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Implementation in a pharmacy service of a big data analysis programme for successful treatment

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European Statement

Patient Safety and Quality Assurance

Author(s)

LUCIA SOPENA, ALBERTO FRUTOS, VICENTE GIMENO, OLGA PEREIRA, RAQUEL FRESQUET, ARITZ MERCHAN, REYES GARCIA, PAULA GOMEZ, ALBERTO APESTEGUIA, MARIA ANGELES ALLENDE, TRANSITO SALVADOR

Why was it done?

The growing technological development of pharmacy services involves the coexistence of traditional warehouses with automated medicine dispensing systems controlled by different computer programs. The information is split into different systems and databases giving rise to possible errors due to the greater complexity. This is a threat but also an opportunity for the hospital pharmacist to lead the development, review, and improvement of medicine use processes and the use of health technologies to improve quality of care, patients’ safety and reduce costs. KNIME data analysis covered the need of our Pharmacy Service to blend data from any source in a single file simplifying the process.

What was done?

The Pharmacy Service of a university hospital has implemented Konstanz Information Miner (KNIME) data analysis and develop successful treatment project to optimise the stock management of several medicines.

How was it done?

An initial algorithm was designed by the union of seven files and can be executed at any time to obtain the updated data.

What has been achieved?

This file provides up-to-date information about the stocks, stock-outs, consumptions, orders and purchasing data of all medicines (average price, laboratory, date and number of orders, units to be received).
In addition, KNIME calculated the coverage time in days and months from weekly and monthly consumption, and the current stock in the warehouses, obtaining a global vision of highest turnaround pharmaceuticals drugs.
The program also allows to link and merge data of the list for shortages of medicines, supply disruptions and restocking time, and to improve the storing, delivering and administering of COVID-19 vaccines.
KNIME program has been especially important in our Pharmacy Service to get better care outcomes and more precise medication ordering, which allows significantly higher patient safety.

What next?

KNIME is a tool that could be successfully implemented and appropriately generalised as recommended to all Pharmacy Services that use different data sources and want to have a generalised view of the information. KNIME represents an advance in the stock and purchase management of medicines specialties to work more efficiently, which improve patient care and safety. Digital medication management also contributes to greener pharmacies by preventing unnecessary overstocking and thus excessive disposal arising from expired medications.

Implementation of an artificial intelligence tool for the detection of drug safety problems

European Statement

Patient Safety and Quality Assurance

Author(s)

Noe Garin, Laia Lopez-Vinardell, Pau Riera, Adrian Plaza, Ivan Castellvi-Barranco, Jose Mateo-Arranz, M. Antonia Mangues

Why was it done?

APS is a rare disease with a high risk of thromboembolism. Recently, some data suggested an increased risk of thrombotic events with direct-acting anticoagulants (DOAC) compared with vitamin K antagonists in APS. Some agencies advise against the use of DOACs in these patients.

This methodology can be extrapolated to other risk situations, so this was a first step with AI to further detection of safety issues.

What was done?

We implemented an Artificial intelligence (AI) tool based on natural language processing (SAVANA®) to identify patients at risk of thromboembolism, defined as Antiphospholipid Syndrome (APS) diagnosis treated with direct-acting anticoagulants (DOAC). SAVANA® is an AI tool able to extract information contained in free-text from electronic clinical records.

A prior operation work was conducted, involving: direction, pharmacy, documentation, IT, SAVANA®, data protection. The work and previous meetings evaluated: feasibility, previous requirements, privacy issues, IT involvement and contract signings.

How was it done?

The implementation consisted of:
– Transference of medical record information to the SAVANA® cloud.
– Identification of the health problem (APS) and initial search.
– Search algorithm optimization in a multidisciplinary team.
– Evaluation of the search by SAVANA® by peer review in a sample of randomly selected cases (n=200).
– Precision and sensitivity analysis. Algorithm improvement.
– Obtaining the Gold Standard and validation.
– Definitive search for the detection of patients with APS in treatment with DOACs and performance of interventions.

What has been achieved?

The project implementation is at a very advanced stage. The algorithm has currently been evaluated and is being refined after precision and sensitivity analysis. Final validation and definitive identification of patients at risk is expected at the end of 2021. Patients detected during the implementation method have been evaluated with the haematology team.

What next?

This methodology can be implemented in any centre with computerized medical records. The use of AI is the only tool available for the identification of certain groups of patients when health problems are not coded. In other cases, its use regarding the extraction of lists allows a great capacity for analysis, absence of biases derived from human error, guarantee of reproducibility and complementary data obtention, mainly in samples of high size.

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