Rheumatologists rely on patient-completed self-assessments to assist in gauging their patient’s arthritis disease activity and quality of life. In many clinics these questionnaires are completed by hand or not completed at all. The results of the questionnaires are numeric, and the rheumatologist is able to compare the new result with the previous and assess the effectiveness of the current treatment strategy.
Administrative staff must transcribe the results into a digital format from the patient’s hardcopy. Writing down systematic and routine data is inefficient, while transferring between hardcopies is redundant and can be inaccurate.
Additionally, the entire process only yields a single data point from the perspective of the doctor and that data point is only generated for the day of the appointment, failing to capture any time-based variation. Trends in a patient’s responses are not easily detected.
Several peer-reviewed patient self-assessments exist in rheumatology, these assessments have been validated for their ability to predict disease activity in patients.
Through our web-app, we provide rheumatologists with a portal for managing their patient self-assessment results and viewing their analytics reports highlighting key metrics and trends.
Rheumatologists will be able to create new patient accounts and choose the frequency and type of assessment for each patient.
Once the account is created the patient can complete their self-assessment and review their results. Through the app patients can also communicate with their physician, access information related to their disease and participate in patient communities.
Following the development of our minimum viable product, we will be looking to work with our users to refine our product and maximize its value. Join us now and help improve your patients' outcomes!
Digitizing the self-assessment questionnaire and remotely (i.e., cloud) storing the patient’s responses greatly streamlines the process while allowing it to become mobile.
By providing patients the ability to complete their self-assessments outside of their appointments we are able to increase the data sampling frequency, giving a much more detailed picture of their response to treatment. Critical changes in that response can be detected and managed sooner.
Remotely stored response data can now be analyzed in ways which would not have been possible previously using cloud computing. The dramatic increase in data resolution provides the opportunity for a more in-depth understanding of treatment response.
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