Use of e-Health in Norwegian FACT Teams: A User Perspective

Flexible assertive community treatment (FACT) is a model for delivering long-term, integrated and comprehensive treatment and follow-up for patients with severe mental illness. The objective of this study was to examine ICT challenges of Norwegian FACT teams. Doing observations in 3 teams and interviews with 5 teams we examined use of ICT systems, identifying challenges with the use of the electronic whiteboards, electronic health records, and team calendars. Better ICT systems and infrastructure are needed to support Norwegian FACT teams.

Implementation of the Flexible Assertive Community Treatment (FACT) Model in Norway: eHealth Assessment Study

Background

Flexible Assertive Community Treatment (FACT) is a model for treatment of long-term severe mental disorders. This method has become more widespread in Norway.

Objective

The objective of our study was to examine how the implementation of FACT teams in Norway has been affected by eHealth policy, infrastructure, and regulations. Another objective was to examine existing literature on eHealth interventions and challenges within FACT teams.

Methods

We have examined Norwegian policy regulating mental health services, laws and regulations, eHealth infrastructure, relevant literature on FACT teams, and current implementation of FACT in Norway.

Results

FACT teams are a wanted part of the Norwegian service system, but the current eHealth infrastructure makes sharing of data within teams and levels of health care challenging, even if eHealth regulations allow such sharing. This has been shown to be an issue in the current implementation of FACT teams in Norway. There is little or no existing research on the eHealth challenges facing FACT teams.

Conclusions

Weaknesses in the Norwegian eHealth infrastructure have been a barrier for an easy implementation of FACT teams in Norway. It is difficult to share information between the different levels of health care. We need systems that allow for easy, secure sharing of health information to and between the FACT team members and other involved health care workers.

Combining multivariate statistics and the think-aloud protocol to assess Human-Computer Interaction barriers in symptom checkers

Symptom checkers are software tools that allow users to submit a set of symptoms and receive advice related to them in the form of a diagnosis list, health information or triage. The heterogeneity of their potential users and the number of different components in their user interfaces can make testing with end-users unaffordable. We designed and executed a two-phase method to test the respiratory diseases module of the symptom checker Erdusyk. Phase I consisted of an online test with a large sample of users (n=53). In Phase I, users evaluated the system remotely and completed a questionnaire based on the Technology Acceptance Model. Principal Component Analysis was used to correlate each section of the interface with the questionnaire responses, thus identifying which areas of the user interface presented significant contributions to the technology acceptance. In the second phase, the think-aloud procedure was executed with a small number of samples (n=15), focusing on the areas with significant contributions to analyze the reasons for such contributions. Our method was used effectively to optimize the testing of symptom checker user interfaces. The method allowed kept the cost of testing at reasonable levels by restricting the use of the think-aloud procedure while still assuring a high amount of coverage. The main barriers detected in Erdusyk were related to problems understanding time repetition patterns, the selection of levels in scales to record intensities, navigation, the quantification of some symptom attributes, and the characteristics of the symptoms.

Risk analysis of information security in a mobile instant messaging and presence system for healthcare

INTRODUCTION

Instant messaging (IM) is suited for immediate communication because messages are delivered almost in real time. Results from studies of IM use in enterprise work settings make us believe that IM based services may prove useful also within the healthcare sector. However, today's public instant messaging services do not have the level of information security required for adoption of IM in healthcare. We proposed MedIMob, our own architecture for a secure enterprise IM service for use in healthcare. MedIMob supports IM clients on mobile devices in addition to desktop based clients.

METHODS

Security threats were identified in a risk analysis of the MedIMob architecture. The risk analysis process consists of context identification, threat identification, analysis of consequences and likelihood, risk evaluation, and proposals for risk treatment.

RESULTS

The risk analysis revealed a number of potential threats to the information security of a service like this. Many of the identified threats are general when dealing with mobile devices and sensitive data; others are threats which are more specific to our service and architecture. Individual threats identified in the risks analysis are discussed and possible counter measures presented.

DISCUSSION

The risk analysis showed that most of the proposed risk treatment measures must be implemented to obtain an acceptable risk level; among others blocking much of the additional functionality of the smartphone. To conclude on the usefulness of this IM service, it will be evaluated in a trial study of the human-computer interaction. Further work also includes an improved design of the proposed MedIMob architecture.

PaSent--the patient's personal health adviser

We have attempted to automate the search for medical information that is relevant to a patient s electronic record. We used anonymous medical reports sent from the University Hospital of Tromsø to general practitioners as input data. The medical reports were matched against the NEL, a Web-based medical dictionary and document collection service for general practitioners. We evaluated only the three top-scoring documents automatically selected by the system. A medical professional evaluated whether a selected document from the NEL was very relevant to the contents of the input report, relevant or irrelevant. Initial tests showed that 60% of the top three selected documents found by the PaSent system were very relevant or relevant to the contents of the medical report used as input data. Our preliminary conclusion is that automatic location of relevant information is possible using medical reports as input data.