Journal of Clinical Pharmacy and Therapeutics, 2014, 39, 343–348

doi: 10.1111/jcpt.12168

Review Article

Review of computerized clinical decision support in community pharmacy C. Curtain*† BPharm (Hons) GradDipComp MPS and G. M. Peterson*† BPharm (Hons) PhD MBA FSHP FACP MPS AACPA *Pharmacy, School of Medicine, University of Tasmania, Hobart, TAS, and †Unit for Medication Outcomes Research and Education, University of Tasmania, Hobart, TAS, Australia

Received 15 August 2011, Accepted 3 April 2014

Keywords: community pharmacy, computerized decision support systems, medication, pharmacists, prescribing

SUMMARY

WHAT IS KNOWN AND OBJECTIVE

What is known and objective: Clinical decision support software (CDSS) has been increasingly implemented to assist improved prescribing practice. Reviews and studies report generally positive results regarding prescribing changes and, to a lesser extent, patient outcomes. Little information is available, however, concerning the use of CDSS in community pharmacy practice. Given the apparent paucity of publications examining this topic, we conducted a review to determine whether CDSS in community pharmacy practice can improve medication use and patient outcomes. Methods: A literature search of articles on CDSS relevant to community pharmacy and published between 1 January 2005 and 21 October 2013 was undertaken. Articles were included if the healthcare setting was community pharmacy and the article indicated that pharmacy use of CDSS was part of the study intervention. Results and discussion: Eight studies were found which assessed counselling, selected drug interactions, inappropriate prescribing and under-prescribing. One study was halted due to insufficient data collection. Six studies showed statistically significant improvements in the measured outcomes: increased patient counselling, 31% reduced frequency of drug–drug interactions (DDIs), reduced frequency of inappropriate medications in the elderly (2
2–1
8% patients) and in pregnant women (5
5–2
9% patients), and increased pharmacists’ interventions for under-prescribed low-dose aspirin (1
74 vs. 0
91 per 100 patients with type 2 diabetes) and over-prescribed high-dose protonpump inhibitors (PPIs) (1
67 vs. 0
17 interventions per 100 highdose PPI prescriptions). What is new and conclusion: Most studies showed improved prescribing practice, via direct communication between pharmacists and doctors or indirectly via patient education. Factors limiting the impact of improved prescribing included alert fatigue and clinical inertia. No study investigated patient outcomes and little investigation had been undertaken on how CDSS could be best implemented. Few studies have been undertaken in community pharmacy practice, and based on the positive findings reported, further research should be directed in this area, including investigation of patient outcomes.

Healthcare practitioners are inundated with numerous and complex medical guidelines, as well as the copious output of evidencebased research.1 The use of computer technology can assist with timely knowledge acquisition through the use of online databases and medical guideline software.2 Clinical decision support software (CDSS), concisely defined by Musen et al. as ‘. . .any computer program designed to help healthcare professionals to make clinical decisions’,3 has been recommended to improve the quality use of medication.2 CDSS includes a range of tools such as passive electronic reference or information management software, reminders and alerts, data-entry workflow and patient-tailored recommendations.3,4 Typically, community pharmacists employ computerized dispensary software to record patient and medication details. Dispensing prescriptions by entering medication information against a patient record provides the opportunity to incorporate computerized checks and alerts. An advantage of CDSS in this environment is drawing attention to best practice, or warning of potential problems at the time of dispensing medication, through the use of prompts, such as reminders and alerts. The advantage for the patient may be more efficacious and safer drug therapy, including reduced likelihood of drug–drug and drug–disease interactions, incorrect dosing or allergic reactions. Reviews across healthcare sectors have identified improved prescriber practice from their use of CDSS.5–10 Yet, evidence of improved patient outcomes has been limited.5,8,9 Reviewers have also identified the limited effectiveness of CDSS, including a relatively small impact on measured outcomes and lack of uptake by healthcare professionals.11,12 Criticisms of CDSS have included alert fatigue and, at least for commercial software, low relevance and specificity of alerts.13–16 There has been limited examination of CDSS in pharmacy practice.8,17 Calabretto et al. showed an increase in prescriptionrelated interventions by pharmacists,17 and Robertson et al. found CDSS generally improved medication safety, through a reduction in the frequency of drug interactions and improved drug dosing.8 Our aim was to review the recent literature on the use of CDSS in community pharmacy practice. METHODS A review of the use of CDSS in community pharmacy practice was published in 2005;17 therefore, the date range for this review was narrowed to articles published from 1 January 2005 to 21 October 2013. All searches were conducted through PubMed. The

Correspondence: C. Curtain, Pharmacy, School of Medicine, University of Tasmania, Private Bag 26, Hobart, TAS 7001, Australia. Tel.: +61 3 6226 1096; fax: +61 3 6226 2870; e-mail: [email protected]

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C. Curtain and G. M. Peterson

Review of pharmacy computerized decision support following terms were used: (‘2005’ [Publication Date]: ‘3000’ [Publication Date]) AND Decision Support Systems, Management [MeSH Terms]; (‘2005’ [Publication Date]: ‘3000’ [Publication Date]) AND Decision Support Techniques [MeSH Terms] and pharmacy; (‘2005’ [Publication Date]: ‘3000’ [Publication Date]) AND Decision Support Systems, Clinical [MeSH Terms] and pharmacy; (‘2005’ [Publication Date]: ‘3000’ [Publication Date]) AND Clinical Pharmacy Information Systems [MeSH Terms]; ((‘2005’ [Publication Date]: ‘3000’ [Publication Date]) AND decision making, computer-assisted [MeSH Terms]) AND pharmacy; (‘2005’ [Publication Date]: ‘3000’ [Publication Date]) AND pharmacy and decision support. Articles were included if they were written in English, the healthcare setting was community or outpatient pharmacy, and the article indicated that the use of CDSS was part of the study. Articles were excluded if they concerned reviews of other studies or if the study did not assess the effect of CDSS within the community pharmacy setting.

account for individual patient characteristics, nor did they limit presentation to once per patient. However, another study of inappropriate medication prescribing in the elderly did not show a decline of effect during the study period.23 The alerts in this study were implemented on newly prescribed rather than repeat prescriptions of inappropriate medications. Mitigation of alert fatigue was attempted by Humphries et al. through restricting alerts to eight clinically important DDIs displayed once per patient.21 However, pharmacists still found the majority of DDIs identified (81%) were clinically irrelevant. Insufficient patient context did not allow for refinement of the alerts. An investigation of pharmacy software with alerts warning of high-risk medications in pregnancy required pharmacists to consult with physicians prior to dispensing category D or X medications.19 The investigation was halted early despite showing a positive effect on reduced inappropriate prescribing. Forty per cent of software alerts, which could not be deactivated, were for medications to be used with caution in pregnancy (risk category C). Alerts also occurred when women were not pregnant, due to the pregnancy status not being updated, highlighting the importance of having accurate and current information available for reliable decision support. Several studies found only modest improvements in effect, in part moderated by resistance to change. Reeve et al.22 noted that 10 of the 31 pharmacies allocated to the decision support arm of their trial did not record any clinical interventions. Raebel et al.23 noted the decision support intervention addressing appropriate prescribing in the elderly produced only a modest improvement and suggested that improvement in prescriber behaviour remained a challenge.

RESULTS The search terms resulted in 2736 articles. Of these, eight met the inclusion criteria (Table 1). These studies were performed in the community pharmacy setting, and most were aimed at improved prescribing practice; one study was terminated through insufficient data collection.18 Furthermore, a study by Raebel et al.19 was halted due to limitations involving identification of targeted medications and patients (contraindicated medications in pregnancy). No studies investigated patient outcomes arising from the decision support intervention. Most studies19–24 produced a statistically significant positive effect of CDSS on pharmacist activity, exhibited as improved counselling,24 improved prescribing and better direct collaboration with prescribers19,21,23 or, indirectly, via patient education.20,22 Pharmacist counselling for assessment and treatment for allergic rhinitis was improved through the use of a Web-based CDSS, with a median seven of nine mandatory guideline questions asked compared with two of nine (P < 0
001).24 Pharmacist–patient interventions were a reduction in chronic high-dose proton-pump inhibitors (PPIs) (1
67 active vs. 0
17 control interventions per 100 high-dose PPI prescriptions)20 and introduction of an antiplatelet agent for prevention of stroke in diabetic patients22 (1
74 active vs. 0
91 control interventions per 100 patients, P < 0
001). Pharmacist– prescriber interventions were a reduction in inappropriate medication prescribing in the elderly23 (2
2–1
8% patients, P = 0
002) and in pregnant women19 (5
5–2
9% patients, P < 0
001), improved prescribing of antimicrobials18 (no quantitative results provided) and a reduction in prescribed drug–drug interactions (DDIs) (21
3– 14
7 per 10 000 prescriptions, P = 0
0125).21 One study surprisingly showed that increased dispensing of DDIs correlated with provision of more comprehensive computerized information (P = 0
008).25 Pharmacists had a direct and positive effect on prescriber practice in several studies.18,19,21,23 Pharmacists also improved prescribing indirectly through educating and empowering patients with evidence-based verbal and written information.20,22 Patient empowerment concerning prescribed high-dose PPIs was shown to result in a change of therapy in the majority of surveyed patients who consulted their prescriber.20 Pharmacy software alerts promoting discussion with patients concerning aspirin use in diabetes and reduction in high-dose PPI therapy declined in effect over time.20,22 These alerts did not

DISCUSSION Very few CDSS studies have been undertaken in community pharmacy practice; however, the majority of these studies have shown positive results. Assisted patient questioning with minimal impact on counselling time may improve medication selection or the requirement for patient referral.24 Resistance to change was noted in several trials22,23 and is a known barrier to change in community pharmacy practice.26 A report identified pharmacists’ barriers to clinical services as entrenched work practices: focus on dispensing, low consumer contact and lack of time.27 Workload and, by inference, lack of time was identified as a barrier by Malone et al.25 An interesting finding was that the provision of more DDI information via CDSS to pharmacists resulted in more DDI prescriptions being dispensed.25 The comprehensive information provided may have allowed pharmacists to exercise greater judgement as to the relevance of each DDI in the context of each patient. This reflects findings of studies which have shown the deliberate overriding of computerized alerts due to the lack of clinical relevance.14,28,29 The main limitation of CDSS in community pharmacy is the generally narrow set of information available for contextualized alerts and reminders based on relevant patient circumstances. It is essential in providing a quality pharmacy service to have access to a wide source of patient information in order to exercise the best professional judgement, whether assisted by computer software or not. The incorporation of extra patient information beyond what is presented by a prescription or prescription history is essential to minimize the effect of alert fatigue by increasing the opportunity to

© 2014 John Wiley & Sons Ltd

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345

Community pharmacy, USA (n = 2)

Madaras-Kelly et al. (2006)18 Cohort trial

Community pharmacy, Australia, (n = 185)

Curtain et al. (2011)20 Randomized control trial

Health maintenance organization outpatient pharmacies, USA

Community pharmacy, Germany

Bertsche et al. (2011)24

Humphries et al. (2007)21 Interrupted time series

Setting

Study

Table 1. Summary of reviewed studies

Patients with prescription for broad-spectrum antibiotic (control n = 133, intervention n = 59). Patients in whom prescriber was contacted (n = 4)

Outpatient pharmacists Patients

Community pharmacists Patients with prescriptions for pantoprazole 40 mg and esomeprazole 40 mg

Community pharmacists

Participants

Patient interview followed by CDSS-assisted pharmacist diagnosis concerning appropriateness of the antibiotic. Pharmacist to contact physician where discrepancies occurred between prescribed and CDSS recommended antibiotic

DDI alert for 8 important drug interactions. Alert included therapy resolution advice. Prescription not dispensed until prescriber consulted

Pharmacies allocated to automated dispensing system prompt. Prompt activated on entering prescriptions of 40 mg esomeprazole or 40 mg pantoprazole. Prompt advised pharmacists to discuss the need for continued therapy with high-dose proton-pump inhibitors

Web-based allergic rhino-conjunctivitis CDSS to support guideline questioning of patient self-medication

Intervention

Control phase and intervention phase

No alert. Usual care

No prompt, usual care

No CDSS. Usual counselling

Comparison

Insufficient data collected due to detailed consent process. Pharmacists were able to obtain patient disease information. Physicians were receptive to pharmacist intervention.

Number of dispensed drug i nteractions

Pharmacist record of intervention Patient medication history, change of therapy Intervention arm, patient survey responses

Number of nine mandatory guideline self-medication questions asked

Outcome measures

(continued)

Intervention: median 7 of 9 questions asked vs. control: median 2 of 9 questions asked, (P < 0
001) Intervention: 282 per 16 924 prescriptions, control 48 per 27 467, (P < 0
001) Therapy change: 6 control, 28 intervention Intervention arm 76 patient survey responses. 19 intended to consult GPs. 48 GP consultations, therapy change in 31 cases Reduced dispensing of interacting drugs for 3 of 8 drug interactions. Trend to reduced dispensing for 2 drug interactions. Overall reduction from 21
3 events to 14
7 events per 10 000 None

Result

Review of pharmacy computerized decision support C. Curtain and G. M. Peterson

Journal of Clinical Pharmacy and Therapeutics, 2014, 39, 343–348

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346 Community pharmacists Patients prescribed oral diabetic medication

Pharmacists Pregnant women (n = 11 100)

Pharmacists Patients from 65 years of age and older (n = 59 680)

Community pharmacists Prescription claims

Participants

CDSS, clinical decision support software; DDI, drug–drug interaction.

Community pharmacy, Australia (n = 52)

Reeve et al. (2007)22 Randomized control trial

Health maintenance organization, USA. Medical offices (n = 18), pharmacies (n = 21)

Raebel et al. (2007)23 Randomized control trial

Health maintenance organization, USA

Community pharmacy, USA (n = 672)

Malone et al. (2007)25 Cross-sectional study

Raebel et al. (2007)19 Randomized control trial

Setting

Study

Table 1 (continued)

Interventions for aspirin use in diabetic patients aided by decision support

Alert activated for category D or X medication in pregnant patient

Computerized alerts activated for newly prescribed selected sedative or anticholinergic medications

Customization of alerts between pharmacies for DDIs Amount of information provided by decision support for DDIs

Intervention

Interventions for aspirin use in diabetic patients without decision support

No alert. Usual care

No alert. Usual care

Customization of alerts between pharmacies for DDIs Amount of information provided by decision support for DDIs

Comparison

Rate of specific interventions recorded in diabetic patients. Rate of interventions per patient overall

Proportion of pregnant women dispensed FDA category D or X medications. Total number of first dispensing of specific medications

25 specific DDIs from 3 months of prescription claims data, pharmacist survey and factors affecting the dispensing of these DDIs Rates of dispensed DDIs between pharmacies Rate: DDIs per prescriptions Customization of DDI alert and provision of DDI information on rate of dispensed DDIs Number of prescriptions of selected medications dispensed for patients 65 years of age and older

Outcome measures

Significant reduction of prescribing of targeted medications from 2
2–1
8% patients, P = 0
002. Amitriptyline (0
59–0
37 per 100 patients, P < 0
001) and diazepam (0
71–0
56 per 100 patients, P = 0
02) 2
9% intervention patients compared with 5
5% of control patients dispensed specific medications, P < 0
001. 238 D or X medications prescribed in intervention arm, 361 D or X medications prescribed in control arm, P = 0
03 201 interventions exclusive to decision support arm at a rate of 4
82 interventions per 100 diabetic patients. Overall rate 1
74 interventions per 100 patients prompt arm compared with 0
91 control arm (P < 0
001)

No difference for customizable alerts (P = 0
68) Difference with computer provision of information (P = 0
008), Provision of more information associated with more DDIs dispensed

Result

Review of pharmacy computerized decision support C. Curtain and G. M. Peterson

Journal of Clinical Pharmacy and Therapeutics, 2014, 39, 343–348

C. Curtain and G. M. Peterson

Review of pharmacy computerized decision support

contextualize and selectively deliver CDSS alerts. Two studies utilized more patient information than is often available based on presented prescriptions and prescription history.19,23 Incorporation of patient age for appropriate prescribing in the elderly23 allowed the development of an alert with suitable patient sensitivity. Similarly, the incorporation of pregnancy status was utilized for the application of a context-sensitive alert for pregnant women.19 Two trials noted the decline of effect of the CDSS over time.20,22 Although alert fatigue is likely to be an implicit cause, this may have been compounded by another factor. The patient population who were recipients of advice from pharmacists may be expected to be frequently attending patients at trial pharmacies. Over time, the population of patients who had not received the pharmacist intervention would substantially decline, reducing the opportunity for intervention. No pharmacy studies obtained clinical outcome data, subsequent to CDSS intervention. Many CDSS studies in hospital and general practice have also obtained little information concerning patients’ outcomes.10

WHAT IS NEW AND CONCLUSION Community pharmacy CDSS research has explored self-medication counselling and the appropriateness of prescribed medication or DDIs, which required pharmacist collaboration with patients or prescribers for initiation of therapeutic change. No investigations into clinical outcomes subsequent to prompted interventions have been undertaken. Further study examining the influence of community pharmacists’ recommendations, following CDSS, on the actions of patients and physicians should be undertaken to assist in improving future implementations of CDSS in pharmacy. Also, although some authors discussed the alert fatigue effect in their studies and one study attempted to mitigate this effect, little investigation has been undertaken into how to maximize the effectiveness and specificity of CDSS alerts in community pharmacy practice. As the practice of community pharmacy expands into greater health services, the potential uses of CDSS may broaden, particularly in the light of the mostly positive results identified through this review.

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