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DOI: 10.1055/s-0041-1740921
Effectiveness of Clinical Decision Support Systems on the Appropriate Use of Imaging for Central Nervous System Injuries: A Systematic Review
Funding This work was supported by the Kashan University of Medical Research Council (grant no.: 96190) and the National Agency for Strategic Research in Medical Education (NASR grant number 970478).![](https://www.thieme-connect.de/media/10.1055-s-00035026/202201/lookinside/thumbnails/10-1055-s-0041-1740921_210201rev-1.jpg)
Abstract
Background One of the best practices for timely and efficient diagnoses of central nervous system (CNS) trauma and complex diseases is imaging. However, rates of imaging for CNS are high and impose a lot of costs to health care facilities in addition to exposing patients with negative impact of ionizing radiation.
Objectives This study aimed to systematically review the effects and features of clinical decision support systems (CDSSs) for the appropriate use of imaging for CNS injuries.
Method We searched MEDLINE, SCOPUS, Web of Science, and Cochrane without time period restriction. We included experimental and quasiexperimental studies that assessed the effectiveness of CDSSs designed for the appropriate use of imaging for CNS injuries in any clinical setting, including primary, emergency, and specialist care. The outcomes were categorized based on imaging-related, physician-related, and patient-related groups.
Result A total of 3,223 records were identified through the online literature search. Of the 55 potential papers for the full-text review, 11 eligible studies were included. Reduction of CNS imaging proportion varied from 2.6 to 40% among the included studies. Physician-related outcomes, including guideline adherence, diagnostic yield, and knowledge, were reported in five studies, and all demonstrated positive impact of CDSSs. Four studies had addressed patient-related outcomes, including missed or delayed diagnosis, as well as length of stay. These studies reported a very low rate of missed diagnosis due to the cancellation of computed tomography (CT) examine according to the CDSS recommendations.
Conclusion This systematic review reports that CDSSs decrease the utilization of CNS CT scan, while increasing physicians' adherence to the rules. However, the possible harm of CDSSs to patients was not well addressed by the included studies and needs additional investigation. The actual effect of CDSSs on appropriate imaging would be realized when the saved cost of examinations is compared with the cost of missed diagnosis.
Keywords
clinical decision support system - computer-assisted decision-making - appropriateness reviews - hospitals - radiology - imaging - central nervous system - evaluation of impactProtection of Human and Animal Subjects
The study is approved by the ethics review board of the Vice-Chancellorship for Research Affairs of Kashan University of Medical Sciences which confirmed the study by the ethical code: IR.KAUMS.MEDNT.Rec.1396.095. Consent to participations is not applicable.
Publication History
Received: 26 July 2021
Accepted: 08 November 2021
Article published online:
12 January 2022
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References
- 1 Goldzweig CL, Orshansky G, Paige NM. et al. Electronic Health Record-Based Interventions for Reducing Inappropriate Imaging in the Clinical Setting: A Systematic Review of the Evidence. Washington (DC): Department of Veterans Affairs (U.S.); 2015
- 2 Friedman DP, Smith NS. Impact of a collaborative radiology utilization management program: does the specialty of the referring provider matter?. Am J Roentgenol 2016; 207 (01) 121-125
- 3 Global GBD. GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol 2019; 18 (01) 56-87
- 4 Laughlin S, Montanera W. Central nervous system imaging. When is CT more appropriate than MRI?. Postgrad Med 1998; 104 (05) 73-76
- 5 Pitts SR, Niska RW, Xu J, Burt CW. National Hospital Ambulatory Medical Care Survey: 2006 emergency department summary. Natl Health Stat Rep 2008; (07) 1-38
- 6 Sharp AL, Nagaraj G, Rippberger EJ. et al. Computed tomography use for adults with head injury: describing likely avoidable emergency department imaging based on the Canadian CT head rule. Acad Emerg Med 2017; 24 (01) 22-30
- 7 Harms P. Automated usability evaluation of virtual reality applications. ACM Trans Comput Hum Interact 2019; 26 (03) DOI: 10.1145/3301423.
- 8 Sarma A, Heilbrun ME, Conner KE, Stevens SM, Woller SC, Elliott CG. Radiation and chest CT scan examinations: what do we know?. Chest 2012; 142 (03) 750-760
- 9 Lehnert BE, Bree RL. Analysis of appropriateness of outpatient CT and MRI referred from primary care clinics at an academic medical center: how critical is the need for improved decision support?. J Am Coll Radiol 2010; 7 (03) 192-197
- 10 Fitzgerald R. Error in radiology. Clin Radiol 2001; 56 (12) 938-946
- 11 Sheng AY, Castro A, Lewiss RE. Awareness, utilization, and education of the ACR appropriateness criteria: a review and future directions. J Am Coll Radiol 2016; 13 (02) 131-136
- 12 Graves JM, Fulton-Kehoe D, Jarvik JG, Franklin GM. Impact of an advanced imaging utilization review program on downstream health care utilization and costs for low back pain. Med Care 2018; 56 (06) 520-528
- 13 Tham E, Swietlik M, Deakyne S. et al; Pediatric Emergency Care Applied Research Network (PECARN). Clinical decision support for a multicenter trial of pediatric head trauma: development, implementation, and lessons learned. Appl Clin Inform 2016; 7 (02) 534-542
- 14 Ip IK, Gershanik EF, Schneider LI. et al. Impact of IT-enabled intervention on MRI use for back pain. Am J Med 2014; 127 (06) 512-8.e1
- 15 Deakyne SJ, Bajaj L, Hoffman J. et al; Pediatric Emergency Care Applied Research Network (PECARN). Development, evaluation and implementation of chief complaint groupings to activate data collection: a multi-center study of clinical decision support for children with head trauma. Appl Clin Inform 2015; 6 (03) 521-535
- 16 Rawson JV, Cronin P. Decision support: the super highway between health services research and change in clinical practice. Acad Radiol 2014; 21 (09) 1081-1082
- 17 Rousseau JF, Ip IK, Raja AS. et al. Can automated retrieval of data from emergency department physician notes enhance the imaging order entry process?. Appl Clin Inform 2019; 10 (02) 189-198
- 18 Easter JS, Bakes K, Dhaliwal J, Miller M, Caruso E, Haukoos JS. Comparison of PECARN, CATCH, and CHALICE rules for children with minor head injury: a prospective cohort study. Ann Emerg Med 2014; 64 (02) 145-152
- 19 Stiell IG, Clement CM, Rowe BH. et al. Comparison of the Canadian CT Head Rule and the New Orleans Criteria in patients with minor head injury. JAMA 2005; 294 (12) 1511-1518
- 20 Hoffman JR, Mower WR, Wolfson AB, Todd KH, Zucker MI. National Emergency X-Radiography Utilization Study Group. Validity of a set of clinical criteria to rule out injury to the cervical spine in patients with blunt trauma. N Engl J Med 2000; 343 (02) 94-99
- 21 Hynes JP, Hunter K, Rochford M. Utilization and appropriateness in cervical spine trauma imaging: implementation of clinical decision support criteria. Ir J Med Sci 2020; 189 (01) 333-336
- 22 Stiell IG, Clement CM, Grimshaw JM. et al. A prospective cluster-randomized trial to implement the Canadian CT Head Rule in emergency departments. CMAJ 2010; 182 (14) 1527-1532
- 23 Harnan SE, Pickering A, Pandor A, Goodacre SW. Clinical decision rules for adults with minor head injury: a systematic review. J Trauma 2011; 71 (01) 245-251
- 24 Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998; 280 (15) 1339-1346
- 25 Desai S, Liu C, Kirkland SW, Krebs LD, Keto-Lambert D, Rowe BH. Effectiveness of implementing evidence-based interventions to reduce C-spine image ordering in the emergency department: a systematic review. Acad Emerg Med 2018; 25 (06) 672-683
- 26 Liu C, Desai S, Krebs LD, Kirkland SW, Keto-Lambert D, Rowe BH. PRIHS-2 Choosing Wisely Team. Effectiveness of interventions to decrease image ordering for low back pain presentations in the emergency department: a systematic review. Acad Emerg Med 2018; 25 (06) 614-626
- 27 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 (06) 401-408
- 28 The Effective Public Health Practice Project (EPHPP). Quality assessment tool for quantitative. Accessed November 29, 2021: https://link.springer.com/content/pdf/bbm%253A978-3-319-17284-2/1.pdf
- 29 Higgins J, Altman D, Sterne J. Eds. Assessing risk of bias in included studies. In: Higgins JPT, Green S. eds. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. Accessed November 29, 2021: https://handbook-5-1.cochrane.org/
- 30 Jackson N, Waters E. Guidelines for Systematic Reviews in Health Promotion and Public Health Taskforce. Criteria for the systematic review of health promotion and public health interventions. Health Promot Int 2005; 20 (04) 367-374
- 31 Nabovati E, Vakili-Arki H, Taherzadeh Z. et al. Information technology-based interventions to improve drug-drug interaction outcomes: a systematic review on features and effects. J Med Syst 2017; 41 (01) 12
- 32 Collaboration TC. Review Manager (RevMan) [Computer program]. Version 5.3. Copenhagen: The Nordic Cochrane Centre. Accessed at September 12, 2019 at: https://training.cochrane.org/online-learning/core-software-cochrane-reviews/revman
- 33 Blackmore CC, Mecklenburg RS, Kaplan GS. Effectiveness of clinical decision support in controlling inappropriate imaging. J Am Coll Radiol 2011; 8 (01) 19-25
- 34 Doyle J, Abraham S, Feeney L, Reimer S, Finkelstein A. Clinical decision support for high-cost imaging: a randomized clinical trial. PLoS One 2019; 14 (03) e0213373
- 35 Zarchi TB. Intent of health care providers to adopt a clinical decision support tool in the management of minor pediatric head injuries. J Am Assoc Nurse Pract 2020; 32 (02) 168-175
- 36 Ballard DW, Kuppermann N, Vinson DR. et al; Pediatric Emergency Care Applied Research Network (PECARN), Clinical Research on Emergency Services and Treatment (CREST) Network, Partners HealthCare. Implementation of a clinical decision support system for children with minor blunt head trauma who are at nonnegligible risk for traumatic brain injuries. Ann Emerg Med 2019; 73 (05) 440-451
- 37 Dayan PS, Ballard DW, Tham E. et al; Pediatric Emergency Care Applied Research Network (PECARN), Clinical Research on Emergency Services and Treatment (CREST) Network, and Partners Healthcare; Traumatic Brain Injury-Knowledge Translation Study Group. Use of traumatic brain injury prediction rules with clinical decision support. Pediatrics 2017; 139 (04) e20162709
- 38 Goergen SK, Fong C, Dalziel K, Fennessy G. Can an evidence-based guideline reduce unnecessary imaging of road trauma patients with cervical spine injury in the emergency department?. Australas Radiol 2006; 50 (06) 563-569
- 39 Engineer RS, Podolsky SR, Fertel BS. et al. A pilot study to reduce computed tomography utilization for pediatric mild head injury in the emergency department using a clinical decision support tool and a structured parent discussion tool. Pediatr Emerg Care 2018; DOI: 10.1097/pec.0000000000001501.
- 40 Sharp AL, Huang BZ, Tang T. et al. Implementation of the Canadian CT Head Rule and its association with use of computed tomography among patients with head injury. Ann Emerg Med 2018; 71 (01) 54-63
- 41 Min A, Chan VWY, Aristizabal R. et al. Clinical decision support decreases volume of imaging for low back pain in an urban emergency department. J Am Coll Radiol 2017; 14 (07) 889-899
- 42 Bookman K, West D, Ginde A. et al. Embedded Clinical decision support in electronic health record decreases use of high-cost imaging in the emergency department: EmbED study. Acad Emerg Med 2017; 24 (07) 839-845
- 43 Ip IK, Raja AS, Gupta A, Andruchow J, Sodickson A, Khorasani R. Impact of clinical decision support on head computed tomography use in patients with mild traumatic brain injury in the ED. Am J Emerg Med 2015; 33 (03) 320-325
- 44 Gupta A, Ip IK, Raja AS, Andruchow JE, Sodickson A, Khorasani R. Effect of clinical decision support on documented guideline adherence for head CT in emergency department patients with mild traumatic brain injury. J Am Med Inform Assoc 2014; 21 (e2): e347-e351
- 45 Altman MR, Colorafi K, Daratha KB. The reliability of electronic health record data used for obstetrical research. Appl Clin Inform 2018; 9 (01) 156-162
- 46 Homco J, Carabin H, Nagykaldi Z. et al. Validity of medical record abstraction and electronic health record-generated reports to assess performance on cardiovascular quality measures in primary care. JAMA Netw Open 2020; 3 (07) e209411-e209411
- 47 Parsons A, McCullough C, Wang J, Shih S. Validity of electronic health record-derived quality measurement for performance monitoring. J Am Med Inform Assoc 2012; 19 (04) 604-609
- 48 Sierzenski PR, Linton OW, Amis Jr. ES. et al. Applications of justification and optimization in medical imaging: examples of clinical guidance for computed tomography use in emergency medicine. J Am Coll Radiol 2014; 11 (01) 36-44
- 49 Sharp AL, Cobb EM, Dresden SM. et al. Understanding the value of emergency care: a framework incorporating stakeholder perspectives. J Emerg Med 2014; 47 (03) 333-342
- 50 Main C, Moxham T, Wyatt J, Kay J, Anderson R, Stein K. Computerised decision support systems in order communication for diagnostic, screening or monitoring test ordering: systematic reviews of the effects and cost-effectiveness of systems. In: NIHR Health Technology Assessment programme: Executive Summaries. Southampton, United Kingdom: NIHR Journals Library; 2010
- 51 Zare S, Meidani Z, Shirdeli M, Nabovati E. Laboratory test ordering in inpatient hospitals: a scoping review on the effects and features of clinical decision support systems. BMC Med Inform Decis Mak 2021; 21 (01) 20
- 52 Roshanov PS, You JJ, Dhaliwal J. et al; CCDSS Systematic Review Team. Can computerized clinical decision support systems improve practitioners' diagnostic test ordering behavior? A decision-maker-researcher partnership systematic review. Implement Sci 2011; 6: 88
- 53 Brunner MC, Sheehan SE, Yanke EM. et al. Joint design with providers of clinical decision support for value-based advanced shoulder imaging. Appl Clin Inform 2020; 11 (01) 142-152
- 54 Babl FE, Borland ML, Phillips N. et al; Paediatric Research in Emergency Departments International Collaborative (PREDICT). Accuracy of PECARN, CATCH, and CHALICE head injury decision rules in children: a prospective cohort study. Lancet 2017; 389 (10087): 2393-2402
- 55 Bressan S, Romanato S, Mion T, Zanconato S, Da Dalt L. Implementation of adapted PECARN decision rule for children with minor head injury in the pediatric emergency department. Acad Emerg Med 2012; 19 (07) 801-807
- 56 Bowen S, Johnson K, Reed MH, Zhang L, Curry L. The effect of incorporating guidelines into a computerized order entry system for diagnostic imaging. J Am Coll Radiol 2011; 8 (04) 251-258