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Rahimi F, Rabiei R, Seddighi AS, Roshanpoor A, Seddighi A, Moghaddasi H. Features and functions of decision support systems for appropriate diagnostic imaging: a scoping review. Diagnosis (Berl) 2024; 11:4-16. [PMID: 37795534 DOI: 10.1515/dx-2023-0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 09/10/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Diagnostic imaging decision support (DI-DS) systems could be effective tools for reducing inappropriate diagnostic imaging examinations. Since effective design and evaluation of these systems requires in-depth understanding of their features and functions, the present study aims to map the existing literature on DI-DS systems to identify features and functions of these systems. METHODS The search was performed using Scopus, Embase, PubMed, Web of Science, and Cochrane Central Registry of Controlled Trials (CENTRAL) and was limited to 2000 to 2021. Analytical studies, descriptive studies, reviews and book chapters that explicitly addressed the functions or features of DI-DS systems were included. RESULTS A total of 6,046 studies were identified. Out of these, 55 studies met the inclusion criteria. From these, 22 functions and 22 features were identified. Some of the identified features were: visibility, content chunking/grouping, deployed as a multidisciplinary program, clinically valid and relevant feedback, embedding current evidence, and targeted recommendations. And, some of the identified functions were: displaying an appropriateness score, recommending alternative or more appropriate imaging examination(s), providing recommendations for next diagnostic steps, and providing safety alerts. CONCLUSIONS The set of features and functions obtained in the present study can provide a basis for developing well-designed DI-DS systems, which could help to improve adherence to diagnostic imaging guidelines, minimize unnecessary costs, and improve the outcome of care through appropriate diagnosis and on-time care delivery.
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Affiliation(s)
- Fatemeh Rahimi
- Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Rabiei
- Department of Health Information Technology and Management, Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir Saied Seddighi
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Roshanpoor
- Department of computer, Yadegar-e-Imam Khomeini (RAH), Janat-abad Branch, Islamic Azad University, Tehran, Iran
| | - Afsoun Seddighi
- Functional Neurosurgery Research Center, Shohada Tajrish Comprehensive Neurosurgical Center of Excellence, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Moghaddasi
- Department of Health Information Technology and Management, Health Information Management & Medical Informatics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Darband St., Tehran, Iran
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2
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Valtchinov VI, Murphy SN, Lacson R, Ikonomov N, Zhai BK, Andriole K, Rousseau J, Hanson D, Kohane IS, Khorasani R. Analytics to monitor local impact of the Protecting Access to Medicare Act's imaging clinical decision support requirements. J Am Med Inform Assoc 2022; 29:1870-1878. [PMID: 35932187 PMCID: PMC9552289 DOI: 10.1093/jamia/ocac132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 05/19/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE This study aimed is to: (1) extend the Integrating the Biology and the Bedside (i2b2) data and application models to include medical imaging appropriate use criteria, enabling it to serve as a platform to monitor local impact of the Protecting Access to Medicare Act's (PAMA) imaging clinical decision support (CDS) requirements, and (2) validate the i2b2 extension using data from the Medicare Imaging Demonstration (MID) CDS implementation. MATERIALS AND METHODS This study provided a reference implementation and assessed its validity and reliability using data from the MID, the federal government's predecessor to PAMA's imaging CDS program. The Star Schema was extended to describe the interactions of imaging ordering providers with the CDS. New ontologies were added to enable mapping medical imaging appropriateness data to i2b2 schema. z-Ratio for testing the significance of the difference between 2 independent proportions was utilized. RESULTS The reference implementation used 26 327 orders for imaging examinations which were persisted to the modified i2b2 schema. As an illustration of the analytical capabilities of the Web Client, we report that 331/1192 or 28.1% of imaging orders were deemed appropriate by the CDS system at the end of the intervention period (September 2013), an increase from 162/1223 or 13.2% for the first month of the baseline period, December 2011 (P = .0212), consistent with previous studies. CONCLUSIONS The i2b2 platform can be extended to monitor local impact of PAMA's appropriateness of imaging ordering CDS requirements.
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Affiliation(s)
- Vladimir I Valtchinov
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,i2b2 tranSMART Foundation, Wakefield, Massachusetts, USA
| | - Ronilda Lacson
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nikolay Ikonomov
- Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria
| | - Bingxue K Zhai
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Katherine Andriole
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Justin Rousseau
- Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA
| | - Dick Hanson
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.,i2b2 tranSMART Foundation, Wakefield, Massachusetts, USA
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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3
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Kjelle E, Andersen ER, Soril LJJ, van Bodegom-Vos L, Hofmann BM. Interventions to reduce low-value imaging - a systematic review of interventions and outcomes. BMC Health Serv Res 2021; 21:983. [PMID: 34537051 PMCID: PMC8449221 DOI: 10.1186/s12913-021-07004-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/02/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND It is estimated that 20-50% of all radiological examinations are of low value. Many attempts have been made to reduce the use of low-value imaging. However, the comparative effectiveness of interventions to reduce low-value imaging is unclear. Thus, the objective of this systematic review was to provide an overview and evaluate the outcomes of interventions aimed at reducing low-value imaging. METHODS An electronic database search was completed in Medline - Ovid, Embase-Ovid, Scopus, and Cochrane Library for citations between 2010 and 2020. The search was built from medical subject headings for Diagnostic imaging/Radiology, Health service misuse or medical overuse, and Health planning. Keywords were used for the concept of reduction and avoidance. Reference lists of included articles were also hand-searched for relevant citations. Only articles written in English, German, Danish, Norwegian, Dutch, and Swedish were included. The Mixed Methods Appraisal Tool was used to appraise the quality of the included articles. A narrative synthesis of the final included articles was completed. RESULTS The search identified 15,659 records. After abstract and full-text screening, 95 studies of varying quality were included in the final analysis, containing 45 studies found through hand-searching techniques. Both controlled and uncontrolled before-and-after studies, time series, chart reviews, and cohort studies were included. Most interventions were aimed at referring physicians. Clinical practice guidelines (n = 28) and education (n = 28) were most commonly evaluated interventions, either alone or in combination with other components. Multi-component interventions were often more effective than single-component interventions showing a reduction in the use of low-value imaging in 94 and 74% of the studies, respectively. The most addressed types of imaging were musculoskeletal (n = 26), neurological (n = 23) and vascular (n = 16) imaging. Seventy-seven studies reported reduced low-value imaging, while 3 studies reported an increase. CONCLUSIONS Multi-component interventions that include education were often more effective than single-component interventions. The contextual and cultural factors in the health care systems seem to be vital for successful reduction of low-value imaging. Further research should focus on assessing the impact of the context in interventions reducing low-value imaging and how interventions can be adapted to different contexts.
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Affiliation(s)
- Elin Kjelle
- Institute for the Health Sciences at the Norwegian University of Science and Technology (NTNU) at Gjøvik, NTNU Gjøvik, Postbox 191, 2802 Gjøvik, Norway
| | - Eivind Richter Andersen
- Institute for the Health Sciences at the Norwegian University of Science and Technology (NTNU) at Gjøvik, NTNU Gjøvik, Postbox 191, 2802 Gjøvik, Norway
| | - Lesley J. J. Soril
- Department of Community Health Sciences and The Health Technology Assessment Unit, O’Brien Institute for Public Health, University of Calgary, 3280 Hospital Dr NW, Calgary, Alberta T2N 4Z6 Canada
| | - Leti van Bodegom-Vos
- Medical Decision making, Department of Biomedical Data Sciences, Leiden University Medical Center, P.O. Box 9600, 2300 RC, Leiden, the Netherlands
| | - Bjørn Morten Hofmann
- Institute for the Health Sciences at the Norwegian University of Science and Technology (NTNU) at Gjøvik, NTNU Gjøvik, Postbox 191, 2802 Gjøvik, Norway
- Centre of Medical Ethics, University of Oslo, Postbox 1130, Blindern, 0318 Oslo, Norway
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4
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Hogan J, Frasso R, Hailu T, Tate A, Martin R, Sze R. Optimizing Imaging Clinical Decision Support: Perspectives of Pediatric Emergency Department Physicians. J Am Coll Radiol 2020; 17:262-267. [DOI: 10.1016/j.jacr.2019.08.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 08/14/2019] [Accepted: 08/25/2019] [Indexed: 11/25/2022]
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5
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Pourjabbar S, Cavallo JJ, Arango J, Tocino I, Staib LH, Imanzadeh A, Forman HP, Pahade JK. Impact of Radiologist-Driven Change-Order Requests on Outpatient CT and MRI Examinations. J Am Coll Radiol 2020; 17:1014-1024. [PMID: 31954708 DOI: 10.1016/j.jacr.2019.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/13/2019] [Accepted: 12/18/2019] [Indexed: 10/25/2022]
Abstract
PURPOSE To assess impact of electronic medical record-embedded radiologist-driven change-order request on outpatient CT and MRI examinations. METHODS Outpatient CT and MRI requests where an order change was requested by the protocoling radiologist in our tertiary care center, from April 11, 2017, to January 3, 2018, were analyzed. Percentage and categorization of requested order change, provider acceptance of requested change, patient and provider demographics, estimated radiation exposure reduction, and cost were analyzed. P < .05 was used for statistical significance. RESULTS In 79,310 outpatient studies in which radiologists determined protocol, change-order requests were higher for MRI (5.2%, 1,283 of 24,553) compared with CT (2.9%, 1,585 of 54,757; P < .001). Provider approval of requested change was equivalent for CT (82%, 1,299 of 1,585) and MRI (82%, 1,052 of 1,283). Change requests driven by improper contrast media utilization were most common and different between CT (76%, 992 of 1,299) and MRI (65%, 688 of 1,052; P < .001). Changing without and with intravenous contrast orders to with contrast only was most common for CT (39%, 505 of 1,299) and with and without intravenous contrast to without contrast only was most common for MRI (26%, 274 of 1,052; P < .001). Of approved changes in CT, 51% (661 of 1,299) resulted in lower radiation exposure. Approved changes frequently resulted in less costly examinations (CT 67% [799 of 1,198], MRI 48% [411 of 863]). CONCLUSION Outpatient CT and MRI orders are deemed incorrect in 2.9% to 5% of cases. Radiologist-driven change-order request for CT and MRI are well accepted by ordering providers and decrease radiation exposure associated with imaging.
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Affiliation(s)
- Sarvenaz Pourjabbar
- Department of Radiology and Biomedical Imaging, Yale-New Haven Hospital, Yale School of Medicine, New Haven, Connecticut
| | - Joseph J Cavallo
- Department of Radiology and Biomedical Imaging, Yale-New Haven Hospital, Yale School of Medicine, New Haven, Connecticut
| | - Jennifer Arango
- Department of Radiology and Biomedical Imaging, Yale-New Haven Hospital, Yale School of Medicine, New Haven, Connecticut
| | - Irena Tocino
- Vice Chair of IT, Department of Radiology and Biomedical Imaging, Yale-New Haven Hospital, Yale School of Medicine, New Haven, Connecticut
| | - Lawrence H Staib
- Department of Radiology and Biomedical Imaging, Yale-New Haven Hospital, Yale School of Medicine, New Haven, Connecticut
| | - Amir Imanzadeh
- Department of Radiology and Biomedical Imaging, Yale-New Haven Hospital, Yale School of Medicine, New Haven, Connecticut
| | - Howard P Forman
- Faculty director for Finance, Department of Radiology. Professor, Radiology and Public Health (Health Policy), Professor in the Practice of Management; Professor of Economics; Director, MD/MBA Program @ Yale; Director, Executive MBA Program (Healthcare focus area); Health Care Management Program (HCM) at Yale School of Public Health, New Haven, Connecticut
| | - Jay K Pahade
- Vice Chair of Quality and Safety, Yale Department of Radiology and Biomedical Imaging; Radiology Medical Director for Quality and Safety, Yale New Haven Health; Associate Professor, Abdominal Imaging and Ultrasound, Department of Radiology and Biomedical Imaging, Yale-New Haven Hospital, Yale School of Medicine, New Haven, Connecticut.
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Cochon L, Lacson R, Wang A, Kapoor N, Ip IK, Desai S, Kachalia A, Dennerlein J, Benneyan J, Khorasani R. Assessing information sources to elucidate diagnostic process errors in radiologic imaging - a human factors framework. J Am Med Inform Assoc 2019; 25:1507-1515. [PMID: 30124890 DOI: 10.1093/jamia/ocy103] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/10/2018] [Indexed: 01/01/2023] Open
Abstract
Objective To assess information sources that may elucidate errors related to radiologic diagnostic imaging, quantify the incidence of potential safety events from each source, and quantify the number of steps involved from diagnostic imaging chain and socio-technical factors. Materials and Methods This retrospective, Institutional Review Board-approved study was conducted at the ambulatory healthcare facilities associated with a large academic hospital. Five information sources were evaluated: an electronic safety reporting system (ESRS), alert notification for critical result (ANCR) system, picture archive and communication system (PACS)-based quality assurance (QA) tool, imaging peer-review system, and an imaging computerized physician order entry (CPOE) and scheduling system. Data from these sources (January-December 2015 for ESRS, ANCR, QA tool, and the peer-review system; January-October 2016 for the imaging ordering system) were collected to quantify the incidence of potential safety events. Reviewers classified events by the step(s) in the diagnostic process they could elucidate, and their socio-technical factors contributors per the Systems Engineering Initiative for Patient Safety (SEIPS) framework. Results Potential safety events ranged from 0.5% to 62.1% of events collected from each source. Each of the information sources contributed to elucidating diagnostic process errors in various steps of the diagnostic imaging chain and contributing socio-technical factors, primarily Person, Tasks, and Tools and Technology. Discussion Various information sources can differentially inform understanding diagnostic process errors related to radiologic diagnostic imaging. Conclusion Information sources elucidate errors in various steps within the diagnostic imaging workflow and can provide insight into socio-technical factors that impact patient safety in the diagnostic process.
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Affiliation(s)
- Laila Cochon
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Ronilda Lacson
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Aijia Wang
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Neena Kapoor
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Ivan K Ip
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Sonali Desai
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Allen Kachalia
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Jack Dennerlein
- Center for Work, Health, and Wellbeing, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - James Benneyan
- Healthcare Systems Engineering Institute, Northeastern University, Boston, Massachusetts, USA
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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7
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Varada S, Lacson R, Raja AS, Ip IK, Schneider L, Osterbur D, Bain P, Vetrano N, Cellini J, Mita C, Coletti M, Whelan J, Khorasani R. Characteristics of knowledge content in a curated online evidence library. J Am Med Inform Assoc 2019; 25:507-514. [PMID: 29092054 DOI: 10.1093/jamia/ocx092] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 08/09/2017] [Indexed: 11/12/2022] Open
Abstract
Objective To describe types of recommendations represented in a curated online evidence library, report on the quality of evidence-based recommendations pertaining to diagnostic imaging exams, and assess underlying knowledge representation. Materials and Methods The evidence library is populated with clinical decision rules, professional society guidelines, and locally developed best practice guidelines. Individual recommendations were graded based on a standard methodology and compared using chi-square test. Strength of evidence ranged from grade 1 (systematic review) through grade 5 (recommendations based on expert opinion). Finally, variations in the underlying representation of these recommendations were identified. Results The library contains 546 individual imaging-related recommendations. Only 15% (16/106) of recommendations from clinical decision rules were grade 5 vs 83% (526/636) from professional society practice guidelines and local best practice guidelines that cited grade 5 studies (P < .0001). Minor head trauma, pulmonary embolism, and appendicitis were topic areas supported by the highest quality of evidence. Three main variations in underlying representations of recommendations were "single-decision," "branching," and "score-based." Discussion Most recommendations were grade 5, largely because studies to test and validate many recommendations were absent. Recommendation types vary in amount and complexity and, accordingly, the structure and syntax of statements they generate. However, they can be represented in single-decision, branching, and score-based representations. Conclusion In a curated evidence library with graded imaging-based recommendations, evidence quality varied widely, with decision rules providing the highest-quality recommendations. The library may be helpful in highlighting evidence gaps, comparing recommendations from varied sources on similar clinical topics, and prioritizing imaging recommendations to inform clinical decision support implementation.
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Affiliation(s)
- Sowmya Varada
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Ronilda Lacson
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Ali S Raja
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ivan K Ip
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Louise Schneider
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - David Osterbur
- Harvard Medical School, Boston, MA, USA.,Countway Library of Medicine, Boston, MA, USA
| | - Paul Bain
- Harvard Medical School, Boston, MA, USA.,Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Nicole Vetrano
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jacqueline Cellini
- Harvard Medical School, Boston, MA, USA.,Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Carol Mita
- Harvard Medical School, Boston, MA, USA.,Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Margaret Coletti
- Agoos Medical Library/Knowledge Services, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Julia Whelan
- Agoos Medical Library/Knowledge Services, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Ramin Khorasani
- Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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8
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Use of Imaging in the Emergency Department: Do Individual Physicians Contribute to Variation? AJR Am J Roentgenol 2019; 213:637-643. [DOI: 10.2214/ajr.18.21065] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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9
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Hentel KD, Menard A, Mongan J, Durack JC, Johnson PT, Raja AS, Khorasani R. What Physicians and Health Organizations Should Know About Mandated Imaging Appropriate Use Criteria. Ann Intern Med 2019; 170:880-885. [PMID: 31181572 DOI: 10.7326/m19-0287] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The Appropriate Use Criteria Program, enacted by the Centers for Medicare & Medicaid Services in response to the Protecting Access to Medicare Act of 2014 (PAMA), aims to reduce inappropriate and unnecessary imaging by mandating use of clinical decision support (CDS) by all providers who order advanced imaging examinations (magnetic resonance imaging; computed tomography; and nuclear medicine studies, including positron emission tomography). Beginning 1 January 2020, documentation of an interaction with a certified CDS system using approved appropriate use criteria will be required on all Medicare claims for advanced imaging in all emergency department patients and outpatients as a prerequisite for payment. The Appropriate Use Criteria Program will initially cover 8 priority clinical areas, including several (such as headache and low back pain) commonly encountered by internal medicine providers. All providers and organizations that order and provide advanced imaging must understand program requirements and their options for compliance strategies. Substantial resources and planning will be needed to comply with PAMA regulations and avoid unintended negative consequences on workflow and payments. However, robust evidence supporting the desired outcome of reducing inappropriate use of advanced imaging is lacking.
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Affiliation(s)
| | - Andrew Menard
- Johns Hopkins Medicine, Baltimore, Maryland (A.M., P.T.J.)
| | - John Mongan
- University of California, San Francisco, San Francisco, California (J.M.)
| | - Jeremy C Durack
- Memorial Sloan Kettering Cancer Center, New York, New York (J.C.D.)
| | | | - Ali S Raja
- Massachusetts General Hospital, Boston, Massachusetts (A.S.R.)
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Magarik M, Fowler LC, Robertson A, Ehrenfeld JM, McEvoy MD, Deitte LA. There’s an App for That: A Case Study on the Impact of Spaced Education on Ordering CT Examinations. J Am Coll Radiol 2019; 16:360-364. [DOI: 10.1016/j.jacr.2018.10.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 10/17/2018] [Accepted: 10/25/2018] [Indexed: 10/27/2022]
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11
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Reed MH. Diagnostic Imaging Referral Guidelines: Where Are We Now? Can Assoc Radiol J 2019; 70:3-4. [DOI: 10.1016/j.carj.2018.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 11/07/2018] [Indexed: 10/27/2022] Open
Affiliation(s)
- Martin H. Reed
- Department of Diagnostic Imaging, Children's Hospital, Winnipeg, Manitoba, Canada
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