Bhagvan Kommadi [Value Momentum] | TeleHealth Platform: DevOps-Based Progressive Delivery | InfluxDays NA 2021

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© 2021 InﬂuxData Inc. All Rights Reserved.
DevOps Based Progressive Delivery
Telehealth Platform
Bhagvan Kommadi
Director of Product Engineering,
ValueMomentum

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Mind Map - DevOps
Telehealth Solution
Architectural Components
Deep Learning Solution
Implementation
What’s Next ?
Sneak Preview of Upcoming Features
Agenda
DevOps
Agility
Flexibility
Quality

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© 2021 InﬂuxData Inc. All Rights Reserved. 3
Mind map
No Code
Platforms
Low Code
Platforms
Market Requirements
Health Insurance
Payment Processing
AI
Digital health
Data Sources
Knowledg
e
Resources
Legacy Systems & Migration
Budget
In House Applications
Health Care
Application Upgrades & Update
Training
Metadata
Customizations
Licensing & Subscription
Life insurance
Tech Stack
Wellness Apps
Hospital
Management
Application Scope
Neural
Sketch
Learning
AI/ML
Devops
Containers
Cloud
CI/CD
Version
Control
Testing
GIT
IDE
Automation
Virtualization
Network
Monitoring
System Admin

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4
Telehealth Solution

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Patient Journey
Book an
Appointment
Appointment
Reminder
Schedule
Tracking
Intimate Delays
Manage
Appointments
Post
Appointment
Engagement

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What Patients Want & Doctor’s View

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Voice Assistants

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Process Flow
Filters
NLP API
Web
Mobile
Desktop
Queries
Registry
Data
Sources
Links
Knowledge
Base
Updates
1) Diseases
2) Data
Sources
3) Artifacts
Top Down
As Knowledge
Domain Expert
Bottom Up
Registry & knowledge
updates
• Orphanet
• Johns Hopkins
• WHO
• NORD

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Knowledgebase Architecture
Service Layer
Visualization Layer
Data Access Layer
Knowledge
Updates
Internet
Data Sources
Search
Manager
Knowledge
Base Manager
axios
cheerios
node
Disease
Registry
Disease
Knowledge

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Digital Assistants & Video Bots
Taking clinical
notes
Retrieving
cases
Updating
Prescriptions
Creating
consultation
appointments

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Architectural
Components

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Bug Tracking
Systems
Monitoring
Systems
Configuration
Management
OS Patching
CI/CD
Pipelines
Containers
Source Code
Control
Systems
Collaboration
Infrastructure
Foundational Tools & Infrastructure

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DevOps Evolution - Metrics
Metric Value
Deployment
Frequency
Monthly
Lead Time Between
a week
and
6months
MTTR Less than
a week
Change
Failure Rate
Less than
15%
Low
Mid High
Metric Value
Deployment
Frequency
Between Daily
and Weekly
Lead Time Less than a
week
MTTR Less than a day
Change Failure
Rate
Less than 15%
Metric Value
Deployment
Frequency
On Demand
Lead Time Less than an
hour
MTTR Less than an
hour
Change Failure
Rate
Less than 5%

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DevOps Evolution - Challenges
• Resistance to Change
• Legacy Architecture
• Shortage of Skills
• Lack of Automation
• Unclear Objectives
Low
• Resistance to Change
• Legacy Architecture
• Lack of Automation
Mid • Legacy Architecture
High

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Flow of Change
Business
Domain
Business
Domain
Stream Aligned Team
Stream Aligned Team
Enabling
Team
Platform Team
Complex Subsystem
Team
Flow of Change
Collaboration
X as a Service
Facilitating

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Jenkins Devops - Features
Authorization
Administration
Analytics
Scalability
Availability
Distributed Teams
Container Support
Quality
Cost
Product
Time

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Jenkins Pipeline – Devops Workﬂow
Code
Checkout
Build the
code
Test the
code
Stage the
build
Release
the build
Deploy the
build
Version
Control
Continuous
Integration
Continuous
Delivery
Continuous
Deployment

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Progressive Delivery of Web Apps
Automating the Delivery of Web Apps
Jenkins
Server
GitRepo
Update
Jenkins
Trigger
Jenkins Build
Process
AWS
Deployment
Live Web
App

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Progressive Delivery of Mobile Apps
Automating the Delivery of Mobile and Web Apps
Jenkins
Server
GitRepo
Update
Jenkins
Trigger
Jenkins Build
Process
Google
Play Store
Apple
App Store

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Jenkins Devops : Beneﬁts
Autonomous
Teams
Simple setup
Delivers
Release in
Time
Faster Delivery
Higher
Productivity
Software
Predictivity
Business
Sustainability
Autonomous
Teams
Audit Trails
End
to
End
Integration
Tests
Functional
Tests
Unit Tests

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Jenkins – Agent based Architecture
Jenkins
Server
Git Lab
Jenkins Agent
Jenkins Agent
Jenkins Agent

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Dev Ops Best Practices
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Automating Software Delivery
Treating Release Orchestration as a Business Imperative
Adopting Best-of-Breed Solutions but Doing so Judiciously
Practicing Value Stream Thinking
Using Feature Flags for Safer, Faster Software Delivery

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23
Demo

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24
Deep Learning
Solution
HealthCare Data Analysis

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Input Layer
Hidden
Layer
Hidden
Layer
Output
Layer
Hidden
Layer
Hidden
Layer
Convolutional Neural Network

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Image Pixels
Pixel Value
Color
Depth
Grey Scale
Intensity
CNN
Layers
Convolution
Rectified
linear unit
(ReLu)
Pooling
Fully
Connected
Features
Extraction
Image Recognition

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CNN Models
Keras
pre-trained
CNNs
Xception
VGG16
VGG19
ResNet5
0
Inception V3
Inception
ResNetV2
Mobile
Net
Dense
Net
NASNet
MobileNet
V2

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Atelectasis 192
Cardiomegaly 50
Consolidation 72
Edema 41
Effusion 203
Emphysema42
Fibrosis 38
Hernia 5
Infiltration 503
Mass 99
No Finding 3044
Nodule 144
Pleural_Thickening 65
Pneumonia 14
Pneumothorax 114
Total Result 5606
Atelectasis
Pneumothorax
Gender : Male, Female
Xray Position: Frontal , Back, Side
Lung Disease Analysis

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Training
Testing
Validation
Normal
Infection
by Virus
Infection
by Bacteria
True
Positive
False
Positive
False
Negative
False
Positive
Actual
P
r
e
d
i
c
t
e
d
Precision = True Positive/Actual Results
Recall = True Positive/Predicted Results
Accuracy = (True Positive + True Negative)/Total
Actual Results = True Positive + False Positive
Predicted Results = True Positive + False Negative
CNN
Scale
Shift
Rotate
Salt and
Pepper noise
Flip
Lung Disease – Deep Learning

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Total params: 12,167,424
Trainable params: 12,167,424
Non-trainable params: 0
Precision: 0.734694 %
Recall: 0.073022 %
Fscore: 0.261248 %
Training Data Bias = Average Predicted Value - Average Actual Value
Convolutional Neural Network Results

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Convolutional Neural Network - Basic

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Convolutional Neural Network – Complete Dataset

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Implementatio
n

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White Coats Platform
Network
App
Practice Plus
Clinic Plus StaffPlus Patient Plus
Registered
Doctors*
3,00,000
+
Locations
3,000
+
Onboarded
Doctors (App)
50,000
+
Doctors from
Metro/Tier 1 & 2
~68
%
Doctors from Tier
3 & 4
~32
%
# of Specialties
125+
General
Practitioners
~35
%
% of Drs :
Specialists
~65
%
• App & Web Views
Q1- 2.05L ; Q2- 2.8L (Growth 36%)
• Social Interactions (APP)
Q1- 1.27L ; Q2- 1.58L (Growth 25%)
• Network Cases
Q1- 800 ; Q2- 1000 (Growth 25%)
White Coats Platform

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What’s Next ?
Relation of programming paradigms to CD and deployment experience
Differences between prevalence and effects of intentional vs. unintentional violations of
SOLID OO design principles
Count of manual interventions required to go from dev to production and relation to
software architecture and testing strategy
Relation of database normalization to deployment and incident/rollback frequency
Complexity of DevOps pipeline in terms of tool count
Overall effect of CD on application quality and security
Prevalence of version control for various definable (source, build scripts, infrastructure
configuration, etc.)
SRE

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Moving From DevOps to ->
DevOps
Data
Ops
Dev
SecOps
AI Ops
MLOps
Development
Operatio
ns
Security
Data
DevOps
DevSecOps
DataOps

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DevOps - Tools
Plan
• Clickup
• JIRA
Code
• JIRA
• Clickup
• GitLab
• Confluenc
e
Build
• Maven
• Gradle
• Sbt
• Ant
Test
• Selenium
• Junit
• Watir
Release
• Jenkins
• CodeShip
Deploy
• Docker
• Clouds
Monitor
• Nagios
• DataDog
• Splunk

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Ops Roles - Future
Vendor Engg Product Engg
SocioTechnical
Systems Engg
Portfolio
Management

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SAFE - CALMR
Culture, Automation, Lean ﬂow, Measurement, and Sharing
Increased frequency, quality, and
security of product innovation
Decreased deployment risk with
accelerated learning cycles
Accelerated solution
time-to-market
Improved solution quality and
shortened lead time for fixes
Reduced severity and frequency
of failures and defects
Improved Mean Time to Recover
(MTTR) from production incidents

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Site Reliability Engineering
DevOps SRE
Waste
Reduction
Automation
Scale
Systematic
Perspective
Delivery
Release
Automation
Infrastructure
as Code
Config
Management
Reliability
Performance
Monitoring
Efficiency
Improvement

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Clinical Knowledge - EBM
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• Guidelines
• Decisions
• Study
Results
• Observations
Data Evidence
Guidance
Action
Omni Channel Telehealth
Platform
Genomic Applications
Personalized Medicine
Evidence Based Medicine
Predictive Toxicology
QSAR
Chemo-Informatics

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Questions?

Bhagvan Kommadi [Value Momentum] | TeleHealth Platform: DevOps-Based Progressive Delivery | InfluxDays NA 2021

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Bhagvan Kommadi [Value Momentum] | TeleHealth Platform: DevOps-Based Progressive Delivery | InfluxDays NA 2021