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Routine generation of real-world evidence: an integrated system for the systematic recording of clinical data on innovative outpatient hospital medicines

European Statement

Patient Safety and Quality Assurance

Author(s)

Manuel Mazarío-García, Amparo Molina Lázaro, Gemma Garrido Alejos, Ferran Sala-Piñol, Núria Juvanet Ribot, Lluís de Haro i Martin

Why was it done?

In 2011, the Catalan Health Service (CatSalut) introduced the Registry of Patients and Treatments of Outpatient Hospital Medicines (RPT-OHM) within their Standard Health Record Platform (SHRP) for the comprehensive evaluation, effectiveness and safety of OHM. CatSalut requires fulfilling RPT-OHM before medicines billing. However, SHRP lacked integration with ICS’ clinical and billing information systems.

What was done?

The Catalan Health Institute (ICS) is the largest healthcare provider in Catalonia and is made up of eight hospitals. The ICS developed and implemented a structured data collection tool named the Hospital-Information-System-integrated Registry of Patients and Treatments (RPT-HIS). This tool systematically collects a range of critical information, spanning from prescription inception to treatment cessation, such as:
• Administrative patient, prescriber, and treatment particulars.
• Active ingredients or combinations, initiation and termination dates, therapeutic indications, and ICD-10 diagnoses.
• Baseline clinical variables recorded upon treatment commencement.
• Dynamic clinical data captured or typed from medical records throughout follow-up according to predefined intervals.
• Cessation variables detailing reasons for treatment discontinuation.

How was it done?

At ICS’ headquarters, a dedicated team of internal business analysts and functional support officers, along with contracted developers, collaborated to design, implement, and maintain RPT-HIS. Monthly coordination meetings ensure efficient integration of new OHM and monitor the registry’s progress. Simultaneously, a network of local reference pharmacists emerged in all eight ICS hospitals, fostering continuous knowledge exchange and driving innovative enhancements.

What has been achieved?

In the first half of 2023, 52,907 initiation, follow-up, or discontinuation forms were completed. Among these, 93.7% met all preset treatment-specific validation rules, underscoring the initiative’s effectiveness. Subsequently, utilization and budgetary impact reports across ICS hospitals have been elaborated, covering general and special patient populations, indicating a positive impact on operational efficiency and patient care.

What next?

In the near future, the real-world data amassed and stored in RPT-HIS could help to underpin refined drug therapy, optimize health outcomes, and strategically position medicines for specific conditions, also aiding in selection and purchase. This initiative serves as a model of good practice, demonstrating the potential of integrated data collection systems, aligned with the routine process of OHM utilization, to improve patient care. The successful implementation of RPT-HIS suggests its viability for adoption in other healthcare settings.

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.

TELEPHARMACY ANALYTICS AND DATA VISUALIZATION THROUGH BUSINESS INTELLIGENCE

European Statement

Clinical Pharmacy Services

Author(s)

Cristina González Pérez, Laura Llorente Sanz, Ángel Liras Medina, Ana Andrea García Sacristán, María Molinero Muñoz, Lidia Ybañez García, José Alberto Peña Pedrosa, Henar González Luengo, María Luaces Méndez, José Manuel Martínez Sesmero

Why was it done?

Telepharmacy implementation in the context of SARS-CoV-2 pandemic conducted us through the management of a high volume of complex, real-time both clinical and economic data. A multidisciplinary working group (biomedical engineers from the Innovation Unit, clinicians, managers and hospital pharmacists) developed a software tool in April-May 2021.

What was done?

The design of an agile, customizable and dynamic dashboard for the visualization and analysis of Telepharmacy key performance indicators (KPI) through Business Intelligence (BI).

How was it done?

Phases:
1. Situation analysis. KPI definition. Ethics committee approval submission.
2. Extraction and processing of raw databases (Telepharmacy database, outpatient dispensing program, hospital admission database, drug catalog) through data mining.
3. Co-creation of the comprehensive dashboard in PowerBI®, by integrating database sources. Different panels have been designed where filters such as age, time frame, medical service, pathology, etc. can be applied.
• Description of general variables: patients, demography, shipments, time frame, medical department.
• Geolocation of the destinations of the patients’ home delivery.
• Pharmacological profile: top 10 drugs, distribution by active ingredient and drug classification group.
• Relative analysis of the beneficiary patients of Telepharmacy vs global outpatients
4. Pilot project by different types of users (administrative staff, clinicians and managers)
5. Structure design for automatic updating of the panels from the successive updates of the source databases
The quality of the raw databases can be a limitation. It has been necessary to define how to handle missing and duplicate data. Pre-processing, normalization and transformation data processes have been applied too.
Working within the hospital network ensures that there are no security gaps in terms of patient data protection.
For the external use of the dashboard, the granularity of the data is modulated to ensure enough clustering to avoid the identification of individual patients.

What has been achieved?

Processing the huge dataset (more than 2.4 million records) was possible by BI tools that synthesizes data, provides dynamic and engaging visualization (charts and graphs), allows the interactive reports customization for more effective communication of results and apply analysis based on Artificial Intelligence.

What next?

Applying new technologies will help us improve strategic decisions: interactions, behaviors and trends perceiving, weak points identifying, uncertainty reducing and over time monitoring.

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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