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Abstract

Multidose drug dispensing (MDD) is the dispensing of different drugs in dose bags containing one, some, or all units of medicine that a patient needs to take at specific times. The aim of this narrative review is to provide an overview of the literature describing the use of MDD systems in community healthcare settings in patients with multimorbidity and polypharmacy. A literature search identified 14 studies examining adherence, medication knowledge, quality of drug prescription (including inappropriate drug use, drug-drug interactions), medication incidents, and drug changes after MDD initiation, as well as healthcare professional (HCP) and patient perspectives. There are limited data on MDD in community healthcare settings, particularly on outcomes such as adherence. Studies are mostly from Northern Europe. Patients selected for MDD are more likely to be older, female, cognitively impaired, and have a higher number of disease diagnoses and drugs than those who do not receive drugs through MDD. MDD is generally initiated for patients who have decreased capacity for medication management. Several advantages of MDD have been reported by patients and HCPs, and studies indicate that MDD can be improved by medication review, defining clear roles and responsibilities of HCPs in the medication management chain, and comprehensive follow-up of patients. Future development, implementation, and assessment of MDD systems in community healthcare should be designed in collaboration with HCPs and patients, to identify ways to optimize the systems and improve patient outcomes.

Keywords

automated dose dispensing community healthcare community pharmacy multidose drug dispensing polypharmacy

Impact Statements

There are limited data on multidose drug dispensing (MDD) in community healthcare settings.

Patients selected for MDD are more likely to have a higher number of disease diagnoses and drugs than non-MDD patients.

MDD is generally initiated for patients who have decreased capacity for medication management.

Combining MDD with other elements, such as integrated care, medication review, and patient follow-up, may help to manage patients with multimorbidity and polypharmacy.

A multidisciplinary, multi-professional approach is needed for MDD in the community.

Introduction

Multimorbidity^1,2 and polypharmacy—which refers to the use of multiple medicines, with definitions of the number of medicines being variable³—are prevalent in the older population. Both multimorbidity and polypharmacy are associated with numerous negative health outcomes, including falls, adverse drug events, frailty, hospitalization, mortality, and impaired functioning and cognition,^2,4
-11 as well as increased healthcare utilization and costs.^12,13

Non-adherence places a significant cost burden on healthcare systems¹⁴ and increases the risk of hospitalization, medical visits, and mortality.^15,16 Adherence is the extent to which a patient’s behavior corresponds with recommendations from a healthcare professional (HCP), including taking medication, following a diet, and/or executing lifestyle changes. Approximately 1 in 2 patients are not adherent to their medication regimens.^17,18

Barriers to medication adherence are complex and include patient-, medication-, physician-, and healthcare system-related factors.¹⁹ The more medications a patient is prescribed, the higher the risk of non-adherence to their treatment regimen.²⁰ Patients with polypharmacy often have difficulties in managing their medications due to complex therapeutic regimens or practical problems, such as halving tablets or opening drug packages.^21,22 Complex drug regimens from multiple prescribing physicians²³ and drug handling factors ^[19 are associated with non-adherence; therefore, systems that simplify medication regimens and handling are needed.

Multidose drug dispensing (MDD) is the dispensing of different drugs in dose bags containing one, some, or all units of medicine that a patient needs to take at specific times. Dose dispensing aids include reusable pillboxes, multidrug punch cards, and blister pouches produced by automated packaging machines.²⁴ Unit dose dispensing systems—refer to automated systems for producing individually packaged and labeled drugs for inpatients—can reduce medication administration errors and improve inpatient safety²⁵; however, a meta-analysis reported that automated dose dispensing (ADD) systems in hospital settings did not significantly lower the probability of medication errors compared with manual systems.²⁶ There is a paucity of studies investigating use of ADD in community healthcare.²⁷ Results of one study investigating use of MDD in patients discharged from hospital showed medication errors in MDD orders for 25% of patients due to prescribing and transcribing errors during the transfer between healthcare settings.²⁸ Nonetheless, there is increasing interest in the use of MDD services in community healthcare settings.

The aim of this narrative review is to provide an overview of the literature describing the use of MDD systems in community healthcare settings in patients with multimorbidity and polypharmacy. The Objectives include to assess whether patients in the community whose drugs are dispensed via MDD (referred to as MDD patients) have greater treatment adherence than patients who receive traditionally administered drugs (referred to as non-MDD patients); to describe the characteristics of MDD patients; and to provide HCP and patient perspectives on MDD.

Methods

The literature search for relevant papers published (year range set for the search results 2001-2020) was conducted using 4 databases: (1) PubMed; (2) Web of Science; (3) Embase; and (4) Cochrane. Keywords included comorbidity, polypharmacy, multimorbidity, multiple chronic diseases, polytherapy, medication appropriateness, overprescribing, multidrug, multiple medications, multiple drugs, medication systems, medication reconciliation, unit dose drug distribution systems, multidose dispensing, and dose dispensing. The search was performed in January 2022 and identified 66 studies. As our initial exploratory literature search revealed few randomized control trials (RCTs), the search criteria were expanded to include observational studies; furthermore, due to heterogeneity in the outcomes investigated, a narrative, rather than a systematic, review was conducted.

Two authors conducted a first pass review of papers identified from the database searches to select for papers relating to automatic and multidose dispensing systems in a community healthcare setting. References from selected papers were screened and forward and backward reference searching was conducted for 40 of these papers; 26 additional papers were identified by forward and backward reference searching. The 2 authors independently screened the titles and abstracts according to Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) (Table 1). The authors discussed the papers, selected appropriate articles, and extracted information on study methodology, sample size, patient profile, outcomes, and results.

Table 1.

Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) Criteria Used to Define the Literature Search.

• Population: community care patients, outpatients with multimorbidity (2+ concurrent diseases) and polypharmacy (5+ prescribed drugs), residents in long-term care facilities. • Intervention : automatic and multidose dispensing systems. • Comparison : ordinary prescription and dispensing of drugs. • Outcomes : adherence, medication errors or incidents (timing errors, omission, dosage errors), adverse events, potentially inappropriate medications, drug-drug interactions, drug-related problems, and drug changes. • Study design: the literature search was expanded to include observational studies, as few randomized controlled trials were identified.

To assess the quality of the included studies, we used the Newcastle-Ottawa Scale (NOS) for observational studies and the Cochrane Risk of Bias tool for RCTs. The NOS assesses studies based on 3 broad criteria: selection of the study groups, comparability of the groups, and ascertainment of either the exposure or outcome of interest. The Cochrane Risk of Bias tool evaluates studies on several domains including selection bias, performance bias, detection bias, attrition bias, reporting bias, and other biases. Each study was independently assessed by 2 reviewers, and any discrepancies were resolved through discussion.

Results

Fourteen studies were identified for this narrative review (Table 2). These studies examined adherence (n = 2)^29,30 medication knowledge (n = 2)^29,30 quality of drug prescription (n = 7),^31
-38 medication incidents (n = 1),³⁹ and drug changes after MDD initiation (n = 4).^38,40
-42 All the studies were conducted in Northern Europe, specifically Sweden (n = 5), Finland (n = 3), the Netherlands (n = 4), and Norway (n = 2). Most of the studies used real-world observational and registry data. There was only 1 RCT.³⁵

Table 2.

Characteristics and Main Results of Included Studies.

Author, year [Reference]	Design	Number of patients and characteristics	Outcome definition	Results
Bobrava et al³¹	Retrospective, registry-based study in Finland on patients initiating MDD	n = 208≥65 yearsPatients initiating MDD69% femaleMean age 84.6 years	PIM—appropriateness of medications was evaluated manually by comparing the patient’s medication list at baseline and 6 months to the EU(7)-PIM listDDI—clinically significant drug-drug interactions were identified manually using Inxbase system	73% of patients were prescribed at least one medication from the EU (7)-PIM list at baseline, and 77% after 6 months64% of PIMs were clinically significantMost common potentially harmful PIMs were acetylsalicylic acid, pantoprazole, carbamazepine and risperidone2.4% of patients were prescribed category D clinically significant DDIs61% of patients had an increase in the number of medications prescribed
Cheung et al³⁹	Observational study in community pharmaciesEvaluation of medication incidents submitted to the Dutch Central Medication incidents Registration (CMR) reporting system	15 113 incidentsStudy compares incidents in community pharmacies (24.4%) with those in hospital (75.6%)	Medication incidents—researchers collected information from incident reports, including nature of incident from the HCP’s perspective and patient’s perspectiveIn community pharmacies, 6.2% of incidents were related to ADD	Nature of incident from the patient’s perspectiveToo many tablets in ADD bag: 25.6%Too few tablets in ADD bag: 27.8%Wrong tablet in ADD bag: 8.8%Wrong patient: 4.8%Wrong information on administration list: 4.8%Providing separate medicine beside the ADD bag: 5.3%Nature of incident from the HCP’s perspectiveSelecting wrong patient: 6.2%Choosing wrong dose/frequency: 5.7%Wrong processing in order system: 8.4%Choosing wrong administration time: 3.5%
Halvorsen et al³²	Cross-sectional study of older people using MDD in Norwegian nursing homes or using home nursing services	n = 2986 MDD users in nursing homesn = 8268 MDD users with home nursing services≥65 yearsMean age 83.6 years69%-72% female	PIMs (NORGEP criteria)DDI (DRUID criteria)	Patients used a mean of 5.7 ± 2.6 MDD regular drugs26% had at least one PIM (31% in nursing homes and 25% in home nursing services, P < .001)PIMs more common in women (26.3%) than men (20.9%), P < .001DDI = 55% (48% in nursing homes and 57% in home nurse services, P < .001)Most commonly prescribed drugs: antithrombotics (49%), diuretics (44%), beta-blockers (40%), drugs affecting the renin–angiotensin system (37%), and antidepressants (31%)
Johnell and Fastbom³³	Cross-sectional register-based study, Sweden	n = 731 105Age ≥75 years61.6% women16.5% used MDD83.5% non-MDD (prescription users)	Potentially IDU, ie, anticholinergic drugs, long-acting benzodiazepines, concurrent use of ≥3 psychotropic drugs, and combinations of drugs that may lead to potentially serious DDIsPotentially serious DDIs classified according to the Swedish Sjoqvist system	Any IDU: MDD = 40.3%, non-MDD = 13.6%Anticholinergic drugs: MDD = 15.3%, non-MDD = 4.9%Long-acting benzodiazepines: MDD = 8.8%, non-MDD = 4.7%≥3 psychotropic drugs: MDD = 22.1%, non-MDD = 2.4%Potentially serious DDIs: MDD = 8.8%, non-MDD = 3.7%Odds ratio for MDD versus non-MDD (reference category) in womenAny IDU: OR = 1.43 (1.40-1.46)Anticholinergic drugs: OR = 1.51 (1.47-1.56)Long-acting benzodiazepines: OR = 0.69 (0.66-0.71)≥3 psychotropic drugs: OR = 3.96 (3.85-4.09)Potentially serious DDIs: OR = 0.80 (0.77-0.83)Odds ratio for MDD versus non-MDD (reference category) in menAny IDU: OR = 1.72 (1.67-1.78)Anticholinergic drugs: OR = 2.05 (1.97-2.14)Long-acting benzodiazepines: OR = 0.94 (0.89-1.0)≥3 psychotropic drugs: OR = 4.93 (4.70-5.17)Potentially serious DDIs: OR = 0.70 (0.66-0.74)OR adjusted for age and number of dispensed drugs, any IDU, anticholinergic drugs, and 3 or more psychotropic drugs were associated with multi-dose drug dispensingSubgroup analysis in the younger age group (75-79 years) showed that MDD was even more strongly associated with any IDU, anticholinergic drugs, ≥3 psychotropic drugs in this age group
Josendal et al³⁴	Cross-sectional study on medication lists of MDD patients, Norway	n = 45 593 MDD patientsAge ≥70 years	Proportion of PIMs according to Norwegian General Practice CriteriaDDIs according to Norwegian Medicines Agency database	Patients prescribed 10.6 ± 5.0 drugs, of which 6.1 were dispensed via MDD27% had ≥1 potentially inappropriate medications, most common:-Concomitant use of ≥3 psychotropic drugs (10.8%)-Prescribing of diazepam (6.4%)-DDIs (59%)-Serous DDIs (2.7%)Women were more frequently exposed to:PIMs OR = 1.5 (1.4-1.6) and DDIs OR = 1.4 (1.4-1.5)Older people had a lower probability of PIMS and DDIs versus aged 75-79 years:Aged 80-89 years: PIMs OR = 0.6 (0.5-0.6) and DDIs OR = 0.9 (0.89-0.95)Aged 90+ years: PIMs OR = 0.6 (0.5-0.6) and DDIs OR = 0.8 (0.7-0.8)
Kwint et al²⁹	Case-control matched on age and sexPatients from Dutch pharmacies≥65 years≥5 oral chronic drugsAt least onedrug dispensed by MDD	MDD n = 127Non-MDD n = 96MDD62% femaleAge 76-83 yearsMean drugs 9.7 ± 3.0Non-MDD66% femaleAge 77-84 yearsMean drugs 10 ± 3.2	Medication adherence: Self-reported with the Medication Adherence Reporting Scale (MARS)Adherent = MARS score >22Medication knowledge: Percentage of drugs for which patient knew indicationAdequate knowledge = knowing the indication	Adherence: MDD = 81% vs non-MDD = 58%, P < .0001Difference independent of medication knowledge and MMSE scoreNon-adherence in MDD was most often reported for drugs for peptic ulcer and gastro-esophageal reflux disease (12%) and high-ceiling diuretics (9%)Medication knowledge: Percentage of drugs for which patient knew indication (mean)MDD = 62.8 ± 30.5 vs non-MDD = 85.0 ± 23.2, P < .001Patients with adequate knowledge: MDD = 40% vs non-MDD = 79%, P < .00186% of MDD users also used manually dispensed drugsWithin these patients, adherence was higher for manually dispensed drugs (97%) than MDD (93%), P < .0001.Adequate medication knowledge was higher for manually dispensed drugs (92%) than MDD (35%)
Kwint et al³⁵	Pragmatic RCTPatients from Dutch pharmacies≥65 years, ≥5 oral chronic drugs, ≥1 drug dispensed by MDDMediation review by 2 pharmacist reviewers, sent to community pharmacists, to be discussed with GP	Allocated to medication review n = 63Waiting-list group, usual care (medication review after 6 months) n = 56	6 months after the medicationreview:-Number of DRPs leading to a recommendation fordrug change-Total number of drug changes andthe number of drug changes related to a recommendation	After 6 months, the number of DRPs leading to a recommendation for drug changeMedication review: decreased by 29%Waiting-list group: decreased by 5%, P < .01Mean number of drug changes:Medication review: 2.2, Waiting-list group: 1.0, P < .02Of these drug changes, the % related to a recommendation:Medication review: 62%, Waiting-list group: 20%, P = .003Recommendations for cessation of a drug were more frequently accepted than recommendations to add a new drug (82% vs 44%, P = .01)
Mertens et al³⁰	Cross-sectional study in the Netherlands, semi-structured interviews conducted by community pharmacists in patient’s home	Home dwelling≥65 years≥5 oral chronic drugsMDD, n = 188Non-MDD, n = 203	Medication management problems, assessment tool with 22 potential problems, covering 4 domains: functional (7), organizational (7),medication adherence (6), and medication knowledge (2)	MDD vs non-MDD were older, more likely to be female, using more drugs, and higher % possibly cognitively impaired (42% vs 20%) or frail (63% vs 27%)Medication adherence problemsMDD: 1, Non-MDD: 0, P < .016+ medication management problemsMDD: 70%, non-MDD: 22%
Sinnemäki et al⁴⁰	Cohort study with matched controls,Finland	Primary care patients ≥65 yearsADD n = 2073Controls n = 2073 matched by sex, age, area of patient’s residence and number of the prescription drugs reimbursed73% female, mean age 82 yearsMean drugs 6.5 ± 3.5	Change in drug use in 1-year periods before and after ADD enrollment - DDD per day	Drug use decreased after initiation of ADD11 of 20 active substances studied were reduced significantly (P < .001-.041) when drug use was adjusted by the number of chronic diseases2 were hypnotics (temazepam and zopiclone)6 were for cardiovascular diseases (simvastatin, ramipril, amlodipine, isosorbide mononitrate, bisoprolol and metoprolol)ADD patients had more starts and discontinuations in their drug use than controls
Sjöberg et al⁴¹	Cohort of hip fracture patients after discharge followed for 6 monthsSweden	≥65 yearsMDD, n = 107Non-MDD, n = 47	Change in drugs over 6 months extracted from medical records and drug registry: withdrawn, dosage adjusted or added, or unchanged	Drugs unchangedMDD: 42%Non-MDD: 29%Odds of drugs being unchanged in MDD vs non-MDDOR = 1.66 (1.20-2.31), adjusted for age, sex, cognition, year of study
Sjöberg et al³⁶	Cross-sectional study in Sweden	Total n = 24 146MDD n = 4928 (20.4%)non-MDD n = 19 219≥65 years≥1 prescribed drug≥2 health care visits for ≥2 diagnoses forobstructive pulmonary disease, diabetes mellitus, and/or cardiovascular diseaseMean age 77 years51% female	Quality of drug treatment according to establish indicators: ≥10 drugs, long-acting benzodiazepines, drugs withanticholinergic action, ≥3 psychotropics, drug combinations that should be avoided	For all quality indicators, the proportion of patientswith poor quality of drug treatment was greater in patients with MDD than non-MDD, P < .0001≥10 drugs (55.1% vs 19.1%, OR = 3.9, 3.9-4.2)ORs adjusted for age, sex, number of diagnoses, nursing home residencyLong-acting benzodiazepines (5.9% vs 3.2%, OR = 1.6, 1.4-1.9)Drugs with anticholinergic action (12.8% vs 5.4%, OR = 2.3, 2.1-2.6)≥3 psychotropics (16.1% vs 2.6%, OR = 5.5, 4.8-6.3)Drug combinations that should be avoided (8.1% vs 5.7%, OR = 1.3, 1.2-1.6)ORs also significant after adjustment for “any psychiatric diagnosis”
Tora et al³⁷	Observational study using registry data in Sweden	n = 180 059 electronically stored prescriptions for patients with MDDMean age: 75.8 ± 17.5Mean 10.1 ± 4.7 different medications	DRPs detected over 3 months through electronic expert support system	76% of patients had an alert for potential DRPMean alerts per patient: 2.2 ± 2.4More frequent in younger patientsMost frequent categories- drug-drug interactions: 37%- duplicate therapy: 30%- geriatric warnings for high dose or inappropriate drugs: 23%Psycholeptics, psychoanaleptics, antithrombotic agents, antiepileptics, renin-angiotensin system agents, and analgesics represented 71% of all drugs involved in alerts
Vallius et al⁴²	Case-control, Finland, MEDALZ study (registry data)Community dwelling people with and without Alzheimer’s disease (AD)Each MDD patient matched no-MDD control by age, sex, AD, region of residence, and time since AD diagnosis	N = 70 718Patients initiating MDDAD and MDDn = 15 604AD and no-MDD n = 15 604No AD and MDD n = 5224No AD and no MDD n = 5224Female 73.6%-74.9%Mean age 82.7-86.4 yearsMean 2.9 years since AD diagnosis	Drug changes: Drug use before and after initiation of MDD (12 months before and 24 months after)Drugs identified from the Prescription Register. Drug use was modeled using mathematical modeling method PRE2DUP (duration of use and defined daily dose, etc.)	MDD seems to be initiated when use of psychotropics is initiated and the number of drugs increasesAt the start of MDD, the prevalence of drug use increased particularly for antipsychotics, antidepressants, opioids, paracetamol and use of ≥ 10 drugs among patients with and without ADThe peak in the number of drugs was observed at MDD initiation (AD mean 7.4 ± 3.1, no AD mean 8.2 ± 3.1)Two years after MDD initiation, the number of drugs was slightly lower (AD 7.0 ± 3.2, no AD 7.7 ± 3.3) in both cohortsThe most commonly used drugs at the initiation of MDD were similar for AD and no AD patients (apart from anti-dementia drugs): cardiovascular drugs, paracetamol, antidepressants, and proton-pump inhibitors
Wallerstedt et al³⁸	SwedenRegister-based data	N = 30 922≥65 years with first MDD prescriptionMean age 83.2 yearsFemale 59.9%	Change in number of drugs: prescribed drugs were estimated at 3-month intervals before and after (maximum 3 years) first MDD(index date)Medication list of each measure date was evaluated according to indicators of prescribing quality	Temporal association between the transition to MDD and an increased number of unique drugs:3 months before MDD initiation: 5.4 ± 3.93 months after MDD initiation: 7.5 ± 3.8Potentially harmful drug treatment peaked at MDD initiation and maintained a high level afterward for 10≥ drugs and 3≥ psychotropicsDrugs with anticholinergic effects and antipsychotics increased at MDD initiation without subsequent declineLong-acting benzodiazepines and drug combinations that should be avoided peaked at MDD initiation, then declined to previous levels or lowerParacetamol, furosemide, cyanocobalamin, omeprazole, and simvastatin were the drugs most frequently added to the medication list when a patient was entered the MDD system

Note. AD = Alzheimer’s disease; ADD = automated dose dispensing; DDD = defined daily doses; DDI = drug-drug interactions; DRP = drug-related problems; EU = European Union; GP = general practitioner; HCP = healthcare professional; IDU = inappropriate drug use; MDD = multidose dispensing; MMSE = Mini-Mental State Examination; OR = odds ratio; PIMs = potentially inappropriate medications; RCT = randomized controlled trial; ± = standard deviation.

The quality assessment of the included studies revealed several strengths and limitations. Among the 14 studies, the majority were observational in nature with only 1 randomized controlled trial (RCT). Using the Newcastle-Ottawa Scale (NOS), the observational studies generally scored well on selection and comparability but varied in the ascertainment of outcomes. Common issues included limited follow-up periods and potential selection biases due to the retrospective nature of some studies.

Type of Patients Receiving Drugs via Multidose Dispensing

Compared with non-MDD patients, MDD patients were older (eg, median 80.5 vs 76 years³⁰; mean 81.2 vs 75.5 years³⁶), and were more likely to be female,^30,36 use a higher number of drugs,^30,36 have cognitive impairment or frailty,³⁰ and have more disease diagnoses.^36,40 Mertens et al³⁰ reported that 70% of MDD patients had 6 or more medication management problems compared with 22% of non-MDD patients. General practitioners (GPs) believed that 85% of MDD patients had lost the capacity to manage their medication compared with 18% of non-MDD patients.³⁰

Medication Adherence and Knowledge

A case-control study in the Netherlands showed significantly greater medication adherence in MDD patients (81%) than in non-MDD patients (58%) (P < .001).²⁹ Patients who had both MDD and manually dispensed (also referred to as traditionally dispensed) drugs showed significantly greater adherence to the manually dispensed (97%) than the MDD (93%) medications (P < .001). This difference in adherence was independent of medication knowledge and level of cognitive functioning. Non-adherence was most common for drugs for peptic ulcer and gastro-esophageal reflux disease (12%) and high-ceiling (loop) diuretics (9%). Adequate medication knowledge—defined as knowing the indication of at least 75% of their drugs—was lower among MDD patients (40%) than non-MDD patients (79%) (P < .001). In line with this, the mean percentage of drugs for which patients knew the indication was lower for MDD patients (63%) than for non-MDD patients (85%) (P < .001); and the percentage of patients with adequate knowledge (ie, knowing the indication) of MDD drugs (35%) was lower than that of manually dispensed drugs (92%) (P < .001).²⁹

Patients may have greater knowledge of manually dispensed drugs as they have more direct interaction with the medication packets and instructions compared with MDD regimens. Although fewer patients on MDD have adequate medication knowledge, this does not necessarily affect adherence to MDD regimens; after stratification for medication knowledge, adherence remained higher for MDD patients.²⁹ However, in another Dutch study, significantly more medication adherence problems were reported in MDD patients than in non-MDD patients (P < .01).³⁰

As only 2 observational studies from the literature search examined adherence,^29,30 and neither had a measure of adherence before MDD initiation, no conclusions can be drawn about the impact of MDD on adherence. Differences in the characteristics of MDD patients are likely to affect outcomes such as adherence.

Quality of Drug Treatment

The results from a Dutch study showed that 6.2% of medication incidents in community pharmacies were related to ADD.³⁹ The most common incidents reported by patients were too many (25.6%) or too few (27.8%) tablets in the dose bag. HCPs reported incorrect handling in the order system (8.4%), selecting the wrong patient (6.2%), and choosing the wrong dose or frequency (5.7%). A Swedish study revealed that 3-quarters of MDD patients had an alert for potential drug-related problems over 3 months, the most common being drug-drug interactions (DDIs; 37%), duplicate therapies (30%), and geriatric warnings for high dose or inappropriate drugs (23%).³⁷

All studies reported high numbers of potentially inappropriate medications (PIMs) in MDD patients,^31
-34,36 and all but one³³ showed higher DDIs. A Finnish study showed that 64% of PIMs were clinically significant in MDD patients.³¹ In a cross-sectional study in Norway,³² 26% of MDD patients had at least one PIM and 55% had DDIs. In a Swedish registry study, MDD was associated with inappropriate drugs, particularly in patients aged 75-79 years.³³ Conversely, MDD patients had a lower probability of potentially serious DDIs, possibly because physicians identified DDIs during the drug overview.³³ A Norwegian study showed that 27% of MDD patients had 1 or more PIMs, specifically DDIs (59%), serious DDIs (2.7%), at least 3 psychotropic drugs (10.8%), and diazepam prescribing (6.4%).³⁴ Another study conducted in Sweden indicated that the MDD system is a prominent determinant for poor quality of drug treatment.³⁶ Specifically, MDD patients had a higher probability than non-MDD patients of using 10 or more drugs, long-acting benzodiazepines, drugs with anticholinergic action, 3 or more psychotropics, and drug combinations that should be avoided, even after adjustment for psychiatric diagnoses.³⁶

Drug Changes After Initiation of Multidose Dispensing

In a prospective study conducted in Sweden, MDD patients aged ≥ 65 years were at significantly higher risk of developing incident polypharmacy (hazard ratio = 1.5) compared with non-MDD patients.⁴³ The results of a Finnish study showed that drug use decreased after initiation of ADD but there were more drug starts and discontinuations.⁴⁰ In a Swedish study, patients transitioning to an MDD system had an increased number of drugs (mean ± SD number of drugs: 5.4 ± 3.9 three months before and 7.5 ± 3.8 three months after MDD initiation), more frequent potentially harmful drug treatment, and fewer changes in drug treatment, compared with pre-MDD.³⁸ Paracetamol, furosemide, cyanocobalamin, omeprazole, and simvastatin were most frequently added to the medication list, and administration of drugs with anticholinergic effects and antipsychotics increased at MDD initiation and did not subsequently decline. A Dutch study investigating MDD adjustments among 250 patients reported that 35% of drug changes involved the addition of a new drug, 34% were related to a change in dosage, and 26% to drug discontinuation.⁴⁴ Findings of a Finnish study exploring drug use among persons with Alzheimer’s disease (AD) before and after transition to automated MDD suggested that MDD use started when psychotropic drugs were initiated and resulted in increase in an increase in the number of drugs being used (eg, antipsychotics, antidepressants, opioids, paracetamol, and ≥10 drugs); the authors reported similar findings for MDD and non-MDD users without AD.⁴² However, a Swedish study of patients with hip fracture reported that MDD is associated with fewer changes in drug treatment compared with standard prescribing (odds ratio of 1.66-1.77 for a drug to be classified as unchanged when prescribed via MDD compared with standard prescribing, after adjustment for age, sex, and cognition).⁴¹

Medication Review

In a pragmatic RCT, MDD patients who received their drugs from community pharmacies were randomly allocated to receive a medication review, the results of which were to be discussed with the patients’ GPs.³⁵ After 6 months, the number of drug-related problems leading to a recommendation for drug change decreased by 29% in the group receiving medication review compared with 5% in waiting-list controls (P < .01). Recommendations for cessation of a drug were more frequently accepted by HCPs than recommendations to add a new drug (82% vs 44%; P = .01). The study authors recommended that all patients with MDD should have a thorough medication review by pharmacists or prescribers.³⁵

Use of Multidose Dispensing for Specific Drug Types

Two studies in the Netherlands on patients using a vitamin K antagonist showed that MDD use was associated with improvements in the length of time vitamin K antagonist was in the therapeutic range, which reflects treatment stability and is a proxy for adherence.^45,46

Implementing Multidose Dispensing Systems

A health technology assessment in Denmark revealed a variety of opinions among key individuals involved in implementing an ADD scheme in the primary sector; these included representatives from the pharmacies, general medical practice, hospital doctors, main authorities, professional organizations for nurses, doctors and pharmacists, the drug industry and patients.⁴⁷ Based on this qualitative research, the authors proposed several recommendations for decision makers and practitioners, including documenting safety problems and new types of error, and ensuring that ADD is only used for relevant target groups.⁴⁷ In a study in Norway, introducing e-prescribing (vs fax or ordinary mail) for MDD in a community healthcare setting led to greater integration between systems, both within the existing MDD system and across care levels, which facilitated faster information exchange and easier access to more complete patient information.⁴⁸ The study authors highlighted a need for clearer understanding of the roles and responsibilities of the different professionals in the medication management chain, as the increased integration means that professionals outside of the current MDD system (eg, hospital doctors, community pharmacists) could influence MDD prescribing and dispensing. A Danish study explored challenges at the primary-secondary care interface by investigating a group of patients receiving MDD on/after admission to hospital.⁴⁹ This study showed that information on MDD was recorded in the physician case record in <4% of patients admitted to an acute care ward.⁴⁹ Although changes were made to MDD for more than half the patients during admission, these changes were notified to GPs or community pharmacies for only 13.6% of patients. Studies in Sweden showed that MDD patients had a 5.9-fold higher risk of medication errors in their discharge summary during transition from hospital to primary/community care compared with non-MDD patients⁵⁰ and there was a 3- to 4-fold higher probability of medication discrepancies associated with MDD at discharge ^[.⁵¹ However, a Norwegian study reported a 34% reduction in the number of discrepancies between medication records from home-care services versus from GPs after MDD initiation.⁵² Two further studies, one in the Netherlands⁴⁴ and one in Finland,⁵³ described the process of making MDD adjustments and reconciling medication lists. A comprehensive medication management process integrating recommended risk management interventions into regular MDD services was proposed for older home-care patients in Finland.⁵⁴ This process comprised medication reconciliation, review, and follow-up, with explicit defining of the roles and responsibilities of each HCP in the care team.

Healthcare Professional Perspectives

The perspectives of HCPs automated MDD have been explore in several studies. For instance, a Swedish study reported that HCPs believe that MDD can reduce duplication of medication, contribute to correct dosages, help patients take their medication at the correct time, and reduce patient confusion. However, some HCPs believed that generic substitution by automated MDD made it more difficult for patients to identify their medications.⁵⁵ A study of Norwegian GPs also reported a positive attitude toward MDD and reported improved cooperation between GPs and pharmacies; however, the study highlighted that pharmacists carry additional responsibility for renewing MDD prescriptions, and that the most common prescription problems were expired prescriptions, drug shortages, missing prescriber signatures, and unclear/missing medication names or medication strengths.⁵⁶

A qualitative study in Norway explored GPs’ experiences of implementing MDD with e-prescribing.⁵⁷ The perceived benefits of MDD e-prescribing included GPs having a complete overview of patients’ medication lists, requiring reduced prescribing time, and increasing collaboration between HCPs; challenges included technological issues during the introduction phase, the need for training on MDD e-prescribing, concerns about the delay between information being added to patients’ medication list and being updated on the system, and concerns about medication interactions, and risk of incorrect medications.⁵⁷ In terms of implementing MDD, the needs of GPs vary regarding training, information, competence, and motivation in the use of digital tools.⁵⁸

A system approach for integrating medication risk management interventions (eg, medication reconciliation, review, and follow-up) into regular ADD in home-care patients was described in a Finnish study.⁵⁴ After implementing the system, the tasks and responsibilities of each HCP involved in the care team were explicitly defined and available resources were utilized more effectively.⁵⁴

Patient Perspectives

Most patients (90%) in a study of community pharmacies in the Netherlands felt that MDD supported their medication management, leading to improved medication adherence and medication safety (59%).⁵⁹ Although 69% considered there were no disadvantages with MDD, problems opening packaging (24%) and legibility issues (13%) were reported.⁵⁹ A qualitative study of ADD users in Denmark indicated that non-compliance was usually conscious (ie, not taking tablets from the dose bags or not following date or time instructions).⁶⁰ Several gaps between the perspectives of HCPs and patients were observed, highlighting the need to include feedback from all users in the development and implementation of such systems.

Discussion

There are limited data on MDD in community healthcare settings, particularly on outcomes such as adherence. Studies on this topic are heterogeneous, and there are few RCTs. Patients selected for MDD are more likely to have a higher number of disease diagnoses and drugs than patients using standard prescription and dispensing systems. In addition, they are more likely to be older, female, and cognitively impaired. MDD is generally initiated for patients who have decreased capacity for medication management.³⁰ Most data come from Northern European settings and results cannot be generalized to other healthcare systems.

Future research is needed to examine the effect of MDD on clinical outcomes beyond adherence. For example, in a Spanish study to investigate the clinical impact of a pharmaceutical professional service intervention, with or without multi-compartment medication (manual or semi-automated), in non-adherent, polymedicated patients with uncontrolled chronic hypertension, there was a significant reduction of systolic blood pressure after 6 months.⁶¹ RCTs designed to assess whether MDD can increase adherence and improve clinical outcomes are needed.

The increasing use of smart technology in conjunction with MDD should also be investigated. Various systems that enable real-time medication adherence monitoring are available. In a recent review, patients described being satisfied with smart oral MDD systems that include real-time adherence monitoring, but technical knowledge was required to operate these systems, which may limit their use in older populations.⁶² Also, some patients felt uncomfortable that their medications were being monitored.⁶² In 5 of 7 studies there was a reported improvement in medication adherence and in 2 studies there was a decline after using smart oral MDD systems.⁶² Another review investigating ADD robots placed in patients’ homes, concluded that patients had high medication adherence and were generally satisfied with the acceptability and functionality of the robots.⁶³

A multidisciplinary, multi-professional approach is needed for MDD in the community, including close collaboration between prescribing physicians and pharmacists. There needs to be a clear evaluation of the patient’s physical and cognitive functioning, and motivation for using the system to determine which patients are appropriate candidates for MDD. Medication review is also essential to evaluate a patient’s suitability and needs, and the complexity of their drug regimen; however, pharmacists conducting medication review for MDD patients report problems discussing the review with GPs.³⁵ Combining MDD with other elements, such as integrated care, medication review, and patient follow-up, may help to manage patients with multimorbidity and polypharmacy. It is important to enhance communication between HCPs and patients to raise patient awareness and knowledge of which of their medications relates to which indication. Interventions that combine educational and behavioral components may improve medication adherence.^64,65

HCPs have requested more training in medication review and automated MDD,⁶⁶ and pharmacists have emphasized the need to increase the efficiency and patient safety of MDD systems by clearly defining the roles and responsibilities of different HCPs.⁵⁶ When implementing e-prescribing MDD systems,⁵⁷ it is important to have a start-up phase, including meetings with pharmacists and technology suppliers. HCPs should screen older patients with polypharmacy on their medication management capacity to see if they might benefit from MDD. MDD could be improved for patients by making dose bags easier to open and increasing legibility of drug packaging text.⁵⁹ Future development, implementation, and assessment of MDD in community healthcare should be designed in collaboration with HCPs and patients to identify ways to optimize the systems and improve patient outcomes.

Limitations of the current review include the paucity of data on use of MDD in community healthcare settings, which resulted in the need to conduct a narrative as opposed to a systematic. Among the studies that were identified, there was considerable heterogeneity in the outcomes investigated, and the majority were observational studies. We were unable to draw conclusions can about the impact of MDD on adherence due to a lack of evidence. Despite these shortcomings, our review highlights unmet needs regarding the use of MDD, in particular inadequate documentation and communication regarding patients being discharged with MDD from hospital settings; the review also underlines the need for more research on the use of MDD in a community healthcare setting.

Conclusions

The results of our narrative review highlight that patients currently selected for MDD are more likely to have multimorbidity and polypharmacy, and a decreased capacity for medication management, than patients who are not selected for MDD. Consistent with these observations is the finding that patients receiving their medications via MDD are generally less aware of the drugs they are receiving and the indication, compared with those receiving medications via manual means. Our review highlights that more evidence is needed to support use of MDD in a community healthcare setting. Future studies should explore ways to mitigate against medication incidents, including clearly identifying the roles and responsibilities of those involved in MDD prescribing and dispensing, and identifying ways to reduce errors that occur during the transfer between of patients between secondary care and community settings.

Footnotes

Acknowledgements

N/A

Author Contribution Statement

Conceptualization: Javier Anitua, Lloyd Price, Danute Ducinskiene, Jessica Schlageter, Shaantanu Donde, Adela Martín-Oliveros, Javier Plaza Zamora, Alessandro Monaco; Methodology: Jessica Schlageter, Shaantanu Donde, Alessandro Monaco; Literature search: Alessandro Monaco, Danute Ducinskiene, Jessica Schlageter; Writing—original draft preparation: Adela Martín-Oliveros, Javier Plaza Zamora, Alessandro Monaco; Writing—review and editing: Adela Martín-Oliveros, Javier Plaza Zamora, Alessandro Monaco, Javier Anitua, Lloyd Price, Danute Ducinskiene, Jessica Schlageter, Shaantanu Donde.

Data Access Statement

Authors had complete access to the study data, with an ongoing access through online search engines.

Data Availability Statement

Not applicable

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Javier Anitua, Lloyd Price, Danute Ducinskiene, Jessica Schlageter, and Shaantanu Donde are employees of Viatris. Adela Martín-Oliveros, Javier Plaza Zamora, and Alessandro Monaco have no relevant financial or non-financial interests to disclose.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Statement

Our study did not require an ethical board approval because it did not involve human or animal participants.

ORCID iD

Alessandro Monaco

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Multidose Drug Dispensing in Community Healthcare Settings for Patients With Multimorbidity and Polypharmacy

Abstract

Keywords

Introduction

Methods

Results

Type of Patients Receiving Drugs via Multidose Dispensing

Medication Adherence and Knowledge

Quality of Drug Treatment

Drug Changes After Initiation of Multidose Dispensing

Medication Review

Use of Multidose Dispensing for Specific Drug Types

Implementing Multidose Dispensing Systems

Healthcare Professional Perspectives

Patient Perspectives

Discussion

Conclusions

Footnotes

Acknowledgements

Author Contribution Statement

Data Access Statement

Data Availability Statement

Declaration of Conflicting Interests

Funding

Ethical Statement

ORCID iD

References