Hybrid Architecture with Ike & Data Libraries

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OVERCOMING THE OBSTACLES TO A LEARNING HEALTH CARE SYSTEM GLOBALLY 06/07/2022 MedDATA FOUNDATION © 2013 1 "For an idea that does not at first seem insane, there is no hope." - Albert Einstein ews expressed herein are soley those of Stephen A. Weitzman, J.D. Executive Director of MedDATA Foundation.

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Transcript of Hybrid Architecture with Ike & Data Libraries

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OVERCOMING THE OBSTACLESTO A LEARNING HEALTH CARE SYSTEM GLOBALLY

04/10/2023

MedDATA FOUNDATION © 2013 1

"For an idea that does not at first seem insane, there is no hope." - Albert Einstein

The views expressed herein are soley those of Stephen A. Weitzman, J.D. LL.M. Executive Director of MedDATA Foundation.

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THERE ARE THREE TECHNICAL PROBLEMS

A platform and methods for sharing data in a way that it can be analyzed for all the good purposes

Standards and Common Data Models for all disease areas

Incentives for the Pharma and Healthcare Systems Silos to share data – (The Silos include – holders of post market patient medical records- and pharma companies that hold the premarket data that shows safety and efficacy or lack thereof)

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INFRASTRUCTURE:You can’t communicate or share

without it.

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Maybe I’ll Wait

2012

20251956

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Federal Aid Highway Act of 1956

I Guess Not!

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SECOND PROBLEM: SHARING DATA

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THERE ARE TWO SETS OF SILOS

POST MARKET MEDICAL RECORD DATA

PREMARKET CLINICAL DATA SHOWING SAFETY AND EFFECTIVENESS OF THERAPIES

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THE SECOND PROBLEM

It is argued that we do not have data standards. That is not true. We do have medical record formats in current use by pharma companies by which data is collected in clinical studies and submitted to FDA or EMA for evaluation of safety and efficacy. If we use these data structures then we can collect and merge post market data with premarket data in the same way that FDA evaluates data.

It is time to create incentives for pharma to make disclosure – full transparency- of protocols and clinical data of approved therapies available – to advance creation of the next generation of therapies.

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Solutions to Infrastructure

Central DatabaseDistributed DatabaseHybrid Database

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In the United Kingdom

Data in Silos are collected into a Central Database for Querying and Analysis

The database is the GPRD/CPRD with 12 million patients and over 60.0 million records

The database is to be expanded to 55 million patients

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PCAST 2010

“In other sectors, universal exchange standards have resulted in new products that knit together fragmented systems into a unified infrastructure.”

“The resulting ‘ network effect’ then increases the value of the infrastructure for all, and spurs rapid adoption.”

“By contrast, health IT has not made this transition.” “The market for new products and services based on

health IT remains relatively small and undeveloped compared with corresponding markets in most other sectors of the economy, and there is little or no network effect to spur adoption.”

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In other sectors, universal exchange standards have resulted in new products that knit together fragmented systems into a unified infrastructure.”“The resulting ‘ network effect’ then increases the value of the infrastructure for all, and spurs rapid adoption.” “By contrast, health IT has not made this transition.” “The market for new products and services based on health IT remains relatively small and undeveloped compared with corresponding markets in most other sectors of the economy, and there is little or no network effect to spur adoption.”

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U.S. Solution: A Distributed SystemReasons

1. Data is kept in the hands of the original data holders

2. Decrease proprietary and liability concerns 3. Decrease risk and severity of data breaches4. Data holders know their data; improve value

and better interpretation of findings5. Minimize data transfer; minimum necessary6. Voluntary – data partner autonomy7. Reciprocity – value for participation8. Partnership9. Well-defined purpose

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Solution: Query Distribution/Response

1- User creates and submits query (a computer program)

2- Data partners retrieve query

3- Data partners review and run query against their local data

4- Data partners review results

5- Data partners return results via secure network

6 Results are aggregated

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Jeffrey Brown, PhD and Richard Platt, MD

Harvard Pilgrim Health Care Institute

/ Harvard Medical School

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Disagreement and Agreement with Rationale For Distributed Model

(OK for Epidemiology but not for Decision Support or other Real-Time needs)

1. Data must be kept in the hands of the original data holders – (In the U.S. we will never get a central database)

2. Decrease proprietary and liability concerns – Can Be Handled3. Decrease risk and severity of data breaches – Disagree4. Data holders know their data; improve value and better

interpretation of findings – DisagreeData in distributed system is not uniformly indexed or coded

5. Minimize data transfer; minimum necessary –(Security Issue)6. Voluntary – Data partner autonomy (Same as 1)7. Reciprocity – Value for Participating: Access more data8. Partnership 9. Well-defined purpose

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1 – Mirror Data and 2

Index

1. Data held by partners is mirrored at their location (Silo)2. Mirrored data is "reindexed" 24/7 in a uniform manner using NLP and Auto-Coding3. Indexes (inverted files) of partners are aggregated in central computer 24/74. User selects data sources and creates and submits query to "central" portal 5. Query locates data in the partner sites through the central index6. Data relevant to the query is aggregated in a cloud 7. Analytics is applied to generate the report 8. Obtain results and publish with reference to sources of data (trail)9. Erase data

Data Partner 4 – Select Data

Sources; Run Query

Obtain Results

Mirrored Data and

Index

Mirrored Data and

Index

Mirrored Data and

Index

Mirrored Data and

Index

Mirrored Data and

Index

5 - CentralCatalog - Index

Data Partner

Data Partner

Data Partner

Data Partner

Data Partner

Alternative: Hybrid/Library/Query /Response

7-Aggregate Data; Analyze;

and

Index Path

Data Path

9-Erase Data

3

6

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1 – Mirror Data and 2

Index

1. Data held by partners is mirrored at their location (Silo)2. Mirrored data is "reindexed" 24/7 in a uniform manner using NLP and Auto-Coding3. Indexes (inverted files) of partners are aggregated in central computer 24/74. User selects data sources and creates and submits query to "central" portal 5. Query locates data in the partner sites through the central index6. Data relevant to the query is aggregated in a cloud 7. Analytics is applied to generate the report 8. Obtain results and publish with reference to sources of data (trail)9. Erase data

Data Partner 4 – Select Data

Sources; Run Query

Obtain Results

Mirrored Data and

Index

Mirrored Data and

Index

Mirrored Data and

Index

Mirrored Data and

Index

Mirrored Data and

Index

5 - CentralCatalog - Index

Data Partner

Data Partner

Data Partner

Data Partner

Data Partner

Alternative: Hybrid/Library/Query /Response

7-Aggregate Data; Analyze;

and

Index Path

Data Path

9-Erase Data

3

6

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Alternative: A Hybrid System& Reasons for Hybrid

The System is Data Agnostic, and Query System Agnostic Can access all available data for that user based upon data use agreements Data is kept in the hands of the original data holders (Same as distributed) Hybrid system is more efficient - Scalable (New Silos add Pointers to Index,

“Catalog”) Hybrid system can obtain results faster Hybrid system can be multi-purpose

Outcomes Research (CER) Drug Safety Signaling (surveillance) Personalized medicine Make Clinical Research More Efficient

Rapidly design and implement observational trials Quickly and affordably conduct randomized studies Significantly reduce usual expenses associated with start-up and shut-down of clinical

research studies Identify patients for clinical studies

Data is uniform – NLP and Coded to Snomed-CT Reciprocity – value for participation (Same as distributed) Partnership (Same as distributed) Well-defined purpose (Same as distributed)

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SHARE DATA AND NOT JUST INFORMATION

www.smartplanet.com © CBS Interactive

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