Introduction to the cognitive hospital

The Green Surgical Block 4.0: Automation of the operating theatre's climate conditions through a real-time patient-flow solution

The energy consumed by surgery units constitutes a staggering part of the overall healthcare carbon footprint. Partly due to the demanding climate conditions necessitated by operating theatres (OTs), energy is also wasted because of poorly managed heating, ventilation and air-conditioning systems (HVACs) that consume up to 57% of the total energy used in a hospital. With their goal of optimising OTs' performance and reducing patient waiting lists, heads of surgery units worldwide do not dare to risk cancelling scheduled surgery because of problems with the OT environment conditions (ie temperature, humidity, pressure). Current solutions are monolithic, complex and completely disconnected from healthcare logic, failing to take into account the idiosyncrasy of hospitals. This article presents an innovation that uses real-time patient flow data to automate and optimise the OT's climate conditions.

Intercepting Medication Errors in Pediatric In-patients Using a Prescription Pre-audit Intelligent Decision System: A Single-center Study

OBJECTIVES

Medication errors can happen at any phase of the medication process at health care settings. The objective of this study is to identify the characteristics of severe prescribing errors at a pediatric hospital in the inpatient setting and to provide recommendations to improve medication safety and rational drug use.

METHODS

This descriptive retrospective study was conducted at a tertiary pediatric hospital using data collected from Jan. 1st, 2019 to Dec. 31st, 2020. During this period, the Prescription Pre-audit Intelligent Decision System was implemented. Medication orders with potential severe errors would trigger a Level 7 alert and would be intercepted before it reached the pharmacy. Trained pharmacists maintained the system and facilitated decision making when necessary. For each order intercepted by the system the following patient details were recorded and analyzed: patient age, patient's department, drug classification, dosage forms, route of administration, and the type of error.

RESULTS

A total of 2176 Level 7 medication orders were intercepted. The most common errors were associated with drug dosage, administration route, and dose frequency, accounting for 35.2%, 32.8% and 13.2%, respectively. Of all the intercepted oerrors. 53.6% occurred in infants aged < 1 year. Administration routes involved were mainly intravenous, oral and external use drugs. Most alerts came from the neonatology department and constituted 40.5% of the total alerts, followed by the nephrology department 15.9% and pediatric intensive care unit (PICU) 11.3%. As to dosage forms, injections accounted for 50.4% of alerts, with 21.3% attributable to topical solutions, 9.1% to tablets, and 5.7% to inhalation. Anti-infective agents were the most common therapeutic drugs prescribed with errors.

CONCLUSIONS

The Prescription Pre-audit Intelligent Decision System, with the supervision of trained pharmacists can validate prescriptions, increase prescription accuracy, and improve drug safety for hospitalized children. It is a medical service model worthy of consideration.