Dr Irosh Fernando

A modified case-based reasoning method is introduced aimed to fulfill the need for a triage tool that differentiates likely psychiatric diagnoses and associated risk level. Clinical cases are represented as a set of clinical features rated on a numerical scale according to level of severity. One standard case is used for each diagnostic category, represented as a vector denoting the expected severity of each clinical feature. A new case represented as another vector denoting the severity of observed clinical features in a patient is assessed against the standard cases. Measurement based on orthogonal vector projection was used as a clinically intuitive measurement of similarity. Using thirty different test cases representing six different diagnostic categories, this measure and alternative similarity measures consisting of cosine similarity and Euclidean distance were evaluated. Results indicated that orthogonal vector projection was superior to the other two methods in differentiating diagnoses and predicting severity.

Mental state examination (MSE) involves assessing the overall severity of illness and also differentiating likely diagnoses. When it is performed on patients serially during the period of their illness, the consecutive estimates can serve as an important way to track their recovery. However, the traditional approach to mental state examination that uses clinician's subjective judgement and results in subjective estimates can be unreliable and prone to inconsistencies. Using the approach introduced in this paper, a case is represented as a vector of thirty five different clinical features, which are rated using a numerical scale according to the severity of each clinical feature. The vector length is used as a measure of the overall severity of the illness. The case base consists of one standard case for each of 6 diagnostic categories. Each standard case represents a typical case for its diagnostic category, with each clinical feature rated according to the maximum level of severity that can be expected for that category. Evaluation of a given clinical case, with clinical features as rated by a clinician with regard to the likely diagnoses involves measuring the similarity of the resulting case vector with the standard vectors in the case base. Whilst cosine similarity and Euclidean distance are alternative measures of similarity, a more clinically intuitive and accurate measure based on orthogonal vector projection is proposed. The orthogonal vector projection approach to case based assessment was evaluated using thirty different test cases representing six different common diagnostic categories. For each of the test cases similarity measures obtained using orthogonal vector projection were compared with measures obtained using cosine similarity and Euclidean distance. The results indicated that the orthogonal vector projection approach was able to differentiate both the diagnosis and severity of illness more accurately than the other two similarity measures. The proposed approach has the potential to be used as a standardised clinical tool for both establishing the diagnosis and severity of illness, and also measuring the recovery from illness. In particular, the estimates of recovery obtained from this approach can serve as an important index in healthcare economics.

Objective: To demonstrate how the Pattern-based Formulation can be used to integrate biological, psychological and sociocultural factors in constructing the case formulation in a patient who developed schizophrenia and post-psychotic depression. Conclusions: Three new patterns are introduced and used to construct a comprehensive case formulation. This expands the suite of patterns in the pattern-based method of psychiatric case formulation, and further demonstrates its broad utility as an educational resource in psychiatry training.

Objectives: To teach psychiatric case formulation; to build a repertoire of patterns that can be reused as building blocks in constructing case formulations. Method: Pattern-based Formulation. Results: Demonstration of a case formulation and introducing three patterns. Conclusion: The demonstration will assist learning case formulation using the Pattern-based Formulation, while the three patterns introduced can be reused when formulating relevant cases.

Objective: The objective of this study is to determine if pattern-based formulation (PBF) can accurately contribute to case formulation. Conclusions: The application of three PBFs accurately contributed to the development of this patient's case formulation. The case formulation demonstrated here, and the patterns introduced in this paper, will serve as educational materials for teaching psychiatric case formulation.

Objectives: To provide a further example of the utility of the pattern-based model in formulation, and to introduce some further patterns. Methods: A case study was carried out using the Pattern-based Formulation (PBF). Results: Based on the case of a patient with a past history of trauma who developed late onset somatic symptoms, post-traumatic stress disorder and major depression, the PBF approach enabled development of a comprehensive formulation to explain the patient's current presentation. Four patterns were utilised. Conclusions: The PBF method of using patterns as building blocks enables development of a psychobiological formulation that can accommodate considerable complexity. PBF represents a broadly applicable method that may assist psychiatry trainees and others to develop good quality formulations.

Objective: There is a need for a simple and brief tool that can be used in routine clinical practice for the quantitative measurement of mental state across all diagnostic groups. The main utilities of such a tool would be to provide a global metric for the mental state examination, and to monitor the progression over time using this metric. Method: We developed the mental state examination scale (MSES), and used it in an acute inpatient setting in routine clinical work to test its initial feasibility. Results: Using a clinical case, the utility of MSES is demonstrated in this paper. When managing the patient described, the MSES assisted the clinician to assess the initial mental state, track the progress of the recovery, and make timely treatment decisions by quantifying the components of the mental state examination. Conclusion: MSES may enhance the quality of clinical practice for clinicians, and potentially serve as an index of universal mental healthcare outcome that can be used in clinical practice, service evaluation, and healthcare economics.

The drill-locate-drill (DLD) algorithm models the expert clinician's top-down diagnostic reasoning process, which generates a set of diagnostic hypotheses using a set of screening symptoms, and then tests them by eliciting specific clinical information for each differential diagnosis. The algorithm arrives at final diagnoses by matching the elicited clinical features with what is expected in each differential diagnosis using an efficient technique known as the orthogonal vector projection method. The DLD algorithm is compared with its rival select-test (ST) algorithm and its design/implementation in psychiatry, and evaluation using actual patient data is discussed.