Applying CHAID algorithm to investigate critical attributes of secured interoperable health data exchange

Abstract

With enhanced interoperability in healthcare environment the exchange of electronic health records (EHRs), both intra and inter organisations, has increased manifold. Sharing of the EHR creates room for illegal disclosures and confidentiality breaches. Interoperable healthcare is a complex system with many independent components. To design a secured framework for such a system, one need to understand the most important security attributes and predict various dependencies among them. The security attributes selected for statistical analysis are taken from the real-time study of patient-doctor relationship existing in any hospital or clinic. Hospitals with functional EHR-systems are the prerequisite of this study. The dependencies in the obtained data are generated through classification technique, chi-squared automatic interaction detection (CHAID). The decision tree obtained is analysed and verified using regression. The paper enabled the identification of the salient feature controlling which would maximally reduce security threats while sharing EHRs in interoperable healthcare environment.

A TOGAF based interoperable health information system needs assessment for practitioner–patient interaction

“…privacy and confidentiality of patient data thereby making it mandatory to preserve and prohibit access to data without the patient's consent (Bhartiya & Mehrotra, 2014). In as much as security protocols and rules are developed in HISs, confidentiality breaches will always be a threat in interoperable healthcare systems, and as such adequate authentication measures must be developed and enforced (Tipton et al, 2016).…”

“…Availability of information is mainly threatened by communication network reliability. Denial of service (DoS) attacks due to network conflicts and other vulnerabilities render service unavailable to seemingly connected applications sharing access to database servers (Bhartiya & Mehrotra, 2014; Shave, 2018).…”

“…On the other hand, “Privacy” refers to the right of patients to prevent their information from being revealed to unintended stakeholders or fostering individual ownership of data thereby explicitly protecting it from unauthorized access and disclosure (Olaronke & Rhoda, 2013; Shave, 2018). Therefore, the integration of e‐healthcare systems should be implemented in such a way that does not compromise on data privacy and confidentiality of patient data thereby making it mandatory to preserve and prohibit access to data without the patient's consent (Bhartiya & Mehrotra, 2014). In as much as security protocols and rules are developed in HISs, confidentiality breaches will always be a threat in interoperable healthcare systems, and as such adequate authentication measures must be developed and enforced (Tipton et al, 2016).…”

Pragmatic Approaches to Interoperability – Surmounting Barriers to Healthcare Data and Information Across Organizations and Political Boundaries

“…This leads to inconsistencies in file storage, with some images being complete and others lacking data. 48 Integrating the Healthcare Enterprise Launched in 1998, the IHE initiative was a joint collaboration between the Healthcare Information and Management Systems Society (HIMSS) and the Radiological Society of North America (RSNA) to coordinate "the use of many different standards by layering on the context of healthcare delivery." 49 The IHE framework utilizes system integration profiles that are based on clinical scenario use cases, considering both the associated actors, i.e., systems, and transaction activities.…”

Individualized visual narratives: type 1 diabetes management strategies among three runners in the London Marathon

“…Data-related challenges in healthcare share commonalities with other domains, encompassing issues of data integration, manipulation, accessibility, and comprehensibility (Bhartiya & Mehrotra, 2014). However, the healthcare field presents distinct domain-specific complexities that warrant special attention, such as the breath of use, data complexity and statistical rigor (Gotz & Borland, 2016).…”