RESUMO
BACKGROUND: With the rapid improvement in healthcare technologies, the security and privacy of the most sensitive data are at risk. Patient privacy has many components, even when data are in electronic format. Although patient privacy has extensively been discussed in the literature, there is no study that has presented all components of patient privacy. METHODS: This study presents a complete assessment framework, develops an inventory as an assessment tool, and examines the reliability and validity of the inventory. The study was carried out in three phases: conceptual framework development, inventory development, and an evaluation case study. Fuzzy conjoint analysis was used in the evaluation to deal with subjectivity and ambiguity. As a result of the evaluation, the case study institution was given a patient privacy maturity level between 1 and 5, where 1 is the worst and 5 is the best. RESULTS: The case study evaluated the largest hospital in Turkey, which employs 800 nurses. Half of the nurses, 400, participated in the study. According to the literature, healthcare institutions do not invest enough in protecting patients' privacy, and the results of the study support this finding. The institution's maturity level was 2, which is poor. CONCLUSIONS: This study measured privacy maturity with many assessment components. The result of the assessment explains to patients and the public whether their data are secure or not. With the implementation of this maturity level, patients have an idea about which institution to choose, and the public can infer the reliability of institutions in terms of patient privacy.
RESUMO
Healthcare is a technology-intensive industry. Although all healthcare staff needs qualified computer support, physicians and nurses need more. As nursing practice is an information intensive issue, understanding nurses' expectations from healthcare information systems (HCIS) is a must issue to meet their needs and help them in a better way. In this study perceived importance of nurses' expectations from HCIS is investigated, and two HCIS is evaluated for meeting the expectations of nurses by using fuzzy logic methodologies.
