Table 1 Characteristics of the papers included (n = 12)

Siebert et al. (2019) [37]

RCT,

Multi

center

To assess a mobile device application during simulation-based resuscitations in various hospital settings

Switzerland

Academic

Standalone

Homegrown

Nurse

Pediatric emergency

Resuscitation drugs,15 drugs for continuous infusion, 19 drugs for direct intravenous injection

American Heart Association pediatric cardiac arrest algorithm

Medication errors, mean time to drug preparation and drug delivery were reduced

    1A

Parush et al. (2020) [45]

Non-

randomized

CT, Cross over

to design and empirically test a graphic dosage calculator tailored for pediatric medication calculation in prehospital emergency care

    Israel

Academic

Standalone

Homegrown

Paramedics

Pediatric

emergency

pediatric emergency drugs

          N/A

significantly decreasing time to calculate doses with the graphic calculator compared with the handbook

   1C

Ni et al. (2018) [42]

Posttest

Specific aims: 1. to develop an automated system utilizing comprehensive EHR information to detect dosing related medication error in real time, 2. To prospectively evaluate system performance in NICU prior to clinical integration, and

3. to estimate the system’s potential to mitigate MAE harm for neonatal patients

     USA

Academic

Integrated

Homegrown

Clinician

   NICU

10 high-risk

continuous

intravenous

infusions and medications. TPN, lipids, intravenous

fluids, insulin,

morphine,

fentanyl,

milrinone,

vasopressin,

dopamine,

and epinephrine

   NCC MERP^a

improving significantly MAE detection by the system. decreasing the time of patient exposure to harm due to drug errors by the system

     2C

Zahn et al. (2021) [39]

Case study, development

to describe the development and history of the pediatric drug information system (PDIS) for Germany and its evaluation by German healthcare professionals

  Germany

Academic

Standalone

Homegrown

Clinician

General

pediatric

            N/A

SmPC (Summary of Product Characteristics.), ATC-code

              N/A

     3A

Reynolds et al. (2019) [41]

Multicenter

pretest–posttest,

control

to evaluate end-user acceptance and the effect of a commercial handheld decision support device in pediatric intensive care settings

      USA

Non-academic

Standalone

Commercial

Nurse

NICU, PICU

intravenous and other liquid medications

   NCC MERP^a

this study did not reveal significant differences in cognitive load and administration errors after deploying the system

     2A

Dodson et al. (2021) [36]

Case study,

development

to evaluate the perceptions of a prototype of a clinical decision support tool through a mobile application for pharmacokinetics

      USA

Academic

Standalone

Homegrown

Nurse

General

pediatric

            N/A

           N/A

               N/A

     3A

Ateya et al. (2017) [44]

Posttest, control

To describe the insulin calculator tool, workflow, and satisfaction of clinical users and their perception of its impact on work efficiency, and quality of patient care, and measure its impact on the incidence of hypoglycemia to assess the safety of its utilization

      USA

Academic

Integrated

Homegrown

Nurse

General

pediatric

           Insulin

          N/A

there was no significant difference in hypoglycemia rates, severe hypoglycemia rates and length of stay by using the system.

    2B

Levy et al. (2011) [40]

Case study, development

to describe the experience in relation to the deployment of the system, including integration of multiple clinical information Systems and oncology-specific configurations

     USA

Academic

Integrated

Homegrown

Nurse

Pediatric

chemotherapy

              N/A

ASCO/ONS guidelines for Chemotherapy administration

                  N/A

    3A

Bury et al. (2005) [38]

RCT, cross over

to describe the design, implementation, and preliminary evaluation of the LISA system

     UK

Academic

Integrated

Homegrown

Clinician

Pediatric chemotherapy

Oral chemotherapy

        PROforma

using LISA reduced the

time novices, while

increasing the time taken

by experts and did not

have a significant impact

on the time taken by intermediates in dose

adjustment decreasing

error in dose calculation

    1B

^aDamhoff et al. (2014) [43]

Non-randomized CT, Crossover

to assess the accuracy of the eBroselow system and the time needed to prepare medications during pediatric simulated resuscitations compared with standard dosing references

     USA

Academic

Standalone

Commercial

Nurse

Pediatric

emergency

Pediatric

emergency

drugs

NDC (National

drug code)

Elimination of dose calculation errors by

the system, decreasing

the time to prepare medications

     1C

Shannon et al. (2002) [35]

Non-randomized CT

to design a computerized system for calculating resuscitation requirements and testing this system to ensure that it gives accurate and fast results

     UK

Non- academic

Standalone

Homegrown

Clinician

Pediatric emergency

10 different drugs

for resuscitation

and Antibiotic

           N/A

decreasing dose errors by the system, decreasing the time to prepare medications

    1D

Ellis et al

(2012) [34]

Non-randomized CT, Crossover

to assess whether a graphic dose calculator, in comparison to standard paper/pencil and calculator, can support the double-checking process and reduce the rate of potential errors with high-alert drugs

   Canada

Academic

Standalone

Homegrown

nurse, student

nurse

Pediatric emergency

Intravenous

morphine

Lexicomop /Micromedex (2011)

no significant difference

to detecting error

by the system and

traditional method

No difference to take the

time to preparing

medication in two groups

    1C