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Background: Adverse events are common in health care. In psychiatric treatment, compensation claims for patient injuries appear to be less common than in other medical specialties. The most common types of patient injury claims in psychiatry include diagnostic flaws, unprevented suicide, or coercive treatment deemed as unnecessary or harmful. Objectives: The objective was to study whether it is possible to form different categories of patient injury types associated with the psychiatric evaluations of compensation claims and to base machine learning classification on these categories. Further, the binary classification of positive and negative decisions for compensation claims was the other objective. Methods: Finnish psychiatric specialist evaluations for the compensation claims of patient injuries were classified into six different categories called classes applying the machine learning methods of artificial intelligence. In addition, another classification of the same data into two classes was performed to test whether it was possible to classify data cases according to their known decisions, either accepted or declined compensation claim. Results: The former classification task produced relatively good classification results subject to separating between different classes. Instead, the latter was more complex. However, classification accuracies of both tasks could be improved by using the generation of artificial data cases in the preprocessing phase before classifications. This preprocessing improved the classification accuracy of six classes up to 88% when the method of random forests was used for classification and that of the binary classification to 89%. Conclusion The results show that the objectives defined were possible to solve reasonably.

Background: Adverse events are common in health care. In psychiatric treatment, compensation claims for patient injuries appear to be less common than in other medical specialties. The most common types of patient injury claims in psychiatry include diagnostic flaws, unprevented suicide, or coercive treatment deemed as unnecessary or harmful. Objectives: The objective was to study whether it is possible to form different categories of patient injury types associated with the psychiatric evaluations of compensation claims and to base machine learning classification on these categories. Further, the binary classification of positive and negative decisions for compensation claims was the other objective. Methods: Finnish psychiatric specialist evaluations for the compensation claims of patient injuries were classified into six different categories called classes applying the machine learning methods of artificial intelligence. In addition, another classification of the same data into two classes was performed to test whether it was possible to classify data cases according to their known decisions, either accepted or declined compensation claim. Results: The former classification task produced relatively good classification results subject to separating between different classes. Instead, the latter was more complex. However, classification accuracies of both tasks could be improved by using the generation of artificial data cases in the preprocessing phase before classifications. This preprocessing improved the classification accuracy of six classes up to 88% when the method of random forests was used for classification and that of the binary classification to 89%. Conclusion The results show that the objectives defined were possible to solve reasonably.

Objective: This paper presents a summary of web-based data collection, impact evaluation, and user evaluations of an Internet-based peer support program for Menieres disease (MD). Design: The program is written in html-form. The data are stored in a MySQL database and uses machine learning in the diagnosis of MD. The program works interactively with the user and assesses the participants disorder profile in various dimensions (i.e., symptoms, impact, personal traits, and positive attitude). The inference engine uses a database to compare the impact with 50 referents, and provides regular feedback to the user. Data were analysed using descriptive statistics and regression analysis. Study sample: The impact evaluation was based on 740 cases and the user evaluation on a sample of 75 cases of MD respectively. Results: The web-based system was useful in data collection and impact evaluation of people with MD. Among those with a recent onset of MD, 78% rated the program as useful or very useful, whereas those with chronic MD rated the program 55%. Conclusions: We suggest that a web-based data collection and impact evaluation for peer support can be helpful while formulating the rehabilitation goals of building the self-confidence needed for coping and increasing social participation., Funding Agencies|Finnish Slot Machine Association, RAY

Purpose: In this article, we present the details and the pilot outcome of an Internet-based self-help program for Menieres disease (MD).amp; para;amp; para;Method: The Norton-Kaplan model is applied to construct a strategic, person-focused approach in the enablement process. The program assesses the disorder profile and diagnosis. In the therapeutic component of the program, the participant defines vision and time frame, inspects confounding factors, determines goals, establishes a strategy, and starts to work on the important problems caused by the disorder. The program works interactively, utilizes collaboration with significant others, and enhances positive thinking. Participants took part in an Internet-based self-help program. Data were collected interactively using open-ended and structured questionnaires on various disease-specific and general health aspects. The pilot outcome of 41 patients with MD was evaluated.amp; para;amp; para;Results: The analysis of the pilot data showed statistically significant improvement in their general health-related quality of life (p amp;lt; .001). Also, the outcome of the Posttraumatic Growth Inventory (Cann et al., 2010) showed small to moderate change as a result of the intervention.amp; para;amp; para;Conclusions: The Internet-based self-help program can be helpful in the rehabilitation of patients with MD to supplement medical therapy., Funding Agencies|Finlands Slot Machine Association