A Process Cube based Approach of Process Mining in Analysing

Frailty Progression Exploiting Electronic Frailty Index

N. F. Farid

1,2,* a

, Marc de Kamps

and Owen A. Johnson

School of Computing, Universiti Utara Malaysia (UUM), Kedah, Malaysia

School of Computing, University of Leeds, U.K.

Keywords: Process Mining, Process Cube, Frailty Progression, Electronic Frailty Index, Electronic Health Record.

Abstract: Process mining is a data analytics technique that is used in healthcare to develop insights into care processes,

care pathways and disease progression using event data extracted from Health Information Systems. The most

widely used application is process discovery where models of healthcare processes are automatically inferred

and visualized. These have been applied to frailty, a common geriatric condition in elderly people typically

described in terms of progression through a number of stages. In this paper we use the Electronic Frailty Index

which is calculated using 36 indicators of frailty deficits. We use process mining to analyse frailty progression

using data from the SystmOne GP system used in UK primary care. We propose an approach for analysing

frailty progression using a process cube analysis through slicing and dicing sets of attributes related to clinical

frailty events. Different combinations of process cube dimensions allow us to model and analyse a

comprehensible frailty progression. We illustrate the method through a case study investigating the

association between frailty stages and three common issues; falls, hypertension and polypharmacy.

1 INTRODUCTION

Frailty affects us all. In the UK frailty is recognised

as a geriatric condition affecting 26% of people over

85 (Clegg et al., 2013). With an aging global

population, the impact of frailty on elderly people,

their families and society has attracted the attention

of researchers. Frailty affects health outcomes,

quality of life as well as rising costs associated with

healthcare and the support required for daily living

(Han et al., 2019).

Frailty is often described in terms of an

accumulation of health characteristics, called deficits,

that reduce physical capability (Clegg and Young,

2011; Xue, 2011). As physiological functions of the

body decline the body is more susceptible to internal

and external events that can further worsen the

condition. As a result, frailty leads to an increase risk

of hospital admissions, institutionalisation,

dependency and other adverse health consequences

(Fried et al., 2004).

https://orcid.org/0000-0001-5213-0016

https://orcid.org/0000-0003-3998-541X

Corresponding author

Process mining is a data analytics technique that

is used in healthcare to develop insights into care

processes, care pathways and disease progression

using event data extracted from Health Information

Systems (HIS). The three types of process mining are

process discovery, which reveals how processes

occurs; conformance checking, identifying

differences between models of the process and the

data from actual events; and process enhancement,

which includes steps to improve the actual process

(van der Aalst, 2016). Process mining can be seen as

the overlap between the two disciplines of data

mining and process analysis (van der Aalst, 2011).

Example applications of process mining in healthcare

include patient safety, process improvement and

exploration of care pathways (Mans et al., 2013;

Kurniati et al., 2020; Kusuma et al., 2020). In earlier

work we investigated applications of process mining

to frailty and identified a limited literature base and

opportunities to help better understand frailty

progression through the use of routine healthcare data

(Farid et al., 2019). In this paper we develop a method

Farid, N., de Kamps, M. and Johnson, O.

A Process Cube based Approach of Process Mining in Analysing Frailty Progression Exploiting Electronic Frailty Index.

DOI: 10.5220/0010879200003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 605-613

ISBN: 978-989-758-552-4; ISSN: 2184-4305

605

suitable for understanding frailty progression and

illustrate the method through a case study

investigating the association between frailty stages

and three common issues: falls, hypertension and

polypharmacy.

2 BACKGROUND

2.1 Process Mining

Patient level information about the delivery of

healthcare, treatments and outcomes encoded within

HIS can be used to identify common patterns that can

be used to generate insights to inform practitioners,

healthcare providers and health informatics research.

Process mining applies this process-based approach

by using a log of relevant events extracted from HIS.

Three types of process mining are common: i)

process discovery, to create process models from the

log of events, ii) conformance checking to ensure the

model produced is highly representative of the log

and iii) enhancement, steps taken to improve the

process (van der Aalst, 2016).

2.2 Process Cube

Process mining can be extended with the concept of

process cubes used to scope and organise event data

based on classic Online Analytical Processing

(OLAP) procedures including dice, slice, drill down

and roll up (van der Aalst, 2013). Process cubes are

used to characterise large datasets where each

dimension is linked to properties of patients and

events. In our analysis we used gender, time (in one-

year increments) and a range of clinical concepts.

Figure 1 shows the view of our process structure with

three dimensions.

Figure 1: Structure of the process cube for the dataset.

The four common process cube operations

following the standard OLAP procedures are slice,

dice, drill down and roll up. Slice operations select

parts of the values from a dimension while

eliminating the dimension from the sub-cube. The

second operation, dice is like a slice operation without

removing any dimensions from the process cube.

Drill down and roll up are operations that deal with

changing the level of a dimension’s granularity.

2.3 Frailty in Elderly People

Even though frailty is common in elderly people and

associated with natural aging, it is not an unavoidable

process. Frailty is recognised as a dynamic process

where the people transition from fit to moderate

frailty and to more advanced frail states over time

(Gill et al., 2006; Lang et al., 2009). Frailty is

generally seen as a trajectory with progression highly

likely to end in a frail state (Setiati et al., 2019).

However, frailty progression varies widely between

individuals, their health and their circumstance and

understanding this complex variation is imperative.

An improved understanding of frailty progression

may help clinicians identify those at high risk of

deterioration and to develop effective intervention

plans.

2.3.1 Electronic Frailty Index

A variety of frailty assessment tools have been

developed with supporting guidance and resources

including PRISMA-7, Tillburg Frailty Indicator,

SHARE-FI (Pialoux, Goyard and Lesourd, 2012) and

the Electronic Frailty Index (eFI) (Clegg et al., 2016).

The eFI has now been adopted across UK primary

care settings and is embedded within General

Practitioner (GP) primary care systems such as

SystmOne, it is in regular use by GPs to assess the

level of frailty and is calculated automatically using

data from the patient’s record (Lansbury et al., 2017).

2.4 Related Work

Disease progression, also known as disease

trajectory, has been modelled effectively as a network

graph where nodes represent the first report of a

disease and edges show the direction (trajectory) of

disease progression (Pescosolido, 2013; Jensen et al.,

2014). The practicality of applying process mining to

study disease progression has been demonstrated by

(De Toledo et al., 2019) and (Kusuma et al., 2020).

However, work on frailty progression based on the

clinical assessment and/or demographics records

have not utilised process mining techniques so far

(Chamberlain et al., 2016; Rogers et al., 2017;

Verghese et al., 2021). While these works analyse

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disease patterns, no performance indicators such as

intervals between disease progress were discussed.

3 METHODOLOGY

Process Mining Project Methodology (PM2) is the

general methodology used to conduct this work (Van

Eck et al., 2015). It comprises of six phases. The

focus of work is established by creating research

questions as a guideline in phase (I) planning. In

phase (II) data extraction is done by selecting

appropriate event data and defining a scope of work.

Next, data processing and transformation in phase

(III) is performed in refining the data to create event

logs in next phase (IV) of mining and analysis. The

evaluation phase (V) is done by diagnosing and

verifying the work findings. The last phase (VI)

process improvement and support was not relevant to

this work. Several iterations were done involving the

last three phases of the methodology. These included

the implementation of two process cube operations in

different iterations.

4 CASE STUDY

The dataset used as our case study is from General

Practices from the city of Bradford in West

Yorkshire, England. The primary care professionals

use SystmOne to record clinical findings during

consultation which includes history, symptoms,

diagnosis, observations, referrals, and treatments.

The case study following the PM2 methodology

explained in previous section, the activities executed

at each stage are described below.

4.1 Phase I: Planning

The planning phase involved developing the research

questions to explore frailty progression. They were

derived from a review of previous studies and

confirmed by clinical domain experts working in

frailty care in the local region. The domain experts

identified three commonly acknowledged clinical

problems with frailty progression which are

hypertension, falls and polypharmacy. Our aim was

to determine the association between frailty stages

with these areas of concern. The research questions

were:

RQ1) Can process mining detect and quantify the

differences in frailty progression?

RQ2) Is it possible to uncover the differences in

sequence of deficits of concern using

process mining?

RQ3) Can process mining determine variations

between patterns of concern?

4.2 Phase II: Extraction

Anonymised patient record data was extracted by the

connected Bradford research data service

www.bradfordresearch.nhs.uk/our-research-

teams/connected-bradford/ Data was extracted from

the SystmOne electronic health care record system for

participating GP practices and loaded into an SQL

server database management system for analysis. The

extract covered Electronic Health Records (EHR)

from elderly patients aged 65 years for a period of 1

January 2003 until 31 August 2018. Data for 86,919

elderly patients with over 2 million events records

were extracted.

Three inclusion criteria were employed to obtain

a patient cohort appropriate for the work. The first

criterion was to include patients who had at least one

year’s data within the dataset, the second was for

patients aged over 84 years with their final frailty

category in the middle or later stage of frailty, and the

third inclusion criterion was the maximum average of

frailty deficits accumulation is three in a year. The

last inclusion criterion follows Bartosch, McGuigan

and Akesson (2018) who found that most frail elderly

people experienced about 6%-7% of deficits

increment in a year. This phase identified two cohorts

of patient with three deficits of concern (n = 8,547)

and without (n = 3,848).

4.3 Phase III: Data Processing and

Transformation

The third phase of this work involved the preparation

of event logs suitable for loading into process mining

tools. The processing step included creating views

based on the structure of data and research aim to

investigate the association between frailty

progression and deficits of concern. Other data

processing and transformation steps includes frailty

index score identification at each visit to General

Practice, log enriching and securing the sequence of

the events that shared similar timestamp details. Each

of the transformation steps are explained as follows:

 Calculation of Electronic Frailty Index (eFI)

score - Frailty scores were determined at every

visit that the patient made to the General

Practice based on those frailty deficits

identified at the time of the visit.

A Process Cube based Approach of Process Mining in Analysing Frailty Progression Exploiting Electronic Frailty Index

607

 Log Enriching – Following Clegg et al. (2016)

frailty is classified into four stages known as fit

(0 – 4 deficits), mild (5 – 8 deficits), moderate

(9 – 12 deficits), and severe (13 or more

deficits). The log was enriched by creating

additional events for the transition between

each stage.

 Securing the Events Sequence – Where the

transition to a new frailty stage has the same

timestamp as a frailty deficit the order is

assumed to be frailty stage first. In this work,

only the first occurrence of deficits associated

with frailty are considered.

We used the commercial process mining tool

Fluxicon Disco (https://fluxicon.com/disco) and the

popular open-source tool ProM

(https://www.promtools.org). The processed event

logs of the two cohorts were loaded into the process

mining tools to produce visual models of frailty

progression.

4.4 Phase IV: Mining and Analysis

The fourth phase involves process mining and

analytical techniques implemented on the patient

cohorts during the mining and analysis. Control-flow

and time are two process mining perspectives applied

in this work. In addition to process mining techniques

using process cube and variant analysis, an analytical

technique is performed to analyse the frailty

progression. Process mining and analytical

techniques will be discussed in detail below.

4.4.1 Process Cube based Analysis

A process cube based analysis is implemented to

produce a simplified and understandable process

models for frailty progression. The dice operation of

process cube techniques was applied where we only

consider the dimension of clinical concepts. The

traces to create event log are retrieved from the

attributes of events and frailty stages. Table 1 shows

the descriptive statistics generated from the process

cube of one dimension; a) sub-cohort with the deficits

of concern and b) sub-cohort without the deficits of

concern.Table 1 shows the measurement values for

conformance checking produced using the plugin in

ProM called Replay a Log on Petri Net for

Conformance Analysis and Measure Precision and

Generalization. Fitness measures how much the

model allows behaviour recorded in the event log,

precision measures the behaviour allowed in the

model but not being expressed in the event log, lastly

generalization measures the future behaviour

expressed by the process model (Buijs, van Dongen

and van der Aalst, 2012). Both models are highly

representative from the event logs, and high precision

indicate that models only represent behaviour of the

event log. Furthermore, generalization (0.53, out of 1)

in the model without deficits of concern is medium

which demonstrates medium possibility for it to

accept behaviour that does not present in the log.

Table 1: The descriptive statistics of two sub-cohorts

following one dimension of process cube.

Cohort a Cohort b

# patients 8,547 3,848

# events 30,754 5,385

Events per

patient

3.6 (~4) 1.4 (~1)

Trace Fitness 0.96 1.00

Precision 1.00 1.00

Generalisation 0.77 0.53

The process discovery of sub-cohorts is illustrated

in Figure 2 using the performance view of Disco. The

thickness of the edges indicates the longer mean

duration within frailty stages or transition points. The

transition point is defined as the interval between end

of current frailty stage to the start of the next frailty

stage. The transition point from fit stage to mild stage

is recognised as point 1, from mild to moderate stage

is point 2 and point 3 is from moderate to severe stage.

It is observed that mean duration is shorter in Figure

2(b) within both frailty stage and transition point,

except for the fit frailty stage.

a) With deficits of concern

b) Without deficits of concern

Figure 2: Process models generated from Disco with mean

duration executed in between activities.

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The evaluation of duration distributions of frailty

stages and transition points were computed using the

independent t-test. The general hypothesis is that time

taken to reach the subsequent stages is influenced by

the presence of deficits of concern

Table 2: The descriptive statistics in segments of stages

with highlighted cells showing statistical significance

between sub-cohorts.

Frailty

Stages

Cohort a Cohort b

value

Median Duration (IQR) in months

Fit

26.6 (13.3 - 47.1)

N = 8,547

46.6 (25.0 - 81.9)

N = 3,848

0.00

Mild

27.8 (14.3 - 47.8)

N = 8,514

1.0 (0 - 18.6)

N = 1,432

0.00

Moderate

19.2 (5.8 - 36.3)

N = 7,023

0.0 (0.0 – 9.9)

N = 101

0.00

Severe

11.1 (0.0 – 33.7)

N = 3,335

0.0 (0.0 – 0.0)

N = 2

0.25

Transition

Point

Median Duration (IQR) in months

value

7.0 (2.1 – 16.6)

N = 8,514

9.0 (2.6 – 23.6)

N = 1,432

0.00

6.7 (2.0 – 16.5)

N = 7,023

5.5 (1.7 – 14.6)

N = 101

0.32

6.0 (1.9 – 14.6)

N = 3,335

4.4 (3.6 – 5.2)

N = 2

0.49

Table 2 demonstrates numerical measurements of

two sub-cohorts comprises of median duration

between segments of stages with the Interquartile

Range (IQR) at 25% and 75%. The statistical

significance component is highlighted in Table 2

using a chosen p-value of less than 0.05. It defined by

the time taken to reach the subsequent stages from

current stage. In segment I (Fit) the duration is

between the start of the fit to the end of the fit stage,

segment II (Mild) between the start of the mild to the

end of the mild stage, segment III (Moderate)

between the start of the moderate to the end of the

moderate and the last segment IV (Severe) is between

the start to the end of severe stage.

The comparison between sub-cohorts in general

showed statistically significant differences in frailty

stages of fit, mild, and moderate. Whereas transition

point 1 is the only statistically significant difference

found between sub-cohorts. It is observed that the

duration is longer in sub-cohort b at mild and

moderate stage, while sub-cohort a experienced a

longer duration in the fit stage.

4.4.2 Variant Analysis

The association between the deficits of concern is

further investigated using different process cube

operations. A slice operation is implemented to pick

specific value as the attributes from selected

dimension of cube. The disease state of clinical

concept is chosen where the value of attributes are the

deficits of concern.

Figure 3: Trace variants with the deficits of concern

generated from the ProM.

Figure 3 shows the trace variant from the sub-

cohort with deficits of concern. The goal of trace

variant analysis is to generate the pattern of sequence

between deficits fall, hypertension and

polypharmacy. It reveals that the trace variants follow

three distinct patterns based on when an elderly

person has their first fall event. The dominant pattern

is that a fall happens after both hypertension and

polypharmacy occurred (61%), while the second most

common pattern is that a fall is recorded in between

hypertension and polypharmacy (23%) and third

pattern is that a fall precedes hypertension and

polypharmacy (16%).

Table 3: The descriptive statistics of pattern of sequence.

The duration of y value indicates year and m indicates

month. *PoS is for Pattern of Sequence.

PoS*

Cases

Case

Portion

Mean

Case

duration

Median Case

Duration [IQR]

I 5,256

F: 60

Mi: 815

Mo: 2,190

Se: 2,236

10y, 6m

11y,1m

[8y,1m – 13y,6m]

II 2,244

F: 9

Mi: 464

Mo: 1,004

Se: 767

9y, 10m

10y,2m

[7y,2m – 12y,9m]

III 1,047

F: 9

Mi: 212

Mo: 494

Se: 332

9y, 6m

10y,0m

[6y,6m – 12y,5m]

The statistical significance difference test is

assessed on case duration of all three patterns of

sequence using Analysis of Variance (ANOVA).

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Next, post-hoc test, Tukey significant difference is

conducted to find which pattern is different. The

hypothesis to test if there is difference among patterns

of sequence with case duration.

Table 3 shows three patterns of sequence derived

from trace variant 1 and 2 (from Figure 3) for pattern

of sequence I, trace variant 3 and 4 for pattern of

sequence II and last two variants from Figure 3 for

pattern of sequence III. The case portion column

presents the acronym for a patient in the fit category

as ‘F’, in the mild category as ‘Mi’, in the moderate

category as ‘Mo’ and in the severe category as ‘Se’.

The general observation between the three process

models is that the combination of an individual deficit

of hypertension and/or polypharmacy usually

happened before reaching the Mild frailty stage.

Though, it occurred only in pattern of sequence I and

II (from Figure 3 and 4), it is affecting about 84% of

cases from sub-cohort a.

Figure 4: Process model from Pattern of Sequence I.

On the other hand, the difference that we can

observe based on 50% frequent path illustrated from

three patterns in Figure 4-6 is that a fall occurred

before reaching the initial frailty stage for pattern of

sequence II (Figure 5) and III (Figure 6). While a fall

in pattern sequence I (Figure 4) commonly happened

after reaching mild stage. Furthermore, it appears that

the average duration of reaching the final frailty

stages is shortest in pattern of sequence I (Figure 4).

It took about 14 months to enter the final frailty stage,

severe. The transition to severe stage is observed after

a fall had occurred. Meanwhile, in pattern of

sequence II (Figure 5) the average duration (43.6

months) to reach the severe stage is longer than

pattern of sequence III in Figure 6 (42.5 months).

Figure 5: Process model from Pattern of Sequence II.

Figure 6: Process model from Pattern of Sequence III.

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Although the pattern of sequence I has the longest

mean and median case duration, the elderly patients

with this pattern of sequence experienced quicker

change to the severe frailty stage.

4.5 Phase V: Evaluation

The last phase is evaluation with the goal to evaluate

the feasibility of the approach taken in the work. The

results from the implementation of a process cube and

descriptive statistical analysis produced insight on the

variation of frailty progression towards deficits of

concern. The RQ1 acts as a baseline in driving the rest

of the work to achieve the aim of incorporating the

process cube and variant analysis. Furthermore,

discussions with domain experts at the early stage of

work helped us focus on the critical matters

underlying frailty progression in elderly.

Based on the observation from process models

(Figure 3-5), pattern of sequence I recorded the

longest case duration but with the shortest interval to

reach the severe frailty stage. It suggests that patients

with the pattern of sequence I are becoming severe

more quickly especially when a fall occurred after

reaching the moderate frailty stage. Apart from that,

longest mean case duration recorded for patient with

pattern of sequence I as a high proportion of patients

(43%) have their final frailty stage as severe, in Table

3 compared to pattern of sequence II (34%) and III

(32%). This reflects findings that patients with severe

frailty are at greater risk of hospital admission and

longer duration of hospital stays (Clegg et al., 2016).

These factors contribute to the increased mean case

duration within the study period.

5 DISCUSSION AND

LIMITATIONS

This work explored the association of deficits of

concern with frailty progression. Falls, hypertension,

and polypharmacy are deficits of concern and three

widely known issues prevalent in the elderly. The

implementation of process cube for exploiting the

slicing operation found variations in frailty

progression between cohorts of patient with and

without deficits of concern which answered RQ1. The

approach enabled analysis of progression at each

frailty stage, and this was illustrated using process

models. The slicing operation supports filtering based

on specific values within a dimension to discover

sequences for the deficits of concern. Trace variant

analysis identified patterns that addressed RQ2. The

relationship between falls, hypertension and

polypharmacy was explored. RQ3 is answered by

comparative analysis from the process models.

Statistical analysis supports the findings.

This is the first study which includes process

mining techniques to determine the association of

frailty progression with deficits of concern.

Polypharmacy often appears in the initial stage of

frailty suggesting that it could possibly correlate with

early frailty progression from fit to mild. While we

can identify correlations, we should be cautious of

making causal assumptions, it may be worsening

frailty that leads to polypharmacy. On the other hand,

there is anecdotal evidence from clinical domain

experts that polypharmacy may be a risk factor for

falls. One limitation with the eFI method is that, once

a deficit has occurred once, it is permanently

identified as a deficit so reducing polypharmacy

would not reduce the eFI score. The eFI score is a

useful tool to indicate frailty but it is not a definitive

assessment of a patient’s true condition.

6 CONCLUSIONS AND FUTURE

WORK

We have established an approach for exploring the

association between frailty progression and three

deficits of concern: falls, hypertension and

polypharmacy using process mining techniques and

routine patient records data from primary care. The

approach comprises of analysis based on process

cubes and trace variant analysis to explore the

sequence of deficits of concern and identify emerging

patterns of frailty progression. This study contributes

insights for the process mining community and

practitioners within frailty domain.

While a process and data driven approach has

been our focus in this work, future work is needed to

explore the interaction of frailty progression with the

presence of polypharmacy at multiple points along

study duration. To achieve this, a more extensive

process mining and statistical investigation is

required.

ACKNOWLEDGEMENTS

This work is part of first author’s PhD study funded

by Higher Education Ministry of Malaysia. Research

data was provided by the Connected Bradford

research data service which has ethical approval for

anonymised data extracts for analysts and

A Process Cube based Approach of Process Mining in Analysing Frailty Progression Exploiting Electronic Frailty Index

611

researchers. IRAS ID: 227117 REC reference:

17/EM/0254.

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