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There is a limited understanding of information technology’s (IT) role as an enabler of patient agility and the department’s ability to respond to patients’ needs and wishes adequately.
This study aims to contribute to the insights of the validity of the hypothesized relationship among IT resources, practices and capabilities, and hospital departments’ knowledge processes, and the department’s ability to adequately sense and respond to patient needs and wishes (ie, patient agility).
This study conveniently sampled data from 107 clinical hospital departments in the Netherlands and used structural equation modeling for model assessment.
IT ambidexterity positively enhanced the development of a digital dynamic capability (
IT ambidexterity promotes taking advantage of IT resources and experiments to reshape patient services and enhance patient agility.
In the age of digital transformation, modern hospitals need to simplify their current care delivery processes and sustainable business models to contain the rising health care costs and address the needs of the more engaged and informed patient. At the same time, hospitals need to adequately address the confluence of dynamic and unpredictable market forces in which they operate, optimally deploy, and enable their information technology (IT) assets; resources; and organizational, IT, and knowledge capabilities and focus on the state-of-the-art patient service delivery [
Despite a wealth of attention for IT adoption and IT-enabled transformation in health care research [
Gaining these insights is essential, as hospitals are actively exploring their digital options and innovations, and transforming their clinical processes and interactions with patients using digital technologies [
These insights are also important for hospitals in the Netherlands, as Dutch hospitals are bound to care production agreements (ie, so-called turnover ceilings) between hospitals and health insurers. The Dutch Healthcare Authority, an autonomous administrative authority falling under the Dutch Ministry of Health, Welfare, and Sport, oversees that these agreements focus more on patient quality and value creation than production. Therefore, more contract negotiations will be driven by focusing on the quality of care and patient value, achieving patient agility seems a valuable endeavor. Thus, this research tries to extend existing work on IT-enabled transformation in health care and does so by sufficiently capturing clinicians’ attitudes toward IT ambidexterity, digital dynamic capability, knowledge processes, and patient agility of their hospital departments. In doing so, we adopt a practitioner-based approach [
Throughout this study, the dynamic capabilities framework is embraced [
To summarize, the study’s main research questions are:
How does IT ambidexterity lead to perceived patient agility of the hospital department?
What is the role of digital dynamic capability and knowledge processes in the process of converting the contributions of IT ambidexterity on the department’s perceived patient agility enhancements?
This study’s IT business value approach aligns with the industries’ focus on operational and clinical excellence, patient-centered value, and a streamlined patient journey [
Hence, this paper proceeds as follows. First, it reviews the theoretical development by highlighting key literature on IT resources and ambidexterity, the dynamic capabilities view (DCV), and organizational agility. Second, section 3 highlights the study’s research model and associated hypotheses. Third, section 4 details the methods used in this study, after which section 5 outlines key results. Finally, this study discusses the outcomes, including theoretical and practical contributions, and ends with concluding remarks.
Organizations need to pursue and make trade-offs in practice between two seemingly opposing paths (ie, the ability to adapt existing IT resources to the current business environment and demands, and their focus on developing IT resources that contribute to long-term organizational benefits) [
Digital dynamic capabilities can be considered the “organization’s skill, talent, and expertise to manage digital technologies for new product development” [
As its definition and conceptualizations suggest, digital dynamic capability builds upon a rich foundation of the DCV [
These dynamic capabilities have been defined and conceptualized as sets of measurable and identifiable routines that have been widely validated through empirical studies [
Although the knowledge-based view of organizations strongly builds upon the organizational learning theories and literature [
The DCV argues that organizations can respond to changing conditions while simultaneously and proactively enacting influence in the environment. Organizational agility has been considered a critical capability for sustained organizational success under the DCV [
By addressing these crucial questions, this paper contributes to the medical informatics and IS literature by unfolding the mechanisms through which the dual capacity of IT exploration and IT exploitation simultaneously drives patient agility in hospital departments.
IT ambidexterity as a core organizational IT resource is expected to enhance hospital departments’ level of patient sensing and responding capability (both conceptualized as higher-order dynamic capability) through digital dynamic capability (as a lower-order technical dynamic capability) and knowledge processes.
Research model. H: hypothesis; IT: information technology.
IT can be a transformative force in hospitals and contribute to enhanced patient services, efficiency and effectiveness gains, and clinical care [
IT exploration can be considered an enabler of digital dynamic capability. This mode promotes the use of and experimentation with new IT resources (eg, new IT platform implementation, decision support functionality, big data and clinical analytics, and social media) as a basis to reshape existing patient services. On the other hand, IT exploitation focuses on using, enhancing, and repackaging existing IT resources (eg, reuse or redesigning current electronic medical record for new patient service development and ensuring hospitalwide accessibility to clinical patient data and information). Therefore, digital dynamic capability relates well to the dual capacity to aim for two disparate modes of operandi in managing the department’s skills, qualities, and competencies to manage digital technologies and developments—like mobile, social media, big data analytics, robotic process automation, AI, cloud computing, and Internet of Things—for new patient service delivery. However, in practice, many organizations struggle to reach IT ambidexterity results because of resource constraints and conflicting ambitions and motives [
Thus, IT resources play a substantial role in acquiring, processing, organizing, and distributing knowledge, and providing digital processes and knowledge options as enablers of agility [
In line with this reasoning, this study defines the following hypotheses:
Hypothesis 1: The greater the hospital department’s IT ambidexterity, the higher the degree of its digital dynamic capability will be.
Hypothesis 2: The greater the hospital department’s IT ambidexterity, the more effective its knowledge processes will be.
Digital dynamic capability is a crucial dynamic capability necessary to innovate and enhance business operations [
A technological-driven capability is crucial for hospital departments that want to strive for patient agility in clinical practice because the process of achieving new digital patient service solutions is exceedingly dependent on its ability to manage digital technologies [
The digital dynamic capability allows hospital departments, for example, to absorb and process sensitive patient information better, support clinicians in their decision-making processes, exchange clinical data, and facilitate patient health data accessibility [
Hence, hospitals that actively invest and develop such a capability are likely to anticipate their patients’ needs (of which they might be physically and mentally unaware) and respond fast to changes in the patient’s health service needs using digital innovations and assessments of clinical outcomes [
Hypothesis 3: The more developed the hospital department’s digital dynamic capability, the higher the hospital department’s patient agility.
Hypothesis 4: The more developed the hospital department’s digital dynamic capability, the more effective the hospital department’s knowledge processes will be.
Previous scholarship demonstrated that knowledge-based capabilities and agility are two crucial capabilities that mediate the impact of IT resources and capabilities on business benefits [
In sum, this study argues that knowledge processes are crucial in the process of reconfiguring its existing patient sensing and responding capabilities [
Hypothesis 5: The more effective the hospital department’s knowledge processes, the higher the hospital department’s patient agility.
A deductive and quantitative approach was used to address the study objectives. Hence, hypothesized relationships among key constructs are analyzed by first cross-sectionally collecting field data and then analyzing the obtaining survey data.
An online survey was developed to capture clinicians’ attitudes toward IT ambidexterity, digital dynamic capability, knowledge processes, and patient agility of their hospital departments. Hence, we adopted a practitioner-based approach that used subjective measures because hospitals are typically more willing to provide subjective data than sensible objective performance metrics (eg, [
This survey was pretested on multiple occasions by 5 master’s students and 6 medical practitioners and scholars to improve the survey items’ content and face validity. The medical practitioners all had sufficient knowledge and experience to assess the survey items effectively to provide valuable improvement suggestions. Within the survey, comprehensible construct definitions were provided, and the survey followed a logical structure. In one of the final questions, the participants were asked if they wanted to receive critical insights from the study. Various controls were also built during the data collection process so that each department completed the survey only once.
The target population was (clinical) department heads and managers, team leads, and physicians under the assumption that, at the hospital department level, these health care providers are actively involved in contact with patients or at least have intelligible insight into the department’s patient interactions and the use of IT. Moreover, these are the foremost stakeholders who can provide insights into the unique and sometimes complicated situations where medical knowledge is exploited, enabling a unique treatment course [
Data were conveniently collected between November 10, 2019, and January 5, 2020, sampled from Dutch hospitals through the 5 master’s students’ professional networks within Dutch hospitals. Convenient sampling is a nonprobability sampling method where the sample is taken from a group of people easy to contact or reach and fit the profile [
The survey software registered 230 active and unique participants. However, 101 cases had to be removed because of unreliable data entries or no entries at all. Additionally, 1 respondent (administrative function) did not belong to our target population and had to be removed from the sample. In a final step, 21 additional participants had to be removed due to substantial missing values (ie, more than 15%). Therefore, this study used 107 complete survey responses for final analyses.
The selection of constructs and measures was made following previous empirically validated work. Additionally, this study includes only measures that were suitable for departmental-level analyses. Since this research was done in a health care setting, some original items had to be slightly reworded to fit the particular context.
This study controlled the outcomes for both
Participants were allowed to complete the survey anonymously, and we did not log anything in the survey system that could trace participants. The participants could withdraw their entries if they wanted to. In addition, reusable personal data was not requested, and the survey did not include questions about personal or sensitive topics [
The research model’s hypothesized relationships are tested using partial least squares (PLS) structural equation modeling (SEM). To estimate and model parameters, SmartPLS version 3.2.9. was used [
Another reason to justify the variance-based approach is that the current sample is relatively small [
This study accounts for possible nonresponse bias by using a
Within the final sample of 107 participants, 36 (33.6%) work for a university medical center, 41 (38.3%) work for a specialized top clinical (training) hospital, and the final 30 (28%) work for general hospitals.
Demographics of participating hospital departments.
Element and categories | Participants, n (%) | |
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University medical center | 36 (33.6) |
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Top clinical training hospital | 41 (38.3) |
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General hospital | 30 (28) |
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0-5 | 28 (26.2) |
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6-10 | 20 (18.7) |
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11-20 | 20 (18.7) |
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20-25 | 8 (7.5) |
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>25 | 31 (29.0) |
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0-5 | 49 (45.8) |
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6-10 | 18 (16.8) |
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11-20 | 28 (26.2) |
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20-25 | 6 (5.6) |
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>25 | 6 (5.6) |
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<4000 | 25 (23.4) |
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4000-6500 | 21 (19.6) |
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6500-9000 | 12 (11.2) |
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9000-11,500 | 12 (11.2) |
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11,500-14,000 | 11 (10.3) |
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≥14,000 | 26 (24.3) |
Various analyses were done to determine the reliability and validity of the study constructs. This is a crucial step before testing the study’s hypotheses and evaluating the quality of the research.
In the first step, the internal consistency reliability is investigated using both the Cronbach alpha measure and the composite reliability estimation value. In a subsequent step, this study assessed the convergent validity—using the average variance extracted (AVE)—of the first-order latent constructs [
As the reliability and validity of the model are now established, the model’s associated fit indices can be assessed as well as the hypothesized relationships using the structural model.
Convergent and discriminant validity assessment.
Constructs | AVEa | CAb | CRc | EXPLRd | EXPLOe | DDCf | PSCg | PRCh | KPi |
EXPLR | 0.888 | .867 | 0.919 |
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EXPLO | 0.790 | 0.937 | 0.960 | 0.502 |
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DDC | 0.783 | 0.862 | 0.916 | 0.584 | 0.631 |
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PSC | 0.723 | 0.904 | 0.929 | 0.375 | 0.502 | 0.588 |
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PRC | 0.792 | 0.934 | 0.950 | 0.313 | 0.341 | 0.452 | 0.508 |
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KP | 0.616 | 0.875 | 0.906 | 0.463 | 0.512 | 0.552 | 0.713 | 0.393 |
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aAVE: average variance extracted.
bCA: Chronbach alpha measure.
cCR: composite reliability estimation.
dEXPLR: information technology exploration.
eEXPLO: information technology exploitation.
fDDC: digital dynamic capability.
gPSC: patient sensing capability.
hPRC: patient responding capability.
iKP: patient knowledge processes.
jThe square root of the AVE was compared with cross-correlation values.
This study used three metrics, that is, (1) standardized root mean square residual (SRMR; note, however, that the first two metrics for model fit should be interpreted with caution as these metrics are not fully established PLS-SEM evaluation criteria), (2) Stone-Geisser test, and (3) the variance explained by the model (
First, the newly developed SRMR metric was calculated. The SRMR metric calculates the difference between observed correlations and the model’s implied correlations matrix [
Finally,
Following the model fit assessments and the assessment of the measurement model, we tested the hypotheses of the study that we developed in the section Research Model and Hypotheses.
Based on the outcomes of the nonparametric bootstrap resampling procedure [
Digital dynamic capability was positively associated with patient agility (
The bootstrapped PLS results showed nonsignificant effects for the included control variables:
Structural model results. IT: information technology.
The digital transformation brings about an unprecedented challenge for modern day hospitals [
Using data from 107 hospital departments in the Netherlands, this study showed that the simultaneous engagement of IT exploration and IT exploitation within hospital departments (ie, IT ambidexterity) enhances the qualities and competencies to manage innovative digital technologies for new patient service development (ie, digital dynamic capability). In addition, the outcomes showcase that the greater the hospital departments’ IT ambidexterity, the more effective are its knowledge processes. Furthermore, our results show that actively investing in digital dynamic capability is essential when departments want to enhance quality and patient clinical journeys. In particular, our study demonstrates that the more developed the hospital department’s digital dynamic capability, the more effective will be the hospital department’s knowledge processes. This outcome is important, as knowledge processes facilitate transforming clinical data into patient-related insights, thereby supporting clinicians within hospitals to make informed decisions concerning diagnosis and treatment. Our study shows that these data-driven processes allow clinicians to improve the patient treatment process and medical quality services and be more agile in their work, which is in line with the theory by Li et al [
This study makes substantial theoretical and practical contributions, which will be discussed next.
The process of digitizing existing patient services and developing new digital solutions remains time-consuming and challenging in many ways. In addition, from a research perspective, there is still a limited understanding of how IT resources and the digital capability-building processes can facilitate patient agility and contribute to the much needed insights on obtaining value from IT at the departmental level [
Evidence unfolded that digital dynamic capability partially mediates the relationship between IT ambidexterity and knowledge processes. Similarly, a partial mediation characterizes the triangular relationship between digital dynamic capability, knowledge processes, and patient agility. These outcomes corroborate existing IT-enabled agility and dynamic capability studies [
This study embraces the dynamic capabilities and knowledge-based view of IT resource deployments and advances the current insights on the resource and capability-building perspective [
Outcomes of this study suggest that hospitals—that are committed to the process of ambidextrously managing their IT resources—are more proficient in promptly sensing and responding to patients’ medical needs and demands. These theoretical contributions are valuable as these particular insights remained unclear in the extant literature, and future research can take these insights into account when investigating the IT benefits in hospitals. Likewise, unfolding the benefits of hospital departments’ dual capacity to aim for two disparate things at the same time using empirical data is relevant from a practical perspective, as the business value of IT and the preceding IT investments can be justified [
This study provides hospital department managers and decision makers with valuable practical implications. Hospital departments must direct IT investments to bring about the highest IT business value, given the many substantial challenges to ensure high quality across the patient care delivery continuum. This research shows that IT ambidextrous departments can adequately develop new innovative digital opportunities and patient services to enhance the hospital department’s knowledge processes and patient agility levels. This development path is crucial for successful hospital departments that strive to enhance the patient’s clinical journey and provide patients with fitting health services. However, it is important to note that IT ambidexterity can help hospital departments indirectly obtain high levels of patient agility. However, this development might be hindered if departments do not fully leverage their dual IT exploration and exploitation capacity to drive digital dynamic capability and knowledge processes and enhance patient agility.
Digital dynamic capability is crucial in the development of knowledge processes and patient agility. Hospital department managers should develop the core competencies, knowledge, and skills to process patient information better, adequately respond to digital transformation, master the state-of-the-art digital technologies, and deliberately develop innovative patient services using digital technology. Hospital department managers should also be aware of the crucial role of knowledge processes. Mature knowledge processes enhance decision-making processes and drive patient agility in hospital departments. Therefore, they should dedicate their resources to leverage these capabilities fully so that they are better equipped to search, identify, and absorb new technological innovations; integrate, process, and exchange patient information; and use them for decision-making processes, and to anticipate and respond fast to changes in the patient’s health service needs. Our study results highlight the need for hospital departments to focus more on patient agility, a crucial antecedent of enhanced patient care. Hospital department managers and decision makers should also deliberately pay attention to end user’s psychological meaningfulness, stakeholder involvement, and providing adequate resourcing and infrastructures when implementing new digital technologies [
In summary, hospital departments should strive to be agile in the modern turbulent economic environment. This study provides crucial insights and guidance to achieve this.
Several study limitations should be mentioned. These limitations suggest future research avenues. This study used self-reported data to test the developed hypotheses as obtaining objective measures is typically a challenging endeavor. The decision to use self-reported data is still justifiable as empirical outcomes, as these data types are strongly correlated to objective measures [
Finally, this study did not include patient service performance outcomes and benefits beyond this paper’s scope. Hence, it would be interesting to investigate the relationships between patient agility and the hospital department’s performance outcomes, as patient agility is considered a crucial ingredient in delivering high-quality patient value and overall streamlined patient journeys. Hence, this research’s outcomes inform further research about whether patient agility impacts clinical care quality and efficacy. Scholars could then investigate patient agility’s contribution to increasing, for example, clinical productivity and quality enhancement during different stages of the COVID-19 pandemic [
Survey constructs and items and descriptive statistics.
Survey response per medical department.
Cross-loading analysis for the first-order factors.
artificial intelligence
average variance extracted
dynamic capabilities view
heterotrait-monotrait ratio of correlations
information system
information technology
partial least squares
structural equation modeling
standardized root mean square residual
variance inflation factor
We want to thank Josja Willems, Reinier Dickhout, Rick Smulders, Yves-Sean Mahamit, and Renaldo Kalicharan for their valuable contributions to the data collection and for sharing their perspectives in numerous discussions. Additionally, we would like to express our gratitude to the 107 participating hospitals. Your active role made this a success.
None declared.