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Implementing big data interventional radiology research: lessons learned
S232
Posters and Exhibits
3. Baker DW, et al. Design and use of performance measures to decrease lowvalue services and achieve cost-conscious care. Ann Intern Med 2013; 158(1): p. 55–59. 4. Kinney ED. Comparative effectiveness research under the Patient Protection and Affordable Care Act: can new bottles accommodate old wine? Am J Law Med 2011; 37(4):522–566. 5. Slutsky J, et al. AHRQ series paper 1: comparing medical interventions: AHRQ and the effective health-care program. J Clin Epidemiol 2010; 63 (5):481–483. 6. Giffin RB, Woodcock J. Comparative effectiveness research: who will do the studies? Health Aff (Millwood) 2010; 29(11):2075–2081.
Abstract No. 522 Introduction to cost analysis in interventional radiology: challenges and opportunities
Posters and Exhibits
B. Sayyar Roudsari1, J. McWilliams1, B. Bresnahan2, S. Padia3; 1UCLA, Los Angeles, CA; 2U Washington, Seattle, WA; 3Seattle, WA Purpose: To review the most commonly used cost analysis methods relevant to the economic evaluations of IR interventions. Materials: Interventional radiology (IR) has a unique position in the healthcare system because of the field’s constant evolution and rapid advancement in technology. However, challenges exist in implementing these technologies into clinical practice and assessing value. Efficacy data often focuses on short-term technical and clinical outcomes as opposed to longterm results. New interventions are often expensive, which can be a barrier when considering payer, patient, or societal perspectives. With ongoing cuts in reimbursements as a result of health care reform and a general lack of evidence of value for medical interventions, there is greater demand on care providers to demonstrate added value for interventions. Therefore, robust assessment of economic value of IR interventions will play a critical component when adapting new techniques. Results: The following methods of cost analysis relevant to the IR are discussed in this review. 1. Cost minimization study; 2. Cost-effectiveness assessment (CEA); 3. Cost utility analysis including the concept of QALY (quality adjusted life years); 4) cost benefit analysis. We have discussed the issue of cost estimation in the economic analysis of IR interventions. We have highlighted that the use of hospital charges as a surrogate for real cost of a service is problematic and should be avoided. We have discussed different methods that effectiveness could be measured in the IR-related economic studies. Finally we have discussed the most common modeling approaches that could be adopted for sensitivity analysis. Conclusions: It is quite clear that comparative effectiveness research will soon target more and more IR interventions. In the interest of both IR patients and IR providers, interventionalists must take a more active role in leading such efforts and being part of multi-disciplinary teams that investigate the added benefits of new modalities relative to current standards of care. To do so, it is critical for interventionalists to gain a basic understanding of the aforementioned analytic tools, study designs, and models.
Educational Exhibit
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JVIR
Abstract No. 523
Implementing big data interventional radiology research: lessons learned V. Arendt1, R. Shah2, S. Weber3, W. Kuo2, D. Wang2, L. Hofmann2; 1Stanford University School of Medicine, Palo Alto, CA; 2Stanford University Medical Center, Palo Alto, CA; 3Stanford University, Palo Alto, CA Learning Objectives: We describe the lessons learned in implementing a prospective database collection tool into a tertiary care interventional radiology practice. Background: In the wake of the Accountable Care Act, it will be necessary for interventional radiologists to be able to document their patients’ outcomes. We explored and utilized a variety of research database tools to prospectively collect procedural data, imaging data, and clinical data on interventional radiology patients. We believe that patient outcome databases should be used at the point of care, capture all relevant data, securely archive that data, and allow for easy data retrieval, analysis and visualization. Clinical Findings/Procedure Details: A simple spreadsheet is familiar to most users and easy to create. However, it does not readily represent relational data structures and it is a single file that can easily be deleted or corrupted. A second tool is is a desktop database with a graphical user interface (GUI) like Microsoft Access. This method of data storage is similar to spreadsheets, but it allows records to be linked together in meaningful ways (e.g. linking multiple procedures to one patient) and has forms for easy and reliable input, as dropdown menus and validation rules can be used to control data input. However, desktop databases are difficult to secure and do not easily interface with other databases, such as electronic medical records (EMRs). In general, desktop databases may be used when the study is small or medium-sized and records do not need to be linked to records in other systems. A Structured Query Language (SQL) relational database often takes the most time and effort to develop, but is also the most customizable and functional. Most large healthcare databases use SQL for data storage, which allows for easy communication between SQL-based relational databases and healthcare databases like EMRs. In general, personalized SQL-based relational databases should be used when the study is medium or large-sized and/or needs to interface with other SQL databases. Conclusions: Given the customizability and security of an SQL-database, we advocate using this tool to track patient outcomes.
Educational Exhibit
Abstract No. 524
Bariatric embolization for obesity: a critical review O. Akinwande1, P. Genyk1, D. Kraitchman1, A. Arepally2, C. Weiss1; 1Johns Hopkins University, Baltimore, MD; 2 Piedmont Healthcare, Atlanta, GA Learning Objectives: 1. To introduce interventional radiologists to the science behind Bariatric Arterial
Posters and Exhibits
3. Baker DW, et al. Design and use of performance measures to decrease lowvalue services and achieve cost-conscious care. Ann Intern Med 2013; 158(1): p. 55–59. 4. Kinney ED. Comparative effectiveness research under the Patient Protection and Affordable Care Act: can new bottles accommodate old wine? Am J Law Med 2011; 37(4):522–566. 5. Slutsky J, et al. AHRQ series paper 1: comparing medical interventions: AHRQ and the effective health-care program. J Clin Epidemiol 2010; 63 (5):481–483. 6. Giffin RB, Woodcock J. Comparative effectiveness research: who will do the studies? Health Aff (Millwood) 2010; 29(11):2075–2081.
Abstract No. 522 Introduction to cost analysis in interventional radiology: challenges and opportunities
Posters and Exhibits
B. Sayyar Roudsari1, J. McWilliams1, B. Bresnahan2, S. Padia3; 1UCLA, Los Angeles, CA; 2U Washington, Seattle, WA; 3Seattle, WA Purpose: To review the most commonly used cost analysis methods relevant to the economic evaluations of IR interventions. Materials: Interventional radiology (IR) has a unique position in the healthcare system because of the field’s constant evolution and rapid advancement in technology. However, challenges exist in implementing these technologies into clinical practice and assessing value. Efficacy data often focuses on short-term technical and clinical outcomes as opposed to longterm results. New interventions are often expensive, which can be a barrier when considering payer, patient, or societal perspectives. With ongoing cuts in reimbursements as a result of health care reform and a general lack of evidence of value for medical interventions, there is greater demand on care providers to demonstrate added value for interventions. Therefore, robust assessment of economic value of IR interventions will play a critical component when adapting new techniques. Results: The following methods of cost analysis relevant to the IR are discussed in this review. 1. Cost minimization study; 2. Cost-effectiveness assessment (CEA); 3. Cost utility analysis including the concept of QALY (quality adjusted life years); 4) cost benefit analysis. We have discussed the issue of cost estimation in the economic analysis of IR interventions. We have highlighted that the use of hospital charges as a surrogate for real cost of a service is problematic and should be avoided. We have discussed different methods that effectiveness could be measured in the IR-related economic studies. Finally we have discussed the most common modeling approaches that could be adopted for sensitivity analysis. Conclusions: It is quite clear that comparative effectiveness research will soon target more and more IR interventions. In the interest of both IR patients and IR providers, interventionalists must take a more active role in leading such efforts and being part of multi-disciplinary teams that investigate the added benefits of new modalities relative to current standards of care. To do so, it is critical for interventionalists to gain a basic understanding of the aforementioned analytic tools, study designs, and models.
Educational Exhibit
’
JVIR
Abstract No. 523
Implementing big data interventional radiology research: lessons learned V. Arendt1, R. Shah2, S. Weber3, W. Kuo2, D. Wang2, L. Hofmann2; 1Stanford University School of Medicine, Palo Alto, CA; 2Stanford University Medical Center, Palo Alto, CA; 3Stanford University, Palo Alto, CA Learning Objectives: We describe the lessons learned in implementing a prospective database collection tool into a tertiary care interventional radiology practice. Background: In the wake of the Accountable Care Act, it will be necessary for interventional radiologists to be able to document their patients’ outcomes. We explored and utilized a variety of research database tools to prospectively collect procedural data, imaging data, and clinical data on interventional radiology patients. We believe that patient outcome databases should be used at the point of care, capture all relevant data, securely archive that data, and allow for easy data retrieval, analysis and visualization. Clinical Findings/Procedure Details: A simple spreadsheet is familiar to most users and easy to create. However, it does not readily represent relational data structures and it is a single file that can easily be deleted or corrupted. A second tool is is a desktop database with a graphical user interface (GUI) like Microsoft Access. This method of data storage is similar to spreadsheets, but it allows records to be linked together in meaningful ways (e.g. linking multiple procedures to one patient) and has forms for easy and reliable input, as dropdown menus and validation rules can be used to control data input. However, desktop databases are difficult to secure and do not easily interface with other databases, such as electronic medical records (EMRs). In general, desktop databases may be used when the study is small or medium-sized and records do not need to be linked to records in other systems. A Structured Query Language (SQL) relational database often takes the most time and effort to develop, but is also the most customizable and functional. Most large healthcare databases use SQL for data storage, which allows for easy communication between SQL-based relational databases and healthcare databases like EMRs. In general, personalized SQL-based relational databases should be used when the study is medium or large-sized and/or needs to interface with other SQL databases. Conclusions: Given the customizability and security of an SQL-database, we advocate using this tool to track patient outcomes.
Educational Exhibit
Abstract No. 524
Bariatric embolization for obesity: a critical review O. Akinwande1, P. Genyk1, D. Kraitchman1, A. Arepally2, C. Weiss1; 1Johns Hopkins University, Baltimore, MD; 2 Piedmont Healthcare, Atlanta, GA Learning Objectives: 1. To introduce interventional radiologists to the science behind Bariatric Arterial