From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value

Teresa Martin-Carreras; Po-Hao Chen

doi:10.1055/s-0039-3400269

Seminars in Musculoskeletal Radiology, Inhaltsverzeichnis

Semin Musculoskelet Radiol 2020; 24(01): 65-73
DOI: 10.1055/s-0039-3400269

Review Article

Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value

Teresa Martin-Carreras

¹Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania

,

Po-Hao Chen

²Imaging Informatics Section, Imaging Institute, Cleveland Clinic, Cleveland, Ohio

³Musculoskeletal Radiology, Imaging Institute, Cleveland Clinic, Cleveland, Ohio

› Institutsangaben

Abstract

The radiology practice has access to a wealth of data in the radiologist information system, dictation reports, and electronic health records. Although many artificial intelligence applications in radiology have focused on computer vision and the interpretive use cases, many opportunities exist to enhance the radiologist's value proposition through business analytics. This article explores how AI lends an analytical lens to the radiology practice to create value.

Keywords

artificial intelligence - business analytics - natural language processing

Volltext

Referenzen

References
1 Drucker PF, Maciariello JA. Management Cases. Rev. ed. New York, NY: HarperCollins; 2008
2 Boland GW, Enzmann DR, Duszak Jr R. Actionable reporting. J Am Coll Radiol 2014; 11 (09) 844-845
3 Boland GW, Duszak Jr R, Kalra M. Protocol design and optimization. J Am Coll Radiol 2014; 11 (05) 440-441
4 Boland GW, Duszak Jr R, McGinty G, Allen Jr B. Delivery of appropriateness, quality, safety, efficiency and patient satisfaction. J Am Coll Radiol 2014; 11 (01) 7-11
5 Boland GW, Duszak Jr R, Dreyer K. Appropriateness, scheduling, and patient preparation. J Am Coll Radiol 2014; 11 (03) 225-226
6 Boland GW, Duszak Jr R, Mayo-Smith W. Optimizing modality operations. J Am Coll Radiol 2014; 11 (07) 654-655
7 Boland GW, Duszak Jr R, Larson PA. Communication of actionable information. J Am Coll Radiol 2014; 11 (11) 1019-1021
8 Boland GW, Thrall JH, Duszak Jr R. Business intelligence, data mining, and future trends. J Am Coll Radiol 2015; 12 (01) 9-11
9 Cook TS, Nagy P. Business intelligence for the radiologist: making your data work for you. J Am Coll Radiol 2014; 11 (12 Pt B): 1238-1240
10 Porter ME. On Competition. Updated and expanded ed. Boston, MA: Harvard Business Review Press; 2008
11 Porter ME, Teisberg EO. Redefining competition in health care. Harv Bus Rev 2004; 82 (06) 64-76 , 136
12 Mieloszyk RJ, Rosenbaum JI, Hall CS, Raghavan UN, Bhargava P. The financial burden of missed appointments: uncaptured revenue due to outpatient no-shows in radiology. Curr Probl Diagn Radiol 2018; 47 (05) 285-286
13 Harvey HB, Liu C, Ai J. , et al. Predicting no-shows in radiology using regression modeling of data available in the electronic medical record. J Am Coll Radiol 2017; 14 (10) 1303-1309
14 Bech M. The economics of non-attendance and the expected effect of charging a fine on non-attendees. Health Policy 2005; 74 (02) 181-191
15 Huang Y, Hanauer DA. Patient no-show predictive model development using multiple data sources for an effective overbooking approach. Appl Clin Inform 2014; 5 (03) 836-860
16 Berg BP, Murr M, Chermak D. , et al. Estimating the cost of no-shows and evaluating the effects of mitigation strategies. Med Decis Making 2013; 33 (08) 976-985
17 Curtis C, Liu C, Bollerman TJ, Pianykh OS. Machine learning for predicting patient wait times and appointment delays. J Am Coll Radiol 2018; 15 (09) 1310-1316
18 Centers for Medicare & Medicaid Services. Appropriate Use Criteria Program. 2019 . Available from: https://www.cms.gov/medicare/quality-initiatives-patient-assessment-instruments/appropriate-use-criteria-program/index.html . Accessed March 31, 2019
19 Sistrom CL, Dang PA, Weilburg JB, Dreyer KJ, Rosenthal DI, Thrall JH. Effect of computerized order entry with integrated decision support on the growth of outpatient procedure volumes: seven-year time series analysis. Radiology 2009; 251 (01) 147-155
20 Bennett CC, Hauser K. Artificial intelligence framework for simulating clinical decision-making: a Markov decision process approach. Artif Intell Med 2013; 57 (01) 9-19
21 American College of Radiology. Clinical Decision Support and the ACR Appropriate Use Criteria FAQ. Available from: https://www.acr.org/Clinical-Resources/Clinical-Decision-Support/CDS-FAQ . Accessed September 14, 2019
22 Schneider E, Zelenka S, Grooff P, Alexa D, Bullen J, Obuchowski NA. Radiology order decision support: examination-indication appropriateness assessed using 2 electronic systems. J Am Coll Radiol 2015; 12 (04) 349-357
23 American Medical Association. 2019 MIPS strategic scoring guide. 2019 Available from: https://www.ama-assn.org/system/files/2019-05/2019-mips-scoring-guide.pdf . Accessed September 10, 2019
24 American College of Radiology. MACRA resources. Available from: https://www.acr.org/Practice-Management-Quality-Informatics/MACRA-Resources . Accessed September 10, 2019
25 Nickel S, Schmidt UA. Process improvement in hospitals: a case study in a radiology department. Qual Manag Health Care 2009; 18 (04) 326-338
26 Trivedi H, Mesterhazy J, Laguna B, Vu T, Sohn JH. Automatic determination of the need for intravenous contrast in musculoskeletal MRI examinations using IBM Watson's natural language processing algorithm. J Digit Imaging 2018; 31 (02) 245-251
27 Rudie JD, Mattay RR, Schindler M. , et al. An initiative to reduce unnecessary gadolinium-based contrast in multiple sclerosis patients. J Am Coll Radiol 2019; 16 (9 Pt A): 1158-1164
28 Raghavan UN, Hall CS, Tellis R, Mabotuwana T, Wald C. Probabilistic modeling of exam durations in radiology procedures. J Digit Imaging 2019; 32 (03) 386-395
29 Reiner BI, Siegel EL, Carrino JA, Goldburgh MM. SCAR Radiologic Technologist Survey: analysis of the impact of digital technologies on productivity. J Digit Imaging 2002; 15 (03) 132-140
30 Reiner BI. Creating accountability in image quality analysis part 1: the technology paradox. J Digit Imaging 2013; 26 (02) 147-150
31 Nagy PG, Pierce B, Otto M, Safdar NM. Quality control management and communication between radiologists and technologists. J Am Coll Radiol 2008; 5 (06) 759-765
32 Nagy PG, Warnock MJ, Daly M, Toland C, Meenan CD, Mezrich RS. Informatics in radiology: automated Web-based graphical dashboard for radiology operational business intelligence. Radiographics 2009; 29 (07) 1897-1906
33 Minnigh TR, Gallet J. Maintaining quality control using a radiological digital X-ray dashboard. J Digit Imaging 2009; 22 (01) 84-88
34 Küstner T, Liebgott A, Mauch L. , et al. Automated reference-free detection of motion artifacts in magnetic resonance images. MAGMA 2018; 31 (02) 243-256
35 Lee H, Tajmir S, Lee J. , et al. Fully automated deep learning system for bone age assessment. J Digit Imaging 2017; 30 (04) 427-441
36 Meeks SL, Tomé WA, Willoughby TR. , et al. Optically guided patient positioning techniques. Semin Radiat Oncol 2005; 15 (03) 192-201
37 Wang LT, Solberg TD, Medin PM, Boone R. Infrared patient positioning for stereotactic radiosurgery of extracranial tumors. Comput Biol Med 2001; 31 (02) 101-111
38 Willoughby TR, Forbes AR, Buchholz D. , et al. Evaluation of an infrared camera and X-ray system using implanted fiducials in patients with lung tumors for gated radiation therapy. Int J Radiat Oncol Biol Phys 2006; 66 (02) 568-575
39 Winkel DJ, Heye T, Weikert TJ, Boll DT, Stieltjes B. Evaluation of an AI-based detection software for acute findings in abdominal computed tomography scans: toward an automated work list prioritization of routine CT examinations. Invest Radiol 2019; 54 (01) 55-59
40 Oh SC, Cook TS, Kahn Jr CE. PORTER: a prototype system for patient-oriented radiology reporting. J Digit Imaging 2016; 29 (04) 450-454
41 Martin-Carreras T, Kahn Jr CE. Coverage and readability of information resources to help patients understand radiology reports. J Am Coll Radiol 2018; 15 (12) 1681-1686
42 Shinagare AB, Lacson R, Boland GW. , et al. Radiologist preferences, agreement, and variability in phrases used to convey diagnostic certainty in radiology reports. J Am Coll Radiol 2019; 16 (04) 458-464
43 Alkasab TK, Bizzo BC, Berland LL, Nair S, Pandharipande PV, Harvey HB. Creation of an open framework for point-of-care computer-assisted reporting and decision support tools for radiologists. J Am Coll Radiol 2017; 14 (09) 1184-1189
44 Porter MF. An algorithm for suffix stripping. Program 1980; 14 (03) 130-137
45 Church K, Gale W. Inverse document frequency (IDF): a measure of deviations from Poisson. In: Armstrong S, Church K, Isabelle P, Manzi S, Tzoukermann E, Yarowsky D. , eds. Natural Language Processing Using Very Large Corpora. Dordrecht, Netherlands: Springer; 1999: 283-295 . Available from: http://link.springer.com/10.1007/978-94-017-2390-9_18 . Accessed May 23, 2017
46 Wu HC, Luk RWP, Wong KF, Kwok KL. Interpreting TF-IDF term weights as making relevance decisions. ACM Trans Inf Syst 2008; 26 (03) 1-37
47 Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. Abstract available at: https://arxiv.org/abs/1301.3781 . Accessed January 13, 2016
48 Enzmann DR. Radiology's value chain. Radiology 2012; 263 (01) 243-252
49 Zafar HM, Chadalavada SC, Kahn Jr CE. , et al. Code abdomen: an assessment coding scheme for abdominal imaging findings possibly representing cancer. J Am Coll Radiol 2015; 12 (09) 947-950
50 Chen PH, Zafar H, Galperin-Aizenberg M, Cook T. Integrating natural language processing and machine learning algorithms to categorize oncologic response in radiology reports. J Digit Imaging 2018; 31 (02) 178-184
51 Yuan J, Zhu H, Tahmasebi A. Classification of pulmonary nodular findings based on characterization of change using radiology reports. AMIA Jt Summits Transl Sci Proc 2019; 2019: 285-294
52 Ward R, Purysko A, Chen PH. Developing a semiautomated data pipeline for use in prostate MRI quality reporting. Available from: https://cdn.ymaws.com/siim.org/resource/resmgr/siim2019/posters/Developing_a_Semiautomated_C.pdf . Accessed September 10, 2019
53 Patel TA, Puppala M, Ogunti RO. , et al. Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods. Cancer 2017; 123 (01) 114-121
54 Okawa G, Ching K, Qian H, Feng Y. Automatic release of radiology reports via an online patient portal. J Am Coll Radiol 2017; 14 (09) 1219-1221
55 Duszak Jr R, Nossal M, Schofield L, Picus D. Physician documentation deficiencies in abdominal ultrasound reports: frequency, characteristics, and financial impact. J Am Coll Radiol 2012; 9 (06) 403-408
56 Atutxa A, Perez A, Casillas A, Atutxa A, Perez A, Casillas A. Machine learning approaches on diagnostic term encoding with the ICD for clinical documentation. IEEE J Biomed Health Inform 2018; 22 (04) 1323-1329
57 Denck J, Landschütz W, Nairz K, Heverhagen JT, Maier A, Rothgang E. Automated billing code retrieval from MRI scanner log data. J Digit Imaging. 2019 . Available from: http://link.springer.com/10.1007/s10278-019-00241-z . Accessed August 27, 2019
58 Quality Payment Program. Advanced Alternative Payment models (APMs). Centers for Medicare & Medicaid Services. Available from: https://qpp.cms.gov/apms/advanced-apms . Accessed September 10, 2019
59 Seidenwurm D, Lexa FJ. A Radiologist's primer on bundles and care episodes. J Am Coll Radiol 2016; 13 (09) 1029-1031
60 Rosenkrantz AB, Hirsch JA, Allen Jr B, Harvey HB, Nicola GN. Identifying radiology's place in the expanding landscape of episode payment models. J Am Coll Radiol 2017; 14 (07) 882-888
61 Pham T, Tran T, Phung D, Venkatesh S. Predicting healthcare trajectories from medical records: a deep learning approach. J Biomed Inform 2017; 69: 218-229
62 Gold HT, Slover JD, Joo L, Bosco J, Iorio R, Oh C. Association of depression with 90-day hospital readmission after total joint arthroplasty. J Arthroplasty 2016; 31 (11) 2385-2388
63 Tiberi III JV, Hansen V, El-Abbadi N, Bedair H. Increased complication rates after hip and knee arthroplasty in patients with cirrhosis of the liver. Clin Orthop Relat Res 2014; 472 (09) 2774-2778
64 Lee HK, Jin R, Feng Y. , et al. An analytical framework for TJR readmission prediction and cost-effective intervention. IEEE J Biomed Health Inform 2019; 23 (04) 1760-1772
65 Waduud MA, Wood B, Keleabetswe P. , et al; vascular surgeons and interventional radiologists at the Leeds Vascular Institute. Influence of psoas muscle area on mortality following elective abdominal aortic aneurysm repair. Br J Surg 2019; 106 (04) 367-374
66 Roemer FW, Hunter DJ, Winterstein A. , et al. Hip Osteoarthritis MRI Scoring System (HOAMS): reliability and associations with radiographic and clinical findings. Osteoarthritis Cartilage 2011; 19 (08) 946-962
67 Hunter DJ, Guermazi A, Lo GH. , et al. Evolution of semi-quantitative whole joint assessment of knee OA: MOAKS (MRI Osteoarthritis Knee Score). Osteoarthritis Cartilage 2011; 19 (08) 990-1002