How MongoDB is Transforming Healthcare Technology
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The document discusses the application of MongoDB in the healthcare sector, highlighting its advantages such as a flexible data model, scalability, and robust querying capabilities that allow for effective management of patient information. It presents various use cases, including patient population management, fraud detection, and compliance auditing, emphasizing MongoDB's role in achieving a 'single view' of patient data from multiple sources. Additionally, it outlines challenges faced by traditional relational databases and how MongoDB's features address these in enhancing healthcare service delivery and operational efficiency.
![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](https://image.slidesharecdn.com/mongodbehr1-170711161128/85/How-MongoDB-is-Transforming-Healthcare-Technology-14-320.jpg)
![Document Data Model
Relational MongoDB
{
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’ }
]
}
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](https://image.slidesharecdn.com/mongodbehr1-170711161128/85/How-MongoDB-is-Transforming-Healthcare-Technology-25-320.jpg)
![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](https://image.slidesharecdn.com/mongodbehr1-170711161128/85/How-MongoDB-is-Transforming-Healthcare-Technology-26-320.jpg)
![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
}](https://image.slidesharecdn.com/mongodbehr1-170711161128/85/How-MongoDB-is-Transforming-Healthcare-Technology-27-320.jpg)
![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’,
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’ }
]
}](https://image.slidesharecdn.com/mongodbehr1-170711161128/85/How-MongoDB-is-Transforming-Healthcare-Technology-30-320.jpg)
How MongoDB is Transforming Healthcare Technology
- 1. MongoDB in Healthcare Jay Runkel Principal Solutions Architect
- 2. Agenda Phreesia Story Challenge with Healthcare How MongoDB is being Used in Healthcare Why MongoDB for Healthcare?
- 4. Contact Info Medical History Family History Medications Current Symptoms Reason for Visit Other Physicians Patient Intake
- 5. Challenges Information varies by: • Physician speciality • Physician preference • Insurance provider Large volumes • 50M Patients • 10Ms of transactions per year RDBMS
- 6. 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
- 9. Single View of Patient • Collects all patient information in a central repository Patient Records Medications Lab Results Procedures Hospital Records Physicians Patients Nurses Billing
- 10. 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
- 11. 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
- 12. 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.
- 14. 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
- 15. 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+ …
- 16. 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
- 17. How MongoDB is being Used in Healthcare
- 18. 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
- 19. 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
- 20. Transforming Healthcare Customer Service Leverage predictive analytics to reduce customer service time Problem Why MongoDB ResultsProblem Solution Results 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
- 21. HIPAA Security Auditing Enhanced application performance, scalability, and analytics Problem Why MongoDB ResultsProblem Solution Results 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
- 22. Why MongoDB for Healthcare
- 23. Why MongoDB for Healthcare Flexible data model Powerful query language Analytics Scalability/Availability Ease of Operations Security
- 25. Document Data Model Relational MongoDB { 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’ } ] } 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
- 26. 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
- 27. 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 }
- 28. 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
- 30. 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’, 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’ } ] }
- 31. Morphia MEAN Stack Java Python PerlRuby Drivers & Ecosystem Support for the most popular languages and frameworks
- 32. Development – The Past
- 33. Development – With MongoDB
- 34. Analytics
- 35. Model of the Aggregation Framework
- 36. 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
- 37. “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
- 39. Automatic Sharding Three types: hash-based, range-based, location-aware Increase or decrease capacity as you go Automatic balancing
- 40. Query Routing Multiple query optimization models Each sharding option appropriate for different apps
- 41. 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
- 43. 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
- 44. Automated Available On-Demand Secure Highly Available Automated Backups Elastically Scalable Database as a Service for MongoDB
- 45. 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
- 46. • 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
- 47. 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
- 48. Security
- 49. *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
- 50. 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
- 51. 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)