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Published in: Systematic Reviews 1/2021

Open Access 01-12-2021 | Protocol

Effectiveness and harms of clinical decision support systems for referral within chronic pain practice: protocol for a systematic review and meta-analysis

Authors: Hervé Tchala Vignon Zomahoun, Regina Visca, Nicole George, Sara Ahmed

Published in: Systematic Reviews | Issue 1/2021

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Abstract

Background

Chronic pain is a common public health problem with negative consequences for individuals and societies. Fortunately, interdisciplinary chronic pain management has been shown to be effective for improving patients’ outcomes and strongly recommended in clinical practice guidelines. Appropriate referral within the healthcare system based on individuals’ needs and available services is essential to optimise health-related outcomes and maximise resources. Clinical decision support systems have been shown to be effective for supporting healthcare professionals in different practices. However, there is no knowledge synthesis on clinical decision support systems for referral within chronic pain practice. We aim to identify the clinical decision support systems for referral within chronic pain practices and assess their content, effectiveness, harms, and validation parameters.

Methods

Using the methodology of Cochrane reviews, we will perform a systematic review and meta-analysis based on studies meeting the following criteria: Population, patients with chronic pain and/or healthcare professionals working in chronic pain; Intervention, clinical decision support systems for referral within chronic pain practice; Comparison, any other clinical tool, any usual care or practices; Outcomes, clinical outcomes of patients measuring how patients feel, function or survive including benefits, adverse effects, continuity of care, care appropriateness, care satisfaction, quality of life, healthcare professional performance, and cost outcomes; and Study design: randomized controlled trials, non-randomized controlled trials, before and after controlled studies and interrupted time series. We will search relevant literature with the support of an information specialist using Medline, Embase, PsycInfo, CINHAL, Web of Science and Cochrane Library from their inception onwards. Two reviewers will independently complete study selection, data extraction and risk of bias assessment. We will analyse data to perform both narrative syntheses and meta-analysis if appropriate.

Discussion

Findings of this review will contribute to enhancing chronic pain care and research. Clinical decision support systems identified as effective in this review can be investigated for implementation in clinical practice and impact on improving patient, clinical and health system outcomes. Clinical decision support systems not yet ready for implementation that require further improvement will also be identified.

Systematic review registration

PROSPERO registration: CRD42020158880.
Appendix
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Literature
1.
go back to reference Raja SN, Carr DB, Cohen M, Finnerup NB, Flor H, Gibson S, Keefe FJ, Mogil JS, Ringkamp M, Sluka KA, Song XJ, Stevens B, Sullivan MD, Tutelman PR, Ushida T, Vader K. The revised International Association for the Study of Pain definition of pain: concepts, challenges, and compromises. Pain. 2020;161(9):1976–82. https://doi.org/10.1097/j.pain.0000000000001939. Raja SN, Carr DB, Cohen M, Finnerup NB, Flor H, Gibson S, Keefe FJ, Mogil JS, Ringkamp M, Sluka KA, Song XJ, Stevens B, Sullivan MD, Tutelman PR, Ushida T, Vader K. The revised International Association for the Study of Pain definition of pain: concepts, challenges, and compromises. Pain. 2020;161(9):1976–82. https://​doi.​org/​10.​1097/​j.​pain.​0000000000001939​.
2.
go back to reference Steingrimsdottir OA, Landmark T, Macfarlane GJ, Nielsen CS. Defining chronic pain in epidemiological studies: a systematic review and meta-analysis. Pain. 2017;158(11):2092–107.CrossRef Steingrimsdottir OA, Landmark T, Macfarlane GJ, Nielsen CS. Defining chronic pain in epidemiological studies: a systematic review and meta-analysis. Pain. 2017;158(11):2092–107.CrossRef
3.
go back to reference Jackson T, Thomas S, Stabile V, Shotwell M, Han X, McQueen K. A systematic review and meta-analysis of the global burden of chronic pain without clear etiology in low- and middle-income countries: trends in heterogeneous data and a proposal for new assessment methods. Anesth Analg. 2016;123(3):739–48.CrossRef Jackson T, Thomas S, Stabile V, Shotwell M, Han X, McQueen K. A systematic review and meta-analysis of the global burden of chronic pain without clear etiology in low- and middle-income countries: trends in heterogeneous data and a proposal for new assessment methods. Anesth Analg. 2016;123(3):739–48.CrossRef
4.
go back to reference Mills SEE, Nicolson KP, Smith BH. Chronic pain: a review of its epidemiology and associated factors in population-based studies. Br J Anaesth. 2019;123(2):e273–e83.CrossRef Mills SEE, Nicolson KP, Smith BH. Chronic pain: a review of its epidemiology and associated factors in population-based studies. Br J Anaesth. 2019;123(2):e273–e83.CrossRef
5.
go back to reference Vos TAC, Arora M, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211–59.CrossRef Vos TAC, Arora M, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390(10100):1211–59.CrossRef
6.
go back to reference Fayaz A, Ayis S, Panesar SS, Langford RM, Donaldson LJ. Assessing the relationship between chronic pain and cardiovascular disease: a systematic review and meta-analysis. Scand J Pain. 2016;13:76–90.CrossRef Fayaz A, Ayis S, Panesar SS, Langford RM, Donaldson LJ. Assessing the relationship between chronic pain and cardiovascular disease: a systematic review and meta-analysis. Scand J Pain. 2016;13:76–90.CrossRef
7.
go back to reference Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J, et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976). 2009;34(10):1066–77.CrossRef Chou R, Loeser JD, Owens DK, Rosenquist RW, Atlas SJ, Baisden J, et al. Interventional therapies, surgery, and interdisciplinary rehabilitation for low back pain: an evidence-based clinical practice guideline from the American Pain Society. Spine (Phila Pa 1976). 2009;34(10):1066–77.CrossRef
8.
go back to reference Oslund S, Robinson RC, Clark TC, Garofalo JP, Behnk P, Walker B, et al. Long-term effectiveness of a comprehensive pain management program: strengthening the case for interdisciplinary care. Proc (Bayl Univ Med Cent). 2009;22(3):211–4.CrossRef Oslund S, Robinson RC, Clark TC, Garofalo JP, Behnk P, Walker B, et al. Long-term effectiveness of a comprehensive pain management program: strengthening the case for interdisciplinary care. Proc (Bayl Univ Med Cent). 2009;22(3):211–4.CrossRef
9.
go back to reference Hylands-White N, Duarte RV, Raphael JH. An overview of treatment approaches for chronic pain management. Rheumatol Int. 2017;37(1):29–42.CrossRef Hylands-White N, Duarte RV, Raphael JH. An overview of treatment approaches for chronic pain management. Rheumatol Int. 2017;37(1):29–42.CrossRef
10.
go back to reference Shortliffe EH. Computer programs to support clinical decision making. JAMA. 1987;258(1):61–6.CrossRef Shortliffe EH. Computer programs to support clinical decision making. JAMA. 1987;258(1):61–6.CrossRef
11.
go back to reference Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001;8(6):527–34.CrossRef Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H, et al. Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc. 2001;8(6):527–34.CrossRef
12.
go back to reference Wright A, Sittig DF, Ash JS, Feblowitz J, Meltzer S, McMullen C, et al. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc. 2011;18(3):232–42.CrossRef Wright A, Sittig DF, Ash JS, Feblowitz J, Meltzer S, McMullen C, et al. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc. 2011;18(3):232–42.CrossRef
13.
go back to reference Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly. 2014;144:w14073.PubMed Beeler PE, Bates DW, Hug BL. Clinical decision support systems. Swiss Med Wkly. 2014;144:w14073.PubMed
14.
go back to reference Evans RS, Kfoury AG, Horne BD, Lloyd JF, Benuzillo J, Rasmusson KD, et al. Clinical decision support to efficiently identify patients eligible for advanced heart failure therapies. J Card Fail. 2017;23(10):719–26.CrossRef Evans RS, Kfoury AG, Horne BD, Lloyd JF, Benuzillo J, Rasmusson KD, et al. Clinical decision support to efficiently identify patients eligible for advanced heart failure therapies. J Card Fail. 2017;23(10):719–26.CrossRef
15.
go back to reference Jenkins HJ, Hancock MJ, French SD, Maher CG, Engel RM, Magnussen JS. Effectiveness of interventions designed to reduce the use of imaging for low-back pain: a systematic review. CMAJ. 2015;187(6):401–8.CrossRef Jenkins HJ, Hancock MJ, French SD, Maher CG, Engel RM, Magnussen JS. Effectiveness of interventions designed to reduce the use of imaging for low-back pain: a systematic review. CMAJ. 2015;187(6):401–8.CrossRef
16.
go back to reference Hervé Tchala Vignon Zomahoun, Sara Ahmed, Regina Visca, George N. Effectiveness of decision supports for referral within chronic pain practice: a systematic review and meta-analysis. PROSPERO. 2020;CRD42020158880:1-4. Hervé Tchala Vignon Zomahoun, Sara Ahmed, Regina Visca, George N. Effectiveness of decision supports for referral within chronic pain practice: a systematic review and meta-analysis. PROSPERO. 2020;CRD42020158880:1-4.
17.
go back to reference Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1.CrossRef Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1.CrossRef
18.
go back to reference Higgins JPT TJ, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019) 2019. Available from: Available from www.training.cochrane.org/handbook. [cited 2019 10/09] Higgins JPT TJ, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.0 (updated July 2019) 2019. Available from: Available from www.​training.​cochrane.​org/​handbook. [cited 2019 10/09]
21.
go back to reference McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement. J Clin Epidemiol. 2016;75:40–6.CrossRef McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement. J Clin Epidemiol. 2016;75:40–6.CrossRef
22.
go back to reference Landis JR, Koch GG. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics. 1977;33(2):363–74.CrossRef Landis JR, Koch GG. An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics. 1977;33(2):363–74.CrossRef
23.
go back to reference Sterne JAC SJ, Page MJ, Elbers RG, Blencowe NS, Boutron I, Cates CJ, Cheng H-Y, Corbett MS, Eldridge SM, Hernán MA, Hopewell S, Hróbjartsson A, Junqueira DR, Jüni P, Kirkham JJ, Lasserson T, Li T, McAleenan A, Reeves BC, Shepperd S, Shrier I, Stewart LA, Tilling K, White IR, Whiting PF, Higgins JPT. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ (in press). 2019. Sterne JAC SJ, Page MJ, Elbers RG, Blencowe NS, Boutron I, Cates CJ, Cheng H-Y, Corbett MS, Eldridge SM, Hernán MA, Hopewell S, Hróbjartsson A, Junqueira DR, Jüni P, Kirkham JJ, Lasserson T, Li T, McAleenan A, Reeves BC, Shepperd S, Shrier I, Stewart LA, Tilling K, White IR, Whiting PF, Higgins JPT. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ (in press). 2019.
24.
go back to reference Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JP. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919. https://doi.org/10.1136/bmj.i4919. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, Carpenter JR, Chan AW, Churchill R, Deeks JJ, Hróbjartsson A, Kirkham J, Jüni P, Loke YK, Pigott TD, Ramsay CR, Regidor D, Rothstein HR, Sandhu L, Santaguida PL, Schünemann HJ, Shea B, Shrier I, Tugwell P, Turner L, Valentine JC, Waddington H, Waters E, Wells GA, Whiting PF, Higgins JP. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919. https://​doi.​org/​10.​1136/​bmj.​i4919.
25.
go back to reference Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. Chichester: Wiley; 2009. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. Chichester: Wiley; 2009.
26.
go back to reference DerSimonian R, Laird N. Meta-analysis in clinical trials revisited. Contemp Clin Trials. 2015;45(Pt A):139-45. DerSimonian R, Laird N. Meta-analysis in clinical trials revisited. Contemp Clin Trials. 2015;45(Pt A):139-45.
27.
go back to reference Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.CrossRef Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58.CrossRef
28.
go back to reference Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.CrossRef Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.CrossRef
29.
go back to reference Zomahoun HTV, Guenette L, Gregoire JP, Lauzier S, Lawani AM, Ferdynus C, et al. Effectiveness of motivational interviewing interventions on medication adherence in adults with chronic diseases: a systematic review and meta-analysis. Int J Epidemiol. 2017;46(2):589–602.PubMed Zomahoun HTV, Guenette L, Gregoire JP, Lauzier S, Lawani AM, Ferdynus C, et al. Effectiveness of motivational interviewing interventions on medication adherence in adults with chronic diseases: a systematic review and meta-analysis. Int J Epidemiol. 2017;46(2):589–602.PubMed
30.
go back to reference Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ. 1995;310(6973):170.CrossRef Bland JM, Altman DG. Multiple significance tests: the Bonferroni method. BMJ. 1995;310(6973):170.CrossRef
31.
go back to reference Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63.CrossRef Duval S, Tweedie R. Trim and fill: a simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics. 2000;56(2):455–63.CrossRef
32.
go back to reference Schünemann HJ, Higgins JPT, Vist GE, Glasziou P, Akl EA, Skoetz N, et al. Completing ‘Summary of findings’ tables and grading the certainty of the evidence. . 2020 September [cited 2020-11-15]. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors) Cochrane Handbook for Systematic Reviews of Interventions (updated September 2020). Cochrane. Available from: www.training.cochrane.org/handbook. [cited 2020-11-15] Schünemann HJ, Higgins JPT, Vist GE, Glasziou P, Akl EA, Skoetz N, et al. Completing ‘Summary of findings’ tables and grading the certainty of the evidence. . 2020 September [cited 2020-11-15]. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors) Cochrane Handbook for Systematic Reviews of Interventions (updated September 2020). Cochrane. Available from: www.​training.​cochrane.​org/​handbook. [cited 2020-11-15]
Metadata
Title
Effectiveness and harms of clinical decision support systems for referral within chronic pain practice: protocol for a systematic review and meta-analysis
Authors
Hervé Tchala Vignon Zomahoun
Regina Visca
Nicole George
Sara Ahmed
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Systematic Reviews / Issue 1/2021
Electronic ISSN: 2046-4053
DOI
https://doi.org/10.1186/s13643-021-01596-7

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