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The contribution of information
and communication technologies
on performance management and
measurement in healthcare:
a systematic review of
the literature
Christian Di Falco, Guido Noto, Carmelo Marisca and Gustavo Barresi
Department of Economics, University of Messina, Messina, Italy
Abstract
Purpose –This article aims to provide the current state of the art of the literature on the contribution of
information and communication technologies (ICTs) on the measurement and management of performance in
the healthcare sector. In particular, the work aims to identify current and emerging ICTs and how these relate
to the performance measurement and management (PMM) cycle of healthcare organizations.
Design/methodology/approach –To address the research objective, we adopted a systematic literature
review. In particular, we used the preferred reporting items for systematic reviews and meta-analysis
(PRISMA) methodology to select articles related to the investigated topic. Based on an initial screening of
560 items retrieved from Scopus and ISI Web of Knowledge, we identified and analyzed 58 articles dealing with
ICTs and PMM in the healthcare sector. The last update of the dataset refers to February 2024.
Findings –Although we attempted to address a relevant topic for both research and practice, we noticed that
a relatively small sample of articles directly addressedit. Through this literature review, in addition to providing
descriptive statistics of researchon ICTs and PMM in healthcare, we identified six theoretical clustersof scientific
streams focusing onthe topic and elevencategories of ICTseffectively tackled by the literature.We then provided
a holistic framework to link technologies to the different PMM phases and functions.
Practical implications –Nowadays, the availability of ICTs to support healthcare organizations ’processes and
services is extensive. In this context, managers at various organizational levels need to understand and evaluate
how each ICT can support different activities to benefit most from their adoption. The findings of this study can
offer valuable insights to top and line managers of healthcare organizations for planning their investments in both
existing and emerging ICTs to support the various stages of development and functions of PMM.
Originality/value –Most of the current literature focusing on ICTs in the healthcare sector refers to the
contribution that technology provides to clinical processes and services, devoting limited attention to the impact of
ICTs on administrative processes, such as PMM. To the best of the authors’knowledge, this represents the first
literature review on the contribution of ICTs to PMM in the healthcare sector. The review, differently from other
research focused on specific ICTs and/or specific PMM functions, provides a holistic perspective to understand how
these technologies may support healthcare organizations and systems in measuring and managing their performance.
Keywords ICT, Performance management, Performance measurement, Healthcare, Hospital, Technology
Paper type Literature review
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371
© Christian Di Falco, Guido Noto, Carmelo Marisca and Gustavo Barresi. Published by Emerald
Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0)
licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both
commercial and non-commercial purposes), subject to full attribution to the original publication and
authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/
legalcode
The authors would like to acknowledge the Italian Ministry of University and Research as this
research is part of the PRIN 2022 project titled “Furthering Performance Measurement and Management
Systems in Healthcare through New Digital Technologies”(2022WPXPFE) funded by the European
Commission - NextGenerationEU.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1754-2731.htm
Received 23 December 2023
Revised 22 March 2024
11 June 2024
Accepted 30 July 2024
The TQM Journal
Vol. 36 No. 9, 2024
pp. 371-391
Emerald Publishing Limited
1754-2731
DOI 10.1108/TQM-12-2023-0425
Introduction
According to a widely shared definition, performance management and measurement (PMM)
is defined as an area of scientific and practical interest that focuses on the planning and
implementation of appropriate tools and devices for the measurement, monitoring and
evaluation of organizational results (i.e. outputs and outcomes) and underlying methods
(namely, the means) used to obtain these (Anthony, 1965;Otley, 1980;Lebas, 1995;Bititci
et al., 2012). Taticchi et al. (2010) identified three phases (or stages) of PMM, namely, the
development of performance indicators, measurement frameworks and management
frameworks. As technology advances, PMM systems have become increasingly linked
to information and communication technologies (ICT) and, more generally, to information
systems (Geddes, 2020).
The healthcare sector and related organizations have not escaped the introduction of
PMM systems aimed at supporting decision-makers at various levels in the achievement of
desired performance results (Nuti et al., 2018;Vainieri et al., 2020).
As a matter of fact, recent decades have been characterized by the introduction of new
emerging ICTs which are supporting healthcare organizations toward improving the
processes, collection, analysis and management of data (Laurenza et al., 2018;Ciasullo et al.,
2022a). Consequently, several scholars have begun to concentrate on how this new type of
technology is contributing to healthcare organizations and systems (Marques and Ferreira,
2020;Tortorella et al., 2022;Ciasullo et al., 2022b,c;Lim et al., 2024). According to these
scholars, the adoption of ICTs in healthcare may foster both clinical and administrative
processes –such as PMM (Tortorella et al., 2020). Due to the fact that many of these ICTs have
only recently been introduced in the healthcare sector, the need to both rationalize and
contextualize the studies undertaken on this topic up until now has become increasingly
relevant. Moreover, it is important to highlight that most of the extant literature focuses on
the overall impact of ICTs in healthcare (Aceto et al., 2018;Dal Mas et al., 2023), with a
particular emphasis on clinical processes and services (Rouleau et al., 2017;Laurenza et al.,
2018;Saig
ı-Rubi
oet al., 2021) and devoting limited attention to the effects of the different ICTs
adoption on administrative processes and activities, such as PMM.
As such, the aim of this study is to shed light on the state of current literature concerning
the link between PMM and ICTs within the broad field of healthcare. More specifically, our
investigation is aimed at highlighting evidence useful for cataloguing and showing the
work undertaken so far in this field. As such, this article provides the identification and
analysis of current and emerging ICTs and how these relate to the PMM phases identified by
Taticchi et al. (2010).
In order to achieve this, we have carried out a systematic review of the literature adopting
the preferred reporting items for systematic reviews and meta-analysis (PRISMA) method,
which allowed us to obtain a set of articles and data to be analyzed and compared. In
particular, our research aimed at identifying and contextualizing trends in the key
contributions of ICTs in the management and measurement of performance in the healthcare
sector, both at the organizational and system level.
This article is structured into different sections. The next section provides a theoretical
background on the evolution of PMM in healthcare, taking into account the technological
progress. The third section describes the adopted methodology for developing the systematic
review of the literature. In the fourth section, results are developed and outlined. In the last
sections, discussion and conclusions are presented.
Theoretical background: the evolution of PMM in the healthcare sector
PMM systems are described as structured information-based processes and methods that
steer an organization or social system toward realizing objectives and targets to fulfill its
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mission and strategy (Ouchi, 1979). While performance measurement is the activity of
collecting data, defining indicators and computing such indicators to evaluate the ability of a
certain entity to achieve strategic goals, performance management is instead focused on the
utilization of such information in decision-making processes (Lebas, 1995;Bititci et al., 2012).
Taticchi et al. (2010) identified three stages of development of PMM: performance indicators –
which refer to the ability to measure specific items/dimensions of organizational
performance; measurement framework –which deals with the arrangement of data and
performance indicators in an evaluation framework and management framework –focusing
on the utilization of data to support decision-making in organizations.
PMM holds significant importance for any healthcare organization or system, as it
facilitates evidence-based management (Prenestini and Noto, 2023). Decision-makers should
rely on concrete data and facts rather than intuition and hunches. Furthermore, PMM
establishes crucial mechanisms for overseeing and managing resources and is ultimately
accountable for sustaining the alignment and coordination of the entire organization (Simons
et al., 2000).
PMM systems have been widely introduced in Western public and healthcare sectors
starting from the New Public Management (NPM) reforms introduced so as to overcome the
limits of the Weberian bureaucratic model previously adopted (Hood, 1991;Nuti et al., 2018).
This last began to show signs of weakness in the 1980s and 1990s, not due to the use of rules
as the main tool in the management of labor and external relations, but rather due to the
failure to adopt more modern and flexible methods of coordination with respect to
standardization and hierarchy (Hood, 1991;O’Flynn, 2007).
The objects of control of the first PMM systems adopted in healthcare organizations were
originally those related to the traditional accounting measures such as inputs –e.g. financial
and human resources –and outputs –e.g. the volume of services provided (Nuti et al., 2018).
After the year 2000, various shortcomings and unintended consequences emerged in
different sectors due to the initial focus of PMM (Bevan and Hood, 2006;Wadmann et al.,
2013). This led to the development of a new generation of PMM systems, claiming to enhance
the complexity of measures for greater comprehensiveness (in terms of multiple dimensions)
and to support inter-organizational performance as well as collaborative activities among
different units within the same organization (Kaplan and Norton, 2005;Bititci et al., 2012;Nuti
et al., 2018). To achieve this, new tools and devices were required to facilitate goal alignment,
information exchange and collaborative actions among healthcare providers, marking a
paradigm shift in healthcare system management. As such, during the last decades, PMM
systems of healthcare organizations have progressively adopted measures and indicators
related to outcomes both at the individual (De Rosis et al., 2020;Ferr
e, 2024) and societal level
(Vainieri et al., 2020;Noto et al., 2023).
Another noteworthy transformation in PMM systems involves the evolution of the
performance concept tied to value (Porter and Teisberg, 2006;Porter, 2010;Gray et al., 2017;
De Rosis et al., 2023;Ferr
e, 2024). Consequently, this novel concept translated into the
development of new measurement and accountability tools. Lastly, periods of crisis, such as
the recent fiscal crisis and the COVID-19 epidemic, emphasized that performance should also
encompass the principles of sustainability and resilience (Vainieri et al., 2020;Kaswan et al.,
2022,2024;Rathi et al., 2023).
The measurement and assessment of the above-mentioned concepts (i.e. outcomes, value,
sustainability and resilience) mark a significant departure from traditional accounting
systems (Bititci et al., 2012) and pose multiple challenges mainly related to the ability to collect
and analyze data and to attribute the related responsibilities at the organizational and system
level (Geddes, 2020;Noto et al., 2023). In this sense, new ICTs may represent a driver to foster
the collection, analysis and reporting of data to perform outcome measurement and
assessment (Aceto et al., 2018;Brusati et al., 2018;Dal Mas et al., 2023).
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ICTs were introduced in the healthcare sector in the early 1990s, impacting positively on
the access, efficiency and quality of virtually any process related to healthcare (Aceto et al.,
2018) and are becoming even more relevant in the last decade (Marques and Ferreira, 2020).
Moreover, the COVID-19 pandemic accelerated the digital transformation of healthcare
organizations (Tortorella et al., 2022), such as in other sectors (Kumar et al., 2023a,b).
Despite the topic having been widely studied, there are still some gaps in the literature.
In particular, although most studies have focused on the introduction of ICTs to address
specific clinical needs and processes (Corny et al., 2020;Rolls et al., 2020), fewer studies
focused on how ICTs might improve managerial processes (Behkami and Daim, 2012).
Among these, PMM is deemed of great interest to scholars due to its relevance both in theory
and in practice.
The advent of ICTs and computer-based software for PMM, particularly in the late 1980s
and early 1990s, significantly propelled the development of new tools and frameworks for
performance evaluation (Paolini, 2022). Health ICTs are digital technologies applied in the
field of healthcare to improve the management, delivery and accessibility of healthcare
services and to improve communication and information exchange between patients and
healthcare providers (De Rosis et al., 2020;Wyers, 2024). This technological shift aimed to
enhance the timeliness and accuracy of measurement and reporting as well as forecast the
impacts of actions on desired performance through cause-and-effect relationships (Noto et al.,
2023). In particular, according to Porter and Teisberg (2006), every ICT provides the
backbones for collecting, compiling and utilizing information on patients, activities, methods,
costs and results. However, ICTs are not an end themself but should be conceived as an
enabler of value-based healthcare that brings together clinical, administrative and financial
information together (Porter and Teisberg, 2006;Feeley et al., 2020). The implementation of
multidimensional PMM frameworks required support from integrated ICT systems. These
played a crucial role in governing the complexity of the PMM function, given the elevated
number of performance indicators, facilitating the arrangement of measures to establish
cause-and-effect chains and ensuring the quality and accuracy of data and information
(Tortorella et al., 2020). The emergence of new ICTs like Big data, Business intelligence
systems, Artificial intelligence (AI), Cloud computing, Blockchain, etc. underpins this further
evolution of PMM systems.
In the realm of health systems and organizations, ICTs have the potential to contribute
significantly by enabling the collection, management and analysis of new and large datasets
(Deveraj et al., 2013;Kamble et al., 2018;Hasselgren et al., 2020;Secundo et al., 2021). However,
there is a notable gap in the literature, as few studies have framed the contribution that ICTs
offer and may provide to decision-making and accountability in healthcare. Most published
studies tend to focus on the introduction of specific technologies addressing clinical needs,
lacking a holistic view. Consequently, there is a pressing need to design strategies that
support the successful adoption of ICTs in the health sector at every governance level,
including health systems, health authorities and public and private healthcare providers.
Methodology
This literature review concerning the link between PMM and new ICTs in the healthcare
sector was conducted via a systematic approach (Tranfield et al., 2003;Denyer and Tranfield,
2008;Kumar et al., 2023a,b). The need for a systematic review stems from a desire to minimize
bias by adopting a replicable, meaningful and transparent process (Tranfield et al., 2003).
This approach has made it possible to establish conceptual boundaries that help us in the
selection of relevant contributions from literature which may answer our review questions.
However, the limitations of such a method may make the search process too rigid, leaving no
space for exceptions during the article selection procedure (Wang and Chugh, 2014). The risk
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of excluding articles that have abstract or misleading titles is also present (Pittaway et al.,
2004;Wang and Chugh, 2014). To overcome this rigidity, some authors (Lee, 2009;Wang and
Chugh, 2014) have considered the SR process as a “guiding tool.”Our work, in reference to the
SR process, was constructed with the specific needs of our research in mind.
Similar to our study, other research adopted the SR to address similar topics and similar
research purposes (see, for instance, Lettieri and Masella, 2009;Rouleau et al., 2017;Marques
and Ferreira, 2020;Chatterjee et al., 2021;Kumar et al., 2023).
Our research protocol is defined by three macro-phases: identification of the literature;
screening and analysis.
The first phase aimed at identifying those articles to be subsequently processed. This
phase represents the start of the literature review, beginning with the definition of the
boundaries relevant to the research topic and ending with the extraction of initial results. As a
data source, we used Scopus and ISI Web of Science, inserting the following search criteria
into data sources:
TITLE-ABS-KEY (technolog* AND healthcare OR “health care”AND “performance
manag*”OR “performance measur*”) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO
(LANGUAGE, “English”)).
The search was carried out first in July 2023 and then updated in February 2024. As one
may notice, we used general and broad terms to avoid exclusions of relevant literature. The
research string was refined through multiple tests and by presenting the preliminary work to
two international conferences.
After the extraction, the initial results gave us a total set of 589 items (351 results on
Scopus and 238 results on WoS). On the basis of the aforementioned criteria, duplicate items
were eliminated (142), providing a total of 447 items. The first set of articles were then filtered
twice on the basis of their scientific content during the screening phase by following the
PRISMA method. In particular, once the set of articles had been identified, we read their
abstracts, leading to the exclusion of 279 articles. For the remaining 168 articles, the full text
was examined. To address possible biases, our team employed a thorough and methodical
strategy for selecting articles. The process began with two authors independently selecting
articles, which was then followed by collaborative brainstorming meetings. These meetings
aimed to reach a consensus on the articles for which there was initially no unanimous
agreement. To ensure efficient decision-making, we scheduled these review and update
sessions to occur regularly. This structured approach allowed us to refine our selection
process continuously and to optimize decision-making.
The results allowed us to confirm a final selection sample of 58 scientific articles (see
Appendix). Exclusion criteria mainly concerned articles just mentioning PMM in their
abstract but not focused on this perspective; articles exclusively focused on clinical results;
articles not referring to ICTs but focused on clinical technology (e.g. medical devices). The
process described above is listed in Figure 1.
The third phase of the research protocol is related to the analysis of the sample selected. In
this section, we have further detailed the process that allowed us to formulate descriptive
statistics concerning the characteristics of the analyzed articles.
Following a careful reading of the articles contained in our set, this phase involved the
selection of all basic information on which an accurate analysis could be constructed. From a
general point of view, the characteristics that we identified are: the year of publication and the
geographical location of the study (i.e. the country where the first author’s university/
research institute is based).
A second distinctive feature among the scientific articles analyzed concerned the
methodology used. We distinguished work undertaken according to theoretical or empirical
research, subdividing the latter into subclasses: qualitative, quantitative and mixed model.
Moreover, we detailed the specific research tools adopted.
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Last, we analyzed the theoretical background and the technologies analyzed and discussed
in the articles selected, identifying the most relevant theories contributing to the topic. To do
this, authors first organize brainstorming sessions to establish shared guidelines. Then, two
separate groups of authors identified the theoretical backgrounds and the technologies
adopted by each article. Last, two final sessions of brainstorming were organized to group
both theories and technologies in homogeneous clusters.
As a final step, we developed a framework to provide a holistic representation of the
contribution that ICTs provide to PMM in healthcare, addressing our research objective.
Results
The first type of data acquired when searching for information is the year of publication of the
articles belonging to our set. Observing the number of articles published in reference to
the year of their publication as identified in our survey provides us with a preliminary
understanding of the contribution interval of research in the relationship between PMM and
ICTs in the healthcare sector. In particular, this investigation has given us the opportunity to
understand when scientific interest in our research topic began and increased as well as how
scientific contribution trends in this subject matter have evolved (see Figure 2).
Figure 2 shows that research started to focus on the link between PMM and ICTs quite
recently. In fact, the first publication in our set is the one by Bomba et al. (1995). This article
addresses one of the main administrative dilemmas facing the national health system in
Australia, namely, the need to reform practices associated with substantial data and
information overload. The authors of this theoretical research discuss the use of ICTs in
beginning the digitalization process. In particular, the adoption of ICTs was expected to bring
Figure 1.
Selection of the
relevant literature
following the PRISMA
method
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benefits through better identifying and understanding community healthcare trends and
applying ICTs to the efficient collection of data for the development of more appropriate
definitions of performance measures and indicators (Bomba et al., 1995).
Continuing the study of historic publication trends, we observed that 2014 was the year
when interest in this field first peaked. We found articles such as Rosen et al. (2015) about the
use of sensors to measure teamwork performance in healthcare that resulted to be one of the
most cited of our sample.
After a couple of years of decreasing interest, the last five years (2020–2024) show a rising
scientific production in this field. In this sense, the COVID-19 pandemic may have represented
a driver to focus scholars’attention on this topic. In 2022, most of the research considered has
been published in management journals –i.e. Span
o and Ginesti (2022),Tortorella et al. (2022)
and Srivastava and Srivastava (2022). A similar trend can be noted in 2023 (Ippolito et al.,
2023;Korhonen et al., 2023). Last, even though only the first two months of 2024 have been
analyzed, the trend of published research appears promising with three articles already
published.
We noticed that the investigated topic has collected contributions published in several
journals belonging to different research areas (see Figure 3).
We also classified the journals publishing this research based on their main topics’
disciplinary areas, identifying “Health and Medicine,”“Management and Economics”(that
includes healthcare management) and Information Technology “IT.”“Health and Medicine”
groups journals that are mainly referring to public health and other medical disciplines.
“Management and Economics”is the category to which all scientific journals focused on
management, accounting, operations, economics and so on belong. Last, “IT”mainly hosts
journals that focus on technical issues related to ICT development and design.
As one may notice from Figure 3, most of the articles of our sample have been published in
“Health and Medicine”journals; also, in the last years, the interest in the topic has also spread
to “Management and Economics”journals.
Another interesting analysis is related to the geographical location of the studies, in terms
of the countries of the universities where the authors conducting the study belong. This type
of information provides us with an understanding of how scientific interest is distributed
0
10
20
30
40
50
60
70
1995
1997
2000
2001
2003
2005
2008
2009
2010
2011
2012
2014
2015
2016
2018
2019
2020
2021
2022
2023
2024
Nr of arcle per year Cumulated value
Source(s): Figure by authors
Figure 2.
Temporal distribution
of scientific products
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according to territorial boundaries. The idea seeks to identify the nations and therefore the
researchers who are contributing the most to scientific literature. In Figure 4, we are able to
identify the distribution of the 58 articles in relation to their country of origin.
The USA represents the driving country for scientific research in this field, counting an
overall number of articles published equal to 29 results. Among the major countries for
publications pertinent to our line of research, we find Italy with five publications. Italian
research mainly focuses on two theoretical strands: PMM, as exemplified by the study of
Span
o and Ginesti (2022) and operation management, e.g. Lettieri et al. (2008). Also, Canada,
0
5
10
15
20
25
30
35
Source(s): Figure by authors
0
1
2
3
4
5
6
7
8
Health & Medicine IT Management & Economics
Source(s): Figure by authors
Figure 4.
Geographical
distribution of the
studies
Figure 3.
Distribution of articles
per research area
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Australia and India appear to be among the nations which have contributed most to current
literature, with five and four (Australia and India) publications each on this topic. As stated in
the previous paragraph, the first article of our set originated in Australia.
Methodological perspective
Another useful piece of information acquired to perform our analysis concerns the method
used by scholars in conducting their research.
First of all, we distinguished between theoretical and empirical research. The first one is
concerned with studying a phenomenon through the analysis of the theories that characterize
this, leading to theoretical conclusions –e.g. literature reviews and conceptual analysis.
Empirical research, on the other hand, is concerned with the collection and analysis of
observable data to answer specific research questions or test hypotheses. In our sample,
34 out of 54 articles refer to empirical research, while the remaining 20 adopt theoretical
approaches. This high percentage of theoretical research can be explained by the novelty of
the topic. In fact, to run empirical analysis, technology should be already implemented,
and most of the theoretical papers have been published in the first period, i.e. 1995–2010,
when the adoption of many of these technologies was not spread.
Empirical research can be performed through qualitative, quantitative or mixed methods.
In our sample of articles, we noticed that the investigated topic has been analyzed with both
quantitative (17 articles) and qualitative methods (14 articles) as well as mixed methods
(seven articles).
The research technique adopted the most (15 articles) is the case study by both qualitative
and quantitative studies. Another widely used research tool is the survey that has been
employed in eight articles. Advanced statistical tools, such as multivariate analysis and
regression on panel data, were employed in four research.
The adoption of different tools and methodological approaches grounded in different
epistemological approaches is consistent with the complexity and multidisciplinary
embedded in the topic that requires contributions from both the hard and social sciences.
Theoretical perspective
As we continue to examine our data, we can compare the different theoretical backgrounds
used by the scientific community in carrying out this research.
In order to homogenize our results as much as possible, we have created clusters of similar
theoretical backgrounds so as to obtain unique results. These are “Management Control”,
“Digital Transformation,”“Operation Management,”“Quality,”“Health Technology
Assessment”and “Evidence-Based Management.”
The most populated clusters emerging from the analysis of our results are “Management
Control”(MC) and “Digital Transformation”(DT).
In the MC cluster, we included all the articles that aim to contribute to the literature on
how to design, develop and implement PMM systems and how ICT can contribute to this
function in healthcare systems and organizations. Examples of articles in this cluster are the
ones written by Rosen et al. (2015),Span
o and Ginesti (2022),Ippolito et al. (2023) and
Korhonen et al. (2023).Rosen et al. (2015) focused on sensor-based technology as a
methodology to measure and evaluate teamwork in healthcare organizations. Span
o and
Ginesti (2022) studied the role of Big data in fostering acceptance of PMM in healthcare
organizations. Ippolito et al. (2023) outlined the contribution of technological innovations in
PMM in a public university hospital through the implementation of a multidimensional
management dashboard. Last, Korhonen et al. (2023) contribute to the literature on
management control by showing how financial and well-being anchors influence horizontal
performance measurement in a healthcare digitalization project. Overall, these articles
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highlight how ICTs and digitalization foster planning and cybernetic controls within
healthcare organizations and between stakeholders and health governance levels.
The other frequently adopted theoretical framework has been named “Digital
Transformation,”defined as the use of new ICTs to enable major organizational
improvements (Fitzgerald et al., 2014). In this cluster, we find articles mainly focused on
the adoption process of ICT in performing or contributing to PMM in healthcare systems and
organizations. Belonging to this cluster is the work of Holden et al. (2011), who studied how
health information technology (HIT) may transform the work process and system, impacting
the outcomes achieved. Other examples of articles belonging to this cluster may be found in
Restuccia et al. (2012),Zhao et al. (2019) and Mishra et al. (2022), which focused on how HITs
impact quality and performance through PMM processes. Last in this cluster, the study by
Zhang (2024) focuses on how quantum healthcare models may allow the best use of data
collected from Internet of Things (IoT) technologies.
A third cluster is defined as “Operation Management”and includes articles aiming at
contributing to this specific managerial discipline. In particular, the papers of Lettieri et al.
(2008) and Nagy et al. (2008) focus on how HITs allow gathering and managing data that can
be used to measure and monitor health processes and operations, improving their
performance. Mettler and Vimarlund (2009),Li et al. (2021) and Mukherjee et al. (2021)
focus on how ICTs may support health organizations in pursuing compliance, patient safety
and satisfaction through their support of the healthcare organization’s operations. Testi et al.
(2009) and Pennathur et al. (2011) deal with well-known operation management topics, i.e.
waiting lists and emergency departments.
The “Quality”cluster includes articles focusing on how ICTs may support PMM to foster
quality improvement and better outcomes for patients and/or the population. The most
cited article in this cluster is Wechsler et al. (2017), which focuses on telestroke and, in
particular, on how telestroke may enhance the possibility of measuring and monitoring
quality of care. Another relevant article in this cluster is the one drafted by Weiner et al.
(2012), which studied the impact of electronic health records (EHR) and other related HIT
in defining and computing quality measures.
An emerging cluster identified by our analysis is the “Health Technology Assessment”
(HTA) one. HTA is an emerging stream of literature that focuses on methods to produce
information to guide decision-making regarding technologies’adoption, reimbursement and
utilization (Banta, 2003). Belonging to this cluster are the articles of Sideman and BenDak
(1997),Hebert (2001) and Badnjevi
cet al. (2019). These articles have been considered relevant
for our literature review as investigating the poorly explored twofold relationship between
PMM and HTA. On the one hand, HTA uses information coming from PMM systems to
perform the evaluation of new technologies –this is the case of Hebert (2001) performing HTA
for telehealth implementation using performance measures; on the other hand, HTA informs
PMM processes on what should be measured to guide decision-making in healthcare
organizations –see Sideman and BenDak (1997).Badnjevi
cet al. (2019) focus on the
contribution that machine learning may provide to PMM in supporting HTA processes.
Last, another cluster resulting from our analysis is “Evidence-based management”(EBM).
This refers to a well-know “evidence-based”paradigm used both by medicine (Sackett et al.,
1996) and management scholars (Aloini et al., 2018). The articles belonging to this cluster are
the ones by Minard et al. (2014) that focus on the incorporation of information on EHRs to
create performance measures that can be used as evidence-guiding decisions and Siddiqui
et al. (2021) that focus on generating and using quality data for evidence-based decision-
making to overcome barriers inherent in immunization systems.
Figure 5 displays the representation of the theoretical cluster above described.
The graph in Figure 6 confirms how management disciplines (Management control and
Operation management) are progressively raising their attention on the investigated topics.
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Technological perspective
Finally, the last analysis performed refers to the identification of the technologies analyzed
and mentioned by the articles selected. As one may notice from Figure 6, most of the papers
refer to the general denomination of HIT, an umbrella term that includes all the ICTs
specifically used in healthcare.
Among the specific technologies mentioned, the most recurrent is the dashboard (Nagy
et al., 2008;Ward et al., 2014;Barbazza et al., 2021;Watkins et al., 2022;Ippolito et al., 2023).
Overall, these articles explain how ICTs applied to healthcare enable the creation of a
dashboard that results in being effective in guiding and supporting decision-making
processes at different governance levels.
0
1
2
3
4
5
6
7
8
DT HTA Operaon management Quality MC EBM
0
5
10
15
20
25
30
35
Source(s): Figure by authors
Figure 5.
Theoretical
background
composition and
evolution
Figure 6.
Technological focus
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381
Another frequently mentioned technology is the EHR (Weiner et al., 2012;Hirsh et al., 2014;
Minard et al., 2014;Landis-Lewis et al., 2015;Nicola et al., 2018). Based on our analysis, EHR
appears to be a pre-condition to foster PMM in healthcare through ICTs that may impact on
the payment system, stratification of population needs, etc.
Telemedicine is also mentioned in four contributions (Hebert, 2001;Wechsler et al.,
2017;Sasikala et al., 2018;Bui et al., 2021). Although telemedicine –including
telemonitoring, telehealth, telementoring, etc. –is a wide discipline mainly concerned
with the provision of care through health technology, we included these articles as they
provide interesting insights for PMM, especially for data production and quality
measurement purposes.
Other mentioned technologies are IoT (Rosen et al., 2015;Li et al., 2021;Zhang, 2024),
Artificial intelligence (AI) (Hou et al., 2014;Badnjevi
cet al., 2019;Soltan et al., 2024), Big data
(Wehrens et al., 2020;Span
o and Ginesti, 2022), Blockchain (Mukherjee et al., 2021;Srivastava
and Srivastava, 2022), Cloud computing (Eze et al., 2020); Business intelligence (BI) (Mettler
and Vimarlund, 2009) and Platforms (O’Connell and Cherry, 2000).
Discussion
The main purpose of the research is to report and examine the state-of-the-art literature on
PMM and ICTs in the healthcare sector and, in particular, to identify current and emerging
technologies and how these relate to PMM. This is considered to be a relevant research area as
digital transformation in healthcare is providing new opportunities not only at the clinical
level but also at the administrative and managerial levels (Tortorella et al., 2020). However, to
the authors’knowledge and consistently with the results obtained by this systematic
literature review, this last aspect has been poorly considered by the scientific literature. In
fact, differently from other literature reviews focusing on ICTs in healthcare that focus on the
broader impact of technologies on healthcare organizations with a clear emphasis on the
clinical processes and services context –see, for instance, Aceto et al. (2018),Marques and
Ferreira (2020),Dal Mas et al. (2023) –our review addresses a less explored topic, that is the
contribution of ICTs on PMM in healthcare. This review aimed at performing a
systematization of the existing knowledge to address and foster future research on the topic.
Our review identified 58 scientific articles that directly deal with the contributions that
ICTs provide to PMM in the healthcare sector. These articles adopt different theoretical
perspectives and focus on different technologies.
What emerged from our study is that the journals that first focused on the investigated
topic were the health and medicine ones. Public health journals are the ones that first
understand and deepen the opportunities that new ICTs could have provided on quality
measurement and how this improved function may have contributed to the production of
evidence for decision-making purposes. Besides the constant interest of these journals in the
contribution of ICTs on PMM in healthcare, in the last years, a growing interest has also been
demonstrated by management, economics and IT. Studies on these disciplines may be needed
to further advance knowledge on the topic.
For what concerns the theoretical perspective adopted in these studies, a consistent share
of the articles directly contributes to the measurement and management of healthcare
performance (i.e. Management control), investigating how ICTs may contribute to the related
systems design, functioning and implementation. These results confirm the relevance of the
first future research perspective outlined by Marques and Ferreira (2020) in their review
concerning the integrated management of ICT to support information exchange within
healthcare organizations. The same amount of studies have been classified within the DT
framework, which includes research aimed at understanding the impact of the introduction of
new ICTs in healthcare organizations.
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382
Two other relevant clusters relate to the management of processes and operations to
improve quality and other performance results, consistent with what was outlined back in
2006 by Porter and Teisberg. These clusters are “Operation management”and “Quality.”
This last, in particular, is linked to diverse public health contributions, as in this discipline the
concept of quality of care is well-known and debated. Public health scholars are also
contributing by using the theoretical perspective of EBM.
Moreover, an interesting result of our research is related to the emergence of HTA as a
promising theoretical perspective relevant to this topic. In particular, from the studies
belonging to this cluster, it emerged a possible synergy between HTA and PMM that
healthcare management could leverage to improve their decision-making processes. This
relationship is certainly mediated by the adoption of ICTs.
For what concern the technologies that these studies take into account, we may notice that
much of the literature generally refers to HIT. This is even more evident in the first
contribution published on the topic (Bomba et al., 1995). Considering that most of these
articles adopt a theoretical methodological perspective, we can explain this by referring to the
fact that in those years most of the technologies were not widely spread and implemented
worldwide, with few exceptions. Another interesting result emerging from the analysis of the
specific technologies treated by these studies is that the EHR can be considered an enabler for
the adoption of other ICTs (Weiner et al., 2012). This result is consistent with what was
highlighted by Feeley et al. (2020) that EHR is “the sole source of costing information so that
accurate costs for every encounter can be tracked, aggregated, shared and used for
improvement”(p. 1). From this evidence, we may derive the policy recommendation that
EHR implementation represents the key priority to foster ICT adoption in PMM and to get the
best value from their adoption. Other ICTs studied in the selected sample of articles range
from “traditional”ones, such as dashboards and BI, to emerging ones, such as IoT, AI,
Blockchain, etc.
Based on the results previously outlined, we developed a framework that outlines the key
contribution that each ICT provides to PMM in healthcare according to the framework
provided by Taticchi et al. (2010) that distinguishes three main phases, namely performance
indicators, measurement framework and management framework (see Table 1). The purpose
of this framework is to provide a holistic view of the contribution of ICTs to PMM in
healthcare that may support both theory –in advancing research on specific technologies or
PMM functions –and practice –in supporting investment in ICTs.
Table 1 attempts at providing a holistic view of what type of technology can support what
stage of PMM, providing a novel contribution to the topic, as most of the studies focus on
individual technologies. For each technology, the framework outlines in what phase of PMM
ICT PMM phase PMM function
EHR Performance indicators Production of new data
IoT Production of new data
Telemedicine Production of new data
AI Measurement framework Analysis of data
Big data Collection and analysis of data
Platform Collection and analysis of data
BI Management framework Management of data
Blockchain Management of data
Cloud computing Sharing and management of data
Dashboard Use and reporting of data
Source(s): Table by authors
Table 1.
Linking ICTs
and PMM
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383
the ICT contributes the most –i.e. measurement or management; and what specific PMM
function may be empowered by the use of technology.
In particular, ICTs that provide an important contribution to the creation of specific and
new performance indicators are EHR, IoT and Telemedicine. These have been linked to this
PMM phase as, consistently with what emerges from the studies mentioning them, they allow
the production of new data that can be used for performance measurement purposes. For
example, with the use of sensors and telemedicine applications, it is possible to produce data
relevant to PMM (Wechsler et al., 2017;Sasikala et al., 2018). Having access to new data in a
timely manner is key to supporting results monitoring and thus to pursuing better patient
outcomes.
For what concerns the development of measurement frameworks, what emerged from
our review is that the technologies mainly contributing to this phase are AI, Big data and
Platforms. In particular, within the measurement phase, our search outlined that AI
(including machine learning) may support the analysis of evidence to identify emerging
patterns and trends that may be used for decision-making purposes (Hou et al., 2014). On the
other hand, Platforms and Big data support healthcare organizations in the collection
of existing data that can be digitalized and/or included in PMM systems (O’Connell and
Cherry, 2000;Span
o and Ginesti, 2022). In particular, ICTs allow us to interpret the data
obtained in a faster and less biased way, thus contributing significantly to decision-making
processes.
For what concern the management phase, that is utilization of performance measures in
decision-making processes (Lebas, 1995;Bititci et al., 2012), Blockchain has been useful in the
management of data in terms of storing and processing health data (Mukherjee et al., 2021;
Srivastava and Srivastava, 2022). Other ICTs supporting the management and analysis of
data are BI and Cloud computing. This last may be employed to correlate shared data from
multiple stakeholders into a common PMM system (Eze et al., 2020). Last, dashboards may be
effective in using and analyzing data as well as reporting performance measures to internal
(Ward et al., 2014) and external (Barbazza et al., 2021) stakeholders.
The overall take-home message of these studies is that ICTs enable and/or empower the
collection of data and measurement abilities of healthcare providers. Also, ICTs support
managers in analyzing and understanding performance results; this allows them to improve
decision-making processes. Last, ICTs improve reporting activities and, more in general, the
use of data for quality improvement and efficiency gains.
Conclusions
This article attempts to fill a gap in the systematization of existing research regarding the
contribution of ICTs to PMM in the healthcare sector.
Besides the identification of the most relevant and adopted ICTs that support the
measurement and management of the performance of healthcare organizations and systems,
this article aimed to contribute to theory by providing a holistic framework that links
technologies with PMM phases and most recurring management, economics and public
health theories.
In summary, the integration of technologies in healthcare performance management
offers significant opportunities to improve the quality of care, operational efficiency and
transparency of healthcare organizations. However, it is crucial to advance research so as to
ensure the success of these initiatives and to continue to explore new avenues for the further
improvement of the healthcare sector.
Future research may focus specifically on the emerging technologies that, once EHR is
fully implemented, may release all the potential support to PMM functions. In particular,
future research should address the following research questions:
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(1) How do ICT-enabled PMMs support quality improvement initiatives in healthcare,
such as patient safety, clinical outcomes and patient satisfaction?
(2) What are the perspectives of healthcare professionals and administrators regarding
the usability, acceptance and perceived benefits of ICT-based performance
management systems?
(3) What are the main barriers and challenges associated with the implementation and
adoption of ICT for performance management and measurement in healthcare, and
how can these be addressed?
(4) How are privacy and security concerns addressed in ICT systems used for
performance management and measurement in healthcare, especially considering
the sensitivity of patient data?
Moreover, future research may deepen the relationship between PMM and HTA,
understating how ICTs may provide new sources of data and measurement to support
related decision-making processes.
The contribution to practice is mainly linked to the provision of insights to healthcare
organizations’top and line managers for planning their investments in ICTs to support the
various stages of development and functions of PMM. In particular, our framework may
support decision-makers in choosing the “right”ICT for the “right”purpose.
Limitations of the study are mainly related to the limits of the systematic review approach.
In fact, despite efforts to create comprehensive search strategies, it is possible that some
relevant studies may be missed. Moreover, the process of data extraction and synthesis
involves subjective decisions by the research team, leading to potential bias.
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Appendix
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Corresponding author
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