An Automated Novel Multidisciplinary Skull Base Data Tracking System: A 14-Year Experience

Objective: To present a novel system used to organize and track data from a regional biweekly CME accredited multidisciplinary skull base conference based on an ongoing 14-year, 3,490 case experience.

Methods: The system uses an Excel based platform automated and modified to track 42 parameters of which 13 are patient specific and 29 are data compilation at a regional biweekly CME accredited multidisciplinary skull base conference. For each patient presented, data metrics included presenting physician, date of presentation, initials, sex, age, diagnosis, radiology, pathology, T stage, N stage, M stage, and p16 /−, and recommendations. All physician and patient data was then automatically generated into an analysis representing all conferences to present. The automated data tracking portal tracks the total number of patients including separation by gender, average age, median age, age range, total number of cases per year (prospective and total), number of conferences per year, average cases per conference (prospective and total), average conferences per year, and frequency of diagnosis, including separation by gender. This particular software is nonproprietary and easily utilized with off the shelf software programs and can be easily adapted. At multidisciplinary meetings, patient data was input manually into the data tracking platform. Then, individual physician and analysis data was generated automatically in the final sheet of the database. The relevant data metrics of the program were chosen by points of reference deemed useful for physicians and their practices through the evolution of multidisciplinary skull base data tracking of 14 years. Web-based data tracking portals for physicians can allow access to individual case presentations and automated data.

Discussion: Employing and consistently using an automated system that computes data points can save time while generating important and relevant information in real time. Financially, hospitals and practices can better allocate resources to benefit patients based on the trends in their specific population. Tracking metrics allows for quantitative data representing the most prevalent diseases in an area potentially allowing for investigation into possible causes/correlations. In addition, this could allow doctors to better qualify their personal practice statistics. Furthermore, this program could act as a database for patients who may qualify for clinical trials by providing capability to search for patients who fit specific criteria. Using a secure, collaborative platform offers a web-based workplace for document management for storage, and data can be accessed remotely. An interconnection between systems that facilitates input from medical professionals not present at the meetings could allow for further data management and analysis. The program is currently limited in that it requires input of pertinent information to be entirely manual. Future efforts should be made to completely automate this process, furthering the usability of the program.

Conclusion: The presenter intends to demonstrate the automated software and compilation of 42 data metrics with ongoing experience of 14 years and 3,490 cases and the capital investment and programmatic impact on the decision-making processes.