# DiSCERN: Advanced PD Therapy Candidacy and Evaluation System

> **NIH NIH R44** · GREAT LAKES NEUROTECHNOLOGIES · 2020 · $696,634

## Abstract

Summary
 The objective is to design, develop, and clinically assess DiSCERN, a standardized telemedicine tool for
identifying patients with Parkinson’s disease (PD) who would benefit from advanced therapies (AT) and
determining when AT recipients need therapy adjustments. Once chronic PD medication usage results in
motor fluctuations and dyskinesias and all non-invasive therapies have been exhausted, AT (e.g., deep brain
stimulation, drug pumps) is often recommended. While experts at academic medical centers may appropriately
identify AT candidates, AT is underutilized due to limited access and inequitable utilization of limited evaluative
resources for a sizable subset of the PD population. Remote screening and monitoring with DiSCERN will
improve patient selection, reduce disparities, and expand access for rural populations and disadvantaged
communities. The system will engage and empower patients, providers, and healthcare institutions and lead to
improved health, healthcare delivery, and the reduction of health disparities. This mobile health technology will
include a patient friendly smartphone app, non-motor assessments, and wireless wearable sensors for
continuously monitoring PD motor symptoms, complications, and quality of life (QoL). We have previously
commercialized wearables and mobile apps for remote monitoring of PD motor symptoms and side effects,
which will significantly de-risk the project. Still, novel development and validation efforts are required to
commercialize this new technology. Innovations include: 1) integration of PD monitoring algorithms with
context aware activity detection for improved PD motor assessment and QoL quantification; 2) implementation
of the algorithms on a smartphone and wearable device; 3) development of a predictive model that uses motor
and non-motor features to accurately identify PD patients who would be good candidates for AT; and 4)
implementation of a model that alerts clinicians when an AT recipient needs a therapy adjustment. Through
integration with AT systems, DiSCERN will improve the clinician experience and allow the limited availability of
specialists to scale care to a diverse and growing PD population, who may not otherwise have access to AT.
Phase I includes: 1) validation of context aware activity detection algorithms on PD patient data; 2) determining
the extent specific activities or activity levels correlate with PD QoL; 3) using clinician feedback to identify
collected data features that are useful in informing AT clinical decisions; and 4) identification of wearables to be
used in the final system. Phase II includes: 1) transition of context aware activity detection and PD symptom
quantification algorithms onto a smartphone and wearable chips; 2) development of a smartphone app that
integrates data collection, non-motor assessment, and data-transfer to the cloud; and 3) collecting data from
AT candidates in the months before and after AT is initiated to develop models that accur...

## Key facts

- **NIH application ID:** 9807285
- **Project number:** 4R44MD013767-02
- **Recipient organization:** GREAT LAKES NEUROTECHNOLOGIES
- **Principal Investigator:** Dustin A. Heldman
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $696,634
- **Award type:** 4N
- **Project period:** 2018-09-20 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9807285

## Citation

> US National Institutes of Health, RePORTER application 9807285, DiSCERN: Advanced PD Therapy Candidacy and Evaluation System (4R44MD013767-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9807285. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*