The document discusses the integration of clinician-centric data and AI in healthcare decision-making, emphasizing the importance of structured, clean data to improve patient outcomes. It explores clinician thought processes when accessing data, challenges with ambiguity in clinical data, and the need for interoperability through standards like FHIR and SDMN. Additionally, it addresses the role of AI in enhancing healthcare delivery, highlighting the necessity of data cleansing and efficient data management.

1. Clinician-Centric Data
and AI Integration in
Healthcare

2. Abstract
Gain insights into the orchestration of data, knowledge and AI in support of decision-making in
healthcare.
We explore the thought processes of clinicians when accessing data for decision-making. We then
discuss concepts and semantic lifting using concept maps, highlighting the importance of context in
interpreting data. The session also covers structured data for FHIR interoperability through SDMN,
demonstrating the significance of data reuse and integration in healthcare.
By focusing on cleanliness and relevance, we examine the role of data in various AI approaches,
including machine learning and generative AI. This webinar aims to showcase how clean, well-
structured data can empower clinicians and improve patient outcomes.

3. Data in Healthcare
• Generally, when people talk about data in healthcare
they do so generically.
• There are many facets to data in Healthcare for
examples:
• The clinician's thought processes when using data
• Concepts and semantic lifting from data
• Structured data for Interoperability
• Data and AI
• We will explore these four particular facets in this
presentation.

4. There are many types of healthcare data:
• Structured
• Unstructured
• Time series
• Interdependent or relative data
• Raw or normalized

5. The clinician's
thought
processes when
using data
BPMN Notional model

6. Why is data relevant?
Everything in healthcare depends on data.
Clinicians look at data from a range of contexts:
• Diagnosis
• Determination of disease severity
• Prediction and prognosis
• Drug selection and dosing
• Tracking status over time
• Response to therapy

7. Questions that a provider may ask about data:
• What data do I need to meet my goal?
• Is the data available, and at what cost?
• Is the data relevant?
• Is the data valid, accurate and precise?
• Is the data consistent with the clinical status of the patient?
• Do I need to transform the data to make it more useful?

9. Concepts and
semantic lifting
from data
KEM and Concept Maps

10. Ambiguity exists in clinical data
• The same data term may have different meanings in different
contexts (e.g., weight ?= usual weight, weight at cancer
treatment planning, weight during cancer treatment..)
• A single analyte can be measured by multiple methods, and
each method may give different results with different
measurement units.
• A single concept may be referenced in different ways
(e.g., diabetes may be indicated by an ICD-10 code, HgbA1C
value, insulin dependency, or elevated fasting blood glucose.)

11. There is a need to avoid ambiguity
Ambiguity can lead to:
• Confusion
• Inefficiency
• Misapplication
Approaches to disambiguate may include:
• Terminology
• Concept Maps
• Business Rules (offering constraints of usage)

12. From Narrative to Operationalization
Disambiguation Operationalization
Concept Maps
Verb concepts
Business Rules
Noun concepts
Narrative
Text
Processes
and
Decisions
Source

13. Knowledge Entity Models (KEM) and Concept Maps

14. Knowledge Entity Models (KEM): Terminology and
Rules

15. Structured data
for
Interoperability
FHIR using SDMN

16. FHIR can capture different types of
data
• Structured
• Unstructured
• Time series
• Interdependent or relative data
The HL7®
FHIR®
standard is a free and open standards framework
created by Health Level Seven®
International (HL7®
) and intended to
facilitate the interoperability of computerized healthcare systems.

17. Unstructured Data Example: Patient
Notes
• Patient Notes are essential in Healthcare as a documentation
and communication tool.
• Creation of notes can be a source of problems (for example,
"plagiarism" when copying and pasting).
• Technology provides new opportunities to improve the patient
note and reduce clinical burden.
• By having better and more complete notes you reduce risk of
omissions and improve compliance.

18. SDMN for reuse of data structures
• The Shared Data Model and Notation (SDMN) is a visual
language for mapping logical data structures that can be re-
used within processes (BPMN), decisions (DMN) and cases
(CMMN).

19. SDMN and FHIR how do they fit
• A FHIR resource can be visualized in SDMN and thus be reused
into processes, cases and decisions.

20. Data and AI
ML or GenAI

21. AI is on everyone’s radar (AI is even on FHIR)
• People talk about AI very generically.

22. Generative AI
(GenAI)
Different Kinds of AI
Machine Learning (ML)
Deep Learning (DL)
Expert Systems
Fuzzy Logic
Neural Networks
Reinforcement Learning
Evolutionary Algorithms
Bayesian Networks
Recommender Systems
Predictive Analytics
Genetic Algorithms
Constraint Satisfaction
Multi-Agent Systems
Rule Based Systems

23. Solving clinical problems with AI
• Making predictions (Using predictive models
with PMML)
• Communicating (Using GenAi to write
communication adapted to the situation and
patient in the desired language)
• Interpreting Health Imaging (image
recognition models)
• Handling large amount of data
• Automating repetitive tasks
• Orchestrating diverse clinical teams

24. Motivators for large data sets
Discovering
• Search for new
insights.
Improving care
• Greater sensitivity
and specificity.
Automating
• Cover shortages
of personnel and
do more with less.

25. Challenges
• AI = garbage in garbage out
• Clean data is a must
• Ethical issues
• Copyrights and ownership of data
• Dataset bias

26. Data cleansing
• Health organizations generate vast
amounts of data.
• Not all of it is accurate, organized or
well-formed.
• Data cleansing is the process of
detecting and fixing or removing
incorrect, corrupted, incorrectly
formatted, duplicate, or incomplete
data within a dataset.

27. Data efficiency
• Not having the right data when it is needed
is problematic.
• Interrupting a process to obtain data can be
disruptive.
• Some tests that are ordered are unnecessary
or the results unused.
• There is a need to create protocols to ensure
collection of the data that is needed.
• It is important not waste the available
information.

28. From Data to a Knowledge Context
Data
Decision
Centric
Orchestration
Computable Data
Aggregated Data
Data Automation
Information Automation
Orchestration of knowledge
Workflow and Decision Automation
Data in context
+ Common schema
+ Metadata
+ Context
+ Events awareness
+ Tasks and activities
+ Roles and responsibilities
+ Case management
+ Event orchestration
+ AI and ML
+ Decision
delivers knowledge orchestration in support of decisions

29. Predictive
AI
Customer Data
Generative AI
Trisotech Decision Centric Orchestration
System of Engagement
System of Insight
System of Records
Service API
Widget CSS Scripts
Data Connector Events
Symbolic AI
PMML
Workflow Automation Case Automation
Service API Chat
Customer UX
AI Models
Decision Orchestration Layer
orchestrating clinical decisions

30. Conclusion
• The clinician's thought processes when using data
• We introduced a BPMN Notional model the clinician’s
thought process.
• Concepts and semantic lifting from data
• We introduced Knowledge Entity and concept Models.
• Structured data for Interoperability
• We introduced SDMN and its combined use with FHIR.
• Data and AI
• We discussed the usage of AI in the context of clinical
usability.

31. Any questions?
THANKS!
31