1
|
Burns JL, Gichoya JW, Kohli MD, Jones J, Purkayastha S. Theory of radiologist interaction with instant messaging decision support tools: A sequential-explanatory study. PLOS DIGITAL HEALTH 2024; 3:e0000297. [PMID: 38408043 PMCID: PMC10896537 DOI: 10.1371/journal.pdig.0000297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 01/17/2024] [Indexed: 02/28/2024]
Abstract
Radiology specific clinical decision support systems (CDSS) and artificial intelligence are poorly integrated into the radiologist workflow. Current research and development efforts of radiology CDSS focus on 4 main interventions, based around exam centric time points-after image acquisition, intra-report support, post-report analysis, and radiology workflow adjacent. We review the literature surrounding CDSS tools in these time points, requirements for CDSS workflow augmentation, and technologies that support clinician to computer workflow augmentation. We develop a theory of radiologist-decision tool interaction using a sequential explanatory study design. The study consists of 2 phases, the first a quantitative survey and the second a qualitative interview study. The phase 1 survey identifies differences between average users and radiologist users in software interventions using the User Acceptance of Information Technology: Toward a Unified View (UTAUT) framework. Phase 2 semi-structured interviews provide narratives on why these differences are found. To build this theory, we propose a novel solution called Radibot-a conversational agent capable of engaging clinicians with CDSS as an assistant using existing instant messaging systems supporting hospital communications. This work contributes an understanding of how radiologist-users differ from the average user and can be utilized by software developers to increase satisfaction of CDSS tools within radiology.
Collapse
Affiliation(s)
- John Lee Burns
- Department of Radiology & Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana, United States of America
- Department of BioHealth Informatics, Indiana University Luddy School of Informatics, Computing, and Engineering, Indianapolis, Indiana, United States of America
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Marc D Kohli
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America
| | - Josette Jones
- Department of BioHealth Informatics, Indiana University Luddy School of Informatics, Computing, and Engineering, Indianapolis, Indiana, United States of America
| | - Saptarshi Purkayastha
- Department of BioHealth Informatics, Indiana University Luddy School of Informatics, Computing, and Engineering, Indianapolis, Indiana, United States of America
| |
Collapse
|
2
|
Esposito G, Allarà C, Randon M, Aiello M, Salvatore M, Aceto G, Pescapè A. A Biobanking System for Diagnostic Images: Architecture Development, COVID-19-Related Use Cases, and Performance Evaluation. JMIR Form Res 2023; 7:e42505. [PMID: 38064636 PMCID: PMC10760513 DOI: 10.2196/42505] [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: 09/06/2022] [Revised: 09/22/2023] [Accepted: 09/27/2023] [Indexed: 12/22/2023] Open
Abstract
BACKGROUND Systems capable of automating and enhancing the management of research and clinical data represent a significant contribution of information and communication technologies to health care. A recent advancement is the development of imaging biobanks, which are now enabling the collection and storage of diagnostic images, clinical reports, and demographic data to allow researchers identify associations between lifestyle and genetic factors and imaging-derived phenotypes. OBJECTIVE The aim of this study was to design and evaluate the system performance of a network for an operating biobank of diagnostic images, the Bio Check Up Srl (BCU) Imaging Biobank, based on the Extensible Neuroimaging Archive Toolkit open-source platform. METHODS Three usage cases were designed focusing on evaluation of the memory and computing consumption during imaging collections upload and during interactions between two kinds of users (researchers and radiologists) who inspect chest computed tomography scans of a COVID-19 cohort. The experiments considered three network setups: (1) a local area network, (2) virtual private network, and (3) wide area network. The experimental setup recorded the activity of a human user interacting with the biobank system, which was continuously replayed multiple times. Several metrics were extracted from network traffic traces and server logs captured during the activity replay. RESULTS Regarding the diagnostic data transfer, two types of containers were considered: the Web and the Database containers. The Web appeared to be the more memory-hungry container with a higher computational load (average 2.7 GB of RAM) compared to that of the database. With respect to user access, both users demonstrated the same network performance level, although higher resource consumption was registered for two different actions: DOWNLOAD & LOGOUT (100%) for the researcher and OPEN VIEWER (20%-50%) for the radiologist. CONCLUSIONS This analysis shows that the current setup of BCU Imaging Biobank is well provisioned for satisfying the planned number of concurrent users. More importantly, this study further highlights and quantifies the resource demands of specific user actions, providing a guideline for planning, setting up, and using an image biobanking system.
Collapse
Affiliation(s)
- Giuseppina Esposito
- Bio Check Up Srl, Naples, Italy
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Ciro Allarà
- Bio Check Up Srl, Naples, Italy
- Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy
| | | | | | | | - Giuseppe Aceto
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
| | - Antonio Pescapè
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
| |
Collapse
|
3
|
Yates T, Rotolo P, Bryce Y. The Money Side: Billing, Coding, Reimbursement, Costs, and Marketing. Tech Vasc Interv Radiol 2022; 25:100859. [DOI: 10.1016/j.tvir.2022.100859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
4
|
Allenby MC, Woodruff MA. Image analyses for engineering advanced tissue biomanufacturing processes. Biomaterials 2022; 284:121514. [DOI: 10.1016/j.biomaterials.2022.121514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/02/2022]
|
5
|
Reponen J, Niinimäki J. Emergence of teleradiology, PACS, and other radiology IT solutions in Acta Radiologica. Acta Radiol 2021; 62:1525-1533. [PMID: 34637341 DOI: 10.1177/02841851211051003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
For this historical review, we searched a database containing all the articles published in Acta Radiologica during its 100-year history to find those on the use of information technology (IT) in radiology. After reading the full texts, we selected the presented articles according to major radiology IT domains such as teleradiology, picture archiving and communication systems, image processing, image analysis, and computer-aided diagnostics in order to describe the development as it appeared in the journal. Publications generally follow IT megatrends, but because the contents of Acta Radiologica are mainly clinically oriented, some technology achievements appear later than they do in journals discussing mainly imaging informatics topics.
Collapse
Affiliation(s)
- Jarmo Reponen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Jaakko Niinimäki
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| |
Collapse
|
6
|
Wang D, Liu C, Wang X, Liu X, Lan C, Zhao P, Cho WC, Graeber MB, Liu Y. Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma. FRONTIERS IN BIOINFORMATICS 2021; 1:718697. [PMID: 36303770 PMCID: PMC9581043 DOI: 10.3389/fbinf.2021.718697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 09/28/2021] [Indexed: 09/01/2023] Open
Abstract
Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate dehydrogenase 1 (IDH1) mutations aids the diagnostic classification of these tumors and the prediction of their clinical outcomes. While histology continues to play a key role in frozen section diagnosis, as a diagnostic reference and as a method for monitoring disease progression, recent research has demonstrated the ability of multi-parametric magnetic resonance imaging (MRI) sequences for predicting IDH genotypes. In this paper, we aim to improve the prediction accuracy of IDH1 genotypes by integrating multi-modal imaging information from digitized histopathological data derived from routine histological slide scans and the MRI sequences including T1-contrast (T1) and Fluid-attenuated inversion recovery imaging (T2-FLAIR). In this research, we have established an automated framework to process, analyze and integrate the histopathological and radiological information from high-resolution pathology slides and multi-sequence MRI scans. Our machine-learning framework comprehensively computed multi-level information including molecular level, cellular level, and texture level information to reflect predictive IDH genotypes. Firstly, an automated pre-processing was developed to select the regions of interest (ROIs) from pathology slides. Secondly, to interactively fuse the multimodal complementary information, comprehensive feature information was extracted from the pathology ROIs and segmented tumor regions (enhanced tumor, edema and non-enhanced tumor) from MRI sequences. Thirdly, a Random Forest (RF)-based algorithm was employed to identify and quantitatively characterize histopathological and radiological imaging origins, respectively. Finally, we integrated multi-modal imaging features with a machine-learning algorithm and tested the performance of the framework for IDH1 genotyping, we also provided visual and statistical explanation to support the understanding on prediction outcomes. The training and testing experiments on 217 pathologically verified IDH1 genotyped glioma cases from multi-resource validated that our fully automated machine-learning model predicted IDH1 genotypes with greater accuracy and reliability than models that were based on radiological imaging data only. The accuracy of IDH1 genotype prediction was 0.90 compared to 0.82 for radiomic result. Thus, the integration of multi-parametric imaging features for automated analysis of cross-modal biomedical data improved the prediction accuracy of glioma IDH1 genotypes.
Collapse
Affiliation(s)
- Dingqian Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Cuicui Liu
- Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Xuejun Liu
- Department of Radiology, Hospital Affiliated to Qingdao University, Qingdao, China
| | - Chuanjin Lan
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - Peng Zhao
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| | - William C. Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Kowloon, Hong Kong, SAR China
| | - Manuel B. Graeber
- Ken Parker Brain Tumor Research Laboratories, Brain and Mind Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Yingchao Liu
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
- Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, China
| |
Collapse
|
7
|
Coppola F, Faggioni L, Gabelloni M, De Vietro F, Mendola V, Cattabriga A, Cocozza MA, Vara G, Piccinino A, Lo Monaco S, Pastore LV, Mottola M, Malavasi S, Bevilacqua A, Neri E, Golfieri R. Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging. Front Psychol 2021; 12:710982. [PMID: 34650476 PMCID: PMC8505993 DOI: 10.3389/fpsyg.2021.710982] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 09/02/2021] [Indexed: 12/22/2022] Open
Abstract
Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.
Collapse
Affiliation(s)
- Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Fabrizio De Vietro
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Vincenzo Mendola
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Alberto Piccinino
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Silvia Lo Monaco
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Luigi Vincenzo Pastore
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| | - Margherita Mottola
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Silvia Malavasi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Alessandro Bevilacqua
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Emanuele Neri
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
- Academic Radiology, Department of Translational Research, University of Pisa, Pisa, Italy
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
| |
Collapse
|
8
|
Ojeda PI, Hannan LM, Mieloszyk RJ, Hall CS, Mileto A, Harris WP, Park JO, Baheti AD, Hippe DS, Bhargava P. Is There a Difference Between LI-RADS 3 to LI-RADS 5 Progression Assessment Using CT Versus MR? A Retrospective, Single-Center, Longitudinal Study of Patients Who Underwent 5082 Radiologic Examinations for Surveillance of Hepatocellular Carcinoma Over a 43-Month Period. Curr Probl Diagn Radiol 2021; 51:176-180. [PMID: 33980417 DOI: 10.1067/j.cpradiol.2021.03.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 03/22/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVE The Liver Imaging Reporting and Data System (LI-RADS) has been widely applied to CT and MR liver observations in patients at high-risk for hepatocellular carcinoma (HCC). We investigated the impact of CT vs MR in upgrading LI-RADS 3 to LI-RADS 5 observations using a large cohort of high-risk patients. METHODS We performed a retrospective, longitudinal study of CT and MR radiographic reports (June 2013 - February 2017) with an assigned LI-RADS category. A final population of 757 individual scans and 212 high-risk patients had at least one LI-RADS 3 observation. Differences in observation time to progression between modalities were determined using uni- and multivariable analysis. RESULTS Of the 212 patients with a LI-RADS 3 observation, 52 (25%) had progression to LI-RADS 5. Tp ranged from 64 - 818 days (median: 196 days). One hundred and three patients (49%) had MR and 109 patients (51%) had CT as their index study. Twenty-four patients with an MR index exam progressed to LI-RADS 5 during the follow-up interval, with progression rates of 22% (CI:13%-30%) at 1 year and 29% (CI:17%-40%) at 2 years. Twenty-eight patients with a CT index exam progressed to LI-RADS 5 during follow-up, with progression rates of 26% (CI:16%-35%) at 1 year and 31% (CI:19%-41%) at 2 years. Progression rates were not significantly different between patients whose LI-RADS 3 observation was initially diagnosed on MR vs CT (HR: 0.81, P = 0.44). DISCUSSION MR and CT modalities are comparable for demonstrating progression from LI-RADS 3 to 5 for high risk patients.
Collapse
Affiliation(s)
- Patricia I Ojeda
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Lindsay M Hannan
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Rebecca J Mieloszyk
- Department of Radiology, University of Washington School of Medicine, Seattle, WA; Microsoft Research, Redmond, WA
| | - Christopher S Hall
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Achille Mileto
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - William P Harris
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - James O Park
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Akshay D Baheti
- Department of Radiology, University of Washington School of Medicine, Seattle, WA; Department of Radiology, Tata Memorial Center, Mumbai, India; Department of Radiology, Homi Bhabha National Institute, Mumbai, India
| | - Daniel S Hippe
- Department of Radiology, University of Washington School of Medicine, Seattle, WA
| | - Puneet Bhargava
- Department of Radiology, University of Washington School of Medicine, Seattle, WA.
| |
Collapse
|
9
|
Creamer DK, Bagadia A, Daniels C, Pitcher RD. A silver bullet? The role of radiology information system data mining in defining gunshot injury trends at a South African tertiary-level hospital. SA J Radiol 2021; 25:2018. [PMID: 33824747 PMCID: PMC8008079 DOI: 10.4102/sajr.v25i1.2018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 12/17/2020] [Indexed: 11/28/2022] Open
Abstract
Background South Africa (SA) has no national injury surveillance system, and hence, non-fatal gunshot injuries are not routinely recorded. Most firearm-related injuries require multi-detector computer tomography (MDCT) assessment at a tertiary-level facility. MDCT scanning for victims with gunshot injuries thus provide an indication of the societal burden of firearm trauma. The potential of the modern radiology information system (RIS) to serve as a robust research tool in such settings is not fully appreciated. Objective The aim of this study was to evaluate the use of institutional RIS data in defining MDCT scanning trends for gunshot victims presenting to a tertiary-level SA hospital. Method A single-institution, retrospective, comparative study was conducted at the Tygerberg Hospital (TBH) Trauma Unit for the years 2013 and 2018. Using data-mining software, customised RIS searches for information on all gunshot-related emergency computed tomography scans in the respective years were performed. Demographic, temporal, anatomical and scan-protocol trends were analysed by cross tabulation, Chi-squared and Fisher’s exact tests. Results Gunshot-related emergency MDCT scans increased by 62% (546 vs. 887) from 2013 to 2018. Lower-limb CT angiography was the commonest investigation in both periods. A higher proportion of victims in 2018 sustained thoracic injuries (12.5% vs. 19.8%; p < 0.01) and required imaging of more than two body parts (13.1% vs. 19.2%; p < 0.01). Conclusion By using RIS data to demonstrate the increasing gunshot-related MDCT workload in the review period, as well as a pattern of more complex and potentially life-threatening injury, this study highlights the burden of firearm trauma in the society and the potential role of the modern RIS as a robust research tool.
Collapse
Affiliation(s)
- Dale K Creamer
- Division of Radiodiagnosis, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Asif Bagadia
- Division of Radiodiagnosis, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Tygerberg Hospital, Stellenbosch University, Cape Town, South Africa
| | - Clive Daniels
- South African Health Informatics Association (SAHIA), Cape Town, South Africa.,Private Healthcare Information Standards Committee (PHISC), Cape Town, South Africa
| | - Richard D Pitcher
- Division of Radiodiagnosis, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| |
Collapse
|
10
|
Dick EA, Raithatha A, Musker L, Redhead J, Mehta A, Amiras D. Remote reporting in the COVID-19 era: from pilot study to practice. Clin Radiol 2020; 75:710.e5-710.e8. [PMID: 32654757 PMCID: PMC7328588 DOI: 10.1016/j.crad.2020.06.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 06/25/2020] [Indexed: 11/26/2022]
Abstract
AIM To assess the benefits and challenges of remote reporting using an intra-departmental teleradiology system. MATERIALS AND METHODS A pilot of an in-hospital Trust radiologist reporting on in-hospital Trust patients via a remote login was undertaken. Reporting output, training impact, and quality improvement were measured. RESULTS Reporting output increased by 140%. Trainee satisfaction was high in a qualitative survey, particularly for out-of-hours support and teaching. Clinicians found the service to be similar to the same service provided by a locally based radiologist. CONCLUSION In the COVID-19 era, remote working has developed rapidly. This study shows that radiology departments can provide remote reporting that is equal in standard to reporting from within the hospital, and in addition, that there are advantages to output and training.
Collapse
Affiliation(s)
- E A Dick
- Department of Radiology, Imperial College NHS Trust, London, UK; University of Sydney, Sydney, NSW, Australia.
| | - A Raithatha
- Department of Radiology, Imperial College NHS Trust, London, UK
| | - L Musker
- Department of Radiology, Imperial College NHS Trust, London, UK
| | - J Redhead
- Medical Director's Office, Imperial College NHS Trust, UK
| | - A Mehta
- Department of Radiology, Imperial College NHS Trust, London, UK
| | - D Amiras
- Department of Radiology, Imperial College NHS Trust, London, UK
| |
Collapse
|
11
|
MRI-guided vacuum-assisted breast biopsy: experience of a single tertiary referral cancer centre and prospects for the future. Med Oncol 2020; 37:36. [PMID: 32221708 DOI: 10.1007/s12032-020-01358-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 03/02/2020] [Indexed: 01/11/2023]
Abstract
MRI-guided vacuum-assisted breast biopsy (VABB) is used for suspicious breast cancer (BC) lesions which are detectable only with MRI: because the high sensitivity but limited specificity of breast MRI it is a fundamental tool in breast imaging divisions. We analyse our experience of MRI-guided VABB and critically discuss the potentialities of diffusion-weighted imaging (DWI) and artificial intelligence (AI) in this matter. We retrospectively analysed a population of consecutive women underwent VABB at our tertiary referral BC centre from 01/2011 to 01/2019. Reference standard was histological diagnosis or at least 1-year negative follow-up. McNemar, Mann-Whitney and χ2 tests at 95% level of significance were used as statistical exams. 217 women (mean age = 52, 18-72 years) underwent MRI-guided VABB; 11 were excluded and 208 MRI-guided VABB lesions were performed: 34/208 invasive carcinomas, 32/208 DCIS, 8/208 LCIS, 3/208 high-risk lesions and 131/208 benign lesions were reported. Accuracy of MRI-guided VABB was 97%. The predictive features for malignancy were mass with irregular shape (OR 8.4; 95% CI 0.59-31.6), size of the lesion (OR 4.4; 95% CI 1.69-9.7) and mass with irregular/spiculated margins (OR 5.4; 95% CI 6.8-31.1). Six-month follow-up showed 4 false-negative cases (1.9%). Invasive BC showed a statistically significant higher hyperintense signal at DWI compared to benign lesions (p = 0.03). No major complications occurred. MR-guided VABB showed high accuracy. Benign-concordant lesions should be followed up with breast MRI in 6-12 months due to the risk of false-negative results. DWI and AI applications showed potential benefit as support tools for radiologists.
Collapse
|
12
|
Pesapane F, Suter MB, Rotili A, Penco S, Nigro O, Cremonesi M, Bellomi M, Jereczek-Fossa BA, Pinotti G, Cassano E. Will traditional biopsy be substituted by radiomics and liquid biopsy for breast cancer diagnosis and characterisation? Med Oncol 2020; 37:29. [PMID: 32180032 DOI: 10.1007/s12032-020-01353-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 02/26/2020] [Indexed: 02/06/2023]
Abstract
The diagnosis of breast cancer currently relies on radiological and clinical evaluation, confirmed by histopathological examination. However, such approach has some limitations as the suboptimal sensitivity, the long turnaround time for recall tests, the invasiveness of the procedure and the risk that some features of target lesions may remain undetected, making re-biopsy a necessity. Recent technological advances in the field of artificial intelligence hold promise in addressing such medical challenges not only in cancer diagnosis, but also in treatment assessment, and monitoring of disease progression. In the perspective of a truly personalised medicine, based on the early diagnosis and individually tailored treatments, two new technologies, namely radiomics and liquid biopsy, are rising as means to obtain information from diagnosis to molecular profiling and response assessment, without the need of a biopsied tissue sample. Radiomics works through the extraction of quantitative peculiar features of cancer from radiological data, while liquid biopsy gets the whole of the malignancy's biology from something as easy as a blood sample. Both techniques hopefully will identify diagnostic and prognostic information of breast cancer potentially reducing the need for invasive (and often difficult to perform) biopsies and favouring an approach that is as personalised as possible for each patient. Nevertheless, such techniques will not substitute tissue biopsy in the near future, and even in further times they will require the aid of other parameters to be correctly interpreted and acted upon.
Collapse
Affiliation(s)
- Filippo Pesapane
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy.
| | | | - Anna Rotili
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Silvia Penco
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Olga Nigro
- Medical Oncology, ASST Sette Laghi, Viale Borri 57, 21100, Varese, VA, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Massimo Bellomi
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiology, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Barbara Alicja Jereczek-Fossa
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
- Department of Radiation Oncology, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| | - Graziella Pinotti
- Medical Oncology, ASST Sette Laghi, Viale Borri 57, 21100, Varese, VA, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO European Institute of Oncology IRCCS, Via Giuseppe Ripamonti, 435, 20141, Milan, MI, Italy
| |
Collapse
|
13
|
Wildman-Tobriner B, Thorpe MP, Said N, Ehieli WL, Roth CJ, Jaffe TA. Moving Radiology Workflow to the Electronic Health Record: Quantitative and Qualitative Experience From a Large Academic Medical Center. Acad Radiol 2020; 27:253-259. [PMID: 30876710 DOI: 10.1016/j.acra.2019.02.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/01/2019] [Accepted: 02/07/2019] [Indexed: 11/19/2022]
Abstract
RATIONALE AND OBJECTIVES To objectively and subjectively evaluate a large, academic radiology department's transition to electronic health record (EHR) centered workflow. MATERIALS AND METHODS Multiple metrics were compared from before and after the move to EHR-driven workflow. Examination ordering and reading priority data were obtained for 30 days both before and after the transition. Sixteen radiologists were observed opening a computed tomography (CT) examination, and time to open, mouse clicks, and keystrokes were recorded. Information available to the radiologist during interpretation was also compared. Additionally, a 12 question survey was sent out to the residents and faculty both before and after the transition. RESULTS Implementation of an eight-level reading priority system increased worklist granularity and improved identification of more urgent studies to read. Radiologists opened CT studies in picture archiving and communications system-driven workflow in 52.4 ± 16.9 seconds using 9.5 ± 3.9 clicks and 6.3 ± 2.9 keystrokes, compared to 17.3 ± 9.5 seconds, 4.8 ± 1.5 clicks, and 0.1 ± 0.3 keystrokes in EHR-driven workflow (p < 0.001 for each measure). More information was available to the radiologist during examination interpretation, and 54.7% of radiologists rated the ease of use of the new system as good or very good (compared to 4.2% for the old system, p < 0.001). CONCLUSION Transitioning to an EHR-driven workflow at a large academic medical center improved efficiency, was favorable to radiologists, and enhanced examination prioritization.
Collapse
Affiliation(s)
- Benjamin Wildman-Tobriner
- Department of Radiology, Duke University Medical Center, North Carolina, 2301 Erwin Rd, Durham, NC 27710.
| | - Matthew P Thorpe
- Department of Radiology, Duke University Medical Center, North Carolina, 2301 Erwin Rd, Durham, NC 27710
| | - Nicholas Said
- Department of Radiology, Duke University Medical Center, North Carolina, 2301 Erwin Rd, Durham, NC 27710
| | - Wendy L Ehieli
- Department of Radiology, Duke University Medical Center, North Carolina, 2301 Erwin Rd, Durham, NC 27710
| | - Christopher J Roth
- Department of Radiology, Duke University Medical Center, North Carolina, 2301 Erwin Rd, Durham, NC 27710
| | - Tracy A Jaffe
- Department of Radiology, Duke University Medical Center, North Carolina, 2301 Erwin Rd, Durham, NC 27710
| |
Collapse
|
14
|
Ahsen ME, Ayvaci MUS, Raghunathan S. When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis. INFORMATION SYSTEMS RESEARCH 2019. [DOI: 10.1287/isre.2018.0789] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Affiliation(s)
- Mehmet Eren Ahsen
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York 10029
| | | | | |
Collapse
|
15
|
Jalal S, Nicolaou S, Parker W. Artificial Intelligence, Radiology, and the Way Forward. Can Assoc Radiol J 2019; 70:10-12. [DOI: 10.1016/j.carj.2018.09.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 09/10/2018] [Indexed: 12/18/2022] Open
Affiliation(s)
- Sabeena Jalal
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Savvas Nicolaou
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - William Parker
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
16
|
Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018; 2:35. [PMID: 30353365 PMCID: PMC6199205 DOI: 10.1186/s41747-018-0061-6] [Citation(s) in RCA: 310] [Impact Index Per Article: 51.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 07/31/2018] [Indexed: 02/08/2023] Open
Abstract
One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6–9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.
Collapse
Affiliation(s)
- Filippo Pesapane
- Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Marina Codari
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy.
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy.,Department of Biomedical Sciences for Health, Università degli Studi di Milano, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy
| |
Collapse
|
17
|
Islam MM, Poly TN, Li YCJ. Recent Advancement of Clinical Information Systems: Opportunities and Challenges. Yearb Med Inform 2018; 27:83-90. [PMID: 30157510 PMCID: PMC6115226 DOI: 10.1055/s-0038-1667075] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objectives:
Clinical information systems (CISs) have generated opportunities for meaningful improvements both in patient care and workflow but there is still a long way to perfection. Healthcare providers are still facing challenges of data exchange, management, and integration due to lack of functionality among these systems. Our objective here is to systematically review, synthesize, and summarize the literature that describes the current stage of clinical information systems, so as to assess the current state of knowledge, and identify benefits and challenges.
Methods:
PubMed, EMBASE, and the bibliographies of articles were searched for studies published until September 1, 2017, which reported on significant advancement of clinical information systems, as well as problems and opportunities in this field. Studies providing the most detailed information were included and the others were kept only as references.
Results:
We selected 23 papers out of 1,026 unique abstracts for full-text review using our selection criteria, and 20 out of these 23 studies met all of our inclusion criteria. We focused on three major areas: 1) Ambulatory and inpatients clinical information systems; 2) Specialty information systems; and 3) Ancillary information systems. As CIS can support evidence-based practices that, in turn, improve patient's safety, quality and efficacy of care, advancement, acceptability, and adaptability of CIS have increased worldwide. Although, the demand for CIS functionality is rising fast, current CISs still have data integration challenges and lack of functionality to exchange patient information from all or some parts of the healthcare system. These limitations can be attributed to technical, human, and organizational factors
Conclusion:
Clinical information systems provide tremendous opportunities to reduce clinical errors such as medication errors and diagnostic errors and to support healthcare professionals by offering up-to-date patient information. They promise to improve workflow and efficiency of care, thus boosting the overall quality of healthcare.
Collapse
Affiliation(s)
- Md Mohaimenul Islam
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chuan Jack Li
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan.,TMU Research Center of Cancer Translational Medicine, Taipei, Taiwan
| |
Collapse
|
18
|
Sardanelli F. Trends in radiology and experimental research. Eur Radiol Exp 2017; 1:1. [PMID: 29708170 PMCID: PMC5909338 DOI: 10.1186/s41747-017-0006-5] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 03/15/2017] [Indexed: 12/19/2022] Open
Abstract
European Radiology Experimental, the new journal launched by the European Society of Radiology, is placed in the context of three general and seven radiology-specific trends. After describing the impact of population aging, personalized/precision medicine, and information technology development, the article considers the following trends: the tension between subspecialties and the unity of the discipline; attention to patient safety; the challenge of reproducibility for quantitative imaging; standardized and structured reporting; search for higher levels of evidence in radiology (from diagnostic performance to patient outcome); the increasing relevance of interventional radiology; and continuous technological evolution. The new journal will publish not only studies on phantoms, cells, or animal models but also those describing development steps of imaging biomarkers or those exploring secondary end-points of large clinical trials. Moreover, consideration will be given to studies regarding: computer modelling and computer aided detection and diagnosis; contrast materials, tracers, and theranostics; advanced image analysis; optical, molecular, hybrid and fusion imaging; radiomics and radiogenomics; three-dimensional printing, information technology, image reconstruction and post-processing, big data analysis, teleradiology, clinical decision support systems; radiobiology; radioprotection; and physics in radiology. The journal aims to establish a forum for basic science, computer and information technology, radiology, and other medical subspecialties.
Collapse
Affiliation(s)
- Francesco Sardanelli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano and Unit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan Italy
| |
Collapse
|
19
|
Zhang R, Chen D, Shang X, Zhu X, Liu K. A Knowledge-Constrained Access Control Model for Protecting Patient Privacy in Hospital Information Systems. IEEE J Biomed Health Inform 2017; 22:904-911. [PMID: 28436908 DOI: 10.1109/jbhi.2017.2696573] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Current access control mechanisms of the hospital information system can hardly identify the real access intention of system users. A relaxed access control increases the risk of compromise of patient privacy. To reduce unnecessary access of patient information by hospital staff, this paper proposes a knowledge-constrained role-based access control (KC-RBAC) model in which a variety of medical domain knowledge is considered in access control. Based on the proposed Purpose Tree and knowledge-involved algorithms, the model can dynamically define the boundary of access to the patient information according to the context, which helps to protect patient privacy by controlling access. Compared with the role-based access control model, KC-RBAC can effectively protect patient information according to the results of the experiments.
Collapse
|
20
|
Hemmat M, Ayatollahi H, Maleki MR, Saghafi F. Future Research in Health Information Technology: A Review. PERSPECTIVES IN HEALTH INFORMATION MANAGEMENT 2017; 14:1b. [PMID: 28566991 PMCID: PMC5430110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 09/28/2022]
Abstract
INTRODUCTION Currently, information technology is considered an important tool to improve healthcare services. To adopt the right technologies, policy makers should have adequate information about present and future advances. This study aimed to review and compare studies with a focus on the future of health information technology. METHOD This review study was completed in 2015. The databases used were Scopus, Web of Science, ProQuest, Ovid Medline, and PubMed. Keyword searches were used to identify papers and materials published between 2000 and 2015. Initially, 407 papers were obtained, and they were reduced to 11 papers at the final stage. The selected papers were described and compared in terms of the country of origin, objective, methodology, and time horizon. RESULTS The papers were divided into two groups: those forecasting the future of health information technology (seven papers) and those providing health information technology foresight (four papers). The results showed that papers related to forecasting the future of health information technology were mostly a literature review, and the time horizon was up to 10 years in most of these studies. In the health information technology foresight group, most of the studies used a combination of techniques, such as scenario building and Delphi methods, and had long-term objectives. CONCLUSION To make the most of an investment and to improve planning and successful implementation of health information technology, a strategic plan for the future needs to be set. To achieve this aim, methods such as forecasting the future of health information technology and offering health information technology foresight can be applied. The forecasting method is used when the objectives are not very large, and the foresight approach is recommended when large-scale objectives are set to be achieved. In the field of health information technology, the results of foresight studies can help to establish realistic long-term expectations of the future of health information technology.
Collapse
Affiliation(s)
- Morteza Hemmat
- Department of Health Information Management at Iran University of Medical Sciences in Tehran, Iran
| | - Haleh Ayatollahi
- Department of Health Information Management at Iran University of Medical Sciences in Tehran, Iran
| | - Mohammad Reza Maleki
- Department of Health Management, Iran University of Medical Sciences in Tehran, Iran
| | - Fatemeh Saghafi
- School of New Sciences and Technologies at Tehran University in Tehran, Iran
| |
Collapse
|
21
|
Dodd GD, Restauri NL, Kondo KL, Lewis PJ. Driving Innovation in Radiology: A Summary of the 2015 Intersociety Committee Summer Conference. J Am Coll Radiol 2016; 13:1477-1482. [DOI: 10.1016/j.jacr.2016.06.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 06/16/2016] [Accepted: 06/17/2016] [Indexed: 11/24/2022]
|
22
|
Jain A, Gupta P, Anand SPS, Dang A. Digital Paper Prints as Replacement for LASER Films: A Study of Intra-Observer Agreement for Wrist Radiographic Findings in Rickets. J Clin Diagn Res 2016; 10:TC11-4. [PMID: 27656525 DOI: 10.7860/jcdr/2016/21057.8275] [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: 05/01/2016] [Accepted: 05/23/2016] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Replacement of conventional LASER films with digital paper prints as supplement to radiology reports may serve as an economical and environment friendly method. However, it is essential that such a change does not compromise patient's intended diagnostic outcome. AIM The aim of this study was to assess the reliability and acceptability of digital paper prints for the radiographic images by the treating physicians and radiologists. MATERIALS AND METHODS This observational analytical study was done at a tertiary care hospital of New Delhi, India. A total of 58 consecutively ordered wrist radiographs of paediatric patients (6 months to 12 years of age) for ruling out rickets were retrieved from the PACS (Picture Archiving and Communication System). These 58 radiographs, out of which 21 (36.2%) had radiological evidence of rickets over PACS were printed on two different media i.e., LASER films and glossy photographic paper. An objective scoring for the severity of rickets was done on both LASER films and paper prints by six observers independently. Overall comfort level with paper prints was rated on a 1-5 point Likert scale. Data was analysed using STATA 14.0 (Stata Corp, College Station, TX). RESULTS Intra-observer percentage agreement and value of Cohen's kappa for PACS vs. LASER films and PACS vs. paper prints was equal i.e., 98.3% and 0.97, respectively. Intra-observer agreement between LASER films and paper prints for all six observers was excellent, ranging from 0.92 to 1.00; percentage agreement ranging from 94.8% to 100%. Fracture of ulna/radius present in 4 sets of the X-rays was well demonstrated in both LASER films and paper prints. Comfort level with paper prints was rated as 5 out of 5 by all due to no requirement of any special illuminated view box and dark room. CONCLUSION This study concludes that the use of paper prints may serve as a reliable alternative to LASER films to communicate the report of wrist radiographs for the treating physicians without any compromise over diagnostic information in cases of rickets.
Collapse
Affiliation(s)
- Abhinav Jain
- Assistant Professor, Department of Radiodiagnosis, Hamdard Institute of Medical Sciences and Research and Hakeem Abdul Hameed Centenary Hospital , New Delhi, India
| | - Priyanka Gupta
- Associate Professor, Department of Paediatrics, Hamdard Institute of Medical Sciences and Research and Hakeem Abdul Hameed Centenary Hospital , New Delhi, India
| | - Surinder Pal Singh Anand
- Professor, Department of Radiodiagnosis, Hamdard Institute of Medical Sciences and Research and Hakeem Abdul Hameed Centenary Hospital , New Delhi, India
| | - Archana Dang
- Ph.D. Scholar, Delhi School of Economics, University of Delhi , India
| |
Collapse
|
23
|
Schultz TJ, Hannaford N, Mandel C. Patient safety problems from healthcare information technology in medical imaging. BJR Case Rep 2015; 2:20150107. [PMID: 30363695 PMCID: PMC6180868 DOI: 10.1259/bjrcr.20150107] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Revised: 08/26/2015] [Accepted: 11/24/2015] [Indexed: 11/06/2022] Open
Abstract
Health information technology (HIT) systems have been deployed extensively by healthcare organizations and promoted as a panacea to many of the challenges faced by medical imaging departments, particularly with respect to workflow, efficiency and diagnostic accuracy. This report describes how inadequate planning, integration, training and testing of HIT can impact on patient safety and result in patient harm.
Collapse
|
24
|
PACS administrators' and radiologists' perspective on the importance of features for PACS selection. J Digit Imaging 2015; 27:486-95. [PMID: 24744278 DOI: 10.1007/s10278-014-9682-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Abstract
Picture archiving and communication systems (PACS) play a critical role in radiology. This paper presents the criteria important to PACS administrators for selecting a PACS. A set of criteria are identified and organized into an integrative hierarchical framework. Survey responses from 48 administrators are used to identify the relative weights of these criteria through an analytical hierarchy process. The five main dimensions for PACS selection in order of importance are system continuity and functionality, system performance and architecture, user interface for workflow management, user interface for image manipulation, and display quality. Among the subdimensions, the highest weights were assessed for security, backup, and continuity; tools for continuous performance monitoring; support for multispecialty images; and voice recognition/transcription. PACS administrators' preferences were generally in line with that of previously reported results for radiologists. Both groups assigned the highest priority to ensuring business continuity and preventing loss of data through features such as security, backup, downtime prevention, and tools for continuous PACS performance monitoring. PACS administrators' next high priorities were support for multispecialty images, image retrieval speeds from short-term and long-term storage, real-time monitoring, and architectural issues of compatibility and integration with other products. Thus, next to ensuring business continuity, administrators' focus was on issues that impact their ability to deliver services and support. On the other hand, radiologists gave high priorities to voice recognition, transcription, and reporting; structured reporting; and convenience and responsiveness in manipulation of images. Thus, radiologists' focus appears to be on issues that may impact their productivity, effort, and accuracy.
Collapse
|
25
|
Cook TS, Nagy P. Business intelligence for the radiologist: making your data work for you. J Am Coll Radiol 2014; 11:1238-40. [PMID: 25467900 DOI: 10.1016/j.jacr.2014.09.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2014] [Accepted: 09/10/2014] [Indexed: 11/29/2022]
Abstract
Although it remains absent from most programs today, business intelligence (BI) has become an integral part of modern radiology practice management. BI facilitates the transition away from lack of understanding about a system and the data it produces toward incrementally more sophisticated comprehension of what has happened, could happen, and should happen. The individual components that make up BI are common across industries and include data extraction and transformation, process analysis and improvement, outcomes measures, performance assessment, graphical dashboarding, alerting, workflow analysis, and scenario modeling. As in other fields, these components can be directly applied in radiology to improve workflow, throughput, safety, efficacy, outcomes, and patient satisfaction. When approaching the subject of BI in radiology, it is important to know what data are available in your various electronic medical records, as well as where and how they are stored. In addition, it is critical to verify that the data actually represent what you think they do. Finally, it is critical for success to identify the features and limitations of the BI tools you choose to use and to plan your practice modifications on the basis of collected data. It is equally important to remember that BI plays a critical role in continuous process improvement; whichever BI tools you choose should be flexible to grow and evolve with your practice.
Collapse
Affiliation(s)
- Tessa S Cook
- Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
| | - Paul Nagy
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Maryland; Johns Hopkins Armstrong Institute for Patient Safety and Quality, Baltimore, Maryland
| |
Collapse
|
26
|
Abstract
Over the past 20 years, imaging informatics has been driven by the widespread adoption of radiology information and picture archiving and communication and speech recognition systems. These three clinical information systems are commonplace and are intuitive to most radiologists as they replicate familiar paper and film workflow. So what is next? There is a surge of innovation in imaging informatics around advanced workflow, search, electronic medical record aggregation, dashboarding, and analytics tools for quality measures (Nance et al., AJR Am J Roentgenol 200:1064-1070, 2013). The challenge lies in not having to rebuild the technological wheel for each of these new applications but instead attempt to share common components through open standards and modern development techniques. The next generation of applications will be built with moving parts that work together to satisfy advanced use cases without replicating databases and without requiring fragile, intense synchronization from clinical systems. The purpose of this paper is to identify building blocks that can position a practice to be able to quickly innovate when addressing clinical, educational, and research-related problems. This paper is the result of identifying common components in the construction of over two dozen clinical informatics projects developed at the University of Maryland Radiology Informatics Research Laboratory. The systems outlined are intended as a mere foundation rather than an exhaustive list of possible extensions.
Collapse
|
27
|
Kein Informationsaustausch ohne Technik. Radiologe 2014; 54:32-9. [DOI: 10.1007/s00117-013-2538-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
28
|
Hempel JM, Jungmann F, Klöckner R, Pinto dos Santos D, Kurz H, Düber C. [IT systems in radiology and IT systems for radiologists]. Radiologe 2013; 54:40-4. [PMID: 24366353 DOI: 10.1007/s00117-013-2539-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Radiology plays an important role in introduction and use of information technology (IT) systems in the daily clinical routine. The radiology information system (RIS) and picture archiving and communication system (PACS) are the main systems used in a digital radiology department. In this article the basic principles and functions of these systems and trends in development are described.
Collapse
Affiliation(s)
- J-M Hempel
- Klinik und Poliklinik für Diagnostische und Interventionelle Radiologie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland,
| | | | | | | | | | | |
Collapse
|