Digital Medication Management in Polypharmacy

Findings of a Cluster-Randomized, Controlled Trial With a Stepped-Wedge Design in Primary Care Practices (AdAM)

Medication errors are responsible for around 30 to 50 percent of all medical errors (1, 2, 3). Approximately seven percent of hospital admissions are for adverse drug reactions (ADRs), of which two percent are fatal and 30 to 70 percent avoidable (4, 5, 6). Polypharmacy, usually defined as the simultaneous use of five or more medicines (7), affects more than one third of all adults (8) and is associated with adverse health outcomes, poor medication safety, and poor utilization of the healthcare system, which is reflected in higher rates of hospital (re)admissions, emergency admissions, and care home placements (1).

Clinical decision support systems (CDSSs) can improve prescribing quality and patient safety by preventing medication errors, ADRs, and the prescription of potentially inappropriate medications (PIMs) (9, 10, 11). They can also support primary care physicians in carrying out risk-benefit assessments of drug therapy (12, 13).

Although CDSSs improve process parameters and increase medication safety (14), patient-relevant outcomes are inconsistent: So far, no randomized study has been able to demonstrate effects of CDSSs on mortality or hospital admissions, often due to a lack of statistical reliability (14).

The aim of the AdAM study (Application of an Electronic Medication Management Support System) was therefore to examine in a cluster-randomized controlled trial whether the application of a user-initiated CDSS by primary care physicians can reduce hospital admissions and mortality in adults with polypharmacy.

Methods

The AdAM project primarily investigated whether a CDSS-supported medication review in primary care practices in Westphalia-Lippe, Germany, leads to a reduction in all-cause hospital admissions and/or all-cause mortality in adults with polypharmacy (at least five medicines). As secondary outcome measures, an assessment was also conducted as to whether the intervention

• reduces all-cause mortality,
• lowers all-cause hospital admissions, and
• improves prescribing quality and medication safety.

A detailed description of the methodology can be found in the eMethods and in the numerous Tables in the eSupplement. The study was registered with ClinicalTrials.gov (NCT03430336), funded by the Innovation Fund (01NVF16006) of the German Federal Joint Committee, and approved by the Ethics Committee of the North Rhine Medical Association (Nr. 2017184, 24.07.2017).

Description of the intervention

AdAM is a multifactorial intervention to support primary care physicians in user-initiated annual CDSS-supported medication reviews. For this purpose, primary care physicians accessed the CDSS, which is not connected to the practice management system, via a secure web-based portal (KV-SafeNet) and which provided the user with information at practice level and – after obtaining informed consent – at patient level. The CDSS contains diagnosis and treatment data relating to drug and non-pharmacological treatments based on performance data of the Barmer health insurance fund (Barmer). It supports cross-patient risk management at practice level (for patients receiving medication mentioned in Direct Healthcare Professional Communications [DHPCs]) as well as patient-specific medication reviews. For the medication review, the doctors accessed the information provided in the CDSS on the enrolled patients, updated and supplemented it with information that was not (yet) available in the Barmer performance data (new prescriptions, dosages, body weight, laboratory results). In response to the review, the CDSS displayed alerts (e.g., on drug interactions, contraindications, dosing errors, and duplicate medications). The physician was then able to optimize the medication and provide patients with a print-out in the format of the nationally standardized medication plan with multilingual explanations on drug therapy. Enrolled patients could be accessed in the CDSS as often as required, and a medication review conducted at least once a year was remunerated with 85 euros per patient. Before starting the intervention, the doctors were given the opportunity to participate in face-to-face and online training sessions on polypharmacy and the technical use of the CDSS. A process evaluation planned in advance examined user behavior and the implementation of the intervention (15, 16).

Study design

The study was originally planned as a cluster-randomized controlled trial with a parallel group design (parallel c-RCT). Practices in the intervention group applied the new electronic medication management support system when treating their patients, while control practices continued to provide standard care. Simulation calculations were conducted due to low recruitment rates of primary care physicians and patients – particularly as a result of the flu outbreak and coronavirus pandemic. We subsequently switched to a stepped-wedge design (SWD) in order to achieve a power of 80 percent. This meant that all the practices of the control group changed to the intervention phase after five quarters. Overall, the observation period was extended to include the data before randomization and after the end of the parallel c-RCT (eFigure).

eFigure

Schematic representation of the AdAM-study in chronological order. Switching the study design to a stepped-wedge design with an open cohort resulted in an extension of the observation period. The parallel group comparison was subsequently extended as follows: The starting point was the practices included in the parallel group comparison, for which additional data for the period before and after the planned parallel group comparison phase (c-RCT period, shaded) were included in the assessment. This resulted in a control phase and a subsequent intervention phase for each practice (compare waitlist control group design).

Study population

General practices in Westphalia-Lippe, Germany, were informed about the study via regional media, were contacted by the Association of Statutory Health Insurance Physicians of Westphalia-Lippe (KVWL) by postal mail and invited to participate. Barmer also sent out information flyers to its insurees in the region. General practices willing to participate with

• healthcare services for patients insured with Barmer
• physicians with a specialist qualification in general medicine, internal medicine, or without a specialist qualification
• at least ten potentially eligible patients (“potential patients”)
• access to the KVWL website via a secure connection, and
• consent of the physicians to fulfill the contractual obligations arising from the study were admitted to the trial and randomized to the intervention or control group.

Potential patients are adult patients of the participating primary care practices insured with Barmer with at least five different medication prescriptions (defined as number of the ATC codes) for at least two quarters. They were identified during the observation period using Barmer performance data. All potential patients of the control and intervention practices whose GP participated in the study were included in the analysis if a contact had taken place during the observation phase. The patients were included in the analysis cohort of the corresponding period (open cohort) as of the quarter in which they met the entry criteria, regardless of consent (secondary data analysis). Primary care practices and their potential patients were excluded from the main analysis if none of their patients participated during the intervention period (inactive practices), as this is due to various organizational reasons (17).

Primary and secondary endpoints

The primary combined dichotomous endpoint comprised mortality and hospital admission (whichever occurred first). The principle secondary endpoints were mortality, hospital admissions, and a combined dichotomous endpoint which represented the 19 high-risk prescriptions for gastrointestinal bleeding, cardiovascular risks, and falls (eSupplement Table 2). The endpoints were measured quarterly at patient level for 14 quarters (October 01, 2017, thru March 31, 2021). The information regarding patients eligible to participate was obtained in pseudonymized form from Barmer performance data.

Statistical analysis

The analysis was conducted according to the intention-to-treat principle (modified ITT after inactive practices were excluded) using the Barmer performance data (secondary data analysis) and employing a mixed logistic model.

Three sensitivity analyses were performed:

• an analysis of the originally planned parallel group comparison (parallel c-RCT without inactive practices), i.e. the practices and their patients were monitored for five quarters after randomization,
• an analysis which included only the quarters before the first COVID-19-related lockdown (until March 31, 2020) (when routine care was restricted in hospitals and general practices), and
• an analysis which included the data of all randomized practices (including the inactive practices) but also took into account only the quarters before the COVID-19 pandemic.

Additionally, a Cox regression analysis with robust variance estimation taking cluster effects into consideration was conducted for sensitivity analysis 1 in the secondary endpoint of all-cause mortality (was not originally planned).

In a dose-response analysis, a mixed Poisson model was used to calculate at cluster level whether a higher proportion of patients treated with CDSS leads to a greater intervention effect of the practice.

Results

From June 2017 thru July 2019, 1348 practices (identified using main practice number) in Westphalia-Lippe were invited to participate in AdAM; of these, 688 practices were randomized (intervention/control: 343/345). A total of 937 primary care physicians agreed to participate in the study, of which four study physicians had no potential patients. Since access to software was linked to the primary care physician, several physicians could participate in the study per practice premises. Similarly, the medical staff could change premises during the study or work at several (main/subsidiary) premises. This increased the total number of clusters (practices) in the intention-to-treat analyses to 746 practices (Figure 1).

Figure 1

Flow chart for the project “Application of an Electronic Medication Management Support System” (AdAM)

The data of 42 700 patients were included in the intention-to-treat analyses. Of these, 23 582 were monitored both during the control period and during the intervention period (blue and orange boxes, respectively, in the eFigure). Data for 6181 patients were available only for the control period and for 12 937 only for the intervention period. A total of 391 994 patient-quarters were monitored during the period October 01, 2017, thru March 31, 2021 (eFigure).

Of the 746 analyzed practices, 411 (55%) were active practices, the median (IQR) of the enrolled patients was 18 (8 to 33), corresponding to a median (interquartile range – IQR) enrollment rate of 35.8 percent (14.8 to 53.6%) of the potential patients. Enrolled patients were on average somewhat older, less in need of care, and took slightly more prescribed medications (eSupplement Table 25).

Table 2

Main and sensitivity analyses of the primary endpoint and the principle secondary endpoints

Characteristics at baseline

There were no significant differences between the two groups at the baseline examination (Table 1).

Table 1

Characteristics of patients, physicians, and practices at baseline

eTable

Overview of the analyses

Primary Endpoint

In the main analysis, no significant decrease was observed in den intervention periods for the combined endpoint (odds ratio [OR] 1.00; 95% confidence interval: [0.95; 1.04]; p = 0.872; violet circle in Figure 2). Sensitivity analysis 1 in the parallel c-RCT period, where each cluster represented either a control practice or an intervention practice, produced similar results (OR 0.99 [0.93; 1.06]; p = 0.852; blue square in Figure 2).

Figure 2

Main and sensitivity analysis of the primary endpoint and the principle secondary endpoints. The results for the different populations to be analyzed (colors) and the different adaption variants (symbols) are presented.

A significant decline in hospital admissions and, with it, also in the combined primary endpoint was noted at the start of the COVID-19 pandemic. Before the start of the pandemic, a primary endpoint event occurred in 16.6 percent (18 599 of 111 811) of the intervention quarters, compared with 17.3 percent (21 993 of 126 886) in the control periods. This had an impact on the analyses with the exclusion of the COVID quarters: The effect estimates for the OR were larger in favor of the intervention in both sensitivity analysis 2 (OR 0.97 [0.92; 1.02]; p = 0.237; green circle in Figure 2) and sensitivity analysis 3 (OR 0.97 [0.93; 1.01]; p = 0.138; red circle in Figure 2) for the pre-pandemic quarters.

Secondary Endpoints

As regards the principle secondary endpoints, the intervention did not result in any reductions in the main analysis (hospital admissions: OR 1.00 [0.95; 1.05]; mortality: OR 1.04 [0.92; 1.17]; high-risk prescription: OR 0.98 [0.92; 1.04]; violet circles in Figure 2 and Table 2). Sensitivity analysis 1 in the parallel c-RCT period produced similar results (hospital admissions: OR 1.00 [0.94; 1.06]; mortality: OR 0.93 [0.8; 1.07]; high-risk prescriptions: OR 0.98 [0.89; 1.08]; blue squares in Figure 2).

The post-hoc analysis of the Cox model showed a lower probability of death for the intervention (hazard ratio [HR] 0.89 [0.787; 0.997]; p = 0.0451; Figure 3).

Figure 3

Kaplan-Meier plot of time since randomization (month) and after the randomized treatment group in the originally planned c-RCT period.

The dose-response analysis showed that treating more patients per practice with the new model of care was associated with a greater reduction in the event rate (relative risk 0.95 [0.90; 0.99]; p = 0.0182; corresponding to the assumptions regarding the effect size when planning the study).

Discussion

On the whole, the planned analyses did not reveal any significant effect of the intervention on the combined primary endpoint and the principle secondary endpoints. Given the inadequate practice and patient recruitment and the high rate of inactive practices (45%), extensive simulations were carried out, which revealed a strong loss of power of the original study design. By switching the study design to an SWD and the associated extension of the observation period as well as an evaluation strategy using mixed effects logistic regression with repeated measurements (GLMM), it was possible to ensure the required 80 percent power in the simulations. Therefore, the preplanned GLMM was used for the analysis of all endpoints (18).

The COVID-19 pandemic had a significant impact on the study, its endpoints, and analyses, as care for chronically ill patients was restricted during this time, they were hospitalized less frequently (19, 20), and the SWD responds sensitively to time effects (21, 22, 23). The parallel c-RCT design, which is insensitive to time effects, was therefore adopted as the planned sensitivity analysis for the primary endpoint and the principle secondary endpoints. An unplanned Cox model analysis was conducted as a further sensitivity analysis, since this provides unbiased estimates of mortality in a parallel group design that is insensitive to time effects. The Cox model analysis revealed that mortality during the intervention period fell by ten percent. The effect estimates of other sensitivity analyses, which included only the pre-pandemic quarters, pointed in the same direction (19, 20, 21, 22, 23). Practices with above-average patient inclusion showed a higher intervention effect (dose-response analysis), indicating that the low patient recruitment due to the pandemic prevented a stronger effect on the primary endpoint. Another effect of the pandemic was that many of the original control practices did not conduct any intervention after the switch (inactive practices), which in turn reduced the power particularly in the main analysis.

We consider this to be the first prospective, randomized controlled study to indicate that a CDSS-supported medication review in adult patients with polypharmacy in primary care could prevent deaths. A recently published non-randomized retrospective study showed that a collaborative medication management involving physicians and pharmacists significantly lowered mortality of patients with polypharmacy (24). Furthermore, a meta-analysis showed a relative mortality reduction of 26 percent (corresponding to an absolute reduction of 1.4%) in an elderly population with polypharmacy who were subjected to a comprehensive medication review (25). A systematic review covering a greater number of studies, however, did not reveal any reduction in mortality (26). In line with previous studies on polypharmacy, our intervention had no impact on hospital admissions (26, 27, 28, 29). Unlike our study, Dreischulte et al. showed a decrease in hospital admissions after high-risk prescription against which targeted interventions were directed (30). Interventions to reduce PIM prescriptions more often resulted in an improvement in health care processes (14, 26, 27, 28), the relevance of which is uncertain for patient-relevant outcomes, however, as PIMs are not the main cause of medication-related hospitalizations (31).

The cluster design of the AdAM study and the exclusive use of health insurance data have the advantage that relevant data is available without omissions and measurement bias is largely avoided (26, 32).

The AdAM study had a number of limitations, especially case number limitations which made the design switch to SWD necessary, as well as unfavorable time effects of the pandemic (21, 22, 23). Time effects result from the fact that in the SWD the observations of the control phases took place earlier in the course of the study than those of the intervention phases. The pandemic-related reduction of hospital admissions occurred above all during the intervention phase. A further time effect-related restriction consisted of the need to adjust for covariates, such as disease burden progression and aging. By adjusting the prognostic index of the medication-based Chronic Disease Score (med-CDS) and the care levels in the analyses on a quarterly basis, a potential improvement resulting from the intervention was reduced. In addition, our effect size may have been underestimated because, firstly, the selection criteria favored the inclusion of low-risk patients (low prevalences of PIM prescription, anticholinergic burden, low med-CDS score, eSupplement Table 12), who may not, or may not significantly, benefit from the intervention (33). Secondly, the benefits of the intervention may not have been fully exploited: incomplete learning curves due to low patient numbers in the clusters, possibly incomplete data entry for medication review due to technical barriers and lack of integration of the CDSS into practice management systems, time constraints on the part of medical staff due to pandemic management, and lack of training (16).

Although the AdAM intervention did not show any significant effects in the planned analyses, there is evidence that CDSS-assisted medication reviews and treatment planning could potentially reduce mortality in adult patients with polypharmacy in primary care, given that the effect estimates of all sensitivity analyses conducted before the COVID-19 pandemic support this hypothesis. Together with other studies, in which the quality of prescribing and patient safety were improved, these results justify a regular CDSS-supported medication review. Further studies are needed to improve implementation, integrate CDSS into workflows (34), and intensify training in the use of CDSS and medication optimization (35, 36, 37, 38).

In summary, the present study was unable to show any significant effects on the primary endpoint and the principle secondary endpoints in the planned analyses. Due to the pandemic and recruitment difficulties, unplanned analyses were conducted that provided indications of a possible reduction in mortality in the intervention group. Controlled trials with appropriate follow-up and a better implementation strategy are needed to prove that a CDSS has significant effects on mortality.

Acknowledgments

This work was created in close cooperation with the entire AdAM study group. Apart from the involved authors, this includes: Lara Düvel, Till Beckmann (Barmer, Wuppertal); Reinhard Hammerschmidt, Julia Jachmich, Eva Leicher, Benjamin Brandt, Johanna Richard, Frank Meyer, Dr. Mathias Flume, Thomas Müller (Association of Statutory Health Insurance Physicians Westphalia-Lippe, Dortmund); Prof. Dr. Ferdinand M. Gerlach, Dr. Beate S. Müller, Dr. Benno Flaig, Dr. Ana Isabel González-González, Truc S. Dinh, Kiran Chapidi (Institute of General Medicine, Goethe University, Frankfurt am Main); Ingo Meyer (PMV Research Group, University Hospital of Cologne); Prof. Dr. Hans J. Trampisch, Renate Klaaßen-Mielke (Department of Medical Informatics, Biometry and Epidemiology, Ruhr University, Bochum,); Prof. Dr. Holger Pfaff, Prof. Dr. Ute Karbach (Institute for Medical Sociology, Health Services Research and Rehabilitation Science, University of Cologne); Karolina Beifuß, Sarah Meyer (Chair of Health Services Research and Health Economic Evaluation, Bergische University, Wuppertal); Simone Grandt (RpDoc Solutions, Saarbrucken).

Affiliations of the other authors:

Conflict of interest statement
Payments were made from the Federal Joint Committee’s innovation fund (funding code 01NVF16006) to the authors’ institutions (except for CM: to the Goethe-University Frankfurt, Institute of General Practice; except for DG: to the German Society for Internal Medicine); SH, PG and RP have not received any money from the Innovation Fund.

RB is a delegate of the Chamber of Pharmacists of Hesse and a member of the Executive Committee of the Pension Fund of the Chamber of Pharmacists of Hesse.

DG has been the author of Barmer’s annual drug report since 2016 and prepares analyses on deficits and strategies for optimizing the AMTS of Barmer insurees; he is a member of the scientific advisory board of RpDoc Solutions (technology partner of the project) and head of the AMTH & AMTS commission of DGIM; his wife is managing partner of RpDoc Solutions.

CM is Editor and co-author of the book “Praxishandbuch Multimorbidität” [Practical Handbook on Multi-Morbidity] (Elsevier). She has received funding from the Innovation Fund for the following projects at Goethe University: Project EVITA, Fkz 01VSF16034, Project PROPERmed, Fkz 01VSF16018, Guideline Multimedication, Fkz 01VSF22012. She has also received funding from the Innovation Fund for the PARTNER project, Fkz 01VSF21038, at Bielefeld University.

SH, PG and RP declare that they no conflicts of interest exists.

Manuscript received on 12 July 2023, revised version accepted on 11 January 2024

Translated from the original German by Dr Grahame Larkin

Corresponding author
Dr. rer. med. Robin Brünn

Pharmacy of University Hospital Frankfurt

Theodor-Stern-Kai 7
60590 Frankfurt am Main

Cite this as:
Brünn R, Basten J, Lemke D, Piotrowski A, Söling S, Surmann B, Greiner W, Grandt D, Kellermann-Mühlhoff P, Harder S, Glasziou P, Perera R, Köberlein-Neu J, Ihle P, van den Akker M, Timmesfeld N, Muth C, on behalf of the AdAM study group: Digital medication management in polypharmacy—findings of a cluster-randomized, controlled trial with a stepped-wedge design in primary care practices (AdAM). Dtsch Arztebl Int 2024; 121: 243–50. DOI: 10.3238/arztebl.m2024.0007

References

1. Davies LE, Spiers G, Kingston A, Todd A, Adamson J, Hanratty B: Adverse outcomes of polypharmacy in older people: systematic review of reviews. J Am Med Dir Assoc 2020; 21: 181–7

2. Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ: Temporal trends in rates of patient harm resulting from medical care. N Engl J Med 2010; 363: 2124–34

3. Meyer-Massetti C, Meier CR, Guglielmo BJ: The scope of drug-related problems in the home care setting. Int J Clin Pharm 2018; 40: 325–34

4. Davies EC, Green CF, Taylor S, Williamson PR, Mottram DR, Pirmohamed M: Adverse drug reactions in hospital in-patients: a prospective analysis of 3695 patient-episodes. PLoS One 2009; 4: e4439

5. Pirmohamed M, James S, Meakin S, et al.: Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ 2004; 329: 15–9

6. Schneeweiss S, Hasford J, Göttler M, Hoffmann A, Riethling AK, Avorn J: Admissions caused by adverse drug events to internal medicine and emergency departments in hospitals: a longitudinal population-based study. Eur J Clin Pharmacol 2002; 58: 285–91

7. Masnoon N, Shakib S, Kalisch-Ellett L, Caughey GE: What is polypharmacy? A systematic review of definitions. BMC Geriatr 2017; 17: 230

8. Delara M, Murray L, Jafari B, et al.: Prevalence and factors associated with polypharmacy: a systematic review and meta-analysis. BMC Geriatr 2022; 22: 601

9. Clyne B, Bradley MC, Hughes C, Fahey T, Lapane KL: Electronic prescribing and other forms of technology to reduce inappropriate medication use and polypharmacy in older people: a review of current evidence. Clin Geriatr Med 2012; 28: 301–22

10. Lainer M, Mann E, Sönnichsen A: Information technology interventions to improve medication safety in primary care: a systematic review. Int J Qual Health Care 2013; 25: 590–8

11. Vélez-Díaz-Pallarés M, Pérez-Menéndez-Conde C, Bermejo-Vicedo T: Systematic review of computerized prescriber order entry and clinical decision support. Am J Health Syst Pharm 2018; 75: 1909–21

12. Avery AJ, Rodgers S, Cantrill JA, et al.: A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis. Lancet 2012; 379: 1310–9

13. Horsky J, Aarts J, Verheul L, Seger DL, van der Sijs H, Bates DW: Clinical reasoning in the context of active decision support during medication prescribing. Int J Med Inform 2017; 97: 1–11

14. Anderson LJ, Schnipper JL, Nuckols TK, et al.: A systematic overview of systematic reviews evaluating interventions addressing polypharmacy. Am J Health Syst Pharm 2019; 76: 1777–87

15. Brünn R, Lemke D, Chapidi K, et al.: Use of an electronic medication management support system in patients with polypharmacy in general practice: study protocol of a quantitative process evaluation of the AdAM trial. Ther Adv Drug Saf 2022; 13: 20420986211073215

16. Brünn R, Lemke D, Basten J, et al.: Use of an electronic medication management support system in patients with polypharmacy in eneral practice: a quantitative process evaluation of the AdAM trial. Pharmaceuticals (Basel) 2022; 15: 759

17. Committee for Medicinal Products for Human Use (CHMP)/The International Conference on Harmonization (ICH): E 9 Statistical principles for clinical trials. London 1998.

18. Müller BS, Klaaßen-Mielke R, Gonzalez-Gonzalez AI, et al.: Effectiveness of the application of an electronic medication management support system in patients with polypharmacy in general practice: a study protocol of cluster-randomised controlled trial (AdAM). BMJ Open 2021; 11: e048191

19. Reinstadler SJ, Reindl M, Lechner I, et al.: Effect of the COVID-19 pandemic on treatment delays in patients with ST-segment elevation myocardial infarction. J Clin Med 2020; 9: 2183

20. Statistisches Bundesamt: 13% weniger stationäre Krankenhausbehandlungen im Jahr 2020. Pressemitteilung Nr. 445 vom 22. September 2021. www.destatis.de/DE/Presse/Pressemitteilungen/2021/09/PD21_445_231.html (last accessed on 19 August 2022).

21. Thompson JA, Fielding KL, Davey C, Aiken AM, Hargreaves JR, Hayes RJ: Bias and inference from misspecified mixed-effect models in stepped wedge trial analysis. Statist Med 2017; 36: 3670–82

22. Nickless A, Voysey M, Geddes J, Yu LM, Fanshawe TR: Mixed effects approach to the analysis of the stepped wedge cluster randomised trial-investigating the confounding effect of time through simulation. PLoS One 2018; 13: e0208876

23. Rennert L, Heo M, Litwin AH, de Gruttola V: Accounting for confounding by time, early intervention adoption, and time-varying effect modification in the design and analysis of stepped-wedge designs: application to a proposed study design to reduce opioid-related mortality. BMC Med Res Methodol 2021; 21: 53

24. Meid AD, Wirbka L, Moecker R, et al.: Mortality and hospitalizations among patients enrolled in an interprofessional medication management program—results of the medicines initiative Saxony-Thuringia (Arzneimittelinitiative Sachsen-Thüringen, ARMIN). Dtsch Arztebl Int 2023; 120: 253–60

25. Bloomfield HE, Greer N, Linsky AM, et al.: Deprescribing for community-dwelling older adults: a systematic review and meta-analysis. J Gen Intern Med 2020; 35: 3323–32

26. Johansson T, Abuzahra ME, Keller S, et al.: Impact of strategies to reduce polypharmacy on clinically relevant endpoints: a systematic review and meta-analysis. Br J Clin Pharmacol 2016; 82: 532–48

27. Smith SM, Wallace E, O‘Dowd T, Fortin M: Interventions for improving outcomes in patients with multimorbidity in primary care and community settings. Cochrane Database Syst Rev 2016; 3: CD006560

28. Tecklenborg S, Byrne C, Cahir C, Brown L, Bennett K: Interventions to reduce adverse drug event-related outcomes in older adults: a systematic review and meta-analysis. Drugs Aging 2020; 37: 91–8

29. Junius-Walker U, Krause O, Thürmann P, et al.: Drug safety for nursing-home residents—findings of a pragmatic, cluster-randomized, controlled intervention trial in 44 nursing homes. Dtsch Arztebl Int 2021; 118: 705–12

30. Dreischulte T, Donnan P, Grant A, Hapca A, McCowan C, Guthrie B: Safer prescribing—a trial of education, informatics, and financial incentives. N Engl J Med 2016; 374: 1053–64

31. Schmiedl S, Rottenkolber M, Szymanski J, et al.: Preventable ADRs leading to hospitalization—results of a long-term prospective safety study with 6,427 ADR cases focusing on elderly patients. Expert Opin Drug Saf 2018; 17: 125–37

32. French DP, Miles LM, Elbourne D, et al.: Reducing bias in trials due to reactions to measurement: experts produced recommendations informed by evidence. J Clin Epidemiol 2021; 139: 130–9

33. Muth C, Uhlmann L, Haefeli WE, et al.: Effectiveness of a complex intervention on Prioritising Multimedication in Multimorbidity (PRIMUM) in primary care: results of a pragmatic cluster randomised controlled trial. BMJ Open 2018; 8: e017740

34. Söling S, Köberlein-Neu J, Müller BS, et al.: From sensitization to adoption? A qualitative study of the implementation of a digitally supported intervention for clinical decision making in polypharmacy. Implement Sci 2020; 15: 82

35. Leiva-Fernández F, Prados-Torres JD, Prados-Torres A, et al.: Training primary care professionals in multimorbidity management: educational assessment of the eMULTIPAP course. Mech Ageing Dev 2020; 192: 111354

36. Prados-Torres A, Del Cura-González I, Prados-Torres JD, et al.: MULTIPAP Study: improving healthcare for patients with multimorbidity. Br J Gen Pract 2020; 70 (suppl 1): bjgp20X711257

37. Mann C, Shaw ARG, Guthrie B, et al.: Can implementation failure or intervention failure explain the result of the 3D multimorbidity trial in general practice: mixed-methods process evaluation. BMJ Open 2019; 9: e031438

38. Salisbury C, Man MS, Bower P, et al.: Management of multimorbidity using a patient-centred care model: a pragmatic cluster-randomised trial of the 3D approach. Lancet 2018; 392: 41–50

e1. Müller BS, Klaaßen-Mielke R, Gonzalez-Gonzalez AI, et al.: Effectiveness of the application of an electronic medication management support system in patients with polypharmacy in general practice: a study protocol of cluster-randomised controlled trial (AdAM). BMJ Open 2021; 11: e048191

e2. van den Bussche H, Koller D, Kolonko T, et al.: Which chronic diseases and disease combinations are specific to multimorbidity in the elderly? Results of a claims data based cross-sectional study in Germany. BMC Public Health 2011; 11: 101

e3. Quinzler R, Freitag MH, Wiese B, et al.: A novel superior medication-based chronic disease score predicted all-cause mortality in independent geriatric cohorts. J Clin Epidemiol 2019; 105: 112–24

e4. Holt S, Schmiedl S, Thürmann PA: Potentially inappropriate medications in the elderly: the PRISCUS list. Dtsch Arztebl Int 2010; 107: 543–51

e5. Renom-Guiteras A, Meyer G, Thürmann PA: The EU(7)-PIM list: a list of potentially inappropriate medications for older people consented by experts from seven European countries. Eur J Clin Pharmacol 2015; 71: 861–75

e6. Carnahan RM, Lund BC, Perry PJ, Pollock BG, Culp KR: The anticholinergic drug scale as a measure of drug-related anticholinergic burden: associations with serum anticholinergic activity. J Clin Pharmacol 2006; 46: 1481–6

e7. Durán CE, Azermai M, Vander Stichele RH: Systematic review of anticholinergic risk scales in older adults. Eur J Clin Pharmacol 2013; 69: 1485–96

e8. Hilmer SN, Mager DE, Simonsick EM, et al.: A drug burden index to define the functional burden of medications in older people. Arch Intern Med 2007; 167: 781–7

e9. Vestal RE: Aging and pharmacology. Cancer 1997; 80: 1302–10

e10. Koper D, Kamenski G, Flamm M, Böhmdorfer B, Sönnichsen A: Frequency of medication errors in primary care patients with polypharmacy. Fam Pract 2013; 30: 313–9

e11. Thompson JA, Fielding KL, Davey C, Aiken AM, Hargreaves JR, Hayes RJ: Bias and inference from misspecified mixed-effect models in stepped wedge trial analysis. Stat Med 2017; 36: 3670–82

e12. Nickless A, Voysey M, Geddes J, Yu L-M, Fanshawe TR: Mixed effects approach to the analysis of the stepped wedge cluster randomised trial—Investigating the confounding effect of time through simulation. PLoS One 2018; 13: e0208876

e13. Hussey MA, Hughes JP: Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials 2007; 28: 182–91

e14. Hemming K, Haines TP, Chilton PJ, Girling AJ, Lilford RJ: The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ 2015; 350: h391

e15. Hemming K, Taljaard M, Forbes A: Modeling clustering and treatment effect heterogeneity in parallel and stepped-wedge cluster randomized trials. Stat Med. 2018; 37: 883–98

e16. Rudolf H, Klaaßen-Mielke R, Timmesfeld N: Terminale Endpunkte bei Cluster-randomisierten Stepped-Wedge-Designs: eine Simulationsstudie. German Medical Science GMS Publishing House 2019.