Task support of electronic patient care report (ePCR) systems in emergency medical services: An elaboration likelihood model lens

Healthcare information management and operational cost performance: empirical evidence

Healthcare knowledge management systems can mitigate hospitals' operational inefficiency. As a healthcare information technology, the electronic health record (EHR) receives much attention from medical institutions due to its considerable impact on operational cost performance. This paper focuses on EHR systems to address operational inefficiency by which patients pay more for health care services, and many U.S. hospitals are filing for bankruptcy. From the theoretical perspective of the practice-based view, this paper introduces a path to implement EHR systems for improving cost performance. The empirical investigation is archival data of 200 hospitals collected from the U.S. healthcare agencies. Findings contribute to prior work by hypothesizing moderating and mediating roles in EHR systems implementation. This paper introduces absorptive capacity and monitoring mechanisms as enablers of implementing EHR systems. The results showed that hospital monitoring strengthens the relationship between absorptive capacity and electronic health record systems implementation, which results in better operational cost performance. Theoretically, this study supports the long-term potential benefits of EHR adoption, and its findings are consistent with optimizing efficiency through data standardization and interoperability. From a practical perspective, this study supports hospitals' investments in evolving healthcare information technology systems through the development of a knowledge-based system employing EHR, particularly when hospitals are merging or need a financial strategic plan to control expenses.

Continuance intention and digital health resources from the perspective of elaboration likelihood model and DART model: a structural equation modeling analysis

Background

Internet hospitals, online health communities, and other digital health APPs have brought many changes to people's lives. However, digital health resources are experiencing low continuance intention due to many factors, including information security, service quality, and personal characteristics of users.

Methods

We used cross-sectional surveys and structural equation modeling analysis to explore factors influencing user willingness to continue using digital health resources.

Results

Information quality (β = 0.31, p < 0.05), service quality (β = 0.19, p < 0.05), platform reputation (β = 0.34, p < 0.05), and emotional support (β = 0.23, p < 0.05) have significant positive effects on user value co-creation behavior. Additionally, user trust and perceived usefulness could mediate the association between user value co-creation behavior and continuance intention, with mediation effects of 0.143 and 0.125, respectively. User involvement can positively moderate the association between user value co-creation behavior and user trust (β = 0.151, t = 2.480, p < 0.001). Also, user involvement can positively moderate the association between value co-creation behavior and perceived usefulness (β = 0.103, t = 3.377, p < 0.001).

Conclusion

The keys to solving the problem of low continuance intention are improving the quality and service level of digital health resources, and promoting users' value co-creation behavior. Meanwhile, enterprises should build a good reputation, create a positive communication atmosphere in the community, and enhance user participation and sense of belonging.

Digital Health Data Quality Issues: Systematic Review

BACKGROUND

The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.

OBJECTIVE

The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.

RESULTS

The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.

CONCLUSIONS

The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.

Digital Future of Emergency Medical Services: Envisioning and Usability of Electronic Patient Care Report System

Despite the efforts of emerging technologies in the healthcare system, there is still a slower rate of acceleration in prehospital settings compared with the hospitals in digital transformation adaptation. The acknowledgment that digital transformation is significant to healthcare is reflected in planning for the future of digital healthcare. Thus, this study aimed to measure the usability of the electronic patient care report (ePCR) system among emergency medical services (EMS) staff who work in prehospital settings. A descriptive cross-sectional correlation study was used. Two hundred fifty EMS staff who are working in the prehospital setting at Saudi Red Crescent Authority in the Kingdom of Saudi Arabia were surveyed, and the response rate was 79.2% (198). An adapted tool of the Computer System Usability Questionnaire survey was used to collect data. The data were coded numerically and subjected to descriptive and inferential statistical analysis including Pearson’s correlation coefficient using the statistical software (SPSS 21). The majority of the participants rate their ePCR system as “useable” at a high level with a score of 3.41 (SD = 1.021). The overall mean of the ePCR system’s three subscales: system usefulness, information quality, interface quality, and overall satisfaction were 3.39 (SD = 1.152), 3.30 (SD = 1.052), 3.57 (SD = 1.064), and 3.37 (SD = 1.239), respectively. The least liked aspect of ePCR system software was information quality 81 (40.9%). Furthermore, there was a significant correlation between the age of EMS staff and the usability of the ePCR system (r = −0.150 ${\hspace{0.17em}}^{\ast }$ , $P=0.035$ ). The results suggest that healthcare institutions’ policy and decision-makers pay close attention to performing standardized training for the staff on their ePCR system before going to the field to increase efficiency and productivity. Furthermore, the users in this study identified other system features that, if included, could have enhanced usability, and improved functions and capabilities of the design to meet the EMS staff’s expectations.

Source Credibility and the Information Quality Matter in Public Engagement on Social Networking Sites During the COVID-19 Crisis

During the coronavirus disease 2019 (COVID-19) pandemic, people use social networking sites (SNSs) to seek social support, ease the move toward the social distance, and communicate and engage with one another. However, there is growing evidence that trustworthiness and quality of information can affect individuals' online engagement behaviors. This study proposes a theoretical model to test people's online engagement during the COVID-19 pandemic by applying the elaboration likelihood model (ELM). Through a questionnaire survey of 630 SNS users, the study examines whether and how source credibility and information quality affect people's online engagement during the COVID-19 pandemic. The model was tested using structural equation modeling. The findings show that source credibility and information quality have a significantly positive relationship with perceived benefit, while negative and significantly associated with perceived risk. Furthermore, perceived benefit is a stronger predictor of online public engagement than the perceived risk. To improve online public engagement as a crisis response strategy, careful source selection and careful generation of online crisis information should not be overlooked.