Ensuring patients are given the correct medication, in the right dosage, with proper instruction, is absolutely critical. However, the job of medication reconciliation is currently an onerous task that is tedious and time-consuming, even with the best software. Somebody needs to manually check every medication a patient is taking, to ensure that instructions are clear and no mistakes are made.
What if you could automate the process so that it was faster and more accurate?
This is exactly what Principal Investigator Randall Britten1 and a team of highly experienced pharmacists and clinicians have set out to discover in their project Smart Medication Reconciliation. It is not a simple quest because the way that dose instructions are described varies considerably in different parts of the health system. For example, “Paracetamol 1000 mg orally at 4-6 hourly for pain, max 4000 mg per 24 hours” is the same instruction as “Panadol 500 mg tablet: film-coated, 100 tablets, blister pack. Take 2 tablets 4-6 hourly, no more than 8 tablets per day”. As thousands of instructions like these are passed around the health system, the potential benefit of standardising and automating is immense.
The PDH-supported research team has assembled anonymised databases that can be used as training sets for machine learning applications such as Natural Learning Processing (NLP). They are using de-identified data from Waitemata District Health Board which combines both primary data (general practice product orders and pharmacy dispensing) and secondary data (hospital inpatient dose-based orders), along with primary care data from Northern Ireland.
The project team will develop a system whereby the most relevant information can be automatically identified, analysed and categorised, so that it becomes easily recognised data, or what’s known as “structured data”. Once this is achieved, the system will become the foundation for follow-on projects that could see the development of features such as automated de-duplication within each patient’s list of medications during the reconciliation process.
The benefits of this project will be immediately realised by enhancing existing medical reconciliation software, such as Orion Health’s Medicines application, which is designed to improve patient safety and support clinical decision making. The research outcomes will also enable medication data to be used for pharmaceutical data mining to help in the development of new drug formulations, dose profiles and advanced pharmacodynamics (that is high-risk drugs with narrow therapeutic indices).
The days of labour-intensive medication reconciliation are indeed numbered.
1Randall Britten, Lead Research Engineer at Orion Health is assisted by Aaron Jackson, Solution Director – Medicines, Orion Health; Dr Jens Andreas, Medicines Management Expert, Orion Health; Dr Edmond Zhang, Senior Data Scientist, Orion Health; Dr Jerome Ng – Waitemata District Health Board, Honorary Lecturer at University of Auckland; Delwyn Armstrong, Head of Analytics, Waitemata District Health Board; Dr Robyn Whittaker, Waitemata District Health Board, Honorary Associate Professor, University of Auckland.