PREDICTION OF SITUATION OF THE FORENSIC PSYCHIATRIC PATIENTS AT THE GALYA RAJANAGARINDRA INSTITUTE

Abstract

Objectives: To study the situation of forensic psychiatric patients. Statistical Prognosis Appropriate for Prognosis and forecasting trends in forensic psychiatric patients receiving service at the Galya Rajanagarindra Institute.

Material and Methods: The research aims to studying the forecasting methods by using secondary time series data. The data used was information of outpatients who received psychiatric services from October 2016 - September 2019. To find the suitable statistical forecasting method. The accuracy of the forecast was measured by absolute percentage deviation (MAPE) and it was applied to forecast the trend from October 2019 - September 2021. 

Results: The results showed that most of the forensic psychiatric patients are males, single status, aged around 40 - 49 years old, living in the 13th health area and paying their own treatment cost. They were mostly diagnosed with ICD-10 code F20-F29 (schizophrenia, schizophrenic behavior and delusions) with the highest number of F20 (schizophrenia). The right to self-pay treatment, sources brought from the court, type of cases, the Drug Addiction Rehabilitation Act. An appropriate statistical forecasting method is Exponential Smoothing using Winters' Additive. The forecasting indicated that the number of patients receiving services is likely to increase every year. It is expected that there will be 221 (556 cases) and 264 (719 cases) patients in the year 2020 and 2021, respectively. The highest number of patients were in September 38 (60 cases) and 42 (74 cases). The most common diagnosis was F20-F29 that estimated to 71 (268 cases) in 2020 and 82 (338 cases) in 2021. and the number of 83 people (399 cases) due to the COVID situation. As a result, the number of service recipients decreased, the number of people decreased, but the number of visits was close to the forecast value.

Conclusion: There should be plans for service system development, budget management and medical supplies according to the predicted increasing number of patients.