As Chief Informatics Officer at Cleveland Clinic Imaging Institute, I work alongside a team of 60 imaging informatics professionals. In this role, I manage a portfolio of radiology applications valuated at over $10M yearly. Recently, the team deployed a new Picture Archive and Communication System (PACS) over 300 radiologists nationwide serving 3 million imaging exams yearly.
Jointly appointed as Section Head of Clinical Informatics, I work with a team of physician-informaticists to integrate new technology into our radiology practice. One of our main projects is an "actionable results module," which tracks the imaging findings of over 100,000 patients.
In the area of imaging artificial intelligence (AI), I lead Cleveland Clinic's Imaging Data Science Center, introducing foundational tools like a high throughput data extraction and deidentification pipeline, full-text report search engine, and buidling a clinical AI workflow engine for radiology. With the Clinical Informatics section, we also brought several AI solutions to clinical practice, including tools in neuroradiology, breast imaging, thoracic radiology, and cardiovascular imaging.
In response to a cybersecurity breach in 2019, I led the transition of a local hospital to an analog workflow and the ensuing development of operational recovery plans for over 25 imaging locations in the Cleveland Clinic catchment. We set up a Cyber Attack Response Task Force to respond more quickly to future incidents.
As a clinical radiologist, my expertise is in musculoskeletal imaging, performing image-guided biopsies, joint injections, as well as a variety of diagnostic work in XR, CT, and MRI. I hold a board certification in both diagnostic radiology and nuclear medicine.
In the field of research, I have focused on the application of AI and advanced imaging techniques as an Assistant Professor at Cleveland Clinic Lerner College of Medicine. My work includes research in musculoskeletal, cardiovascular, and oncologic imaging as co-investigator or principal investigator in projects totaling approximately $5.4M of grant funding.
I am active in professional societies, contributing to the development of the Informatics e-Learning Hub with the ACR Commission for Informatics, chairing the ACR Informatics Advisory Council and ACR's annual Data Science Summit, and leading the RSNA's Informatics Policy Committee. For SIIM, I chair the Security Subcommittee to bring awareness and resilience to enterprise radiology operations.
As a dedicated clinical radiologist and informaticist, I harbor a deep passion for optimizing workflow, reducing burnout, and enhancing patient care through the strategic use of innovative technology. Guided by this passion, I actively engage in creating and implementing sophisticated digital solutions, recognizing that our ability to provide exceptional care is intrinsically tied to the efficiency and effectiveness of our technological systems.
You can find my profile on Twitter, LinkedIn, or follow the blog Figure Stuff Out. I enjoy imaging informatics, healthcare innovation, and good comfort food. The content of this website and the blog are of my personal opinion and do not represent the view of Cleveland Clinic.
Natural Language Processing for Imaging Protocols
AI for Acute Aortic Syndrome Detection
Integrated Workflow Peer Learning Case Submission
ARIES - Deep Learning + Bayesian System for Radiology Education and Dx Support
RadCare - Resident-Led Radiology Consultation for General Medicine Wards
Capricorn - Radiology Residency Analytics Tool
Centaur - Rapid-Fire Learning Module for Basic Imaging Findings
Integrating natural language processing and machine learning algorithms to categorize oncologic response in radiology reports
Unsupervised genomic and epigenetic analysis
- Xavier BA, Chen PH. Natural Language Processing for Imaging Protocol Assignment: Machine Learning for Multiclass Classification of Abdominal CT Protocols Using Indication Text Data. J Digit Imaging [ePub ahead of print]. 2022 Jun 2
- Chen P-H, Bodak R, Gandhi NS. Ransomware Recovery and Imaging Operations: Lessons Learned and Planning Considerations. J Digit Imaging. 2021 Jun;34(3):731-740.
- Rudie JD, Duda J, Duong MT, Chen PH, Xie L, Kurtz R, et al. Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance. J Digit Imaging.s 2021 Aug;34(4):104958.
- Shah C, Kohlmyer S, Hunter K, Jones SE, Chen P-H. A translational clinical assessment workflow for the validation of external artificial intelligence models. In: Park BJ, Deserno TM, editors. Medical Imaging 2021: Imaging Informatics for Healthcare, Research, and Applications. Online Only, United States: SPIE; 2021. p. 14.
- Martin-Carreras TT, Li H, Chen P-H. Interpretative applications of artificial intelligence in musculoskeletal imaging: concepts, current practice, and future directions. J Med Artif Intell. 2020 Sep;3:1313.
- Shah C, Cook TS, Chen P-H, Hyland S, Heavener R, Kahn CE, et al. Improving Triage of After-Hours Radiology Examinations Through Worklist Unification. J Am Coll Radiol. 2020 Mar;S1546144020301459.
- Martin-Carreras T, Chen P-H. From Data to Value: How Artificial Intelligence Augments the Radiology Business to Create Value. Sem Musc Radiol. 2020 Feb;24(01):6573.
- Chen P-H. Essential Elements of Natural Language Processing: What the Radiologist Should Know. Acad Radiol. 2020 Jan;27(1):612.
- Duong MT, Rauschecker AM, Rudie JD, Chen P-H, Cook TS, Bryan RN, et al. Artificial intelligence for precision education in radiology. Br J Radiol. 2019 Jul 26;20190389.
- Gillman J, Wu SE, Rowland J, Scanlon M, Chen P-H. Comparison of In-Person and Digital Radiology Resident Consultation Services. J Am Coll Radiol. 2019 Feb 4; S1546144018314856
- Chen P-H, Scanlon MH. Teaching Radiology Trainees from the Perspective of a Millennial. Academic Radiology. 2018 Jun;25(6):794-800.
- Chen P-H, Cross N. IoT in Radiology: Using Raspberry Pi to Automatically Log Telephone Calls in the Reading Room. Journal of Digital Imaging. 2018 Jun;31(3):371-8.
- Deitte LA, Chen P-H, Scanlon MH, Heitkamp DE, Davis LP, Urban S, et al. Twenty-four-Seven In-house Faculty and Resident Education. Journal of the American College of Radiology. 2018 Jan;15(1):90a.
- Chen P-H, 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 Apr;31(2):17884.