Looking to automate document processing in your healthcare organization? Learn from Provectus & AWS experts how to make data capture, conversion, and analytics more efficient. Process and manage documents faster and on a larger scale with AI & Machine Learning. In this presentation, we offer management and engineering perspectives on document processing with AI, to help you explore available options. Whether you are looking for a ready-made solution or plan to build a custom solution of your own, this webinar will help you find the best fit for your healthcare use cases.

1. Choosing the Right Document
Processing Solution for
Healthcare Organizations
Presented by:
Iskandar Sitdikov, ML Solutions Architect @ Provectus
Stepan Pushkarev, CTO @ Provectus
Andy Schuetz, PhD, Sr. Solutions Architect @ AWS

2. Webinar Objectives
1. Provide an overview of the market for document processing solutions
2. Outline critical factors for choosing the right document processing solution
for your healthcare use case
1. Strategize on whether you should look for a ready-made solution to purchase,
or to build a custom solution of your own
1. Get qualified for the Provectus IDP Solution Discovery Program

3. 1. Introduction
2. Healthcare use cases
3. Document processing in 60 seconds
4. Solutions map, advantages and problems
5. Evaluation
Agenda

4. Introductions
Iskandar Sitdikov
ML Solutions Architect,
Provectus
Andy Schuetz, PhD
Sr. Solutions Architect,
Healthcare and Life Science,
AWS
Stepan Pushkarev
Chief Technology
Officer, Provectus

5. AI-first Consultancy & Solutions Provider
500 employees and
growing
Established in 2010
HQ in Palo Alto
Offices in North
America, LATAM, and
Europe
Machine Learning DevOps
Big Data Analytics
We are obsessed about leveraging cloud, data, and AI to reimagine the way
businesses operate, compete, and deliver customer value

6. Our Clients
Innovative Tech Vendors (ISV & DNB)
Seeking for niche expertise to differentiate
and win the market
Midsize to Large Enterprises
Seeking to accelerate innovation, achieve
operational excellence

7. Healthcare Use Cases
Document processing 101

8. Use cases: Clinical notes, medical
records, insurance medical claims,
clinical studies, medical imaging
reports, lab reports, and transfers.
Administrative overhead to process
data from these types of documents is
huge.
Main benefits: Operational speed and
cost reduction. In our practice, we see
2-8x+ reduction in costs compared to a
fully manual process and 30%+ savings
in comparison to legacy OCR solutions.
Healthcare Use Cases

9. General goal is to spot main entities in
the document (paragraphs, forms, tables,
etc.) and then successfully identify
written text in them (segmentation and
OCR).
Both problems can be resolved
separately or using end-to-end networks.
IDP / CV

10. Context search on data from OCR + segmentation
Forms and tables greatly impact overall performance. Data extraction from forms is resolved (due to a
straightforward key-value structure). Tables are still a pain point for all data extractors. For unstructured texts,
deep networks are a solution at this point. Ex: BERT — good for finding key-value (question / answer) pairs
in context.
IDP / Data Extraction

11. Evaluation of the document
processing model is a task in
progress.
Results with a low-confidence
score and missing information
are forwarded to human experts.
Samples of successfully extracted
information are also forwarded to
human experts for evaluation.
IDP / Evaluation and Monitoring

12. Data lake + Ontology specifications
Fast Healthcare Interoperability Resources (FHIR)
is a standard describing data formats and
elements and an application programming
interface for exchanging electronic health
records. The standard was created by the Health
Level Seven International healthcare standards
organization.
IDP / Storage

13. Automation encapsulates all processes mentioned above
and unites them into one single product, featuring:
● Document capture
● Model lifecycle
○ Labeling
○ (Re)Training
○ Evaluation
○ Monitoring
● Human-in-the-loop
● Integrations
● System monitoring
IDP / Automation

14. IDP is more than just OCR. To resolve the problem in-house, you need
to take care of data capture, data ingestion, preprocessing, OCR, data
extraction, evaluation, and further integrations to destination
systems.
Bottleneck: Tables and unstructured text
IDP / Takeaways

15. Solutions Landscape
Market Overview

16. Documents are everywhere... and solutions for document processing are everywhere, too!
Competitive Landscape

17. Major technology platforms offer general-
purpose technology components for
document processing, such as:
● Amazon Textract + Comprehend
● Google Document AI
● Microsoft Azure Form Recognizer
Solutions: Cloud Vendors
Pros:
● Cloud infrastructure and integration
● Long lifespan and support
● Constant development
Cons:
● General purpose a.k.a require
additional work to extract necessary
information and integrate with
current workflows

18. This is a “younger” group of up-and-coming
vendors who have built solutions using AI-
native platforms to tackle the most demanding
automation challenges. Generally, they can
handle documents that are more complex or
have greater variation. As a result, they often
can deliver a greater business impact than
older technologies. Since they are free from
legacy technical debt, it is easier for them to
build next-gen, future-oriented solutions.
Solutions: Startups
Pros:
● Modern tech
● Constant development
● More focused applications
● Support — For a new independent player, support is
one of the highest priorities to gain customer
loyalty
Cons:
● Only few startups in this market can survive
competition with big vendors
● Challenging to customize
● May not align with your cloud strategy
● Support — On the other hand, new startups might
struggle with support

19. Legacy vendors typically build IDP
solutions on top of legacy platforms.
Niche vendors that are focused on limited
types of documents and use cases. You
might find hidden gems here!
Vendors that restructure your documents
workflow by introducing standard types of
documents, which are really easy to
process.
Solutions: Other Vendors
Pros:
● Wide variety of integrations
● Niche use cases
● Large portfolio of clients
Cons:
● In some cases, they rely outdated, less
performant technologies
● Document flow restructure

20. System Integrators may offer IDP
as part of their portfolio of
solutions. Their IDP offering may
be a solution from another IDP
vendor or developed in-house.
Solutions: System Integrators

21. © 2021, Amazon Web Services, Inc. or its Affiliates.
The AWS ML stack
Broadest and most complete set of machine learning capabilities
ML FRAMEWORKS
& INFRASTRUCTURE
TensorFlow, PyTorch,
Apache MXNet
Deep learning
AMIs & containers
GPUs Inferentia Elastic inference FPGA
AI SERVICES
Vision
Rekognition
Speech
Polly
Transcribe
Chatbots
Lex
Contact centers
Contact Lens
Connect Voice ID
Code + DevOps
CodeGuru
DevOps Guru
Text
Comprehend
Translate
Textract
Business tools
Personalize
Forecast
Fraud Detector
Lookout for Metrics
Search
Kendra
Industrial
Panorama Appliance and
SDK, Monitron, Lookout for
Equipment, Lookout for Vision
Healthcare
HealthLake
Comprehend Medical
Transcribe Medical
Label
data
Data
collection prep
Store
features
Detect bias
and explain
predictions
Visualize in
notebooks
Pick
algorithm
Manage
& monitor
Train
models faster
Deploy in
production
Tune
parameters
Manage edge
devices
SAGEMAKER STUDIO IDE
CI/CD
AMAZON
SAGEMAKER

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Purpose-built, HIPAA-eligible services
Amazon
Comprehend
Medical
Amazon
Transcribe Medical
Amazon
HealthLake
(now in preview)
NEW

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Digital transformation in healthcare
Uses <30 of
variables
200K- 300K data points
Today’s clinical
models
Future ML models
Use <30
data points
Use 200K–300K
data points
Clinician notes
Claims
Lab reports
Medical record
Clinical
studies
Transfer summaries Medical imaging
reports

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Quickly and easily import medical records including clinical notes, lab reports,
and more
Amazon
HealthLake
A HIPAA-eligible service that
enables healthcare providers,
health insurance companies,
and pharmaceutical companies
to store, transform, query, and
analyze health data at petabyte
scale
IMPORT
Understand relationships in the data with integrated analytics and ML capabilities
ANALYZE
Powerful query and search capabilities to ask questions of the data
QUERY & SEARCH
Tag and index unstructured data using specialized ML models
TRANSFORM
Stored in the AWS Cloud in a secure, compliant, and auditable way
STORE

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Making sense
of health data
See how you can benefit
from Amazon HealthLake
Automatically understand and extract meaningful medical information from raw,
disparate data. Revolutionize a process that was traditionally manual, error-
prone, and costly
Transform data seamlessly
Presents a a chronological order of medical events so that you can look at
trends over time. Unlock novel insights with ML models to find patterns and
identify anomalies
Identify trends and make predictions
Create a complete view of each patient’s medical history and structure it in the
Fast Healthcare Interoperability Resources (FHIR) standard format to facilitate
the exchange of information across multiple applications
Support interoperability

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Abbreviations, and time stamps
patient is a 62-year-old female with a history of Type 2 diabetes mellitus with insulin use (1/14/2019). Admitted ER 1/11/2020 for an
elevated BP with no previous history of HTN. 3/28/2020 she was admitted for hypothyroidism and prescribed metformin (GLUCOPHAGE)
1000 mg take daily by mouth in evening. Follow up clinic visit (9/20/2020) with A1C results of...
Medical conditions
Transform Data Seamlessly

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
ICD-10
RxNorm
“METFORMIN (GLUCOPHAGE) 1,000 mg tablet E 1 AND 1/2 TABLETS
BY MOUTH IN THE MORNING... “
“Type 2 diabetes mellitus with hyperglycemia,
without long-term current use of insulin (HCC)…”
Assign medical codes to text

28. 28
© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Access all the information
on a patient

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
Support Interoperability
Interoperability
Pharmaceutical
companies
Patients
Hospitals
Health plans
Providers
Labs

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© 2021 Amazon Web Services, Inc. or its affiliates. All rights reserved |
How it Works

31. Vendors Evaluation Methodology

32. What to Choose?
Now, you have all the information about
possible go-to solutions in your market
segment. What’s next?
You need to fairly compare each and every
solution to choose one that fits and aligns
with your use case the most.
Deep evaluation is key to making the right
decision.

33. Data
● EDA (exploratory data analysis) — Knowing your
data is the key to success
● Sample data based on EDA
● Use this data as the evaluation dataset for
measuring performance of solutions on the
market / in the segment
Metrics
● F1, Accuracy, Recall, etc.
● Key, value extraction
● Table data
● Language, character recognition, spelling,
handwritten text
Provectus Evaluation Methodology

34. Evaluation / Composite Index
Name Score
Provider 1 0.64
Provider 2 0.81
Provider 3 0.78
Composite Index

35. Evaluation / Text Index

36. Evaluation / Robustness Index

37. TCO and Case Study: Under NDA Client
General TCO structure:
● Infrastructure (data pipelines, storage, control panel)
● CV, NLP, Human-in-the-loop
● R&D costs (if building in house)
● Support
TCO targets for end-to-end solution: ~20-30 cents per
document for simple use cases and 50+ cents for specific
“complex” documents
Result: The cost of processing one document was reduced
from 24 to 11 cents, since the right OCR/CV vendor was
selected (it saved almost 10 cents per document). Also,
serverless architecture was leveraged to reduce
infrastructure costs.
OCR/CV solutions performance vs. cost: For a
given use case, the most expensive solution
delivered the worst result. A second to best
result was demonstrated by the vendor with
the second to cheapest solution.

38. Takeaways
1. Ecosystem matters: Data integration with built-in industry specific connectors, data pipelines,
OCR, NLP, security, storage, and a human-in-the-loop workflow — All these elements should be
integrated with each other for optimal performance.
1. Use unbiased benchmarking framework for evaluating real performance
of different providers, based on your use case and datasets.
1. Work with Provectus to reduce your Document Processing costs
a. By 2-8x compared to manual workflows
b. By 30%+ compared to legacy OCR solutions
c. By 10%+ compared to modern cloud solutions

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Suite 295, Palo Alto
California, 94301
provectus.com
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We would be happy to answer!