A Model of Perception of Privacy, Trust, and Self-Disclosure on Online Social Networks.

Online Social Networks are used widely, raising new issues in terms of privacy, trust, and self-disclosure. For a better understanding of these issues for Facebook users, a model was built that includes privacy value, privacy risk, trust, privacy control, privacy concerns, and self-disclosure. A total of 602 respondents participated in an online survey, and structural equation modeling was used to evaluate the model. The findings indicate significant relationships between the constructs in this study. The model from our study contributes new knowledge to privacy issues, trust and self-disclosure on Online Social Networks for other researchers or developers of online social networks.

Patient education on safety and privacy.

The individual's right to determine if, when and how data about them will be collected, stored, used and shared with others is called the right to privacy. The problem of data collection and sharing arises daily among health services. Among medical staff, there are numerous message flows, including the medical records of patients and other patient's personal data. These data are often completely unprotected and available to anyone who knows where it is. Unfortunately, the same data might not be available for patients, despite the fact that each individual has the right to view their own medical record and despite the fact that many other persons connected directly or indirectly to the patient has this access without limitations. In the paper, we will not concentrate on actions that medical staff has to perform nor on the knowledge that they have to have in order to protect a patient's personal data and enable a patient's access to their own data, but we will concentrate on the problem of educating patients about their rights and duties with regard to safety and privacy. Our educational suggestions will be given on the basis of corresponding Slovenian legislation and guidelines for medical staff regarding the protection of personal data.

Knowledge Formalization to Support Decision-Making in Heart Failure Treatment.

This paper presents medical knowledge representation of data provided within Clinical Practice Guidelines for Heart Failure. The formalization is provided in order to support taking decisions on an appropriate treatment strategy for a specific patient. An intuitive and efficient mechanism of medical knowledge formalization, called extended Timed Transition Diagram (eTTD), is used to represent acquired medical knowledge. The presented models can be used to help students in their training as well as to support physicians with their decision-making tasks.

Formalization and acquisition of temporal knowledge for decision support in medical processes.

BACKGROUND: In medical practice, long term interventions are common and they require timely planning of the involved processes. Unfortunately, evidence-based statements about time are hard to find in Clinical Practice Guidelines (CPGs) and in other sources of medical knowledge. At the same time, health care centers use medical records and information systems to register data about clinical processes and patients, including time information about the encounters, prescriptions, and other clinical actions. Consequently, medical records and health care information systems are promising sources of data from which we can detect temporal medical knowledge. OBJECTIVE: The objectives were to (1) Analyze and classify the sorts of time constraints in medical processes, (2) Propose a formalism to represent these sorts of clinical time constraints, (3) Use these formalisms to enable the automatic generation of temporal models from clinical data, and (4) Study the adherence of these intervention models to CPG recommendations. METHODS: In order to achieve these objectives, we carried out four studies: The identification of the sort of times involved in the long-term diagnostic and therapeutic medical procedures of fifty patients, the supervision of the indications about time contained in six CPGs on chronic diseases, the study of the time structures of two standard data models, as well as ten languages to computerize CPGs. Based on the provided studies, we synthesized two representation formalisms: Micro- and macro-temporality. We developed three algorithms for automatic generation of generalized time constraints in the form of micro- and macro-temporalities from clinical databases, which were double tested. RESULTS: A full classification of time constraints for medical procedures is proposed. Two formalisms called micro- and macro-temporality are introduced and validated to represent these time constraints. Time constraints were generated automatically from the data about 8781 Arterial Hypertension (AH) patients. The generated macro-temporalities restricted visits to be between 1-7 weeks, whereas CPGs recommend 2-4 weeks. Micro-temporal constraints on drug-dosage therapies distinguished between the initial dosage and the target dosage, with visits every 1-6 weeks, and 2-5 months, respectively. Our algorithms obtained semi-complete maps of dosage increments and the maximum dosages for 7 drug types. Data-based time limits for lifestyle change counsels and blood pressure (BP) check-ups were fixed to 6 and 3 months, for patients with low- and high-BP, respectively, when CPGs specify a general 3-6 month range. CONCLUSIONS: Experience-based temporal knowledge detected using our algorithms complements the evidence-based knowledge about clinical procedures contained in the CPGs. Our temporal model is simple and highly descriptive when dealing with general or specific time constraints' representations, offering temporal knowledge representation of varying detail. Therefore, it is capable of capturing all the temporal knowledge we can find in medical procedures, when dealing with chronic diseases. With our model and algorithms, an adherence analysis emerges naturally to detect CPG-compliant interventions, but also deviations whose causes and possible rationales can call into question CPG recommendations (e.g., our analysis of AH patients showed that the time between visits recommended by CPGs were too long for a proper drug therapy decision, dosage titration, or general follow-up).

Agent oriented approach to handling medical data.

Medical treatment of a patient could be represented as a circle of the following actions: examination, diagnostics, and therapy. The aims of the actions are to find out the patient's state of health and consequently to conclude about possible diseases and finally to choose a suitable therapy. In long term, the circle of actions repeat as long as the patient is not healthy. Efficiency of this treatment depends on the knowledge and the experiences of the physicians involved. Information technology offers many possibilities to help the physicians increase the efficiency and the quality of this work. In the article, we present an agent-oriented computer-based health care service, which uses information from different data sources that are physically distributed across several sites. Such a decentralized approach mirrors the organizational structure of a health service and it is very similar to an agent-oriented view of the world.

Diagnostic process from the data quality point of view.

The spread of electronic use of data in various areas has put importance of data quality to higher level. Data quality has syntactic and semantic component; the syntactic component is relatively easy to achieve if supported by tools (either off-the-shelf or our own), while semantic component requires more research. In many cases such data come from different sources, are distributed across enterprise and are at different quality levels. Special attention needs to be paid to data upon which critical decisions are met, such as medical data for example. The starting point for research is in our case the risk of the medical area. In the paper we will focus on the semantic component of medical data quality.

Protecting medical data for decision-making analyses.

In this paper, we present a procedure for data protection, which can be applied before any model building based analyses are performed. In medical environments, abundant data exist, but because of the lack of knowledge, they are rarely analyzed, although they hide valuable and often life-saving knowledge. To be able to analyze the data, the analyst needs to have a full access to the relevant sources, but this may be in the direct contradiction with the demand that data remain secure, and more importantly in medical area, private. This is especially the case if the data analyst is outsourced and not directly affiliated with the data owner. We address this issue and propose a solution where the model-building process is still possible while data are better protected. We consider the case where the distributions of original data values are preserved while the values themselves change, so that the resulting model is equivalent to the one built with original data.