Tarik K. Alkasab, MD, PhD, Massachusetts General Hospital and Harvard Medical School; Mitchell A. Harris, PhD; Michael E. Zalis, MD; Keith J. Dreyer, DO, PhD; Daniel I. Rosenthal, MD
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Radiologists make many diagnoses, but the process of learning the outcomes of their patients is so cumbersome that they rarely find out whether their diagnoses were proved correct. This clinical feedback is important for all physicians’ self-assessment and personal quality improvement as they seek to optimize their diagnostic skill, both during and after training.[1] Currently, if a radiologist wants to learn about a patient’s subsequent clinical course, they must record the patient’s medical record number (MRN) by hand and return to the electronic medical record (EMR) later and manually search for relevant new information. As radiologists see a large volume of patients with minimal continuity of care, the process of data aggregation and outcome inference takes substantial time and effort. As radiology workloads have increased, and the volume of data contained in the EMR has expanded, the required chart review and data assimilation has become a rate-limiting factor. With current EMR portals, diligent radiologists could spend several hours each week following up their patients, and even then only follow the most remarkable cases. As a result, many opportunities for feedback and improvement are missed.
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We have addressed this problem by creating a case-tracking application that we have dubbed “RaceTrack” (for “Radiology Case Tracking”). The database-backed web application integrates with our department’s existing radiology information systems, including the radiology information system (RIS: IDXRad, GE Healthcare), picture archiving and communication system (PACS: IMPAX, Agfa), and dictation software (Commissure, Nuance). The RaceTrack system also interfaces with the Queried Patient Inference Dossier (QPID), which aggregates and indexes electronic medical records for integration of tracked cases with clinical data.[2]
This integration allows radiologists to create, manipulate, and follow their tracked cases with a minimum of effort and interruption of their workflow. The process begins when the radiologist presses a “hotkey” in either the PACS or the dictation environment. This opens a simple entry form that is automatically pre-filled with patient MRN and study information gathered from the radiologist’s current work context and the RIS. The radiologist types a brief description of why they wish to follow the case, and dismisses the form. The system then creates an entry in the radiologist’s personal database of tracked cases. Later, to review cases, the radiologist logs into the RaceTrack system from an office computer, a personal laptop, or a home computer via a virtual private network. The system displays a personalized table of tracked cases, that includes links, directly to the patient’s EMR entry and to a web-based PACS display of the study of interest. At no time does the radiologist have to re-type the MRN or accession number.
Further, the tracking system integrates with electronic medical records to help radiologists place radiology examinations in a broader clinical context. RaceTrack periodically searches the patient’s record via the QPID system for subsequent relevant clinical information, such as new operative notes, pathology reports, or follow-up imaging examinations. When such data are found, they are displayed as part of the case description in the RaceTrack tool without the radiologist having to pore through medical records manually. The radiologist can then choose relevant reports and create annotated links to those selected medical record items which are maintained with the radiologist’s personal description of the case. This drastically decreases the amount of work required for radiologists to find the subsequent clinical course of their patients and helps keep track of subsequent diagnoses.
As an example, consider a busy radiologist interpreting a series of CT scans in the reading room. On a non-enhanced scan of a middle-aged man’s abdomen, he sees a renal mass with an unusual, but non-specific, appearance and recommends follow-up imaging to further characterize it. Since he wishes to find out himself what the subsequent workup of the lesion reveals, he activates the RaceTrack tool. A form is brought up which is pre-filled with the accession number for the study he is viewing. He types a brief description (“Strange renal mass, not definitely cystic”) and dismisses the form. The case is now stored in his personal database of cases, and he can return to his queue of studies which need to be read. Later that week, he logs in to the RaceTrack system, peruses his list of cases in his office, and notices the new entry. He remembers the case, and brings up the web-based PACS system so he can show the lesion to a colleague, who is equally mystified by the lesion, which further raises his interest in the case. Three weeks later, the radiologist is again reviewing his list of cases, and notices a flag on the case of the renal mass, which indicates that new clinical information is available. He clicks on the case, and finds that there has been a renal MR exam (read by one of his colleagues), which demonstrates enhancement concerning for a neoplasm. He adds an annotated link of the MR report to his record of the case. Finally, a month later, while going through his case list, he finds that there has been a nephrectomy, and the surgical pathology report is available: the lesion is diagnosed as an oncocytoma. The radiologist creates another annotated link to the pathology report with a “diagnostic” flag raised indicating that the item provides the diagnosis for the case. As a result of using the RaceTrack system, the radiologist has found the clinical and pathological outcome of the workup initiated by his recommendation.
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