IT ambidexterity driven patient agility and hospital patient service performance: a variance approach (2107.09415v1)

Abstract: Hospitals are currently exploring digital options to transform their clinical procedures and their overall engagement with patients. This paper investigates how hospital departments can leverage the ability of firms to simultaneously explore new IT resources and practices (IT exploration) as well as exploit their current IT resources and practices (IT exploitation), i.e., IT ambidexterity, to adequately sense and respond to patients' needs and demands, i.e., patient agility. This study embraces the dynamic capability view and develops a research model, and tests it accordingly using cross-sectional data from 90 clinical hospital departments from the Netherlands through an online survey. The model's hypothesized relationships are tested using Partial Least Squares (PLS) structural equation modeling (SEM). The outcomes demonstrate the significance of IT ambidexterity in developing patient agility, positively influencing patient service performance. The study outcomes support the theorized model can the outcomes shed light on how to transform clinical practice and drive patient agility.

• The paper reveals that IT ambidexterity significantly boosts patient agility by balancing IT exploration and exploitation (β = 0.48, p < 0.0001).
• The paper demonstrates that enhanced patient agility substantially improves patient service performance through effective sensing and responding (β = 0.47, p < 0.0001).
• The paper confirms that patient agility fully mediates the relationship between IT ambidexterity and service performance, explaining 23% and 22% of the variance respectively.

This paper investigates how hospital departments can effectively use information technology (IT) to become more agile in responding to patient needs and ultimately improve patient service performance (2107.09415). It addresses gaps in understanding how IT investments translate into tangible benefits at the departmental level, particularly concerning patient-centered care and dynamic capabilities.

The core concepts explored are:

1. IT Ambidexterity: This refers to a hospital department's ability to simultaneously explore new IT resources and practices (e.g., experimenting with AI-driven diagnostics, adopting new patient portals) and exploit existing IT resources and practices (e.g., optimizing the current Electronic Medical Record (EMR) system, reusing existing IT skills and infrastructure). The paper argues that balancing these two aspects is crucial.
2. Patient Agility: Framed within the Dynamic Capability View (DCV), this is conceptualized as a higher-order capability comprising two dimensions:
   • Sensing Capability: The ability to continuously monitor, anticipate, and understand patient needs, preferences, and emerging trends.
   • Responding Capability: The ability to quickly react and adapt services, processes, and resources based on sensed patient needs and environmental changes.
3. Patient Service Performance: Measured through outcomes like enhanced service quality, improved accessibility of medical services, and increased patient satisfaction.

The paper proposes and tests two main hypotheses using data collected via an online survey from 90 clinical hospital departments in the Netherlands:

• H1: IT ambidexterity positively enhances the patient agility of the hospital department.
• H2: Patient agility positively enhances the hospital department's patient service performance.

Data analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

Key Findings:

• Both hypotheses were supported. IT ambidexterity was found to be a significant driver of patient agility (β = 0.48, p < 0.0001).
• Patient agility, in turn, significantly positively influenced patient service performance (β = 0.47, p < 0.0001).
• The results indicated that patient agility acts as a full mediator between IT ambidexterity and patient service performance. This means that the positive effect of balancing IT exploration and exploitation on patient service performance occurs through the development of better sensing and responding capabilities (patient agility).
• IT ambidexterity explained 23% of the variance in patient agility, and patient agility explained 22% of the variance in patient service performance. Control variables like department size and age did not show significant effects.

Practical Implications for Implementation:

1. Balanced IT Strategy: Hospital departments should consciously manage their IT portfolio to balance investments in new, exploratory technologies with efforts to optimize and leverage existing systems. Simply focusing on one aspect (e.g., only implementing cutting-edge tech or only refining current systems) is less effective than pursuing both simultaneously.
2. Justifying HIT Investments: The findings provide evidence that strategic IT management (ambidexterity) can build crucial organizational capabilities (patient agility) that lead to better patient-related outcomes. This helps justify HIT investments beyond simple efficiency gains.
3. Developing Agility: To improve patient service, departments should focus on enhancing their ability to sense patient needs (e.g., through patient feedback systems, trend analysis) and respond quickly (e.g., flexible clinical pathways, rapid deployment of service adjustments). IT ambidexterity is a key enabler for developing these sensing and responding routines.
4. Diagnostic Tool: Departments struggling with digital transformation or patient service performance can use the concepts of IT ambidexterity and patient agility (sensing/responding) to diagnose weaknesses. Are they failing to explore new IT? Are they inefficiently exploiting current IT? Are they slow to sense or respond to patient needs?
5. Change Management: Implementing changes requires addressing both individual behaviors and institutional factors. Decision-makers should ensure end-user involvement, provide adequate resources, and foster a culture that values both exploring new possibilities and refining existing processes when introducing new digital technologies.

The paper acknowledges limitations, including the use of single informants per department and a relatively small sample size, suggesting future research could use matched-pair data collection and larger samples for increased robustness.