An Electronic Clinical Decision Support System for the Management of Low Back Pain in Community Pharmacy: Development and Mixed Methods Feasibility Study

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Abstract

Background

People with low back pain (LBP) in the community often do not receive evidence-based advice and management. Community pharmacists can play an important role in supporting people with LBP as pharmacists are easily accessible to provide first-line care. However, previous research suggests that pharmacists may not consistently deliver advice that is concordant with guideline recommendations and may demonstrate difficulty determining which patients require prompt medical review. A clinical decision support system (CDSS) may enhance first-line care of LBP, but none exists to support the community pharmacist–client consultation.

Objective

This study aimed to develop a CDSS to guide first-line care of LBP in the community pharmacy setting and to evaluate the pharmacist-reported usability and acceptance of the prototype system.

Methods

A cross-platform Web app for the Apple iPad was developed in conjunction with academic and clinical experts using an iterative user-centered design process during interface design, clinical reasoning, program development, and evaluation. The CDSS was evaluated via one-to-one user-testing with 5 community pharmacists (5 case vignettes each). Data were collected via video recording, screen capture, survey instrument (system usability scale), and direct observation.

Results

Pharmacists’ agreement with CDSS-generated self-care recommendations was 90% (18/20), with medicines recommendations was 100% (25/25), and with referral advice was 88% (22/25; total 70 recommendations). Pharmacists expressed uncertainty when screening for serious pathology in 40% (10/25) of cases. Pharmacists requested more direction from the CDSS in relation to automated prompts for user input and page navigation. Overall system usability was rated as excellent (mean score 92/100, SD 6.5; 90th percentile compared with similar systems), with acceptance rated as good to excellent.

Conclusions

A novel CDSS (high-fidelity prototype) to enhance pharmacist care of LBP was developed, underpinned by clinical practice guidelines and informed by a multidisciplinary team of experts. User-testing revealed a high level of usability and acceptance of the prototype system, with suggestions to improve interface prompts and information delivery. The small study sample limits the generalizability of the findings but offers important insights to inform the next stage of system development.

Related collections

Most cited references 53

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Non-specific low back pain.

Rachelle Buchbinder, Martin Underwood, Chris Maher (2017)

Non-specific low back pain affects people of all ages and is a leading contributor to disease burden worldwide. Management guidelines endorse triage to identify the rare cases of low back pain that are caused by medically serious pathology, and so require diagnostic work-up or specialist referral, or both. Because non-specific low back pain does not have a known pathoanatomical cause, treatment focuses on reducing pain and its consequences. Management consists of education and reassurance, analgesic medicines, non-pharmacological therapies, and timely review. The clinical course of low back pain is often favourable, thus many patients require little if any formal medical care. Two treatment strategies are currently used, a stepped approach beginning with more simple care that is progressed if the patient does not respond, and the use of simple risk prediction methods to individualise the amount and type of care provided. The overuse of imaging, opioids, and surgery remains a widespread problem.

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Effect of clinical decision-support systems: a systematic review.

Tiffani Bright, Anthony Wong, Ravi Dhurjati … (2012)

Despite increasing emphasis on the role of clinical decision-support systems (CDSSs) for improving care and reducing costs, evidence to support widespread use is lacking. To evaluate the effect of CDSSs on clinical outcomes, health care processes, workload and efficiency, patient satisfaction, cost, and provider use and implementation. MEDLINE, CINAHL, PsycINFO, and Web of Science through January 2011. Investigators independently screened reports to identify randomized trials published in English of electronic CDSSs that were implemented in clinical settings; used by providers to aid decision making at the point of care; and reported clinical, health care process, workload, relationship-centered, economic, or provider use outcomes. Investigators extracted data about study design, participant characteristics, interventions, outcomes, and quality. 148 randomized, controlled trials were included. A total of 128 (86%) assessed health care process measures, 29 (20%) assessed clinical outcomes, and 22 (15%) measured costs. Both commercially and locally developed CDSSs improved health care process measures related to performing preventive services (n= 25; odds ratio [OR], 1.42 [95% CI, 1.27 to 1.58]), ordering clinical studies (n= 20; OR, 1.72 [CI, 1.47 to 2.00]), and prescribing therapies (n= 46; OR, 1.57 [CI, 1.35 to 1.82]). Few studies measured potential unintended consequences or adverse effects. Studies were heterogeneous in interventions, populations, settings, and outcomes. Publication bias and selective reporting cannot be excluded. Both commercially and locally developed CDSSs are effective at improving health care process measures across diverse settings, but evidence for clinical, economic, workload, and efficiency outcomes remains sparse. This review expands knowledge in the field by demonstrating the benefits of CDSSs outside of experienced academic centers. Agency for Healthcare Research and Quality.

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Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality.

David Bates, Gilad Kuperman, Samuel Wang … (2003)

While evidence-based medicine has increasingly broad-based support in health care, it remains difficult to get physicians to actually practice it. Across most domains in medicine, practice has lagged behind knowledge by at least several years. The authors believe that the key tools for closing this gap will be information systems that provide decision support to users at the time they make decisions, which should result in improved quality of care. Furthermore, providers make many errors, and clinical decision support can be useful for finding and preventing such errors. Over the last eight years the authors have implemented and studied the impact of decision support across a broad array of domains and have found a number of common elements important to success. The goal of this report is to discuss these lessons learned in the interest of informing the efforts of others working to make the practice of evidence-based medicine a reality.

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Author and article information

Contributors

Aron Simon Downie:

ORCID: https://orcid.org/0000-0002-9888-3854

Institute for Musculoskeletal HealthSydney School of Public Health, Faculty of Medicine and HealthThe University of SydneyLevel 10 KGV BuildingMissenden RoadCamperdown, 2050Australia61 2 9850 6382aron.downie@sydney.edu.au

Journal

Journal ID (nlm-ta): JMIR Med Inform

Journal ID (iso-abbrev): JMIR Med Inform

Journal ID (publisher-id): JMI

Title: JMIR Medical Informatics

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-9694

Publication date Collection: May 2020

Publication date (Electronic): 11 May 2020

Volume: 8

Issue: 5

Electronic Location Identifier: e17203

Affiliations

[1 ] Institute for Musculoskeletal Health Sydney School of Public Health, Faculty of Medicine and Health The University of Sydney Camperdown Australia

[2 ] Faculty of Science and Engineering Macquarie University Macquarie Park Australia

[3 ] Faculty of Medicine, Health and Human Sciences Macquarie University Macquarie Park Australia

[4 ] Sydney Pharmacy School, Faculty of Medicine and Health University of Sydney Sydney Australia

[5 ] Centre for Health Informatics, Australian Institute of Health Innovation Faculty of Medicine and Health Sciences Macquarie University Macquarie Park Australia

[6 ] Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

[7 ] Hunter New England Population Health Hunter New England Local Health District Newcastle Australia

[8 ] Institute for Evidence-Based Healthcare, Faculty of Health Sciences and Medicine Bond University Gold Coast Australia

Author notes

Corresponding Author: Aron Simon Downie aron.downie@ 123456sydney.edu.au

Author information

Aron Simon Downie https://orcid.org/0000-0002-9888-3854

Mark Hancock https://orcid.org/0000-0002-9277-5377

Christina Abdel Shaheed https://orcid.org/0000-0003-1258-5125

Andrew J McLachlan https://orcid.org/0000-0003-4674-0242

Ahmet Baki Kocaballi https://orcid.org/0000-0002-8328-5317

Christopher M Williams https://orcid.org/0000-0001-8896-0978

Zoe A Michaleff https://orcid.org/0000-0002-0360-4956

Chris G Maher https://orcid.org/0000-0002-1628-7857

Article

Publisher ID: v8i5e17203

DOI: 10.2196/17203

PMC ID: 7248808

PubMed ID: 32390593

SO-VID: 9d260393-a3b4-4cb6-b5e3-7d414e57b325

Copyright © ©Aron Simon Downie, Mark Hancock, Christina Abdel Shaheed, Andrew J McLachlan, Ahmet Baki Kocaballi, Christopher M Williams, Zoe A Michaleff, Chris G Maher. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 11.05.2020.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 26 November 2019

Date revision requested : 5 January 2020

Date revision received : 6 February 2020

Date accepted : 6 February 2020

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