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PHARMACEUTICAL ALGORITHMS TO PERFORM MEDICATION PHARMACEUTICAL ANALYSIS (submitted in 2019)

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European Statement

Patient Safety and Quality Assurance

Author(s)

Arnaud Potier, Béatrice Demoré, Alexandre Dony, Emmanuelle Divoux, Emmanuelle Boschetti, Laure-Anne Arnoux, Cédric Dupont, Jean-Christophe Calvo, David Piney, Virginie Chopard, Nathalie Cretin, Edith Dufay

Why was it done?

Drug iatrogenia costs global health systems $52 billion annually. The third global patient safety challenge aims at reducing the global burden of iatrogenic medication-related harm by 50% within 5 years [1]. Pharmaceutical analysis is a fundamental activity, a regulatory obligation in many countries but remains a challenge. This practice is highly variable. A graphic definition of the target pharmaceutical analysis has been formalised in December 2017 which sets the basis for its digitalisation, effectively implemented since January 2019. The aim is to build a corpus of the most relevant PA to facilitate clinical pharmacist practice.

What was done?

A computerised clinical pharmacy tool is integrated into the health information system of our group of hospitals (5000 beds) to promote efficiency of pharmaceutical analysis in order to improve patient safety. Pharmaceutical algorithms (PA) are conceptualised to improve drug related problems (DRP) detection and their resolution through pharmaceutical intervention (PI) according to a defined conduct to be held: anamnesis of subjective and objective elements of appreciation, DRP characterisation and PI transmission. Pharmaceutical analysis is performed by the use of PharmaClass® (Keenturtle). This software has been interfaced with 5 health data flow of two health facilities (1000 of the 2000 beds were tested): identity and patient flow, medication data, laboratory results examination, medical history, physiological constants. PA are partially encoded as rules in Pharmaclass® that issues alerts analysed by a pharmacist.

How was it done?

Health data are lacking of semantic interoperability which Pharmaclass® aims at overcoming from Electronical health record (EHR) queries in real time. A corpus of PA has been structured integrating the conduct to be held. PA were created by modeling the pharmaceutical experiment with the thread of criticality. PA were validated by consensus.

What has been achieved?

80 PA were encoded into Pharmaclass®: 40 are targeting serious adverse drug events. 1516 alerts were analysed and 539 PI transmitted during the 9-month test period.

What next?

This practice is applicable to any pharmaceutical analysis that uses data from an EHR. Clinical pharmacy societies should host and take care of updating corpus of PA. Its educational interest should be exploited. A European interest group for artificial intelligence in clinical pharmacy is being created.

DISCHARGE MANAGEMENT: SAFER DISCHARGES AND IMPROVED INFORMATION TRANSFER METRICS

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European Statement

Clinical Pharmacy Services

Author(s)

Marie-Claire Jago-Byrne, Sinead McCool, Caroline Reidy, Stephen Byrne

Why was it done?

Published research had demonstrated that 50% of discharge prescriptions were non-reconciled(1). A recent study demonstrated that 43% of patients experienced post-discharge medication errors(2). The prevalence of polypharmacy (>5 medications) has increased over the 15 years to 2012, from 17.8% to 60.4% in people 65 years and older in Ireland(3).

What was done?

The aim of this project was to improve medication safety at the point of hospital discharge by using targeted medication reconciliation and producing a computer-generated prescription. This new model for discharge prescribing was introduced for patients who met both of the following criteria in two acute hospitals:
• Prescribed 9 or more medications, at the time of admission.
• Aged 70 years and over

How was it done?

The new model for discharge prescribing used collaborative medication reconciliation and the e-Discharge software to improve the quality of discharge prescriptions. The model was introduced in both hospitals and received support from community and hospital colleagues. Clinical pharmacists became the project champions and worked closely with medics during the change process. Key safety aspects were:
• Clinical double check for this high-risk process- the pharmacist and the doctor sign the prescription.
• Increased legibility
• Explanation for all prescription changes to community colleagues.
Phase 2: The software was further tested on 200 patients in a bench top exercise

What has been achieved?

Phase 1: The overall compliance with the national discharge prescription standards increased from 50.4% to 96.9% with the new model for discharge prescribing. The biggest change in compliance was observed in the three communication categories, which explain to community healthcare providers the rationale behind the medication changes made during the hospital stay. A user acceptability survey of HCP involved in the project demonstrated that all those involved had benefited from improved workflows in hospital and community settings, and more appropriate and efficient use of resources. All users requested expansion of this service.
Phase 2: This review allowed for the improvement of the e-Discharge Software using anonymised patient cases to test issues identified in Phase 1.

What next?

In Phase 3 the model will be introduced to a third hospital to evaluate transferability of the concept alongside current practice outlined above

BARCODESCANNING IN THE PHARMACY FOR A SAFER THERAPY

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European Statement

Selection, Procurement and Distribution

Author(s)

T. De Rijdt

Why was it done?

Medication errors find their origin mostly in prescribing, transcribing and administration of medication. Only 4 % of the errors occurs in the pharmacy process. As we covered the major reasons by deployment of a electronic prescribing system with decision support and bedside scanning before administration the next step in augmenting patient safety is preventing dispension errors in the pharmacy.

What was done?

All medication orders from the electronic prescribing system are revised by a hospital pharmacist for appropriateness and send to a set of handheld barcode scanners for guiding the pharmacy technicians through the picking process. They identify themselves, the ship label, the picking location and the medication by scanning. The scanner checks if the right drugs are dispensed for the right patient.

How was it done?

Due to bedside scanning all orders are electronically available and all medication have barcodes on the single dose. All locations are barcoded for reasons of replenishment of stock. By simply sending the orders to handheld terminals it’s a small effort to verify the picking.

What has been achieved?

All electronic medication orders are checked by barcode scanning or a second hospital pharmacist resulting in a diminishment of picking errors to (nearly) zero. We can show an online status of the medication order to nurses and physicians and we shifted pharmacist time from checking drugs to checking appropriateness of therapy.

What next?

In a next step we will also check retour medication by barcode scanning preventing possible misplacement.

INTEGRATION OF THE PHARMACEUTICAL CARE RECORD INTO THE MULTIDISCIPLINARY ELECTRONIC OUTPATIENT RECORD

European Statement

Patient Safety and Quality Assurance

Why was it done?

So far, only pharmacists could access the pharmaceutical care record. The creation of the electronic medical record in the Health System has allowed patient data to be centralised and can be accessed easily and quickly. It is an opportunity to integrate our care service as pharmacists within the multidisciplinary care as well as to facilitate data consultation to other professionals that care for the same patient.

What was done?

Integrate the outpatient pharmaceutical care record to the electronic patient record via an application form included in the process of each pathology.

How was it done?

First question was where the application form should be included when the patient came in for a consultation. In order to unify all the actions relating to the process, it was decided to include the form in the main process instead of creating a specific process for the pharmacy department. For example, a form for an HIV patient should be included in his Infectious Disease Consult process.

Another issue was to define what items should be taken into account for the follow up.

Finally, the following items were included: reason for the visit, pathology, clinical data, outpatient treatment, regular treatment, drug-related problems, adverse drug allergies/past issues, adherence, checks to mark whether the patient has received the oral and written information, the leaflet from the host to the Outpatient Pharmacy Service and a free text to write down given recommendations.

What has been achieved?

Improvement of patient care, increased safety in the use of drugs and in the avoidance of medical errors has all been achieved, as well as the promotion of teamwork amongst professionals who attend to the patient. From the information technology perspective, data export can be provided for future researches.

What next?

All professionals should integrate their activities to take advantage of collaboration and increasing synergies.

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