• French

Driving research of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME / CFS),
Post Treatment Lyme Disease Syndrome (PTLDS), Fibromyalgia and Post COVID .

Computerized Adaptive Testing


Using machine learning and artificial intelligence PASS will enable the patient to create a symptom summary, in less than 30 minutes, that accurately describes their individual current symptoms (including the symptom character and its priorities from their own perspective) in preparation for their upcoming clinician visit. From the patient’s perspective, there are as many as 65 different symptom categories in chronic, complex diseases. This patient summary (and also a clinician summary version) will then be readily available for their clinician to review when the patient arrives for his or her visit. The clinician might choose to confirm facts about those symptoms emphasized in the summary as well as ensure that other relevant symptoms may or may not be present.


The development of this tool requires many consensus meetings (maybe a dozen or more) that include 30 or more patients, clinician, and scientists all of whom are familiar with the diseases and the tools required to create the PASS. These meetings are designed to review all existing questionnaires, criteria, and other tools currently used in evaluation of ME/CFS, PTLD, and FM. The intent is to adapt questions from all currently available tools into an extensive database whose content has been fully developed and supported by clinicians and patients. These extensive questions need to be field tested by thousands of ME/CFS, PTLD, and FM patients using online methodologies and the results evaluated statistically to find those questions or combinations of questions that display accurate and precise statistical performance.

Further consensus meetings are required to further narrow down to only those questions with superior performance to be included in the version 1.0 of PASS. This beta version (1.0) can then be field-tested once again with thousands of patients online. These results will then be used to create a version 2.0 that can be considered the final product of PASS. This will represent the first final PASS version but we all recognize that subsequent versions of PASS will be required as various updates will continue to improve the product.