Anales de la RANM

100 A N A L E S R A N M R E V I S T A F U N D A D A E N 1 8 7 9 INTEGRATED DIAGNOSIS: EARLY EXPERIENCE Pablo R. Ros An RANM · Año 2019 · número 136 (02) · páginas 99 a 102 Further, Institutes are characterized by having a sin- gle clinical and administrative leadership and therefo- re a single strategy, marketing plan and budget. Its sin- gle focus makes Institutes efficient and valuable, ma- king its proliferation commonplace as stand-alone or within AMCs or other large medical centers. In either case, traditional physician-centered Departments continue to operate to ensure specialty-based training and research programs (5). Considering the increasing success of disease and or- gan based Institutes, the integration of the core diag- nostic disciplines has started to draw interest by health care systems’ administrators and practicing diagnosti- cians alike. Establishing a Diagnostic Institute makes sense since it has the potential on one hand to increa- se patient satisfaction and quality of service delivered and on the other, to be innovative and bring market differentiation. In short, a Diagnostic Institute brings value, which is the Holy Grail in health care nowa- days, since its establishment may increase quality whi- le decreases cost. A Diagnostic Institute rides not only the new wave of integration in healthcare but also takes advantage of today’s other hot topic, the application of Artificial Inte- lligence (AI) in clinical care. Diagnostic disciplines with their immense digital data banks consisting of medical images, anatomic and clinical pathology (laboratory) information and genomics constitute the ideal field to apply AI, machine learning and big data analytics (1). With the advent of digital pathology, the playing field for cross-disciplinary information technology (IT) tools greatly expands. Moreover, there is a strong trend for quantification of image contents to enable large-scale computational analysis. This is equally applicable for pathology and radiology, and in the lat- ter case, it is known as radiomics (3). The disciplinary border actually becomes blurred and irrelevant when computational approaches, such as deep learning, are applied to quantify imaging features - the computatio- nal methods are the same regardless of the data sour- ce. In addition, the possibility of combining radiologic and pathologic imaging in machine learning approa- ches is a particularly promising aspect of ID (1) Therefore, advances in IT provide the technological background for meaningful integration of diagnostic data and allow the take off of clinically feasible ID. To understand the many facets of Diagnostic Institu- tes it is essential to become familiar with its organi- zation overview based on the typical structure of Ra- diology, Pathology and Genetics Departments in US AMCs (Figure 1). Departments of Radiology are typically organized in Divisions based on organ systems, such as: Abdomi- nal Imaging, Neuroradiology, Breast Imaging, Vascu- lar, Musculoskeletal and Cardio-Thoracic Radiology. Divisions can be also based on patient’s age, such as Pediatric Radiology; type of service, such as Emergen- cy Radiology, Oncoradiology and Community or Re- gional Radiology; and intrinsic nature (Interventional and Diagnostic Radiology). In addition, Radiology is also divided in technology-based modalities, such as Radiography or plain films, Ultrasonography, Compu- ted Tomography, Nuclear Medicine, Magnetic Reso- nance Imaging and Angiography (7). Departments of Pathology in the United States and Canada are very complex since include, along with Anatomic Pathology, the laboratory services of the medical center (8). The traditional divisions are: 1) Anatomic Pathology, 2) Clinical Pathology (Labo- ratory Medicine) and more recently, 3) Genomics and 4) Community or Regional Pathology. Anato- mic Pathology is subdivided in: 1) Surgical Pathology Subspecialty Services (i.e. Neuro, GI, Breast, Cardiac, Bone, Uropathology, etc.), 2) Cytology and 3) Autop- sy. Clinical Pathology or Laboratory is subdivided in: 1) Chemistry, 2) Hematopathology, 3) Microbiolo- gy, 4) Human Leukocyte Antigen (HLA) / Transplant, 5) Molecular Pathology, 6) Transfusion Medicine, 7) Coagulation. Genomics is subdivided in: 1) Cytogene- tics and 2) Molecular Genetics Departments of Genetics have a Clinical Division, fre- quently subdivided into: 1) Prenatal, 2) General Ge- netics, 3) Inborn Errors of Metabolism and 4) Cancer Genetics. As above mentioned, Institutes are structured based on COEs. This concept is easy to understand in disea- DIAGNOSTIC INSTITUTE DEPARTMENTS OF RADIOLOGY, PATHOLOGY AND GENETICS IN THE UNITED STATES Diagnostic Institute Structure OvervieOOverviewOverview AnatomicPathology: SurgicalPathology (Subspecialty Services) Cytology Autopsy ClinicalPathology: Chemistry Hematopathology Microbiology HLA/transplant MolecularDiagnostics TransfusionMedicine Coagulation Genomics: Cytogenetics Moleculargenetics RegionalPathology AbdominalImaging BreastImaging Diagnostic Neuroradiology& Neurointervention EmergencyRadiology Musculoskeletal Imaging NuclearMedicine PediatricRadiology Cardiothoracic Radiology Vascular& Interventional Radiology RegionalRadiology Pathology Radiology Genetics Ultrasound CT MRI Radiography HumanGeneticsClinicalServices: Prenatal GeneralGenetics InbornErrorsofMetabolism CancerGenetics Figure 1. Example of the organizational structure of a Diagnosis Institute in an Academic Medical Center in the US. Note the Departments of Radiology, Pathology and Genetics are divided in organ, technology or disease based Divisions, Sections and in some situations Centers. CENTERS OF EXCELLENCE IN A DIAGNOSTIC INSTITUTE