Pharmaceutical Care Interventions Of Inpatient Prescriptions At Hospital Pakar Sultanah Fatimah Muar
Abd Majid, R.; Abdul Manap, N. A.; Farid Basheer, F.; Yahaya, R.; Mohd Sabari, N. H.
Show abstract
IntroductionPrescribing medications plays a vital role in patient healthcare. The rational drug use is now a significant concern for public health due to incorrect medication prescribing. In promoting rational evidence-based prescribing, prescriptions will be screened and reviewed by pharmacists before medications are dispensed whereby pharmaceutical care intervention (PCI) will be conducted. MethodsThis study aimed to evaluate the prevalence and types of PCI detected at an inpatient pharmacy and to identify the stage of dispensing process where the PCI are frequently detected. The PCI that included was focusing on the prescribing errors. A cross-sectional observational study was conducted over a period of three months started from 1st March 2023 until 31st May 2023 where new medication orders using Pharmacy Information System (PhIS) of all patients warded screened by Inpatient Pharmacy Department were included. ResultsThe prevalence of PCI was 0.006%. The most common type of interventions performed were the prescribed frequency (31.5%) followed by dose (30.0%), drug (19.2%) and polypharmacy (10.8%). The drug category based on ATC classification with a high percentage of interventions was anti-infective for systemic use (34.6%) followed by nervous system (16.2%) and alimentary tract and metabolism (14.7%). Half of the PCIs were detected during screening stage (51.2%) whiles 36.0% were detected during counterchecking and the least detected is during medication filling (12.8%). The highest prescribing errors was from medical wards (50.7%), followed by surgical (24.1%) and orthopaedics (14.8%). ConclusionThe prescribers and clinical pharmacist and inpatient pharmacist are doing well in maintaining patient care. Prescriptions that involve drug category of anti-infective required more attentions especially on drug choice, dose, frequency, and polypharmacy.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Perspectives of pharmacy employees on an inappropriate use of antimicrobials in Kathmandu, Nepal 98%
- Assessment of knowledge and perception of prescribers towards rational medicine use in the Ashanti Region of Ghana 97%
- Personality traits and other factors associated with psychotropic medication non-adherence at two hospitals in Uganda. A cross-sectional study 94%
Similar papers in this journal
- Stakeholder Perspectives on the Adaptability of Hospital Drug Formularies to Disease Patterns: A Modified Q-Methodology Study in Vietnam 92%
- Menstrual hygiene management practice and factors affecting it among high school females in Ambo City, Oromia state, Ethiopia, 2018: A cross-sectional mixed method. 91%
- Stethoscope and non-infrared thermometer disinfection among physicians: A cross-sectional study with implications for the control of COVID-19 91%
Similar papers in this journal
Similar papers in this journal
- Drug Supply Management at First-level Public Health Facilities: Case of Pyay District, Myanmar 95%
- Access to essential medicines for noncommunicable diseases during conflicts: the case cardiovascular diseases, diabetes and epilepsy in Northern Syria 93%
- Antibiotic susceptibility patterns of pathogens isolated from laboratory specimens at Livingstone Central Hospital in Zambia 93%
Similar papers in this journal
- Caregivers’ Perspective: Satisfaction With Healthcare Services At The Paediatric Specialist Clinic Of The National Referral Centre In Malaysia 92%
- Patterns of physical activity among the students of an Indian university and their perceptions about the curricular content concerned with health 91%
- Seroprevalence of SARS-CoV-2 in Niger State: A Pilot Cross Sectional Study 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.