Akaza announces successful deployment of Best Estimate Diagnosis System

March 1, 2003 - Akaza Research announces successful deployment of a Best Estimate Diagnosis System for the Psychiatric and Neurodevelopmental Genetics Unit (PNGU) at Massachusetts General Hospital. The unit, headed by psychiatrist Dr. David Pauls, has been working with Akaza to develop time-saving and money-saving technology to allow clinicians at sites across the world review psychiatric data, make diagnoses, and collaborate with other clinicians in order to reach consensus on those diagnoses.

The Best Estimate System is one of the first products of a larger initiative undertaken by Akaza and MGH to develop a portable, extensible platform for collecting, managing, and analyzing complex phenotypic data in concert with genotypic data. The system, called Pheno, is capable of handling multiple projects and research sites, is expected to be in place at PNGU by the end of the year. The team plans eventually to extend this system to the greater academic research community.

The Best Estimate System is currently being used by the Tourette Syndrome Association’s International Consortium for Genetics, headed by Dr. Pauls, to study the genetic roots of Tourette Syndrome. The Best Estimates system provides a web-based tool that allows the Project Manager at PNGU assign patient cases to clinicians in the consortium for diagnosis. It provides the clinicians with a secure web-based interface to review data collected on those patients and enter their best estimate diagnosis.

Once data has been collected and entered for a given case via the Pheno System, the Project Manager may assign clinicians to review and diagnose the case. When clinicians log in to the system, they see a list of cases in an anonymized format that have been assigned to them. Selecting a ‘Review’ link for a case brings up the electronic diagnosis coding sheet, where the clinician can a) select links to view the patient’s raw data in a separate window, and b) use drop-down menus to input the list of relevant disorders the patient is or may be affected by, as well as their severity and age of onset. Once clinicians have submitted a case and marked it as ‘final’, the system automatically reconciles these clinicians’ diagnoses with each other, noting any changes in the diagnosis. Should they match, it notifies the clinicians and the project manager via email that the diagnosis is complete. If differences exist, the system lists those differences in an email to the parties, facilitating collaboration to help reach a final consensus diagnosis, which both clinicians must verify in the system before it is marked as complete.

Project Managers have access to a robust reporting module, allowing them to see all original and final diagnoses and to filter records by patient, by diagnosis, or by status.

Some of the most useful features the system offers include:

• User-friendly presentation of raw interview data to clinicians.
• Customizable diagnosis coding sheets presented as web-based forms.
• Automatic reconciliation of disparate diagnoses among clinicians.
• Detailed reporting for tracking clinician progress and revealing diagnosis results.
• Secure access to anonymized data using SSL encryption.

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