What’s in a name Business Vocabularies, Business Rules and DMN- Denis GagneDenis Gagné
Names are arbitrary labels. So why do we assign so much meaning to names? In certain context, certain names can create confusion. In others, a name can disambiguate the intended meaning being communicated. In the context of business decisions, the names given to various business concepts play a crucial role in providing meaningful and unambiguous business decisions. This becomes even more important in the context of life critical business decisions such as those made in a healthcare clinical context. Medical information systems need to be able to communicate complex and detailed medical data securely and efficiently. This is obviously a difficult task and requires a profound analysis of the structure and the concepts of medical terminologies.
In this presentation we will explore the use of disambiguated business terms to express decision requirements and decision logic in DMN.
Obtaining consent for a Total Knee Arthroplasty (TKA) surgeryDenis Gagné
We present a proof of concept for eConsent based on BPM+ Health models, CDS Hooks and the Consent FHIR resource. The PoC is based on Trisotech Digital Enterprise Suite services connected to the Healthcare synthetic ecosystem IOL from the Interoperability Institute (IOI).
Presentation given at the Expanding eConsent: Advanced Care Planning in the 21st Century event.
Introduction to some DMN patterns and their valueDenis Gagné
Businesses continuously make Business Decisions. Some of these decisions are strategic business decisions, but a lot are operational business decisions taken every day within every transaction. With the ever-increasing number of laws and regulations that may apply or regulate these operational business decisions, business analysts are more often called upon to document/specify how these business decisions are to be taken in order to provide transparency and to offer auditable traces of the actual decisions taken. In this insightful session, we will introduce how business analysts can use DMN to capture the requirements for operational business decisions, some of the recurring basic patterns in modeling these business decisions and will even show how to transform these decision models into actual executable business decision services.
Healthcare Concept Maps combined with a FHIR Accelerator Denis Gagné
BPM+ models are based on open standards that can be used to visually depict the structure and behavior of healthcare workflows and decisions.
If these workflow and decision models are to completely model healthcare clinical guidelines, then they also need to orchestrate logical data structures of medical concepts and data in the electronic health record.
In this session we will:
introduce two knowledge entity models of the most common medical conditions and observations
bind them to logical data structures based on FHIR, referred to the FHIR accelerator
demonstrate them in action.
From allotrope to reference master data management OSTHUS
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
Challenges & Opportunities of Implementation FAIR in Life SciencesOSTHUS
Speak in common terms – identify Business Outcomes (value) as well as technology
Don’t say “semantics”, “FAIR”, “ontologies”, etc. – talk about outcomes and results
Drive projects through results – QUICK WINS
Identify the right data – build off of that (evolution not revolution)
Think about legacy systems, provenance, governance, stewardship, etc. – have answers to the nay-sayers.
Be honest what this will do and what it won’t
ROI – have this in mind (Business Value not Tech Value)
Cost savings (reduced hours, faster search, accurate reporting, better visibility, etc.)
Risk Mitigation (improved regulatory, corporate knowledge vs. indivual, M&A, etc.)
Innovation (what is the value to being a thought leader?)
What’s in a name Business Vocabularies, Business Rules and DMN- Denis GagneDenis Gagné
Names are arbitrary labels. So why do we assign so much meaning to names? In certain context, certain names can create confusion. In others, a name can disambiguate the intended meaning being communicated. In the context of business decisions, the names given to various business concepts play a crucial role in providing meaningful and unambiguous business decisions. This becomes even more important in the context of life critical business decisions such as those made in a healthcare clinical context. Medical information systems need to be able to communicate complex and detailed medical data securely and efficiently. This is obviously a difficult task and requires a profound analysis of the structure and the concepts of medical terminologies.
In this presentation we will explore the use of disambiguated business terms to express decision requirements and decision logic in DMN.
Obtaining consent for a Total Knee Arthroplasty (TKA) surgeryDenis Gagné
We present a proof of concept for eConsent based on BPM+ Health models, CDS Hooks and the Consent FHIR resource. The PoC is based on Trisotech Digital Enterprise Suite services connected to the Healthcare synthetic ecosystem IOL from the Interoperability Institute (IOI).
Presentation given at the Expanding eConsent: Advanced Care Planning in the 21st Century event.
Introduction to some DMN patterns and their valueDenis Gagné
Businesses continuously make Business Decisions. Some of these decisions are strategic business decisions, but a lot are operational business decisions taken every day within every transaction. With the ever-increasing number of laws and regulations that may apply or regulate these operational business decisions, business analysts are more often called upon to document/specify how these business decisions are to be taken in order to provide transparency and to offer auditable traces of the actual decisions taken. In this insightful session, we will introduce how business analysts can use DMN to capture the requirements for operational business decisions, some of the recurring basic patterns in modeling these business decisions and will even show how to transform these decision models into actual executable business decision services.
Healthcare Concept Maps combined with a FHIR Accelerator Denis Gagné
BPM+ models are based on open standards that can be used to visually depict the structure and behavior of healthcare workflows and decisions.
If these workflow and decision models are to completely model healthcare clinical guidelines, then they also need to orchestrate logical data structures of medical concepts and data in the electronic health record.
In this session we will:
introduce two knowledge entity models of the most common medical conditions and observations
bind them to logical data structures based on FHIR, referred to the FHIR accelerator
demonstrate them in action.
From allotrope to reference master data management OSTHUS
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
Challenges & Opportunities of Implementation FAIR in Life SciencesOSTHUS
Speak in common terms – identify Business Outcomes (value) as well as technology
Don’t say “semantics”, “FAIR”, “ontologies”, etc. – talk about outcomes and results
Drive projects through results – QUICK WINS
Identify the right data – build off of that (evolution not revolution)
Think about legacy systems, provenance, governance, stewardship, etc. – have answers to the nay-sayers.
Be honest what this will do and what it won’t
ROI – have this in mind (Business Value not Tech Value)
Cost savings (reduced hours, faster search, accurate reporting, better visibility, etc.)
Risk Mitigation (improved regulatory, corporate knowledge vs. indivual, M&A, etc.)
Innovation (what is the value to being a thought leader?)
Rapid changes in the technology lead to increased variety of data sources. These varied data sources
generating data in the large volume and with extremely high speed. To accommodate and use this data in decision
making systems is the big challenge. To make fullest use of the valuable data generated by different systems, target
users of the analysis systems need to be increased. In general knowledge discovery process using the tools which are
available requires the handsome expertise in the domain as well as in the technology. The project ITDA (Integrated
Tool for Data Analysis) focuses to provide the complete platform for multidimensional data analysis to enhance the
decision making process in every domain. This projects provides all the techniques required to perform
multidimensional data analysis and avoids the overheads occurred by the traditional cube architecture followed by
most of the analytics system. Modelling the available data in the multidimensional form is the basis and crucial step
for multidimensional analysis. This work describes the multidimensional modelling aspect and its implementation
using ITDA project.
Learnings and Anecdotes from transitioning to a Headless CMS Content Structure and its implications for the underlying Frontend and Backend Architecture.
Integration Patterns for Big Data ApplicationsMichael Häusler
Big Data technologies like distributed databases, queues, batch processors, and stream processors are fun and exciting to play with. Making them play nicely together can be challenging. Keeping it fun for engineers to continuously improve and operate them is hard. At ResearchGate, we run thousands of YARN applications every day to gain insights and to power user facing features. Of course, there are numerous integration challenges on the way:
* integrating batch and stream processors with operational systems
* ingesting data and playing back results while controlling performance crosstalk
* rolling out new versions of synchronous, stream, and batch applications and their respective data schemas
* controlling the amount of glue and adapter code between different technologies
* modeling cross-flow dependencies while handling failures gracefully and limiting their repercussions
We describe our ongoing journey in identifying patterns and principles to make our big data stack integrate well. Technologies to be covered will include MongoDB, Kafka, Hadoop (YARN), Hive (TEZ), Flink Batch, and Flink Streaming.
Crossing the Analytics Chasm and Getting the Models You Developed DeployedRobert Grossman
There are two cultures in data science and analytics - those that develop analytic models and those that deploy analytic models into operational systems. In this talk, we review the life cycle of analytic models and provide an overview of some of the approaches that have been developed for managing analytic models and workflows and for deploying them, including using analytic engines and analytic containers . We give a quick overview of languages for analytic models (PMML) and analytic workflows (PFA). We also describe the emerging discipline of AnalyticOps that has borrowed some of the techniques of DevOps.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian from the University of Oxford explains Data Science and its relationship with Big Data and Cloud Computing. Then he illustrates using AzureML to perform a simple data science analytics.
Inteligencia de Negocios en SQL Server 2008 y Minería de Datos.
Ing. Eduardo Castro Martinez, PhD
Microsoft SQL Server MVP
http://ecastrom.blogspot.com
http://comunidadwindows.org
Creating Low-Code Loan Applications using the Trisotech Mortgage Feature SetDenis Gagné
Qualifying, underwriting, and selling a loan requires adherence and respect of various laws and regulations. The Trisotech Mortgage Feature Set (MFS) offers fully integrated features and functions leveraging open industry standards such as BPMN, DMN, and MISMO to allow mortgage lending organizations to include critical GSE and Regulation Z decisions into their workflows. Join us to discover a streamlined, compliant approach to mortgage lending with Trisotech MFS.
Generative AI and Regulatory ComplianceDenis Gagné
Generative AI can aid businesses, especially in the banking and finance industry, to meet regulatory compliance challenges by extracting important terms, creating concept models, and generating code to align with specified obligations. By utilizing a knowledge entity model (KEM), organizations can achieve traceable implementations, reduce errors, and minimize subjective interpretations when integrating decision models with regulatory requirements.
More Related Content
Similar to Data Type reused in Digital Modeling Suite
Rapid changes in the technology lead to increased variety of data sources. These varied data sources
generating data in the large volume and with extremely high speed. To accommodate and use this data in decision
making systems is the big challenge. To make fullest use of the valuable data generated by different systems, target
users of the analysis systems need to be increased. In general knowledge discovery process using the tools which are
available requires the handsome expertise in the domain as well as in the technology. The project ITDA (Integrated
Tool for Data Analysis) focuses to provide the complete platform for multidimensional data analysis to enhance the
decision making process in every domain. This projects provides all the techniques required to perform
multidimensional data analysis and avoids the overheads occurred by the traditional cube architecture followed by
most of the analytics system. Modelling the available data in the multidimensional form is the basis and crucial step
for multidimensional analysis. This work describes the multidimensional modelling aspect and its implementation
using ITDA project.
Learnings and Anecdotes from transitioning to a Headless CMS Content Structure and its implications for the underlying Frontend and Backend Architecture.
Integration Patterns for Big Data ApplicationsMichael Häusler
Big Data technologies like distributed databases, queues, batch processors, and stream processors are fun and exciting to play with. Making them play nicely together can be challenging. Keeping it fun for engineers to continuously improve and operate them is hard. At ResearchGate, we run thousands of YARN applications every day to gain insights and to power user facing features. Of course, there are numerous integration challenges on the way:
* integrating batch and stream processors with operational systems
* ingesting data and playing back results while controlling performance crosstalk
* rolling out new versions of synchronous, stream, and batch applications and their respective data schemas
* controlling the amount of glue and adapter code between different technologies
* modeling cross-flow dependencies while handling failures gracefully and limiting their repercussions
We describe our ongoing journey in identifying patterns and principles to make our big data stack integrate well. Technologies to be covered will include MongoDB, Kafka, Hadoop (YARN), Hive (TEZ), Flink Batch, and Flink Streaming.
Crossing the Analytics Chasm and Getting the Models You Developed DeployedRobert Grossman
There are two cultures in data science and analytics - those that develop analytic models and those that deploy analytic models into operational systems. In this talk, we review the life cycle of analytic models and provide an overview of some of the approaches that have been developed for managing analytic models and workflows and for deploying them, including using analytic engines and analytic containers . We give a quick overview of languages for analytic models (PMML) and analytic workflows (PFA). We also describe the emerging discipline of AnalyticOps that has borrowed some of the techniques of DevOps.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian from the University of Oxford explains Data Science and its relationship with Big Data and Cloud Computing. Then he illustrates using AzureML to perform a simple data science analytics.
Inteligencia de Negocios en SQL Server 2008 y Minería de Datos.
Ing. Eduardo Castro Martinez, PhD
Microsoft SQL Server MVP
http://ecastrom.blogspot.com
http://comunidadwindows.org
Creating Low-Code Loan Applications using the Trisotech Mortgage Feature SetDenis Gagné
Qualifying, underwriting, and selling a loan requires adherence and respect of various laws and regulations. The Trisotech Mortgage Feature Set (MFS) offers fully integrated features and functions leveraging open industry standards such as BPMN, DMN, and MISMO to allow mortgage lending organizations to include critical GSE and Regulation Z decisions into their workflows. Join us to discover a streamlined, compliant approach to mortgage lending with Trisotech MFS.
Generative AI and Regulatory ComplianceDenis Gagné
Generative AI can aid businesses, especially in the banking and finance industry, to meet regulatory compliance challenges by extracting important terms, creating concept models, and generating code to align with specified obligations. By utilizing a knowledge entity model (KEM), organizations can achieve traceable implementations, reduce errors, and minimize subjective interpretations when integrating decision models with regulatory requirements.
Automating and Orchestrating Processes and Decisions Across the EnterpriseDenis Gagné
PRESENTED BY
Carl Lehmann – Principal Analyst, 451 Research
Denis Gagne – CEO and CTO, Trisotech
DESCRIPTION
When business processes must execute complex decisions across the enterprise, most process automation platforms and rules management engines fall short. While competent in rules-based process modeling and automation they’re unable to model, automate and orchestrate complex decision-making processes, for example, in areas such as in clinical contexts, insurance risk management, and structured financial services, among others.
451 Research is tracking an emerging class of automation and orchestration technology that is becoming competent in both.
Join us to explore the industry trends driving the need for joint process and decision automation.
The benefits derived from a unified approach to both.
The technical apparatus needed to automate and orchestrate process and decision models.
This presentation aims to provide a comprehensive understanding of a form of neuro-symbolic AI, prompt engineering, and the role of process and decision orchestration in achieving optimal outcomes. By exploring the building blocks of prompt orchestration and showcasing examples, we seek to inspire the audience to harness the power of prompt engineering and contribute to the development of responsible and effective AI systems.
Data Validation in a Low-Code EnvironmentDenis Gagné
Experience the Power of Trisotech Digital Enterprise Suite - Discover the cutting-edge features that revolutionize data validation in a low-code environment. With Trisotech, you can effortlessly validate data based on their defined data types, empowering both IT and business professionals. By externalizing and generalizing data validation aspects, you can optimize decision-making, streamline processes, and enhance case management. Boost your operational efficiency with automated services, while minimizing production errors.
Learn the art of defining constraints using personalized messages and system codes, and witness how these constraints seamlessly integrate into the services you create and deploy.
From Laws and Regulations to Decision AutomationDenis Gagné
Regulations are a set of obligations that apply to corporations and individuals. They can be established through laws or under the authority of a governing body. Regulations may explicitly define processes and rules, but often they prescribe outcomes or performances without detailing how to achieve them.
When an organization must comply with a regulation, it aligns its operations with the obligations specified in the regulation. Compliance is the action of ensuring this alignment. However, demonstrating compliance can be a challenge because organizations must be able to trace their implementation back to the regulation.
To create traceability, a knowledge entity model (KEM) is developed. This model represents the regulation using vocabulary, concept maps, and business rules. The KEM is derived from the text of the regulation, breaking it down into vocabulary terms, concept connections, and business rules.
Using the KEM, an automated solution can be created using decision automation and business process automation (DMN and BPMN). This solution links the business rules to the decision or process as a knowledge source, creating a traceable solution.
Smart Drug Package Inserts using Clinical Workflows and DecisionsDenis Gagné
Are you ready to take your drug information game to the next level? Join us for an exciting and informative webinar where we dive into the world of Drug Package Inserts and explore how new technologies and models can enhance the way we access and use this valuable information. With over decades of experience, the Drug Package Insert is a trusted source for healthcare providers, but it's time to bring it into the modern era. Discover the 5 ways Workflow and Decision models can improve patient care and make drug information more accessible than ever before. Don't miss out on this straight-to-the-point presentation, register now!
Deployment, Performance, Agility and Flexibility using Trisotech Digital Dist...Denis Gagné
Trisotech Digital Distributed Containers offers containerized services to allow advanced scaling both vertically and horizontally across geographies on Kubernetes and container-based infrastructure for performance and high-availability configuration. Learn how you can deploy workflows (BPMN/CMMN) and decisions(DMN) into single-service or multi-service containers using flexible API driven DevOps. Topics will also include persistence storage and scaling along with runtime configurations.
In addition to the operational and administrative processes existing in the Pharmaceutical industry, there are numerous processes such as drug research, clinical trial, risk reduction and patient safety.
BPM+ Workflows and Decisions are standard visual depictions (models) that are both human readable and machine automatable. When combined to the FHIR interoperability standard, these models represent the future of PharmaTech.
In this session, we explore the challenges posed by these Pharma Clinical Processes, introduce BPM+ Workflows and Decision Automation as a potential way to efficiently address these challenges, and then finally demonstrate this automation.
Modelling the Preoperative Surgical JourneyDenis Gagné
This webinar provides an introduction to BPM+ using the Preoperative Surgical Journey as an example.
Speakers demonstrate visual modelling and automation for the Preoperative Surgical Journey based on the three open standards that make up BPM+.
BPM+ Health is a multidisciplinary initiative, with high levels of participation from clinicians, to improve the quality and consistency of healthcare delivery. It is achieving this by applying business process modelling standards to clinical best practices, care pathways and workflows directly at the point of care.
Further information on BPM+ Health can be found at https://www.bpm-plus.org
Intelligent Assistance for Knowledge Workers.pptxDenis Gagné
Knowledge Workers, a term coined by Peter Drucker, are workers whose job is to think for a living. Knowledge work can be differentiated from other forms of work by emphasizing continuously evolving non-routine problem-solving based on information. As businesses increase their dependence on information technology via digital transformation, the number of fields in which knowledge workers must operate has expanded dramatically.
Today, much of the knowledge work accomplished involves informal collaborations via emails supported by attached documents (PDFs and others). Fundamentally, knowledge workers spend much of their time acting as human integrators of unstructured information exchanged via unstructured communications and collaborations.
In support of these efforts, Intelligent Document Process (IDP) technologies were introduced by various vendors to transform unstructured and semi-structured information into usable data. The ultimate objective of most IDP capabilities is to integrate with downstream systems such as ERP. They tend to be based on pattern matching supported by Machine Learning (ML) technologies. To become effective, these approaches require varying quantities of representative information being available or supervised learning and labeling techniques that is yet another form of knowledge work. But what if an adequate sample of examples or information are not available for a particular type of knowledge work? And how do we support knowledge workers and their actual flow of work?
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Digital transformation and the accelerated transition to remote work are contributing to a perfect technological storm. This perfect storm is indifferently hitting every industry around us. A particularly challenging vector of this technological storm is the ever-growing need for business automation to achieve digital transformation conflated with an ever-growing shortage of technology professionals and software developers. With most companies turning to technology to transform how they engage with customers; software developers are in high demand and short supply. It is clear that we cannot rely on this small number of specialized workers - software developers - to carry out the massive undertaking of digital transformation in organizations.
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Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
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Editable Toolkit to help you reuse our content: 700 Powerpoint slides | 35 Excel sheets | 84 minutes of Video training
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4. Trisotech.com
Advantages of reusing existing data types
Leverage existing material in the enterprise
Keep things familiar
Easier integration with exiting system (internal or external)
5. Trisotech.com
Scenario
Calculate the BMI (Body Mass Index) of a patient
oUsing structure from XSD in Workflow Modeler
oUsing data from Odata in Decision Modeler
oUsing data from an OpenAPI in Workflow Modeler
6. Trisotech.com
XSD
Standard of the W3C
Defines the structure of an XML file
Modelers supports include and import statements
Can be imported in all modelers supporting data types
8. Trisotech.com
OData
OData (Open Data Protocol) is an ISO/IEC approved, OASIS
standard that defines a set of best practices for building and
consuming RESTful APIs.
More focused on data with defined set of operations to
access them
Can be imported in Decision and Workflow Modeler; creates
both the data and the operations
10. Trisotech.com
OpenAPI
Standardized way to describe REST API that can be consumed
by human and machines to know how to interact with a
system without any supplementary documentation required.
Under the Linux Foundation
Version 2 and 3 can be imported in the Decision Modeler and
the Workflow Modeler
Creates both the data types and the operations
16. Trisotech.com
Data type reuse by reference/by copy
Data types are indexed in the Digital Enterprise Graph
They can either be used by reference or by copy
By reference:
osynchronized each time the model is open
owhen they are changed while the model is opened
By copy:
onot automatically synchronized
ocan be synchronized manually
17. Trisotech.com
Pro and Cons of using by reference
Pro
oAlways in sync with their source so there’s a greater
consistency accross all models
Cons
oNo modification can be done locally on the element
oChanges done to the type might break the model
18. Trisotech.com
Pro and Cons of using by copy
Pro
oType can be modified locally to suite your needs
oNo external changes will affect your model
Cons
oLess consistency accross all model
oChanges must be fetched manually
Today we are going to focus on three of the modelers available in the Digital Modeling Suite: Workflow, Decision and Knowledge Entity.
The Digital Modeling suites allow business user and technical users to modelise the business logic of their enterprise. Workflow Modeler focus on the process of the enterprise while the Decision Modeler focus is the business logic based
The Knowledge Entity Modeler is more like a centralized glossary where you can keep the terms used in your enterprise, the types and even the rules.
We will show you how you can imported existing data types from XSD, Odata and OpenAPI to the modelers. We will do a small détour to show how those data can be fetch to use in the Digital Automation Suite.
Then we will demonstrate how you can create new terms and types from the KEM. From there, we’ll show you how those types can be used through the Digital Enterprise graph either by reference or by copy.
World Wide Web Consortium
Published 20 years ago to replace the Document Type Definitions (DTDs)
Open Claculate Patient BMI - XSD
Show the XSD file than import in the modeler
Show the item definition
Do the mapping
Publish the model, execute from an XML file
Open Calculate Body Mass Index Odata
Show the Odata file, espcecially the entity Patient
Import the Odata; enter the url https://odata.triso.tech/simon/simon
Show the different operations available
Show the imported data type
Go to BKM get patient, change to O, select operation and interface
Go to Invoke get Patient, do the invocation and the mapping of id
Set output type to Patient
Go toe Determine BMI Category and set the parameter patient.weight/height
Execute in the modeler directly
Broader than Odata since you can add all of your operations and also how the security is implemented
Open Claculate Patient BMI – Open API
Show the OpenAPI file
Import it
Show the operation and the ouptut parameter
Show the data type
Assign th e type to patient
Map the service task to the operation and interface
No exécution for this one
Open the HeatlthCare model
Show the Patient entry and the Blood Pressure one.
Show the type of Patient
Explain how this centralizes knowlege helps. This is common knowlege can then be used to build all the models. In our example we could have share use of the same patient definition to remove the need of complex mapping.
Create a new Workflow model
Drop Patient
Show the definition, the data type with all its sub types
Go to Data type and show the reuse from graph. Import Appointement
Drop a new data objec and assign Appointement to it
Unlock Appointement
Go back to the KEM. Change patient (add an item component Birthdate)
Change appointement, remove reason.
Save the model.
Go back to the workflow modeler. Show the change in the Patient type. Show Appointement, it hasn’t changed. Synchronized manually.
Presentation will be available soon on our website