2. THE PROBLEM
‘One Size fits all’ approach no longer works
Broad Based Prescriptions
11 Drug classes to treat Type-2 Diabetes
No Big-Data driven solution
Petabytes of data on EHR just being stored
and not used for aiding physicians’ day-to-
day activities
Individual Incompatibility
‘Biological algorithm’ can take into
consideration only a limited no. of factors
prior to making prescription decision
Individual Incompatibility
‘Biological algorithm’ can take into
consideration only a limited no. of factors
prior to making prescription decision
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4. THE SOLUTION
Physician Ordered Prescription Guidance Platform for Type 2 Diabetes
Doctor Performs
Diagnosis
Doctor conducts patient
interview,
clinical investigations &
diagnoses
patient with Type-2
Diabetes
EMR Data
Extraction
Precision Rx extracts
patient info (clinical
observations, lab results,
ADR history) from the EMR
relevant for making a
prescription decision
Big Data
Analysis
Precision Rx parses the
patient data
through a Supervised ML
Model,
benchmarking the patient
medical data
against similar patients’
data from
across the world
Prescription
Guidance Report
Precision Rx generates a
prescription guidance
report sorting all 11 T2D
drug classes based on
potential toxicity/efficacy
for the patient. The Doctor
can now make a data-
driven and statistically
sound prescription decision
Drug Response
Modelling
Precision Rx models out
projected drug responses
for the 11 T2D Drug
classes for the current
patient and attributes
risk/toxicity scores for all
the 11 drug classes
5. THE TEAM
Magic behind the lens
Yash Sagar Santani Puneet Chadha Christin Bivens Pallav Prakash Michael Melnick Bellari Kanjilal
CEO Ops & Biz Dev Data Science Business Strategy ML Architect Data Scientist
6. OUR ADVISORS
Expert feedback & Domain Expertise
Dr. Rachel M. Shing Dr. Yasmin Sarfraz Dr. Riaz Sirajuddin Ales Varabyou
7. Integrated
Healthcare Systems
Stand-Alone
Physician FacilitiesUniversity Hospitals
TARGET
CUSTOMERS
Kaiser Permanente,
Ascension, Veterans
Affairs
Johns Hopkins University,
Vanderbilt, OU Med,
Stanford
Local facilities with >=6
physicians
REVENUE MODEL
How to make money
*APU – Active Physician User. Internal KPI to be used to track customer acqusition
9. MARKET SIZING
Addressable Market*
*Retrieved from: Henry Kaiser Foundation (2019): Available from: https://www.kff.org/other/state-indicator/primary-care-physicians-by-field/ and
https://www.kff.org/other/state-indicator/physicians-by-specialty-area/
10. Doctor Shadowing
6 local doctors – regularly treat
T2D patients
Shadowing, questioning,
continuous feedback loop for
2.5 months
Clinical Workflow
Construction
First static digitized version of
workflow for selecting optimum
HbA1C band & drug class for
T2D patient
Neatly documented
methodology for treating T2D
patients
ML modelling
Initiated
Parsed through 30,000 patients’
worth of data from OSU-CHSI’s*
Cerner EHR Export
Findings, outcomes, model
selection + Cerner Data Tier 2.0
Access
MOVING FORWARD
ML Model training, testing, optimization and inter-model benchmarking
TRACTION
Progress Till Date & Steps Moving Forward
*Oklahoma State University’s Centre for Health Systems Innovation
11. Jul
2020
Proof Of Concept
Ready
Model selection and
optimization
accomplished, Cerner
Data fully utilized
Prototype Ready
Additional testing and
training of model with
more data
Oct
2020
Seed Round Raised
Runway to develop
shippable product +
conduct pilot studies
Nov
2020
MVP ready
Shippable product ready
for pilot testing studies
Apr
2021
Large-Scale Trial
Live study with an
integrated provider
network facility
Jul
2021
Series A Financing
Raised
Large-scale
implementation, EHR
integration and customer
acquisition initiated
Feb
2022
DEVELOPMENT LIFECYCLE
Action-Plan
12. OneOme Quest PGx Geneticure Promethease Aevus PRx
Disease group
specialization
Privately funded
Big Data Analysis
Physician Ordered
EHR System
Integration
Machine Learning
Modelling
SaaS Business
Model
Revenue Generation
Currently
Recurring Revenue
Generation Model
Genetic Counselor
support
COMPETITION
