The Role of Data in Crisis Management Models in the Health Care

Context

Hannele Väyrynen

, Annamaija Paunu and Nina Helander

Information and Knowledge Management, Tampere University, Tampere, Finland

Keywords: Data, Crisis Management, Crisis Management Model, Information Technology, Health Care.

Abstract: Successful crisis management is consisted of different factors, varying actors and operation environments.

Health care system is one of the most critical sectors in societies to operate also in a crisis situation. In the

middle of a crisis, digitalization and access to data can have an important role as an enabler. In this paper, the

role of data in crisis management models in health care context is studied. The theoretical frame is derived

from the crisis management literature review. The study is able to identify the role of data in seven critical

elements in crisis management models that need consideration during crisis, namely data has supporting,

enabling as well as critical role in technology, strategy, government, adaptation mechanisms, scenarios,

security of supply chain and co-operation in crisis management. As a result of the study, different aspects of

data in promoting successful crisis management are proposed.

1 INTRODUCTION

There are different kind of crises such as economic,

war, natural disaster, health, technological and

human-made crisis to mention just a few. Crises can

be described through three elements: depth, duration

and resilience (Maritsa and Kalemis, 2020). We live

in a constant cycle of various crises and we need to

prepare, adjust, manage and learn from crises. Thus,

crisis management (CM) is very much needed

throughout different sectors of society. However,

different CM strategies and models are needed for

different crises (Khodarahmi, 2009).

In a crisis, it needs also to be considered that

different stakeholders (Shallmo and Williams, 2020)

have to explore several environments around them in

order to build a situational picture: political,

economic and social environments, technology,

health and science and international relationships (Li

et al., 2021). This kind of building of a situational

picture requires access to relevant data.

Health care systems are one of the most critical

sectors in societies being a solid foundation for daily

life (Keskimäki et al., 2019). In a crisis, situations are

resolved with ad hoc solutions causing complex

networks of a complex of human-technology mixture

https://orcid.org/0000-0002-3636-280X

https://orcid.org/0000-0003-2201-6444

(Bakos 2020). There may be signals for sudden crisis

and the preparedness and resilience to shocks of

health systems vary (European Observatory on

Health Systems and Policies, 2020).

Previous research has acknowledged the crucial

role of information technology in successful CM in

the health care context. Difficulties during the

management of emergencies and crisis are most often

related to proper information management, as

relevant and adequate data is needed promptly for the

decision makers. Information technology (IT) can

provide useful tools to ensure access to data, but we

also need to gain a proper understanding of the real

needs for information in the middle of a crisis,

existing information exchange practices, and ways of

communication in the stakeholders’ society (Wybo

and Lonka, 2003Telemedicine has enabled virtual

care and emergency consultation (Hollander and

Carr, 2020) although the role of telecare may still be

unclear (Singh et al., 2021).

Digitalization offers opportunities in CM

(Reeves et al., 2020; Gkeredakis et al., 2021);

however benefits are not actualized in the crisis

situations in health care context in an optimal way,

e.g. too few platforms or too little data utilization is

(Schofield et al., 2019) or challenges in IT integration

Väyrynen, H., Paunu, A. and Helander, N.

The Role of Data in Crisis Management Models in the Health Care Context.

DOI: 10.5220/0011589500003335

In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR, pages 337-344

ISBN: 978-989-758-614-9; ISSN: 2184-3228

337

or data quality and cooperation of professionals

regarding the data face challenges (Hong et al., 2020).

During the 2020-2021 CORONA-19 crisis, different

strategies in health care were incentivized. However,

there is a need for strategy as long-term planning has

been identified as promoting health care digital

transformation and crisis anticipation (Pérez Sust et

al., 2020) and continuous anticipation to manage

complexity and investments in human resources

(ESPAS 2015).

IT and the available data can support health care

personnel in their daily work, enable supply chain

management, ensure health care financing with

effeciency processes, and produce transparent

processes for the governance and service delivery

(Otto et al. 2015). Concerning the practical

infrastructure level, transportation, power and water

network, internal and external organization

communication systems, and crucial supplies like

oxygen, blood, medical equipments, and medication

are subject to technological reliability (Zhao et al.,

2019). All these functions produce fragmented data

and technology platforms are one way to unify

scattered data and information (Cimellaro et al,

2018).

However, data as such is not valuable but has to

be transformed into understandable information that

brings some value to the recipient. It has been said

that “healthcare is undergoing a data revolution”

(Panesar 2019). Inreasingly, real-time data analysis to

create predictive modeling during the crisis has

supported the mitigation of risks (Mensah et al., 2015;

Lo Sardo et al., 2019). The challenges of data

utilization culminate in unintegrated information

management systems or non-syncretized data

formulating barriers for data and information

exchange between institutions (Liapiz et al. 2015).

The challenges of health-care informatics were

identified nearly twenty years ago (Guah, 2004) and

the same stumbling blocks still exist. Beside

technology solutions, the management of information

is needed as well (Bose, 2003).

In this paper the aim is to study the role of data in

CM. The paper describes key elements of CM models

and the role of data in them in health care context and

seeks answers to the following research question:

What is the role of data in successful CM in health

care?

The article proceeds as follows: after this

introductory section the theoretical bases of data and

crisis management are presented after which the

methodology part of the literature review is

described. The fourth section presents the main

results followed by the conclusions in the last section

with suggested avenues for future research.

2 THEORETICAL BASES OF

DATA AND CM

Crisis management is composed of different factors,

and has been called a “multi-faceted crisis response

mechanism” (Liapis et al., 2015), thus it requires

multidisciplinary examination (Pearson and Clair,

1998). Crisis management models are designed and

planned to prepare for a crisis and act as

prespecifications or guidance of how to operate.

There are examples of models that focus, e.g.

information exchange (Shooley et al., 2014), effects

on the economy (Estrada and Arturo, 2020), strategy

(Radonjic-Simic et al. 2021), infrastructure (Mihai,

2020), technical efficiency (Ortega-Díaz et al., 2020),

education (Estrada and Arturo, 2020) or signal

detection, prevention and preparedness, damage

limitation, recovery and learning (Pearson and

Mitroff, 1993).

In general, several challenges have been identified

in the literature related to the use of data and

information technology. There are challenges related

to poor quality of data leading to potential

misinterpretations, problems with information

systems, rigid processes, strict legislation and

resource challenges (Vuori et al., 2019). Typically

difficulties can also exist in accessing relevant

information (finding the right documents or

databases) due to the lack of sufficient employee

training, as people do not have the skills to search for

the right information (Corallo et al., 2012).

All of these challenges are also typical for health

data, but there are some special characteristics that

potentially hinder the usage of health data. First,

health data is characterized by highly confidential

personal health data leading to the challenge of

securing the privacy of health data. Second, other

challenges related to the use of health data are related

to the heterogeneity of the data sources,

interoperability between different IT systems and the

fragmentation and strictness of regulatory

frameworks. (OECD, 2015)

3 RESEARCH SETTING

The applied research methodology was a scoping

literature review. The review was selected to provide

valuable information about the topic for both

KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval

338

academia and business (Tranfield et al. 2003). The

databases used for the literature search were Web of

Science, Scopus and Ebsco. The time horizon was

2009-2021 because health care technology as well as

data science developed rapidly during this decade. To

get a more general picture of the literature, crisis

management was considered with the concepts of

health AND data; and crisis AND management AND

model.

In the first phase of the literature review, a total of

633 articles were identified and furthermore,

altogether 74 CM models were identified from these

articles. Altogether these 74 identified CM model

papers were further analysed using the Atlas content

analysis program to code the content into clusters.

Atlas is the qualitative data analysis and research

software to analyse the content of the selected

literature and cluster the themes (Atlas 2022).

Altogether seven clusters were identified and the role

of data in these clusters were further identified. Figure

1 illustrates these phases of the literature review and

analysis.

Figure 1: Illustration of the research method process

(o* = occurrence of articles).

4 RESULTS

Different CM models can guide the stakeholders to

co-operate with other agents and institutions (Li, e.g.,

2021). A policy framework for technology and data

utilization is needed, and a crisis situation may push

policy development and reformulation of operational

strategies (Kuščer et al., 2022) as well as data

collection and communication planning (Barkbook-

Johnson et al., 2017). Open-source software for data

exchange and co-operation in a crisis is one way to

advance situational picture formulation and problem

solving (Falenski et al., 2013).

More advanced data utilization in crises enables

data-aided predictive modelling and scenario analysis

for environment change forecasting and pandemic

crisis, for example (Danesh-Yazdi and Aiei-Ashtiani,

2019). Big data analysis is used to support multi-

criteria decision-making processes in crisis

management with estimation and evaluation

techniques (Ersoy and Alberto, 2019; Alkahami et al.,

2020) and to study the effect of crises on business

management (Chen and Biswas, 2021). One critical

factor is security of supply chain and resource

management, and pandemic big data can navigate

optimal resourcing in a crisis (Bag et al., 2021; Das et

al., 2022).

Next, the key findings are listed in Table 1,

showing the role of data in CM that need to be

critically considered.

Table 1: Summary of the role of data in empirical findings.

Critical

element

Effect factors that

need to be ensured

in CM

Role of data in

promoting successful

Technology

• Remote care

functionality and

efficiency

• Medical device

usage

• Sensor data,

network between

sensors and IT

• Dashboard with

real-time data

• Human-robotics

interaction

• Data policy

• Real time data and

information,

minimum level of

information

blackouts

• Big data analysis

• Facilitate decision

making

• Human-technology,

human-robotics in

eration

Strategy

• Systematic data

generation

• Communication

strategy

• Education

programs

• Information for

providers and policy

makers

• Analyses how the

system responds

• Simulation of

damage scenarios

and alternative

strate

testin

Government

• Guidance means

for data and

technology

utilization in

services

• IT and data policy

• Produce data and

information for

decision makers to

prepare legislative

enablers

• Health and rescue

clinical governance

Adaptation

mechanisms

• Health IT

acceptance

• Platform for

health and

government

actors

• Virtual

interaction

• Education

• Transparent

information on

technology

implementation

processes

• Health care

professional

education provides

civic resilience

The Role of Data in Crisis Management Models in the Health Care Context

339

Table 1: Summary of the role of data in empirical findings

(cont.).

Critical

element

Effect factors that

need to be ensured

in CM

Role of data in

promoting successful

Scenarios

• Prevention and

recovery

• Simulation (live,

agent based

computer, virtual

reality)

• Systematic

databased

evaluation for the

situational

picture

• Machine learning

in scenario

develo

ment

• Tools for data

analysis and KM

tools for evaluation

• Data mining

techniques

• CM information

literacy

• Situational picture

formation

Supply

chain

• Integration of

different

technologies

(e.g.,

transportation

• Goods and

service delivery

• Resource and

inventory

management

• Multimodal

communication

technology that

enables risk

management and

decision making

• Health and clinical

management

• Absorbing best

solutions and

practices

from other industries

• Identification new

problems

• Network

mana

ement

Co-operation

• Collaboration

processes among

health care

industry

stakeholders

• Strategy,

operation, and

innovations for

prevention of

crisis effects

• Shared knowledge

and other resources

• National and global

knowledge sharing

between

professionals

• Complexity control

• E-learning.

Nutley et al.’s (2014) decision making model for

health data context addresses “behavioral, technical,

and organizational constraints to data use” to support

decision making. These data related factors proposed

by Nutley et al. (2014) were also present in the CM

models analysed in this paper. For example, the data

infrastructure factor revealed that technology is

already smart, but technology utilization may be

challenging. There are many technological systems,

solutions and applications with several measuring and

controlling means (e.g., sensors, internet of things

IoT, wireless technology) in health care. However,

connecting the different IT systems e.g. between

different hospitals, rescue services or transportation is

not an easy task. Integration of the IT systems

internally in a hospital is challenging e.g. because the

challenge of data interoperability. Even a simple

digital application utilization requires a health care

ecosystem around it: the manufacturer of the

platform, the equipment service or device provider,

and furthermore, an ecosystem for the

communications, e.g., the telecommunications

infrastructure provider and the institution (Jeong and

Shin, 2016). However, when integration is successful,

the literature results show that communication can be

strengthened and real time data made possible, e.g.,

by sensors and IoT (ibid.); but successful

communication either via technology or humans

requires strong cooperation (Sentell et al., 2019).

Technology utilization and the benefits obtained from

the technology solution need to be “sold” to the actors

to be implemented. It is important to identify how the

benefits occur in practice, at the different levels, e.g.,

at state level, corporation, profession or family and

individual level (Oborn et al., 2021).

Furthermore, the factor of data availability faces

similar challenge to infrastructure, i.e., how to obtain

integrated data. Different solutions produce data, but

it is another matter whether the actors have access to

the data or whether the data is usable (Lenert, 2012).

The literature covers information management in data

availability in more general terms than just in a crisis

context, often in the knowledge management (KM)

context (Alhuwail, 2021; Thye et al., 2020.) Although

the data sources can provide valuable information for

the actors in the crisis (Shooley et al., 2014) what kind

of means and possibilities there are for data exchange

between institutions. Access to a certain data source

(e.g. geographic information) is possible in one

industry while surprisingly, in another industry

(health care sector) access is rejected.

From this we reach data policy factor; data

availability and data policy should go hand in hand to

secure appropriate data and information usage as well

as enabling data. Platforms, data and information

usage need policy and protection methods and clear

guidelines on how to utilize and document them

(Tang et al., 2011; Shi, 2020). Moreover, the data

quality factor raises discussion of the challenges of

data or integration of other data sources. Therefore

again, in a wider national and operation context,

guidelines and data sharing programs and

standardization for data management are needed.

Data is an ever-changing resource and data

development and suitable technology solutions are

KDIR 2022 - 14th International Conference on Knowledge Discovery and Information Retrieval

340

essential to optimize data utilization as well as a

necessary part of CM. (Alhuwail, 2021) However,

data and information are useless until they are

utilized. CM models can advise the actors to adopt

technology solutions under pressure due to a crisis (Li

et al., 2021).

5 CONCLUSIONS

The previous literature has stated that the benefits of

information technology are not being actualized in

health care in optimal manner even though advances

e.g., in IT integration and AI and machine learning in

data analysis have a lot of potential (Hong et al.,

2020). The aim of this paper was to study the role of

data in the CM in health care context through a

literature review.

Altogether 74 different CM models were

identified from the literature and clustering seven key

elements: technology, strategy, government,

adaptation mechanisms, scenarios, security of supply

chain and co-operation. Data has an important role in

every other element as an enabler in crisis operation.

In successful CM in health care, real-time

information sharing between multiple actors and

organizations is essential to formulate the situational

picture (e.g., Ross Ashley, 2003). Furthermore, data

was identified as playing a supporting role; with new

or innovative technological solutions for the crisis

operation and data utilization, guiding strategy

modification of what needs to be done in a changing

situation and how technology could be utilized. Data

has also a critical role in formulating governmental

actions (data policy or data production for decision

makers), enabling information transparency, and

providing tools for education and leadership. Most

importantly, IT and modern digital solutions are

needed to gather, process, and share data to enable the

scenario and situational picture formulation in a

crisis. One critical area to ensure is the supply chain

and IT offers means of communication and network

management tools to enable essential national and

international co-operation in a crisis with knowledge

sharing.

Governmental actions are needed (legislation,

public-private data or platform sharing) to support the

health care transformation to survive in crisis

situations and to gain the benefits of innovative

technologies, such as AI and machine learning for

data analytics (Mahmood et al., 2020). Human-

technology interaction is an emerging trend in crisis

operations; however, it clearly needs a social

approach besides technology, such as change of

attitudes and education to promote competence for

technology utilization (Sentell et al., 2021) as well as

equality in access e.g. to virtual care services

(Schofield et al., 2019). New management models

and strategy development are also needed to turn a

crisis into an asset (Pérez Sust et al., 2020).

Although this paper managed to give a review, it

has some limitations. First, the data set was gathered

from a limited search engine and does not capture the

scholarly literature comprehensively. Secondly, there

are many excellent examples of data utilization and

technology innovations produced in a crisis

operational environment that merit additional reviews

in the future.

Further study of the role of data and information

exchange (Vujadinovic, 2020) would be especially

interesting. Cyber security was excluded from this

study and, when considering digital platforms, data

and medical applications, and data exchange, security

issues are an important research topic. Third potential

research avenue for IT and CM are simulations in

crisis. Simulations offer a tool for evaluating different

emergency plans and communication technologies

during a crisis.

ACKNOWLEDGEMENTS

This work is a part of the RECPHEALS project,

funded by Academy of Finland, Special funding for

research into crisis preparedness and security of

supply.

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