Date post: | 09-Jul-2015 |
Category: |
Health & Medicine |
Upload: | madpow-health-20 |
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Proprietary and Confidential to Eliza Corporation. Copying or distribution by any method is prohibited without prior authorization from Eliza Corporation
HEALTH DATA MANAGEMENT IN THE CLOUD OR
EVERYTHING I KNOW ABOUT TECHNOLOGY PROJECTS I LEARNED FROM WATCHING
STAR TREK RE-‐RUNS
Proprietary and Confidential to Eliza Corporation. Copying or distribution by any method is prohibited without prior authorization from Eliza Corporation 2
Topics
v Context – Background on Eliza v Eliza’s Experience with AWS v Lessons Learned v Q&A
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Automated, interacDve outreach that uses data-‐driven approaches to engage people in conversaDons that drive
healthier behavior.
Our clients measure it in terms of healthier, beKer informed members resulDng in improved clinical outcomes, reduced
costs, increased revenues, and higher brand loyalty.
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Delivering the highest levels of performance and flexibility
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Unique and predicQve data assets
B E N C HM A R K P E R F O RM A N C E
S E G M E N T P O P U L A T I O N
P R E D I C T B E H A V I O R
I D E N T I F Y A R E A S F O R I M P R O V E M E N T
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Current CharacterisQcs and Trends
v Current CharacterisDcs o MulD-‐channel, rules driven, member centric o High volume, with significant variability/bursts o Tight integraDon with customer systems and processes o Very high expectaDons for quality, responsiveness, upDme
v Trends and Demands o Eliza, customer, and now third party data o 3600 member views, flexible dashboards, benchmarking o The what AND the who o ConDnued high growth with higher expectaDons….
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Memories….
Contact Manager
SBC
Phone Switch
ESR
User Jobs Server
Report Server
Member MGR
Job Init Service
Tran
scrib
er
Rules Engines
ApplicaQon Coordinator
Names
NAS
Call Host
Call Host
Call Host
Call Host
User Jobs
App Code Deploy Server
App Ext. EID
ApplicaQons
WAVs Logs
Invite
Invite
Sessions
Inbound Invite
Refer (ext+uid)
Refer
inbound Invite With app info (parameters
Voice
Sessions
Cube Procs
Data Extract System
Portal Cube
AnalyQc Cube
Billing Cube
Web Service
App Framework Web Services
config config App Coordinator Web Services
Sessions
User Jobs
Voice
Sessions Data
Portal/ReporQng
DES Tool
Billing Tool AnalyQcs
Report Extracts
Batch Input
Real Time Input
network
MEP Bulk Email
Call Data
TTS Prompt Request Prompt
Engagement Engine
Data Warehouse
Staging
User Data Files
Sessions Wave
Prompt Recording (Sputnik)
Prompt
NAT
Higgins
DFs DFs DFs
SS01
Tumbleweed
AS2
ED SVC.
ED repository
ESR Config SVC.
RTNS
MQ
Telco config
TBD
Names Blacklist
Transfer DB
PGP PGP1
User Data
User Data
User
Config
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Eliza’s Plaaorm on AWS -‐ Goals
v Simplify delivery of real Dme operaDons reporDng and alerDng v Support flexible reports and analyDc dashboards
o Rapid creaDon of new views, alternaDve formats, adding and removing filters, measures, etc. o AddiDon/placement of widgets for threshold se^ng, date selecDon, case management, other funcDons
o Flexible, extensible data model o Member centric profiles o Import, storage, and management of both Eliza and non-‐Eliza data o “3600 paDent view”
v Improved security and reliability o Data fully encrypted in transit (SSL) and at rest (256 bit AES) without sacrificing performance o Fewer systems, fewer hands/eyes on data, wide area data replicaDon in real-‐Dme
v Improved quality of interacDons
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Eliza’s Plaaorm on AWS
Availability Zone A Availability Zone n
Eliza Services(Metras)
Auto Scaling VM’s Auto Scaling
VM’s
Amazon DynamoDB
Amazon Simple Queue Service
Amazon Simple Storage Service
ELB with SSL Termination
Virtual Private Gateway
Internet Gateway
Internet
Eliza Call Host Cloud
Web Service Client
FTP Server
Management Tools
Eliza Services(Metras)
Eliza PhD
Eliza Corp Architecture Diagram – Service Level View
Amazon Route 53DNS
Encrypted Data Stores
Cloud
Boundary
Private Subnet Private Subnet
Redshift Cluster
Redshift Cluster
Key
EC2 Instance
HTTPS Connection
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Member profile: complete contact drilldown
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Member profile: drilldown into specific outreach details
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AcQve Monitoring soluQon tracks projected gap closures
v Offers insight into member behavior beyond tradiDonal claims data
v Member-‐level v A^tudes v MoDvaDons v Barriers
v Clearly idenDfies non-‐compliant members who can -‐ with a liKle extra encouragement -‐ close gaps
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OperaQonal Metrics: Outages, Bugs, and Costs, Oh My….
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Lessons Learned
v Technology v People and Process v Vendors, Contracts, and Costs v Customers
vs.
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Lessons Learned
v Technology o The sojware is the infrastructure o Architecture: design for failure o The benefits of frequent, early mistakes o Distributed data is “eventually” consistent o Data access paKerns (really) maKer o Some cloud services are more equal than others o Pay close aKenDon to the roadmap of the vendor you choose
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Lessons Learned
v People and Process o Agile and DevOps o Vendor training programs are actually preKy good o Not all tech staff will be willing or able to make the move (especially in IT and SQL areas) o Spend more Dme than you think you need to on architecture o Spend more Dme than you think you need to explaining the vision, and the roles and responsibiliDes needed
to get there
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Lessons Learned
v Vendors, Contracts, and Costs o Amazon does not negoDate…. o But price is not an issue, its come down nineteen Dmes since we started the project…. o ….but other terms can be an issue o You will be required to use dedicated instances o If you have exisDng vendors, be prepared for squirrely behavior o Feed your inner bean counter
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Lessons Learned
v Customers o Actually more open to the idea than we thought o But they expect that you’ve done your homework o Many will require a deep dive review on your architecture, security/privacy controls, audits, and DRP o Cloud ops must be integrated into your informaDon security management system, just like in-‐house
resources o Be prepared to address reports/headlines about provider outages o Be prepared to answer the price discounDng quesDon
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Epilogue