Which kind of knowledge is suitable for redesigning

hospital logistic processes?

Laura Măruşter and René J. Jorna

Faculty of Management and Organization, University of Groningen, The Netherlands

AIME’ 05, Aberdeen, Scotland

The problem

The number of “multi-disciplinary” patients is growing: aging population, an increased specialization of doctors

special centers have emerged, comprising different specialisms

- we need knowledge expressed in explicit models to create multi-disciplinary units: different specialisms coordinate the treatment of specific groups of patients

How to redesign the hospital logistic processes?

Our proposed approachWe propose a knowledge management perspective for

business process modelling – Knowledge content: analysing, modelling and

reorganizing processes– Knowledge types: sensory, coded and theoretical

knowledge [Boisot (1995), Jorna&vanHeusden (2000)]

Sensory knowledge: based on sensory experience; it is very difficult to code.

Coded knowledge: based on a conventional relation between the representation and that which is being referred to: texts, drawings, or mathematical formulas.

Theoretical knowledge: why certain pieces of knowledge belong together; it is often used to identify causal relations (i.e. if-then-relations)

The knowledge management perspective for process modeling

A. Knowledge creation: 1. Row Data → Coded Knowledge (RD →CK),

2. Coded Knowledge → Theoretical Knowledge (CK →TK)

B. Knowledge use and transfer: - used for analyzing, diagnosing and reorganizing the

logistic hospital process- easily transferred to other people, or parts of the

organization.

I. Knowledge creation

Result1: Developing Logistic Patient Groups

1. Operationalizing logistic complexity (6 logistic variables): RD → CK

2. Clustering logistic variables: CK → TK => two clusters: “moderately complex patients”,

“complex patients”

3. Characterizing the clusters, via a rule-based model : CK → TK

OGH LNG

ADI

RDLFNK

INTCRD

NRL

CHR

Predictive rules for "complex" patients

Rule 1: IF renal_failure=yes AND ...Rule 2: IF diabetes=yes AND diabetic_foot=yes AND no_of_diagnoses <=7 AND .......

Predictive rules for "moderately complex" patients

Rule 1: IF renal_failure=no AND diabetic_foot=no AND no_of_diagnoses <=2 AND age>55 AND .........

"moderately complex" patients

"complex" patients

CHR - surgery, CRD - cardiology, INT – internal medicine, NRL - neurology, NEUR - neurosurgery, FNKT (FNKC) – functional investigations. RONT (ROEH, RMRI, RKDP, ROZA) - radiology.

1. CK → TK

2. CK → TK

I. Knowledge creation (cnt.)

Result2: Constructing process models: CK → TK

a). Petri net process models– Process mining: deriving process models

from data, recorded at runtime in a process log.

b). Instance graph process models- a graph where each node representsone log entry of a specific instance

The Petri net process model for “moderately complex patients”

The Petri net process model for “complex patients”

3. CK → TK

The instance graph for five patients in the ``moderately complex'' cluster, with diagnosis “d440” – atherosclerosis.

4. CK → TK

Knowledge use and transfer

Knowledge use: two new multi-disciplinary units can be created:

1. “moderately complex”: CHR, CRD, INT, NRL, NEUR 2. “complex”: CHR, CRD, INT, NRL, NEUR, OGH,

LNG,ADI

Knowledge transfer: • the knowledge should be transparent, understandable

and easy to be checked by all involved parties; • our approach provides robust theoretical knowledge of a

highly abstract kind of the logistic hospital process.

So, which kind of knowledge is suitable for redesigning hospital logistic processes?

OGH LNG

ADI

RDLFNK

INTCRD

NRL

CHR

Predictive rules for "complex" patients

Rule 1: IF renal_failure=yes AND ...Rule 2: IF diabetes=yes AND diabetic_foot=yes AND no_of_diagnoses <=7 AND .......

Predictive rules for "moderately complex" patients

Rule 1: IF renal_failure=no AND diabetic_foot=no AND no_of_diagnoses <=2 AND age>55 AND .........

"moderately complex" patients

"complex" patients