mixed methods papers would be included, providing they report statistical evidence related to medication adherence, as per the aims of the review. The main outcome of this review was primary medication adherence/compliance. Due to the lack of a standard approach to measuring medication adherence, all measures of primary medication adherence were included in the review. However, studies on secondary medication adherence and persistence were excluded. There was no limitation on the language, time of publication, or the geographical location where the study was conducted.
Table 1
Inclusion and exclusion criteria
| Components | Inclusion criteria | Exclusion criteria |
|---|---|---|
| Population | Outpatients, primary care, or ambulatory care patients | Studies on animals and inpatients (i.e., patients on admission) |
| Intervention | Electronic prescribing or computerized physician order entry (CPOE) | – |
| Comparator | Handwritten or paper-based prescribing | – |
| Outcome | Primary medication adherence of any measure | Secondary medication adherence and persistence |
| Study design | Quantitative primary studies (experimental and observational) | Qualitative studies, master's thesis, conference abstracts and press release (non–peer reviewed) |
| Time of study | No limit on the time of study | – |
| Language | No limit on the language of publication | – |
| Location | No limit on the country where the study was conducted | – |
Study Screening and Selection
The first stage of the process involved reading the titles and abstracts of the search results. The studies were then classified into excluded, included, and undecided based on eligibility on the exclusion and inclusion criteria. Studies that fell under included and undecided were taken forward to the second stage of screening and selection which involved obtaining and reading the full texts and applied the exclusion and inclusion criteria again to further screen the papers to be included in the final review. Two researchers (D.A. and P.R.) independently performed the screening and selection and differences between reviewers on eligible studies were resolved by common agreement in accordance with the specified inclusion/exclusion criteria. The specificity of these ensured that there were no disparities between reviewers.
Data Extraction and Quality Assessment
The data extraction and quality appraisal were performed by D.A. and E.A.O., and discrepancies were resolved through discussion to achieve consensus. Data relevant to the PICO components of the review question were extracted using a bespoke data extraction form pilot-tested beforehand (seeSupplementary Appendix E[available in the online version] for a sample data extraction form). The included studies were then appraised for methodological quality and risk of bias using the CASP quality assessment framework (https://casp-uk.net/casp-tools-checklists/).31The critical appraisal process examined parameters such as selection bias, randomization, accounting for potential confounding factors, choice of statistical tests, follow-up, treatment effect, measurement, recall and classification biases. The intervention of interest in this review was electronic prescription or computerized physician order entry (CPOE), while the comparator group comprised of paper-based/handwritten prescription.
Data Synthesis
The results of the included studies vary significantly in the PICO measure, thereby limiting the ability to perform a meta-analysis due to clinical dissimilarities in the research designs.3233To address for such observed heterogeneity/dissimilarities in included studies, narrative synthesis was employed in synthesis of data. Narrative synthesis, an alternative approach, has been criticized as a subjective process that could lead to bias in the data synthesis which may decrease transparency.343536Despite the low recognition of narrative synthesis as a discrete method of data synthesis similar to meta-analysis, narrative synthesis can allow different study designs, participants, interventions, or outcome measures (heterogeneity) to be incorporated in a systematic review.36The SWiM30reporting guideline, an extension of the PRISMA,29was utilized to improve the rigor of this systematic review, since it examined the quantitative effect of two interventions for which meta-analysis of effect estimates could not be applied.37The narrative synthesis is the summary of the current state of knowledge and it attempts to answer the focused question of the review.38
Results
Selection of Studies
The screening and selection process is illustrated by the PRISMA flow diagram (Fig. 1). A total of 2,430 articles were retrieved from the databases searched (EMBASE [58], CINAHL [16], MEDLINE [34], and Google Scholar [2,322]). An additional 12 articles were recovered through other sources including the searching of gray literature and reading of reference lists. The total sum of retrieved articles was reduced to 2,418 following the removal of 24 duplicates. A further 2,402 articles were removed after the reading of the titles and abstracts. The full text could not be accessed for 1 of the 16 remaining articles after several efforts, including contacting the authors.39The full texts of the 15 articles were retrieved and screened using the inclusion and exclusion criteria (second stage of the screening). Five full text articles were then excluded (a short discussion paper,40a press release,41a master's thesis,42one study had no comparator group,1and the other study did not use primary medication adherence as an outcome measure43). The final review included the remaining 10 studies.25444546474849505152
Characteristics of Studies
All 10 included studies were published journal articles (Table 2). Nine of the 10 articles were observational research designs (cohort, cross-sectional, and case-control studies)254445464849505152and the last one was a prospective randomized study (experimental).47Even though the studies were conducted in different settings, only one of the studies used a population outside the United States.45The included studies reported population sample sizes ranging from 143 patients50to 10 million index prescriptions.46
Table 2
Study and participants characteristics
| Study (year) | Publication type | Study design | Country | Setting | Population/sample size | Duration of study |
|---|---|---|---|---|---|---|
| Craghead and Wartski (1989)44 | Journal | Cross-sectional study | The United States | Ireland Army Community Hospital (IACH), Fort Knox Kentucky | 295,932 handwritten prescriptions and 15,945 e-prescriptions | January 1987–March 1988 |
| Ekedahl and Mansson (2004)45 | Journal | Cross-sectional study | Sweden | Three health care districts served by 22 pharmacies | 240,000 inhabitants | March 2000–October 2000 |
| Shrank et al (2010)46 | Journal | Cross-sectional cohort study | The United States | Data from CVS Pharmacy chain and Caremark Pharmacy Benefit Manager company | 10,349,139 index prescriptions filled by 5,249,380 patients | January 2008–December 2008 |
| Fischer et al (2011)25 | Journal | Case control | The United States | Outpatients in Multiple States | 423,616 prescriptions | July 2007–June 2009 |
| Fernando et al (2012)47 | Journal | Prospective randomized control study | The United States | Ronald Reagan University of California Los Angeles Medical Center Emergency Department, California | 224 discharged patients | 1 year (7–31 days follow-up duration and 52.4% successful follow-up rate) |
| Bergeron et al (2013)48 | Journal | Cross-sectional evaluation | The United States | One academic general internal medicine ambulatory care clinic | 344 adult patients | 2 years (2009–2011) |
| Pevnick et al (2014)49 | Journal | Case control | The United States | Outpatients of primary care physicians in New Jersey | 12,389 initial claims | June 2003–July 2006 |
| Anderson et al (2015)50 | Journal | Case control | The United States | Outpatient university dermatology clinic, Wake Forest Baptist Medical Center, North Carolina | 143 patients (40 males and 103 females) | 3 months |
| Forestal et al (2016)51 | Journal | Cross-sectional | The United States | Noninstitutionalized elderly patients (65 years and over) in Pennsylvania | 148,325 prescription claims | September 2014 |
| Adamson et al (2017)52 | Journal | Case control | The United States | Outpatient dermatological clinic at Parkland Memorial Hospital in Dallas, Texas | 2,496 patients and 4,318 prescriptions | January 2011–December 2013 |
Results of Studies Included
The summary of the findings of the 10 studies included in the review is presented inTable 3. All of the studies used electronic prescribing/prescription as the intervention and, paper and/or other prescriptions such as telephone, telefax, and pharmacy order as the comparator group. The included studies reported using electronic prescribing existing either as a standalone systems or integrated with EMR. Furthermore, the included studies made use of different measures of medication adherence such as self-report, pharmacy records, claim data, and patient interview. The results of four of the studies showed a statistically significant (p < 0.05) increase in primary medication adherence following the adoption of electronic prescribing,25495052while four of the studies indicated significantly higher primary medication adherence in paper-based prescriptions compared with electronic prescriptions.44454651The remaining two studies suggested no significant (p > 0.05) difference in primary medication compliance between electronic and paper-based prescriptions.4748
Table 3
Summary of study findings
| Study | Intervention (I) | Comparator (C) | Primary adherence measure | Adherence results (p-values and 95% CI) | Adherence result information |
|---|---|---|---|---|---|
| Craghead and Wartski (1989)44 | Electronic prescription (from EP integrated with EMR) | Handwritten prescription | Prescription claim data | I (981.6 per 1,000 prescriptions) C (998.8 per 1,000 prescriptions) | Increased adherence with handwritten prescriptions |
| Ekedahl and Mansson (2004)45 | Electronic prescription (from EP integrated with EMR) | All other prescriptions including telephone, telefax, and paper | Pharmacy record of prescription claim data | I = 97.63% C = 99.89% p < 0.05 | Increased adherence with paper-based prescriptions |
| Shrank et al (2010)46 | Electronic prescription | All other types of prescription | Pharmacy data and insurance claim | I = 97.7% (RR =1.64) C = 98.3% (RR =1.00) p < 0.001 | Increased adherence with paper-based prescriptions |
| Fischer et al (2011)25 | Electronic prescribing (from standalone EPS) | Paper/printed prescription | Insurance claim | I: OR =1.00 C: OR = 0.54; 95% CI: 0.52–0.57 p < 0.001 | Increased adherence with e-prescribing system |
| Fernando et al (2012)47 | Electronically delivered prescription (from EP integrated with EMR) | Standard written prescription | Self-report (telephone interview) | I = 86.3% C = 88.8% p = 0.578 | No significant difference |
| Bergeron et al (2013)48 | Electronic prescribing (from EP integrated with EMR) | Paper prescribing | Patient interview | I = 89.4% at 6 months I = 97.5% at 12–18 months) C = 93.1%) p = 0.07 | No significant difference |
| Pevnick et al (2014)49 | Electronic prescribing (from standalone EPS) | Traditional paper-based and other methods of delivering prescriptions. | Claim data | Electronic prescribing led to about a 3.6% increase in primary medication adherence (p = 0.04) | E-prescribing increases adherence |
| Anderson et al (2015)50 | Electronic prescription (from EP integrated with EMR) | Paper prescription | Self-report | I = 94%) C = 67%) p<0.001 | E-prescription increases adherence |
| Forestal et al (2016)51 | Electronic prescription | All other prescriptions including written, telephone, fax, and pharmacy | Prescription claim data | Electronic prescriptions (I) were more likely to be reversed at day 0 (I = 50%, any other [AO] = 49%,p < 0.05) and after day 0 (E = 58%, AO = 42%,p < 0.05) | Increased adherence with paper-based prescriptions |
| Adamson et al (2017)52 | Electronic prescription (from EP integrated with EMR) | Paper prescription | Prescription fill and pick up (pharmacy record) | I = 84.8%) C = 68.5%) p < 0.01 | E-prescription increases adherence |
Abbreviations: C, comparator; CI, confidence interval; EP, electronic prescribing; EPS, electronic prescription system; EMR, electronic medical record; I, intervention; OR, odd ratio; RR, relative risk.
Risk of Bias Assessment
In this assessment, the percentage of positive answers to the questions gave the final score of the study (Table 4). Studies scoring 50% and below of positive answers were classified as having high risk of bias, while studies that scored 51 to 74% were classified as moderate risk of bias. Studies that scored 75% and above were classified as low risk of bias. Four studies were appraised as having high risk of bias45485051and another four studies as moderate risk of bias.44474952The remaining two studies were assessed as low risk of bias.2546The main weaknesses were pertaining to sample recruitment/selection,4546505152method used to measure adherence,47484950identifying and accounting for potential cofounding factors,4445474851and applicability of findings.4445474849505152Moreover, some of the studies collected their data at the early stage of implementation of electronic prescription systems and there could be differences in the characteristics of both the prescribers and patients who utilized and did not utilize electronic prescribing systems at this early stage.444549The prescribers could also be given the choice to use or not to use the electronic prescription system at this stage of adoption, thereby creating an opportunity for selection bias in the studies.
Table 4
Risk of bias assessed by the critical appraisal skills program (CASP) quality tools
| Authors | Study design | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | Q13 | %yes/risk of bias |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Craghead and Wartski (1989)44 | Cross-sectional | Y | Y | Y | Y | – | U | U | – | – | U | Y | N | Y | 60%/moderate |
| Ekedahl and Mansson (2004)45 | Cross-sectional | Y | Y | Y | U | – | Y | N | – | – | N | Y | N | N | 50%/high |
| Shrank et al (2010)46 | Cross-sectional cohort | Y | Y | U | U | – | Y | Y | Y | Y | Y | Y | U | Y | 75%/low |
| Fischer et al (2011)25 | Case control | Y | Y | Y | Y | – | Y | U | – | – | Y | Y | U | Y | 80%/low |
| Fernando et al (2012)47 | Randomized control study | Y | Y | Y | Y | Y | N | Y | N | N | N | N | N | Y | 54%/moderate |
| Bergeron et al (2013)48 | Cross-sectional | Y | Y | Y | Y | – | N | U | – | – | N | N | N | Y | 50%/high |
| Pevnick et al (2014)49 | Case control | Y | Y | Y | Y | – | N | U | – | – | Y | Y | N | Y | 70%/moderate |
| Anderson et al (2015)50 | Case control | Y | Y | N | Y | – | N | U | – | – | Y | N | N | Y | 50%/high |
| Forestal et al (2016)51 | Cross-sectional | Y | Y | N | U | – | Y | U | – | – | N | N | N | Y | 40%/high |
| Adamson et al (2017)52 | Case control | Y | Y | U | Y | – | Y | U | – | – | U | Y | N | Y | 60%/moderate |
Abbreviations: (–), not applicable; N, no; Q1, Did the study address a clearly focused issue?; Q2, Did the authors use an appropriate method to answer their question?; Q3, Were the cases/cohort recruited in an acceptable way?; Q4, Were the controls selected in an acceptable way?; Q5, Was the assignment of patients to treatments randomized/blinded?; Q6, Was the outcome accurately measured to minimize bias?; Q7, Aside from the experimental intervention, were the groups treated equally?; Q8, Was the follow-up of patients complete enough?; Q9, Was the follow up of subjects long enough?; Q10, Have the authors taken account of the potential confounding factors in the design and/or in their analysis?; Q11, Do you believe the results?; Q12, Can the results be applied to the local population?; Q13, Do the results of this study fit with other available evidence?; U, unclear; Y, yes.
Discussion
This systematic review was aimed at determining the impact of electronic prescribing compared with paper-based/handwritten prescribing on primary medication adherence among outpatients. The lack of randomized control studies minimizes firm conclusions.31All four studies that indicated an increase in primary medication adherence following the introduction of an electronic prescribing system were of retrospective case-control design.25495052Only one of the studies which was appraised with high risk of bias lasted for a duration fewer than 2 years, recruited a convenient sample of 143, and applied a subjective measure of primary medication adherence (self-report).50The use of subjective measures of medication adherence is widely criticized due to the associated social desirability and recall biases which could lead to artificial inflation in adherence value.53All four studies made use of electronic prescribing as the intervention and paper prescription as the comparator. Two of the studies reported using data from a standalone electronic prescribing system2549while the other two studies reported obtaining data retrospectively from an EMR (integrated) system.5052
On the other hand, all four studies that reported an increase in initial medication adherence with paper-based prescriptions were of cross-sectional design.44454651Although the studies recruited large sample sizes, this study design is often placed below case-control design in the hierarchy of evidence since they are quick and easy to undertake and may not permit distinction of cause and effect.5455Furthermore, the utilization of a heterogeneous comparator group by the three of the four studies that included other types of prescriptions in addition to paper prescription, such as telephone, telefax, and pharmacy order, may perhaps impact the first-fill adherence of paper prescription.454651This may be responsible for the observed decrease in medication adherence after the implementation of the electronic prescription system as reported by the studies. Additionally, these four studies made use of claim-based measures of medication adherence which may not contain information to determine whether the prescriptions were retrieved.51Insurance claim measure of adherence gives only information about prescriptions that have been filled. Even though only new prescriptions were considered in the research, misclassification can happen when new prescriptions are paid by the patients themselves thereby not reflecting in the insurance claim database.51It is also possible for a subset of the population to have other sources of insurance coverage that may not be captured by the claim database.25Also, the study by Forestal et al51analyzed claim data for September 2014 only. A sample with a prolonged duration may give a more accurate outcome measure of medication adherence across the methods of prescribing. Some experts have cautioned against the use of a claim-based measure of initial medication adherence.5657Only two of the studies gave information about using an electronic prescription system integrated with EMR.4445
Among the two primary studies that found no significant difference in primary medication adherence between the two methods of prescribing were a prospective randomized control47and cross-sectional studies.48Both studies recruited small sample sizes of 224 and 344 patients, respectively, measured medication adherence through patient interviews and made use of electronic prescription integrated with EMR. Moreover, the use of a short follow-up duration (7–31 days) and a low successful follow-up rate (52.4%) by Fernando et al47could affect the adherence measure since continuity of care with enhanced follow-up has been found to increase the patient adherence to medication.58
The difficulty observed in comparing studies on adherence was because of these variations in follow-up durations, population demographics, and the reliability of the different methods of measuring medication adherence. Higher incomes may be associated with increased adherence and the factors affecting medication adherence across different countries include the availability of medicines, prevalence of disease conditions, and variations in health insurance systems.59The copayment to be paid by the patient could be the strongest predictor of primary medication nonadherence and there might be a significant relationship between the income levels of the patients and the rate of adherence.46Lower socioeconomic status has been found to discourage adherence to prescribed medications.6061
Electronic prescribing integrated with EMR can enable the health care provider to monitor the patient medication regimen and initiate targeted intervention when the need arises.62Additionally, prescriptions transmitted electronically to the pharmacy could be filled before the patients arrive to pick them up, thereby reducing the pharmacy wait time.44This in turn saves time and improves the quality of prescriptions delivered at the pharmacy for the patient. Furthermore, automated electronic reminders, such as text message notifications and phone calls, could remind patients to pick their prescriptions when transmitted electronically to the pharmacy. These are expected to reduce the number of unclaimed prescriptions and increase primary medication adherence. However, the decrease in medication adherence that could be associated with electronic prescriptions implementation may be caused by the lack of patient-initiated steps.46Electronic prescriptions are likely to be automatically delivered to the pharmacy for patients who do not intend to fill them, leading to intentional nonadherence.51The automatic transmission of electronic prescription can also increase the likelihood of forgetfulness by the patients leading to unintentional nonadherence but a printed copy of the prescription can serve as a physical reminder for the patients to pick their prescription at the pharmacy.5152In addition, the early increase in nonadherence observed following the adoption of electronic prescription systems may be attributed to the adaptation by both the patients and prescribers to the change in practice.48A learning curve may exist in the implementation process that would later resolve at which the nonadherence rate falls below the baseline levels.48The education of the prescribers and patients about electronic prescribing/prescription would quicken this process of adaptation.
Limitations and Recommendations
The incomplete reporting of effect estimates and the significant variations in the characteristics of the included studies made it difficult to achieve the consistency required to conduct a meta-analysis.37The actual rate of medication adherence could be higher because nonadherent patients are prone to be underrepresented in clinical research.63Unlike electronic prescribing which transmits all prescriptions directly to the pharmacy, it is difficult to track and trace the filling of handwritten/paper prescriptions generally and in the included studies since they could be lost, forgotten, misplaced, or ignored.445164This could make the measurement of initial medication adherence in handwritten/paper-based prescriptions challenging. Furthermore, some prescriptions may be printed and given to the patient at the pharmacy without actually being dispensed resulting in the overestimation of primary medication compliance. Future research comparing the effect of the two methods of prescribing on primary medication adherence should utilize a standardized objective measure of medication adherence with prolonged follow-up durations. This can allow the effect sizes to be combined through meta-analysis to ascertain the effect of electronic prescribing on primary medication adherence. Nine out of the ten included papers recruited their study sample from the United States where the health system is primarily insurance-based. And there could be a relationship between the lack of medical insurance and nonadherence to prescribed medications.65These could limit the application of the findings of the studies in countries where the health system differs. Further studies should be performed in both varying settings and countries to give a more precise representation of adherence. The unavailability of full text for one study,39after several efforts including contacting the authors, can affect the thoroughness of this systematic review as the findings of this article might influence the review's methodology and conclusion.
Conclusion
This systematic review has reemphasized the need for standardization in methods to measure medication adherence. The wide variations in the characteristics of the included studies limited the opportunity to pool the effect estimates via meta-analysis and arrive at a definite conclusion. Medication adherence should be a shared responsibility between the health care provider, pharmacy, and patient, and an ideal method of prescribing must incorporate the advantages of both paper and electronic prescriptions to facilitate efficiency, minimize cost, and maximize the treatment outcome.
Clinical Relevance Statement
Evidence from the retrieved articles demonstrates the need for a standardized objective method for measuring medication adherence and the scarcity of high-quality studies of the randomized control type. This would permit for the meta-analysis of the effect estimates of electronic versus paper-based prescribing on initial medication adherence. Finally, it has highlighted the importance of further research in this area to be conducted in different countries to give a more accurate representation of primary medication adherence.
Multiple Choice Questions
Which of the following is a subjective method of measuring medication adherence?
pharmacy record
patient Interview
insurance claim
prescription refill
Correct Answer:The correct answer is option b. The subjective methods involve the evaluation of adherence by the biases, for example, patient's self-reports and health care professional assessments. They are vulnerable to recall and social desirability biases.
Electronic prescription systems can exist either as standalone or integrated with …?
internet
text messages
phone calls
electronic health records
Correct Answer:The correct answer is option d. Electronic prescription systems were introduced as standalone systems ab initio and were later integrated with electronic health records.
Conflict of Interest None declared.
Protection of Human and Animal Subjects
This is a secondary study that synthesized the findings of original studies. No human or animal subjects were recruited.
Supplementary Material
Supplementary MaterialSupplementary Material(1.2M, pdf)
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