salus d2.3.3 version 1 - srdc · - we registered all background ip brought into the project. - we...

FP7-287800 SALUS

SALUS-FP7-287800• D2.3.3 Version 0.2 30/01/2015 of 85

SALUS “Scalable, Standard based Interoperability Framework for

Sustainable Proactive Post Market Safety Studies”

SPECIFIC TARGETED RESEARCH PROJECT PRIORITY Objective ICT-2011.5.3b) Tools and environments enabling the re-use of electronic health records

SALUS D2.3.3 Report on Intellectual Property Management (b)

Due Date: January 31, 2015 Actual Submission Date: Project Dates: Project Start Date : February 01, 2012

Project End Date : January 31, 2015 Project Duration : 36 months

Deliverable Leader: Agfa HealthCare

Project co-funded by the European Commission within the Seventh Framework Programme (2007-2013)

Dissemination Level

PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)

FP7-287800 SALUS


Document History:

Version Date Changes From Review

V0.1 December 5, 2014 First Draft Els Lion Dirk Colaert

V0.2 January 5, 2015 Final document for review Els Lion Consortium

V0.3 January 16, 2015 Input from ERS, INSERM,

Roche, SRDC

René Schippers,

Damien Leprovost,

Hans Cools, Anil

Sinaci

V1.0 January 29, 2015 Document ready for

submission

FP7-287800 SALUS


Contributors (Benef.) Dirk Colaert (AGFA)

Kristof Depraetere (AGFA)

Jos De Roo (AGFA)

Gerard Freriks (ERS)

René Schippers (ERS)

Jos Devlies (EUROREC)

Sajjad Hussain (INSERM)

Andrea Migliavacca (LISPA)

Marco Eichelberg (OFFIS)

Frerk Müller (OFFIS)

Gokce Banu Laleci Erturkmen (SRDC)

Tomas Bergvall (UMC)

Responsible Author Dirk Colaert Email [email protected]

Beneficiary Agfa Phone +32 3444 8408

FP7-287800 SALUS


SALUS Consortium Contacts:

Beneficiary Name Phone Fax E-Mail

SRDC Gokce Banu Laleci Erturkmen

+90-312-2101763 +90(312)2101837 [email protected]

EUROREC Georges De Moor +32-9-2101161 +32-9-3313350 [email protected]

UMC Niklas Norén +4618656060 +46 18 65 60 80 [email protected]

OFFIS Wilfried Thoben

+49-441-9722131

+49-441-9722111

[email protected]

AGFA Dirk Colaert +32-3-4448408 +32 3 444 8401 [email protected]

ERS Gerard Freriks +31 620347088 +31 847371789 [email protected]

LISPA Alberto Daprà +390239331605 +39 02 39331207 [email protected]

INSERM Marie-Christine Jaulent +33142346983 +33153109201 [email protected]

TUD Peter Schwarz +49 351 458 2715 +49 351 458 7319 [email protected]

ROCHE Bharat Thakrar +41-61-687 4606 +41 61 68 88412 [email protected]

mailto:[email protected]

mailto:[email protected]

FP7-287800 SALUS


Table of contents

TABLE OF CONTENTS ........................................................................................................................ 5

1. INTRODUCTION ........................................................................................................................ 6

1.1 EXECUTIVE SUMMARY ................................................................................................................ 6 1.2 WHAT WE DID IN DELIVERABLES D2.3.1 AND D2.3.2 ................................................................. 6 1.3 DESCRIPTION OF THE CONTENT OF THIS DELIVERABLE .............................................................. 6

2. OVERVIEW OF THE MEMOS OF INVENTION BY PARTNER ........................................ 7

2.1 SRDC .......................................................................................................................................... 7 2.1.1 De-Identification/Anonymization Service ............................................................................ 7 2.1.2 SIL-LISPA .......................................................................................................................... 11 2.1.3 Ontmalizer ......................................................................................................................... 15 2.1.4 PMSST ............................................................................................................................... 19 2.1.5 Semantic MDR ................................................................................................................... 25

2.2 OFFIS........................................................................................................................................ 29 2.2.1 ANT .................................................................................................................................... 29

2.3 AGFA........................................................................................................................................ 35 2.3.1 Semantic Mediation Framework ........................................................................................ 35

2.4 UMC ......................................................................................................................................... 39 2.4.1 TAST .................................................................................................................................. 39 2.4.2 CSCT .................................................................................................................................. 46 2.4.3 PHT .................................................................................................................................... 54

2.5 INSERM ................................................................................................................................... 65 2.5.1 IRT ..................................................................................................................................... 65

3. RESULT OF IP SEARCH ......................................................................................................... 71

3.1 SRDC ........................................................................................................................................ 71 3.1.1 De-Identification/Anonymization Service .......................................................................... 71 3.1.2 SIL-LISPA .......................................................................................................................... 73 3.1.3 Ontmalizer ......................................................................................................................... 76 3.1.4 PMSST ............................................................................................................................... 78 3.1.5 Semantic MDR ................................................................................................................... 79

3.2 OFFIS........................................................................................................................................ 80 3.2.1 ANT .................................................................................................................................... 80

3.3 AGFA........................................................................................................................................ 81 3.3.1 Semantic Mediation Framework ........................................................................................ 81

3.4 UMC ......................................................................................................................................... 82 3.4.1 TAST .................................................................................................................................. 82 3.4.2 CSCT .................................................................................................................................. 83 3.4.3 PHT .................................................................................................................................... 83

3.5 INSERM ................................................................................................................................... 84 3.5.1 IRT ..................................................................................................................................... 84

4. CONCLUSION ........................................................................................................................... 85

FP7-287800 SALUS


1. INTRODUCTION

1.1 Executive Summary

The contributions to technical deliverables and project results have been scrutinized in the context of existing patents, potentially preventing the partners to further use their own project results. Although at project design time this was already taken into account, the consortium thought it was still useful to do an end-of-project check and systematically screen project results on potential breaching of existing IP. As a result of the exercise, the consortium is confident that no patents have been breached by the work of SALUS. To collect the necessary information a template was distributed, used internally at Agfa to submit patents. This template is designed to describe an artifact in a condensed but still comprehensive way. This led to an interesting side effect of this exercise: this deliverable now contains a nice and useful description of the main technical results of the project. The reader is invited to look at the deliverable as the ‘short track’ to become acquainted with the technical work, done by the consortium.

1.2 What we did in deliverables D2.3.1 and D2.3.2

In the deliverables D2.3.1 IP Rights Agreement and D2.3.2 Report on IP Management (a) we collected the following information.

- We registered all background IP brought into the project. - We registered all foreground IP, produced by the project, in connection with the use

cases. - We decided principally on the open-source nature of the foreground, for which partners

could make exceptions. - We decided on making no official open-source projects, but instead making source

code available on request.

1.3 Description of the content of this deliverable

The main content of this deliverable is devoted to the search of potential IP breaches of existing patents by our work. The deliverable describes the methodology and the results of this investigation. In a nutshell, we did the following:

- We sent to the partners a template (‘memo of invention’) to be filled in and to be sent back. This template was based on Agfa’s internal template to submit patents. Acting like if we ‘would submit a patent’, we were pretty sure to capture all necessary information.

- Partners filled in the template. The content of this is reflected in chapter 2. - Based on the returned information the Agfa patent office searched for similar

technologies on different patent databases. - The Agfa patent office came up with a list of patents that potentially could be breached

by project artifacts or could be very close to it. - This list was sent back to each partner concerned in order to be checked as

ultimately only the partner can decide whether its technology breaches one of the patents or not. The assessment from Agfa’s patent office, as well as the check from the partners is listed in chapter 3.

FP7-287800 SALUS


In this document we report all the artifacts produced in this process. The document ends with a final conclusion.

2. OVERVIEW OF THE MEMOS OF INVENTION BY

PARTNER

2.1 SRDC

2.1.1 De-Identification/Anonymization Service

This document should capture the specifics of the artifacts produced by the Salus partners. This will then be used by the Agfa patent Office to scan for potential breaches of existing patents.

Title of the module:

De-Identification/Anonymization Service

A. Partner: SRDC

Name: SRDC Ltd. Address: ODTU Teknokent Silikon Blok Kat 1, No 14, Ankara Turkey

B. Information about Making the Invention:

C. Information about Use of the Invention:

The invention was first thought of on or about: December 2010

date

The invention was first explained to: To SALUS Partners during Kickoff meeting

Name of Person or Persons

on or about February 2012

The first drawing or sketch of the invention was made on

or about

February 2012 date

The first written description of the invention was made on or about

March 2013 date

FP7-287800 SALUS


A device, product or process embodying or using the invention has been made or used and tested.

Yes No Date: November 2013

A device, product or process incorporating or using the invention has been offered for sale, sold or used making a product or performing a service that has been sold

or used

Yes No

A technical paper, article, advertisement, other printed document or verbal

communication describing the invention has been distributed or communicated outside the consortium.

Yes No

Date of such communication, name of person to whom disclosed and person's organization:

Date: March 2013

Name: Elif Eryilmaz, Gokce B. Laleci Erturkmen Security and Privacy Issues for enabling the Secondary

use of EHRs in Clinical Research Med-e-Tel Conference 2013, Luxembourg, April 10-12, 2013

Organization: Medetel Conference

Is organization a potential customer?

Yes No

Was the invention disclosed under a

confidentiality agreement?

Yes

No

Unknown

SKETCH OR DRAWING: (In the space below, include a drawing, sketch, schematic, or diagram of the

invention and graphs, charts, etc. where applicable, or attach same to this form or list nodeID.)

Date of first sale/use

November 2013

Date of first commercial production using invention

N/A

FP7-287800 SALUS


D. Invention Disclosure: PURPOSE OF INVENTION:

(Explain the problem to be solved, results sought to be accomplished, difficulties to be overcome or eliminated and advantages to be gained by the invention.)

De-Identification of the EHR data to be used for clinical research purposes

BRIEF DESCRIPTION OF THE INVENTION: (Describe the machine, circuit, method, product or composition of matter that is

the subject of this Disclosure. Attach sketches or diagrams where applicable using drawing reference numbers in the description. Be sure to describe the preferred form of the invention, but identify alternate forms where appropriate.

Describe which products the invention could be used in.)

De-identification is the process of removing personal identity revealing attributes and replacing the required identifiers and attributes for research purposes either with pseudonyms or when possible with more generalized categories (like year of birth instead of exact birth date).

There is no single de-identification procedure that will meet the diverse needs of all the medical uses while ensuring privacy of patient data. Therefore, we have developed flexible de-identification architecture. De-identification methods are developed in a modular way, which can be extended by implementing new methods when necessary. Also configuration architecture is developed so that for each element a different de-identification method can be chosen by collaborating with the respective stakeholders after analyzing and assessing the risks for each data element set that needs to be exchanged in a de-identified manner. As a result, it is possible to set different methods and contextualized thresholds for the specific cases, which are not very common and may result with identification of individuals when commonly suggested de-identification methods are applied.

To be compliant with the existing methodologies at the European level, we have used guidelines provided by ISO about how some particular data elements within Electronic Health Records can be de-identified. IHE has reorganized these data elements in the IHE IT Infrastructure White Paper and has defined the following de-identification algorithms:

Redaction: Removing an atomic data element

Fuzzing: Adding “noise” to an atomic data element

FP7-287800 SALUS


Generalization: Making an atomic data element less specific

Longitudinal consistency: Modifying data so that it is shifted by a specific amount

Text Processing: Special considerations for free-format text

(Recoverable) Substitution: Changing one data element into another data element

Pass-through: No change

Before the implementation of De-Identification Service for clinical content represented in CDA based on the algorithms described by IHE, we have first conducted an analysis for each SALUS content model, which specifies the data elements to be exchanged within the scope of each SALUS use case.

After this phase, for each data element in the content model, the table presented in the figure below is filled, indicating the type of the transformation algorithm (redaction, generalization, pass through etc.) that can be applied to each data element as a part of de-identification.

After agreeing on the de-identification algorithms to be applied for each data element in the content model, these are mapped to the relevant CDA elements in the clinical instances. We have used the concept of Service-Oriented Architecture (SOA) with RESTful implementations on top of HTTP to implement these de-identification algorithms that enables extendible development within or across the current infrastructure. As a result, we have successfully de-identified some CDA document instances that are based on IHE Patient Care Coordination (PCC) and HL7/ASTM Continuity of Care Document (CCD) templates, by using the implemented de-identification algorithms. As the outcome of the De-Identification process, the de-identified medical data together with a study specific PID are passed to the Pseudonymization Service, so that a unique Pseudonym can be assigned replacing this PID. After that, the totally de-identified and pseudonymized medical data can be passed to the Research Zone.

DISTINCTIVE FEATURES: (How does the disclosed invention differ from earlier attempts to solve the same problem?)

We have implemented these methods by using service-oriented methodology providing extensible approach to implement additional methods with respect to the requested features of the end-users. As a result, we have achieved a novel extensible security infrastructure for the secondary use of EHRs represented as CDA documents in clinical research. The novel aspects can be summarized as:

FP7-287800 SALUS


De-identification is processed on top of the queried clinical data instances instead of all data elements in a data warehouse,

An extensible de-identification framework is created based on SOA principles using RESTful implementations in a modular way that makes further development easy based on the needs of the end-users,

A flexible de-identification framework is created where the de-identification method can be configured for each data element after analyzing and assessing the risks for each data element set that needs to be exchanged in a de-identified manner with the respective stakeholders.

E. Prior Art: Identify the most closely related device, product or process existing before the

invention: (a) in the company; and (b) outside the company.

-ISO Pseudonymization Guidelines

-Pommerening Approach

Identify the published description(s) in a technical paper, advertisement, patent, etc. of the device, product or process that according to your knowledge is closest to the invention. (If possible and as appropriate, furnish copies and point out

relevant portions.)

[1] Klaus Pommerening, Michael Reng. Secondary use of the Electronic Health Record via pseudonymisation. In:

L. Bos, S. Laxminarayan, A. Marsh (eds.): Medical Care Compunetics 1, IOS Press, Amsterdam 2004; pp.

441–446

[2] Thielscher, C., Gottfried,M., Umbreit, S., Boegner, F., Haack, J., Schroeders, N. Patent: Data processing

system for patient data. Int. Patent, WO 03/034294 A2 (2005).

[3] Noumeir R, Lemay A, Lina JM. Pseudonymization of radiology data for research purposes. J Digit Imaging.

2007 Sep;20(3):284-95.

Identify any persons that you know of who may have knowledge of additional information relevant to parts mentioned above.

None.

F. Addendum None.

2.1.2 SIL-LISPA

This document should capture the specifics of the artifacts produced by the SALUS partners. This will then used by the Agfa patent Office to scan for potential breaches of existing patents.


LISPSemantic Interoperability Service

FP7-287800 SALUS


A. Partner: SRDC







or used Yes No




date

The invention was first explained to: Submitted as a part of SALUS Proposal to the commission. So it is shared with SALUS Project partners, and SALUS

evaluators, Commission Officers


on or about January 2011

date

The first drawing or sketch of the invention was made on or about

January 2011 date

The first written description of the invention was made on

or about

January 2011 date

Date of first

sale/use

November 2013- At LISPA

Deployment as a part of SALUS Project

Date of first commercial production using

invention

NA

FP7-287800 SALUS


Yes No


Date: Submitted on

December 2011

Name: Gokce B. Laleci, Mustafa Yuksel, Asuman Dogac, Providing Semantic Interoperability between Clinical Care and Clinical Research Domains, IEEE

Transactions on Information Technology in BioMedicine, Volume: 17, Issue: 2, March 2013

(online since Sept. 2012), Page(s): 356-369.

Organization: Publicly available as a publication


Yes No

Was the invention disclosed under a confidentiality agreement?

Yes

No Unknown

SKETCH OR DRAWING:

(In the space below, include a drawing, sketch, schematic, or diagram of the invention and graphs, charts, etc. where applicable, or attach same to this form or

list nodeID.)

FP7-287800 SALUS


D. Invention Disclosure:

PURPOSE OF INVENTION: (Explain the problem to be solved, results sought to be accomplished, difficulties to be overcome or eliminated and advantages to be gained by the invention.)

The objective is to make the XML based HL7 CCD based medical summaries

available in SALUS Common Information Model in RDF format.

BRIEF DESCRIPTION OF THE INVENTION:

(Describe the machine, circuit, method, product or composition of matter that is the subject of this Disclosure. Attach sketches or diagrams where applicable

using drawing reference numbers in the description. Be sure to describe the preferred form of the invention, but identify alternate forms where appropriate. Describe which products the invention could be used in.)

This Semantic Interoperability Service is capable of converting medical summaries

represented through HL7 CCD based templates (identified in SALUS D4.1.1) to SALUS Common Information Model.

The EHR RDF Service gets the data of the eligible patients in native XML representation of the CCD/PCC templates, after which data formalization takes

place. In order to perform comprehensive transformations of XML Schemas (XSD) and XML data to RDF automatically, we have implemented a tool named Ontmalizer. Through this tool, the CCD/PCC template instances complying with

HL7 CDA Schema are automatically RDFized by creating the corresponding ontology instances. The outcome is always a one-to-one correspondence of the

input data, but represented as RDF entities to foster further semantic processing. Semantic Interoperability Layer - Data Services (SIL-DSs) for Lombardy is

responsible for converting the medical summaries of the eligible population represented in local ontologies, i.e. CDA/CCD Content Entity Model instances received from EHR RDF Service to instances represented in SALUS CIM Ontology.

In order to perform this operation, a set of conversion rules in Notation3 (N3) has been implemented in Euler Yap Engine (EYE).

DISTINCTIVE FEATURES: (How does the disclosed invention differ from earlier attempts to solve the same

problem?) Similar approaches exist in the literature as presented below.

E. Prior Art:

Identify the most closely related device, product or process existing before the

invention: (a) in the company; and (b) outside the company. Similar approach has been followed in Artemis project.

There is a similar Patent: Semantic interoperability system for medicinal information: WO 2011032086 A2

FP7-287800 SALUS


Identify the published description(s) in a technical paper, advertisement, patent,

etc. of the device, product or process that according to your knowledge is closest to the invention. (If possible and as appropriate, furnish copies and point out relevant portions.)

a) Dogac A., Laleci G., Kirbas S., Kabak Y., Sinir S., Yildiz A., Gurcan Y.

Artemis: Deploying Semantically Enriched Web Services in the Healthcare Domain Information Systems Journal (Elsevier), Volume 31, Issues 4-5, June-July

2006, pp.321-339

b) https://www.google.com/patents/WO2011032086A2 Identify any persons that you know of who may have knowledge of additional

information relevant to parts mentioned above. For Artemis project SRDC can provide information.

F. Addendum None.

2.1.3 Ontmalizer

This document should capture the specifics of the artifacts produced by the Salus partners. This will then used by the Agfa patent Office to scan for potential breaches of existing patents.


Ontmalizer

A. Partner: SRDC

Name: SRDC Ltd.

Address: ODTU Teknokent Silikon Blok Kat 1, No 14, Ankara Turkey



date

The invention was first explained to: Submitted as a part of SALUS Proposal to

the commission. So it is shared with SALUS Project partners, and SALUS evaluators, Commission Officers


on or about January 2011

date

FP7-287800 SALUS


C. Information about Use of the Invention: A device, product or process embodying or using the invention has been made or

used and tested.


A device, product or process incorporating or using the invention has been offered

for sale, sold or used making a product or performing a service that has been sold or used

Yes No

A technical paper, article, advertisement, other printed document or verbal communication describing the invention has been distributed or communicated outside the consortium.

Yes No


Date: Submitted on December 2011

Name: Gokce B. Laleci, Mustafa Yuksel, Asuman Dogac,

Providing Semantic Interoperability between Clinical Care and Clinical Research Domains, IEEE Transactions on Information Technology in

BioMedicine, Volume: 17, Issue: 2, March 2013 (online since Sept. 2012), Page(s): 356-369.


Is organization a potential

customer?

Yes No


or about

January 2011 date


January 2011 date


November 2013- At LISPA Deployment as a part of

SALUS Project


NA

FP7-287800 SALUS



Yes

No

Unknown

SKETCH OR DRAWING:


list nodeID.)



To perform comprehensive transformations of XML Schemas (XSD) and XML data to RDF/OWL automatically.



using drawing reference numbers in the description. Be sure to describe the preferred form of the invention, but identify alternate forms where appropriate. Describe which products the invention could be used in.) Ontmalizer is a tool developed by the SRDC team that is able to perform comprehensive transformations of XML Schemas (XSD) and XML data to RDF/OWL automatically. Through this tool, it is possible to create RDF/OWL representation of XML Schemas, and XML instances that comply with such XML Schemas. The state of the art open source and/or free tools for RDFizing XSD and XML were not able to handle complex schemas and XML instances such as HL7 Clinical Document Architecture (CDA) R2. Only the commercial versions (standard and maestro) of TopBraid Composer1 were successfully able to handle such complex schemas. However, we do not want to use such commercial tools in SALUS. Besides, TopBraid Composer is able to RDFize XSDs and XMLs through its GUI; it does not provide an API for easy integration. As a result, we implemented our own solution. We make use of Sun's XSOM library2 for processing XML Schemas, Apache Xerces3 for processing XML data and Apache Jena4 for managing RDF data. While proceeding with the implementation of Ontmalizer, we noticed that the TopQuadrant / TopBraid Composer team provided their conversion guidelines through a blog entry5. In this guideline, the TopBraid team explains how they managed to implement

1 TopBraid Composer, http://www.topquadrant.com/products/TB_Composer.html 2 http://xsom.java.net/ 3 http://xerces.apache.org/xerces2-j/ 4 http://jena.apache.org/ 5 Living in the XML and OWL World - Comprehensive Transformations of XML Schemas and XML data to RDF/OWL, http://topquadrantblog.blogspot.com/2011/09/living-in-xml-and-owl-world.html

FP7-287800 SALUS


transformations of XML Schemas and XML data to RDF/OWL for version 3.6.0 of TopBraid Composer. We were happy to see that we were following similar approaches, and we continued the implementation by taking into account their guidelines as well.

DISTINCTIVE FEATURES:

(How does the disclosed invention differ from earlier attempts to solve the same problem?)

The state of the art open source and/or free tools for RDFizing XSD and XML were not able to handle complex schemas and XML instances such as HL7 Clinical Document Architecture (CDA) R2. Only the commercial versions (standard and maestro) of TopBraid Composer6 were successfully able to handle such complex schemas. However, we do not want to use such commercial tools in SALUS. Besides, TopBraid Composer is able to RDFize XSDs and XMLs through its GUI; it does not provide an API for easy integration.

E. Prior Art:

Identify the most closely related device, product or process existing before the invention: (a) in the company; and (b) outside the company.

-Rhizomik XSD2OWL Package - XML2OWL demonstration platform

-REDeFer


relevant portions.)

-http://rhizomik.net/html/redefer/xsd2owl/ -http://xml2owl.sourceforge.net/index.php?input=help -http://www-smis.inria.fr/PUBFILES/2011/2011%20-

%20Transforming%20XML%20Schema%20to%20OWL%20Using%20Patterns.pdf

-code.google.com/p/redefer/source/browse/ReDeFer

Identify any persons that you know of who may have knowledge of additional information relevant to parts mentioned above. None.

F. Addendum None.

6 TopBraid Composer, http://www.topquadrant.com/products/TB_Composer.html

http://rhizomik.net/html/redefer/xsd2owl/

http://xml2owl.sourceforge.net/index.php?input=help

http://www-smis.inria.fr/PUBFILES/2011/2011%20-%20Transforming%20XML%20Schema%20to%20OWL%20Using%20Patterns.pdf



FP7-287800 SALUS


2.1.4 PMSST



Post Marketing Safety Study tool

A. Partner: SRDC




A device, product or process embodying or using the invention has been made or

used and tested.

Yes No Date:


or used Yes No

The invention was first thought of on or about: January 2013

date

The invention was first explained to:

SALUS consortium


on or about November 2013

date


or about

November 2013 date


March 2014 date


Will be used as a part of SALUS Pilots in the second half of 2014


N/A

FP7-287800 SALUS


A technical paper, article, advertisement, other printed document or verbal communication describing the invention has been distributed or communicated

outside the consortium.

Yes No

Date of such communication, name of person to whom disclosed and person's

organization:

Date: March 2014

Name: SALUS D6.2.2 Toolsets for

Enabling Signal Detection on EHRs based on temporal

patterns – R2

Organization: Distributed as a public SALUS Deliverable


Yes No



Yes

No

Unknown

SKETCH OR DRAWING:


list nodeID.)

FP7-287800 SALUS


D. Invention Disclosure: PURPOSE OF INVENTION:

(Explain the problem to be solved, results sought to be accomplished, difficulties to be overcome or eliminated and advantages to be gained by the invention.)

In order to collect data into pharmacoepidemiological databases, common data models are introduced by different initiatives and EHR sources transform data to those models in order to enable the secondary use for clinical research purposes. Either distributed or not, analyses on longitudinal EHR data requires the clinical researchers to implement the designed algorithms and build methods according to the pre-defined data model of the database that they are working on. It is an experienced fact that data requirements on clinical research side, and data availability and quality on EHR side are subject to change in time. As this happens, new initiatives propose new common data models into which collaborating EHR sources have to transform and transfer data, regardless of the system's central or distributed nature. In the Post Marketing Safety Study Tool (PMSST), we address the heterogeneity problem among common data models for clinical researchers who work on EHR data for post-marketing surveillance studies. We demonstrate that this problem of interoperability can be solved in an upper level with the use of Common Data Element (CDE) phenomenon. If the applications share the machine processable definitions of the data elements and there are established links between data elements of different domains (i.e. clinical research and patient care domains), this can be used to facilitate automatic access to data across different domains. Hence, in the context of post-marketing surveillance, uniform observational analysis methods can be designed and implemented independent from the underlying EHR database model, either the source is a pharmacoepidemiological database or directly a hospital information system. In the light of the Common Data Element (CDE) based interoperability approach, we design and implement the Post Marketing Safety Study Tool (PMSST) which can extract any needed information from a patient record after it is retrieved as a result of an eligibility query or it is directly accessed from EHR database within a data mining routine. Our design is built upon the notion of CDEs and makes use of a Semantic Metadata Registry (MDR) to retrieve data element definitions and use their extraction specifications to access data specifications, PMSST lets the researcher to be able to define what need to be extracted from the patient records with the help of the CDEs accessed from a Semantic MDR. With this dynamic behavior, the researcher writes her methods on the schema/template which will be created based on the data elements that she manipulate. With the help of the underlying interoperability framework, post-marketing surveillance methods do not have to be restricted to the data model of the EHR source.


(Describe the machine, circuit, method, product or composition of matter that is the subject of this Disclosure. Attach sketches or diagrams where applicable using drawing reference numbers in the description. Be sure to describe the

preferred form of the invention, but identify alternate forms where appropriate. Describe which products the invention could be used in.) PMSST is composed of several different components among which a number of integration mechanisms exist. In Figure 1 the flow of data between those integrated components are

FP7-287800 SALUS


depicted. Although the usage of the tool starts with defining an eligibility criteria and retrieving EHR data according to that query, our implementation is independent of the content model according to which the EHR data is shaped. For example, if the underlying EHR system can provide HL7 CCD based patient summaries, then PMSST can seamlessly process the data by using the corresponding extraction specification retrieved from the Semantic MDR. This time, the extraction specifications would be XPath expressions and clinical researcher would not be aware of this.

Figure 1 - Step-by-step representation of the data flow between different components. A clinical researcher uses PMSST in order to define a result schema so that when patient data is retrieved from the underlying EHR source(s), data will be automatically transformed to that schema.

Figure 1 shows the steps of the data flow during the execution of PMSST and the steps can be described as follows:

1. The researcher uses a web browser to define the result schema by using the CDEs. Roche uses SDTM variables in our deployment.

2. CDEs are maintained in the Semantic MDR and retrieved through the IHE DEX profile. The user browses to the CDEs starting from the object classes.

3. If the user likes to restrict the value of a selected data element (i.e. set Acute Myocardial Infarction to MHPTCD element), possible values can automatically be searched from the terminology server. PMSST knows in which coding system to look for the term by analyzing the value domain of the CDE definition.

4. The schema definition is sent to the PMSST engine on the server side. 5. Eligibility query is sent to the SALUS system and EHR data is retrieved in the form of

SALUS Common Information Model. This is an RDF based model, hence the extraction specifications of the SALUS CDEs are SPARQL scripts.

6. For each schema item definition, PMSST engine calculates the value from the retrieved EHR data.

FP7-287800 SALUS


7. Result schema is defined by SDTM elements. Semantic MDR keeps the mappings between SDTM and SALUS CDEs. And, SALUS CDEs has the extraction specifications to access the necessary information from the EHR data. CDE definitions, mappings and extractions specifications are retrieved from the Semantic MDR in conformance to the IHE DEX profile.

8. Each schema item is represented as a tree data structure internally. Variable definitions and execution order of different CDEs are managed within these trees. Having retrieved the extraction specifications, this structure is reduced to a graph with the extracted data from the EHR data.

9. If the schema item definition includes a value in one of its defining CDEs, value analysis should be done. However, in our deployment, EHR data is coded with ICD9-CM system while SDTM elements refer to MedDRA or NCI terms. The terminology server includes mappings between these different coding systems and PMSST can do value matching with the help of this terminology server.

10. Data is produced conforming to the result schema defined by the researcher. 11. User can write analysis methods on top of this schema independent from the

underlying EHR source model. In our deployment, Roche implements SAS scripts to do the analysis.

12. Finally the analysis results are presented to the researcher.



Current research on post-marketing surveillance for pharmacovigilance and pharmacoepidemiology tries to unify the available EHR data on a common information model. Most of the time, this forces the EHR systems to implement the necessary adapters for transforming data into the defined common model and persist in a separate database, either central or distributed. On the other hand, some approaches transform the query to the native data model at each transaction. However, data and processing requirements of different areas of clinical research change in time while the quality, quantity and availability of EHR data on patient care side increases. This forces the researchers to update their information models accordingly or come up with the new ones. The literature exemplifies this situation clearly. Vaccine Safety Datalink is an early initiative for transforming EHR data for post marketing safety surveillance of vaccines. FDA’s Sentinel initiative and the Mini-Sentinel pilot system is one of the latest and important efforts for post marketing surveillance, built on the experiences of Vaccine Safety Datalink. Mini-Sentinel builds a distributed system to answer safety queries of clinical researchers through a common information model. OMOP introduces its own Common Data Model (CDM) to transform EHR data. Informatics for Integrating Biology and the Bedside (i2b2) is another parallel effort with similar objectives and exposes its own common information mode. CPRD is a European example of the latest pharmacoepidemiological databases and there are several ongoing projects supported by European Medicines Agency and European Commission using a common information model for surveillance activities. The fact is that those common information models are not so “common”; they are only used within the boundaries of the associated initiatives and projects. PMSST utilizes a different interoperability architecture than existing common information model based efforts. Our architecture makes use of the Common Data Element (CDE) approach in which the abstract CDE definitions are bound to the implementation specific content models through the extraction specifications. This lets the researcher use any set of

FP7-287800 SALUS


abstract CDEs and design its study based on a model depicted by those CDEs. The prerequisite is that the CDEs should be imported to the knowledge base of the Semantic MDR and appropriate links between different CDE sets should be established. In our work, we use automatic content model importers for CDE acquisition and establish the necessary links between SDTM variables and SALUS CDEs. We know that there are several initiatives defining abstract CDE sets and map to existing sets and content models. Hence, we believe these different initiatives can contribute to the post-marketing surveillance activities, and to the field of clinical research informatics in a general sense, if considered like a network of different common information models with the use of extraction specifications as we do with the use of extraction specifications.

E. Prior Art:


OMOP Project I2B2 Project Mini Sentinel Project


etc. of the device, product or process that according to your knowledge is closest to the invention. (If possible and as appropriate, furnish copies and point out

relevant portions.) 1. DeStefano, Frank. "The Vaccine Safety Datalink project." Pharmacoepidemiology and

drug safety 10.5 (2001): 403-406. 2. Murphy, Shawn N., et al. "Serving the enterprise and beyond with informatics for

integrating biology and the bedside (i2b2)." Journal of the American Medical Informatics Association 17.2 (2010): 124-130.

3. Behrman, Rachel E., et al. "Developing the Sentinel System—a national resource for evidence development." New England Journal of Medicine 364.6 (2011): 498-499.

4. Robb, Melissa A., et al. "The US Food and Drug Administration's Sentinel Initiative: expanding the horizons of medical product safety." Pharmacoepidemiology and drug safety 21.S1 (2012): 9-11.

5. Foundation for National Institutes of Health. Observational Medical Outcomes Partnership (OMOP). http://omop.org/ (accessed on 2 June 2014).

Identify any persons that you know of who may have knowledge of additional information relevant to parts mentioned above. None.

F. Addendum None.

http://omop.org/

FP7-287800 SALUS


2.1.5 Semantic MDR



Semantic MDR

A. Partner: SRDC





used and tested.

Yes No Date: January 2013

A device, product or process incorporating or using the invention has been offered for sale, sold or used making a product or performing a service that has been sold or used

Yes No

The invention was first thought of on or about:

December 2011

date

The invention was first explained to: SALUS Consortium


on or about November 2012

date


November 2012 date

The first written description of the invention was made

on or about

December 2012 date

Date of first

sale/use

November 2013- At LISPA

Deployment as a part of SALUS Project


invention

NA

FP7-287800 SALUS



outside the consortium. Yes No


Date: First Submitted to JAMIA on December 2012, then to JBI on February 2013

Name: A. Anil Sinaci, Gokce B. Laleci Erturkmen

Federated Semantic Metadata Registry Framework for Enabling Interoperability across

Clinical Research and Care Domains Journal of Biomedical Informatics Available online 7 June 2013



Yes No


Yes

No

Unknown

SKETCH OR DRAWING:

(In the space below, include a drawing, sketch, schematic, or diagram of the invention and graphs, charts, etc. where applicable, or attach same to this form or list nodeID.)

FP7-287800 SALUS



PURPOSE OF INVENTION: (Explain the problem to be solved, results sought to be accomplished, difficulties

to be overcome or eliminated and advantages to be gained by the invention.) In order to solve the interoperability problem within/between clinical research and care domains, several organizations are publishing common data element dictionaries and common models as described above. Although these efforts ensure interoperability within the selected domain for the selected use cases, interoperability across application domain boundaries is not automatically possible. These stem from the following facts:

Common data element model development efforts are often disparate form each other. Although previous efforts are examined, most of the time, a common model is created from scratch.

Most of the time, the specifications for these CDE sets and common models are in unstructured text files.

Some of these efforts examine previous ones and reuse some CDEs proposed by others, and sometimes provide partial mappings to other CDE dictionaries. For example, S&I CEDD reuses elements from HITSP C154, NEHTA and FHIM; HITSP C32 provides mapping between HITSP C154 data elements to the elements of HL7 CCD. However, these are maintained in several different spreadsheets or in PDF documents. Hence, it is not possible to process or query this data.

We believe there is a need for a more coordinated approach that would allow machine processable definitions of CDEs defined by different efforts to be searched, allow CDEs to be reused and to be linked with each other and the mappings/links/relations between different CDEs in different domains can be queried to address semantic interoperability. In this work, we developed a framework that facilitates all of these through the use of federated semantically enabled metadata registries (MDR) conforming to ISO 11179 standard where CDEs maintained in different MDRs can be uniquely identified, queried and linked with each other through Linked Data principles.



preferred form of the invention, but identify alternate forms where appropriate. Describe which products the invention could be used in.) SALUS federated MDR framework enables the following basic functionalities:

Searching CDEs maintained by different MDRs

Retrieving standard specification of a selected CDE from an MDR

Re-using CDEs maintained in a different MDR by referencing to the respective CDE To facilitate semantic interoperability more effectively across domains, Semantic MDR framework supports the following additional functionality:

Link and semantically associate the CDEs across different MDRs in reference to well-accepted knowledge organization system (KOS) ontologies and terminology systems.

Query these semantic relationships within and across MDRs.

FP7-287800 SALUS


We have chosen to apply Linked Open Data (LOD) principles as the basis of this semantically linked federated MDR framework. Linked Data is a recommended best practice for exposing, sharing and connecting pieces of data, information and knowledge on the Semantic Web using URIs and RDF. It provides a natural way to expose the CDEs maintained in different MDRs openly in the LOD cloud and inter-relate them with each other as depicted in the following figure:


problem?)

There are numerous adoptions of ISO/IEC 11179 registries to address semantic interoperability, several of which are in healthcare domain. These central metadata registries are used to maintain a set of common data elements (CDEs) in the selected domain so that data sources and data requesters can agree on unambiguous semantics of the selected data elements in the chosen domain. To address interoperability at a larger scale, it should be possible to link and reuse CDE definitions across application domains which can be greatly enabled by a semantically interlinked federated MDR framework. Centralized metadata registries would not scale as it is not practical to manage CDEs within different application domains in a single registry; each set of common data elements can evolve in time, there should be a more flexible mechanism to manage and exploit linked set of CDEs across domains. SALUS Semantic MDR addresses these problems and proposes a federated MDR framework that enables the following functionalities:

FP7-287800 SALUS


Searching CDEs maintained by different MDRs

Retrieving standard specification of a selected CDE from an MDR

Re-using CDEs maintained in a different MDR by referencing to the respective CDE

E. Prior Art:

Identify the most closely related device, product or process existing before the invention: (a) in the company; and (b) outside the company. The MeteOr, CaDSR initiatives listed in the following section.



1. Komatsoulis GA et al. caCORE version 3: Implementation of a model driven, service-

oriented architecture for semantic interoperability. J Biomed Inform 2008;41:106–23. 2. UK CancerGrid. <http://www.cancergrid.org/> [accessed 15 Apr 2013]. 3. AIHW. MeteOR: Metadata Online Registry. <http://meteor.aihw.gov.au/> [accessed 15 Apr

2013]. 4. AHRQ. The United States Health Information Knowledgebase. <http://ushik.ahrq.gov/>

[accessed 15 Apr 2013]. 5. Forsberg K, Malfait F. Semantic models for CDISC based standard and metadata

management. CDISC Interchange Europe Brussels, Belgium: 2012. <http://kerfors.blogspot.com/2012/05/semantic-models-for-cdisc-based.html> [accessed 15 Apr 2013].

6. Stausberg J et al. A National Metadata Repository for Empirical Research in Germany. 15th International Open Forum on Metadata Registries. Berlin, Germany: 2012 May.

7. Rimatzki B, Haas P. Implementation of an ISO/IEC 11179 Based Metadata Registry to Foster Interoperability of Health Telematics Applications. 15th International Open Forum on Metadata Registries. Berlin, Germany: 2012 May.

Identify any persons that you know of who may have knowledge of additional

information relevant to parts mentioned above. Authors of the listed papers

F. Addendum None.

2.2 OFFIS

2.2.1 ANT


http://www.cancergrid.org/

http://meteor.aihw.gov.au/

http://ushik.ahrq.gov/

http://kerfors.blogspot.com/2012/05/semantic-models-for-cdisc-based.html

FP7-287800 SALUS



Adverse Drug Event Notification Tool

A. Partner: OFFIS Name: Tobias Krahn

Address: Escherweg 2, 26121 Oldenburg, Germany




used and tested.

Yes No Date: 2014 (SALUS

prototypes, tested in TUD and LISPA setting)


or used Yes No



Yes No


During project proposal (2010/11)

date



on or about

date


2012 date

The first written description of the invention was made on

or about

2013 (D6.1.1) date



invention

FP7-287800 SALUS



organization: Date: 2013/2014 (D6.1.1, D6.1.2) (A); conference papers:

GMDS 2013 (B); DKVF 2013 (C); eHealth 2014 (D);

CBMS 2014 (E); MIE 2014 (F)

Name:

Organization: European commission (A); Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

(B); Deutsches Netzwerk Versorgungsforschung e.V. (C); Medical Future Verlag (D); Computer based medical systems 2014 (Dr. Marina Krol) (E); European

Medical Informatics Conference (31.08-03.09.2014) (F)


customer?

Yes No



Yes

No

Unknown

SKETCH OR DRAWING: (In the space below, include a drawing, sketch,

schematic, or diagram of the invention and graphs, charts, etc. where applicable, or attach same to this form or list nodeID.)

FP7-287800 SALUS


Interoperability Platform

ADE Notification System

ADE Detection ADE Notification

2

EHR

Detection of

ADE indicators

1 Known ADE

detection3 Data mining

EHR Database

Figure 2: conceptual overview of the Adverse Drug Event Notification Tool

Figure 3: Graphical user interface of the Adverse Drug Event Notification Tool

FP7-287800 SALUS




First, the purpose of the Adverse Drug Event Notification Tool (ANT) is to analyze

Electronic Health Records (EHR) for suspected cases of Adverse Drug Events (ADEs). Second, the purpose is to present the analysis results to the treating physician through a Graphical User Interface (see Figure 2) in order to support an

easy decision making process, whether the suspected event is a real ADE.

BRIEF DESCRIPTION OF THE INVENTION: (Describe the machine, circuit, method, product or composition of matter that is the subject of this Disclosure. Attach sketches or diagrams where applicable

using drawing reference numbers in the description. Be sure to describe the preferred form of the invention, but identify alternate forms where appropriate.

Describe which products the invention could be used in.) The ADE-Notification-Tool is a software component to use data-mining-algorithms

on clinical data sources to semi-automatically detect adverse drug events. This software tries both on the one hand to detect already known side effects as also

currently not known side effects of drugs. Therefore several data sources about known ADEs, as also medical knowledge about detecting side effects, has been integrated to the software. The tool communicates with external data sources via

a SPARQL endpoint, which makes it easy to adapt the tool to further information systems. Within SALUS project these SPARQL queries might be translated to IHE

profile based queries to query for example LISPA system, but in case of TUD database, these queries can be performed directly.

Any data source offering a SPARQL endpoint can be therefore directly queried by the ADE notification tool. This component will be released as Open Source

software by the project to the community. This software can be used then as an ADE detection tool for medical professionals, but also as a framework for further

research on side effects and medication issues to handle big data.



One of the major problems of earlier ADE detection and notification systems reported in the literature is that they do not distinguish between the current

knowledge on already known ADEs and insights on new ADEs. Although they can detect suspicious events in the clinical data, added value can be achieved by

comparing these events with the present ADE knowledge. And the chance of success is high, as the amount of machine-processible medical data provided by open and interlinked data sources is constantly growing.

FP7-287800 SALUS


Even though the amount of routinely collected patient data increases, data mining in existing ADE detection approaches is primarily used to analyze spontaneous

ADE reports. In the ANT, we plan to apply data mining on EHR databases. Moreover, ANT combines different ADE detection approaches in a comprehensive

system. Existing ADE detection systems mainly focus on specific detection methods, like monitoring specific laboratory values known to be correlated with

ADEs.

E. Prior Art:


(a) Not applicable (b) Two paper publications

Identify the published description(s) in a technical paper, advertisement, patent, etc. of the device, product or process that according to your knowledge is closest

to the invention. (If possible and as appropriate, furnish copies and point out relevant portions.)

-Honigman B, Lee J, Rothschild J et al. Using computerized data to identify adverse drug events in outpatients. J Am Med Inform Assoc 2001; 8:254–

266. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11320070 -Jha AK, Kuperman GJ, Teich JM et al. Identifying adverse drug events: Development

of a computer-based monitor and comparison with chart review and stimulated

voluntary report. J Am Med Inform Assoc 1998;5:305–314.


Our Consortium Partner UMC

F. Addendum ADE-Notification-Tool Open Source: The ADE-Notification-Tool is a software component to use data-mining-algorithms on clinical data sources to semi-automatically detect adverse drug events. This software tries both on the one hand to detect already known side effects as also currently not known side effects of drugs. Therefore several data sources about known ADEs, as also medical knowledge about detecting side effects, has been integrated to the software. The tool communicates with external data sources via a SPARQL endpoint, which makes it easy to adapt the tool to further information systems. Within SALUS project these SPARQL queries might be translated to IHE profile based queries to query for example LISPA system, but in case of TUD database, these queries can be performed directly.

FP7-287800 SALUS


Any data source offering a SPARQL endpoint can be therefore directly queried by the ADE notification tool. This component will be released as Open Source software by the project to the community. This software can be used then as an ADE detection tool for medical professionals, but also as a framework for further research on side effects and medication issues to handle big data.

2.3 AGFA

2.3.1 Semantic Mediation Framework



LISPSemantic Mediation Framework

A. Partner: Agfa Name: Agfa HealthCare

Address: Septestraat 27, 2640 Mortsel, Belgium




Yes No Date:

The invention was first thought of on or about: 2012

date

The invention was first explained to: Submitted as part of SALUS deliverables 4.4.1 and 4.4.2 to the

European Commission


April 2013 date

The first written description of the invention was made on or

about

April 2013 date

FP7-287800 SALUS



or used Yes No



Yes No


organization:

Date: NA

Name: NA

Organization: NA



Yes

No

Unknown



In the SIL we have semantic services (blue boxes):


NA


invention

NA

FP7-287800 SALUS


SIL-DS

Query

Manager

RDF HQMF query or GET entity

Tool

QueryResult

Calculator

Terminology

Service

Calculate

Convert terms

SIL-DS

(TUD)

SIL-DS

(LISPA)

Query Query

ORBIS SPARQL

EndpointTIL

LISPA DWHORBIS DB

Query Query

The Tools, like Safety Analysis Tools, ICSR Reporting tool, ADE Notification Tool, use the tool specific Query Manager to fetch the needed data. The Query Manager queries using an RDF representation of an HQMF query or retrieves by resolving an entity from the SIL-DS expressed in SALUS CIMv2. Both SIL-DS (TUD) and SIL-DS (LISPA) implement the same RESTful HTTP interface. The SIL-DS (TUD) uses the ORBIS SPARQL Endpoint to issue SPARQL queries to retrieve data from the ORBIS DB. The retrieved data is converted from the Orbis Content Model to the SALUS CIMv2 model by (a sub-service of) the SIL-DS (TUD). The SIL-DS (LISPA) uses the SALUS TIL to retrieve data from the SALUS-LISPA DWH through the IHE QED profile. The SIL-DS (LISPA) converts the retrieved data from the CDA CCD/PCC Content Model to the SALUS CIMv2 model. The Query Manager uses the Terminology Service to translate terminology concepts from a source terminology to a target terminology. The Result Calculator determines derived values from the data. Finally the Query Manager provides the data to the Tool.



The Semantic Mediation Framework prototype is developed to support the semantic interoperability between the clinical research and clinical care systems of the SALUS project. The result of the development process is a constellation of semantic services and tools that together constitute the Semantic Mediation Framework.

FP7-287800 SALUS




preferred form of the invention, but identify alternate forms where appropriate. Describe which products the invention could be used in.)

The SALUS Semantic Interoperability Layer (SIL) is build with a number of RESTful semantic services that enables semantic interoperability between clinical research systems (e.g. Case Series Characterization Tool) and EHR sources. In the SALUS architecture, we collect the EHR data in the local model used by the EHR systems (which can be standard based like HL7/ASTM CCD, or can be proprietary models, like ORBIS Data Model), and then semantically lift the collected data to represent them as RDF entities in local ontologies corresponding to the local models used. To facilitate interoperability, we have collected in task 4.3 a SALUS Common Information Model (CIM) version 2 ontology to act as a mediator between different local ontologies. The SALUS CIMv2 ontology not only represents entities that can be presented within a medical summary resource like medications, conditions, procedures and demographics, but also establishes a link with the terminology system ontologies that are used to code patients’ medical summaries. In SALUS, while the terminology systems are represented as ontologies, the mapping relationships among different terminology system codes are also established through semantic links in relation to well-established ontologies like SKOS. As examples, skos:broader, skos:exactMatch properties are used to represent the semantic relationships between the codes within and across code systems in a machine processable way. This ensures terminology reasoning to address semantic interoperability.


problem?) The novelty lies in the two step approach where the native data source is mechanically lifted to the semantic level with a one-to-one mapping to RDF bearing the original semantics of the data source. Next a content model to domain model conversion is performed to harmonize the semantics of the different data sources.

E. Prior Art:





FP7-287800 SALUS


F. Addendum None.

2.4 UMC

2.4.1 TAST



Temporal Association Screening tool

A. Partner: UMC

Name: Uppsala Monitoring Centre Address: Bredgränd 7, SE-753 20 Uppsala, Sweden


C. Information about Use of the Invention: A device, product or process embodying or using the invention has been made or

used and tested.

Yes No Date: 2005

A device, product or process incorporating or using the invention has been offered for sale, sold or used making a product or performing a service that has been sold or used

The invention was first thought of on or about: 2004

date

The invention was first explained to: Ralph Edwards


on or about April 2004

date


or about

April 2004 date


2004-05-24 date

FP7-287800 SALUS


Yes No



Yes No


organization:

Date: 2004-05-24

Name: Peter Stephens

Organization: IMS Health


Yes No



Yes

No

Unknown

SKETCH OR DRAWING:


list nodeID.)


2007


invention

2007

FP7-287800 SALUS


Figure 4. Login

Figure 5. Settings

FP7-287800 SALUS


Figure 6. Search functionality

Figure 7. Term mappings

FP7-287800 SALUS


Figure 8. Result list

Figure 9. Results with the statistical disproportionality measure (IC low) as the ordered column

FP7-287800 SALUS




The Temporal Association Screening (TAS) tool (Norén 2008, 2010, 2013) enables broad scale screening for potential signals of SALUS EHR data. The tool consists of three main parts: the client, the web service and the method. The web service is built as a RESTful API described below and the method is built with R scripts that handle all of the calculations needed for the statistical measure, ICΔ that is used to detect potential signals. The method runs on top of the SALUS Clinical Data Repository, which contains the population data in OMOP CDM Format. The basis for the TAS tool is to screen EHR data broadly for potential drug related problems without any prior hypothesis. Some common research questions can be “I am interested in the safety of vancomycin, can you give all the events that are likely to be temporally related?” or “I would like to know all drugs that could cause acidosis”.




In the initial implementation of the TAS Tool, TAS API was built on the Representational State Transfer (REST) framework using .NET 4 and C#. However, in the second iteration to be able to have common technical infrastructure throughout the safety analysis tools of SALUS, the REST API is implemented with Java and Jersey. As indicated in the usage of the Temporal Association Screening method, it is possible to make an analysis considering different type of combinations of drug-condition pairs. TAS GUI allows construction of such combinations. During the criteria specification phase, the tool provides auto complete feature for the provided criteria concepts based on the target terminology systems used in EHR sources. For drug criteria, ATC terminology system is used in both of the deployment. However, for the conditions the tool enables user to select the EHR source. Based on the selection, it adapts itself such that ICD9CM would be used in LISPA and ICD10GM would be used in TUD. The results of the Temporal Association Screening are presented in a tabular view in the user interface.

DISTINCTIVE FEATURES: (How does the disclosed invention differ from earlier

attempts to solve the same problem?)

E. Prior Art:


(a) THIN Mining interface funded by the PROTECT project (http://www.imi-protect.eu)

http://www.imi-protect.eu/


FP7-287800 SALUS




Norén GN, Bate A, Hopstadius J, Star K, Edwards IR, editors. Temporal pattern discovery for trends and transient effects: its application to patient

records. ACM SIGKDD international Conference on Knowledge Discovery and Data Mining; 2008 August 24 - 27, 2008; Las Vegas, Nevada, USA. 3.10: KDD '08. ACM.

Norén N, Hopstadius J, Bate A, Star K, Edwards R. Temporal pattern discovery in longitudinal electronic patient records. Data Mining and Knowledge Discovery. 2010(20):361-87. doi:DOI 10.1007/s10618-009-0152-3. Noren GN, Bergvall T, Ryan PB, Juhlin K, Schuemie MJ, Madigan D. Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36 Suppl 1:S107-21. doi:10.1007/s40264-013-0095-x.


Niklas Norén, Ralph Edwards

F. Addendum

The UMC is interested in active exploitation of SALUS (to use the system for pharmacovigilance work). On the concrete organization of Open Source modules (e.g. on Sourceforge.com) UMC indicated that they can make the code and documentation available but can’t commit to keep a real open-source project running on SourceForge. The actual modules used in the different use cases and which are intended to be provided as open source are listed below: Case Series Characterization Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “2.a”. Temporal Pattern Characterization Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “2.b”. We will however not provide the method to create the chronographs as open source. Temporal Association Screening Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “3.a”.

FP7-287800 SALUS


Patient History Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository.

2.4.2 CSCT



Case Series Characterization tool

A. Partner: UMC

Name: Uppsala Monitoring Centre Address: Bredgränd 7, SE-753 20 Uppsala, Sweden




used and tested.

Yes No Date: 2011




2007

date


Ralph Edwards


on or about 2006

date


or about

2007 Date


2007 date

FP7-287800 SALUS


Yes No



Yes No


Date: Sep 24, 2007

Name: Manfred Hauben

Organization: Pfizer


customer?

Yes No


Yes

No

Unknown



Date of first

sale/use

-


-

FP7-287800 SALUS


Figure 10. Defining the foreground population by specifying medication, condition and possibly a temporal constraint between them.

Figure 11. Defining the background population by specifying a medication, condition or using all patients.

FP7-287800 SALUS


Figure 12. List of all results

Figure 13. Results - Gender distribution

FP7-287800 SALUS


Figure 14. Results - Age distribution

Figure 15. Results - Conditions prior to the condition of interest

FP7-287800 SALUS


Figure 16. Medication prior to the condition of interest

Figure 17. Results - shows occurrence of risk factors

FP7-287800 SALUS




Quantitative pharmacovigilance studies aims at finding causal relations between drugs and adverse drug reactions. The endpoint after having defined the foreground population and what to compare against is a statistical disproportionality measure of how often the drug-reaction combination is seen together. To enhance the analytical capabilities other possible explanations are important to discover as early as possible in the analytical process. The Case Series Characterization Tool (CSCT) enables the query of data sources for EHR extracts of selected patient populations to characterize ADE cases that originate from SALUS EHR data and compares the statistics against a custom background population. It provides a graphical interface to identify the eligibility criteria of the selected patient populations, and also to list the required statistical comparisons between the collected data and background population. It also provides a graphical interface to present the resulting statistics to the safety analyst. These results can be used to find other explanation to the statistical pattern or increase the suspicion of a causal relation if no other explanations are found.



using drawing reference numbers in the description. Be sure to describe the preferred form of the invention, but identify alternate forms where appropriate. Describe which products the invention could be used in.)

The scenario starts with a safety analyst who wants investigate the relation between a medication and a medical event. He wants to compare a specified foreground (e.g. using a medication of interest and having a condition of interest) with the selected background population (e.g. other patients on the same drug or other patients with the same disease). The CSCT enables the user to define detailed inclusion/exclusion criteria for both background and foreground populations. The safety analyst performs the following steps to populate the foreground and background queries: For the background population:

He chooses "nifedipine" 5th level term (code C08CA05) from the ATC terminology system as the active ingredient code of a medication by using the typeahead search facility of the tool (which is integrated with our terminology server)

For the foreground population:

He chooses "Myocardial infarction" Preferred Term (PT) (code 10028596) from the MedDRA terminology system as the problem

He chooses "nifedipine" 5th level term (code C08CA05) from the ATC terminology system as the active ingredient code of a medication by using the typeahead search facility of the tool

FP7-287800 SALUS


He adds a temporal constraint between the medication and condition statements s/he just created, for stating that the condition shall occur within two weeks after the medication

Apart from defining the inclusion/exclusion criteria, the safety analyst also chooses the statistics to be checked for stratifying datasets of the selected foreground and selected background populations among the following criteria:

Age and gender distribution

Country of origin

Common medications/events prior to/after the medication/medical event of interest

Apart from the common conditions, and drugs, the safety analyst may also wish to compare the presence of some specific conditions, which are the risk factors of the selected conditions in inclusion criteria of foreground and background population. Risk factors are defined in the same way as defining the foreground and background queries. The analyst chooses a suitable clinical statement type e.g. condition, social history, etc from the left panel and populates it with the required constraints. In our example, the safety analyst defines a risk factor for myocardial infarction again from the Preferred Term (PT) level of the MedDRA by choosing the "Diabetes mellitus" (code 10012601) via the type-ahead search of the CSCT. Although the query definition functionalities remained the same, the result presentation approach has been updated in the latest versions of CSCT. In very generic terms the following updates have been realized.

Results are presented asynchronously

Important results are highlighted

Results are presented in a graphical view

Pseudonymized identifiers are presented for each result

DISTINCTIVE FEATURES: (How does the disclosed invention differ from earlier

attempts to solve the same problem?)

G. Prior Art:


None known


relevant portions.)

Norén GN. Statistical methods for knowledge discovery in adverse drug reaction surveillance [PhD in Mathematical Statistics]. Stockholm: Stockholm Univeristy; 2007.

FP7-287800 SALUS


Juhlin, K., Star, K., Norén, GN. Pinpointing key features of case series in pharmacovigilance-a novel method. Drug Safety. vol. 36, pp. 912{913

(2013) Identify any persons that you know of who may have knowledge of additional

information relevant to parts mentioned above.

Niklas Norén at Uppsala Monitoring Centre

F. Addendum

The UMC is interested in active exploitation of SALUS (to use the system for pharmacovigilance work). On the concrete organization of Open Source modules (e.g. on Sourceforge.com) UMC indicated that they can make the code and documentation available but can’t commit to keep a real open-source project running on SourceForge. The actual modules used in the different use cases and which are intended to be provided as open source are listed below: Case Series Characterization Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “2.a”. Temporal Pattern Characterization Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “2.b”. We will however not provide the method to create the chronographs as open source. Temporal Association Screening Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “3.a”. Patient History Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository.

2.4.3 PHT



Patient history tool

A. Partner: UMC Name: Uppsala Monitoring Centre

FP7-287800 SALUS


Address: Bredgränd 7, SE-753 20 Uppsala, Sweden




Yes No Date: 2005



Yes No



Yes No

The invention was first thought of on or

about:

2004

date

The invention was first explained

to:

Ralph Edwards


on or about April 2004

date


April 2004 date


2004-05-24 date

Date of first

sale/use

2007


2007

FP7-287800 SALUS



Date: 2004-05-24

Name: Peter Stephens

Organization: IMS Health


customer?

Yes No


Yes

No

Unknown

SKETCH OR DRAWING: (In the space below, include a drawing, sketch, schematic, or diagram of the invention and graphs, charts, etc. where applicable,

or attach same to this form or list nodeID.)

Figure 18. Login screen

FP7-287800 SALUS


Figure 19. General patient information

Figure 20. Patient summary

FP7-287800 SALUS


Figure 21. Patient timeline showing all events on a scrollable calendar

Figure 22. All encounters for the patient

FP7-287800 SALUS


Figure 23. Encounter details

Figure 24. All conditions for the patient

FP7-287800 SALUS


Figure 25. Condition details

Figure 26. All medications for the patient

FP7-287800 SALUS


Figure 27. Medication details

Figure 28. All procedures for the patient

FP7-287800 SALUS


Figure 29. Procedure details



During signal evaluation the possibility to view individual patient histories is essential for ensuring that the evidence of a possible causal association between a drug and an event is not confounded by something in the patient. The patient history tool would enable the safety analyst to view the drugs, events, lab tests and patient demographics for single patients to confirm or refute the evidence of potential signals. During its data retrieval and presentation operations, Patient History Tool (Norén 2008, 2010, 2013) makes use of Common Data Elements (CDEs) developed in the scope of SALUS Project for the sake of semantic interoperability. These CDEs are used to automatically extract data from medical summaries to fill in data collection sets through the use of Semantic Metadata Registries (MDR). During this process, the SemanticMDR makes use of IHE DEX profile. CDE Repository plays the role of the Metadata Source while Patient History Tool acts as the Metadata Consumer of IHE DEX profile. The basis for the patient history tool is that during signal evaluation a safety analyst often would like to see more information about the individual patients to rule out any confounding factors hidden by the summarized statistics shown in other tools. A typical research question could be “I have a potential signal with 8 cases diagnosed with Steven Johnsons Syndrome within 3 weeks of first prescription of the antibiotic Cefotaxime. To rule out any confounding factors I need to see if there were any signs of the disease prior to the drug prescription, if the patient was currently

FP7-287800 SALUS


taking drugs known to cause the disease or if there were other patient demographic information that could influence the signal/no signal decision”.


the subject of this Disclosure. Attach sketches or diagrams where applicable using drawing reference numbers in the description. Be sure to describe the


In the first step, Patient History Tool (Norén 2008, 2010, 2013) retrieves the clinical history for a particular patient by interacting with the Semantic Interoperability Layer of SALUS. PHT determines the patient whose clinical summary will be retrieved using one the following options:

Pseudonymized identifier of the patient as it is done in the initialization of Patient History Tool after the Case Series Characterization process

Message number of ICSR

ICSR report identifier and version Patient History Tool has the unique identifiers of the CDEs to be presented to the clinical researcher. After receiving a patient summary from SALUS system by providing ICSR related identifiers, the tool interacts with the CDE Repository for each CDE to receive its extraction specification. This interaction is performed through IHE DEX profile. The extraction specifications are SPARQL scripts because SALUS semantic interoperability layer returns patient summaries as RDF instances conforming to the SALUS Common Information Model ontology. For each CDE, corresponding SPARQL script is executed on the patient summary and data for that CDE is extracted from the patient summary. The tool provides a powerful navigation mechanism to conveniently present this data to the researcher. Patient History Tool is a fully web based utility which can run on any web browser. It has been designed and implemented with HTML5 principles; using Javascript and RESTful interactions. Additionally, the tool communicates with SALUS CDE Repository (which is based on the Semantic MDR) through IHE DEX RetrieveMetadata transaction whose web service has a SOAP binding.


problem?)

E. Prior Art:


(a) THIN Mining interface funded by the PROTECT project (http://www.imi-

protect.eu)



FP7-287800 SALUS




Norén GN, Bate A, Hopstadius J, Star K, Edwards IR, editors. Temporal pattern discovery for trends and transient effects: its application to patient

records. ACM SIGKDD international Conference on Knowledge Discovery and Data Mining; 2008 August 24 - 27, 2008; Las Vegas, Nevada, USA. 3.10: KDD '08. ACM.

Norén N, Hopstadius J, Bate A, Star K, Edwards R. Temporal pattern discovery in longitudinal electronic patient records. Data Mining and Knowledge Discovery. 2010(20):361-87. doi:DOI 10.1007/s10618-009-0152-3. Noren GN, Bergvall T, Ryan PB, Juhlin K, Schuemie MJ, Madigan D. Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: lessons for developing a risk identification and analysis system. Drug Saf. 2013;36 Suppl 1:S107-21. doi:10.1007/s40264-013-0095-x.


Niklas Norén, Ralph Edwards

F. Addendum

The UMC is interested in active exploitation of SALUS (to use the system for pharmacovigilance work). On the concrete organization of Open Source modules (e.g. on Sourceforge.com) UMC indicated that they can make the code and documentation available but can’t commit to keep a real open-source project running on SourceForge. The actual modules used in the different use cases and which are intended to be provided as open source are listed below: Case Series Characterization Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “2.a”. Temporal Pattern Characterization Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “2.b”. We will however not provide the method to create the chronographs as open source.

FP7-287800 SALUS


Temporal Association Screening Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository. It is being used in use case “3.a”. Patient History Tool: UMC doesn’t intend to apply for a patent for this tool. They aim to make this tool available as a part of public SALUS GIT repository.

2.5 INSERM

2.5.1 IRT



SALUS ICSR Reporting Tool

A. Partner: INSERM

Name: INSERM UMRS 1142 (LIMICS)

Address: Centre de recherche des Cordeliers, Escalier D, 2ème étage - 15, rue de l'école de médecine - 75006 Paris, France


The invention was first thought of on or

about:

2009-2010

date


Marie-Christine Jaulent


on or about

date


date


17 January 2011 (in SALUS first version of DOW)

date

FP7-287800 SALUS




Yes No Date: 2014


or used Yes No



Yes No


Date: 28-30 november 2012

Name: Semantic Web Applications and Tools for Life Sciences

Workshop (SWAT4LS 2012)

Organization:


Yes No


Yes

No

Unknown

SKETCH OR DRAWING: (In the space below, include a drawing, sketch,

schematic, or diagram of the invention and graphs, charts, etc. where applicable, or attach same to this form or list nodeID.)



FP7-287800 SALUS


FP7-287800 SALUS




Under-reporting of adverse drug events (ADEs) is a well-known phenomenon that has been extensively studied. It has been estimated that only around 1 to 5% of ADEs are reported to health regulatory authorities through spontaneous reporting systems. This phenomenon

results in delayed acquisition of knowledge about adverse effects and thus potentially causes substantial patient casualties and major costs for public health. One major cause of under-

reporting identified in the literature is the tedious and time-consuming character of the reporting process, double data entry, together with the lack of awareness of the importance of reporting AEs for patient safety.

SALUS ICSR Reporting Tool (IRT) has been developed to shortcut these difficulties. The tool

supports the reporting of ADEs to regulatory authorities with services enabling automatic pre-population of individual case safety reports (ICSR) by extracting data available in the patient’s Electronic Health Record (EHR) and providing assistance to the manual completion of information that couldn’t be automatically prefilled. The main objective of the tool is to reduce the time necessary to complete ICSRs and the errors due to double data entry. If the data needed to complete the ICSR form is available in the EHR of the patient, IRT will enable automatic filling of the form with this data.

We expect that the partial automation of the AE reporting process enabled by SALUS solutions will contribute to reduce AE under-reporting, which is currently one major obstacle in the process of acquisition of pharmacovigilance data.


the subject of this Disclosure. Attach sketches or diagrams where applicable using drawing reference numbers in the description. Be sure to describe the preferred form of the invention, but identify alternate forms where appropriate.

Describe which products the invention could be used in.)

SALUS IRT supports semi-automatic reporting of ADE to regulatory authorities using two data models: (1) the ICH E2B(R2) data model (which is also a protocol for electronic reporting of ADE); (2) the AIFA data model used in Italy to report ADE to local authorities.

IRT also supports several additional functionalities: recording an ICSR to be completed and reported later; accessing previously sent and waiting to be completed ICSRs; updating/nullifying and sending an ICSR reported in a previous session; finalizing and

sending an ICSR. The ICSR pre-population process is always triggered following the HP decision, but this can be done in two different circumstances: (i) the HP detects an ADE on the basis of his own expertise and decides to report it; (ii) the ADE notification tool, a complementary SALUS component performing real-time screening of EHR data, detects a potential ADE and displays an alert message to the HP, proposing him to report the case. But in both cases, the ADE notification tool triggers the IRT (calling its web service) and provides it with the data necessary for creating a new prepopulated ICSR, especially the PID

of the patient.

E2B is an international standard developed by the International Conference on Harmonisation (ICH) and promoted by the World Health Organization (WHO), the FDA and

the European Medicines Agency (EMA) for electronic reporting of ADEs. E2B is used by the Uppsala Collaborating Centre for International Drug Monitoring (UMC), which centralizes a unique database (VigiBase) of spontaneous ADE reports submitted all across the world. The

E2B specification is both (i) a data model in a machine-process able format for providing relevant information when reporting an AE; (ii) a protocol describing how the report should be transmitted electronically to regulatory authorities. The E2B data model includes more

FP7-287800 SALUS


than 230 data elements—only a few of them are mandatory. The commonly used version of E2B is release 2 (R2), but a third release (R3) is achieved and under deployment. E2B(R2) is used in SALUS.

SALUS IRT is composed of several subcomponents (ICSR reporting tool and related Web GUIs, ICSR reporting manager, ICSR report generator, ICSR content model converter), and is closely integrated with several other components of SALUS platform, without which it

could not work: (i) SALUS ADE notification tool, which performs real-time screening of EHR data, detects a potential ADE and displays an alert message to the HP, proposing him to report the case ; SALUS ADE notification tool triggers the ICSR Reporting Tool and provides it with the data necessary for creating a new prepopulated ICSR, especially the PID of the patient, and possibly the codes and names of the ADE and drugs to be reported; (ii) SALUS Semantic Interoperability Layer-Data Service (SIL-DS), which is invoked based on the PID

received from the ADE notification tool to obtain the patient summary for prepopulating the ICSR form; (iii) SALUS terminology service, which provides conversion of

codes from the source terminologies in the EHR to MedDRA, which is requested in the standard E2B reporting form; (iv) SALUS de-identification service, which filters or

encrypts sensible patient data before sending the ICSR to the regulatory authorities.

SALUS platform ensures the possibility of extracting patient data needed to pre-populate ICSR forms, by converting the information model data elements used in the patient data warehouse (DWH) to the E2B data model. The two main components of SALUS platform

enabling this function are Technical Interoperability Layer-Data Service (TIL-DS) and Semantic Interoperability Layer-Data Service (SIL-DS). TIL-DS handles the interaction of the SALUS platform with the local DWH and enables query transactions necessary to extract patient data needed for the filling of the ICSR. SIL-DS is responsible to handle the conversions between the different information models, in that case between the information models used to encode patient data in the local EHR or DWH (typically HL7 CDA PCC information model) and the E2B(R2) information model.

The integration of SALUS ICSR Reporting Tool with the other components of SALUS platform enables the partial automation of the ADE reporting process, its integration into the physician's work-flow with limited disturbance, and a fully secured electronic transmission of the report to the regulatory authorities. SALUS ICSR Reporting Tool also supports several additional functionalities such as recording an ICSR to be completed and reported later or accessing previously sent and waiting to be completed ICSRs.

IRT has been developed in the Eclipse development environment. We have used JavaServer

Faces7 for developing a web application, which allows an easier communication with the Java application for loading SALUS ontologies. JAXB API8 is used to generate an E2B compliant XML file. For loading SALUS ontologies and executing SPARQL queries, we use the JENA API9.

Several journal or conference papers describing ICSR reporting tool have been published or are accepted:

7 http://www.oracle.com/technetwork/java/javaee/javaserverfaces-139869.html 8 http://jaxb.java.net/ 9 http://jena.apache.org/

http://www.oracle.com/technetwork/java/javaee/javaserverfaces-139869.html

http://jaxb.java.net/

http://jena.apache.org/

FP7-287800 SALUS


DECLERCK, G., HUSSAIN, S., DANIEL, C., YUKSEL, M., LALECI ERTURKMEN, G., TWAGIRUMUKIZA, M., JAULENT, M.C. Bridging data models and terminologies to support adverse drug event reporting using EHR data. Methods of Information in Medecine, numéro spécial « Managing Interoperability and compleXity in Health Systems ». Accepted.

PARÈS, Y., DECLERCK, G., HUSSAIN, S., NG, R., JAULENT, M.C. (2013). Building a time-saving and adaptable tool to report adverse drug events. In Lehmann, C.U., Ammenwerth, E. & Nøhr, C. (éds), Medinfo 2013: Proceedings of the 14th World Congress on Medical and Health Informatics, 10-23 août 2013, Copenhague (Danemark), Studies in health technology and informatics, vol. 192, 903-907, IOS Press.

DECLERCK, G., HUSSAIN, S., PARÈS, Y., DANIEL, C., JAULENT, M.C., YUKSEL, M., SINACI, A.A., LALECI ERTURKMEN, G.B. (2012). Semantic-sensitive extraction of EHR data to support adverse drug event reporting. Semantic Web Applications and Tools for Life Sciences Workshop (SWAT4LS 2012), 28-30 november, Paris (France).



Efforts have already been made to support the reporting of ADEs with automated detection systems providing alerts to the physician or extracting patient data from EHRs to prepopulate automatically ADE reporting forms, such as the ASTER (Adverse drug events

Spontaneous Triggered Event Reporting) proof of concept pilot project. Existing solutions have however the two following limitations: (i) the completed form has to be processed manually by an intermediate instance, in charge of putting the form in the proper format expected by the FDA; (ii) the extraction of EHR data is not built to be interoperable with several EHR data models.

In this respect, SALUS interoperability solutions provide an indisputable advantage compared to existing solutions using EHR-enabled pre-population: (i) the possibility of

prepopulating E2B compliant XML reports ensures a minimal workload of the pharmacovigilance authorities when processing the submitted data: the report being ensured in the expected format; and (ii) due to SALUS platform’s scalability, AE reporting tool can be plugged onto new EHR systems with minimal adaptations: the only requirement is to map the new EHR data model to SALUS CIM; additional mappings between the new EHR data model and E2B do not have to be defined. Similarly, when the E2B data model evolves (a third release has recently been published), the alignment to the multiple EHR data models

plugged onto SALUS platform do not have to be amended one by one; only the mapping from E2B to SALUS CIM need to be updated. In addition, the SALUS interoperability solutions are generic: the aim is to make the EHR data available in a common model. In this paper we exemplified how this common model can be used to pre-fill AE reports; however, other pilot applications can be built easily to consume medical summaries in common model,

all needs to be done is writing new SPARQL queries.

E. Prior Art:

Identify the most closely related device, product or process existing before the

invention: (a) in the company; and (b) outside the company.

ASTER (Adverse drug events Spontaneous Triggered Event Reporting) system.



FP7-287800 SALUS


Linder, J.A., Haas, J.S., Iyer, A., Labuzetta, M.A., Ibara, M., Celeste, M., Getty, G., Bates, D.W.

(2010).Secondary use of electronic health record data: spontaneous triggered adverse drug event

reporting. Pharmacoepidemiology Drug Safety,19(12), 1211-5.

Rozich, J. D., Haraden, C. R., & Resar, R. K. (2003). Adverse drug event trigger tool: a practical

methodology for measuring medication related harm. Quality and Safety in Health Care, 12(3),

194-200.

Brajovic, S., Piazza-Hepp, T., Swartz, L., Dal Pan, G.(2012). Quality assessment of spontaneous

triggered adverse event reports received by the Food and Drug Administration.

Pharmacoepidemiology Drug Safety, 21(6), 565-70.

Identify any persons that you know of who may have knowledge of additional

information relevant to parts mentioned above. None

F. Addendum None.

3. RESULT OF IP SEARCH

The tables below reflect the result of the IP search and the reaction from the partners. The Agfa patent office flagged some patents as being ‘relevant’ or ‘possibly relevant’ (column ‘pre-screening’). This was then checked by the partners, knowing the product better and in more detail than the patent office. Their assessment of the relevancy is reflected in the column ‘relevant/not relevant’ and the justification is described in the column ‘comment if not relevant’. Patents were checked in the following database: PatBase of Minesoft.

3.1 SRDC

3.1.1 De-Identification/Anonymization Service

Nr Title Publication nr Patent Assignee Pre-screening

Relevant/ not relevant

Comment if not relevant

1 ANONYMOUSLY LINKING A PLURALITY OF DATA RECORDS

US6397224 BA ARCANVS INC Relevant Not Relevant This patent addresses patient linkage, which is not in the scope of SALUS De-Identification toolset.

2 Data source privacy screening systems and methods

WO03021473 A1 PRIVASOURCE INC

Possible relevant

Not relevant This patent describes a very specific de-identification algorithm which is nit implemented in our system.

3 SYSTEM FOR ENSURING THE CONFIDENTIALITY OF ELECTRONIC DATA, ESPECIALLY PATIENT DATA, IN A NETWORK BY USE OF PSEUDONYMS, WHEREBY A PSEUDONYM GENERATOR USES A POLICY DATABASE FOR PSEUDONYM GENERATING RULES WITH SUPPLIED PATIENT

DE10327291 B4 SIEMENS AG Possible relevant

? It was in German

FP7-287800 SALUS


4 PRIVACY PRESERVING DATA-MINING PROTOCOL

US7823207 BB CROSSIX SOLUTIONS INC

Possible relevant

Not relevant This patent focuses on privacy aware data mining methods; we are not specifically addressing data mining security and privacy.

5 SYSTEMS AND METHODS FOR PROTECTING PRIVACY

US7797725 BB PALO ALTO RES CT INC

Relevant Not relevant This patent particularly focuses on privacy protection of the data that is generated through data mining of public data sources. In SALUS we are not focusing on the results of data mining process on public data.

6 APPARATUS AND METHOD FOR GENERATING REPORTS WITH MASKED CONFIDENTIAL DATA

US8024339 BB BUSINESS OBJECTS S A

Relevant Can be relevant

We have to discuss the relevance, we are not covering all the processes mentioned in the patent, the abstract idea matches, but the process definition given in the patent is not directly applicable for SALUS.

7 METHOD AND SYSTEM OF DE-IDENTIFICATION OF A RECORD

US2007255704 AA

INT BUSINESS MACHINES CORP

Possible relevant

Not relevant This patent addresses de-identification of unstructured data, which is not in our scope.

8 DOUBLE BLINDED PRIVACY-SAFE DISTRIBUTED DATA MINING PROTOCOL

US8577933 BB CROSSIX SOLUTIONS INC

Relevant Not directly relevant

This patent addresses a broader concept: privacy safe data mining protocol. It is not directly applicable to SALUS requirements and the provided solutions.

9 DATA OBFUSCATION SYSTEM, METHOD, AND COMPUTER IMPLEMENTATION OF DATA OBFUSCATION FOR SECRET DATABASES

WO2009139650 A1

BUSINESS INTELLIGENCE SOLUTION

Relevant Not relevant This patent describes a de-identification method for databases, which is not directly covered in SALUS.

10 METHOD AND SYSTEM FOR DE-IDENTIFICATION OF DATA

US2011264631 AA

DATAGUISE INC Relevant Possibly relevant

This patent is a very generic patent describing any de-identification task in a very broad way, so it could be found relevant.

11 SECURED SEARCHING US2013054554 AA

INT BUSINESS MACHINES CORP

Relevant Not relevant This patent addresses a generic secure search algorithm, which is not in our scope.

12 METHOD AND SYSTEM FOR MAKING MULTISITE PERFORMANCE MEASURE ANONYMOUS AND FOR CONTROLLING ACTIONS AND RE-IDENTIFICATION OF ANONYMOUS DATA

WO14091385 A1 PHILIPS KONINKLL NV

Relevant Not relevant This patent covers re-identification, and also focuses on a specific topic: anonymization of performance measures, which is not directly addressed by our solution.

FP7-287800 SALUS


3.1.2 SIL-LISPA


Relevant/ Not relevant


1 METHOD AND APPARATUS FOR REQUESTING, RETRIEVING, AND NORMALIZING MEDICAL INFORMATION

WO0198994 A1 INGENIX INC Relevant Not relevant

The patent introduces a generic approach for an id based patient data querying through an internet website. SIL LISPA does not have a graphical interface and the query is sent to the data provider in a programmatic way.

2 DATA QUERY AND LOCATION THROUGH A CENTRAL ONTOLOGY MODEL

EP1260916 A2 EP1274018 A2 EP1327941 A2

UNICORN SOLUTIONS INC

Relevant Relevant Relevant

Not relevant

EP1260916 A2 states that the system must include a central computer comprising a global ontology directory and several servers responding to queries related to class and relation definitions. However, LISPA SIL is a single application, mapping an RDF instance from one format to other format. EP1274018 A2 includes claims related with navigation, querying of ontology instances. However, LISPA SIL does not provide a graphical interface; and it does not allow querying of ontological resources or relations between them. EP1327941 A2 proposes a methodology for transforming XML to XML or transforming a relational database table to another relational database table by transforming them into ontological format. The actual transformation is done by mapping those ontological formats. However, in our case, we do not have a target data model as a DTD or SQL. We directly make use of the resultant ontology.

3 Method and apparatus for obtaining and distributing healthcare information

US2004172287 AA Relevant Not relevant

In this patent, during the patient data retrieval process, the compliance of the healthcare providers with certain recommended

FP7-287800 SALUS


treatment guidelines is checked. Furthermore, the patent includes consulting service to the healthcare providers. However, LISPA SIL does not include such approaches.

4 HEALTHCARE SEMANTIC INTEROPERABILITY PLATFORM

US2009080408 AA INTEL CORP Relevant Not relevant

This patent defines a network where data from different formats are transformed into a common canonical format and vice versa. Besides, the patent includes a traceability mechanism for the origin of the data transformed into the common format. Similarly, In LISPA SIL, we implement a mechanism in which data in a local format is transformed into a common format. However, we have a single information source and we do not keep track of the origin of the data.

5 METHOD AND MODULE FOR LINKING DATA OF A DATA SOURCE TO A TARGET DATABASE

WO10067295 A1 PHILIPS ELECTRONICS KONINKLL NV

Relevant Not relevant

This patent includes a user interface for converting a relational schema representation of the data source to an ontology. However, in LISPA SIL this operation is done automatically after the data is retrieved from the source database.

6 SEMANTIC INTEROPERABILITY SYSTEM FOR MEDICINAL INFORMATION

WO11032086 A2 II4SM Relevant Not relevant

The patent defines a complex system for managing ontologies; mappings between ontologies; values sets that would be subsets of ontologies. Although there is a transformation operation takes place in SIL LISPA, this component does not have ontology management, rule management and configuration management capabilities.

7 METHOD AND SYSTEM FOR FACILITATING CLINICAL RESEARCH

US2012078659 AA Relevant Not relevant

This patent defines a methodology including two types of criteria to group patients to be used in clinical research studies. Although this part the patent is similar with the approach followed in LISPA SIL, LISPA SIL does not generate

FP7-287800 SALUS


a question to be answered by patients or doctors.

8 DATA AUTOMATION US2013144790 AA Relevant Not relevant

This is a patent which defines a very generic architecture where the data would collected from various types of data sources; encryption of the data; transfer of the data via a communication device; decryption of the transferred data and converting it into a standard format. Although the approach is similar in SIL LISPA, the data is not encrypted before transformation into the standard format.

9 ASSOCIATING MULTIPLE DATA SOURCES INTO A WEB-ACCESSIBLE FRAMEWORK

US2013275361 AA CERNER INNOVATION INC


This patent defines a system to gather patient data considering certain categories named as “topics”. And each topic is associated with a web service and the data gathered through those web services is presented to the clinicians. However, SIL LISPA does not have separate web services to obtain patient data considering a certain topic. Furthermore, the retrieved data is not presented to the clinicians. It is used by other applications in the system in automatically.

10 ONTOLOGY HARMONIZATION AND MEDIATION SYSTEMS AND METHODS

US2014156638 AA ORBIS TECHNOLOGIES INC


The approach described in this patient is very similar to the approach followed in SIL LISPA in terms of representation of the database schemas ontologically and transformation of this ontological database representation into a common ontology format. However, SIL LISPA the data is not store for entity correlation or disambiguation. Furthermore, statistics and metadata regarding said databases is not stored in the scope SIL LISPA.

FP7-287800 SALUS


3.1.3 Ontmalizer




1 Uni-level description of computer information and transformation of computer information between representation schemes

US2002059566 AA OREGON HEALTH AND SCIENCE UNIV

Relevant Not relevant This patent describes a data transformation mechanism between different representation schemes, by using uni-level description of the data. Ontmalizer implements a custom, direct mapping from XML Schema and XML documents to their corresponding OWL/RDF representations without using uni-level descriptions.

2 APPARATUS AND METHOD FOR CONVERTING XML DOCUMENT TO OWL DOCUMENT

KR100509921 B1 KOREA ELECTRONICS TELECOMM

Relevant machine translation

Not relevant This patent introduces a general mapping mechanism to create OWL representations of XML documents by using OWL constructs from an ontology registry which includes a tag set. Ontmalizer does not make use of any tag registries and make a direct conversion from XML Schema and XML documents to owl:Class, owl:DatatypeProperty and owl:ObjectProperty constructs.

3 EXTENSIBLE DATABASE SYSTEM AND METHOD

US7836097 BB CLEVELAND CLINIC FOUNDATION

Possible relevant

Not relevant This patent describes a complete system to transform data conforming to a domain model represented in XML, to another model represented as a graph model. The patent uses a data storage system and an associated graphical user interface. Ontmalizer neither uses a data storage system nor a graphical user interface.

4 TECHNIQUES FOR AUTOMATED GENERATION OF ONTOLOGIES FOR ENTERPRISE APPLICATIONS

US8214401 BB ORACLE INT CORP

Relevant Not relevant This patent introduces a methodology to create OWL ontological models out of XML, XSD, WSDL, WSRP based models. The patent first converts the input data (i.e. XML) to an annotated format and then processes that

FP7-287800 SALUS


format internally through mapping rules. Ontmalizer does not use intermediate annotated formats and directly converts the input XML Schema and XML documents to RDF/OWL ontological formats.

5 EFFICIENT XML/XSD TO OWL CONVERTER

US8745097 BB INFOSYS LTD Relevant Not relevant This patent introduces a method to convert XML Schema and XML documents to OWL ontology documents. The method of this patent merges the plurality of XML schema documents and creates a consolidated XML schema document. Then, it uses this consolidated document to transform XML documents into OWL ontological formats. Ontmalizer does not work with plurality of XML schema documents; it processes a single XML schema document and does not lean on an intermediate consolidated model.

6 ENFORCING POLICIES OVER LINKED XML RESOURCES

US8725774 BB XEROX CORP Relevant Not relevant This patent describes a method to generate an OWL ontology of linked resources where the input is XML documents and they are validated against XML Schema documents and the input resources are linked together with valid URLs. Ontmalizer does not use linked resources; it processes simple (not linked) XML and XML schema files where the URIs can be invalid in the input documents.

7 CONVERSION METHOD FOR AUTOMATICALLY CONVERTING XML DOCUMENT INTO OML DOCUMENT AND DEVICE

CN103123646 A BEIHANG UNIV Possible Relevant machine translation

Not relevant This patent describes a data transformation mechanism from XML documents to OWL documents. The method of the patent generates an XML Schema tree from the given XML document. Unlike this method, Ontmalizer also

FP7-287800 SALUS


receives the XML Schema definition as an input along with the XML document. In addition, internal implementation mechanisms differ majorly.

3.1.4 PMSST




1 Document schema transformation by patterns and contextual conditions

US5915259 A FUJI XEROX CO LTD


The patent describes a system that transforms a document to another with specified input schema, transformation rule and a contextual condition. On the contrary, PMSST does not have any formal input schema and applicable transformation rule. PMSST works on semantic data model.

2 SYSTEM AND METHOD FOR INTEGRATING HETEROGENEOUS BIOMEDICAL INFORMATION

WO07019504 A2 SIEMENS CORPORATE RESEARCH INC


This patent claims that it aggregates the results from a number of healthcare information systems. However, PMSST retrieves results from only one healthcare system at a time and extracts desired data from that result.

3 HEALTH CARE DEVICE AND SYSTEMS AND METHODS FOR USING THE SAME

WO11084470 A1 MYCARE LLC Relevant Not relevant

This patent describes that it does not allow to download or store the information that was accessed, retrieved, processed, or displayed. Unlikely, PMSST allows users to download the information displayed. Furthermore, this patent introduces a alerting feature when the clinical data is modified. PMSST does not have such feature.

4 SYSTEM OF ENTITY- KR101288208 B1 KYUNGPOOK Relevant Not This patent

FP7-287800 SALUS


RELATIONSHIP MODEL REFORMULATION OF SPARQL QUERY RESULTS ON RDF DATA AND THE METHOD

NAT UNIV IND ACAD

Machine translation

relevant introduces a SPARQL query execution on entity-relationship model resulting RDF data. Unlikely, PMSST executes SPARQL queries only on RDF models.

3.1.5 Semantic MDR

Nr Title Publication nr Patent Assignee

Pre-screening



1

SYSTEM AND METHOD FOR FEDERATED MEMBER-BASED DATA INTEGRATION AND REPORTING

US2009012983 AA COGNOS INC


This patent describes a data translation mechanism where XML based documents are used to define the data integration specification and data movement specification. Semantic MDR does not fit into this mechanism where Semantic MDR makes use of extraction specifications for data movement through semantic links. The extraction specifications are dictated by SPARQL, XPath and SQL expressions.


WO11032086 A2 II4SM Relevant Not relevant

This patent describes a rule based ontology mapping mechanism to establish semantic interoperability for medicinal information. Semantic MDR is a totally different system which do not deal with ontology mapping.

3 PLANNING-BASED AUTOMATED FUSING OF DATA FROM MULTIPLE HETEROGENEOUS SOURCES

WO12018475 A2 UNIV CARNEGIE MELLON


This patent introduces a query plan and execution mechanism in order to retrieve data from heterogeneous data sources. Semantic MDR does not include any query planning or execution mechanism in a graph structure as introduced in the patent.

FP7-287800 SALUS


3.2 OFFIS

3.2.1 ANT




1 COMPUTER ALGORITHMS AND METHODS FOR PRODUCT SAFETY

WO02069254 A2 CLASSEN IMMUNOTHERAPIES

Possible relevant

relevant

2 Method and system for identifying and anticipating adverse drug events

WO03107252 A2 VIGILANZ CORP Possible relevant

Not relevant

Focuses on patient care process and is not drug related

3 METHOD AND SYSTEM FOR CREATING, STORING AND USING PATIENT-SPECIFIC AND POPULATION-BASED GENOMIC DRUG SAFETY DATA

US7461006 BB DRUGLOGIC INC Possible relevant

Not relevant

Focuses on other objectives, especially from the genomic perspective

4 Surveillance system for adverse events during drug development studies

US6639515 BB NOVO NORDISK A S Possible relevant

Not relevant

Focuses on a surveillance progress which is different to the one used in ANT

5 ADVERSE TREATMENT EVENT MANAGEMENT SYSTEM

US2008082361 AA

SIEMENS MEDICAL SOLUTIONS

Possible relevant

Relevant

6 SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR PERFORMING AUTOMATIC SURVEILLANCE AND TRACKING OF ADVERSE EVENTS

US2009216555 AA

MCKESSON AUTOMATION INC

Possible relevant

Relevant

7 ARTIFICIAL INTELLIGENCE-ASSISTED MEDICAL REFERENCE SYSTEM AND METHOD

WO10124016 A1 LEAD HORSE TECHNOLOGIES INC

Possible relevant

Not relevant

Approach focuses more on statistical methods, ANT consists more of information-based rule processes.

8 PHARMACOVIGILANCE ALERT TOOL

US2012143776 AA

ORACLE INT CORP Possible relevant

Not relevant

Completely different data analysis approach

9 METHOD AND SYSTEM FOR ONTOLOGY BASED ANALYTICS

US2013096944 AA

JUNIOR UNIV BOARD OF TRUSTEES FOR LEIAND STANFORD

Relevant Relevant

10 SIGNAL DETECTION ALGORITHMS TO IDENTIFY DRUG EFFECTS AND DRUG INTERACTIONS

US2013179375 AA

JUNIOR UNIV BOARD OF TRUSTEES FOR LEIAND STANFORD

Possible relevant

Not relevant

Focuses on another approach of ADE detection

11 DETECTION OF ADVERSE REACTIONS TO MEDICATION USING A COMMUNICATIONS NETWORK

US8473315 BA Possible relevant

Not relevant

Describes another workflow of ADE detection, e.g. the user has to enter ADR information by himself

FP7-287800 SALUS


3.3 AGFA

3.3.1 Semantic Mediation Framework




1 ONTOLOGY HARMONIZATION AND MEDIATION SYSTEMS AND METHODS

US2014156638A ORBIS TECHNOLOGIES INC

Possible relevant

Relevant


US2013030827A II4SM INTERNATIONAL INSTITUTE FOR THE SAFETY OF MEDICINES LTD ; II4SM INTERNATIONAL INSTITUTE FOR THE SAFETY OFMEDICINES LTD ; II4SM INT INST FOR SAFETY OF MEDICINES LTD

Possible relevant

Relevant

3 METHOD AND MODULE FOR LINKING DATA OF A DATA SOURCE TO A TARGET DATABASE

US2012130966A PHILIPS ELECTRONICS KONINKLL NV ; PHILIPS ELECTRONICS NV KONINKLL ; PHILIPS INTELLECTUAL PROPERTY AND STANDARDS GMBH ; KONINKLIJKE PHILIPS ELECTRONICS NV ; KONINKL PHILIPS ELECTRONICS NV

Possible relevant

Relevant

4 AUTOMATED HEALTHCARE INFORMATION COMPOSITION AND QUERY ENHANCEMENT

US2010131498A GENERAL ELECTRIC CO ; GENERAL ELECTRIC CO A NEW YORK CORP ; GENERAL ELECTRIC COMPANY

Possible relevant

Not relevant

We are not dealing with document analysis

5 HEALTHCARE SEMANTIC INTEROPERABILITY PLATFORM

US2009080408A INTEL CORP ; KERMANSHAHCHE KRISTINA ; NATOLI JOSEPH D ; PAINTER JOSHUA ; BOUCHER ALAN

Possible relevant

Relevant

6 SYSTEM AND METHOD FOR CREATING AND SEARCHING MEDICAL ONTOLOGIES

US2009024615A SIEMENS MEDICAL SOLUTIONS USA INC

Possible relevant

Not relevant

We are not doing natural language processing

7 DATA QUERY AND LOCATION THROUGH A CENTRAL ONTOLOGY MODEL

US2003101170A EDELSTEIN JOSEPH ; MARCHANT HAYDEN ; MEIR RANNEN ; FOX JOSHUA ; HELLMAN ZIV Z ; HALBERSTADT BENJAMIN ; AMARU RUTH M ; IBM ; UNICORN SOLUTIONS INC ;

Possible relevant

Not relevant

We are not realizing mapping generation

FP7-287800 SALUS


SCHREIBER MARCEL ZVI ; YUVAL TOM Y ; MELAMED BORIS ; SCHREIBER ZVI ; MEIR RANNEN YOSEF ; SCHREIBER MARCEL Z ; MEIR RANNEN Y ; UNICORN SOLUTIONS ; 2006 TRIDENT CO

8 AUTOMATED MAPPING OF SERVICE CODES IN HEALTHCARE SYSTEMS

US2014095205A SIEMENS MEDICAL SOLUTIONS USA INC

Possible relevant

Not relevant

We have no automatic mapping

3.4 UMC

3.4.1 TAST




1 SYSTEMS AND METHODS FOR DISCOVERING PARTIALLY PERIODIC EVENT PATTERNS

US6996551 BB IBM Possible relevant

Not relevant

We are not searching for periodic patterns

2 METHOD FOR ANALYZING DRUG ADVERSE EFFECTS EMPLOYING MULTIVARIATE STATISTICAL ANALYSIS

WO02088901 A2 QED SOLUTIONS INC

Possible relevant

Not relevant

Not relevant, only the application

3 METHOD AND SYSTEM FOR ANALYZING DRUG ADVERSE EFFECTS

US7542961 BB/ US7979373 BB

DRUGLOGIC INC


Based on spontaneous reports, does not go beyond what we already published before the patent was approved/ US7979373 Continuation of US7542961, no additional relevant details

4 APPARATUS FOR DETERMINING ASSOCIATION VARIABLES

WO06017153 A2 MONITRIX INC Possible relevant

Not relevant

Looks at temporal association but without self-controlled component

5 METHOD, SYSTEM, AND SOFTWARE FOR ANALYZING PHARMACOVIGILANCE DATA

US7650262 BB UBC LATE STAGE INC

Possible relevant

Not relevant

Prosanos patent on URN method, for cross-sectional data with a different statistical approach

6 Methods and Systems for Evaluating Interaction of Medical Products and Dependence on Demographic Variables

US8170889 BB UBC LATE STAGE INC

Possible relevant

Not relevant

Focuses on interactions what is not part of the SALUS tools and no temporal associations. Looks at patient records as flat.

7 FLEXSCAPE: DATA DRIVEN HYPOTHESIS TESTING AND GENERATION SYSTEM

US2011231356 AA

QUANTUM LEAP RESEARCH INC

Possible relevant

Not relevant

Focuses on building graphical models and hypothesis testing which the SAT tools do not.

8 NOVEL SIMULATION AND PERMUTATION METHODS FOR THE DETERMINATION

WO13056061 A1 JOHNS HOPKINS UNIV

Possible relevant

Not relevant

Looks at temporal association but without self-controlled

FP7-287800 SALUS


OF TEMPORAL ASSOCIATION BETWEEN TWO EVENTS

component

3.4.2 CSCT




1 METHOD FOR GRAPHICALLY DEPICTING DRUG ADVERSE EFFECT RISKS

US7925612 BB DRUGLOGIC INC

Possible relevant

Not relevant

No temporal associations, no case series characterization

2 METHOD AND SYSTEM FOR CREATING, STORING AND USING PATIENT-SPECIFIC AND POPULATION-BASED GENOMIC DRUG SAFETY DATA

US7461006 BB DRUGLOGIC INC

Possible relevant

Not relevant

Looks for genotype information not included in SALUS

3 SYSTEMS AND METHODS FOR IDENTIFICATION AND/OR EVALUATION OF POTENTIAL SAFETY CONCERNS ASSOCIATED WITH A MEDICAL THERAPY

US2007294112 AA GEN ELECTRIC Relevant Not relevant

Does not specify any detailed data analysis

4 METHOD, SYSTEM AND DATABASE FOR POST-MARKETING SURVEILLANCE AND EVALUATION OF DRUGS

US2009099903 AA LA SER ALPHA GROUP SARL


Case-control not relevant to any of our inventions

5 A METHOD FOR DETECTING ADVERSE DRUG REACTION SIGNALS BASED ON RETROSPECTIVE COHORT STUDY USING ELECTRONIC MEDICAL RECORD

KR20120125079 A AJOU UNIVERSITY INDUSTRY ACADEMIC COOPERATION FOUNDATION

Possible relevant machine translation

Not relevant

The Chinese version of "KR20120125079A machine translation"

6 SYSTEMS AND METHODS FOR POPULATION HEALTH MANAGEMENT

US2013325505 AA GEN ELECTRIC Possible relevant

Not relevant

Health management tool not targeted at exploratory data analysis

7 SYSTEM AND METHOD FOR PHARMACOVIGILANCE

US8744872 BA AETNA INC Relevant Not relevant

Focuses on risk score that is different from any of the tools provided in SALUS

3.4.3 PHT




1 METHOD, APPARATUS, AND SYSTEM FOR READING, PROCESSING, PRESENTING, AND/OR STORING ELECTRONIC MEDICAL RECORD INFORMATION

US8775213 BB EMERGENT HEALTH CARE SOLUTIONS LLC

Possible relevant "touch sensitive display". PHT is agnostic about that

Not relevant

Related to touch sensitive media, which are not included in the SALUS implementation

FP7-287800 SALUS


3.5 INSERM

3.5.1 IRT




1 SYSTEM AND TECHNIQUES FOR REPORTING ADVERSE EFFECTS

US2009319299 AA MEDIDATA SOLUTIONS INC


The patent discusses how collect and associate drug information and medical information, prompting a user, and display iteratively a cumulative narrative on it. The patent seems not to be relevant with IRT, that doesn't process like this.

2 TRACKING DIRECT REPORTED ADVERSE EVENTS

US2010179821 AA CERNER INNOVATION INC


The patent discusses how are processed the data after the reporting process. The patent seems not to be relevant with IRT, that doesn't cover this.

3 DIRECT REPORTING OF ADVERSE EVENTS

US8457989 BB CERNER INNOVATION INC

Relevant Might be relevant

The patent discusses five points : - monitoring EHR - detect adverse event - form generation with pre-populates - receiving data from user - electronically send data IRT covers only the 3rd, 4th and 5th points, for which the patent is relevant. But SALUS as a whole, specifically the ANT-IRT couple, can be considered as covering all the points. The patent *might be* relevant, depending how ANT works.

4 CLINICAL TRIAL ADVERSE EVENT REPORTING SYSTEM

US2014164265 AA ORACLE INT CORP

Relevant Might be relevant

The patent discusses several points: - monitoring interactions with adverse event component - receiving a first event in response to a first interaction - receiving a second event in response to a second interaction - collecting data that is stored within the electronic data capture system - send data to safety compliance management system. The patent *might be* relevant just as the previous patent.

FP7-287800 SALUS


4. Conclusion

As a conclusion of the current exercise and the current deliverable, but also of the project-wide work on IP the consortium has done the SALUS consortium states:

1. Each of the partners had the opportunity to define background IP, prior to starting the work. After one year, small corrections were done, as described in the deliverable D2.3.1. This had no impact on the potential re-use of project results.

2. Many of the project results (foreground IP) were considered as public. However, the consortium decided to not engage in an official open-source project, because of the heavy overhead and the ‘moral’ obligation to maintain the code, even after the project stops. Potential exploitation is considered to be a bigger incentive for code maintenance. Still, the project partners are willing to share code on request.

3. Project results have been checked against breaching existing patents. The conclusion is that none of the results and artifacts of the SALUS project breach an existing patent. The partners are free to safely use their tools in any exploitation they may consider doing. We refer to the appropriate deliverables on non-commercial and commercial exploitation and dissemination of the generated IP for more details.

END OF DOCUMENT

salus d2.3.3 version 1 - srdc · - we registered all background ip brought into the project. - we...

Documents