Page 123 - Read Online

Langan et al. Art Int Surg 2023;3:140-6 https://dx.doi.org/10.20517/ais.2023.13 Page 5

the EPM software uses Robotic Process Automation (RPA) to automate mundane and repetitive tasks such
as sending letters and tracking if appointments have been made. The advantages of using this Artificial
Intelligence software will be discussed in two categories: the first is identification and the second is
surveillance.

With respect to identification, patients with pancreatic cysts and their respective demographics are
electronically populated from the Electronic Medical Record (EMR) into the EPM dashboard for the end
user. To accomplish this task, radiology reports are analyzed through the CL model, and patients identified
with pancreatic cysts are added to the EPM worklist so they can be tracked and management decisions are
captured. Following identification, at our institution, patients are contacted by letter and phone call by a
nurse navigator and offered a consultation with one of our pancreatic surgeons. The ordering provider is
also sent a letter explaining the identification and program. If the patient elects to be seen, consultation is
scheduled at that time. The use of the AI computational linguistics model, therefore, mitigates patient loss
and prevents the lack of follow-up options for a possible pre-malignant lesion of the pancreas. Moreover, in
our own practice, this software has identified ampullary adenoma, ampullary cancer, metastatic cancer, and
pancreatic neuroendocrine tumors. Therefore, the identification of “at risk” patients goes far beyond
pancreatic cysts.

[22]
Similarly, Kooragayala et al. utilized natural language processing to identify pancreatic cystic lesions .
Overall, from 18,769 subjects, the algorithm could correctly classify CT scan reports and maintain a
diagnostic accuracy of 98.7% (393 reports correctly identified out of 398). IPMNs were the lesions of the
pancreas that were identified most frequently when the Natural Language Processing program was utilized,
20.7% (48 patients). Nineteen percent (44 patients) were found to be due to trauma, 17.7% (41 patients) due
to pancreatitis, 15.5% (36 patients) were pancreatic cysts, 12.9% (30 patients), and 8.2 % (19 patients) were
ductal abnormalities. A negative exam was identified in 2.2% of cases (5 patients), 1.7% (4 patients) had
previous surgery of the pancreas and 2.2% (5 patients) were found to have findings that were unrelated.
Natural Language Processing can improve screening and automate referral for patients with precancerous
[22]
pancreatic lesions .
For longitudinal surveillance, the cloud-based EPM dashboard takes the place of the traditional and
antiquated Excel spreadsheet. The dashboard electronically monitors orders and appointments for the
patients on the worklist. Thus, EPM is able to not only show patients identified and patients seen, but also
to highlight upcoming events, patients who are missing follow-up, and document the follow-up advised by
the pancreatic surgeon. Phone call reminders or reminder letters to patients can be set and reminders for
surveillance imaging and time points can be set to ensure that exams and procedures are scheduled and
performed. The automation rules and electronic monitoring makes it easy for the care team to identify
patients who have missed their interval imaging, endoscopy or surveillance appointment since the EPM
software is organized into work lists to make sure patients are not lost to follow-up. Moreover, on a patient
level, the dashboard allows the care team to see the cyst details longitudinally to assess for concerning
change and can therefore perform real-time risk stratification. Overall, the Eon Patient Management (EPM)
software automates repetitive tasks allowing patient coordinators to spend more time on patient care and
less time on administrative duties. The software ensures patients are tracked and followed according to the
published evidence-based guidelines.

An additional major advantage of this software is the ability to automatically populate a national registry.
Currently, in the Lung Cancer Screening space, Eon is automatically uploading data to the American
College of Radiology Lung Cancer Screening Registry (LCSR). In addition, Eon has created a national