MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-Time Alerting to Improve Business Outcomes

From Pharmacist to
Analyst:
Leveraging MongoDB for
Real-Time Alerting to
Improve Business Outcomes
Presented by: David Munguia
Humana: Healthcare Services / Platforms

HUMANA HCS PLATFORMS
OVERVIEW
• Background
• MongoDB Journey & Lessons Learned
• You Can Do It!
• Closing and Questions

WHO ARE WE?
• High Performance Rule Based
Analytics
• HEDIS Reporting & Compliance
• Improved Member Outcomes
• Batch and Real-time Deployments
• SpecialtyRx
• Official Kickoff November 2017
• Stream Based Platform
• MongoDB Journey Began Here!

HUMANA HCS PLATFORMS: SpecialtyRx
NRT
Pipeline
Analytic
Services
Batch
Driver
Alert
Sink
ETL
Services
Message Bus
• Provide Alerting for Specialty Medications (e.g.
Humera, Embril, etc) at Point-of-Service
• Supports Daily Batch Runs
• Concurrent ETL Jobs
• Modern Streaming Platform
• MongoDB & Apache Spark Central
Technologies
• Language Agnostic (C/C++, Scala, Python)
• Clinical Quality Language Engine (CQL)

HUMANA HCS PLATFORMS: SpecialtyRx – Clinical Quality Language (CQL)

“No Battle Plan Survives
Contact with the Enemy.”
Helmuth von Moltke

“No Battle Plan Survives
Contact with the Enemy.”
Helmuth von Moltke
“Everyone has a plan ‘till you
get punched in the mouth.”
Mike Tyson

LESSON 1 – TRY IT
• I Hated Hearing it!!!
• No Two Use-Cases are the Same
• MongoDB Made it Doable
• Our Mindset

LESSON 2 – USE AVAILABLE RESOURCES
• Befriend your MongoDB
Representative
• Sigfrido Narvaez
• Giovanni DeVita
• Engage MongoDB Professional Services
• Suggest 6 Day Engagement over 2 Visits
• When you have Security Concerns
• Consultants have Undocumented Tools
• MongoDB University
• Stack Overflow
• Github Repositories!

LESSON 3 – MongoDB ≠ Oracle
• SQL-Let it Go!
• JSON is a Blessing
• $lookup is Like C/C++ goto
• Reserved for Special Cases
• We’re not that Special
• MongoDB has Fewer Features
• But, That is Good Thing
• Simplifies ‘Try-ability’
• Plenty of Usable Build Patterns
• Bucket, Subset, Tree & Graph

LESSON 3 – MongoDB ≠ Oracle
• SQL-Let it Go!
• JSON is a Blessing
• $lookup is Like C/C++ goto
• Reserved for Special Cases
• We’re not that Special
• MongoDB has Fewer Features
• That is Good
• Try-ability
• Plenty of Usable Build Patterns
• Bucket, Subset, Tree & Graph
CREATE TABLE member (
member_id NUMBER generated BY DEFAULT AS identity,
first_name VARCHAR2(50) NOT NULL,
last_name VARCHAR2(50) NOT NULL,
PRIMARY KEY(member_id)
);
db.createCollection(‘member’); // assuming member_id = _id
Or
SELECT *
FROM members a,
member_address b
WHERE a.member_id = b.member_id
AND a.member_id = ‘abcd’;
db.patients.find( {_id: ‘abcd’ } );

LESSON 4 – SCHEMAS & INDEXING
• Have a Complete Understanding
of your Access Patterns
• Utilize Covered Queries
• Index Order Matters
• Your First Version will Suck

LESSON 5 – SIZING CONSIDERATIONS
• Stay Clear of 16MB Document Limit
• Keep to Less Than 1MB
• 800B Avg.
• Our Inflection Point: 200M Documents
• Prefer Foreground Indexing
• Background Indexing Can Slow Normal
Operations
• Smaller Index Sizes

LESSON 6 – PRE-SPLITTING
• Pre-Splitting is the Process of Pre-
Chunking and Balancing Data Blocks
• 50% Science, 25% Magic, 25% Voodoo
• Automatic Splitting and Balancing is Slow
and Consumes Resources
• The Choice of Shard Key and Pre-Splitting
Strategy is Crucial
• When do you Pre-Split? Refer to Lesson 1

LESSON 6 – PRE-SPLITTING • Shard Key: member identifier
• Non-Monotonic String Type
• Oracle Remnant of Hash Partitioning

• Shard Key: member identifier
• Oracle Remnant of Hash Partitioning
• Try 1: Naïve member id Splitter
• Divided min/max keys into n bins
• Keys were “clumpy”
LESSON 6 – PRE-SPLITTING

• Oracle Remnant

• Oracle Remnant
Still Created 25-40 Splits When Loading

• Oracle Remnant
• Try 2: MongoDB did Initial Work (5 days/Col)
1. Established IOPS, Network, Oplog Params
2. Stored _id, min/max for ns(database) in new Collection
3. Used python and “master_chunk” collection to auto-
generate pre-splitting JS scripts for each sharded
collection.
4. Load Data

• Oracle Remnant
• Try 2: MongoDB did Initial Work (5 days/Col)
1. Established IOPS, Network, Oplog Params
2. Stored _id, min/max for ns(database) in new Collection
3. Used python and “master_chunk” collection to auto-
generate pre-splitting JS scripts for each sharded
collection.

APACHE SPARK & MONGODB
mongos
CQL
mongos
CQL
[Message Pipeline]
[Message Pipeline]
mongos
[Message Pipeline]
v1
v2
• MongoDB Connector for Apache Spark
• Highly Recommended
• Scalable and Resilient
• Perfect for Many ETL Jobs
• Leverages MongoDB Agg. Pipelines
• Optimized for MongoDB Topology
• Allowed us to Concentrate on Problem
• Thank you Bryan!

APACHE SPARK & MONGODB
mongos
CQL
mongos
CQL
[Message Pipeline]
[Message Pipeline]
mongos
[Message Pipeline]
v1
v2
• MongoDB Connector for Apache Spark
• Scalable and Resilient
• Strong Contender for ETL Jobs
• Leverages MongoDB Agg. Pipelines
• Optimized for MongoDB Topology
• Allowed us to Concentrate on Problem
• Thank you Bryan!

LESSON 7 – TURN THE TABLES
• Exploit New Opportunities
• Active Medications
• Pre-Filtering (CQL)

• Exploit New Opportunities
{
"_id": "076438350931",
"patient_id": "xxxxxxxxxxxxxx",
"claim_status": "D",
"claim_id": "076438xxxxxx",
"ndc": "00310028060",
"date_filled": "2017-07-29:00:00.000Z",
"dispensed_qty": 90,
"days_supply": 90,
"avg_qty_per_day": 1,
"active_end_date": "2017-11-28T00:00:00.000Z",
"rx_number": "000000959377",
}

• Turn Disadvantages into Advantages

• Turn Disadvantages into Advantages
def preFilteringPopulation (
patient_collection: String,
engineList: String,
grpcMessageSize: Int,
batchType: String): Boolean = {
//-- Configs assumed to be passed-in through spark-submit
val sparkSession = SparkSession.builder().getOrCreate()
var status = true
try {
var response = null.asInstanceOf[com.tsi.grpc.EngineServer.BatchPopulationResponse]
val engines = engineList.split(",").map(_.trim).toList
var flag = false
var idx = 0
while ( !flag && idx < engines.length ){
var client = new gRpcEngineClient(engines(idx), grpcMessageSize)
response = client.batchPopulation(patient_collection,batchType,rundate_str)
if (response == null || response.status == false) {
println("Pre-filtering process failed with engine=" + engines(idx))
idx += 1
}…

HUMANA HCS PLATFORMS: LESSON 7
MongoDB in the Cloud & Other Weirdness
• Use Dedicated IOPS (mongod and ETL Instances)
• Take Advantage of Mutable Resources
• “aws ec2 modify-volume --iops 20000 --volume-id vol-02cbed099c22xxxxx --
profile prof1 --region us-east-1”
• Keep Hyperthreading On
• When you have $match Everything Looks Like a Nail
• C++ Stream Builders Are Awesome
• Must Iterate through Iterator when Using $out
• Setting Write Concerns when NOT Writing (not pretty)

}
],
"Encrypted": true,
"VolumeType": "io1",
"VolumeId": "vol-02cbed099c22xxxxx",
"State": "in-use",
"KmsKeyId": "arn:aws:kms:us-east-1:068955290847:key/8f175f51-60fb-4750-bbfe,
"SnapshotId": "",
"Iops": 6000,
"CreateTime": "2019-01-14T19:24:42.150Z",
"Size": 1500
}
]
}

bsoncxx::builder::stream::document filterBuilder;
filterBuilder
<< "patient_id" << patientId
<< "ndc"
<< bsoncxx::builder::stream::open_document
<< "$in" << targetDrugs
<< bsoncxx::builder::stream::close_document
<< "claim_status" << "P"
<< "date_filled"
<< "$lte" << endDate
<< "active_end_date"
<< "$gte" << startDate
<< "provider_id"
<< "$exists" << true
<< "$ne" << ""
<< bsoncxx::builder::stream::close_document;

• C++ Stream Constructors Are Awesome

SO, WHAT HAPPENED…
• Went Live May 2019 with Full
Membership
• Largest Deployment at Humana
• Over 80 Customer Measures
• Batches Went from 30 to 4 Hours
• Compressed 2 Week ETL to 5 Days

CLOSING
• Large Projects are Daunting, But
You Can Do It
• Have a “try it” attitude
• You Have Many Resources
• Let Go of SQL (Spark--I Know)
• Understand Your Access Patterns
• Pre-Splitting
• Turn your Disadvantages to
Advantages

ACKNOWLEDGEMENTS
• Humana
• Christopher Jaramillo
• Sathish John
• Raj Basavaraju
• Walter von Westphalen
• MongoDB
• Sigfrido Narvaez
• Giovanni DeVita
• MongoDB and SoCal Chapter
Team Members that made this possible:

THANK YOUHUMANA HCS INSIGHT PLATFORMS

Humana: Healthcare Services / Platforms
QUESTIONS!

MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-Time Alerting to Improve Business Outcomes

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MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-Time Alerting to Improve Business Outcomes