Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Occupational Rehabilitation
Published in: Journal of Occupational Rehabilitation 3/2016

Open Access 01-09-2016

Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review

Authors: Douglas P. Gross, Susan Armijo-Olivo, William S. Shaw, Kelly Williams-Whitt, Nicola T. Shaw, Jan Hartvigsen, Ziling Qin, Christine Ha, Linda J. Woodhouse, Ivan A. Steenstra

Published in: Journal of Occupational Rehabilitation | Issue 3/2016

Login to get access

Abstract

Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O’Malley’s scoping review framework which progresses through five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies for analysis; (4) charting the data; and (5) collating, summarizing and reporting results. We considered computer-based, and other available tools, such as algorithms, care pathways, rules and models. Since this research crosses multiple disciplines, we searched health care, computing science and business databases. Results Our search resulted in 4605 manuscripts. Titles and abstracts were screened for relevance. The reliability of the screening process was high with an average percentage of agreement of 92.3 %. Of the located articles, 123 were considered relevant. Within this literature, there were 43 CDS tools located. These were classified into 3 main areas: computer-based tools/questionnaires (n = 8, 19 %), treatment algorithms/models (n = 14, 33 %), and clinical prediction rules/classification systems (n = 21, 49 %). Each of these areas and the associated evidence are described. The state of evidentiary support for CDS tools is still preliminary and lacks external validation, head-to-head comparisons, or evidence of generalizability across different populations and settings. Conclusions CDS tools, especially those employing rapidly advancing computer technologies, are under development and of potential interest to health care providers, case management organizations and funders of care. Based on the results of this scoping review, we conclude that these tools, models and systems should be subjected to further validation before they can be recommended for large-scale implementation for managing patients with MSK disorders.
Appendix
Available only for authorised users
Literature
1.
Power JD, Perruccio AV, Desmeules M, Lagacé C, Badley EM. Ambulatory physician care for musculoskeletal disorders in Canada. J Rheumatol. 2006;33(1):133–9.PubMed
2.
Coyte PC, Asche CV, Croxford R, Chan B. The economic cost of musculoskeletal disorders in Canada. Arthritis Care Res. 1998;11(5):315–25.PubMedCrossRef
3.
Gagnon CM, Stanos SP, van der Ende G, Rader LR, Norman Harden R. Treatment outcomes for workers compensation patients in a US-Based Interdisciplinary Pain Management Program. Pain Pract. 2013;13(4):282–8.PubMedCrossRef
4.
Schaafsma F, Schonstein E, Whelan KM, Ulvestad E, Kenny DT, Verbeek JH. Physical conditioning programs for improving work outcomes in workers with back pain. Cochrane Database Syst Rev. 2010; (1):CD001822.
5.
Van Oostrom SH, Driessen MT, De Vet HCW, Franche RL, Schonstein E, Loisel P, Van Mechelen W, Anema JR. Workplace interventions for preventing work disability. Cochrane Database Syst Rev. 2009; (2):CD006955.
6.
Karjalainen K, Malmivaara A, van Tulder M, Roine R, Jauhiainen M, Hurri H, Koes B. Multidisciplinary biopsychosocial rehabilitation for subacute low back pain among working age adults. Cochrane Database Syst Rev. (Online : Update Software) 2003; (2):CD002193.
7.
Gross DP, Haws C, Niemelainen R. What is the rate of functional improvement during occupational rehabilitation in workers’ compensation claimants? J Occup Rehabil. 2012;22(3):292–300.PubMedCrossRef
8.
Hayward RS, El-Hajj M, Voth TK, Deis K. Patterns of use of decision support tools by clinicians. In: AMIA annual symposium proceedings/AMIA symposium, AMIA symposium; 2006, p. 329–33.
9.
10.
Hill JC, Fritz JM. Psychosocial influences on low back pain, disability, and response to treatment. Phys Ther. 2011;91(5):712–21.PubMedCrossRef
11.
Blackmore CC, Mecklenburg RS, Kaplan GS. Effectiveness of clinical decision support in controlling inappropriate imaging. JACR J Am Coll Radiol. 2011;8(1):19–25.PubMedCrossRef
12.
Hemens BJ, Holbrook A, Tonkin M, Mackay JA, Weise-Kelly L, Navarro T, Wilczynski NL, Haynes RB. Computerized clinical decision support systems for drug prescribing and management: a decision-maker–researcher partnership systematic review. Implement Sci. 2011;6(1):1–17.CrossRef
13.
Sahota N, Lloyd R, Ramakrishna A, Mackay JA, Prorok JC, Weise-Kelly L, Navarro T, Wilczynski NL, Haynes RB. Computerized clinical decision support systems for acute care management: A decision-maker–researcher partnership systematic review of effects on process of care and patient outcomes. Implement Sci. 2011;6(1):1–14.CrossRef
14.
Souza NM, Sebaldt RJ, Mackay JA, Prorok JC, Weise-Kelly L, Navarro T, Wilczynski NL, Haynes RB. Computerized clinical decision support systems for primary preventive care: a decision-maker–researcher partnership systematic review of effects on process of care and patient outcomes. Implement Sci. 2011;6(1):1–14.CrossRef
15.
Roshanov PS, Misra S, Gerstein HC, Garg AX, Sebaldt RJ, Mackay JA, Weise-Kelly L, Navarro T, Wilczynski NL, Haynes RB. Computerized clinical decision support systems for chronic disease management: a decision-maker–researcher partnership systematic review. Implement Sci. 2011;6(1):1–16.CrossRef
16.
Haynes RB, Wilczynski NL. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: methods of a decision-maker–researcher partnership systematic review. Implement Sci. 2010;5(1):1–8.CrossRef
17.
Nieuwlaat R, Connolly SJ, Mackay JA, Weise-Kelly L, Navarro T, Wilczynski NL, Haynes RB. Computerized clinical decision support systems for therapeutic drug monitoring and dosing: a decision-maker–researcher partnership systematic review. Implement Sci. 2011;6(1):1–14.CrossRef
18.
Patel S, Brown S, Friede T, Griffiths F, Lord J, Ngunjiri A, Thistlethwaite J, Tysall C, Woolvine M, Underwood M. Study protocol: improving patient choice in treating low back pain (IMPACT–LBP): a randomised controlled trial of a decision support package for use in physical therapy. BMC Musculoskel Disord. 2011;12:1–7.CrossRef
19.
Trafton J, Martins S, Michel M, Lewis E, Wang D, Combs A, Scates N, Tu S, Goldstein MK. Evaluation of the acceptability and usability of a decision support system to encourage safe and effective use of opioid therapy for chronic, noncancer pain by primary care providers. Pain Medicine. 2010;11(4):575–85.PubMedCrossRef
20.
21.
Davis K, Drey N, Gould D. What are scoping studies? A review of the nursing literature. Int J Nurs Stud. 2009;46:1386–400.PubMedCrossRef
22.
Poth C, Ross S. Meta-analysis, systematic review, or scoping review? Comparing methodologies in educational research. In: The Canadian Society for the Study of Education Annual Congress; 2009.
23.
Arskey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32.CrossRef
24.
Levac D, Colquhoun H, O’Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):1–9.CrossRef
25.
26.
Young AE, Roessler RT, Wasiak R, McPherson KM, van Poppel MN, Anema JR. A developmental conceptualization of return to work. J Occup Rehabil. 2005;15(4):557–68.PubMedCrossRef
27.
International Classification of Function, Disability and Health: ICF. In. Geneva, Switzerland: World Health Organization; 2001.
28.
Berlin A, Sorani M, Sim I. A taxonomic description of computer-based clinical decision support systems. J Biomed Inform. 2006;39(6):656–67.PubMedCrossRef
29.
30.
31.
Terwee CB, Bot SDM, de Boer MR, van der Windt DAWM, Knol DL, Dekker J, Bouter LM, de Vet HCW. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol. 2007;60(1):34–42.PubMedCrossRef
32.
Gross DP, Zhang J, Steenstra I, Barnsley S, Haws C, Amell T, McIntosh G, Cooper J, Zaiane O. Development of a computer-based clinical decision support tool for selecting appropriate rehabilitation interventions for injured workers. J Occup Rehabil. 2013;23(4):597–609.PubMedCrossRef
33.
Hill JC, Vohora K, Dunn KM, Main CJ, Hay EM. Comparing the STarT back screening tool’s subgroup allocation of individual patients with that of independent clinical experts. Clin J Pain. 2010;26(9):783–7.PubMedCrossRef
34.
Hill JC, Whitehurst DG, Lewis M, Bryan S, Dunn KM, Foster NE, Konstantinou K, Main CJ, Mason E, Somerville S. Comparison of stratified primary care management for low back pain with current best practice (STarT Back): a randomised controlled trial. Lancet. 2011;378(9802):1560–71.PubMedPubMedCentralCrossRef
35.
Knab JH, Wallace MS, Wagner RL, Tsoukatos J, Weinger MB. The use of a computer-based decision support system facilitates primary care physicians’ management of chronic pain. Anesth Analg. 2001;93(3):712–20.PubMedCrossRef
36.
Shaw WS, Reme SE, Pransky G, Woiszwillo MJ, Steenstra IA, Linton SJ. The pain recovery inventory of concerns and expectations: a psychosocial screening instrument to identify intervention needs among patients at elevated risk of back disability. J Occup Environ Med. 2013;55(8):885–94.PubMedCrossRef
37.
Stephens B, Gross DP. The influence of a continuum of care model on the rehabilitation of compensation claimants with soft tissue disorders. Spine. 2007;32(25):2898–904.PubMedCrossRef
38.
Womack SK, Armstrong TJ. Use of a computerized decision support system for primary and secondary prevention of work-related MSD disability. J Occup Rehabil. 2005;15(3):313–28.PubMedCrossRef
39.
Aravena HI. Utility of the Orebro Musculoskeletal Questionnaire as a Screening and Clinical Decision Support tool in Workers’s Compensation Claims Edmonton. Canada: University of Alberta; 2014.
40.
Speklé EM, Hoozemans MJ, Blatter BM, Heinrich J, van der Beek AJ, Knol DL, Bongers PM, van Dieën JH. Effectiveness of a questionnaire based intervention programme on the prevalence of arm, shoulder and neck symptoms, risk factors and sick leave in computer workers: a cluster randomised controlled trial in an occupational setting. BMC Musculoskel Disord. 2010;11(1):99.CrossRef
41.
Zhang J, Cao P, Gross D, Zaiane OR. On the application of multi-class classification in physical therapy recommendation. Health Inf Sci Syst Health Inf Sci Syst. 2013;1(1):1–15.PubMedCrossRef
42.
Qin Z, Armijo-Olivo S, Woodhouse LJ, Gross DP. An investigation of the validity of the Work Assessment Triage Tool clinical decision support tool for selecting optimal rehabilitation interventions for workers with musculoskeletal injuries. Clin Rehabil. (Epub ahead of print) 2015:0269215515578696.
43.
Qin Z, Armijo-Olivo S, Woodhouse L, Gross D. Evaluation of A Clinical Decision Support Tool for Selecting Optimal Rehabilitation Intervention for Injured Workers. Calgary: Education & Research Archive: University of Alberta; 2014.
44.
Schmidt CO, Pfingsten M, Fahland RA, Lindena G, Marnitz U, Pfeifer K, Kohlmann T, Chenot JF. Assessing a risk tailored intervention to prevent disabling low back pain—protocol of a cluster randomized controlled trial. BMC Musculoskel Disord. 2010;11:1–7.CrossRef
45.
Zhang J, Cao P, Gross D, Zaiane OR. On the application of multi-class classification in physical therapy recommendation. Edmonton: University of Alberta; 2012.
46.
Zhang J, Gross D, Zaïane OR. On the application of multi-class classification in physical therapy recommendation. In: 17th Pacific-Asia conference on knowledge discovery and data mining, PAKDD 2013. vol. 7867 LNAI. Gold Coast, QLD; 2013, p. 143–54.
47.
Hay EM, Dunn KM, Hill JC, Lewis M, Mason EE, Konstantinou K, Sowden G, Somerville S, Vohora K, Whitehurst D. A randomised clinical trial of subgrouping and targeted treatment for low back pain compared with best current care. The STarT Back Trial Study Protocol. BMC Musculoskel Disord. 2008;9(1):58.CrossRef
48.
Main C, Sowden G, Hill J, Watson P, Hay E. Integrating physical and psychological approaches to treatment in low back pain: the development and content of the STarT Back trial’s ‘high-risk’intervention (StarT Back; ISRCTN 37113406). Physiotherapy. 2012;98(2):110–6.PubMedCrossRef
49.
50.
Storheim K. Targeted physiotherapy treatment for low back pain based on clinical risk can improve clinical and economic outcomes when compared with current best practice. J Physiother. 2012;58(1):57.PubMedCrossRef
51.
52.
Speklé EM, Heinrich J, Hoozemans MJ, Blatter BM, van der Beek AJ, van Dieën JH, van Tulder MW. The cost-effectiveness of the RSI QuickScan intervention programme for computer workers: results of an economic evaluation alongside a randomised controlled trial. BMC Musculoskel Disord. 2010;11(1):259.CrossRef
53.
Sowden G, Hill JC, Konstantinou K, Khanna M, Main CJ, Salmon P, Somerville S, Wathall S, Foster NE. Targeted treatment in primary care for low back pain: the treatment system and clinical training programmes used in the IMPaCT Back study (ISRCTN 55174281). Fam Pract. 2012;29:50–62.PubMedCrossRef
54.
Rundell SD, Davenport TE, Wagner T. Physical therapist management of acute and chronic low back pain using the World Health Organization’s International Classification of Functioning, Disability and Health. Phys Ther. 2009;89(1):82–90.PubMedCrossRef
55.
Stanton TR, Fritz JM, Hancock MJ, Latimer J, Maher CG, Wand BM, Parent EC. Evaluation of a treatment-based classification algorithm for low back pain: a cross-sectional study. Phys Ther. 2011;91(4):496–509.PubMedCrossRef
56.
Stanton TR, Hancock MJ, Apeldoorn AT, Wand BM, Fritz JM. What characterizes people who have an unclear classification using a treatment-based classification algorithm for low back pain? A cross-sectional study. Phys Ther. 2013;93(3):345–55.PubMedCrossRef
57.
Strong J, Large RG, Ashton R, Stewart A. A New Zealand Replication of the IPAM Clustering Model for alow back Patients. Clin J Pain. 1995;11(4):296–306.PubMedCrossRef
58.
Widerstrom B, Olofson N, Arvidsson I. Manual therapy and a suggested treatment based classification algorithm in patients with low back pain: a pilot study. J Back Musculoskel Rehabil. 2007;20(2):61–70.
59.
Reme SE, Shaw WS, Steenstra IA, Woiszwillo MJ, Pransky G, Linton SJ. Distressed, immobilized, or lacking employer support? A sub-classification of acute work-related low back pain. J Occup Rehabil. 2012;22(4):541–52.PubMedCrossRef
60.
Shaw WS, Pransky G, Patterson W, Linton SJ, Winters T. Patient clusters in acute, work-related back pain based on patterns of disability risk factors. J Occup Environ Med. 2007;49(2):185–93.PubMedCrossRef
61.
Steenstra IA, Ibrahim SA, Franche R-L, Hogg-Johnson S, Shaw WS, Pransky GS. Validation of a risk factor-based intervention strategy model using data from the readiness for return to work cohort study. J Occup Rehabil. 2010;20(3):394–405.PubMedCrossRef
62.
Fitzgerald G, Axe M, Snyder-Mackler L. A decision-making scheme for returning patients to high-level activity with nonoperative treatment after anterior cruciate ligament rupture. Knee Surg Sports Traumatol Arthrosc. 2000;8(2):76–82.PubMedCrossRef
63.
Hurd WJ, Axe MJ, Snyder-Mackler L. A 10-year prospective trial of a patient management algorithm and screening examination for highly active individuals with anterior cruciate ligament injury: Part 1, outcomes. Am J Sp Med. 2008;36(1):40–7.CrossRef
64.
Sonnabend DH. Treatment of primary anterior shoulder dislocation in patients older than 40 years of age: conservative versus operative. Clin Orthop Relat Res. 1994;304:74–7.PubMed
65.
Spiegl UJ, Ryf C, Hepp P, Rillmann P. Evaluation of a treatment algorithm for acute traumatic osseous Bankart lesions resulting from first time dislocation of the shoulder with a two year follow-up. BMC Musculoskel Disord. 2013;14(1):305.CrossRef
66.
Kodama N, Imai S, Matsusue Y. A simple method for choosing treatment of distal radius fractures. J Hand Surg. 2013;38(10):1896–905.CrossRef
67.
Wang WT, Olson SL, Campbell AH, Hanten WP, Gleeson PB. Effectiveness of physical therapy for patients with neck pain: an individualized approach using a clinical decision-making algorithm. Am J Phys Med Rehabil. 2003;82(3):203–18.PubMed
68.
Björklund M, Djupsjöbacka M, Svedmark Å, Häger C. Effects of tailored neck-shoulder pain treatment based on a decision model guided by clinical assessments and standardized functional tests. A study protocol of a randomized controlled trial. BMC Musculoskel Disord. 2012;13(1):75.CrossRef
69.
Study will use hybrid model to create decision support package for conservative treatment of nonspecific low back pain. Lippincott’s Bone Joint Newsl. 2011; 17(6):67–8.
70.
Shaw WS, Linton SJ, Pransky G. Reducing sickness absence from work due to low back pain: How well do intervention strategies match modifiable risk factors? J Occup Rehabil. 2006;16(4):591–605.PubMedCrossRef
71.
Sueoka SS, LaStayo PC. Zone II flexor tendon rehabilitation: a proposed algorithm. J Hand Ther. 2008;21(4):410–3.PubMedCrossRef
72.
Tuttle N. Is it reasonable to use an individual patient’s progress after treatment as a guide to ongoing clinical reasoning? J Manipulative Physiol Ther. 2009;32(5):396–403.PubMedCrossRef
73.
Van Zundert J, Van Kleef M. Low back pain: From algorithm to cost-effectiveness? Pain Pract. 2005;5(3):179–89.PubMedCrossRef
74.
Wisneski RJRR. The Pennsylvania Plan II: an algorithm for the management of lumbar degenerative disc disease. Instr Course Lect. 1985;34:17–36.PubMed
75.
Forseen SE, Corey AS. Clinical decision support and acute low back pain: evidence-based order sets. J Am Coll Radiol. 2012; 9(10):704–12.e704.
76.
Murphy DR, Hurwitz EL. A theoretical model for the development of a diagnosis-based clinical decision rule for the management of patients with spinal pain. BMC Musculoskel Disord. 2007;8(1):75.CrossRef
77.
Fritz JM, Brennan GP, Leaman H. Does the evidence for spinal manipulation translate into better outcomes in routine clinical care for patients with occupational low back pain? A case–control study. Spine J. 2006;6(3):289–95.PubMedCrossRef
78.
Flynn T, Fritz J, Whitman J, Wainner R, Magel J, Rendeiro D, Butler B, Garber M, Allison S. A clinical prediction rule for classifying patients with low back pain who demonstrate short-term improvement with spinal manipulation. Spine. 2002;27(24):2835–43.PubMedCrossRef
79.
Brence J. Should a prescriptive clinical prediction rule drive our decision process in patients with low back pain? SportEX Med. 2013;58:7–8.
80.
Chen J, Phillips A, Ramsey M, Schenk R. A case study examining the effectiveness of mechanical diagnosis and therapy in a patient who met the clinical prediction rule for spinal manipulation. J Man Manip Ther. 2009;17(4):216–20.PubMedPubMedCentralCrossRef
81.
Childs JD. Validation of a clinical prediction rule to identify patients likely to benefit from spinal manipulation: a randomized clinical trial. Pittsburgh: University of Pittsburgh; 2003.
82.
Childs JD, Flynn TW, Fritz JM. A perspective for considering the risks and benefits of spinal manipulation in patients with low back pain. Man Ther. 2006;11(4):316–20.PubMedCrossRef
83.
Childs JD, Fritz JM, Flynn TW, Irrgang JJ, Johnson KK, Majkowski GR, Delitto A. A clinical prediction rule to identify patients with low back pain most likely to benefit from spinal manipulation: a validation study. Ann Intern Med. 2004;141(12):920–8.PubMedCrossRef
84.
Childs JD, Fritz JM, Piva SR, Erhard RE. Clinical decision making in the identification of patients likely to benefit from spinal manipulation: a traditional versus an evidence-based approach. J Orthop Sports Phys Ther. 2003;33(5):259–72.PubMedCrossRef
85.
Cleland J, Fritz J, Childs JD, Kulig K, Eberhart S, Davenport T, Magel J, Landel RF. Generalizability of a clinical prediction rule for identifying patients with low back pain who are likely to respond rapidly and dramatically to thrust manipulation. In: 14th annual meeting of the American Academy of Orthopaedic Manual Physical Therapists: 2008; Seattle, Washington; 2008, p. 161–81.
86.
Cleland JA, Fritz JM, Kulig K, Davenport TE, Eberhart S, Magel J, Childs JD. Comparison of the effectiveness of three manual physical therapy techniques in a subgroup of patients with low back pain who satisfy a clinical prediction rule: a randomized clinical trial. Spine. 2009;34(25):2720–9.PubMedCrossRef
87.
Cleland JA, Fritz JM, Whitman JM, Childs JD, Palmer JA. The use of a lumbar spine manipulation technique by physical therapists in patients who satisfy a clinical prediction rule: a case series. J Orthop Sports Phys Ther. 2006;36(4):209–14.PubMedCrossRef
88.
Fritz JM, Childs JD, Flynn TW. Pragmatic application of a clinical prediction rule in primary care to identify patients with low back pain with a good prognosis following a brief spinal manipulation intervention. BMC Fam Pract. 2005;6(1):29.PubMedPubMedCentralCrossRef
89.
Hallegraeff HJM, Winters JC, de Greef M, Lucas C. Manipulative therapy and clinical prediction criteria in treatment of acute nonspecific low back pain. Percept Mot Skills. 2009;108(1):196–208.PubMedCrossRef
90.
Hancock MJ, Maher CG, Latimer J, Herbert RD, McAuley JH. Independent evaluation of a clinical prediction rule for spinal manipulative therapy: a randomised controlled trial. Eur Spine J. 2008;17(7):936–43.PubMedPubMedCentralCrossRef
91.
Learman K, Showalter C, Cook C. Does the use of a prescriptive clinical prediction rule increase the likelihood of applying inappropriate treatments? A survey using clinical vignettes. Man Ther. 2012;17(6):538–43.PubMedCrossRef
92.
Maher C, Childs JD, Cleland JA, Vreeman DJ. Clinical prediction rules. Virginia: American Physical Therapy Association Inc.; 2006. p. 759.
93.
May S, Rosedale R. A case of a potential manipulation responder whose back pain resolved with flexion exercises. J Manipulative Physiol Ther. 2007;30(7):539–42.PubMedCrossRef
94.
Resch K. Can a spinal manipulation clinical prediction rule improve decision making for patients with low back pain? Focus Altern Complement Ther. 2005;10(4):309–10.
95.
Schenk R, Dionne C, Simon C, Johnson R. Effectiveness of mechanical diagnosis and therapy in patients with back pain who meet a clinical prediction rule for spinal manipulation. J Man Manip Ther. 2012;20(1):43–9.PubMedPubMedCentralCrossRef
96.
Underwood M. A clinical prediction rule predicted outcome in patients with low back pain having spinal manipulation and exercise treatment. Evid Based Med. 2005;10(4):125–125.CrossRef
97.
Werneke MW, Hart D, Oliver D, McGill T, Grigsby D, Ward J, Weinberg J, Oswald W, Cutrone G. Prevalence of classification methods for patients with lumbar impairments using the McKenzie syndromes, pain pattern, manipulation, and stabilization clinical prediction rules. J Man Manip Ther. 2010;18(4):197–204.PubMedPubMedCentralCrossRef
98.
Apeldoorn AT, Ostelo RW, van Helvoirt H, Fritz JM, de Vet HC, van Tulder MW. The cost-effectiveness of a treatment-based classification system for low back pain: design of a randomised controlled trial and economic evaluation. BMC Musculoskel Disord. 2010;11(1):58.CrossRef
99.
Apeldoorn AT, Ostelo RW, van Helvoirt H, Fritz JM, Knol DL, van Tulder MW, de Vet HC. A randomized controlled trial on the effectiveness of a classification-based system for subacute and chronic low back pain. Spine. 2012;37(16):1347–56.PubMedCrossRef
100.
Beneciuk JM, George S, Fritz J. Treatment-based classification subgroups among STarT Back Screening Tool risk categories in patients seeking outpatient physical therapy. In: CSM 2011 Orthopaedic and Sports Physical Therapy Section Programming: 2011; New Orleans, LA; 2011.
101.
Brennan GP, Fritz JM, Hunter SJ, Thackeray A, Delitto A, Erhard RE. Identifying subgroups of patients with acute/subacute “nonspecific” low back pain: results of a randomized clinical trial. Spine. 2006;31(6):623–31.PubMedCrossRef
102.
Carpenter K, Mintken P. Examination, intervention, and outcomes for 3 patients treated with mechanical traction per the treatment-based classification: a retrospective case series. In: CSM 2009 Orthopaedic and Sports Physical Therapy Section Programming: 2009; Las Vegas, NV; 2009.
103.
Delitto A, Erhard RE, Bowling RW. A treatment-based classification approach to low back syndrome: identifying and staging patients for conservative treatment. Phys Ther. 1995;75(6):470–85.PubMed
104.
Fritz JM, Cleland JA, Childs JD. Subgrouping patients with low back pain: evolution of a classification approach to physical therapy. J Orthop Sports Phys Ther. 2007;37(6):290–302.PubMedCrossRef
105.
George SZ. Characteristics of patients with lower extremity symptoms treated with slump stretching: a case series. J Orthop Sports Phys Ther. 2002;32(8):391–8.PubMedCrossRef
106.
Hebert JJ, Koppenhaver SL, Walker BF. Subgrouping patients with low back pain a treatment-based approach to classification. Sports Health Multidiscip Approach. 2011;3(6):534–42.CrossRef
107.
Henry SM, Fritz JM, Trombley AR, Bunn JY. Reliability of a treatment-based classification system for subgrouping people with low back pain. J Orthop Sports Phys Ther. 2012;42(9):797–805.PubMedCrossRef
108.
Parent EC, FJ, Brennan GP, Hunter SJ, Long A. The effect of a workshop on using specific exercises on the outcomes of patients with low back pain and treatment-based classification. In: CSM 2009 Orthopaedic and Sports Physical Therapy Section Programming: 2009; Las Vegas, NV; 2009.
109.
Scott DR, MA, Walters J: Use of treatment-based classification groups produces significant outcomes in patients with LBP. In: CSM 2008 Orthopaedic and Sports Physical Therapy Section Programming: 2008; Mashville, Tennessee; 2008.
110.
Sions J. Combining mobilization and stabilization clinical prediction rules provide relief for patient with acute exacerbation of chronic low back pain. In: CSM 2009 Orthopaedic and Sports Physical Therapy Section Programming: 2009; Las Vegas; 2009.
111.
Widerström B, Olofsson N, Arvidsson I, Harms-Ringdahl K, Larsson UE. Inter-examiner reliability of a proposed decision-making treatment based classification system for low back pain patients. Man Ther. 2012;17(2):164–71.PubMedCrossRef
112.
Hicks GE, Fritz JM, Delitto A, McGill SM. Preliminary development of a clinical prediction rule for determining which patients with low back pain will respond to a stabilization exercise program. Arch Phys Med Rehabil. 2005;86(9):1753–62.PubMedCrossRef
113.
May S, Gardiner E, Young S, Klaber-Moffett J. Predictor variables for a positive long-term functional outcome in patients with acute and chronic neck and back pain treated with a McKenzie approach: a secondary analysis. J Man Manip Ther. 2008;16(3):155–60.PubMedPubMedCentralCrossRef
114.
Fritz JM, Whitman JM, Flynn TW, Wainner RS, Childs JD. Factors related to the inability of individuals with low back pain to improve with a spinal manipulation. Phys Ther. 2004;84(2):173–90.PubMed
115.
Cai C, Pua YH, Lim KC. A clinical prediction rule for classifying patients with low back pain who demonstrate short-term improvement with mechanical lumbar traction. Eur Spine J. 2009;18(4):554–61.PubMedPubMedCentralCrossRef
116.
Fritz JM, Lindsay W, Matheson JW, Brennan GP, Hunter SJ, Moffit SD, Swalberg A, Rodriquez B. Is there a subgroup of patients with low back pain likely to benefit from mechanical traction? Results of a randomized clinical trial and subgrouping analysis. Spine. 2007;32(26):E793–800.PubMedCrossRef
117.
Hall H, McIntosh G, Boyle C. Effectiveness of a low back pain classification system. Spine J. 2009;9(8):648–57.PubMedCrossRef
118.
Stolze LR, Allison SC, Childs JD. Derivation of a preliminary clinical prediction rule for identifying a subgroup of patients with low back pain likely to benefit from Pilates-based exercise. J Orthop Sports Phys Ther. 2012;42(5):425–36.PubMedCrossRef
119.
Laslett M: Clinical prediction rule for rapid pain relief of low back pain following manipulation. NZ J Physiother. 2006; 34(2).
120.
Vela LI, Haladay DE, Denegar C. Clinical assessment of low-back-pain treatment outcomes in athletes. J Sport Rehabil. 2011;20(1):74–88.PubMed
121.
122.
Cai C, Ming G, Ng LY. Development of a clinical prediction rule to identify patients with neck pain who are likely to benefit from home-based mechanical cervical traction. Eur Spine J. 2011;20(6):912–22.PubMedPubMedCentralCrossRef
123.
Carpenter K, Mintken P, Cleland JA. Evaluation of outcomes in patients with neck pain treated with thoracic spine manipulation and exercise: a case series. NZ J Physiother. 2009;37(2):76.
124.
Childs MJD, Fritz JM, Piva SR, Whitman JM. Proposal of a classification system for patients with neck pain. J Orthop Sports Phys Ther. 2004;34(11):686–700.PubMedCrossRef
125.
Cleland JA, Childs JD, Fritz JM, Whitman JM, Eberhart SL. Development of a clinical prediction rule for guiding treatment of a subgroup of patients with neck pain: use of thoracic spine manipulation, exercise, and patient education. Phys Ther. 2007;87(1):9–23.PubMedCrossRef
126.
Cleland JA, Mintken PE, Carpenter K, Fritz JM, Glynn P, Whitman J, Childs JD. Examination of a clinical prediction rule to identify patients with neck pain likely to benefit from thoracic spine thrust manipulation and a general cervical range of motion exercise: multi-center randomized clinical trial. Phys Ther. 2010;90(9):1239–50.PubMedCrossRef
127.
Cleland JA, CJ, Fritz JM, Whitman JM, Eberhart SL, A clinical prediction rule for classifying patients with neck pain who demonstrate short-term improvement with thoracic spine manipulation. In: 12th annual meeting of the American Academy of Orthopaedic Manual Physical Therapists: 2006; Charlotte, NC; 2006, p. 168–87.
128.
Farrell KPL, Katherine E. Implementation of a treatment based classification system for neck pain: a pilot study. Orthop Phys Ther Pract. 2011;23(2):91–6.
129.
Fritz JM, Brennan GP. Preliminary examination of a proposed treatment-based classification system for patients receiving physical therapy interventions for neck pain. Phys Ther. 2007;87(5):513–24.PubMedCrossRef
130.
Heintz MM, Hegedus EJ. Multimodal management of mechanical neck pain using a treatment based classification system. J Man Manip Ther. 2008;16(4):217–24.PubMedPubMedCentralCrossRef
131.
O’Hearn M, ML, Gillespie C. Physical therapy outcomes of a treatment-based classification scheme for individuals with neck pain. In: CSM 2009 Orthopaedic and Sports Physical Therapy Section Programming: 2009; Las Vegas, NV; 2009.
132.
Puentedura EJ, Cleland JA, Landers MR, Mintken P, Louw A, Fernández-de-las-Peñas C. Development of a clinical prediction rule to identify patients with neck pain likely to benefit from thrust joint manipulation to the cervical spine. J Orthop Sports Phys Ther. 2012;42(7):577–92.PubMedCrossRef
133.
Raney NH, Petersen EJ, Smith TA, Cowan JE, Rendeiro DG, Deyle GD, Childs JD. Development of a clinical prediction rule to identify patients with neck pain likely to benefit from cervical traction and exercise. Eur Spine J. 2009;18(3):382–91.PubMedPubMedCentralCrossRef
134.
Raney NH, PE, Smith TA, Cowan JE, Rendeiro DG, Deyle GD, Childs JD. A clinical prediction rule for classifying patients with neck pain who demonstrate short-term improvement with cervical traction and exercise. In: CSM 2008 Orthopaedic and Sports Physical Therapy Section Programming: 2008; Mashville, Tennessee; 2008.
135.
Tamayo A. The use of a clinical prediction rule for diagnosis and treatment based classification system for the treatment of a cervical radiculopathy patient: a case report. Orthop Phys Ther Pract. 2009;21(1):24–30.
136.
Tseng Y-L, Wang WT, Chen W-Y, Hou T-J, Chen T-C, Lieu F-K. Predictors for the immediate responders to cervical manipulation in patients with neck pain. Man Ther. 2006;11(4):306–15.PubMedCrossRef
137.
Wyatt LH. Commentary on Cleland JA et. al_2007. J Am Chiropract Assoc. 2007; 27.
138.
Schellingerhout JM, Verhagen AP. Letter to the Editor concerning “Development of a clinical prediction rule to identify patients with neck pain likely to benefit from cervical traction and exercise” by Raney N et al.(2009) Eur Spine J 18: 382–391. Eur Spine J. 2010;19(5):833–833.PubMedPubMedCentralCrossRef
139.
Schellingerhout JM, Verhagen AP, Heymans MW, Pool JJM, Vonk F, Koes BW, de Vet HCW. Which subgroups of patients with non-specific neck pain are more likely to benefit from spinal manipulation therapy, physiotherapy, or usual care? Pain. 2008;139(3):670–80.PubMedCrossRef
140.
Barton CJ, Menz HB, Crossley KM. Clinical predictors of foot orthoses efficacy in individuals with patellofemoral pain. Med Sci Sports Exerc. 2011;43(9):1603–10.PubMedCrossRef
141.
142.
Iverson CA, Sutlive TG, Crowell MS, Morrell RL, Perkins MW, Garber MB, Moore JH, Wainner RS. Lumbopelvic manipulation for the treatment of patients with patellofemoral pain syndrome: development of a clinical prediction rule. J Orthop Sports Phys Ther. 2008;38(6):297–312.PubMedCrossRef
143.
Lesher JD, Sutlive TG, Miller GA, Chine NJ, Garber MB, Wainner RS. Development of a clinical prediction rule for classifying patients with patellofemoral pain syndrome who respond to patellar taping. J Orthop Sports Phys Ther. 2006;36(11):854–66.PubMedCrossRef
144.
Vaccaro AR, Lehman RA Jr, Hurlbert RJ, Anderson PA, Harris M, Hedlund R, Harrop J, Dvorak M, Wood K, Fehlings MG. A new classification of thoracolumbar injuries: the importance of injury morphology, the integrity of the posterior ligamentous complex, and neurologic status. Spine. 2005;30(20):2325–33.PubMedCrossRef
145.
Vaccaro AR, Oner C, Kepler CK, Dvorak M, Schnake K, Bellabarba C, Reinhold M, Aarabi B, Kandziora F, Chapman J. AOSpine thoracolumbar spine injury classification system: fracture description, neurological status, and key modifiers. Spine. 2013;38(23):2028–37.PubMedCrossRef
146.
Vicenzino B, Collins N, Cleland J, McPoil T. A clinical prediction rule for identifying patients with patellofemoral pain who are likely to benefit from foot orthoses: a preliminary determination. Br J Sports Med. 2010;44(12):862–6.PubMedCrossRef
147.
Vicenzino B, Smith D, Cleland J, Bisset L. Development of a clinical prediction rule to identify initial responders to mobilisation with movement and exercise for lateral epicondylalgia. Man Ther. 2009;14(5):550–4.PubMedCrossRef
148.
Whitman JM, Cleland JA, Mintken P, Keirns M, Bieniek ML, Albin SR, Magel J, McPoil TG. Predicting short-term response to thrust and nonthrust manipulation and exercise in patients post inversion ankle sprain. J Orthop Sports Phys Ther. 2009;39(3):188–200.PubMedCrossRef
149.
Wixom SM, LaStayo P. A potential classification model for individuals with tennis elbow. J Hand Ther. 2012;25(4):418–21.PubMedCrossRef
150.
Flynn T. Clinical prediction rules for the lumbar spine. N Z J Physiother. 2009;37(3):145–145.
151.
152.
Murphy DR, Hurwitz EL. Application of a diagnosis-based clinical decision guide in patients with low back pain. Chiropr Man Ther. 2011;19(1):1–10.CrossRef
153.
Nee RJ, Coppieters MW. Interpreting research on clinical prediction rules for physiotherapy treatments. Man Ther. 2011;16(2):105–8.PubMedCrossRef
154.
155.
Aebischer B, Hill JC, Hilfiker R, Karstens S. German translation and cross-cultural adaptation of the STarT back screening tool. PLoS ONE. 2015;10(7):1–14.CrossRef
156.
Luan S, Min Y, Li G, Lin C, Li X, Wu S, Ma C, Hill JC. Cross-cultural adaptation, reliability, and validity of the chinese version of the STarT back screening tool in patients with low back pain. Spine. 2014;39(16):E974–9.PubMedCrossRef
157.
Pombo N, Araújo P, Viana J. Knowledge discovery in clinical decision support systems for pain management: a systematic review. Artif Intell Med. 2014;60(1):1–11.PubMedCrossRef
158.
Smith MY, Depue JD, Rini C. Computerized decision-support systems for chronic pain management in primary care. Pain Med. 2007;8(Suppl 3):S155–66.CrossRef
159.
Cresswell K, Majeed A, Bates DW, Sheikh A. Computerised decision support systems for healthcare professionals: an interpretative review. Inform Prim Care. 2012;20(2):115–28.PubMed
160.
Haskins R, Rivett DA, Osmotherly PG. Clinical prediction rules in the physiotherapy management of low back pain: a systematic review. Man Ther. 2012;17(1):9–21.PubMedCrossRef
161.
Patel S, Friede T, Froud R, Evans DW, Underwood M. Systematic review of randomized controlled trials of clinical prediction rules for physical therapy in low back pain. Spine. 2013;38(9):762–9.PubMedCrossRef
162.
Hill JC, Dunn KM, Main CJ, Hay EM. Subgrouping low back pain: a comparison of the STarT Back Tool with the Örebro Musculoskeletal Pain Screening Questionnaire. Eur J Pain. 2010;14(1):83–9.PubMedPubMedCentralCrossRef
163.
Betten C, Sandell C, Hill JC, Gutke A. Cross-cultural adaptation and validation of the Swedish STarT Back Screening Tool. Eur J Physiother. 2015;17(1):29–36.CrossRef
164.
Bruyère O, Demoulin M, Beaudart C, Hill JC, Maquet D, Genevay S, Mahieu G, Reginster JY, Crielaard JM, Demoulin C. Validity and reliability of the French version of the start back screening tool for patients with low back pain. Spine. 2014;39(2):E123–8.PubMedCrossRef
165.
Kongsted A, Johannesen E, Leboeuf-Yde C. Feasibility of the STarT back screening tool in chiropractic clinics: a cross-sectional study of patients with low back pain. Chiropr Man Ther. 2011;19(1):1–10.CrossRef
166.
Morsø L, Albert H, Kent P, Manniche C, Hill J. Translation and discriminative validation of the STarT Back Screening Tool into Danish. Eur Spine J. 2011;20(12):2166–73.PubMedPubMedCentralCrossRef
167.
Gusi N, Del Pozo-Cruz B, Olivares PR, Hernández-Mocholi M, Hill JC. The Spanish version of the “sTarT back screening tool” (SBST) in different subgroups. Aten Prim. 2011;43(7):356–61.CrossRef
168.
Bruyère O, Demoulin M, Brereton C, Humblet F, Flynn D, Hill JC, Maquet D, Van Beveren J, Reginster JY, Crielaard JM, et al. Translation validation of a new back pain screening questionnaire (the STarT Back Screening Tool) in French. Arch Public Health. 2012;70(1):1–12.CrossRef
169.
Piironen S, Paananen M, Haapea M, Hupli M, Zitting P, Ryynänen K, Takala EP, Korniloff K, Hill JC, Häkkinen A et al: Transcultural adaption and psychometric properties of the STarT Back Screening Tool among Finnish low back pain patients. Eur Spine J. 2015; 1–9.
170.
Speklé EM, Hoozemans MJM, van der Beek AJ, Blatter BM, Bongers PM, van Dieën JH. Internal consistency, test–retest reliability and concurrent validity of a questionnaire on work-related exposure related to arm, shoulder and neck symptoms in computer workers. Ergonomics. 2009;52(9):1087–103.PubMedCrossRef
171.
Hoozemans MJM, Speklé EM, Van Dieën JH. Concurrent validity of questions on arm, shoulder and neck symptoms of the RSI QuickScan. Int Arch Occup Environ Health. 2013;86(7):789–98.PubMedCrossRef
172.
Speklé EM, Hoozemans MJM, van der Beek AJ, Blatter BM, van Dieën JH. The predictive validity of the RSI QuickScan questionnaire with respect to arm, shoulder and neck symptoms in computer workers. Ergonomics. 2012;55(12):1559–70.PubMedCrossRef
Metadata
Title
Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review
Authors
Douglas P. Gross
Susan Armijo-Olivo
William S. Shaw
Kelly Williams-Whitt
Nicola T. Shaw
Jan Hartvigsen
Ziling Qin
Christine Ha
Linda J. Woodhouse
Ivan A. Steenstra
Publication date
01-09-2016
Publisher
Springer US
Published in
Journal of Occupational Rehabilitation / Issue 3/2016
Print ISSN: 1053-0487
Electronic ISSN: 1573-3688
DOI
https://doi.org/10.1007/s10926-015-9614-1

Other articles of this Issue 3/2016

Journal of Occupational Rehabilitation 3/2016 Go to the issue

Review

Disability Diversity Training in the Workplace: Systematic Review and Future Directions

OriginalPaper

Work Incapacity and Treatment Costs After Severe Accidents: Standard Versus Intensive Case Management in a 6-Year Randomized Controlled Trial

OriginalPaper

Correspondence in Stakeholder Assessment of Health, Work Capacity and Sick Leave in Workers with Comorbid Subjective Health Complaints? A Video Vignette Study

OriginalPaper

Job Restrictions for Healthcare Workers with Musculoskeletal Disorders: Consequences from the Superior’s Viewpoint

OriginalPaper

A Model of Supervisor Decision-Making in the Accommodation of Workers with Low Back Pain

OriginalPaper

Development of a Modified Version of the Spinal Function Sort (M-SFS): A Mixed Method Approach