A FIRST-ORDER LOGIC

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A FIRST-ORDER LOGIC

MITIGATION FRAMEWORK FOR HANDLING

MULTI-MORBID PATIENTS

Szymon Wilk1_{, Martin Michalowski}2_{, Wojtek Michalowski}3_,

Daniela Rosu3_{, Mounira Kezadri-Hamiaz}3_{, Marc Carrier}4

1_{Institute of Computing Science, Poznan University of Technology, Poland} 2_{Adventium Labs, USA}

3_{Telfer School of Management, University of Ottawa, Canda} 4_{Ottawa Hospital Research Institute, Canada}

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CLINICAL PRACTICE GUIDELINES

A first-order logic mitigation framework for handling multi-morbid patients 26.01.2016

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Clinical Practice Guidelines (CPGs)

 _{Introduced to limit the variations in service delivery and to}

minimize healthcare costs

 _{Initially aimed at nurses and other ancillary personnel, then}

adopted (slowly) by physicians

 _{Increasing popularity of computer-interpretable guidelines}

(CIGs) integrated with clinical systems

A first-order logic mitigation framework for handling multi-morbid patients

Knowledge-based tools for disease-specific patient management

[Rosenfeld and Shiffman, 2009]

26.01.2016

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CPGs (and CIGs) in Clinical Practice

 On the one hand, multiple advantages

 Increased adoption of evidence-based medicine (EBM) and improved

adherence to standards of practice

 Positive impact on patient outcomes (e.g., decreased mortality)

 _{On the other hand, still limited adoption}

 Considered to be “cookbook medicine”  Given mostly in paper format

 Limited standardization of formal representations

No support for multimorbid conditions

Clinical guidelines are only one option for improving the quality of care [Woolf et al., 1999]

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MOTIVATION AND CHALLENGE

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Practical Motivation

 76% of people 65+ years old have 2+ chronic conditions, and their care costs are 5.5 times higher than for non multi-morbid patients [Bähler, et al. 2015]

 Direct application of multiple CPGs “may have undesirable

effects” and “diminish the quality of care” [Boyd et al. 2005]

 No support for multimorbid conditions is a “major shortcoming

of CPG uptake in clinical practice” [Peleg, 2013]

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Methodological Challenge

“The challenge … to identify and eliminate redundant,

contraindicated, potentially discordant, or mutually exclusive guideline based recommendations for patients presenting with comorbid conditions or multiple medications.” [Sittig et al., 2008]

One of the “grand challenges” for clinical decision support

A new, “combinatorial, logical, or semantic” methodological approach is needed [Fox et al. 2010]

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PATIENT PREFERENCES

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P4 Medicine

A new emerging paradigm of medicine [Hood and Flores, 2012]

1. Predictive – prediction of possible (future) diseases ( genome sequencing) 2. Preventive – focus on wellness and avoiding possible diseases

3. Personalized – customization of treatment to specific patient characteristics 4. Participatory – inclusion of patients as active participants/decision makers in

healthcare process

26.01.2016 A first-order logic mitigation framework for handling multi-morbid patients

Patient-centered medicine: to improve health outcomes of

individual patients in everyday clinical practice, taking into account the patient’s objectives, preferences, values as well as the available economic resources [Sacristán, 2013]

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Patient Preferences

 A new and important component of EBM

 _{Preferences are especially relevant when evidence is associated}

with a high level of uncertainty (→ “grey zone” or “preference-sensitive” decisions) [van der Weijden et al., 2013]

 _{Participation of patient groups already in the development of}

CPGs [van der Weijden et al., 2010]

EBM = Evidence + Experience + Preferences

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RELATED WORK

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Related Work on CPGs and CIGs

Relevant research areas [Peleg, 2013]

1. Modeling languages

2. Acquisition and specification methodologies 3. Integration with electronic health record (EHR) 4. Validation and verification

5. Execution engines and supportive tools 6. Exception handling

7. Maintenance (including compliance analysis) 8. Sharing

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Related Work on Patient Preferences

1. Patient decision aid tools

2. Elicitation of patient preferences

• Utilities

• QUALYs (Quality Adjusted Life Years)

3. Integration of patient preferences in

clinical decision support systems

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MITIGATION FRAMEWORK BASED

ON FIRST-ORDER LOGIC

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Goals and Motivations

Goal: a “rich” and flexible framework for

(1) Mitigating adverse interactions between CPGs in multi-morbid patients (2) Customizing resulting therapies based on their preferences

Motivations

(1) Response to important challenges in clinical decision making

(2) Extension of our previous approach to mitigation based on constraint-logic programming (CLP)

(3) Adaptation of the new framework to other settings and situations (e.g. compliance-based mitigation within a single CPG)

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A New Mitigation Framework

 Inspired by our experience with CLP [Wilk et al., 2013] and its

limitations and shortcomings

 Limited expressiveness of representation

 Limited interpretability of generated solutions

 _{Research questions}

(1) How to represent CPGs and secondary domain knowledge to address

limitations of the CLP-based approach?

(2) What techniques to use to “solve” CPGs and to process domain

knowledge encoded in a new formalism?

Answer: first-order logic (FOL),

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Assumptions

 A patient suffers from multiple (two or more) conditions managed according to associated CPGs

 CPGs are given as actionable graphs, specific elements associated with additional properties (timing, dosages, …)

 Secondary clinical knowledge (not available in CPGs) related to

preferences and interactions is available and explicitly codified as complex revision operators

 Patient state characterized by currently available (possibly incomplete) clinical data

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Actionable Graphs

 An intermediate representation based on a task-network model for better interoperability

 Inspired by SDA* – can be automatically obtained from other representations (e.g. GLIF3, SAGE)

 An actionable graph (AG) is a directed graph

 Action, decision and context nodes corresponding to

appropriate steps

 Parallel nodes defining (sub-)paths executed in parallel  Arcs corresponding to transitions between nodes

 A root context node indicating the condition (disease)

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FOL Background

 Logical and non-logical symbols (functions, predicates , …)

 _{Terms, formulas and sentences}

 _A_theory _{𝒟 is a collection of sentences}

 _An_{interpretation} _{ℐ gives the meaning (formal semantics) to}

non-logical symbols

 _{If ℐ satisfies all sentences in 𝒟, then it is called a}_model _for

theory 𝒟 and denoted as ℐ ⊨_𝑚 𝒟

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Theorem Proving and Model Finding

 Theorem proving allows for checking if theory 𝒟 is consistent

(𝑖. 𝑒. , there exists at least one model for 𝒟)

 If theory 𝒟 is consistent, then its models can be identified using

model finding techniques

 Theorem proving is also used for checking logical consequences

(entailments) of a consistent theory 𝒟

 Theory 𝒟 entails sentence 𝜙 (denoted as 𝒟 ⊨ 𝜙), if 𝜙 is satisfied by all models for 𝒟

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Key Components of Our Framework

1. A vocabulary (non-logical symbols) to construct a theory

describing a particular mitigation problem

2. A combined mitigation theory 𝒟_{𝑐𝑜𝑚𝑏} composed of individual

theories that describe various aspects of the mitigation problem (e.g., individual CPGs, patient information)

3. A set of revision operators encoding secondary knowledge

related to patient preferences and adverse interactions

4. A set of procedures that control the application of operators

to 𝒟_{𝑐𝑜𝑚𝑏} and construct a therapeutic scenario 𝒟_𝑡ℎ

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Vocabulary

Constants, variables and predicates, defined under assumption that CPGs are given as actionable graphs (AGs)

Predicate Description

node(x) x is a node in AG

disease(x, d) x is a context node for disease d

action(x, a) x is an action node associated with action a decision(x, t) x is a decision node associated with test t parallel(x) x is a starting/ending parallel node

directPrec(x, y) node x directly precedes node y (arc from x to y) prec(x, y) node x precedes node y (path from x to y)

hasValue(x, v) test associated with node x has value v

dosage(x, n) action node x corresponding to drug administration is

associated with dosage n …

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Combined Mitigation Theory

𝒟_{𝑐𝑜𝑚𝑏} = 𝒟_{𝑐𝑜𝑚𝑚𝑜𝑛}, 𝒟_𝑐𝑝𝑔, 𝒟_𝑝𝑖 , where

 𝒟_{𝑐𝑜𝑚𝑚𝑜𝑛} – a shared and reused component of all combined mitigation

theories with axioms defining universal character of CPGs, e.g.,

• ∀𝑥, 𝑦: 𝑑𝑖𝑟𝑒𝑐𝑡𝑃𝑟𝑒𝑐 𝑥, 𝑦 ⟹ 𝑝𝑟𝑒𝑐 𝑥, 𝑦

• ∀𝑥, 𝑦: 𝑧 𝑝𝑟𝑒𝑐 𝑥, 𝑦 ∧ 𝑝𝑟𝑒𝑐 𝑦, 𝑧 ⟹ 𝑝𝑟𝑒𝑐(𝑥, 𝑧)

• ∀𝑥, 𝑦, 𝑠𝑥, 𝑑𝑥, 𝑜𝑦: 𝑑𝑖𝑟𝑒𝑐𝑡𝑃𝑟𝑒𝑐 𝑥, 𝑦 ∧ 𝑠𝑡𝑎𝑟𝑡 𝑥, 𝑠𝑥 ∧ 𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛 𝑥, 𝑑𝑥 ∧ 𝑜𝑓𝑓𝑠𝑒𝑡(𝑦, 𝑜𝑦) ⟹ 𝑠𝑡𝑎𝑟𝑡(𝑦, 𝑠𝑥 + 𝑑𝑦 + 𝑜𝑦)

• …

 𝒟_𝑐𝑝𝑔 – a union of theories 𝒟_𝑐𝑝𝑔𝑑1 ∪ 𝒟_𝑐𝑝𝑔𝑑2 ∪ ⋯ ∪ 𝒟_𝑐𝑝𝑔𝑑𝑘 that represent AGs

corresponding to CPGs applied to a comorbid patient (𝒟_𝑐𝑝𝑔𝑑𝑖 _associated

with AG/CPG for disease 𝑑_𝑖)

 𝒟_𝑝𝑖 – a collection of available patient data (results of tests and

examinations, prescribed therapies, …), a sentence for each data item

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Revision Operators

𝑅𝑂𝑘 = 𝛼𝑘, 𝑂𝑝𝑘 , where

 𝛽𝑘 – a sentence defining the applicability of the operator (undesired

circumstances that need to be addressed)

 𝑂𝑝𝑘 – a list of operations that need to be applied to 𝒟_𝑐𝑝𝑔 (only)

 Each operation is defined as a sentence ∀𝒙: 𝜙𝑘,𝑖 → 𝜓𝑘,𝑖 describing delete, add and replace modification on 𝒟_𝑐𝑝𝑔

 𝜙𝑘,𝑖 → ∅ – 𝜑_𝑖𝑘 is removed from any sentence in 𝒟_𝑐𝑝𝑔  ∅ → 𝜓𝑘,𝑖 – 𝜙_𝑖𝑘 is added as a new sentence to 𝒟_𝑐𝑝𝑔

 𝜙𝑘,𝑖 → 𝜓𝑘,𝑖 – 𝜙𝑘,𝑖 is replaced by 𝜓𝑘,𝑖 in any sentence in 𝒟_𝑐𝑝𝑔

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Revision Operators

 𝑅𝑂𝑘 is possibly applicable to 𝒟_𝑐𝑝𝑔, iff 𝒟_𝑐𝑝𝑔 ∧ 𝛼𝑘 is consistent

 𝑅𝑂𝑘 is definitely applicable to 𝒟_𝑐𝑝𝑔, iff 𝒟_𝑐𝑝𝑔 ⊨ 𝛼𝑘 is consistent

 Preference- and interaction-related revision operators 𝑅𝑂_{𝑝𝑟𝑒𝑓}𝑘 and 𝑅𝑂_𝑖𝑛𝑡𝑘 with undesired circumstances defining “violated” preferences and adverse interactions, respectively

Possible applicability indicates undesired circumstances that are possible, but may be avoided, while definite applicability signals unavoidable situation.

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Therapeutic Scenario

 𝒟_𝑡ℎ – a theory that represents a preferred and safe course of actions for a given patient and includes

 Clinical actions to be taken (action and dosage predicates) and their order

and timing (prec, start, duration, offset predicates)

 Assumptions related to the future patient’s state (hasValue predicates)

 𝒟_𝑡ℎ contains only sentences that correspond to present and future – focused on actions being executed or suggested and assumed/predicted patient states

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Control Procedures

First-order logic (FOL) level

Regular expression (RE) level

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Customize Procedure

 Revises 𝒟_{𝑐𝑜𝑚𝑏} according to patient preferences ensuring no conflicts with interaction-related revisions are introduced

 Reports resulting 𝒟_𝑡ℎ consistent with introduced revisions

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Mitigate Procedure

 Identifies adverse interactions in 𝒟_{𝑐𝑜𝑚𝑏} that are captured by available interaction-related revision operators

 Revises 𝒟_{𝑐𝑜𝑚𝑏} to address encountered interactions

 Establishes 𝒟_𝑡ℎ from a model for 𝒟_{𝑐𝑜𝑚𝑏}

 _{Based on depth-first search – it is able to backtrack from “dead}

ends”

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Revise Procedure

 _{Revises 𝒟}_{𝑐𝑜𝑚𝑏} _{(its 𝒟}_𝑐𝑝𝑔 _{component) according to operations}

defined by an applied revision operator

 _{Operates on strings and regular expressions}

 _{Employs “smarter” search and replace (→ time-dependent}

starting position)

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Translate Procedure

 Translates between FOL and REs/strings

 _{Employs domain specific heuristics, e.g. for FOL → RE the}

following transformations are used:

 Removal of quantifiers, variables and logical connectives

 Removal of directPrec statements and precedence-based ordering of

node entries

 Proper ordering of entries corresponding to node properties (should

appear after the associated node entry)

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CLINICAL EXAMPLE

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CKD, AFib and HTN

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CKD, AFib and HTN

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FOL Representation – 𝒟

_𝑐𝑝𝑔

𝐴𝐹𝑖𝑏

exists x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12:

(disease(x1,AFIB) /\ parallel(x2) /\ action(x7,BB) /\ parallel(x12)) /\

(((disease(x1,AFIB) /\ parallel(x2) /\ test(x3,AFIB_DUR) /\ hasValue(x3,LT48H) /\ action(x4,FLEC) /\ parallel(x5) /\ action(x8,CCB) /\ parallel(x12)) /\

(disease(x1,AFIB) /\ parallel(x2) /\ test(x3,AFIB_DUR) /\ hasValue(x3,LT48H) /\ action(x4,FLEC) /\ parallel(x5) /\ action(x9,ACEI) /\ parallel(x12))) \/

((disease(x1,AFIB) /\ parallel(x2) /\ test(x3,AFIB_DUR) /\ hasValue(x3,GE48H) /\ parallel(x5) /\ action(x8,CCB) /\ parallel(x12)) /\

(disease(x1,AFIB) /\ parallel(x2) /\ test(x3,AFIB_DUR) /\ hasValue(x3,GE48H) /\ parallel(x5) /\ action(x9,ACEI) /\ parallel(x12)))) /\

((disease(x1,AFIB) /\ parallel(x2) /\ test(x6,CHA2DS2) /\ hasValue(x6,GE1) /\ action(x10,WAR) /\ parallel(x12)) \/

(disease(x1,AFIB) /\ parallel(x2) /\ test(x6,CHA2DS2) /\ hasValue(x6,EQ0) /\ action(x11,ASA) /\ parallel(x12)))

Simplified representation for brevity (e.g. no directPrec)

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Interaction-Related Revision Operators

 𝑅𝑂_𝑖𝑛𝑡1 : if patient diagnosed with HTN and CKD, then remove Step 1 from CPG for HTN

 𝑅𝑂_𝑖𝑛𝑡2 : if patient diagnosed with HTN, AFib and CKD, then remove diuretics from CPG for HTN

 𝑅𝑂_𝑖𝑛𝑡3 : if patient diagnosed with AFib and CKD and anemia is present, then replace NOAC with warfarin in CPG for AFib

 𝑅𝑂_𝑖𝑛𝑡4 : if patient diagnosed with AFib and CKD and GFR < 60, then replace BB with metoprolol in CPG for AFib

𝑅𝑂_𝑖𝑛𝑡4 = 𝛼4, 𝑂𝑝4,1

𝛼4 = ∃𝑥1, 𝑥2, 𝑥3, 𝑥4: 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑥1, 𝐴𝐹𝐼𝐵 ∧ 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑥2, 𝐶𝐾𝐷 ∧ 𝑡𝑒𝑠𝑡 𝑥3, 𝐺𝐹𝑅 ∧ ℎ𝑎𝑠𝑉𝑎𝑙𝑢𝑒 𝑥3, 𝐿𝑇_60 ∧ 𝑎𝑐𝑡𝑖𝑜𝑛 𝑥4, 𝐵𝐵

𝑂𝑝4,1 = ∀𝑥1, 𝑥2: 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑥1, 𝐴𝐹𝑖𝑏 ∧ 𝑝𝑟𝑒𝑐 𝑥1, 𝑥2 ∧ 𝑎𝑐𝑡𝑖𝑜𝑛 𝑥2, 𝐵𝐵 → 𝑑𝑖𝑠𝑒𝑎𝑠𝑒 𝑥1, 𝐴𝐹𝑖𝑏 ∧ 𝑝𝑟𝑒𝑐 𝑥1, 𝑥2 ∧ 𝑎𝑐𝑡𝑖𝑜𝑛(𝑥2, 𝑀𝐸𝑇𝑂)

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Patient Scenario

A 70-years old male with CKD and HTN having the following characteristics:

(1) Decreased kidney function (GRF < 60) and mild anemia require ESA, patient does not have metabolism abnormalities and is on CV risk and lifestyle management

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Patient Scenario

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Patient Scenario

Mitigation framework is invoked

1. Customize procedure applies 𝑅𝑂_{𝑝𝑟𝑒𝑓}1 and revises CPG for Afib 2. Mitigateprocedure checks applicable 𝑅𝑂_𝑖𝑛𝑡𝑘

 𝑅𝑂_𝑖𝑛𝑡1 → changes affect past actions (step 1 in CPG for HTN) and thus they are not

introduced by reviseprocedure

 𝑅𝑂_𝑖𝑛𝑡2 → diuretics are removed from CPG for HTN

 𝑅𝑂_𝑖𝑛𝑡3 → apixaban is discarded and warfarin is restored in CPG for AFib  𝑅𝑂_𝑖𝑛𝑡3 → BB in replaced by metoprolol in CPG for AFib

For the last 12 hours patient has been experiencing irregular pulse, breathlessness, dizziness, and chest discomfort. Upon admission to the ED patient has been diagnosed with AFib that has been confirmed by standard ECG recording. Patient’s CHA₂DS₂ score is 2.

Patient has expressed preferences related to AFib therapy:

𝑅𝑂_{𝑝𝑟𝑒𝑓}1 : if diagnosed with AFib and prescribed warfarin, then replace warfarin with apixaban (one of the NOACs)

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Revised CPGs

Marked elements constitute the therapeutic scenario 𝒟_𝑡ℎ

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Revised CPGs

Marked elements constitute the therapeutic scenario 𝒟_𝑡ℎ

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GRANT APPLICATIONS

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ACMS: Advanced Crew Medical System

 Canadian Space Agency: “development of an

operational concept and a business case for an advanced, fully integrated Crew Medical System targeted for exploration-class missions”

 _{Adaption of our framework for preference- and}

response-related revisions

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AFGuide

 CIHR – 1st _{Live Pilot Project Grant: “to capture ideas with the}

greatest potential to advance health-related knowledge, health research, health care, health systems, and/or health outcomes”  AFGuide – an electronic guide to help primary care physician in

designing anti-coagulation therapy – based on our framework

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CONCLUSIONS, ONGOING

AND FUTURE WORK

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Conclusions

 An attempt to provide a uniform framework for handling multi-morbidities that combines mitigation with preferences

 A significant extension of our previous proposal

 Improves expressiveness and provides explicit representation of

properties and relationships in CPGs (dosages, precedence, …)

 However, increases “internal” complexity and readability for clinicians

 Adaptable to other problems and situations

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Ongoing and Future Work

 Combination with our framework for supporting clinical workflow execution by healthcare teams

 Even more flexible representation of CPGs (loops)

 Implementation of the framework in form of an interactive clinical decision support system (what-if scenarios)

 “Integration” with one of the existing CIG representations (e.g., PROforma or Asbru)

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26.01.2016