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New market metrics using consumer and integrated data Presenter: Steve Davis
New market metrics using consumer and integrated data
Presenter: Steve Davis
Proprietary and Confidential. Do not distribute.
•Longitudinal •Comprehensive
Dataset: 71M Lives
Real World Data Model
Medical groups
Integrated delivery networks
Staging Area
Hospitals
Multi-specialty practices EMR1
Small group practices EMR2
Physician offices
EMR 3
Rx platform
Billing system
Rx platform
Billing system
Rx platform
Billing system
Rx platform
Billing system
EMR1
EMR2
EMR3
• Demographics • Lab results • Phys. notes (NLP) • Procedures • Diagnosis • Medications • Outpatient visits • Vital signs • Hospitalizations • Observations
Data & Analytics for Life Sciences
Analytics for Providers
Processing: Validation. Normalization, Standardization. Mapping.
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Physician Notes and Lab Reports Guide Every Patient Interaction and Decision to Prescribe
4 Confidential property of Optum. Do not distribute or reproduce without express permission from Optum.
An Unmatched Foundation of Clinical and Claims Data
Billions of monthly data inputs are integrated, normalized and validated
Clinical/EHR Data
~78 million patient lives
Claims Data
19 years of longitudinal health
records
155 million administrative lives
Clinical Data Size Snapshot
Across 70+ IDNs and Groups
• Labs: 8 Billion • Written Prescriptions: 1.3 Billion • Clinical Observations: 5.6 Billion • Diagnosis: 4 Billion • Procedures: 3.4 Billion • Notes: 2.3 Billion
Optum’s Proprietary Deterministic Matching Capabilities enable the most widely published Integrated Data Research Platform in the industry
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Expansion of the Optum Data Ecosystem
Media and Digital Exposure
Clinical/EHR Data
~67 million patient lives
Claims Data
165 million administrative
lives
Consumer Data
Oncology
CPG/Front Store Data
Client’s Own Data
Registry Data
All Payer Claims Data
Global Data
Optum’s Proprietary Deterministic Matching Capabilities enable linked data to include the full spectrum of health, media, behavioral and purchasing data at the individual or household level
25 million All Payer Claims Lives will be
delivered in Q2
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Today’s Presentation
• Patients are inundated with data and information about disease state awareness which increases the pressure to take control of ones’ self-health. This pressure calls for pharma to increase the need to transform from existing new-to-brand patient acquisition and relationship management to a patient response management approach providing a 360 degree feedback loop for more comprehensive data insights.
• Today, we will explore some of the ways to proactively identify the lead and
lag indicators by looking at what-if scenarios using electronic health records and predict persistency across new-to-brand patient populations and to continuously monitor the care indicators for better patient health outcomes.
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What it Looks Like to Us
Forecasting
Messaging Engaging
Tracking
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Patient Social Media
Care Provider
Activity
Trackers
Physician
Smart Phone Apps
Payer Friends &
Family
Care Coordination
Apps
Pharmacy
TV/ Print/ Radio
‘It’s not what happens to you. It’s what you do with what happens to you.” – Chris Waddell.
How it Feels to the Patient
Impact of Electronic Health Records (EHR) in Patient Management
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Companies moving progressively and are incorporating EHR data into their strategy
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Forecasting
Messaging Engaging
Tracking
Predicting Disease
Clinical Segmentation
Similar Products in Class
Econometrics Lifecycle Influence
Message – Track – Engage Forecast
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Forecasting with Real World Precision
Traditional Paradigm
• Pre Launch: Demand Study (typically focus group: primary research with KOLs)
• Market Size Analysis (Rx volume, source of business, market share, etc.)
• Competitor Analysis (generic alternatives, competitor price points, formulary coverage etc.)
Real World Paradigm
• Clinical Target: What is my brand’s label (Eg., Inadequate glycemic control, A1C between 7.0-10.0%)? How many patients are treated? Under-treated and not at goal? Are there any contra-indications (Eg., renal impairment)? How large are each of the populations? What is the distribution of clinical target segment by provider type?
• Protocol Guidelines: Are there national prescribing guidelines (Eg., ADA) ? How do providers adhere to the guidelines?
Fore
cast
Inpu
ts
Forecast Models
Setting brand expectations requires a realistic definition of your brand’s clinical target population and a clinically-informed understanding of real world treatment paradigms
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Forecasting strategy can be built based on the brand share by line of therapy
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While patients progressing to earlier lines were progressively less likely to have severe psoriasis, patients on line 5+ were the most likely to be classified as severe.
Diagnosed with Plaque Psoriasis, Severity recorded in 180 days prior to Rx, 366+ days in EHR database prior to Line 1
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Body surface area affected by Psoriasis captured in Notes
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Optum EHR Data Jan 2007 – Dec 2015
Percentage of body surface area affected by Psoriasis using the notes data indicates that Cosentyx may be used to treat severe Psoriasis patients. Below we see the area affected by psoriasis across brands when the diagnosis date and WRx date are aligned
12.5
7.2 6.7 7.0 8.6
10.0
Cosentyx Enbrel Humira Otezla Remicade Stelara
% o
f BSA
Affe
cted
Median BSA % Affected
Median BSA % Affected
n= 5,518 patients diagnosed with psoriasis (696.xx) have NLP mention of BSA %
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Average BSA% for patients 6 months prior to getting a written Rx
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Optum EHR Data Jan 2007 – Dec 2015
Signs, diseases and symptoms allow for insight into treatment choice beyond the structured EHR data. We are able to see the percentage of body surface area affected by psoriasis using the NLP data
14.9 13.5
11.7 11.8
14.8 12.4
Cosentyx Enbrel Humira Otezla Remicade Stelara
% o
f BSA
Affe
cted
Median BSA% Affected Prior to WRx
patients with an WRx and NLP mention of BSA % in six months prior to WRx date
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Message – Track – Engage Forecast
Forecasting
Messaging Engaging
Tracking Patient retention
Predicting disease
Profiling IDN & physician networks Patient acquisition
Predicting Persistency
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Humedica Natural Language Processing – Alzheimer’s
255,830 patients have mentions of Alzheimer’s, captured through Signs, Diseases, and Symptoms in Humedica’s data (from January 1, 2007 – December 31, 2014)
For those patients, there are more than 220,000 mentions describing the disease, the top 12 are as follows:
0
5,000
10,000
15,000
20,000
Mentions of Alzheimer’s Severity
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Natural Language Processing – Scores of Interest
Mini-Mental State Examination (MMSE): • Most commonly used test for complaints of memory problems • Used by clinicians to help diagnose dementia and assess its progression and severity • The MMSE is a series of questions and tests, each of which scores points if answered
correctly; if every answer is correct, a maximum score of 30 points is possible
0%5%
10%15%20%25%30%35%40%45%50%
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
% of MMSE Scores
All Patients Alzheimer's SDS Mention Alzheimer's Diagnosed
80% of test results for Alzheimer’s patients are recorded at below normal*
* Guidelines based on alzheimers.org.uk Source: Humedica Clinical Data January 1, 2007 – December 31, 2014 N=226,663
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Natural Language Processing of clinical notes prior to diagnosis allows for a view into possible key predictors of future disease
16% of patients diagnosed with Alzheimer’s Disease have a mention of a memory loss related concept in the 13-15 months before diagnosis, compared to only 3% of AFIB diagnosed patients.
n=62,253 ALZ pts. n=346,139 AFIB pts.
We can identify the signs & symptoms
prior to AD diagnosis
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Forecasting
Messaging Engaging
Tracking Patient acquisition
Profiling IDN & physician networks
Patient retention
Message – Track – Engage Forecast
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Physicians in the larger IDNs (with the higher PCP prescribing rates and higher avg. A1Cs) are more likely to follow the ADA protocol recommendations than the smaller IDN physicians
Diabetes Market, 4/1/2013 – 9/30/2015, SGLT2s, Type 2 Diabetes, A1C result within 90 days pre-Rx
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Patients within the larger IDNs (with the higher PCP prescribing rates and higher avg. A1Cs) live in neighborhoods with lower average household incomes than the patients in the smaller IDNs
< 30k 30k-40k 40k-50k 50k-60k 60k +
< 30k 30k-40k 40k-50k 50k-60k 60k +
Diabetes Market, 4/1/2013 – 9/30/2015, SGLT2s, Type 2 Diabetes, A1C result within 90 days pre-Rx
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Forecasting
Messaging Engaging
Tracking
Message – Track – Engage Forecast
Signs, Symptoms
Provider notes
Patient retention
Diabetes Therapeutic Area Case Study using physician notes and exam room discussion coupled with
clinical variables such as lab values to identify lead and lag indicators influencing
Patient Persistency
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Analysis overview
EMRs/EHR Claims
Dependent variable is from administrative health claims data as a function of various prescription and medical records data as independent variables
Dependent variable is from administrative health claims data as a function of various independent clinical factors such as: ‘Non Compliance Note pre Rx‘, ‘Time from diagnosis to getting an Rx’, ‘Days Since Previous Hospital Discharge’, ‘Pretreatment A1C levels’ , ‘BMI’, ‘Race’ etc.,
Modeling approach (segmentation and prediction): – Cluster analysis – Performed a Random Forest- to determine the variables of greatest importance – Performed a Multiple Linear Regression- using variables picked by the Random Forest – Used Backwards Selection until model- only included statistically significant predictors of persistency Note: Patient record gaps may exist if treated outside of our data network ; Fill rate for some of the clinical variables is low ; patients must have been in the clinical database for at least a year prior to the Rx and remain in the claims database for at least a year after the Rx
EHR with linked
claims
EHR with linked claims
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Primary Adherence
Notes: Type 2 Diabetes, 365 days eligibility following first fill, persistency measured with a 30 day grace period
A significant drop in percentage of patients filling prescriptions (primary adherence) who were persistent through the following year
EHR with linked claims
20% of written prescriptions are never filled at the pharmacy
One and done (side effects, symptoms,
formulary) Efficacy,
adherence leading to switching
Only 35% of written prescriptions are refilled
for 360+ days
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NO FILL
1. Patient did not fill the brand written, a brand from another brand in the same class, or a brand from a different class in the 90 days after the WRx
WRx Class: 1 Brand: A
Fill Class: No Fill Fill Brand: No Fill
90 DAY LOOK-FORWARD WINDOW
FILLED ANOTHER BRAND (DIFFERENT CLASS)
1. Patient did not fill the brand written or another brand from the same class in the 90 days after the WRx.
2. There was a fill for a brand from a different class. The fill had no prior history in 90 days before the WRx.
Looking for evidence of prior history on fill brand
Fill Class: 2 Brand: C
WRx Class: 1 Brand: A
ASSIGNED TO
90 DAY LOOK-FORWARD WINDOW 90 DAY LOOK-BACK WINDOW
FILLED ANOTHER BRAND (SAME CLASS)
1. Patient did not fill the brand written in the 90 days after the WRx
2. There was a fill observed for a brand from the same class as the written Rx. The fill had no prior history in 90 days before the WRx
Looking for evidence of prior history on fill brand
WRx Class: 1 Brand: A
Fill Class: 2 Brand: C
Fill Class: 1 Brand: B
ASSIGNED TO
90 DAY LOOK-FORWARD WINDOW 90 DAY LOOK-BACK WINDOW
FILLED AS WRITTEN
1. There was a fill observed for the same brand as the written prescription in 90 days after the WRx.
Fill Class: 2 Brand: C
WRx Class: 1 Brand: A
Fill Class: 1 Brand: B
Fill Class: 1 Brand: A
90 DAY LOOK-FORWARD WINDOW
ASSIGNED TO
Primary Adherence Methodology
EHR with linked claims
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We found strong relationship between persistency and group of response variables such as A1c pre-Rx, hospitalization
<--- Prediction time ---> <--- Post period --->sample factors medication not needed
a1c < 6 not persistentamount of exercise
Contr
olled medication needed and taken
HEAL
TH
Income $30-59k WRx f
illed
persistent
BMI high, but not supercomorbidilty lab results hyperlipideia)low copay
Seve
re co
mplic
ation
s
Continuim of hospitalization DCSI high
medication needed, but not taken not persistent
Kidney problems (based on labs)Low income non-compliance note
Diabetes Dx WRx Rx Filled (continuer or new) 1 Year post fill
TIME
EHR with linked claims
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Lab values and clinical factors between brands predicted varying directional impact on persistency
X = p < .05; Y = p < .01; Z = p < .001 ; coefficients direction is positive and negative
Demographics Brand-A Brand-B Brand-C Brand-D Brand-E Brand-F Census Bureau Division Z Y Z Z X Race X Z Z X Y Average HH Income X Z Age Group Y Z Z Z Y X
Pre-Rx Lab Values Brand-A Brand-B Brand-C Brand-D Brand-E Brand-F A1C Z Y Z Z Y BMI Z Z Z LDL Z Z Glucose X Z Y
Comorbidities Brand-A Brand-B Brand-C Brand-D Brand-E Brand-F Hyperlipidemia X Z Y Cardio-Metabolic Syndrome Z
Other Clinical Information Brand-A Brand-B Brand-C Brand-D Brand-E Brand-F Time From Original Diabetes Dx X Z Days From Last Class Y X
Days Since Previous Hospital Discharge Y Z Z
Physician Specialty Y Encounter Type Y Z Z Exercise Level Y Smoking Status X Y Y
Notes Brand-A Brand-B Brand-C Brand-D Brand-E Brand-F SDS Fatigue Z SDS Memory X SDS Kidney X
EHR with linked claims
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Mention of problems in the notes are directly correlated and predictive to lower persistency
• Segments of patients with mentions of Fatigue, Memory and/or other problems are captured from the notes
• Any mention of a note of problems, pain, or memory issues prior to being prescribed a product is associated with a patient being less persistent
Low Persistency
Fatigue
Memory
Kidney
No Mentions
EHR with linked claims
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We built a continuous monitoring system into the care based on patients’ responses using lead/lag indicators to better health management
A1C, BMI, eGFR, ALT, OGTT, FPG, DKA
Comorbidities, Age, Gender, Race, Smoking Status, Exercise Level
EVENT of interest
LEAD INDICATORS(EHR data predictors)
Written Rx to filling
Physician med titration / restart/ add
Procedures & Hospitalizations
Demographics, Formulary & Diagnoses
Lab Results & Vital Signs
Signs, diseases & symptoms
Patient switching/discontinue
LAG INDICATORS (Claims data)
NonCompliance_Note_PreRx 1
A1C 2
Hospitalization 1
BMI Comorbidities
WHAT-IF SCENARIO
Insu
lin B
asal
sG
LP1
Yes
No
74%
95%
0%
20%
40%
60%
80%
100%
Insulin Basals GLP1
Probability of Persistency
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Looking for Commercial Pilot Clients
• 3 Licenses for the All Payer EHR/Claims Data Intersection
• 3 Commercial Analytics on any of the Optum data assets we have discussed today
• 25% Discount for either option in Q2
