Presentation at AIME 2011

Careﬂow Planning:
From Time-annotated Clinical Guidelines to
Temporal Hierarchical Task Networks

Arturo González-Ferrer
Annette ten Teije
Juan Fdez-Olivares
Krystyna Milian

1

miércoles 6 de julio de 2011

MOTIVATION
• Real requirements by doctors of 7 hospitals in our region (Andalucía) in project Oncotheraper:

• Virgen del Rocío (Sevilla)

• Virgen Macarena (Sevilla)

• Carlos Haya (Málaga)

• Torre Cárdenas (Almería)

• Reina Sofía (Córdoba)

• Complejo Hospitalario (Jaén)

• Virgen de las Nieves (Granada)

2

MOTIVATION

3

MOTIVATION
• Doctors in the project need IT support for planning, visualization and execution of a
patient long-term treatment in pediatrics oncology

3

MOTIVATION
• They spend much time preparing the patient plan at hand

3

MOTIVATION
• These protocols are very constrained by multiple temporal annotations (chemotherapy cycles,
delayed synchronizations, ...).

3

MOTIVATION
• We did previous work where we used the BPMN graphical notation to model organizational
processes and obtain process instances adapted to speciﬁc conditions

3

MOTIVATION
• we found that the capability of BPMN to represent complex temporal constraints is still
confusing and incomplete

3

MOTIVATION
• time-BPMN extension (Gagné & Trudel) is only theoretical, not practical

3

MOTIVATION
• time-BPMN extension (Gagné & Trudel) is only theoretical, not practical
• We decided to move the modeling stage to using Computer-interpretable Guidelines

3

“Hardly any of the existing Clinical Decision Support
Systems (CDSS) appear to be aimed at supporting
extended clinical workﬂows, management of information
and decision-making in plans that unfold over time”

J. Fox, D. Glasspool, V. Patkar, M. Austin, L. Black, M. South, D. Robertson, and C. Vincent.
Delivering clinical decision support services: There is nothing as practical as a good theory.
Journal of Biomedical Informatics, 43(5):831-843, 2010
4

MOTIVATION

• We need to represent and reason about actions and decisions, different
patient profiles, temporal patterns and scheduling of resources, in order to
automatically generate personalized care plans

• We need these plans to be visually atractive, and useful for the doctors

• We want to deploy these plans for the patient treatment into a workflow
engine (i.e. careflow)

• This way, both the patients and doctors could follow the treatment using a
more efficient, safe, and high-quality healthcare process

5

CLINICAL GUIDELINES

• Clinical Practice Guidelines: Recommendations on the appropriate treatment and care of
people with speciﬁc diseases and conditions, on the basis of the best available evidence.

• Computer-interpretable Guidelines languages
6

CARE PATHWAYS

Care Pathways: Aim to model a timed process of patient-focused care, by specifying
key events, clinical exams and assessments to produce the best prescribed outcomes,
within the limits of the resources available, for an appropriate episode of care

• It aims to reduce the patient’s stay time in the hospital, delivering a more efﬁcient care process
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AIMS AND GOALS
• Starting from the recommendations of a Computer Interpretable
Guideline for a speciﬁc disease, we want to obtain a Care Pathway:
Clinical Guidelines

8

AIMS AND GOALS
Clinical Guidelines

?

8

AIMS AND GOALS

Care Pathways
Clinical Guidelines

?

8

AIMS AND GOALS

Care Pathways
Patient Proﬁle
Clinical Guidelines

?

8

AIMS AND GOALS

Care Pathways
Patient Proﬁle
Clinical Guidelines

Temporal Patterns
?

8

AIMS AND GOALS

Care Pathways
Patient Proﬁle
Clinical Guidelines

Temporal Patterns
?
Resources

8

AIMS AND GOALS

Care Pathways
Patient Proﬁle
Clinical Guidelines

Temporal Patterns
?
Resources
Visual Plan

8

AIMS AND GOALS

Care Pathways
Patient Proﬁle
Clinical Guidelines

Temporal Patterns
?
Resources
Visual Plan
Workﬂow
8

ASBRU
• It is an XML-based, time-oriented, CIG language, used to embody CPGʼs and
protocols as skeletal plans.

• Each one of these skeletal plans consists of a plan-body that can be
composed of:

• subplans (set of steps in parallel or sequentially),

• cyclical-plans (repeated several times),

• plan-activations (a call to another plan) or

• user-performed steps (a speciﬁc action performed by the user).

• In addition, time-annotated conditions can be attached for the selection of
plans

9

HTN PLANNING
• HTN planning has shown to be very useful on practical human-centric domains, for the
generation of customized plans (tourism, e-learning, emergencies, ...).

• HPDL: It is HTN extension of PDDL language (derived from ﬁrst-order logic), organized as:

planning domain planning problem

compound tasks
hierarchy of objects
primitive actions

decomposition
methods
preconditions initial state

predicates,
functions set of goals

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Computer Interpretable Guidelines are very good for

Representation of actions and decisions in Clinical

Processes, but they need a counterpart Knowledge

Reasoning step to be used as a basis for obtaining

Care Pathways

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WHAT TO DO?

• Temporal HTN planning is appropriate for the aim of generating care pathways
• J. Fdez-Olivares et al. Supporting clinical processes and decisions by hierarchical planning and
scheduling Computational Intelligence, 2011

• But : modeling HTN domains directly can be really complex.
Sometimes, an Art!

• We need a higher level language for representation of the guideline:

• Let use Asbru

• Then what?

• We need some Knowledge Engineering

12

WHAT TO DO?

• Temporal HTN planning is appropriate for the aim of generating care pathways
• J. Fdez-Olivares et al. Supporting clinical processes and decisions by hierarchical planning and
scheduling Computational Intelligence, 2011

• But : modeling HTN domains directly can be really complex.
Sometimes, an Art!

• We need a higher level language for representation of the guideline:

• Let use Asbru Asbru to HPDL?

• Then what?

• We need some Knowledge Engineering

12

What is the most
important aim of
Knowledge
Engineering?

Turning the process of constructing
Knowledge Based Systems
from an Art into an Engineering
Discipline, using better
methodological approaches
[Studer et. al, 1998]

13


METHODOLOGICAL APPROACH
• Experts describe the CPG in text
format

• Knowledge Engineers model it using a
CIG language (e.g. Asbru)

14

format


• Automatically transform the
Asbru representation into an
HTN planning domain model,
considering time constraints

14

format



• Interprete the domain to
obtain a Care Pathway for a
patient, using the available
resources

14

format



resources

• Deploy the Care Pathway
obtained into a BPM engine for
ubiquitous execution.

14

format



resources


already done (see bibliography)

14

format



resources


already done (see bibliography)
work in progress
14

MAPPING ASBRU TO HPDL
• Why is this useful?
• HTN planning is capable to represent clinical protocols and
reason to obtain a pathway:

• not only representing, but also managing complex temporal patterns

• representing custom constraints (e.g. resources) and reasoning about them

• adapted to the speciﬁc conditions of the patient proﬁle

• very useful in low-frequency scenarios, where BPM tools could be interesting

• CIG languages more user-friendly for knowledge representation
• not only for humans, also for managing them in a computer
• some of them use graphical notations, that are underneath stored as XML
15

HIGH OR LOW FREQUENCY DOMAIN?
❖ Traditional use of Asbru in high-frequency domains (e.g. intensive care units)

Asbru
Guideline

Time-
Asbru
annotated
Interpreter
input data

What to do
for every
ICU
input
variable?

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HIGH OR LOW FREQUENCY DOMAIN?
❖ We want to use it for low-frequency domains (long/medium-term care plans)

Asbru
Guideline

Translator

Patient
Conditions Long-term
Guideline in IACTIVE
Care
Hospital HPDL planner
Pathway
Resources

Deliberative
Reasoning

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ASBRU & HPDL: SIMILARITIES
• Both follow a similar Task Network Model (TNM)
• This is also valid for other CIG languages (not only Asbru)

• Asbru skeletal plans similar to HTN
compound/primitive operators.

• Both are able to represent multiple task
ordering schemas

• Both are able to represent multiple
temporal patterns (e.g. Allen’s T.L. )

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Asbru HPDL

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Asbru HPDL

plan-activations A B task calls
activate B

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Asbru HPDL

activate B

A1 cond 1 A1
subplans parallel conditional compound tasks
A A
A2 cond 2 A2

if-then-else task methods
A A1 A2
sequence

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Asbru HPDL

activate B

A1 cond 1 A1
subplans parallel conditional compound tasks
A A
A2 cond 2 A2

if-then-else task methods
A A1 A2
sequence

user-performed A durative-action

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Asbru HPDL

20

Asbru HPDL
duration

A ?start, ?end, ?duration
time annotations
start end <,>,<=,>=

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Asbru HPDL
duration

time annotations
start end <,>,<=,>=

time-annotated
references to plans 28 days after
(ref. points as plan A B temporal landmarks
pointers) (all allen’s relations)

20

Asbru HPDL
duration

time annotations
start end <,>,<=,>=

time-annotated
references to plans 28 days after
(ref. points as plan A B temporal landmarks
pointers) (all allen’s relations)

repeat 5 times
cyclical plans recursive cyclical task
(based on temporal formalism
A frequency of Anselma et. al)
offset

20

RESULTS
• Proof of concept: We focused in the protocol for the Hodgkin’s disease.

• around 70 text pages

• We modeled it with a subset of the Asbru language (using DELT/A)
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RESULTS
• We have developed a KE tool for the translation (Asbru2HPDL)

• We identiﬁed and translated different patterns, not only the hierarchy of tasks:

22

RESULTS
• The translation only cover the workflow of the care process, but not the
modeling of patient profiles and hospital resources
Patient Profile
;;definition of predicates in the domain
(sex ?patient ?s - gender)
(group ?patient ?g - group)

;; instances for patient Alice in the problem
(sex Alice M)
(group Alice Group3)
;;start date for treatment
(startdate Alice "07/11/2011 08:00:00")
Resource
Constraints
(between "07/11/2011 00:00:00" and "07/12/2011 00:00:00" (available John))
(between "07/12/2011 00:00:00" and "07/01/2011 00:00:00" (available Paul))
(between "07/02/2012 00:00:00" and "07/03/2012 00:00:00" (available Paul))

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RESULTS
• A fragment of the generated pathway:

• This pathway can afterwards be translated into a representation
understandable by a BPM engine
• González-Ferrer, A., Fdez-Olivares, J., Sánchez-Garzón, I., & Castillo, L. (2010).
Smart Process Management: Automated Generation of Adaptive Cases
based on Intelligent Planning Technologies. 8th BPM Conference, Demo Track

24

RESULTS
• Personalized access for each doctor to his/her list of time-annotated tasks

• Patient’s follow-up procedure is supported by a BPM runtime console

25

Conclusions
• We presented an AI-based methodology to model and
operationalize care pathways for oncology treatments
• The patterns commonly found in a CIG representation (concretely
Asbru) can be translated into a corresponding HTN representation.
• The methodology could be used for other CIG languages as well
• This is interesting because
• Adding patients and resources information, we can obtain a
customized care pathway that unfolds over time
• CIGs are more user-friendly for the modeling than doing it
directly with cumbersome HTN planning languages
• We can ﬁnally deploy the care plan into a BPM execution engine

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FUTURE WORK
• Use and evaluation in a real hospital scenario
• Apply to other protocols, which patterns could be
also interesting?
• Flexibility: we are progressing to include
monitoring, plan repair and replanning
• Real integration with real HIS and EHR
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see more at ...

• presentation @ KR4HC workshop
• Dr. Juan Fernández-Olivares
• Task Network based modeling, dynamic generation and adaptive
execution of patient-tailored treatment plans based on Smart Process
Management technologies

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Presentation at AIME 2011

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