DATA PRIVACY PRESERVING MODEL FOR HEALTH INFORMATION SYSTEM
Health Information Systems (HIS) provide the bedrock for decision-making and has four key
functions: data generation, compilation, analysis and synthesis, and communication and use. The HIS gathers data from the health sector and other relevant sectors, analyzes the data and ensures their overall relevance, quality, and timeliness, and converts data into information for health-related decision-making. In addition to being essential for monitoring and evaluation, the information system also provides early warning capability, supports patient and health facility management, facilitate planning, supports and stimulates research, permits health situation and trends analysis, supports global reporting, and underpins communication of health challenges to diverse users (WHO, 2009).
To improve the quality of medical care around the globe, efforts are being made to increase the practice of evidence-based medicine through the use of an HIS called Clinical Decision Support Systems (CDSS). Clinical Decision Support provides clinicians, patients, or caregivers with clinical knowledge and patient-specific information to help them reach decisions that enhance patient care (Osheroff, Teich & Middleton, 2011). The patient’s information is matched to a clinical knowledge base, and patient-specific appraisals are then communicated effectively at appropriate times during patient care. Some CDSS include forms and templates for entering and documenting patient information, and alerts, reminders, and order sets for providing suggestions and other support. The use of CDSS comes with many potential benefits. Importantly, CDSS can increase adherence to evidence-based medical knowledge and can reduce unnecessary variation in clinical practice. CDSS can also assist with information management to support the physicians’ decision making abilities, reduce their mental workload, and improve clinical workflows (Karsh et al., 2010). When well designed and implemented, CDSS have prospects that can improve health care quality, and also to increase efficiency and reduce health care costs (Berner, 2010).
Despite the promise of CDSS, there are several barriers that can hinder their development and implementation. Till date, Medical knowledge base is incomplete in part because of insufficient clinical evidence (Englander & Carraccio, 2014). Moreover, methodologies are still being designed to convert the knowledge base into computable code, and interventions for conveying the knowledge to clinicians in a way they can easily use in practice are in the nascent stages of development. Low clinician demand for Clinical Decision Support is another encumbrance to broader CDSS adoption. Clinicians’ lack of motivation to use CDSS appears to be related to usability issues with the Clinical Decision Support intervention, its lack of integration into the clinical workflow, concerns about autonomy, and the legal and ethical implications of adhering to or overriding recommendations made by the CDSS (Berner, 2010). In addition, in many cases, acceptance and use of CDSS are hinged upon the adoption of electronic medical records (EMRs), because EMRs can include Clinical Decision Support applications as part of Computerized Provider Order Entry (CPOE) and electronic prescribing systems.
One of the five recommendations made for CDSS in connection with the practice of Evidence-based Medicine was to “develop maintainable technical and methodological foundations for computer-based decision support” (Sim, Gorman & Greenes, 2011). Also, the medical domain is “characterized by much judgmental knowledge”. Consequently, a CDSS that can provide suggestive knowledge representations based on data sets with patient attributes that are synonymous with the attributes of the patient in context is valuable to a medical practitioner. Invariably, there are situations where the number of local samples to draw conclusions from, is none or few. Several current challenges have not been sufficiently addressed during the development of CDSS. From latest research, the lists of challenges include: improvement of the human-computer interface, dissemination of best practices in CDSS design, development, and implementation, creation of an architecture for sharing executable CDSS modules and services, combination of recommendations for patients with co-morbidities, summary of patient-level information, prioritization and filtering of recommendations to the user, prioritization of CDSS content development and implementation, creation of Internet-accessible clinical decision support repositories, usage of free text information to drive clinical decision support, and mining of huge clinical databases to create new CDSS (Kumar & Prabha, 2016).
Psychiatry is one branch of medicine that urgently needs HIS owing to the fact that there are relatively few specialists in that area of medicine (Saha, Chant, Welham, & McGrath, 2015). According to the National Alliance on Mental Illness, mental illnesses are medical conditions that disrupt a person’s clear thinking, feeling, mood, ability to relate to others, decision making ability and daily functioning (NAMI, 2011). Mental illnesses include schizophrenia, depression, bipolar disorder, obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), borderline personality disorder, anxiety disorder and others. However, schizophrenia involves a relatively higher display of psychotic symptoms than most other mental illnesses (Amin, Agarwal & Beg, 2013).
Schizophrenia is a chronic and debilitating illness characterized by perturbations in cognition, affect and behavior, all of which have a bizarre aspect (Lehman et al., 2010). Due to the fact that schizophrenia is a stigmatized illness it is important for schizophrenic patients’ data to be kept with a high degree of secrecy so as to avoid sensitive patient data being divulged. It is therefore expedient that in Clinical Decision Support Systems that contain data of Schizophrenic patients, access to patient data by the healthcare givers be restricted based on their roles in the hospital. This can be achieved by employing access control. The Mandatory Role-Based Access Control is a type of access control and can be employed for such a study as this. To boost the security of a Health Information System (HIS) through data privacy preservation, this study proposes a model for implementing data privacy preservation in a HIS. This model would help boost the security of the HIS in question through the restriction of access of users to its database.
This study proposes a Data Privacy Preservation (DPP) model for HIS. In order to guarantee the secrecy of sensitive patient data domiciled in a HIS, the study involved the development of an application named Schizoapp which was used to instantiate the proposed DPP model and effected data privacy by blocking attributes on a patient database based on the Mandatory Role-Based Access Control (MAC) model which is used to assign access rights to different categories of health professionals based on their role in the hospital. The study also compared the use of the application (Schizoapp) developed in this study for data privacy preservation with the machine learning approach to data privacy preservation which employed the Random Forest Decision Tree algorithm embedded in the WEKA software.
In healthcare delivery, patients are required to share information with certain categories of health personnel to facilitate correct diagnosis and to determine appropriate treatment. However, patients would most of the time prefer their sensitive information to be kept secret particularly from persons that need not have access to such information especially in cases of health problems such as schizophrenia as the disclosure of such private information may lead to social stigma and discrimination. There have been cases where health personnel who by virtue of their role ought not to have access to certain patient information gained access to such information. Some of these health personnel cause harm to the patient in question by divulging such details to other individuals thereby jeopardizing the patient’s health. Hence, the healthcare system becomes the worse for it as a number of patients may relapse to worse states they already improved from and the retrogression in the patients’ health status will in the long run take a toll on the healthcare system.
The existing Data Privacy Preservation (DPP) models are designed for Clinical Decision Support Systems with inadequate information available for DPP in Health Information Systems (HIS) in Nigeria. This research, therefore focused on the development of a model for Data Privacy Preservation (DPP) in HIS to address this inadequacy.
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