Clinical Advisor

IPH’s Clinical Advisor application helps clinicians drive enhanced patient outcomes by combining digitized medical data with machine learning algorithms to support healthcare analytics. Using a convenient cloud-based platform, clinicians can administer objective digital assessments to patients in real-time and leverage signal capture from multiple sources to create a comprehensive picture of patient performance over time.

Clinicians can expand their working knowledge of patient health performance with cleanly collected measurements sourced from a proprietary network of specialty healthcare providers spanning the spectrum of disease states. By leveraging IPH’s analytics platform and multi-dimensional data to drive clinical decisions, physician practices can reduce costs, improve communication between doctors and patients, and ultimately, enhance patient care and outcomes.

Clinical Advisor advances patient care by contextualizing insights into the efficacy of therapies – at both the patient and population level – through its machine learning algorithms and healthcare analytics.

By creating digital assessments that integrate population health data, objective measurements, patient-reported outcomes, and clinical observations into a single browser-based application, Clinical Advisor provides an objective, evidence-based view of disease trajectories as well as recommendations for the long-term success of therapies.

Clinical Advisor enables providers to reliably measure patient performance and quantify the impact and progression of neurological disease over time. Using objective digital instruments to assess patient performance, Clinical Advisor captures data across the domains of cognition, sleep, pulmonary function, vision, manual dexterity, gait, and more.

IPH’s proprietary machine learning algorithms aid in understanding the trajectory of disease by using ongoing streams of patient data sourced from a network of specialty healthcare providers. Real-time, cloud-based analysis allows for continuous improvements of healthcare analytics models, illuminating the relevant features that forecast both treatments and expected patient-reported outcomes.