Digital Intelligence. Clinical Integration. Real-time Dashboards.
Prescriptive analytics can tell a user what course of action would produce the highest likelihood of maximum benefit when a predicted event does occur. This is the aim of clinical decision support applications and many of the artificial intelligence initiatives that are currently cropping up across the industry.
DADOS Predict allows users to easily integrated predictive models into the clinical or study workflow. Data collected using DADOS is synoptic and of high quality, ideal for training predictive models. The resulting predictive model can be easily integrated within DADOS to be applied to prospective patients. The results of these predictions can be integrated into dynamic visualizations to provide actionable insights to the research or clinical team.
DADOS Predict facilitates the integration of predictive models with the collection of patient-generated health data, which can help to supplement existing clinical data, thereby filling in information gaps and providing a more comprehensive picture of ongoing patient health.
Predictive analytics is the ability to extrapolate the course of future events from descriptive data to provide a more comprehensive picture of patient health.
The algorithms for this clinic support tool were developed by the Arthritis Data Science team at UHN.
Led by Drs. Raja Rampersaud and Christian Veillette, orthopaedic surgeons, and with the support of DADOS and the Techna Institute
How DADOS will leverage Microsoft’s Azure Analytics and Power BI to predict post-surgery patient performance on follow-up visits from baseline
Leveraging Microsoft’s Azure Machine Learning Studio and embedded Power BI reporting into the platform, DADOS can produce a personalized report at the baseline of care to predict how well the patient will do post-surgery with the follow-ups 6 weeks, 3 months, 6 months, and a year after surgery. DADOS Predict can be configured to work with these predictive analysis tools, while also allowing clinicians can review not only their patients’ answers to the questionnaires but also the prediction results in real-time with their patients.
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