Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Medication Monitoring in a Nurse-Led Respiratory Outpatient Clinic: Pragmatic Randomised Trial of the West Wales Adverse Drug Reaction Profile

  • Marie E. Gabe,

    Affiliation College of Human and Health Sciences, Swansea University, Swansea, United Kingdom

  • Fiona Murphy,

    Affiliation College of Human and Health Sciences, Swansea University, Swansea, United Kingdom

  • Gwyneth A. Davies,

    Affiliations ABM University Health Board, Wales, United Kingdom, College of Medicine, Swansea University, Swansea, United Kingdom

  • Ian T. Russell,

    Affiliation West Wales Organisation for Rigorous Trials in Health, and College of Medicine, Swansea University, Swansea, United Kingdom

  • Susan Jordan

    Affiliation College of Human and Health Sciences, Swansea University, Swansea, United Kingdom

Medication Monitoring in a Nurse-Led Respiratory Outpatient Clinic: Pragmatic Randomised Trial of the West Wales Adverse Drug Reaction Profile

  • Marie E. Gabe, 
  • Fiona Murphy, 
  • Gwyneth A. Davies, 
  • Ian T. Russell, 
  • Susan Jordan



To assess the clinical effect of medication monitoring using the West Wales Adverse Drug Reaction (ADR) Profile for Respiratory Medicine.


Single-site parallel-arm pragmatic trial using stratified randomisation.


Nurse-led respiratory outpatient clinic in general hospital in South Wales.


54 patients with chronic respiratory disease receiving bronchodilators, corticosteroids or leukotriene receptor antagonists.


Following initial observation of usual nursing care, we allocated participants at random to receive at follow up: either the West Wales ADR Profile for Respiratory Medicine in addition to usual care (‘intervention arm’ with 26 participants); or usual care alone (‘control arm’ with 28 participants).

Main Outcome Measures

Problems reported and actions taken.


We followed up all randomised participants, and analysed data in accordance with treatment allocated. The increase in numbers of problems per participant identified at follow up was significantly higher in the intervention arm, where the median increase was 20.5 [inter-quartile range (IQR) 13–26], while that in the control arm was −1 [−3 to +2] [Mann-Whitney U test: z = 6.28, p<0.001]. The increase in numbers of actions per participant taken at follow up was also significantly higher in the intervention arm, where the median increase was 2.5 [1][4] while that in the control arm was 0 [−1.75 to +1] [Mann-Whitney U test: z = 4.40, p<0.001].


When added to usual nursing care, the West Wales ADR Profile identified more problems and prompted more nursing actions. Our ADR Profile warrants further investigation as a strategy to optimise medication management.

Trial Registration ISRCTN10386209


Iatrogenic harm is a persistent concern [1]. No pharmacological therapy that is effective is free of adverse effects [2]. Adverse drug reactions (ADRs), defined as any untoward and unintended response in a patient or investigational subject to a medicinal product that relates to any dose administered [3], range from minor and reversible through disability to death. Preventable ADRs account for 3.7% of hospital admissions in developed countries [4], and generate a 4.7% mortality rate in hospital [5]. Five per cent of inpatients experience ADRs [6], rising to 14% in elderly care wards [7]. Thus, this issue causes international concern. In the UK, about 6.5% of hospital admissions are due to ADRs, most of which are preventable [8], [9]; however we know less about the prevalence of ADRs in the community not associated with hospital admission [10].

ADRs are an important source of patient harm and healthcare expenditure. However there is significant heterogeneity between studies in risk of bias, populations, settings and methods used to identify ADRs [8], [11][13]. In the US, ADRs are recognised in the top six causes of death [14], costing $370 per ADR case for emergency department visits [15]. The cost of each ADR case is estimated at €2250 in Germany [16]. In the UK, ADR-related admissions alone cost the NHS an estimated £466 m per annum [10]; however, this estimate does not account for non-hospital contact [17]. Variability in identifying, recording and reporting of adverse events and ADRs accentuates this public health problem. Improved monitoring of, and response to, identified ADRs is an international priority to reduce the unnecessary burden of treatment, yet remains under-researched [18][22].


There are underlying weaknesses in current medication management practice internationally, particularly medication monitoring for known ADRs of prescribed drugs [18], [20], [21], [23][25]. Healthcare systems have traditionally relied on patient and clinician self-reports, prescription-event monitoring and case-control studies. These approaches capture serious and unexpected ADRs, whilst minor to moderate ADRs are typically less well reported [26] but remain burdensome to service users. Despite the advantages of service user ADR reporting [27], under-reporting is widely recognised: in a clinical trial only 1.5–5.1% (5/328-11/216) of ADR-related problems were spontaneously reported to physicians, increasing to 29–49% (96/328-105/216) when a questionnaire on specific known ADRs was administered [28], and the overall ADR reporting rate may be around 1% [29]. A survey of 661 outpatients in Boston identified that 37% of ameliorable ADRs were related to failure of service users to inform physicians (19/51) [30]. Public awareness of ADR reporting remains low [31], [32]: a survey of 2028 UK adults indicated that while 477 (24%) had experienced an ADR, only 172/2028 (9%) were aware of reporting mechanisms [33], [34]. ADR reporting by physicians may retain an element of subjectivity [35], [36], and the long-term effects of educational interventions to increase ADR reporting are unclear [37]. Further investment is needed to develop feasible evidence-based strategies, that engage service users [32], and are not overly time consuming, and explore their clinical impact [18], [19], [20], [23], [24], [38][43].

Respiratory Medicine

Thirty five million Americans live with chronic lung disease [44], and the financial burden of lung disease in Europe is estimated at €100 billion, of which, €6.7 billion is prescription drug costs [45]. In the UK, almost 8 million individuals suffer with lung disease, accounting for 7% of all drugs prescribed [46]. Inhaled beta-agonists, often prescribed in tandem with corticosteroids, are among the top ten drugs causing ADRs [47]. However, no comprehensive and validated medication monitoring profile in respiratory medicine has been located. Guidelines on the management of chronic respiratory diseases are available [48], [49], but do not describe medication monitoring.

To address this deficit, the West Wales ADR (WWADR) Profile for respiratory medicine has been developed from existing literature summarising adverse effects [50][53], and tested for cognitive form, validity and reliability [22], [54]. This paper sets the foundation for larger trials of nurse-led medication monitoring initiatives.


The protocol for this trial and supporting CONSORT checklist are available as supporting information, see Checklist S1 and Protocol S1.

Ethics Statement

Favourable ethical opinion for this study was received from the South West Wales Research Ethics Committee (February 2010 reference 09/WMW02/60). This allowed the study to take place in the National Health Service (NHS) in South West Wales. The University, as trial sponsors, accepted this decision, and did not seek further review. Patient information sheets were available in English and Welsh ahead of gaining informed written consent from all participants. All participant data were anonymised.


We aimed to explore the clinical effect of nurse-led medication monitoring using the West Wales ADR (WWADR) Profile for Respiratory Medicines. Key outcomes were changes in problems found and actions taken during nurse-patient consultations before and after introduction of the ADR Profile.


A nurse-led outpatient respiratory clinic in a large 600-bed general hospital in South West Wales, that serves an urban and rural population of around 600,000.


We undertook a single-site parallel-arm pragmatic randomised trial with stratified random allocation performed by West Wales Organisation for Rigorous Trials in Health (WWORTH), the local clinical trials unit [table 1]. At observation 1, all patients received usual outpatient care, which included checking patients’ medication, symptoms, health and wellbeing. At their next scheduled outpatient visit, observation 2, the control arm received usual care only, while the intervention arm, in addition to usual care, received medication monitoring with the ADR Profile. The researcher observed all consultations. The interval between the first and second round of observations was dictated by the nurses’ clinical judgment, based on participants’ needs.

The Intervention

The WWADR Profile for Respiratory Medicine was used in the intervention arm at observation two [table 1] by the respiratory nurse specialists working in the outpatient clinic. The WWADR Profile [54] is a five-sectioned ADR symptom questionnaire uniting problem detection with actions over five sections comprising 81 items (57 questions, 4 observations, 7 measurements, 13 checks of laboratory data), covering symptoms of the known ADRs of corticosteroids, bronchodilators and leukotriene receptor antagonists. We derived these items from the literature on ADRs [22], [50][53], invited service users and expert clinicians to scrutinise them for relevance, feasibility and accuracy, and investigated validity and reliability [54]. Targeted questions seek to identify problems that may be indicative of ADRs, and merit attention regardless of aetiology. For example: muscular weakness may be symptomatic of steroid-induced metabolic changes or hypokalaemia [50][52], [55]; reports of mood changes may indicate steroid-induced mood disorders [56][58] or hypokalaemia [51][53]. The Profile also includes health promotion advice that is particularly pertinent to users of respiratory medicines. For example, high salt intake may exacerbate steroid-induced fluid retention [52]. The risk of adverse reactions is reduced, but not eliminated, when inhaled preparations are used [53].


We undertook remote randomisation after the first round of observations. We had selected five stratification variables from the literature and previous work [54]: age (more or less than median of 61 years), number of respiratory medicines (more or less than median of two), number of other drugs prescribed (more or less than median of five), sex and day of initial observation (Wednesday or Friday clinic). We passed participant study numbers and stratification variables to WWORTH by telephone. They used a dynamic randomisation algorithm based on weighted squared-difference measures of imbalance to allocate participants in the ratio of 1∶1 and balance the five stratification variables without blocking [59]. We used hospital numbers to track participants’ return to clinic for the second round of observations.


Patients were screened by their consultant and nurses to ensure they were well enough to participate. All eligible patients were approached in respiratory outpatient clinics January 2011–January 2012.

Inclusion Criteria:

  • Established clients of the nurse-led outpatient respiratory clinic
  • Existing chronic respiratory condition
  • Currently prescribed at least one of: bronchodilators, corticosteroids or leukotriene receptor antagonists

Exclusion Criteria:

  • Aged 16 or under
  • Lack of fluency in English or Welsh
  • Considered by their nurses or consultant to lack the capacity to consent, or to be unduly stressed or vulnerable (for example the recently bereaved).

Outcome Measures

Primary outcomes:

  1. Change in numbers of problems found per participant between observations one and two
  2. Change in numbers of actions taken per participant between observations one and two

Secondary outcomes:

  1. Numbers with at least one action taken at observation two
  2. Numbers of actions taken per participant at observation two

Data Collection and Measurement of Outcomes

Data on problems identified and actions taken were collected for each participant at both observations using a structured template of possible ADRs [54] [table 1]. The nurse completed an independent copy of the WWADR Profile for the intervention participants. In addition, the researcher kept freehand field notes, primarily to indicate all nursing actions taken. Nurses and patients were asked to comment on use of the Profile and identify possible harm to patients.

For outcome 2.1, ADR actions were distinguished from all actions: ADR actions were those directly prompted by the WWADR Profile whilst ‘all actions’ also included those identified from usual care, or discussion around questions in the WWADR Profile.

Sample Size

Previously, 14/20 patients receiving usual care and 1/20 patients responding to a structured monitoring instrument had no ADRs on a monitoring profile addressed by their nurses [60], [61]. A sample of 34, 17 in each arm, is sufficient to detect this difference in incidence of nursing actions with 90% power when using 1% significance level [62]. To allow for potential losses to follow up within available resources, the sample was inflated to 54.


At observation one, the nurse, researcher and participants were unaware of intended allocation. However, at observation two, participants, based on their previous experience in clinic, might have guessed that they were receiving the intervention. Nurses were unblinded when delivering the intervention. The researchers were aware of allocation during observation, but were blinded for analysis.

Data Analysis

Data were analysed using IBM SPSS statistics version 19. Data were double entered and checked for data entry errors. No mistakes were identified. To test whether data were normally distributed we used the Shapiro-Wilk test for samples smaller than 50 and the Kolmogorov-Smirnov test for samples larger than 50. To compare between arms those variables found not to be normally distributed we used the Mann-Whitney U test, the non-parametric equivalents of the t test. Categorical variables were tested by chi squared tests or Fisher’s exact test and logistic regression using the backward likelihood ratio criterion. Using ‘increased number of ADR-related actions taken at observation two’ as the outcome variable, arm allocation, age, sex and consultation two duration were entered into univariate models as sole predictor variables. Arm allocation was then tested with each of the four other variables iteratively. Participants’ comments are included for illustration.


A total of 63 eligible patients were seen in clinic, 54 were recruited (86%). No losses occurred and all participants were analysed in accordance with their original allocation [figure 1]. Control participants were asked to attend on Wednesdays and intervention participants on Fridays. Although 18 participants attended on the ‘incorrect’ day, all received the treatment allocated.

Some interval variables were not normally distributed, necessitating non-parametric testing. Table 2 shows that the trial arms, comparable at the first consultation, differed in the length of the second consultation.

Outcomes 1.1 & 1.2. Changes in Numbers of Problems Found and Actions taken per Participant between Observations One and Two

The change in numbers of problems found per participant between observations one and two was significantly different (intervention arm median 20.5, [inter-quartile range (IQR) 13 to 26]; control arm median −1, [IQR −3 to 2]: Mann-Whitney U 1.50, z 6.28, p<0.001) [table 3]. The change in all actions taken was also significant (intervention arm median 2.5, [IQR 1 to 4]; control arm median 0 [−1.75 to +1]: Mann-Whitney U 112.50, z 4.40, p<0.001) [table 3].

Table 3. Changes in numbers of problems found and actions taken between observations 1 and 2: Outcomes 1.1 & 1.2.

Outcome 2.1. Number of Participants with at Least One Action taken at Observation Two

No significant difference was observed in the numbers of participants with at least one action taken at observations one and two [table 4]. However, the difference in ADR specific actions was significant. At observation two, 1 of 26 (4%) intervention participants had no ADR actions taken, compared with 9 of 28 (32%) controls: X2 5.40, df 1, p 0.02, OR 11.84 (1.38–101.70) [table 4].

Table 4. Proportions of participants with at least one action and ADR addressed: Outcome 2.1.

Outcome 2.2. Number of Actions taken per Participant at Observation Two

At observation two, significantly more actions were taken in the intervention arm (median 4, inter-quartile range 3–6) than the control arm (median 2, inter-quartile range 1–3): Mann-Whitney U 112.00, z 4.42, p<0.001, r 0.60 [table 5; figure 2].

Figure 2. Bar chart of problems found and actions taken par participant at observations one and two.

Multivariate Analysis

The only significant predictor of increased number of ADR-related actions was arm allocation.

Other predictors were not significant when tested alone or with ‘trial arm’ [Table S1]. Only one participant had a negative result for ‘at least one action taken at observation 2’ and ‘at least one ADR action taken at observation 2’, precluding multivariate analysis.

Clinical Impact

Problems identified in response to using the WWADR Profile, along with lifestyle factors, included:

  • Muscular weakness affecting activities of daily living (22/26, 84.6%), muscular pain (18/26, 69.2%).
  • Dry mouth, hoarseness, problems with teeth or dentures, changes in taste or smell of the participants’ breath (19/26, 73.1%).
  • Tendency to bruising (17/26, 65.4%), delayed skin healing (16/26, 61.5%). For example: participant 004, who regularly took aspirin and a combined corticosteroid/bronchodilator inhaler, reported “bruises with the slightest knock, sometimes very bad” and attributed this to “just with getting older, normal”.
  • Salt regularly added to meals or during cooking (17/26, 65.4%).
  • Mood problems (16/26, 61.5%). For example, the nurse commented: “I would not have picked up on participant 014’s depression [without the WWADR Profile]”.
  • Fine tremor affecting activities of daily living (12/26, 46.2%). For example: participant 023, who took a high dose of beta agonists, was identified by the ADR Profile as having a fine motor tremor, and commented “they [hands] shake now but I put it down to age… …get annoyed with myself dropping things”.

Actions taken by the participants’ nurses in response to using the WWADR Profile included:

  • 32 referrals to other healthcare professionals. Most referrals were to the patients’ primary care practitioners [27/32, 84.4%] for: urinary dysfunction (4); falls/balance problems (2); mood problems (1); prescription of additional medication, for example to treat oral candidiasis (2), suspected infection (3).
  • 21 occasions where health promotion advice was given, such as maintaining immunisation status (12), smoking cessation (5) and diet (3).
  • 19 instances of advice regarding medication concordance, for example to order replacement inhalers from primary care in a timely manner or change medication regimen. Examples included commencement of gradual withdrawal of corticosteroids (p026) and treatment of oral candidiasis (p026).
  • 12 instances where oral care advice was given: rinsing mouth following administration of inhaled medication (7), visiting dentist to pre-empt problems relating to xerostomia (5).
  • 9 occasions where patients were advised to send a sputum sample for culture and sensitivity before antibiotics were used.


Implementation of the WWADR Profile was neither observed nor reported to be directly harmful. The time taken identifying and responding to problems found may be burdensome to nurses, however, the two nurses involved reported structuring their future care around the Profile. No participant reported negatively on the amount of time spent in clinic or completing the WWADR Profile.


We believe this paper reports the first randomised trial of nurse-led medication monitoring in respiratory medicine. We offer preliminary evidence that formalised and concurrent nurse-led medication monitoring increases the number of patient problems found and responded to; however, interpretation of these findings rests with readers. Using a structured ADR Profile alongside usual care has potential to increase early detection of problems and may therefore ameliorate or pre-empt ADRs and improve patients’ wellbeing and health outcomes [41], [63][66]. Identification of problems may prompt the multidisciplinary team to take preventive actions that allow patients to remain well, in accordance with treatment and disease management priorities [67]. It is unlikely that confounding variables account for the differences found, since dynamic randomisation ensured even distribution of variables, reduced biases and maximised the strength of findings [59], [68], [69], and logistic regression identified treatment allocated as the only significant predictor of having more ADR actions taken.

Medication Monitoring: an Orphan Task

The strength of patient safety initiatives is influenced by the personnel using them and the organisation in which they are used [70]. Whilst nurses, doctors, pharmacists and patients are all eligible to report ADRs, the task of medication monitoring has not been formally embraced by any one profession [24], [60], [71]. Pharmacists have played an integral role in the success of the Finland asthma programme and medication adherence [72], [73], and may be well-placed to monitor medication and review prescriptions [63], [74], [75], but obstacles to their on-going involvement in medication monitoring need to be identified [76]. As inter-professional role boundaries shift and nurses assume roles previously in the medical domain, such as prescribing, there is potential to integrate ADR profiles with nurse-led care. The WWADR Profile helps minimise variation in practice, but organisational uptake should be supported by protected time, learning resources, leadership, dissemination and evaluation [42]. The WWADR Profile may also help overcome some perceived barriers to nurse-led medication monitoring such as inter-professional communication or lack of confidence in knowledge of pharmacology and therapeutics [41], [63][65], [77][80]. With this support, nurses, as the largest group of front-line healthcare professionals working most closely with service users, are well-placed to play a valuable role in medication monitoring [81], [82]. The Profile itself could be easily transferred across healthcare settings and may alleviate under-reporting of drug-related problems [83], [84].

Limitations of the Study

As the lasting impact of responding to drug-related harm could not be explored within the resources available to this trial, we cannot ascertain the impact of the initiative on concordance with therapy, enduring amelioration of problems found or long-term harm from administration of the Profile. The Profile prompted health promotion regarding sub-optimal health behaviours, such as poor mouth care or adding salt to meals, and referrals to other professionals. Although some problems were addressed immediately, such as changes in doses of respiratory medicines, clinical gains from nurse-led medication monitoring in outpatient departments depend, in part, on participants following advice to contact other healthcare professionals [60], [61]: for example to treat suspected infections (12/26), obtain overdue vaccinations (12/26) or investigate falls (2/26). Instigation of further treatment or over-treatment would be at the discretion of professionals receiving the referrals, and not directly attributable to the Profile. We were unable to investigate the costs and benefits of referrals. The relatively small sample size precluded detection of small differences, but we feel that our findings indicate sufficient clinical gain to warrant further exploration.

Potential bias in research should always be considered [85]. This study design risked contamination. Over the course of the project, the nurses may have been sensitised to medication monitoring using the WWADR Profile, and unknowingly delivered some aspect of the Profile to the control participants, diluting the observed effect of structured medication monitoring [86]. However, the large effect sizes suggest that contamination was not a major problem.

Blinding during outcome assessment in non-medicinal product [87] or pragmatic trials may be difficult, or impossible, as in this study [88], [89]. Detection bias of outcomes was minimised, but not removed, by a pre-arranged data collection template and the relatively low subjectivity of outcomes [90] such as observation of tremor and statements of advice. We acknowledge that unblinded assessment may increase the risk of bias [91], in line with observers’ expectations [92], suggesting the need for larger multisite investigations. Systematic review indicates that some 3% assessments are likely to be misclassified [93], which would not materially affect interpretation of our study. Observation 1 preceded randomisation.

This trial captures ADR profiles for patients in a single location; accordingly, findings should be generalised cautiously [94], [95]. However, the trial was undertaken in an outpatient nurse-led clinic typical of many district general hospitals, using broad inclusion criteria [95], and patients’ experiences of long-term use of respiratory medicines are not widely reported elsewhere. We did not test alternative instruments or additional appointments. The Hawthorne effect, and social desirability in the nurses’ and participants’ behaviours and communications, cannot be discounted. However, this would have affected both arms equally. Although a good response rate was obtained, a further important consideration is volunteer bias [96]. Volunteer bias could not be directly explored in the current trial, as recruitment rate was high (85%). Findings cannot be generalised to patients too busy or unwilling to participate in research or those viewing their healthcare providers less favourably. Those who took the time to participate in this research may represent a subset of the population who are particularly engaged with their healthcare providers and prescribed regimens.

The literature offers no consensus on identifying and addressing medicine-related harm and adverse events [82]. Many ADRs are subtle and ill-defined, and may resemble pre-existing conditions or ageing. Their aetiology is often multifactorial or uncertain [3]. It was outside the remit of our nurse-led intervention to determine the cause of each problem. Rather, by addressing the diverse problems vulnerable to exacerbation by prescribed medicines [20], [50][53], the WWADR Profile works towards incremental optimisation of patients’ health status. In view of the paucity of the evidence base on potential adverse drug reactions [97], [98], to optimise clinical gain, we thought it more important to be thorough and detailed when monitoring for potential medication-related harms, and risk over-ascertainment, than to risk overlooking potentially treatable problems of equivocal aetiology. This approach addresses concerns of public bodies [39], [99][101] and participants that “at the moment not enough [is] done to follow-up: how do you know medication’s any good if no one asks you?” (participant 020).


Patient safety might be improved by concurrent medication monitoring using drug specific checklists [61], [80], [102][106]. The WWADR Profile helped nurses structure and prioritise care, and identify previously unsuspected problems. This way, known ADRs may be predicted, pre-empted and addressed, to reduce the burden on long-term medication users. To our knowledge, no previous trials have tested this approach [107]. In this trial, recognising and alleviating symptoms of potential ADRs elicited clinical gains. Completing the WWADR Profile as part of usual care helped protect the time needed to monitor medication. We acknowledge that larger, multisite trials may overturn these findings, but these findings provide the impetus to pursue proactive ADR monitoring research.

Further Research

This paper suggests that medication monitoring profiles support nurse-led medication monitoring [79], improving the process of care, focusing attention and reducing adverse events in health care [104][107]. However, more evidence is needed on the long-term effects of structured medication monitoring on clinical outcomes [41], nursing vigilance, nurses’ workload, nurses’ and service users’ time, and acceptability [20], [39], [40], [108][112]. In this small trial, the benefits of medication monitoring for service users outweighed the risks and larger, mulitsite trials of medication monitoring are needed to explore long-term clinical impact. Piloting similar initiatives in different drug groups and healthcare settings, particularly in primary healthcare, where most chronic diseases are currently managed, may enhance patient care.


Significant differences between intervention and control arms were detected in the recruited sample, supporting our work on antipsychotics [60], [61], [113]. Expanding nursing roles and patient-focused chronic disease management [67] should be encouraged and extended to routine adoption of medicines management policies [114], including standardised medication monitoring profiles [115]. Monitoring should be: accurate, simple, thorough, target-based, capable of detecting insidious and long-term harm, and of improving clinical outcomes [19]. This trial indicates that under-reporting may be improved by structured and standardised ADR profiles [99], [116], [117]. Although many preventable ADRs are due to inadequate patient monitoring [118], many are also due to failure to react to problems found [38]. Actions taken are thus an important focus of this research to ameliorate preventable ADRs [8 38 119–121], and further investigation of ADR Profiles as a strategy to link problem identification with actions is warranted.

Supporting Information

Checklist S1.

CONSORT checklist Schulz KF, Altman DG, Moher D. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. Trials 2010;11∶32.


Protocol S1.

Trial Protocol: Nurse-led medication monitoring.



The authors wish to thank all those involved in bringing this study into fruition and are extremely grateful to the clinicians and service users involved in this trial. We should also like to thank the clinical and university teams who contributed time and effort to this study, most particularly Professor Gary Rolfe, Dr Daphne Russell, Ms Michelle Davies, Ms Linzi Thomas, Ms Nicola Headon, Mr Jonathan Bidmead and Ms Caroline Davies.

Data availability: The dataset, in IBM spss, will be available from the authors on request.

Author Contributions

Conceived and designed the experiments: MG SJ. Performed the experiments: MG. Analyzed the data: MG SJ. Contributed reagents/materials/analysis tools: MG FM GD IR SJ. Wrote the paper: MG FM GD IR SJ.


  1. 1. Baines RJ, Langelaan M, de Bruijne MC, Asscheman H, Spreeuwenberg P, et al. (2013) Changes in adverse event rates in hospitals over time: a longitudinal retrospective record review. Quality and Safety in Health Care 22: 290–298.
  2. 2. Aronson JK (2013) Adverse drug reactions: history, terminology, classification, causality, frequency, preventability In: Talbot J, Aronson JK, editors. Stephens’ Detection and Evaluation of Adverse Drug Reactions: Principles and Practice. 6th edtn. Chichester, UK: Wiley-Blackwell 1–119.
  3. 3. International Conference of Harmonization (1996) ICH Harmonised Tripartite Guideline for Good Clinical Practice. Buckinghamshire, UK: Institute of Clinical Research. Available: (accessed 09 September 2010)
  4. 4. Howard R, Avery A, Slavenburg S, Royal S, Pipe G, et al. (2007) Which drugs cause preventable admissions to hospital? A systematic review. British Journal of Clinical Pharmacology 63: 136–147.
  5. 5. Wu TY, Jen MH, Bottle A, Molokhia M, Aylin P, et al. (2010) Ten-year trends in hospital admissions for adverse drug reactions in England 1999–2009. Journal of the Royal Society of Medicine 103: 239–250.
  6. 6. Ferner RE, Butt TF (2012) Adverse drug reactions. Medicine 40: 366–370.
  7. 7. Cunningham G, Dodd TRP, Grant DJ, McMurdo MET, Richards RME, et al (1997) Drug-related problems in elderly patients admitted to Tayside hospitals, methods for prevention and subsequent reassessment. Age and Ageing 26: 375–382.
  8. 8. Gurwitz JH, Field TS, Avorn J, McCormick D, Jain S, et al. (2000) Incidence and preventability of adverse drug events in nursing homes. American Journal of Medicine 109: 87–94.
  9. 9. Pirmohamed M, James S, Meakin S, Green C, Scott AK, et al. (2004) Adverse drug reactions as cause of admission to hospital: prospective analysis of 18820 patients. BMJ 329: 15–19.
  10. 10. Hakkarainen KM, Hedna K, Petzold M, Hägg S (2013) Percentage of Patients with Preventable Adverse Drug Reactions and Preventability of Adverse Drug Reactions – A Meta-Analysis. PLoS ONE 7: e33236.
  11. 11. Kvasz M, Allen IE, Gordon MJ, Ro EY, Estok R, et al. (2000) Adverse Drug Reactions in Hospitalised Patients: A Critique of a Meta-analysis. Medscape General Medicine 2: E3.
  12. 12. Beijer HJM, de Blaey CJ (2002) Hospitalisations caused by adverse drug reactions (ADR): a meta-analysis of observational studies. Pharmacy World and Science 24: 46–54.
  13. 13. Miguel A, Azevedo LF, Araújo M, Pereira AC (2012) Frequency of adverse drug reactions in hospitalized patients: a systematic review and meta-analysis. Pharmacoepidemiology and Drug Safety 21: 1139–1154.
  14. 14. Lazarou J, Pomeranz BH, Corey PN (1998) Incidence of adverse drug reactions in hospitalised patients: a meta-analysis of prospective studies. Journal of the American Medical Association 279: 1200–1205.
  15. 15. Yee JL, Hasson NK, Schreiber DH (2005) Drug-related emergency department visits in an elderly veteran population. Annals of Pharmacotherapy 39: 1990–1995.
  16. 16. Rottenkolber D, Schmiedl S, Rottenkolber M, Farker K, Saljé K, et al. (2011) Adverse drug reactions in Germany: direct costs of internal medicine hospitalizations. Pharmacoepidemiology and Drug Safety 20: 626–634.
  17. 17. Hohl CM, Nosyk B, Kuramoto L, Zed PJ, Brubacher JR, et al. (2011) Outcomes of emergency department patients presenting with adverse drug events. Annals of Emergency Medicine 58: 270–279.
  18. 18. National Patient Safety Agency (2007) Safety in doses: medication safety incidents in the NHS. London, UK: National Patient Safety Agency. Available: Accessed 12 April 2014
  19. 19. Glasziou P, Aronson J (2008) An introduction to monitoring therapeutic interventions in clinical practice. In: Glasziou P, Irwig L, Aronson JK, editors. Evidence based medical monitoring. Oxford, UK: Blackwell publishing/BMJ books. 3–14.
  20. 20. Jordan S (2011) Adverse events: expecting too much of nurses and too little of nursing research. Journal of Nursing Management 19: 287–292.
  21. 21. Health Service Commissioner for England (2011) Care and Compassion? Report of the Health Service Ombudsman on ten investigations into NHS care of older people. [Online]. London, UK: Parliamentary and Health Service Ombudsman, The Stationery Office. Available: Accessed 8 May 2013.
  22. 22. Gabe ME (2012) Nurse-led medication monitoring and adverse events. PhD Thesis, Swansea University, UK.
  23. 23. Jha AK, Prasopa-Plaizier N, Larizgoitia I (2010) Bates DW; Research Priority Setting Working Group of the WHO World Alliance for Patient Safety (2010) Patient safety research: an overview of the global evidence. Quality and Safety in Health Care 19: 42–47.
  24. 24. Jordan S (2007) Adverse Drug Reactions: Reducing the Burden of Treatment. Nursing Standard 21(34): 35–41.
  25. 25. Brenner S, Detz A, López A, Horton C, Sarkar U (2012) Signal and noise: applying a laboratory trigger tool to identify adverse drug events among primary care patients. BMJ Quality and Safety 21: 670–675.
  26. 26. Alvarez-Requejo A, Carvajal A, Bégaud B, Moride Y, Vega T, et al. (1998) Under-reporting of adverse drug reactions. Estimate based on a spontaneous reporting scheme and a sentinel system. European Journal of Clinical Pharmacology 54: 483–488.
  27. 27. Blenkinsopp A, Wilkie P, Wang M, Routledge PA (2007) Patient reporting of suspected adverse drug reactions: a review of published literature and international experience. British Journal of Clinical Pharmacology 63: 148–156.
  28. 28. Urinary Tract Infection Study Group (1987) Coordinated multicenter study of norfloxacin versus trimethroprim-sulfarnethoxazole treatment of symptomatic urinary tract infections. Journal of Infectious Disease 155: 170–177.
  29. 29. Medawar C, Herxheimer A (2003) A comparison of adverse drug reaction reports from professionals and users, relating to risk of dependence and suicidal behavior with paroxetine. International Journal of Risk and Safety in Medicine 16: 5–19.
  30. 30. Gandhi T, Weingart S, Borus J, Seger AC, Peterson J, et al. (2003) Adverse drug events in ambulatory care. New England Journal of Medicine 348: 1556–1564.
  31. 31. Lorimer S, Cox A, Langford NJ (2012) A patient’s perspective: the impact of adverse drug reactions on patients and their views on reporting. Journal of Clinical Pharmacy and Therapeutics 37: 148–152.
  32. 32. van Hunsel F, Härmark L, Pal S, Olsson S, van Grootheest K (2012) Experiences with Adverse Drug Reaction Reporting by Patients. An 11-Country Survey. Drug Safety 35: 45–60.
  33. 33. Avery AJ, Anderson C, Bond CM, Fortnum H, Gifford A, et al. (2011) Evaluation of patient reporting of adverse drug reactions to the UK ‘Yellow Card Scheme’: literature review, descriptive and qualitative analyses, and questionnaire surveys. Health Technology Assessment 15(20): 1–234, iii–iv. doi:
  34. 34. Fortnum H, Lee AJ, Rupnik B, Avery AJ, Yellow Card Study Collaboration (2012) Survey to assess public awareness of patient reporting of adverse drug reactions in Great Britain. Journal of Clinical Pharmacy and Therapeutics 37: 161–165.
  35. 35. Aziz Z, Siang TC, Badarudin NS (2007) Reporting of adverse drug reactions: predictors of under-reporting in Malaysia. Pharmacoepidemiology and drug safety 16: 223–228.
  36. 36. Gonzàlez-Rubio F, Calderón-Larrañaga1 F, Poblador-Plou B, Navarro-Pemán C, López-Cabañas A, et al (2011) Underreporting of recognized adverse drug reactions by primary care physicians: an exploratory study. Pharmacoepidemiology and Drug Safety 20: 1287–1294.
  37. 37. Molokhia M, Tanna S, Bell D (2009) Improving reporting of adverse drug reactions: Systematic review. Clinical Epidemiology 1: 75–92.
  38. 38. Gurwitz JH, Field TS, Judge J Rochon P, Harrold LR, et al. (2005) The incidence of adverse drug events in two large academic long-term care facilities. The American Journal of Medicine 118: 251–258.
  39. 39. National Co-ordinating Council for Medication Error Reporting and Prevention (2005) The First Ten Years “Defining the Problem and Developing Solutions”. United States Pharmacopia, Rockville, Maryland. Available: Accessed 8 May 2013.
  40. 40. Steinman MA, Handler SM, Gurwitz JH, Schiff GD, Covinsky KE, et al. (2011) Beyond the prescription: Medication monitoring and adverse drug events in older adults. Journal of the American Geriatrics Society 59: 1513–1520.
  41. 41. McKibbon KA, Lokker C, Handler SM, Dolovich LR, Holbrook AM, et al. (2012) The effectiveness of integrated health information technologies across the phases of medication management: a systematic review of randomized controlled trials. Journal of the American Medical Informatics Association 19: 22–30.
  42. 42. Shojania KG, Thomas EJ (2013) Trends in adverse events over time: why are we not improving? Quality and Safety in Health Care 22: 273–277.
  43. 43. O’Leary KJ, Devisetty VK, Patel AR, Malkenson D, Sama P, et al. (2013) Comparison of traditional trigger tool to data warehouse based screening for identifying hospital adverse events. Quality and Safety in Health Care 22: 130–138.
  44. 44. American Lung Association (2011) Fighting for Air. Available: Accessed 8 May 2013.
  45. 45. European Lung Foundation (2011) Lung Health in Europe. Facts and Figures. Available: Accessed 8 May 2013.
  46. 46. The British Thoracic Society (2006) The Burden of Lung Disease. A Statistics Reports from the British Thoracic Society. 2nd Edtn. Available: Accessed 8 May 2013.
  47. 47. Davies EC, Green CF, Taylor S, Williamson PR, Mottram DR, et al. (2009) Adverse Drug Reactions in Hospital In-Patients: A Prospective Analysis of 3695 Patient-Episodes. PloS ONE. 4: e4439.
  48. 48. British Thoracic Society/Scottish Intercollegiate Guidelines Network (2011) Asthma Guideline. Available: Accessed 8 May 2013.
  49. 49. Global Initiative for Chronic Obstructive Lung Disease (2011) Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. Revised 2011.Available: Accessed 8 May 2013.
  50. 50. Aronson JK (2006) Meyler’s Side Effects of Drugs: The International Encyclopaedia of Adverse Drug Reactions and Interactions. Amsterdam, Elsevier.
  51. 51. Lewis KE, Davies GA (2010) Drugs that act on the respiratory tract. In: Aronson JK editor Side Effects of Drugs Annual 32. Amsterdam: Elsevier. 311–331.
  52. 52. Jordan S (2008) The Prescription Drug Guide for Nurses. Maidenhead, UK: Open University Press, McGraw-Hill.
  53. 53. British National Formulary (2013) BNF number 65. London, UK: British Medical Association and the Royal Pharmaceutical Society of Great Britain.
  54. 54. Gabe ME, Jordan S (2013) Development and clinical gains of nurse-led medication monitoring profiles. Journal of Nursing Management: doi: [Epub ahead of print].
  55. 55. Deshmukh CT (2007) Minimizing side effects of systemic corticosteroids in children. Indian Journal of Dermatology, Venereology and Leprology 73(4): 218–221.
  56. 56. Lewis DA, Smith RE (1983) Steroid-induced Psychiatric Syndromes. A Report of 14 Cases and a Review of the Literature. Journal of Affective Disorders 5(4): 319–332.
  57. 57. Schimmer B, Parker K (2006) Adrenocortiotropic hormone; adrenocortical steroids and their synthetic analogues. In: Brunton, L., Lazo J., Parker K. (editors) Goodman and Gilman’s: The Pharmacological Basis of Therapeutics, 11th edition. New York; McGraw-Hill. 1587–1612.
  58. 58. Sirois F (2003) Steroid psychosis: a review. General Hospital Psychiatry 25(1): 27–33.
  59. 59. Russell D, Hoare ZS, Whitaker R, Whitaker CJ, Russell IT (2011) Generalized method for adaptive randomization in clinical trials. Statistics in Medicine 30: 922–934.
  60. 60. Jordan S (2002) Managing Adverse Drug Reactions: An Orphan Task. Developing Nurse-Administered Evaluation Checklists. Journal of Advanced Nursing 38: 437–448.
  61. 61. Jordan S, Tunnicliffe C, Sykes A (2002) Minimising Side Effects: The clinical impact of nurse-administered ‘side effect’ checklists. Journal of Advanced Nursing 37: 155–165.
  62. 62. Uitenbroek DG (1997). SISA (Simple Interactive Statistical Analysis) Binomial. Southampton. Available: Accessed 8 May 2013.
  63. 63. Dilles T, Elseviers MM, Van Rompaey B, Van Bortel LM, Stichele RR (2011) Barriers for nurses to safe medication management in nursing homes. Journal of Nursing Scholarship 43: 171–180.
  64. 64. Lapane KL, Hughes CM, Daiello LA, Cameron KA, Feinberg J (2011) Effect of a pharmacist-led multicomponent intervention focusing on the medication monitoring phase to prevent potential adverse drug events in nursing homes. Journal of the American Geriatrics Society 59: 1238–1245.
  65. 65. Nash M (2011) Improving mental health service users’ physical health through medication monitoring: A literature review. Journal of Nursing Management 19: 360–365.
  66. 66. Jarernsiripornkul N, Senacom P, Uchaipichat V, Chaipichit N, Krska J (2012) Patient reporting of suspected adverse drug reactions to antiepileptic drugs: Factors affecting attribution accuracy. Epilepsy and Behaviour 24: 102–106.
  67. 67. Welsh Assembly Government (2005) Designed for Life: creating world class Health and Social Care for Wales in the 21st Century. Cardiff, UK: Welsh Assembly Government.
  68. 68. Altman DG (1985) Comparability of randomised groups. The Statistician 34: 125–136.
  69. 69. Altman DG, Bland JM (1999) Statistics notes. Treatment allocation in controlled trials: why randomise? British Medical Journal 318: 1209.
  70. 70. Morello RT, Lowthian JA, Barker A McGinnes R, Dunt D, et al. (2013) Strategies for improving patient safety culture in hospitals: a systematic review. Quality and Safety in Health Care 22: 11–18.
  71. 71. Caplan L, Haverhals LM (2012) Barriers and facilitators for preventing adverse drug reactions of long latency: a qualitative study. International Journal of Risk and Safety in Medicine 24: 81–94.
  72. 72. Bell JS, Enlund H, Vainio K (2010) Medication adherence: a review of pharmacy education, research, practice and policy in Finland. Pharmacy Practice 8: 147–161.
  73. 73. Närhi U, Airaksinen M, Enlund H (2002) Pharmacists solving problems in asthma management – experiences from a one-year intervention programme in Finland. International Journal of Pharmacy Practice 10: 55–59.
  74. 74. Närhi U, Airaksinen M, Tanskanen P, Erlund H (2000) Therapeutic outcomes monitoring by community pharmacists for improving clinical outcomes in asthma. Journal of Clinical Pharmacy and Therapeutics 25: 177–183.
  75. 75. Krska J, Cromarty JA, Arris F, Jamieson D, Hansford D, et al. (2001) Pharmacist-led medication review in patients over 65: a randomized, controlled trial in primary care. Age and Ageing 30: 205–211.
  76. 76. Bultman DC, Svarstad BL (2002) Effects of Pharmacists Monitoring on Patient Satisfaction with Antidepressant Medication Therapy. Journal of the American Pharmacist Association 42: 36–43.
  77. 77. Jordan S, Coleman M, Hardy B, Hughes D (1999) Assessing Educational Effectiveness: The Impact Of A Specialist Course On The Delivery Of Care Journal of Advanced Nursing. 30: 796–807.
  78. 78. Kyriacos U, Jordan S, Van Den Heever J (2005) The biological sciences in nursing: a developing country perspective. Journal of Advanced Nursing 52: 91–103.
  79. 79. Cleary A, Walsh F, Connolly H, Hays V, Oluwole B, et al. (2011) Monitoring and documentation of side effects from depot antipsychotic medication: an interdisciplinary audit of practice in a regional mental health service. Journal of Psychiatric and Mental Health Nursing 19: 395–401.
  80. 80. Chopra V, Shojania KG (2013) Recipes for checklists and bundles: one part active ingredient, two parts measurement. Quality and Safety in Health Care 22: 93–96.
  81. 81. Winkeljohn D (2010) Adherence to oral cancer therapies: Nursing interventions. Clinical Journal of Oncology Nursing 14: 461–466.
  82. 82. Gabe ME, Davies GA, Murphy F, Davies M, Johnstone L, et al. (2011) Adverse drug reactions: treatment burdens and nurse-led medication monitoring. Journal of Nursing Management 19: 377–392.
  83. 83. Heeley E, Riley J, Layton D, Wilton LV, Shakir SA (2001) Prescription-event monitoring and reporting of adverse drug reactions. Lancet 358: 1872–1873.
  84. 84. Hazell L, Shakir SA (2006) Under-reporting of adverse drug reactions: a systematic review. Drug Safety 29: 385–396.
  85. 85. Higgins JP, Altman DG, Gøtzsche PC, Juni P, Moher D, et al. (2011) The Cochrane Collaboration’s tool for assessing risk of bias in randomized trials. British Medical Journal 343: d5928
  86. 86. Keogh-Brown MR, Bachmann MO, Shepstone L, Hewitt C, Howe A, et al. (2007) Contamination in trials of education interventions. Health technology Assessment 11(43): iii, ix-107.
  87. 87. Boutron I, Moher D, Altman DG, Schulz KF, Ravaud P, et al. (2008) Extending the CONSORT Statement to Randomized Trials of Nonpharmacological Treatment: Explanation and Elaboration. Annals of Internal Medicine 148: 295–309.
  88. 88. Eldridge S (2010) Pragmatic trials in primary health care: what, when and how? Family Practice 27(6): 591–592.
  89. 89. Zwarenstein M, Treweek S, Gagnier JJ, Altman DG, Tunis S, et al. (2008) Improving the reporting of pragmatic trials: an extension of the CONSORT statement. BMJ 337: a2390 1223–1226.
  90. 90. Higgins JPT, Green S (2011) Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. Oxford, UK: The Cochrane Collaboration. Available: 13 December 2013.
  91. 91. Hróbjartsson A, Thomsen AS, Emanuelsson F, Tendal B, Hilden J, et al. (2012) Observer bias in randomised clinical trials with binary outcomes: systematic review of trials with both blinded and non-blinded outcome assessors. BMJ 344: e1119
  92. 92. Rosenthal R, Jacobson L (1963) Teachers’ expectancies: Determinants of pupils’ IQ gains. Psychological Reports 19: 115–118.
  93. 93. Schulz K, Grimes DA (2006) The Lancet handbook of essential concepts in clinical research. Edinburgh: Elsevier.
  94. 94. Shadish SW, Cook TD, Campbell DT (2002) Experimental and quasi-experimental designs for generalized causal inferences. New York: Houghton Mifflin.
  95. 95. Post PN, de Beer H, Guyatt GH (2012) How to generalize efficacy results of randomized trials: recommendations based on a systematic review of possible approaches. Journal of Evaluation in Clinical Practice doi:–10.1111/j.1365–2753.2012.01888.x
  96. 96. Jordan S, Watkins A, Storey M, Allen SJ, Brooks CJ, et al. (2013) Volunteer Bias in Recruitment, Retention, and Blood Sample Donation in a Randomised Controlled Trial Involving Mothers and Their Children at Six Months and Two Years: A Longitudinal Analysis. PLoS ONE 8(7): e67912
  97. 97. Golder S, Loke Y (2009) Search strategies to identify information on adverse effects: a systematic review. Journal of the Medical Library Association 97(2): 84–92.
  98. 98. Tsang R, Colley L, Lynd LD (2009) Inadequate statistical power to detect clinically significant differences in adverse event rates in randomized controlled trials. Journal of Clinical Epidemiology 62: 609–16.
  99. 99. Audit Commission (2001) A Spoonful of Sugar: Medicines Management in NHS Hospitals. London, UK: The Stationery Office.
  100. 100. Committee of Public Accounts (2006) A Safer Place for Patients: Learning to Improve Patient Safety. London, UK: The Stationery Office.
  101. 101. Francis R (2013) Report of the Mid Staffordshire NHS Foundation Trust Public Inquiry: Executive Summary. London, UK: The Stationery Office. Available: Accessed 15 December 2013.
  102. 102. Jarernsiripornkul N, Krska J, Capps PAG, Richards RM, Lee A (2002) Patient reporting of potential adverse drug reactions: a methodological study. British Journal of Clinical Pharmacology 53: 318–325.
  103. 103. Montesi G, Lechi A (2009) Prevention of medication errors: detection and audit. British Journal of Clinical Pharmacology 67: 651–655.
  104. 104. de Vries EN, Prins HA, Crolla RM, den Outer AJ, van Andel G, et al. (2010) Effect of a comprehensive surgical safety system on patient outcomes. New England Journal of Medicine 363: 1928–1937.
  105. 105. Arditi C, Rège-Walther M, Wyatt JC, Durieux P, Burnand B (2012) Computer-generated reminders delivered on paper to healthcare professionals; effects on professional practice and health care outcomes. Cochrane Database of Systematic Reviews 12: CD001175
  106. 106. Onder G, van der Cammen TJ, Petrovic M, Somers A, Rajkumar C (2013) Strategies to reduce the risk of iatrogenic illness in complex older adults. Age Ageing 42: 284–91
  107. 107. Loke YK (2013) An agenda for UK clinical pharmacology: Adverse drug reactions. British Journal of Clinical Pharmacology 73: 908–911.
  108. 108. Herxheimer A, Sanz E (2008) Social, cultural and ethical aspects of drug use–changes over 40 years: a personal look back. European Journal of Clinical Pharmacology 64: 107–114.
  109. 109. Jordan S (2009) Medication Errors In An Intensive Care Unit: Systems, Pressures And Prioritising. Journal Of Advanced Nursing 16: 2258–2259.
  110. 110. Thompson A, Hetrick SE, Alvarez-Jiménez M, Parker AG, Willet M, et al. (2011) Targeted intervention to improve monitoring of antipsychotic-induced weight gain and metabolic disturbance in first episode psychosis. The Australian and New Zealand Journal of Psychiatry 45: 740–748.
  111. 111. Wightman R, Fielding J, Green S (2011) Audit of antipsychotic prescribing in dementia: Cambridgeshire results and lessons learnt. Psychiatria Danubina 23: S126–129.
  112. 112. Hill KD, Wee R (2012) Psychotropic drug-induced falls in older people: a review of interventions aimed at reducing the problem. Drugs and Aging 29: 15–30.
  113. 113. Jordan S, Gabe M, Newson L, Snelgrove S, Panes G, et al. (2014) Medication Monitoring for People with Dementia in Care Homes: the Feasibility and Clinical Impact of Nurse-led monitoring. The Scientific World Journal Article ID 843621. doi: Available: 12 April 2014
  114. 114. Department of Health (2008) Medicines Management: Everybody’s Business. London, UK: The Stationery Office.
  115. 115. Department of Health (2000) A health Service of all the talents: Developing the NHS workforce. London, UK: The Stationery Office.
  116. 116. Tierney W (2003) Adverse outpatient drug events – a problem and opportunity. New England Journal of Medicine 348: 1587–1589.
  117. 117. Lampela P, Hartikainen S, Sulkava R, Huupponen R (2007) Adverse drug effects in elderly people – a disparity between clinical examination and adverse effects self-reported by the patient. European Journal of Clinical Pharmacology 63: 509–515.
  118. 118. Valentin A, Capuzzo M, Guidet B, Moreno R, Metnitz B, et al. (2009) Errors in administration of parenteral drugs in intensive care units: multinational prospective study. BMJ 12: b814
  119. 119. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates D (2005) Adverse drug events occurring following hospital discharge. Journal of General Internal Medicine 20: 317–323.
  120. 120. Mant D (2008) A framework for developing and evaluating a monitoring strategy. In: Glasziou P, Irwig L, Aronson JK, editors Evidence based medical monitoring. Oxford, UK: Blackwell publishing/BMJ books. 15–30.
  121. 121. Coleman J, Ferner R, Aronson J (2008) Monitoring for the adverse effects of drugs. In: Glasziou P, Irwig L, Aronson JK, eds. Evidence based medical monitoring. Oxford, UK: Blackwell publishing/BMJ books. 194–210.