How MongoDB is Transforming Healthcare Technology

MongoDB in Healthcare
Jay Runkel
Principal Solutions Architect

Agenda
Phreesia Story
Challenge with Healthcare
How MongoDB is being Used in Healthcare
Why MongoDB for Healthcare?

Contact Info
Medical History
Family History
Medications
Current Symptoms
Reason for Visit
Other Physicians
Patient Intake

Challenges
Information varies by:
• Physician speciality
• Physician preference
• Insurance provider
Large volumes
• 50M Patients
• 10Ms of transactions per year
RDBMS

Phreesia Solution
Reliability and Scalability at Lower Cost
• Easily handle new data types
• Easily scale
• Leverage encrypted storage engine to protect
HIPAA and PCI data
MongoDB

Single View of Patient
• Collects all patient information in a central
repository
Patient
Records
Medications
Lab Results
Procedures
Hospital
Records
Physicians
Patients
Nurses
Billing

What is a “Single View” application?
•What
• Single, real-time representation
• Patient, member, claim, etc.
•How
• Gathers and organizes data from multiple,
disconnected sources;
• Aggregates information into a standardized
format and joint information model
•Why
• Improves business visibility
• Serve operational applications
• Foundation for analytics
Single View Solution
Internal
Database
Internal
Files
External
Database
External
Files
Data Aggregation Layer
Presentation Layer

RDBMS
Challenge: Differently shaped data spread across
many systems
Application 1
Application 2
Application n
Source
Database 1
Source
Database 2
Source
Database n
• Reconciling different data schemas from multiple systems
into a single schema is hard and in many cases impossible.
• Relational databases weren’t built for this.
• It is necessary to be able to iterate on the schema quickly
when new data sources need to be added.
• Evolving relational data schemas quickly is not easy.
• A Single View application is only as good as its ability to
serve up fine-grained access to the data within it. Data
access capabilities cannot be sacrificed therefore features
such as ad hoc queries, secondary indexes, and the ability to
aggregate data are critical.
• The required agility often can’t be provided by
relational databases or niche products.
…
COMMON MODEL
CustID | Activity ID | Date |
Type | 100s or 1000s fields
mostly agreed up front

Single View in RDBMS simply don’t scale
The database is forced to take
into account complexity of all
source systems simultaneously.
This leads to an untenable level of
complexity in change
management and data access.

Documents are Rich Data Structures
{
first: ‘Paul’,
last: ‘Miller’,
memberId: 834343444,
city: ‘New London’,
location: [45.123,47.232],
profession: [banking, finance, trader],
medicalHistory: [
{ type: ‘Office visit’,
date: Date(‘2017-04-04’),
providerId: ‘dr9919’ },
{ type: ‘Colonoscopy’,
date: Date(‘2016-11-14’),
providerId: ‘d433000’ }
]
}
Fields can contain an array of sub-
documents
Fields
Typed field values
Fields can contain
arrays

Aggregation with a dynamic schema
Batch or real-time ingestion
of source data (push or pull)
Store raw data &
enable processing
Application 1
Application 2
Application n
Source
database 1
Source
database 2
Source
database n
1
2
4
Queue & distribute updates
for affected source systems
Single View
Application
Holistic view
across all data
3
1
2
3
4 If data can be edited the updates need to be fed back
into relevant source applications to ensure a
consistent state across all systems.
A custom Single View application will surface the
data subject to authentication/authorisation. Data can
either be read-only or also allow users to modify
records.
The flexible data in MongoDB schema allows
systems to send data raw. As second step business
logic is applied to process the data, e.g. to relate
records and detect duplicates.
Depending on the business requirements additional
manual steps can be implemented to verify matches
and/or process data manually if needed.
Related but disconnected data from multiple source
systems is ingested into MongoDB.
COMMON FIELDS
CustomerID | Activity
ID | Type | …
DYNAMIC FIELDS
Can vary from
record to record+
…

Why MongoDB for Single View?
• Dynamic schema → can handle vastly different data together and can keep
improving and fixing issues over time easily
• High scale/performance → directly impacts customer & user experience so
every second counts
• Auto-sharding → can automatically add processing power as data is added
• Rich querying → supporting end users directly requires multiple ways of
access and key/value is not sufficient
• Aggregation framework → database-supported roll-ups for analysis
• MapReduce capability (Native MapReduce or Hadoop Connector)
→ batch analysis looking for patterns and opportunities in the single view

How MongoDB is being Used in
Healthcare

MongoDB Healthcare Use Cases
360 view of a patient
Population management for at-risk
demographics
Lab-data management and analytics
Fraud detection
Health Applications, such as
Remote Monitoring and Body
Area Networks
Mobile Apps for Doctors and Nurses
Pandemic Detection with Real-Time
Geospatial Analytics
Electronic Healthcare Records
(EHR)
Advanced Auditing Systems for
Compliance
Hospital Equipment Management
and Optimization

Government DBaaS
Government agency provides a centralized data store to
manage veterans’ electronic records (VLER DAS)
Problem Why MongoDB ResultsProblem Solution Results
Internal and external systems need to
exchange and store data through
trusted connections to provide a full
range of services to the veteran
Clinicians needs accurate information to
ensure quality patient treatment
Benefits users needs accurate
information for benefits adjudication
Leverage flexible data model to save all
types of electronic records via one
centralized data service
Scales easily using sharding to manage
electronic records for the lifetime (and
beyond) of all veterans
Provides expressive query capabilities
to meet the needs of each line of
business
Succeeded in rolling out system in 9
months, meeting Congressionally
mandated deadline
Common access mechanism to
exchange and store veteran electronic
records
One place to store and manage veteran
electronic records for the lifetime of the
agency

Transforming Healthcare
Customer Service
Leverage predictive analytics to reduce customer service time
Florida Blue wanted to dramatically lower
response times for their claims, benefits,
and customer service calls
As more customers called in, the
additional load on the system would result
in higher response times
Needed to meet HIPAA requirements
Harnessed MongoDB to build a predictive
analytics engine that engages customers
with right customer service questions at
right time
MongoDB and IBM POWER provided a
highly secure platform that enabled
Florida Blue to meet HIPAA and other
regulatory certifications
Enabled Florida Blue to offer faster
customer service resulting in lower
response times
MongoDB on IBM POWER systems cut
hold times from 9 minutes to 30 seconds
IBM POWER’s higher SMT thread count
resulted in 3-5x the improvement over x86
servers that helped significantly lower
response times

HIPAA Security Auditing
Enhanced application performance, scalability, and analytics
Patient EHR and security auditing (record
access history) was stored in same SQL
Server database.
50% of database activity was security
audit records.
Database and application performance
were significantly impaired.
Security audit information stored in
MongoDB
Leverage industry standards for
healthcare security audit logging:
~300 distinct auditable user actions
Required and varying data elements
Responsive interactive audit report
Highly available
Better SLAs than EHR
Scalable
Supports 1000 new documents per
second
10’s of billions of audit event records

Why MongoDB for Healthcare
Flexible data model
Powerful query language
Analytics
Scalability/Availability
Ease of Operations
Security

Document Data Model
Relational MongoDB
{
first: ‘Paul’,
last: ‘Miller’,
location: [45.123,47.232],
profession:
[banking,finance,trader],
medicalHistory: [
date: Date(‘2017-04-04’),
date: Date(‘2016-11-14’),
]
}
memberId First Last City
834343444 Paul Miller New London
736736362 Chris Carr Baltimore
679099222 Tim OBrien New York
memberId providerId date type
834343444 dr9919 2017-04-04 Office Visit
834343444 d433000 2016-11-14 Colonoscopy
679099222 d822811 2016-12-06 X-Ray
Members
Medical History

Documents are Flexible
Documents in the same product catalog collection in MongoDB
{
product_name: ‘Acme Paint’,
color: [‘Red’, ‘Green’],
size_oz: [8, 32],
finish: [‘satin’, ‘eggshell’]
}
{
product_name: ‘T-shirt’,
size: [‘S’, ‘M’, ‘L’, ‘XL’],
color: [‘Heather Gray’ … ],
material: ‘100% cotton’,
wash: ‘cold’,
dry: ‘tumble dry low’
}
{
product_name: ‘Mountain Bike’,
brake_style: ‘mechanical disc’,
color: ‘grey’,
frame_material: ‘aluminum’,
no_speeds: 21,
package_height: ‘7.5x32.9x55’,
weight_lbs: 44.05,
suspension_type: ‘dual’,
wheel_size_in: 26
}

Data Governance with Document Validation
Implement data governance without
sacrificing agility that comes from dynamic
schema
• Enforce data quality across multiple teams and
applications
• Use familiar MongoDB expressions to control
document structure
• Validation is optional and can be as simple as a
single field, all the way to every field, including
existence, data types, and regular expressions

Do More With Your Data
Rich Queries
Find everybody in New London that had a
colonscopy between 1970 and 1980
Geospatial
Find all members within 5 km of Trafalgar
Sq.
Search
Find all the members describing headache
as a symptom. Count them by profession.
(text, facets, collation)
Aggregation
Calculate the average number of office visits
per member per year
Graph
Find all the medical procedures for a Paul’s
family (descendants)
Map Reduce
Based upon past history, predict the areas
with most likely to have a higher incident of
cancer.
{
first: ‘Paul’,
last: ‘Miller’,
location: [45.123,47.232],
profession:
[banking,finance,trader],
medicalHistory: [
date: Date(‘2017-04-04’),
date: Date(‘2016-11-14’),
]
}

Morphia
MEAN Stack
Java Python PerlRuby
Drivers & Ecosystem
Support for the most popular languages and frameworks

Model of the Aggregation Framework

MongoDB Connector for BI
Visualize and explore multi-dimensional
documents using SQL-based BI tools. The
connector does the following:
• Provides the BI tool with the schema of the
MongoDB collection to be visualized
• Translates SQL statements issued by the BI tool
into equivalent MongoDB queries that are sent to
MongoDB for processing
• Converts the results into the tabular format
expected by the BI tool, which can then visualize the
data based on user requirements

“We reduced 100+ lines of integration code to just a single line after moving to the MongoDB Spark connector.”
- Early Access Tester, Multi-National Banking Group Group
Analytics Application
Scala, Java, Python, R APIs
SQL
Machine
Learning
Libraries
Streaming Graph
Spark
Worker
Spark
Worker
Spark
Worker
Spark
Worker
MongoDB Connector for Spark
Advanced Analytics
• Native Scala connector, certified by Databricks
• Exposes all Spark APIs & libraries
• Efficient data filtering with predicate pushdown,
secondary indexes, & in-database
aggregations
• Locality awareness to reduce data movement
• Updated with Spark 2.0 support
MongoDB Connector for Apache Spark

Automatic Sharding
Three types: hash-based, range-based, location-aware
Increase or decrease capacity as you go
Automatic balancing

Query Routing
Multiple query optimization models
Each sharding option appropriate
for different apps

Replica Sets
Replica Set – 2 to 50 copies
Self-healing shard
Data Center Aware
Addresses availability considerations:
High Availability
Disaster Recovery
Maintenance
Workload Isolation: operational & analytics

MongoDB Management & Operations
• Atlas
– MongoDB as a Service
• Cloud Manager
– MongoDB Management Platform running in the cloud
– MongoDB located in cloud or data center
• Ops Manager
– MongoDB Management Platform
– Ops Manager deployed on your own servers

Automated Available On-Demand
Secure Highly Available Automated Backups
Elastically Scalable
Database as a Service for MongoDB

MongoDB Atlas Features
Database as a service for MongoDB
MongoDB Atlas is…
• Automated: The easiest way to build, launch, and scale apps on MongoDB
• Flexible: The only database as a service with all you need for modern applications
• Secured: Multiple levels of security available to give you peace of mind
• Scalable: Deliver massive scalability with zero downtime as you grow
• Highly available: Your deployments are fault-tolerant and self-healing by default
• High performance: The performance you need for your most demanding workloads

• Spin up a cluster in
seconds
• Replicated &
always-on
deployments
• Fully elastic: scale
out or up in a few
clicks with zero
downtime
• Automatic patches
& simplified
upgrades for the
newest MongoDB
features
• Authenticated &
encrypted
• Continuous backup
with point-in-time
recovery
• Fine-grained
monitoring &
custom alerts
Safe &
Secure
Run for
You
• On-demand pricing
model; billed by the
hour
• Multi-cloud support
(AWS available with
others coming
soon)
• Part of a suite of
products & services
designed for all
phases of your app;
migrate easily to
different
environments
(private cloud, on-
prem, etc) when
needed
No Lock-
In
MongoDB Atlas Benefits
Database as a service for MongoDB

MongoDB Ops and Cloud Manager
• Dozens of charts
tracking key
performance
indicators
• Custom alerts that
trigger when key
metrics are out of
range
• RESTful API to
integrate with your
existing APM tools
• Visual displays of
query and write
latency
• Recommendations
for new indexes to
improve query
performance
• One-click rollout of
new indexes across
your deployment;
according to best
practices and with no
downtime
OptimizationMonitoring
• Deploy, resize, and
upgrade your
deployments with
just a few clicks
• Reduce the
operational overhead
of running MongoDB;
enable your ops team
to be 10-20x more
efficient
• RESTful API to
integrate with your
enterprise
orchestration tools
Automation Backup
• Continuous backups
to minimize your
exposure to data loss
• Restore to precisely
the moment you
need with point-in-
time recovery

*Included with MongoDB Enterprise Advanced
BUSINESS NEEDS SECURITY FEATURES
Authentication SCRAM, LDAP*, Kerberos*, x.509 Certificates
Authorization Built-in Roles, User-Defined Roles, Field-Level Redaction
Auditing* Admin, DML, DDL, Role-based
Encryption
Network: SSL (with FIPS 140-2), Disk: Encrypted Storage
Engine* or Partner Solutions
Enterprise-Grade Security

Read-Only Views
MongoDB 3.4 allows administrators to define
dynamically generated views that expose a subset of
data from the underlying collection
• Reduces risk of sensitive data exposure
• Views do not affect source collections
• Separately specified permissions levels
• Allows organizations to more easily meet
compliance standards in regulated industries

MongoDB Compass MongoDB Connector for BI
MongoDB Enterprise Server
MongoDB Enterprise Advanced
CommercialLicense
(NoAGPLCopyleftRestrictions)
Platform
Certifications
MongoDB Ops Manager
Monitoring &
Alerting
Query
Optimization
Backup &
Recovery
Automation &
Configuration
Schema Visualization
Data Exploration
Ad-Hoc Queries
Visualization
Analysis
Reporting
LDAP & Kerberos Auditing FIPS 140-2Encryption at Rest
REST APIEmergency
Patches
Customer
Success
Program
On-Demand
Online Training
Warranty
Limitation of
Liability
Indemnification
24x7Support
(1hourSLA)

How MongoDB is Transforming Healthcare Technology

How MongoDB is Transforming Healthcare Technology

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