Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in UK primary care: An interrupted time series analysis

Niels Peek; Wouter T Gude; Richard N Keers; Richard Williams; Evangelos Kontopantelis; Mark Jeffries; Denham L Phipps; Benjamin Brown; Anthony J Avery; Darren M Ashcroft

doi:10.1371/journal.pmed.1003286

. 2020 Oct 13;17(10):e1003286. doi: 10.1371/journal.pmed.1003286

Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in UK primary care: An interrupted time series analysis

Niels Peek ^1,^2,^3,^*, Wouter T Gude ⁴, Richard N Keers ^1,^5,⁶, Richard Williams ^1,³, Evangelos Kontopantelis ⁷, Mark Jeffries ^1,⁵, Denham L Phipps ^1,⁵, Benjamin Brown ^1,^3,⁸, Anthony J Avery ^1,⁹, Darren M Ashcroft ^1,^2,^5,⁷

Editor: Sean Patrick MacBride-Stewart¹⁰

¹NIHR Greater Manchester Patient Safety Translational Research Centre, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom

²NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom

³Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom

⁴Department of Medical Informatics, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, Netherlands

⁵Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, United Kingdom

⁶Pharmacy Department, Greater Manchester Mental Health NHS Foundation Trust, Manchester, United Kingdom

⁷NIHR School for Primary Care Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom

⁸Centre for Primary Care, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom

⁹Division of Primary Care, School of Medicine, University of Nottingham, Nottingham, United Kingdom

¹⁰NHS Greater Glasgow and Clyde, UNITED KINGDOM

I have read the journal's policy and the authors of this manuscript have the following competing interests: RNK has received an honorarium for presenting on the topic of schizophrenia (epidemiology, aetiology, presentation and management) at the MORPh Consultancy Ltd Primary Care Pharmacists Training Network Mental Health Event, on 23rd October 2019, Liverpool UK.

^✉

* E-mail: niels.peek@manchester.ac.uk

Roles

Niels Peek: Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing

Wouter T Gude: Data curation, Methodology, Writing – original draft, Writing – review & editing

Richard N Keers: Conceptualization, Data curation, Investigation, Writing – review & editing

Richard Williams: Conceptualization, Data curation, Investigation, Software, Writing – review & editing

Evangelos Kontopantelis: Data curation, Formal analysis, Methodology, Writing – review & editing

Mark Jeffries: Conceptualization, Investigation, Writing – review & editing

Denham L Phipps: Conceptualization, Investigation, Writing – review & editing

Benjamin Brown: Conceptualization, Methodology, Writing – review & editing

Anthony J Avery: Conceptualization, Funding acquisition, Methodology, Supervision, Writing – review & editing

Darren M Ashcroft: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Sean Patrick MacBride-Stewart: Academic Editor

PMCID: PMC7553336 PMID: 33048923

Abstract

Background

We evaluated the impact of the pharmacist-led Safety Medication dASHboard (SMASH) intervention on medication safety in primary care.

Methods and findings

SMASH comprised (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedback; and (3) pharmacists reviewing individual at-risk patients, and initiating remedial actions or advising general practitioners on doing so. It was implemented in 43 general practices covering a population of 235,595 people in Salford (Greater Manchester), UK. All practices started receiving the intervention between 18 April 2016 and 26 September 2017. We used an interrupted time series analysis of rates (prevalence) of potentially hazardous prescribing and inadequate blood-test monitoring, comparing observed rates post-intervention to extrapolations from a 24-month pre-intervention trend. The number of people registered to participating practices and having 1 or more risk factors for being exposed to hazardous prescribing or inadequate blood-test monitoring at the start of the intervention was 47,413 (males: 23,073 [48.7%]; mean age: 60 years [standard deviation: 21]). At baseline, 95% of practices had rates of potentially hazardous prescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% CI 20.3% to 36.8%, p < 0.001) at 24 weeks and by 40.7% (95% CI 29.1% to 54.2%, p < 0.001) at 12 months after introduction of SMASH. The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI 0.2% to 50.7%, p = 0.046) at 24 weeks; the change at 12 months (23.5%) was no longer significant (95% CI −4.5% to 61.6%, p = 0.127). After 12 months, 95% of practices had rates of potentially hazardous prescribing between 0.74% and 3.02%. Study limitations include the fact that practices were not randomised, and therefore unmeasured confounding may have influenced our findings.

Conclusions

The SMASH intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices. This reduction was sustained over 12 months after the start of the intervention for prescribing but not for monitoring of medication. There was a marked reduction in the variation in rates of hazardous prescribing between practices.

Niels Peek and co-workers study a medication safety intervention in primary care in Salford, UK.

Author summary

Why was this study done?

Electronic, actionable audit and feedback interventions can reduce the rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practice.
When based on a single feedback cycle, the effect of these interventions tends to wane over time.
Therefore, we evaluated whether such an intervention is associated with sustained changes when it provides continuous, patient-level feedback.

What did the researchers do and find?

We evaluated the impact of the Safety Medication dASHboard (SMASH), a pharmacist-led intervention, in primary care during a phased rollout in Salford, UK (population size, 251,300).
We found that the intervention was associated with a sustained reduction in potentially hazardous prescribing, and a reduction in the variation between practices, until the study was completed (after 12 months).
A marked reduction in inadequate blood-test monitoring was also seen initially, but this reduction was not sustained over time.

What do these findings mean?

Electronic, actionable audit and feedback interventions can provide long-term improvements to medication safety in general practice when they are delivered on an ongoing basis.
More research is needed to obtain sustained reductions in inadequate blood-test monitoring.

Introduction

Improving medication safety is a core objective for healthcare systems worldwide, and was recently identified by the World Health Organization (WHO) as the theme for the Third Global Patient Safety Challenge [1]. Incidents related to drugs and other treatments account for the largest proportion of preventable patient harm [2], and it has been estimated that the cost associated with medication errors reaches US$42 billion annually across the globe [3]. While most medications are prescribed, dispensed, and administered in ambulatory care settings, research and quality improvement have traditionally focused on hospital-based settings. Yet there is clear evidence that medication errors are very common also in ambulatory care and contribute to iatrogenic morbidity [4], despite the fact that individual items carry low risk. The sheer volume of ambulatory care prescribing (over 1 billion prescription items supplied in the community in England each year) means that avoidable deaths in primary care due to medication errors are 7 times likely than in secondary care (627 versus 85) [5]. Stocks et al. [6] found that 5% of patients who are managed in general practices in the UK are exposed to potentially hazardous prescribing, and about 12% had no record of appropriate monitoring. Variation in the prevalence of potentially high-risk prescribing and lack of appropriate monitoring tests between practices was high, even after adjustment for patient- and practice-level variables.

Information technology (IT) has long been recognised as having the potential to increase patient safety [7]. When healthcare providers switched to electronic prescribing in the late 1990s, computerised decision support was identified as a technology that could prevent prescribing errors by issuing alerts during the prescribing process. All electronic prescribing systems used in English primary care include decision support. However, clinicians experience alerts in most cases as ‘too much, too late’ [8], and it has been reported that up to 80% of alerts in ambulatory settings may be overridden [9].

Electronic, actionable audit and feedback [10] is a different approach that informs clinicians when potential errors have happened, prompting them to take remedial action. It can be applied instead of computerised decision support, or provided as an additional ‘defence layer’ [11]. The PINCER trial demonstrated that a pharmacist-led intervention in which trained pharmacists worked collaboratively with general practitioners (GPs) to act upon computer-generated feedback that identified potentially at-risk patients reduced the rates of hazardous prescribing and inadequate monitoring of medication [12]. The intervention was shown to be more effective than feedback alone, indicating that pharmacist visits played a crucial role in solving medication safety errors. However, the effect of PINCER waned by 12 months [12], probably because pharmacist involvement ceased after 3 months and the intervention relied on a single feedback cycle—while feedback is known to be more effective when it is provided more than once [13].

We developed the pharmacist-led Safety Medication dASHboard (SMASH) intervention, building on the principles of the PINCER intervention, in which trained clinical pharmacists work with general practices to act upon feedback that is provided in a continuous fashion. To do this, we developed a novel electronic, interactive medication safety dashboard that identifies patients exposed to potential medication safety hazards and is updated on a daily basis [14], and capitalised on the National Health Service (NHS) England’s policy to increase clinical pharmacists working as part of general practice teams [15–17]. Our objective was to evaluate changes in the prevalence of hazardous (high-risk) prescribing and inadequate blood-test monitoring associated with this intervention.

Methods

Study design

Our study took place during a phased rollout of the intervention in Salford, UK (population size, 251,300). Due to the planned rollout, randomisation was not possible. General practices received the intervention at different points in time, depending on the availability of trained pharmacists to help deliver this intervention within the practices. Once a pharmacist was available, the practice controlled the precise date at which it would start the intervention. We used an interrupted time series (ITS) design to evaluate the impact of the intervention on medication safety in this ‘natural experiment’. Our description follows the TREND statement [18] for improving the reporting quality of nonrandomised evaluations of interventions (see S1 TREND Statement) and the Template for Intervention Description and Replication (TIDieR) Checklist [19] (see S1 TIDieR Checklist).

Safety indicators

We used 12 medication safety indicators (10 relating to potentially hazardous prescribing and 2 to inadequate blood-test monitoring) developed for PINCER [12,20]. These 12 indicators were selected from a broader set of 24 indicators based on their prevalence nationally and in Salford (for an overview, see Stocks et al. [6] and Akbarov et al. [21], respectively), leaving out indicators with a low prevalence. Each indicator consisted of a denominator and a numerator. The denominator included all patients having 1 or more risk factors for being exposed to potentially hazardous prescribing or inadequate monitoring because of an existing diagnosis or prescribing pattern (during a time period specific to the indicator definition and the audit date). The numerator consisted of those patients who actually received the potentially hazardous prescription or had no record of the required monitoring during the time period leading up to the audit date. Operational definitions for each medication safety indicator are provided in Table A in S1 Table.

Participants and recruitment

General practices were eligible to participate in the study if they had access to the Salford Integrated Record (SIR)—a data warehouse containing linked data from primary care and secondary data that is refreshed on a nightly basis [22]. Practices were recruited through local quality networks. In the United Kingdom, patients, including those individuals who are living in care home settings, are registered with 1 general practice that holds the patient’s electronic health record, including diagnoses, test results, and details of prescribed medication. Patients were included in the study if they were registered with one of the participating practices and had the potential to trigger one of the indicators at any time point during the 12 months that the intervention was deployed in their practice.

Intervention

SMASH is an enhanced PINCER intervention as defined by the National Institute for Health and Care Excellence (NICE) medicine optimisation guideline [23], and comprised 3 components. First, clinical pharmacists worked in general practices as members of the practice team. They were trained to deliver the intervention and apply root cause analysis techniques to identify, explore, resolve, and prevent medication errors in partnership with general practice staff [24]. Some pharmacists had existing roles in general practices, and others were recruited into similar roles for practices where no pharmacist was employed previously. Many pharmacists worked across several practices. Second, pharmacists and practice staff were given access to a web-based, interactive dashboard that provided feedback on potentially hazardous prescribing. The dashboard was designed in collaboration with various stakeholders (pharmacists, GPs, and a patient). It generated lists of patients currently exposed to potentially hazardous prescribing or inadequate blood-test monitoring as defined by the 12 indicators, thus providing actionable feedback at the patient level [14]. The dashboard also provided practice-level summary data (the number and percentage of patients who concurrently had risk factors for each indicator, comparisons with the average for each indicator across all practices, and patterns over time) as well as educational material (evidence summaries for each indicator, links to the relevant literature, and details of possible actions that could be taken for each indicator to reduce risk to patients). Dashboard content (numbers and graphs) for each indicator was updated on a daily basis (at midnight) with up-to-date data on new diagnoses, laboratory test results, and prescriptions from the SIR data warehouse. Figs A–D in S1 Fig provide screenshots of the dashboard. Third, pharmacists reviewed individual patients whose records triggered the indicators, and initiated remedial actions (e.g., ordering laboratory tests) or advised GPs on action plans (e.g., discontinuing medication, co-prescribing protective medication) [25,26]. Pharmacists and GPs had access to the dashboard for the entire 12-month study period, and pharmacists were encouraged to continue monitoring the practice’s dashboard and take action when new cases of hazardous prescribing and inadequate monitoring emerged. A small financial reward (£2.56 [US$3.31] per eligible patient) was also provided by the local NHS body responsible for the planning and commissioning of healthcare services to practices implementing the intervention [27].

Outcome measures

Our primary outcome measures were prevalence of exposure to (a) any potentially hazardous prescribing (10 indicators) and (b) any inadequate blood-test monitoring (2 indicators) among patients with risk factors for such prescribing and monitoring (as defined by individual indicators), respectively. These outcomes were assessed through 2 composite measures, defined as the sum of all patients who triggered any prescribing (or, separately, monitoring) indicator divided by the sum of all patients with risk factors at a given audit date. Secondary outcome measures included rates of ongoing (existent for 30 or more days) and new (arisen within the previous 30 days) exposure to any potentially hazardous prescribing and any inadequate blood-test monitoring, and the prevalence of the 12 indicators individually. In each practice, outcomes were assessed every 4 weeks during the 24 months before and the 12 months after the practice started the intervention. The patient cohort in which the outcomes were evaluated was dynamic (i.e., the individual patients and the numbers of patients included in the denominator could change from 1 audit date to the next).

All outcome data were collected by interrogating primary care electronic health records from the SIR data warehouse for relevant diagnoses, prescriptions, and laboratory test results. Patients were censored from the study when they died or when they were no longer registered at one of the participating general practices.

Statistical analysis

On the basis of baseline levels of potentially hazardous prescribing across practices in Salford [6], we assumed a baseline risk of 6%, with the intervention reducing it to 4.5%, and estimated that our study would have 88.3% power to detect this reduction in 2,000 patients across 30 practices with a statistical significance of 0.05.

The analyses were performed according to the intention-to-treat principle; we analysed data from all the eligible patients regardless of whether they actually received a medication review. We used an ITS design, to account for non-flat pre-intervention outcome trends in study outcomes. This involved using autoregressive integrated moving average (ARIMA) models with various post-intervention periods (4 weeks, 12 weeks, 24 weeks, and 12 months), through the itsa command in Stata version 15 [28,29]. Since ARIMA models are linear, outcomes were quantified as percentages and transformed to the log scale prior to analyses. The rates of potentially hazardous prescribing and inadequate monitoring are subject to ceiling and floor effects, which necessarily limits any linear trend in improvement. Therefore, we conducted the analyses on measures transformed to a logit scale, which has no ceiling [30]. Results were back-transformed to percentages to facilitate interpretation.

To account for and quantify practice variability, data from each practice were analysed separately in an ITS model and then aggregated using a random-effects meta-analysis model. More specifically, for each practice we used the estimates that are reported as standard in an ITS analysis (pre-slope, level change, and slope change [31]) in linear post-estimation commands. Through these, we evaluated differences in the outcomes at specific time points post-intervention (observed), compared to model extrapolations from the pre-intervention trend assuming no intervention (expected), on the log scale. The time points were 4 weeks, 12 weeks, 24 weeks, and 12 months after practices started the intervention. Results for these time points were next meta-analysed using a bootstrap version of the DerSimonian–Laird variance estimator [32,33], implemented in the metaan command in Stata [34], which also allows for back-transformation to percentages. This process allowed us to display practice heterogeneity in the intervention with forest plots, and also quantify it with an estimate of the routinely used I² statistic [35]. All statistical analyses were pre-specified except for the post-intervention periods shorter than 12 months (4 weeks, 12 weeks, and 24 weeks) at which we assessed changes in outcomes.

Ethical approval

The study was approved on behalf of the UK’s Health Research Authority by the North West–Greater Manchester East Research Ethics Committee on 24 September 2015 (reference 15/NW/0792). Patient consent was deemed not necessary because all electronic health record data were deidentified before they were shared with the study team.

Patient and public involvement

A member of our patient and public involvement group at the NIHR Greater Manchester Patient Safety Translational Research Centre and other key stakeholders, including 6 GPs and 7 pharmacists, were involved in the design of the dashboard in an iterative process of short interviews and prototype reviews [36]. We met regularly with members of our patient and public involvement group to review progress during the rollout of the intervention and will continue to work with this group to advise on plans for dissemination of these findings.

Results

Participating practices

Of the 44 general practices in Salford, 43 (98%) participated in the study. The missing practice wanted to participate but was ineligible because it did not contribute to the SIR data warehouse. The first practice started receiving the intervention from 18 April 2016, and the last from 26 September 2017. Start dates were not uniformly distributed over time; there was a peak in practices starting the intervention in November–December 2016. The participating practices had a pooled list size of 235,595 patients on the date that the practices started the intervention. A total of 54,044 patients had the potential to be identified against any of the 12 indicators in the 24 months before the start of the intervention, and 53,068 patients had this potential in the 12 months of the study. All these patients were included in the analysis at 1 or more time points.

Table 1 shows the baseline characteristics of the participating practices divided into 3 groups based on their starting period. The number of people registered to practices and having 1 or more risk factors for any of the medication safety indicators at the start of the intervention was 47,413. Of those, 23,073 (48.7%) were male, their mean age was 60 years (standard deviation: 21), and 24,954 (52.6%) had been prescribed 5 or more drugs simultaneously at that point in time. Of the 47,413 patients, there were 1,291 (2.7%) who were then exposed to a form of potentially hazardous prescribing or inadequate blood-test monitoring as defined by the 12 indicators.

Table 1. Baseline characteristics of the included patients and participating practices, according to their start date.

Characteristic	Intervention start date
Characteristic	18 Apr to 12 Sep 2016	31 Oct to 31 Dec 2016	20 Jan to 26 Sep 2017
Practices	11	22	10
Registered patients; mean (range)	8,093 (3,318–15,104)	5,096 (970–11,517)	3,447 (1,951–8,695)
QOF points achieved^†; mean (range)	539 (482–558)	482 (321–558)	511 (450–557)
Patients with 1 or more risk factors at the start of the intervention^‡	17,211	23,744	6,458
Exposure to 1 or more medication safety hazards; n (%)	470 (2.7)	589 (2.5)	232 (3.6)
Age in years; mean (SD)	58 (21)	62 (21)	57 (21)
Male sex; n (%)	8,569 (49.8)	11,302 (47.6)	3,202 (49.6)
Residence in most deprived quintile of Salford postal codes; n (%)	6,738 (39.1)	9,020 (38.0)	3,169 (49.1)
Charlson comorbidity index [46] ≥ 5; n (%)	4,403 (25.6)	7,581 (31.9)	1,683 (26.1)
Polypharmacy (≥5 drugs); n (%)	8,516 (49.5)	13,166 (55.4)	3,272 (50.7)

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^†The Quality and Outcomes Framework (QOF) financially rewards primary care practices according to their performance on a range of clinical and organisational indicators, each of which is associated with a number of maximum achievable points, with each point corresponding to a defined payment. Presented QOF scores are from 2015–2016 and ranged from 0 to 559, with higher scores indicating better performance [47].

^‡Included patients were people registered to practices and having 1 or more risk factors for any of the 12 medication safety indicators at the start of the intervention. This is the denominator for the remaining rows in the table.

Rates of potentially hazardous prescribing and inadequate blood-test monitoring

Fig 1 presents the primary outcomes of potentially hazardous prescribing and inadequate monitoring, relative to the intervention start date in each practice. Table B in S1 Table shows the rates, numerators, and denominators for each of the prescribing and monitoring outcomes at the different follow-up time points. At the start of the intervention, 2.61% (1,230 out of 47,183) of potentially exposed patients had received 1 or more potentially hazardous prescriptions; 13.32% (89 out of 668) had received inadequate blood-test monitoring. At this point in time, 95% of practices had rates of high-risk prescribing between 0.88% and 6.19% and rates of inadequate monitoring between 2.61% and 49.02% (shaded areas in Fig 1). At 12 months after the intervention start, the observed rate of hazardous prescribing had reduced by 1.01 percentage points to 1.60% (95% CI 1.49% to 1.72%; 657 out of 47,163 patients), and inadequate blood-test monitoring by 3.96 percentage points to 9.36% (95% CI 7.23% to 11.86%; 61 out of 652 patients). In terms of variation between practices at 12 months follow-up, 95% of practices had rates of high-risk prescribing between 0.74% and 3.02%, and rates of inadequate monitoring between 1.78% and 36.19%.

Table 2 shows for all indicators the rates at baseline and follow-up time points. In the analysis of the primary outcomes, the rate of potentially hazardous prescribing reduced faster after introduction of the SMASH intervention than would be expected from extrapolation of the pre-intervention trend, with an absolute difference at 4 weeks of −0.36 (95% CI −0.48 to −0.23, p < 0.001); 12 weeks, −0.50 (95% CI −0.60 to −0.39, p < 0.001); 24 weeks, −0.69 (95% CI −0.80 to −0.57, p < 0.001); and 12 months, −0.96 (95% CI −1.12 to −0.79, p < 0.001), corresponding to relative reductions of 14.1% (95% CI 8.0% to 21.4%), 19.9% (95% CI 13.7% to 27.2%), 27.9% (95% CI 20.3% to 36.8%), and 40.7% (95% CI 29.1% to 54.2%), respectively. The observed rate of inadequate blood-test monitoring also reduced faster than the extrapolated pre-intervention trend, with an absolute difference at 4 weeks of −2.40 (95% CI −4.53 to 0.08, p = 0.058); 12 weeks, −2.59 (95% CI −4.70 to −0.13, p = 0.038); and 24 weeks, −2.82 (95% CI −5.15 to −0.18, p = 0.046), corresponding to relative reductions of 17.8% (95% CI −0.5% to 41.4%), 19.6% (95% CI 0.8% to 44.3%), and 22.0% (95% CI 0.2% to 50.7%), respectively. At 12 months this difference was no longer significant (absolute reduction −2.85 [95% CI −5.68 to 0.71]; relative reduction 23.5% [95% CI −4.5% to 61.6%], p = 0.127).

Table 2. Effect of the SMASH intervention on primary and secondary outcomes relating to prescribing and monitoring safety.

Outcome measure	Number of patients exposed to (numerator) and having risk factors for (denominator) prescribing and monitoring hazards at baseline	Baseline percentage of hazardous prescribing and monitoring (95% CI)	Absolute difference in percentage (95% CI) of medication safety hazards at 4 weeks, 12 weeks, 24 weeks, and 12 months after intervention start as compared to extrapolations of the pre-intervention trends
Outcome measure			4 weeks after start	12 weeks after start	24 weeks after start	12 months after start
Any prescribing hazard composite (1–10)	1,230/47,183	2.61 (2.47 to 2.75)	−0.36 (−0.48 to −0.23)	−0.50 (−0.60 to −0.39)	−0.69 (−0.80 to −0.57)	−0.96 (−1.12 to −0.79)
Ongoing prescribing hazards	1,032/47,183	2.19 (2.06 to 2.32)	−0.31 (−0.43 to −0.19)	−0.47 (−0.56 to −0.36)	−0.67 (−0.77 to −0.55)	−0.96 (−1.11 to −0.79)
New prescribing hazards	198/47,183	0.42 (0.36 to 0.48)	−0.05 (−0.09 to −0.01)	−0.04 (−0.08 to −0.00)	−0.03 (−0.06 to 0.01)	−0.01 (−0.05 to 0.07)
1. Prescription of an oral NSAID without co-prescription of an ulcer-healing drug in a patient aged ≥65 years	472/20,746	2.28 (2.08 to 2.49)	−0.17 (−0.33 to 0.00)	−0.31 (−0.47 to −0.13)	−0.47 (−0.67 to −0.23)	−0.63 (−0.98 to −0.21)
2. Prescription of an oral NSAID without co-prescription of an ulcer-healing drug to a patient with a history of peptic ulceration	18/1,407	1.28 (0.76 to 2.01)	0.05 (−0.05 to 0.16)	0.06 (−0.03 to 0.16)	0.08 (−0.01 to 0.19)	0.11 (−0.05 to 0.28)
3. Prescription of an antiplatelet drug without co-prescription of an ulcer-healing drug to a patient with a history of peptic ulceration	111/1,407	7.89 (6.53 to 9.42)	−0.47 (−0.89 to −0.03)	−0.79 (−1.20 to −0.36)	−1.45 (−1.91 to −0.96)	−2.62 (−3.23 to −1.95)
4. Prescription of warfarin or NOAC in combination with an oral NSAID	44/3,545	1.24 (0.90 to 1.66)	−0.13 (−0.25 to −0.00)	−0.14 (−0.25 to −0.02)	−0.13 (−0.24 to 0.01)	−0.08 (−0.26 to 0.12)
5. Prescription of warfarin or NOAC in combination with an antiplatelet drug without co-prescription of an ulcer-healing drug	72/2,096	3.44 (2.70 to 4.31)	−0.50 (−0.95 to 0.03)	−0.64 (−1.09 to −0.11)	−0.83 (−1.30 to −0.27)	−1.17 (−1.76 to −0.42)
6. Prescription of aspirin in combination with another antiplatelet drug without co-prescription of an ulcer-healing drug	144/4,319	3.33 (2.82 to 3.91)	−0.69 (−1.00 to −0.33)	−0.81 (−1.12 to −0.45)	−0.99 (−1.36 to −0.55)	−1.30 (−1.81 to −0.62)
7. Prescription of a non-selective beta-blocker to a patient with asthma	314/23,276	1.35 (1.21 to 1.51)	−0.03 (−0.12 to 0.07)	−0.09 (−0.19 to 0.02)	−0.16 (−0.32 to 0.02)	−0.29 (−0.58 to 0.10)
8. Prescription of a long-acting beta-2 inhaler (excluding combination products with inhaled corticosteroid) to a patient with asthma who is not also prescribed an inhaled corticosteroid	38/277	13.72 (9.89 to 18.34)	−0.52 (−1.71 to 0.75)	−0.96 (−2.11 to 0.27)	−1.39 (−2.72 to 0.06)	−2.35 (−4.38 to 0.01)
9. Prescription of an oral NSAID to a patient with heart failure	37/2,523	1.47 (1.03 to 2.02)	−0.21 (−0.37 to −0.06)	−0.19 (−0.32 to −0.04)	−0.21 (−0.37 to −0.03)	−0.22 (−0.47 to 0.09)
10. Prescription of an oral NSAID to a patient with chronic renal failure (eGFR < 45 ml/min/1.73 m²)	33/2,107	1.57 (1.08 to 2.19)	−0.27 (−0.38 to −0.16)	−0.28 (−0.40 to −0.16)	−0.32 (−0.45 to −0.18)	−0.39 (−0.57 to −0.19)
Any monitoring hazard composite (11–12)	89/668	13.32 (10.84 to 16.14)	−2.40 (−4.53 to 0.08)	−2.59 (−4.70 to −0.13)	−2.82 (−5.15 to −0.04)	−2.85 (−5.68 to 0.71)
Ongoing monitoring hazards	71/668	10.63 (8.39 to 13.22)	−0.99 (−2.50 to 0.81)	−1.32 (−2.86 to 0.53)	−1.76 (−3.43 to 0.30)	−2.16 (−3.99 to 0.21)
New monitoring hazards	18/668	2.68 (1.60 to 4.23)	−0.74 (−1.18 to −2.70)	−0.66 (−1.14 to −1.13)	−0.56 (−1.14 to 0.10)	−0.37 (−1.21 to 0.63)
11. Prescription of methotrexate without both a recent full blood count and a liver function test in the last 3 months	37/466	7.94 (5.65 to 10.78)	0.27 (−0.76 to 1.41)	0.17 (−0.76 to 1.19)	0.00 (−1.00 to 1.12)	−0.31 (−1.80 to 1.44)
12. Prescription of amiodarone for at least 6 months without a thyroid function test within the last 6 months	52/203	25.62 (19.76 to 32.20)	−4.90 (−10.45 to 1.89)	−5.27 (−10.71 to 1.40)	−5.92 (−11.84 to 1.56)	−5.97 (−13.69 to 4.69)

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Bold font indicates significant difference (p < 0.05).

eGRF, estimated glomerular filtration rate; NOAC, non–vitamin K antagonist oral anticoagulant; NSAID, nonsteroidal anti-inflammatory drug; SMASH, Safety Medication dASHboard.

Incident cases (i.e., arisen within the previous 30 days) of both hazardous prescribing (baseline rate, 0.42% [95% CI 0.36% to 0.48%]) and inadequate blood-test monitoring (baseline rate, 2.68% [95% CI 1.60% to 4.23%]) were lower than extrapolations of the pre-intervention trend up to 12 weeks after the intervention start, but not after 24 weeks or 12 months (Table 2).

Sustained reductions at 12 months were found for 5 types of hazardous prescribing, namely, oral NSAID without co-prescription of an ulcer-healing drug in patients aged ≥65 years (baseline rate, 2.28% [95% CI 2.08% to 2.49%]; 12-month change, −0.63 [95% CI −0.98 to −0.21], p = 0.003), antiplatelet drug without co-prescription of an ulcer-healing drug to patients with a history of peptic ulceration (baseline rate, 7.89% [95% CI 6.53% to 9.84%]; 12-month change, −2.62 [95% CI −3.23 to −1.95], p < 0.001), warfarin or NOAC in combination with an antiplatelet drug without co-prescription of an ulcer-healing drug (baseline rate, 3.44% [95% CI 2.70% to 4.31%]; 12-month change, −1.17 [95% CI −1.76 to −0.42], p = 0.001), aspirin in combination with another antiplatelet drug without co-prescription of an ulcer-healing drug (baseline rate, 3.33% [95% CI 2.82% to 3.91%]; 12-month change, −1.30 [95% CI −1.81 to −0.62], p < 0.001), and oral NSAID to patients with poor renal function (baseline rate, 1.57% [95% CI 1.08% to 2.19%]; 12-month change, −0.39 [95% CI −0.57 to −0.19], p < 0.001). The intervention was associated with changes in prescriptions of warfarin or NOAC in combination with an oral NSAID and of oral NSAID to patients with heart failure at, respectively, 12 and 24 weeks, but these changes were not sustained thereafter. For the latter type of hazardous prescribing, the average rate across all practices was still decreased at 12 months, but this decrease was no longer significant due to higher variation between practices (Table 2).

For both outcomes, pre-intervention levels, pre-intervention trends, and changes at follow-up varied substantially between practices. For potentially hazardous prescribing, the 12-month change ranged from −4.34 to +0.93 (I² = 91.1%; Fig G in S1 Fig). In 30 practices there was a decrease in potentially hazardous prescribing after 12 months; in 12 practices there was no statistically significant change; and in 1 practice there was an increase. For inadequate blood-test monitoring, the 12-month change ranged from −94.9 to +42.4 (I² = 83.4%; Fig J in S1 Fig). In several practices (e.g., practices 38 and 39) there was a marked increase in inadequate monitoring before the intervention and a sharp decrease afterwards, leading to high levels of estimated change. These 2 practices and 4 others achieved a significant reduction at 12 months, while in 29 practices there was no significant change, and 8 practices saw a (usually modest) increase. For both outcomes there was a negative correlation between the pre- and post-intervention trends (potentially hazardous prescribing, Pearson’s correlation −0.353, p = 0.020; inadequate blood-test monitoring, Pearson’s correlation −0.558, p < 0.001).

Discussion

We evaluated a complex intervention to improve the safety of medicine prescribing and blood-test monitoring in general practice, combining training of general-practice-based clinical pharmacists, a web-based dashboard providing actionable feedback, and pharmacists reviewing individual patients’ prescriptions and monitoring to initiate remedial actions or advise GPs on doing so. The intervention was associated with substantial and sustained reductions in targeted high-risk prescribing over 12 months after the start of the intervention, and marked reduction in the variation in rates of potentially hazardous prescribing between practices.

A gradual decrease was observed in potentially hazardous prescribing and inadequate monitoring during the first 20 weeks after initiating the intervention. For targeted high-risk prescribing, the decrease continued throughout the 12-month follow-up period, whereas for inadequate blood-test monitoring there was a decrease at 24 weeks, but not so at 12 months. Not surprisingly, we observed larger reductions in ongoing high-risk prescribing than in new high-risk prescribing, given that the intervention prompted review of patients who were already exposed to high-risk prescribing, although both rates were reduced.

We observed a small downward trend in both potentially hazardous prescribing and inadequate blood-test monitoring during the 2 years before the intervention (as shown in Fig 1). This was likely due to increasing awareness among some GPs of the risks associated with commonly prescribed medications targeted by our indicators, but there was substantial variation between practices, highlighting the potential for further improvements in medication safety through targeted practice-level intervention. Notably, the largest reductions were observed in practices with high prior rates of high-risk prescribing, and there was a marked reduction in the variation between practices during the 12-month follow-up period.

There was also marked variation in reductions of different forms of potentially hazardous prescribing. The largest reductions were observed in patients receiving warfarin or NOACs in combination with an antiplatelet drug without gastro-protective drug co-prescription, patients receiving aspirin with another antiplatelet drug; and patients with a history of peptic ulceration receiving antiplatelet drugs. All these forms of potentially hazardous prescribing can be easily resolved by discontinuing 1 or more of the medications if they are no longer clinically indicated or, alternatively, if appropriate, through co-prescribing of gastro-protective medication. In contrast, we did not observe a significant reduction in prescriptions of non-selective beta-blockers to patients with a history of asthma, despite evidence of increased risks of asthma exacerbation [37,38]. A partial explanation may be that GPs are hesitant to halt prescribing started by other specialist practitioners (e.g., psychiatrists or optometrists). Also, there exist instances where no suitable alternative treatment is available for the patient in question [38].

Interviews with healthcare professionals involved in the study [25] and analyses of activity logs of the dashboard [39] have indicated that in many practices, pharmacists were the primary (and sometimes, sole) users throughout the study. Activity logs showed that pharmacists, after an intensive review of patients identified on the dashboard for a given practice, switched to ‘surveillance mode’ for that practice, while focusing most of their efforts on new practices that started the intervention [39]. This surveillance was particularly noticeable for cases of inadequate monitoring which, by definition, started to be re-flagged on the dashboard after 6 months for those patients receiving long-term treatments, such as low dose methotrexate.

Strengths and limitations of this study

To our knowledge no previous studies have evaluated changes associated with complex interventions that include ongoing, patient-level feedback on safe prescribing and monitoring in general practice. Our intervention used state-of-the-art web technology to provide feedback to practitioners using textual, numerical, and graphical information, benchmarking their practice’s performance against the average performance for all participating practices and providing the ability to monitor their own performance over time [14]. This information was updated daily during the 12-month intervention period. At the same time, there was continued support of pharmacists to review high-risk cases and to initiate or advise on remedial actions.

The main strengths of our study are the pragmatic design, the range of clinically relevant outcomes evaluated, and the large number of general practices that took part. Forty-three out of 44 general practices in Salford took part in our study, eliminating potential forms of bias due to selection (e.g., volunteering). The high adoption of SMASH might have been partially driven by a small financial incentive. However, similar incentives have been included in the latest General Medical Services contract framework for general practices in the UK [40].

Our study had the following limitations. We evaluated the intervention in a pragmatic fashion during its rollout, and therefore did not randomise practices to receiving or not receiving the intervention. Instead, we enrolled practices in a ‘naturalistic fashion’ starting on different start dates; for logistical reasons (allocation of clinical pharmacists to practices), these dates could not be randomised. Due to the lack of randomisation, we cannot exclude that unmeasured confounding has influenced our findings. However, the ITS design is recognised as one of the strongest quasi-experimental designs for assessing the impact of interventions in such circumstances [31]. The main threat to validity is bias through secular trends in outcomes caused by other interventions (such as new policies). While no new relevant policies came into force during our study, there was an increasing awareness of the importance of medication safety in primary care in the UK during this period. For this reason, we applied a 2-stage statistical analysis strategy, comparing model extrapolations of pre-intervention trends at the practice level to observed outcomes post-intervention. Because practices started the intervention over a long time period (April 2016 to September 2017), we believe that the risks of bias are low. We do acknowledge that half of the participating practices started the intervention during the last 2 months of 2016 (Table 1), and that there were some differences between this group and the practices that started earlier or later. Furthermore, all our outcomes were measured by interrogating routinely collected electronic health records. It is possible that medication safety risks were not detected due to omissions or mistakes in patients’ records. It is also possible that some apparent instances of hazardous prescribing or inadequate blood-test monitoring were caused by mistakes in patients’ records and were resolved by correcting those mistakes rather than through clinical actions.

Comparison with other studies

Several feedback-based, complex interventions targeting medication safety in primary care have been evaluated in recent years, the results of which are consistent with our findings. Avery et al. [12] randomised 72 general practices in England to simple feedback (lists of at-risk patients plus brief written educational materials) accompanied by pharmacist-led educational outreach meetings (PINCER) versus simple feedback alone. Feedback, consisting of a paper report, was repeated after 6 and 12 months, but pharmacist support was limited to 12 weeks. The effects on high-risk prescribing were similar to changes found in our study at 24 weeks but waned at 12 months. It was already known that feedback is more effective when it is provided more than once [13], but this result shows that when feedback is part of a larger intervention, the entire intervention must be provided more than once—not just the feedback. In our case, the continued support from practice-based pharmacists and ongoing access to up-to-date information through the dashboard made a crucial difference to sustainably target high-risk prescribing in the highly committed general practice environment.

In a cluster-randomised, stepped-wedge trial conducted in 33 practices, Dreischulte et al. [41] evaluated a complex intervention comprising electronic feedback similar to SMASH, 1-hour pharmacist outreach visits and financial incentives. They observed a reduction in high-risk prescribing after 12 months similar to what we found in our study. Building on this, MacBride-Stewart et al. [42] evaluated the impact of a safety improvement initiative comprising an educational workshop, a single round of benchmark feedback, financial incentives, and electronic search tools to identify high-risk patients. There were substantial and sustained reductions in the high-risk prescribing of NSAIDs, although with some waning of effect 12 months after the intervention ceased. The same intervention had no effect on antipsychotic prescribing in people with dementia.

Policy implications

Emerging evidence suggests that general-practice-based pharmacists enhance integrated patient care and can deliver clinical interventions efficiently and in high volume [43,44]. Our study demonstrates how these pharmacists can also play a key role in sustainably improving medication safety in general practice and reducing variation in potentially hazardous prescribing between practices. However, for this to be effective, it is imperative to have an underpinning ‘learning health systems’ [45] capability for continuous data-driven self-study that promotes change and improvement. Specifically, it requires a digital infrastructure for continuous, patient-level feedback that allows practitioners to identify high-risk patients, access educational material, and compare rates of high-risk prescribing and monitoring both between practices and within their practice over time.

Our intervention easily scales up because it is based on the automated analysis of routine healthcare data, and can therefore be used in any environment that uses electronic prescribing. As such it can be used to support the WHO’s Third Global Patient Safety Challenge [1], which focuses on reducing medication-related iatrogenic harm. Specifically, it can be used to target high-risk situations and constitutes a programme of action for designing safe systems and practices for medication in primary care.

Conclusion

Our intervention was associated with reduced rates of potentially hazardous prescribing and inadequate blood-test monitoring in general practices during the first 24 weeks after initiation. This reduction was sustained at 12 months for prescribing but not for monitoring. There were substantial differences in rates of potentially hazardous prescribing between practices prior to the intervention, and there was a marked reduction in the variation between practices over the 12-month follow-up period.

Supporting information

S1 Fig. Supplemental figures.

Screenshots from the SMASH interactive dashboard (Figs A–D) and forest plots showing variation between practices in the reduction in potentially hazardous prescribing and inadequate monitoring (Figs E–J).

(DOCX)

Click here for additional data file.^{(924.3KB, docx)}

S1 Study Protocol. Protocol for the study.

(PDF)

Click here for additional data file.^{(207.3KB, pdf)}

S1 Tables. Supplemental tables.

Table A. Definitions of the medication safety indicators targeted by the SMASH intervention. Table B. Rates of hazardous prescribing and inadequate medication monitoring for 43 general practices in Salford.

(ZIP)

Click here for additional data file.^{(58.3KB, zip)}

S1 TIDieR Checklist. Completed TIDieR Checklist.

(PDF)

Click here for additional data file.^{(83.3KB, pdf)}

S1 TREND Statement. Completed TREND statement.

(PDF)

Click here for additional data file.^{(248.4KB, pdf)}

Acknowledgments

The work uses data provided by patients and collected by the NHS as part of their care and support. PRIMIS at the University of Nottingham has provided detailed specifications for the safety indicators used in SMASH. We are grateful to the NHS Salford Clinical Commissioning Group and to all the pharmacists and all other staff at the general practices for taking part in the study.

Abbreviations:

GP: general practitioner
ITS: interrupted time series
NHS: National Health Service
NOAC: non–vitamin K antagonist oral anticoagulant
NSAID: nonsteroidal anti-inflammatory drug
SIR: Salford Integrated Record
SMASH: Safety Medication dASHboard

Data Availability

The patient-level data for this study cannot be shared publicly because of their sensitive nature (patient-level healthcare data, collected routinely without consent). They were provided for use in this study only by the Salford Integrated Record (SIR) Board. A copy can be requested from the SIR Board via SALCCG.SIRProposals@nhs.net using reference number DATA002. We have made the aggregated data, derived from the patient-level data, available through Mendeley Data at https://doi.org/10.17632/ps8jwmmnkv.1. These are the weekly counts, per participating practice, for numerator and denominator, for each safety indicator over the 3-year study period.

Funding Statement

The National Institute for Health Research (www.nihr.ac.uk) Greater Manchester Patient Safety Translational Research Centre (NIHR Greater Manchester PSTRC; PSTRC-2016-003) supported the time and facilities of NP, RNK, RW, MJ, DLP, AJA, and DMA. MRC (mrc.ukri.org) Health eResearch Centre grant MR/K006665/1 supported the time and facilities of EK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003286.r001

Decision Letter 0

Caitlin Moyer

9 Mar 2020

Dear Dr. Peek,

Thank you very much for submitting your manuscript "Impact of a pharmacist-led actionable audit and feedback intervention to improve medication safety in primary care: interrupted time series analysis" (PMEDICINE-D-19-04218) for consideration at PLOS Medicine.

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-----------------------------------------------------------

Requests from the editors:

We note that you are unable share data publicly (even when de-identified, we assume). Are you able to include aggregate data in supplementary files for the benefit of other researchers, or via a publicly-accessible repository?

Noting the research design, we ask you to adopt softer wording in places where you currently assert that "the SMASH intervention reduced rates ..." and similar. For example, "Implementation of the SMASH intervention was associated with reduced rates of ..." would seem more apt.

Please substitute "this reduction" or "this apparent impact" for "this effect".

We suggest an amended title: "Evaluation of a pharmacist-led audit and feedback intervention for improving medication safety in primary care: an interrupted time-series analysis".

Please reformat your abstract to comprise three subsections: "background"; "methods and findings"; and "conclusions". The final sentence of the "methods and findings" subsection should quote 2-3 of the study's main limitations.

To your abstract, please add aggregate demographic details for study participants; and study dates.

In your abstract and throughout the paper, please quote p values alongside 95% CI, where available.

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Comments from the reviewers:

*** Reviewer #1:

This is a statistical review of manuscript PMEDICINE-D-19-04218. The manuscript reports the results of a pharmacist-led intervention of medication safety in primary care.

My comments are here below.

* Abstract - Results: I suggest that you start by providing the rates at baseline ("At baseline, 95% of practices had rates…"), before presenting your results. This will allow the reader to understand the starting point, and realise what the percentage reductions that are mentioned are relative to in a more "chronological" way.

* Statistical analysis: Could you please provide more information on your sample size calculation? Please summarise in a few words the reference levels for which you use reference 6. Also please clarify what you mean by a before-after study? Is it effectively a change in percentage? What is your estimated population size of a GP? Finally, 88.3% power is slightly unusual. Could you please clarify?

* Statistical analysis: the sample size calculation is 30 GPs. But you include 43 GPs. Please could you clarify why?

* Results: you mention that "the rate of [] prescribing reduced significantly more after introduction of the SMASH intervention compared to extrapolations". Does this mean that you compare the projected rate of 12 months, using data from the pre-intervention period, to the observed rate at 12 months? Or effectively is it the case that you are comparing the observed rates to the rates at baseline?

*** Reviewer #2:

[see attachment]

*** Reviewer #3:

Reviewers Comments

Impact of a pharmacist-led actionable audit and feedback intervention to improve medication safety in primary care: interrupted time series analysis

This paper reports the outcomes of an intervention in an NHS England primary care setting (GP practices in part of Greater Manchester).

The intervention was described as having 3 components

1. the placement of clinical pharmacists into GP practices who are trained to identify and resolve root causes of medication errors,

2. the feedback of potentially hazardous prescribing/blood monitoring via an interactive dashboard that provides patient-level analysis, patient details and daily updates in 12 measures of potential harm (10 prescribing, 2 blood monitoring) selected from PINCER trial measures, and

3. a review of patient records and ordering of blood tests or changing to prescribing (or recommendation to prescriber to change) where appropriate to specifically address the observed potentially hazardous prescribing/blood monitoring.

In the majority of cases the review was undertaken by the clinical pharmacist and only one of these was an independent prescriber so most changes to prescribing were recommended to a GP within the practice.

The effect of the intervention was estimated by linear regression of interrupted series data (log-transformed to adjust for curves resulting from ceiling and floor effects). Pre-intervention trends, step changes and post-intervention trends were used to estimate the expected and observed values and, from these measurements, the effect size at 4, 12, 24 and 52 weeks post-intervention were estimated. These were estimated for the proportion of patients exposed to a composite of the 10 prescribing and separately the 2 blood monitoring measures. Secondary outcomes were the estimated effect size of the intervention for 1. the proportion of patients with long-standing potentially hazardous prescribing/blood monitoring and 2. for the proportion of patients newly exposure to the potentially hazardous prescribing/blood monitoring.

Additional reported analysis were

- the estimated effect size at 4, 12, 24 and 52 weeks was reported for each of the individual indicators.

- the estimated effect size at 52 weeks for each of the two composite measures (prescribing, blood monitoring) was separately analysed then meta-analysed and forest plotted with I2 estimated.

- the rates for the composite potentially hazardous prescribing/blood monitoring (4 weekly intervals for the 2 years prior and 1 year post intervention) are plotted with 95% confidence intervals for all practices.

The outcome was a reduction in the composite measure of potentially hazardous prescribing at 4, 12, 24 and 52 weeks. Reductions in composite measure of blood monitoring were observed at 12, 24 weeks but not at 4 or 52 weeks.

The other reported results show that the intervention had an effect on some but not all potentially hazardous prescribing measures and for neither of the individual blood monitoring measures.

The authors conclude that the intervention has reduced potentially hazardous prescribing which is sustained over time. They highlighted the characteristics of the intervention that are different from others which help to explain why the effect is sustained.

Major issues

The major issue with the evaluation is stating that the intervention resulted in a change when the confidence intervals of the estimated effect size cross zero. For example in the abstract (page 8); results section 2nd sentence is the following statement:

The rate of inadequate blood-test monitoring reduced by 22.0% (95% CI, 0.2% to 50.7%) at six months and by 23.5% (95% CI, -4.5% to 61.6%) at 12 months.

Given the confidence interval of the 12 months figure crosses zero the reduction is not statistically significant (at a 0.05 significance level).

This is repeated in the body of the paper in the Rates of potentially hazardous prescribing and inadequate blood-test monitoring section, on page 15.

The rate of inadequate blood-test monitoring reduced significantly more compared to the extrapolated pre-intervention trend with an absolute difference at four weeks of -2.40 (95% CI, -4.53 to 0.08), twelve weeks -2.594 (95% CI, -4.70 to -0.13), 24 weeks -2.824 (-5.15 to -0.18), and 52 weeks -2.85 (95% CI, -5.68 to 0.71), corresponding to relative reductions of 17.8% (95% CI, 0.5 to 41.4), 19.6% (95% CI, 0.8 to 44.3), 22.0% (95% CI, 0.2 to 50.7%), and 23.5% (95% CI, -4.5 to 61.6) respectively.

Again the confidence intervals for the absolute rates cross zero in the 4 week and 52 week figures so there is no statistically significant reduction (at a 0.05 significance level) for these two times. It would be helpful if the authors could explain how the relative reduction at 4 weeks is statistically significant when the absolute at 4 weeks is not.

In light of these comments the statements in the conclusion would need to be reviewed as there is no statistically significant reduction in the blood monitoring (95% confidence) at 12 months following the implementation of the intervention.

The methods describe describes the practice-level analysis being done at 4, 12, 24 and 52 weeks but only the 52 week results are presented (in the last paragraph of the results section and as the supplementary figures 2 and 3). Related to this, an interpretation of the weaker correlation between pre and post intervention trends in the composite measure of potentially hazardous prescribing compared to the composite measure of blood monitoring would be useful to make it clearer why this statistic is being reported.

In the discussion, there is a statement at the top of page 11 "In other words, our intervention appeared to not only accelerate the pre-intervention trend in practices where it existed but also initiated it where it was absent." No evidence or analysis that showed those practices with higher levels of potentially hazardous prescribing prior to the intervention did not have declining trends leading up to the intervention was provided. Was that analysis done but not provided/reported?

It might be useful to the reader if the authors were able to suggest reasons for the difference in outcome for the difference measures using the results from the qualitative analysis. Understanding why some measures are unchanged by this intervention (e.g. prescribing beta-blockers to patients with a diagnosis of asthma) would help others when they replicate this initiative as it might mean some types of potentially hazardous prescribing require a totally different type of intervention in order to have an effect or might be that an adjustment of this intervention is what is required.

The 12 PINCER indicators were selected based on their prevalence nationally and in Salford; was it low prevalence or high prevalence (or some other criteria) used to select the 12. Readers might benefit from seeing the PINCER measures not selected and the prevalence of all of them.

The paper describing the evaluation of the intervention follows the TREND style. A more structured description of the intervention might help the reader assess whether the intervention could be replicated in their own setting e.g. the template for intervention description and replication (TIDieR). For example, replicating this in other areas it would be useful to know whether the medication reviews were done with or without the patient present.

Minor issues

In Table 1 please provide an explanation for the p-value in the final column - what measures do the p-values relate to?

The results were measured at 4, 12, 24 and 52 weeks but sometimes the 24 week and 52 week results are described as six month and 12 month (and once the 52 weeks is described as one year). It would help if the description of the timeframe of the post-intervention analysis was consistent and accurate.

Results in the text sometimes have 3 significant figures, 2 are used in table 2 and 1 in supplementary table 2. It would help if the used of significant figures was consistent.

In the methods section bottom of page 6 the statement "All outcomes data were collected by interrogating primary care electronic health records" is this a separate extract/database to the SIR database? If it is separate would it be possible to provide additional details sufficient to allow replication by other researchers?

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

Attachment

Submitted filename: PLOS medicine Review TD.docx

Click here for additional data file.^{(15.5KB, docx)}

PLoS Med. 2020 Oct 13;17(10):e1003286. doi: 10.1371/journal.pmed.1003286.r002

Author response to Decision Letter 0

14 Apr 2020

Attachment

Submitted filename: PLOS medicine response letter 20200414 (final).docx

Click here for additional data file.^{(35.9KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1003286.r003

Decision Letter 1

Richard Turner

2 Jul 2020

Dear Dr. Peek,

Thank you very much for re-submitting your manuscript "Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in primary care: an interrupted time series analysis" (PMEDICINE-D-19-04218R1) for consideration at PLOS Medicine.

I have discussed the paper with editorial colleagues and it was also seen again by two reviewers. I am pleased to tell you that, provided the remaining editorial and production issues are dealt with, we expect to be able to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please let me know if you have any questions. Otherwise, we look forward to receiving the revised manuscript shortly.

Sincerely,

Richard Turner, PhD

Senior Editor, PLOS Medicine

rturner@plos.org

------------------------------------------------------------

Requests from Editors:

Please check the Dryad code for data access, which we were unable to access.

Please adapt the title slightly to "... in UK primary care ...". As an alternative, you might wish to add "in Manchester, UK" to the segment of the title before the colon.

Please make that "43" in the abstract.

Please adapt the end of the "Methods and findings" subsection of your abstract so that the final sentence begins "Study limitations include ..." or similar. The quoted limitations could include the fact that practices were not randomized, for example, and the possibility that unmeasured confounders influence your findings.

At the end of p.4 of the PDF, please make that "Our objective was to evaluate".

On p.9 of the PDF, you present various "significant reductions", but we note that not all the point estimates are significant at the 5% level. Please modify the text as appropriate.

Please signpost the discussion of limitations in the main text with "This study had some limitations ..." or similar. As in the abstract, you may wish to note the possible influence of unmeasured confounders on your findings.

On p.12 of the main text, please make that "... it is also possible that some".

Please remove the mention of funding at the end of the main text. This information will appear in the metadata upon publication, via information in the submission form.

We ask you to review the full paper to ensure that the wording does not imply that causality has been established in this research design. For example, at the top of p.11 of the PDF there is the phrase "... no previous studies have evaluated the effects ...", in which "impact" could be substituted. The author summary section also includes similar phrasing, and there is similar wording at the end of p.9 and on p.10 of the PDF ("12-month effect").

Please update reference 38.

The TREND and TIDieR attachments both contain page numbers, which we ask you to remove (as page numbers generally change upon publication). In the checklists, please refer to individual items by section (e.g., "Methods") and paragraph number.

Please refer to the attachments in your methods section (e.g., "See S1_TREND_Checklist").

Comments from Reviewers:

*** Reviewer #1:

This is a statistical review of manuscript PMEDICINE-D-19-04218_R1. The authors have satisfactorily replied to my previous comments. Thank you. I have no further points.

*** Reviewer #3:

Thank you for considering the reviewers comments and agreeing to make changes to the original submitted paper.

I'll limit my feedback to the comments I originally made as reviewer#3 and the changes made.

R3.1. The major issue with the evaluation is stating that the intervention resulted in a change when that change is statistically insignificant. I had thought non-statistically significant changes would not be even mentioned in the abstract or described in words and well as figure. In the abstract the way the following is written it could be misread and readers might think there was a reduction at 12 months where there wasn't - "The rate of inadequate blood-test monitoring (composite of 2 indicators) reduced by 22.0% (95% CI, 0.2% to 50.7%, p=0.046) at 24 weeks and by 23.5% (95% CI, -4.5% to 61.6%, p=0.127) at 12 months." The authors are clearer in the discussion when they write "inadequate blood-test monitoring there was a decrease at 24 weeks, but not so at 12 months."

R3.2. thank you for including all the forest plots

R3.4 thank you for adding some thoughts as to why the response for each safety measure differed

R3.5. thank you for clarifying it was PINCER measures with a higher prevalence included in your feedback

R3.6. thank you for completing the TIDier statement

R3.7. thank you for removing the unnecessary p-values

R3.8. thank you for being consistent when describing the result times. "We agree and now use 4 weeks, 12 weeks, 24 weeks, and 12 months consistently." - it seems a little strange to choose to use a mix of weeks and months

R3.9. thank you for consistently using the same number of significant figures

R3.10. thank you for clarifying the source of the extract used for the analysis

These additional comments relate to changes made to the original paper:

R3.11. in the "What do these findings mean?" section at the start of the paper the authors state

"Electronic, actionable audit and feedback interventions can provide long-term improvements to medication safety in general practice - but only when they are delivered on an ongoing basis."

I am not sure the phrase "but only" can be justified from the findings of this study.

R3.12. In the introduction (top of page 4) the following phrase

"means that medication errors in primary care contribute to more than seven times more avoidable deaths than secondary care (627 versus 85)" has the more in it twice making it difficult to read. Perhaps "means that avoidable deaths in primary care due to medication errors are seven times more likely than in secondary care (627 versus 85)" is clearer.

R3.13. Page 11 in discussion "after an intensive review of patents" should be "after an intensive review of patients"?

R3.14. Page 12 in discussion "The main thread to validity is bias" should be "The main threat to validity is bias"

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Oct 13;17(10):e1003286. doi: 10.1371/journal.pmed.1003286.r004

Author response to Decision Letter 1

12 Aug 2020

Attachment

Submitted filename: 20200709 PLOS medicine response letter (2nd revision).pdf

Click here for additional data file.^{(67.6KB, pdf)}

PLoS Med. doi: 10.1371/journal.pmed.1003286.r005

Decision Letter 2

Richard Turner

8 Sep 2020

Dear Prof. Peek,

On behalf of my colleagues and the academic editor, Dr. Sean MacBride-Stewart, I am delighted to inform you that your manuscript entitled "Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in UK primary care: an interrupted time series analysis" (PMEDICINE-D-19-04218R2) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

PRESS

A selection of our articles each week are press released by the journal. You will be contacted nearer the time if we are press releasing your article in order to approve the content and check the contact information for journalists is correct. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact.

PROFILE INFORMATION

Now that your manuscript has been accepted, please log into EM and update your profile. Go to https://www.editorialmanager.com/pmedicine, log in, and click on the "Update My Information" link at the top of the page. Please update your user information to ensure an efficient production and billing process.

Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Richard Turner, PhD

Senior Editor

PLOS Medicine

plosmedicine.org

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Supplemental figures.

(DOCX)

Click here for additional data file.^{(924.3KB, docx)}

S1 Study Protocol. Protocol for the study.

(PDF)

Click here for additional data file.^{(207.3KB, pdf)}

S1 Tables. Supplemental tables.

(ZIP)

Click here for additional data file.^{(58.3KB, zip)}

S1 TIDieR Checklist. Completed TIDieR Checklist.

(PDF)

Click here for additional data file.^{(83.3KB, pdf)}

S1 TREND Statement. Completed TREND statement.

(PDF)

Click here for additional data file.^{(248.4KB, pdf)}

Attachment

Submitted filename: PLOS medicine Review TD.docx

Click here for additional data file.^{(15.5KB, docx)}

Attachment

Submitted filename: PLOS medicine response letter 20200414 (final).docx

Click here for additional data file.^{(35.9KB, docx)}

Attachment

Submitted filename: 20200709 PLOS medicine response letter (2nd revision).pdf

Click here for additional data file.^{(67.6KB, pdf)}

Data Availability Statement

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PERMALINK

Evaluation of a pharmacist-led actionable audit and feedback intervention for improving medication safety in UK primary care: An interrupted time series analysis

Niels Peek

Wouter T Gude

Richard N Keers

Richard Williams

Evangelos Kontopantelis

Mark Jeffries

Denham L Phipps

Benjamin Brown

Anthony J Avery

Darren M Ashcroft

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Study design

Safety indicators

Participants and recruitment

Intervention

Outcome measures

Statistical analysis

Ethical approval

Patient and public involvement

Results

Participating practices

Table 1. Baseline characteristics of the included patients and participating practices, according to their start date.

Rates of potentially hazardous prescribing and inadequate blood-test monitoring

Fig 1. Observed rates of patients exposed to medication safety hazards in 43 participating practices in Salford.

Table 2. Effect of the SMASH intervention on primary and secondary outcomes relating to prescribing and monitoring safety.

Discussion

Strengths and limitations of this study

Comparison with other studies

Policy implications

Conclusion

Supporting information

Acknowledgments

Abbreviations:

Data Availability

Funding Statement

References

Decision Letter 0

Caitlin Moyer

Roles

Author response to Decision Letter 0

Decision Letter 1

Richard Turner

Roles

Author response to Decision Letter 1

Decision Letter 2

Richard Turner

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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