The Behavioral Intention in Accessing Digital Healthcare Information on Social Media

Purpose: Social media healthcare is becoming increasingly important in healthcare as many individuals seek healthcare information and support through online social media platforms. Social media healthcare is an emerging field; however, it is not clear what factors influence an individual’s acceptance of its usage. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study explores the factors that influence acceptance of social media usage for healthcare by consumers. Design/Method: Using a purposive sampling approach, the researchers sent a validated questionnaire link to participants through social media platforms. A total of 260 responses from respondents were analyzed using SmartPLS3. Results: Analysis of the data revealed that performance expectancy, effort expectancy, social influence, and satisfaction significantly impact consumers’ behavior intention to embrace social media health information. The effect of four identified factors: “PE” (β = .415, t-value = 3.194, p < .001), “EE” (β = -. 3.98, t-value = 2.443, p < .015), “SI” (β = .593, t-value = 3.774, p < .000), “SAT” (β = .312, t-value = 2.676, p < .008). Conclusions: Social media health is vital to healthcare seekers, especially where it enables consumers to manage their health. On implication, the study provides healthcare givers and professionals insight on how to approach and advance social media healthcare education and interaction with consumers.

Blockchain-Based Healthcare Information Preservation Using Extended Chaotic Maps for HIPAA Privacy/Security Regulations

A healthcare information system allows patients and other users to remotely login to medical services to access health data through the Internet. To protect the privacy of patients and security over the public network, secure communication is required. Therefore, the security of data in transmission has been attracting increasing attention. In recent years, blockchain technology has also attracted more attention. Relevant research has been published at a high rate. Most methods of satisfying relevant security-related regulations use modular and exponential calculation. This study proposes a medical care information preservation mechanism that considers the entire process of data storage in devices from wearable devices to mobile devices to medical center servers. The entire process is protected and complies with HIPAA privacy and security regulations. The proposed scheme uses extended chaotic map technology to develop ID-based key negotiation for wearable devices, and thereby reduces the amount of computing that must be carried out by wearable devices and achieve lightness quantify. It also uses the non-tamperability of the blockchain to ensure that the data have not been tampered with, improving data security. The proposed mechanism can resist a variety of attacks and is computationally lighter than the elliptic curve point multiplication that has been used elsewhere, while retaining its security characteristics.

Deriving Key Architectural Features of FHIR-BlockChain Integration through the Qualitative Content Analysis

Background: The blockchain has been highlighted its possibility as a technology to ensure immutability, transparency, and decentralization of data in information systems in various industries. Amongst the possibility of blockchain in the healthcare industry, one of the informatics areas that can leverage the benefits of using blockchain is healthcare information exchange while it is combined with the use of fast healthcare interoperability resources (FHIR).Objective: To investigate the key architectural features of FHIR and blockchain integration that provides the benefits of immutability of transaction data for a decentralized and secured healthcare information exchange framework.Materials and Methods: We conducted an in-depth, individual, semi-structured interview with four domain knowledge experts in the area of FHIR and medical blockchain. Our interview plan were qualified by the COREQ criteria (Consolidated Criteria for Reporting Qualitative Research), which is an analysis method the qualitative content analysis. We conducted the interview based on seven key questions and additional open discussion of technical, business, and legal aspects of the FHIR-blockchain implementations. The results of the interviews were recorded, summarized, and codified in a structured way. Results: The qualitative content analysis revealed the four types of FHIR-blockchain architectures for storing FHIR data and/or transaction and integrating FHIR and blockchain. The four FHIR-blockchain architectures compose of 1) the types of on-chain information from FHIR transactions to be stored inside blocks and 2) the features of the architectures and their possible use case scenarios. In addition to the characterization of the four FHIR-blockchain architectures, we discussed the topics; 1) potential legal issues, 2) justification of using blockchain in healthcare information exchange, and 3) practical implications and guideline of the architectures for implementation.Conclusion: Although FHIR-blockchain integration has been considered as a promising tool for decentralized and secured healthcare information exchange, it should be clarified as to how it aligned with business requirements. A detailed and/or tailored guidelines of implementation in the architectural, functional, and legal perspectives may need to be demonstrated by the benefits of using FHIR-blockchain integration.

Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers

We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental health information of high actionability for people with mental disorders. sensitivity of the classifier using both semantic and structural features as variables achieved statistically higher than that of the binary classifier using either semantic (p < 0.001) or structural features (p = 0.0010). The specificity of the optimized classifier was statistically higher than that of the classifier using structural variables (p = 0.002) and the classifier using semantic variables (p = 0.001). Differences in specificity between the full-variable classifier and the optimised classifier were statistically insignificant (p = 0.687). These findings suggest the optimised classifier using as few as 19 semantic-structural variables was the best-performing classifier. By combining insights of linguistics and statistical analyses, we effectively increased the interpretability and the diagnostic utility of the binary classifiers to guide the development, evaluation of the actionability and usability of mental healthcare information.

Risk in Healthcare Information Technology: Creating a Standardized Risk Assessment Framework

Data breaches are occurring at an unprecedented rate. Between June 2019 and early October 2020, over 564 data breaches affected over 36.6 million patients as posted to the United States Federal government HITECH portal. These patients are at risk for having their identities stolen or sold on alternative marketplaces. Some healthcare entities are working to manage privacy and security risks to their operations, research, and patients. However, many have some procedures and policies in place, with few (if any) centrally managing all their infrastructure risks. For example, many healthcare organizations are not tracking or updating all the known and potential concerns and elements into a centralized repository following industry best practice timetables for auditing and insurance quantification. This chapter examines known and potential problems in healthcare information technology and discusses a new open source risk management standardized framework library to improve the coordination and communication of the aforementioned problematic management components. The healthcare industry would benefit from adopting such a standardized risk-centric framework.