For the past nine years, Cosmika has been working as a biostatistician/bioinformatician, where her research focuses on managing and analysis of large data sets from healthcare and bacterial data. During these projects she has extracted and processed large data sets, both clinical and genomic sequencing data. These large data sets have been purified, classified and stored in university High Performance Computing (HPC) server. Being a trained statistician, Cosmika has expertise on python, bio python, Perl, R and Shell scripting languages. She developed fully automated python-based pipelines that can be run within the HPC server.

Project Information

Research Driver Programme: Medicines in Acute and Chronic Care

Title: Clinical Decision Support Tools (CDST) for antipsychotic prescribing.

Summary: 

High-dose antipsychotic prescribing among hospitalised patients is a common reported problem especially among patients admitted to non-mental health hospitals where there is lack of clinicians specialised in mental health prescribing. Providing a clinical decision support tool(s), therefore, to help and support clinicians to improve appropriate prescribing of antipsychotics will be important. This project aims to improve the appropriate prescribing of antipsychotics among hospitalised people using computerised clinical decision support tool(s), with the ultimate goal of improving patients’ safety and reducing harms. We will first quantify the issue of high-dose antipsychotic prescribing, then understand and profile the characterises of the study cohort; assess the association between high-dose antipsychotic prescribing and clinical outcomes including both harms and benefits. The identified risk factors associated with high-dose antipsychotic prescribing will then be translated into an algorithm to develop a computerised clinical decision support tool(s) within HEPMA. The tool(s) will firstly, identify and highlight patients on high-dose antipsychotic prescribing (to be flagged for monitoring etc) and secondly, alert and guide clinicians when they prescribe antipsychotics.

What is your motivation for undertaking this project and how will this funding impact your research?

This project resonates with my professional interests and skills as a biostatistician/bioinformatician due to its direct alignment with my expertise in managing and analyzing large healthcare datasets. The opportunity to contribute to patient safety by improving the appropriate prescribing of antipsychotics, coupled with the chance to apply innovative solutions like developing a computerized clinical decision support tool, excites me. Collaborating with clinicians, IT professionals, and leveraging my programming abilities to create efficient and automated solutions further adds to the appeal. Ultimately, the project offers a meaningful avenue to apply my knowledge and skills towards positively impacting healthcare outcomes and fostering innovation in the field.