Identifying the risk factors of patient safety in internet hospitals: A mixed methods study

Objective

The purpose of this study was to identify key risk factors and their interrelationships for patient safety in internet hospitals from a system perspective, using mixed methods of qualitative and quantitative analysis.

Methods

This study constructed a comprehensive indicator system of patient safety risk factors in internet hospitals by qualitative analysis using the Patient Safety Systems (SEIPS) model as a framework. Risk factors were initially identified through a literature review and subsequently refined using a Delphi survey involving 24 experts related to internet hospitals in China. The identified indicators were quantitatively analyzed to determine key risk factors and their influencing mechanism using the Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methods.

Results

The qualitative analysis established a patient safety risk factor indicator system for internet hospitals, comprising 23 elements across six dimensions. Quantitative analysis employing the DEMATEL-ISM approach revealed that risk management has the highest centrality. Among cause factors, task complexity exerts the most significant impact on other factors, while network information security exhibits the highest absolute value among result factors. Risk factors are categorized into three levels: surface, deep, and root factors, with task complexity, legal and regulatory, and guidance policy being the root factors at the foundation of the system.

Conclusions

Our study offered a systemic perspective on analyzing risk factors for patient safety in internet hospitals. Policymakers and managers of internet hospitals should take advantage of the interrelationships among these factors to mitigate patient safety risks by effectively controlling key factors.

Public Interest Summary

In the rapidly evolving landscape of internet hospitals, ensuring patient safety is paramount. This study aimed to comprehensively identify and understand key risk factors influencing patient safety within these digital healthcare platforms. Using mixed methods of qualitative and quantitative analysis, the study examined the intricate interplay of factors affecting patient safety. Our methodology involved constructing a risk factors indicator system based on the Patient Safety Systems (SEIPS) model. By employing the integrated Decision-Making Trial and Evaluation Laboratory along with the Interpretive Structural Modeling method, we unveiled the core risk factors and their intricate relationships. Recognizing the interconnectivity of these factors allows us to develop effective risk mitigation strategies that enhance patient safety in internet hospitals. This study encourages stakeholders to leverage the dynamic relationships among these factors to ensure safer online healthcare experiences for patients.