DÄ internationalArchive44/2016Interprofessional Medication Management in Patients With Multiple Morbidities

Original article

Interprofessional Medication Management in Patients With Multiple Morbidities

A Cluster-randomized Trial (the WestGem Study)

Dtsch Arztebl Int 2016; 113(44): 741-8; DOI: 10.3238/arztebl.2016.0741

Köberlein-Neu, J; Mennemann, H; Hamacher, S; Waltering, I; Jaehde, U; Schaffert, C; Rose, O

Background: Medication reviews and medication management are being used more and more around the world to improve medication safety. Both of these tools were originally conceived as pharmaceutical care activities and have recently been developed into interdisciplinary approaches. We studied the efficacy of interprofessional medication management for multimorbid patients that takes their medical conditions, but also their general living situation into account.

Methods: A comprehensive medication management was performed, which involved the collection of information on the drugs each patient took, the way they were stored, the patient’s drug intake and handling, and any problems that arose with pharmacotherapy. The interventional approach was evaluated over a period of 15 months in a cluster-randomized controlled trial with a stepped wedge design. The primary endpoint was the quality of pharmacotherapy, as assessed with the Medication Appropriateness Index (MAI). A mixed model was used to analyze efficacy.

Results: 162 patients were enrolled in the study; 142 were included in the intention-to-treat analysis (53.3% women, mean age 76.8 ± 6.3 years). The mean total MAI score decreased significantly (p ≤ 0.001) from the control phase (29.21, 95% CI [26.09; 32.33]) to the intervention phase (22.27 [19.00; 25.54]), with an effect strength (Cohen’s d) of –0.24 [–0.36; –0.13]. The number of drug-related problems declined as well.

Conclusion: In this study, interprofessional collaboration increased medication safety. Working across disciplinary boundaries allowed for a decrease in drug-related problems and brought up aspects outside the purview of the primary care physician.

LNSLNS

Internationally, medication management is increasingly becoming an established instrument to improve the quality of therapy and medication safety (1). It is used especially in patients with complex polymedication, several treating physicians, and a high probability of developing drug-related problems (2, 3). The latter term describes events or circumstances in drug treatment that actually or potentially prevent the achievement of therapeutic goals (4).

Medication management is based on methods of pharmaceutical care; in recent years, it has become a patient-oriented approach that is implemented in structurally different ways (for example, under the supervision of either physicians or pharmacists, or a cooperation of both professions) (59). In Germany, the Federal Union of German Associations of Pharmacists (Bundesvereinigung Deutscher Apothekerverbände, ABDA) makes a distinction between isolated medication analysis (according to the definition of the Pharmaceutical Care Network Europe, PCNE) (eFigure 1) and medication management with longitudinal patient care (1012).

Definition of medication review
Definition of medication review
eFigure 1
Definition of medication review

Systematic reviews and meta-analyses based on controlled studies have described the effectiveness of medication management in outpatient care, especially with regard to quality improvements in medication therapy and medication safety as well as to changes in diverse clinical parameters (1318). Instruments for measuring the quality of medication therapy—such as a Medication Appropriateness Index (MAI)—allow for the standardized evaluation of the overall therapy (1922). However, on the basis of these results, conclusions about the effectiveness of medication management can only be drawn to a limited degree for the German healthcare context. A recent systematic review by Viswanathan et al. pointed out the great variance of the achieved results, which is caused by the composition of the interventions and their structural basis (18). As far as we are aware, the results of interventional studies of the cooperation between physicians and pharmacists in the outpatient setting in Germany are currently lacking.

On this background, the Westphalian study on a medication therapy management and home care based intervention under gender specific aspects in elderly multimorbid patients (WestGem) was conducted as an interdisciplinary project. The focus was on evaluating the effectiveness of interprofessional medication management in the outpatient care setting. The authors’ primary hypothesis was the expectation that the patient’s participation in medication management would lead to a change of the quality of medication therapy.

Method

Study design

The proof of efficacy was undertaken in the setting of a controlled, cluster-randomized trial, using a stepped wedge design. Compared with the usual parallel group structure, this design allows for each cluster to start in the control group and the intervention is introduced into the clusters at intervals (in steps).

In the WestGem study, an independent biometrician randomized the participating general practices (clusters) to three (changing) cohorts. After a control period, the cohorts switched to the intervention phase at intervals of three months each. The cohort allocation was disclosed only at the time of the changeover.

During the control phase, patients received standard care; in the intervention phase they additionally participated in medication management.

The intervention phase, depending on the timing of the changeover, was six to 12 months, with a subsequent follow-up period of 3 months. Data were collected at the time of inclusion into the study (T0); at the end of the recruitment phase (T1); and after 3 (T2), 6 (T3), 9 (T4), and 15 months (T6).

The study was entered into the current controlled trials register (ISRCTN41595373). The ethics committee at the Medical Association of Westphalia-Lippe (AKZ-2013–292-f-s) approved the study. The published study protocol provides a detailed description of the WestGem study (23).

Intervention

The complete intervention consisted of two overlapping strands of action that were complementary to standard care:

1. medication management, and

2. care provided by the Pflege- und Wohnberatung (PuW, home-care specialists), using a case management concept according to the German Society for Care and Case Management (Deutsche Gesellschaft für Care und Case Management, DGCC) (24).

For the purpose of medication management, primary care physicians (PCP) started off by sending information from their patient records to the home-care specialists (eTable 3). The home-care specialists arranged a home visit, conducted an assessment of the patient situation they found—including, among others: drugs taken, adherence, medication handling and storage, reported problems with medication therapy—and communicated this to the pharmacist, along with the information provided by the primary care physician. The pharmacist then undertook a comprehensive medication review (PCNE type 3). This included drugs taken, medication documented by primary care physicians, available laboratory data, diagnostic data, and insights into every patient’s personal situation as elicited in patient interviews. The results of the analysis were summarized in a letter of recommendation and sent to the home-care specialists, who in turn added information on the patient’s home situation and passed them on to the primary care physicians. Implementing the recommendations was the responsibility of each primary care physician. Details about such patient-related advice from physician to pharmacist and detailed information on the second strand of action can be obtained from the authors.

Reasons for the occurrence/observation of drug-related problems
Reasons for the occurrence/observation of drug-related problems
eTable 3
Reasons for the occurrence/observation of drug-related problems

Setting and study population

The study was conducted in two regions in the Westphalia-Lippe area. In a first step, all general practices in the regions were invited to participate in the study. Subsequently, patients were recruited from September 2013 to December 2013 (eBox 1).

Recruitment of patients and PCPs in detail
Recruitment of patients and PCPs in detail
eBox 1
Recruitment of patients and PCPs in detail

We included patients who met the inclusion and exclusion criteria listed below and who had given written consent to participating in the study:

Inclusion criteria

  • Age ≥ 65 years
  • A minimum of three chronic disorders affecting two different organ systems
  • At least one cardiovascular disease
  • At least one visit to the PCP in each of the preceding three-month intervals
  • Five or more long-term drug treatments (>3 months) with systemic effects
  • Ability to complete questionnaires, with assistance if required

Exclusion criteria

  • Life expectancy of less than 12 months (assessed by the treating primary care physician)
  • Participation in another clinical study

Study endpoints

The primary outcome measure of the study and the goal parameter for medication management was the quality of medication therapy, measured by using the MAI score (eBox 2). The 10 criteria of this score enable standardized assessment of the medication (2527).

MAI score calculation
MAI score calculation
eBox 2
MAI score calculation

Secondary endpoints were the number of drug-related problems (DRPs), potentially inadequate medication (PIM), patients’ quality of life, everyday life skills/competencies, and gait stability/risk of falling.

The attained level of social support was measured as the endpoint to evaluate intervention component 2 (results not shown) (28).

Data collection and measuring instruments

Data were collected by using different documentation media (for a detailed description, see [23]). For the endpoints explained in the Results section, eTable 1 shows a summary of the fundamentals and instruments of documentation.

Description of the complete intervention
Description of the complete intervention
eTable 1
Description of the complete intervention

Sample size calculation and statistical analysis

The sample size estimate was done according to Woertman et al. (29). The required sample size to achieve a power of 1–β = 80%, a two-sided significance level of α = 5%, an effect size of Cohen’s d=0.25, and after defining all required variables (ICC=0.05; for more see [23]), was 240 patients.

The endpoints were evaluated on the basis of an intention-to-treat population. We started with a descriptive data evaluation; results were presented as means and standard deviations. In order to analyze the effectiveness of medication management we developed a mixed model and evaluated this with a significance level of 5%. In order to determine the mere effects of the intervention, we conducted a model specification by considering so-called contrasts, which evaluated the mean change in MAI scores between the control and intervention phases at defined follow-up points (eBox 3). These results are shown as the “mean difference in contrast” with 95% confidence intervals and as effect sizes (Cohen’s d). All secondary endpoints were evaluated in analogy to the primary endpoint. For the latter we conducted additional sensitivity analyses (eBox 3). The significance level for all non-confirmatory tests was 5% and was intended to be exploratory. We used SPSS Statistics 23 (IBM Corp, Armonk, NY, USA) and STATA 14 (StataCorp, College Station, Texas, USA).

Further descriptions of the evaluation of the outcome measures
Further descriptions of the evaluation of the outcome measures
eBox 3
Further descriptions of the evaluation of the outcome measures

Results

Characteristics of the study population

Of the 70 primary care physicians we approached, 13 responded to our invitation; 12 were included in the study and were allocated randomly to the three study cohorts (C1: start after the end of the recruitment period, C2: start after three months, and C3: start after 6 months).

On the assumption of 856 potential study participants, 480 patients were approached about participating in the study (eBox 1). 162 patients consented in writing to participate (4–24 per practice). The intention-to-treat population comprised 142 patients. The CONSORT flowchart in eFigure 2 provides an overview of the flow of patients throughout the study.

CONSORT flowchart
CONSORT flowchart
eFigure 2
CONSORT flowchart

The mean age of the ITT cohort was 76.7±6.5 years, and the collective included 76 (53.5%) women. 33.6% of study participants were living alone at the time of the study. Table 1 describes relevant sociodemographic and clinical characteristics.

Sociodemographic and clinical characteristics of the intention-to-treat patient cohort in total and considered separately by time of changeover
Sociodemographic and clinical characteristics of the intention-to-treat patient cohort in total and considered separately by time of changeover
Table 1
Sociodemographic and clinical characteristics of the intention-to-treat patient cohort in total and considered separately by time of changeover

Medication therapy at baseline

When reconciling the primary care physicians’ documentation with patients’ drug intake at home, we found deviations in 4.8±3.5 medical drugs per patient at baseline, in terms of dosages differing from those prescribed, modified intake modalities, or continued use of drugs that should have been discontinued. 60.3% of drugs affected by deviations were lacking in the primary care physician’s documentation and resulted from medication obtained elsewhere or self-medication. The total number of drugs documented by primary care physicians was 9.4±3.5, the number of drugs actually taken was 10.5±3.6.

The mean number of drug-related problems at baseline was 7.3±3.4 per patient. The most common cause for the occurrence of drug-related problems was the choice of substance (49.8%) (eTable 2). The mean observed number of interaction effects per patient was 5.5±3.9; 26.8% of such interactions were categorized as clinically relevant and 32.7% as partially clinically relevant.

Basis and instruments of documentation of the endpoints shown
Basis and instruments of documentation of the endpoints shown
eTable 2
Basis and instruments of documentation of the endpoints shown

Primary endpoint

The analysis of the Medication Appropriateness Index (MAI) showed that the mean baseline score in patients in cohort 1 was reduced from 30.15±24.14 to 14.09±14.80 points after 15 months. In cohort 2 it was reduced from 43.27±30.39 to 24.47±16.17 points, and in cohort 3 it was reduced from 26.07±17.33 to 18.44±14.67 points (Figure). Table 2 summarizes the numbers and proportions of prescriptions categorized as inappropriate according to the MAI criteria.

Alteration of Medication FIGURE Appropriateness Index Scores over the study period, separated according to changeover groups
Alteration of Medication FIGURE Appropriateness Index Scores over the study period, separated according to changeover groups
Figure
Alteration of Medication FIGURE Appropriateness Index Scores over the study period, separated according to changeover groups
Numbers and proportions of inappropriate prescriptions, coded by domain of the Medication Appropriateness Index (MAI) score
Numbers and proportions of inappropriate prescriptions, coded by domain of the Medication Appropriateness Index (MAI) score
Table 2
Numbers and proportions of inappropriate prescriptions, coded by domain of the Medication Appropriateness Index (MAI) score

The difference in scores between the control period and the first intervention period reached significance with a mean of –4.51 units (95% confidence interval [–6.66;–2.36], P<0.001; effect size d= –0.24, [–0.36; –0.13]). An additional medication review showed a reduction in the mean MAI score compared with patients who had had one assessment by additional –0.99 units [–3.96; 1.97], P=0.510; d= –0.04, [–0.17; 0.08]. Sensitivity analyses (Table 3) showed that the change in the mean MAI score was mainly due to an improved quality of the prescribed medication and not due to a change in the number of drugs.

Results of the intention to treat-analysis for primary and secondary endpoints
Results of the intention to treat-analysis for primary and secondary endpoints
Table 3
Results of the intention to treat-analysis for primary and secondary endpoints

Secondary endpoints

The number of drug-related problems dropped (–0.45, [–0.81;–0.09]; P=0.014; d= –0.13, [–0.23;–0.03]). The second medication review showed no additional reduction. Depending on the category of cause, up to 60% of drug-related problems were solved (eTable 1). Medication management did not show any effect for additional endpoints in this analysis (Table 3).

Discussion

Results in the context of the published literature

The study showed a statistically significant effect of the intervention on the quality of medication therapy; the number of drug-related problems decreased as well. Both aspects are regarded as indicators in medication safety (30).

As far as we are aware, our study is the first published investigation that generates insights into the effectiveness of interprofessional medication management covering a variety of diseases in the outpatient setting in Germany. A study by Wolf et al. (2015) also found positive effects on the reported parameters for inpatient psychiatric care and outpatient follow-up care (30). A study in patients with Parkinson’s disease, reported by Henrichsmann and Hempel (2015), showed an improvement in symptoms (31). International studies have shown similar trends for effects, but different effect sizes, which can be partly explained with a disease-specific focus of the medication management (9, 18. 32). Furthermore, in our study, the type and number of implemented pharmaceutical recommendations affected the observed change in the quality of medication therapy. The process evaluation yielded a mean implementation rate of pharmaceutical suggestions of 54.9%; the rate increased the longer physician and pharmacist had been cooperating (33). The identified number of drug-related problems corresponds to comparable studies (34, 35). The drug-related problems that were included were based on the physicians’ documentation only, in order to assess the control period and the first intervention period on the basis of the same data.

The achieved improvement in the quality of medication therapy can be regarded as an important and obvious step towards increased medication safety, even though current literature does not provide a clear correlation between a MAI score reduction and patient outcomes (18, 19, 21, 32). We are therefore investigating in secondary analyses which change on the MAI score leads to a clinically significant improvement in patient relevant endpoints.

Strengths and limitations

We were able to compile a detailed dataset for 142 multimorbid patients with polymedication, allowing precise insights into patients’ entire life and healthcare situation. The stepped wedge design we used furthermore enables a comprehensive evaluation of the process. The study does, however, include a random regional sample, which includes medical practices that were willing to participate. For this reason, we cannot exclude the possibility of a selection bias (for example, certain prescribing profiles, specialized practices). The differences in the structure of patients in the practices underlines this assumption. It is unlikely, however, that the patient selection was done by the physicians themselves as the included study participants had been drawn randomly from the total number of all relevant patients in a given practice.

The low number of cases in the study is a further limitation. In spite of our recruitment effort we were not able to reach our target of 240 patients. This would have required a longer recruitment period, which was not feasible because of the restricted funding period. The case number was not sufficient especially for the evaluation of the patient-relevant secondary endpoints.

Another limitation of our study was the approach in determining the MAI score. The pharmacists had been blinded when calculating scores as to which cohort a patient was allocated to, but they were involved in some cases in conducting the medication reviews. They can therefore not be regarded as completely independent. For reasons of comparability between the control and the intervention phase, the MAI score is solely based on the physicians’ patient files. The drugs the patients were actually taking were not regarded. This means that we were able to assess individual criteria (Table 2) to a limited extent only.

As far as the generalizability of the intervention and translation to routine healthcare practice is concerned, it should be borne in mind that the participating pharmacists were particularly qualified for the medication reviews (for example most pharmacists had attained a doctorate in clinical pharmacy and were particularly specialized in medication management). The participation of the home-care specialists in the medication management process is a similar limitation. The home-care specialists conducted the so-called “brown bag review” (the inspection of all medication packages that patients had collected) on behalf of the pharmacists and therefore took over an pharmaceutical core activity. This was necessary because the terms of the funding program prohibited pharmacists from direct patient contact as a means to prevent a competitive advantage.

Conclusions

Interprofessional medication management has proved effective in outpatient care in terms of quality improvement of medication therapy in elderly multimorbid patients with polymedication. Furthermore, the number of drug-related problems was reduced. Larger follow-up studies should adapt the intervention to a wider and optimized community setting. The authors’ own secondary analyses demonstrated, for example, that not all patients benefitted equally from participating in medication management (33). In view of the resource implications associated with the interdisciplinary management of patients, criteria for how to select suitable patients might be conceived. For example, the number of drugs taken and the deviation between drugs prescribed and actually taken by the patient were significant factors determining a patient’s individual response to medication management.

Studies with longer follow-up periods should furthermore investigate the effect of medication management on patient relevant endpoints.

Acknowledgment
The study received funding in the context of the Ziel-2-Förderreihe IuK & Gender med.NRW from the federal state of North Rhine Westphalia and the European Union. The funders were not involved in the scientific evaluation of the data and had no influence on the composition of the manuscript and the decision to submit it for publication.

The authors thank Kathrin Czarnecki for her help in coordinating the project; Anna Ghukasyan, Dominik Tosciak, Maxim Tomachevski, Yvonne Winkler, Karolina Beifus, and Vera Weinsheimer for data entry of the survey questionnaires; and Moritz Felsch and Lena Herich for biometrical support. Special thanks go to the team of pharmacists—Carina John, Pharm.D., Dr. Marcus Lautenschläger, Damaris Mertens-Keller, Pharm.D., and Ina Richling, Pharm.D.—the home-care specialists (Pflege- und Wohnberatung, PuW) and general practices, which lived the intervention with great enthusiasm. Furthermore, the authors thank Prof. Dr. Falk Hoffmann (Oldenburg) for his critical review of the manuscript and for numerous valuable suggestions.

Conflict of interest statement
Pharm D Rose has received conference delegate fees, travel expenses, and accommodation costs, as well as honoraria for preparing scientific continuing medical educational events, from Bayer, Boehringer Ingelheim, Medac, MSD, and Omnicell.

Pharm D Waltering has received lecture honoraria from Medac, MSD, and HRA-Pharma.

Prof. Köberlein-Neu, Prof. Mennemann, Stefanie Hamacher, Corinna Schaffert, und Prof. Jaehde declare that no conflict of interest exists.

Manuscript received on 24 March 2016, revised version accepted on
22 August 2016.

Translated from the original German by Birte Twisselmann, PhD.

Corresponding author
Prof. Dr. rer. medic. Juliane Köberlein-Neu
Bergische Universität Wuppertal
Fakultät für Wirtschaftswissenschaft, BKG
Rainer-Gruenter-Str. 21
42119 Wuppertal, Germany
koeberlein@wiwi.uni-wuppertal.de

@Supplementary material
For eReferences please refer to:
www.aerzteblatt-international.de/ref4416

eBoxes, eTables, eFigures:
www.aerzteblatt-international.de/16m0741

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Center for Health Economics and Health Services Research, Schumpeter School of Business and Economics, University of Wuppertal: Prof. Dr. rer. medic. Köberlein-Neu, Corinna Schaffert
Department of Social Work, Münster University of Applied Sciences: Prof. Dr. phil. Mennemann
Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne: Stefanie Hamacher
Department of Pharmaceutical and Medicinal Chemistry, University of Münster:
Isabel Waltering, Pharm.D
Clinical Pharmacy, Institute of Pharmacy, University of Bonn, Bonn, Germany:
Prof. Dr. rer. nat. Jaehde, Olaf Rose, Pharm.D.
Alteration of Medication FIGURE Appropriateness Index Scores over the study period, separated according to changeover groups
Alteration of Medication FIGURE Appropriateness Index Scores over the study period, separated according to changeover groups
Figure
Alteration of Medication FIGURE Appropriateness Index Scores over the study period, separated according to changeover groups
Key messages
Sociodemographic and clinical characteristics of the intention-to-treat patient cohort in total and considered separately by time of changeover
Sociodemographic and clinical characteristics of the intention-to-treat patient cohort in total and considered separately by time of changeover
Table 1
Sociodemographic and clinical characteristics of the intention-to-treat patient cohort in total and considered separately by time of changeover
Numbers and proportions of inappropriate prescriptions, coded by domain of the Medication Appropriateness Index (MAI) score
Numbers and proportions of inappropriate prescriptions, coded by domain of the Medication Appropriateness Index (MAI) score
Table 2
Numbers and proportions of inappropriate prescriptions, coded by domain of the Medication Appropriateness Index (MAI) score
Results of the intention to treat-analysis for primary and secondary endpoints
Results of the intention to treat-analysis for primary and secondary endpoints
Table 3
Results of the intention to treat-analysis for primary and secondary endpoints
Recruitment of patients and PCPs in detail
Recruitment of patients and PCPs in detail
eBox 1
Recruitment of patients and PCPs in detail
MAI score calculation
MAI score calculation
eBox 2
MAI score calculation
Further descriptions of the evaluation of the outcome measures
Further descriptions of the evaluation of the outcome measures
eBox 3
Further descriptions of the evaluation of the outcome measures
Definition of medication review
Definition of medication review
eFigure 1
Definition of medication review
CONSORT flowchart
CONSORT flowchart
eFigure 2
CONSORT flowchart
Description of the complete intervention
Description of the complete intervention
eTable 1
Description of the complete intervention
Basis and instruments of documentation of the endpoints shown
Basis and instruments of documentation of the endpoints shown
eTable 2
Basis and instruments of documentation of the endpoints shown
Reasons for the occurrence/observation of drug-related problems
Reasons for the occurrence/observation of drug-related problems
eTable 3
Reasons for the occurrence/observation of drug-related problems
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