Medication Prescribing Errors Research Articles

The effect of an extended-hours ED clinical pharmacy service on admission medication prescribing errors.

The aim of this study was to determine the effect of a 7-day extended-hours clinical pharmacy service in the ED on medication prescribing errors upon hospital admission and time to medication reconciliation. In this retrospective observational study, high-needs patients reviewed by ED pharmacists were compared against those not reviewed, to determine if the service was associated with reduction in admission medication errors. The primary outcome was the rate of medication errors. Errors were independently rated by two senior clinicians using a risk-probability matrix. Secondary outcomes included service's impact on time to best possible medication history (BPMH) and medication reconciliation. There were 242 patients who met the inclusion criteria: 105 intervention vs 137 control. In the intervention arm, 74 patients had at least 1 medication error compared with 113 in the control arm (total errors 206 vs 407). The error rate per 10 medications (interquartile range) was 1.4 (0, 2.9) in the intervention arm compared with 2.7 (1.2, 4.3) in the control arm (risk ratio 0.66 [95% confidence interval: 0.56-0.78]; P < 0.001). There were 33 moderate-risk and no high-risk errors (intervention), compared with 84 moderate-risk and 3 high-risk errors (control). Percent agreement was 98.98% (weighted kappa: 0.62). Time to BPMH and medication reconciliation were reduced from 40.5 and 45.0 h to 7.8 and 40.0 h, respectively. The 7-day extended-hours ED clinical pharmacy service was associated with a reduction in medication prescribing errors in high-needs patients and improved time to BPMH and medication reconciliation.

Exploring the Impact of Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.

Exploring the Impact of Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.

Telemedicine vs Telephone Consultations and Medication Prescribing Errors Among Referring Physicians

Critically ill children presenting to emergency departments (EDs) in non-children's hospitals are at high risk for experiencing medical errors, including medication errors. Video telemedicine consultations with pediatric specialists have the potential to reduce the risk of medication errors beyond the current standard of care, telephone consultations. To compare the rates of ED physician-related medication errors among critically ill children randomized to receive either video telemedicine or telephone consultations. This cluster randomized, unbalanced crossover trial was conducted at 15 community EDs in northern California between September 2014 and March 2018. Analyses were conducted from May 2022 to January 2023. Participants included acutely ill children younger than 15 years presenting to a participating ED. Participating EDs were randomized to use video telemedicine or telephone for consultations with pediatric critical care physicians according to 1 of 4 unbalanced (3 telemedicine to 1 telephone) crossover treatment assignment sequences. Pharmacists reviewed medical records to document physician-related medication errors using a previously validated instrument. Multilevel logistic regression analyses were performed to create models with the medication order as the unit of analysis and adjusting for age, the log-transformed Revised Pediatric Emergency Assessment Tool score, and hospital study period. A total of 696 patient encounters were included in the trial (mean [SD] age, 4.2 [4.6] years; median [IQR] age, 2.1 [0.5-2.1] years; 304 female [43.7%]), with 537 patient encounters (77.2%) assigned to video telemedicine and 159 patient encounters (22.8%) assigned to telephone. At least 1 physician-related medication error occurred for 87 patients (12.5%), including 20 of 159 patients (12.6%) in the telephone cohort and 67 of 537 patients (12.5%) in the telemedicine cohort. Of the 2414 medication orders, errors occurred in 124 cases (5.1%), including 26 of 513 orders (5.1%) in the telephone cohort and 98 of 1901 orders (5.2%) in the telemedicine cohort. In the multivariable analysis, the adjusted odds ratio of experiencing a medication error among those assigned to telemedicine was 0.86 (95% CI, 0.49-1.52; P = .61). This cluster randomized crossover trial found no statistically significant differences in physician-related medication errors between critically ill children assigned to receive telephone consultations vs video telemedicine consultations. ClinicalTrials.gov Identifier: NCT02877810.

Dabigatran-related serious medication errors: an analysis using data from VigiBase

ObjectiveTo analyze the serious medication errors (MEs) on dabigatran, and their related factors, in order to avoid or reduce the occurrence of adverse events.MethodsSerious MEs related to dabigatran were extracted from the WHO global database of reported potential side effects of medicinal products (VigiBase) by using “Medication errors and other product use errors and issues” High Level Group Term (HLGT) of the international Medical Dictionary for Regulatory Activities (MedDRA). Well-documented reports, vigiGrade completeness score ≥ 0.80, or with an informative narrative were analyzed with a focus on the clinical features of the cases. The PCNE Classification for drug-related problems (DRP) was used to classify medication errors in our analysis of cases.ResultsUntil January 26, 2020, there were 453 cases with serious MEs related to dabigatran in VigiBase, and 113 were well-documented. Among these, 69 patients (61%) were hospitalized or had prolonged hospitalization, 16 (14%) had life-threatening events, and 12 (11%) died. The MEs occurred in the prescription phase in 77 cases, in administration in 35, and at the dispensing stage in one case. The MEs in prescription were related to a drug selection error in 44 cases (24 concerning contraindications and 20 drug interactions) and to dose error in 33 cases (17 with excessive dose; eight with insufficient frequency; four had an incorrect time; in three, the dose was too low; and in one, too frequent). The MEs in administration were medical-staff-related errors in five cases (three with wrong administration route, one administration omission, and one overdose), patient-related errors in 28 (14 insufficient dose or no administration, seven improper drug storage, four wrong administration method, and three over prescribed dose), and other errors in two (without efficacy monitoring). The dispensing error of a wrong drug strength occurred in a pharmacy. The main adverse events in the 113 patients were haemorrhage in 57 cases (50%) and ischemia in 29 cases (26%).ConclusionBased on the analysis of reports in VigiBase, serious MEs related to dabigatran mainly occurred during prescription and administration. Although the incidence of MEs with clinical consequences in the use of dabigatran cannot be determined, attention should be paid to selection of the appropriate dose to a right patient in the prescription, and to patient compliance and storage in drug administration. The patient harm mainly manifested itself as bleeding or ischemia including fatal outcome in rare patients.

FedICU: a federated learning model for reducing the medication prescription errors in intensive care units

FedICU: a federated learning model for reducing the medication prescription errors in intensive care units

Medication Prescribing Errors on a Surgery Service - Addressing the Gap with a Curriculum for Surgery Residents: A Prospective Observational Study.

Educational interventions with proven effectiveness to reduce medication prescribing errors are currently lacking. Our objective was to implement and assess the effectiveness of a curriculum to reduce medication prescribing errors on a surgery service. This was a prospective observational cohort study at a Canadian academic hospital without an electronic order entry system. A pharmacist-led medication prescribing curriculum for surgery residents was developed and implemented over 2 days (2 h/day) in July 2019. Thirteen (76%) out of 17 surgery residents contributed pre-implementation data, while 13 (81%) out of 16 surgery residents contributed post-implementation data. Medication prescribing errors were tracked for 12 months pre-implementation and 6 months post-implementation. Errors were classified as prescription writing (PW) or decision making (DM). There were a total of 1050 medication prescribing errors made in the pre-implementation period with 615 (59%) PW errors and 435 (41%) DM. There were a mean of 87.5 (SD  =  14.6) total medication prescribing errors per month in the pre-implementation period with 51.3 (11.9) PW and 36.3 (6.0) DM errors. There were a total of 472 medication prescribing errors made in the post-implementation period with 260 (55%) PW and 212 (45%) DM errors. There were a mean of 78.7 (10.3) total medication prescribing errors per month in the post-implementation period with 43.3 (9.5) PW and 35.3 (4.2) DM errors. In the first quarter of the academic year, there were significantly fewer mean total errors per month post-implementation versus pre-implementation (77.7(12.7) versus 107.3(8.1); P  =  .035), with significantly fewer PW errors per month (40.7(13.2) versus 68.7(9.3); P  =  .046) and no difference in DM errors per month (37.0(2.0) versus 38.7(5.7);P  =  .671). There were no differences noted in the second quarter of the academic year. Medication prescribing errors occurred from PW and DM. Medication prescribing curriculum decreased PW errors; however, a continued education program is warranted as the effect diminished over time.

Parkinson’s disease medications in hospital: A retrospective audit of prescribing and administration errors

Background: Managing Parkinson’s disease (PD) medications in the acute hospital setting is complex. Mismanagement of PD medications such as inaccurate medication administration time and prescribing errors is common which can lead to the worsening of PD symptoms. Aim and Objectives: The aim of the study was to identify the extent of PD medication prescribing and administration errors in adult inpatients and pharmacists’ impact across a Victorian metropolitan hospital. Materials and Methods: A retrospective review analyzed a sample of 30 patients admitted between January and April 2022. PD medication orders (PDMO) and medication administration records (MAR) were reviewed; charting errors were identified based on reconciliation of PDMO with the pharmacist-led best-possible medication history (p-BPMH). Administration errors were identified through a review of MAR and PDMO charted time or p-BPMH documented administration time for each PD medication. Results: A total of 60 PDMO and 469 administration instances were reviewed. About 83.3% (50) of PDMOs had at least one instance of delay or omission in administration. 45.0% (27) of PDMO charting errors led to delays or omissions in administration. The timing of administration was the most common charting error accounting for 18.3% (11) of PDMOs. Documentation of p-BPMH within 24 h of admission led to a reduction of charting errors (52.9% vs. 76.9%, respectively). PD medications that were administered on time compared to p-BPMH documented time and PDMO charted time were 48.6% (228/469) and 53.7% (252/469), respectively. Conclusion: This study identified a high degree of PDMO charting and administration errors for patients with PD. Strategies to improve PD medication management are warranted.

Identification of Medication Prescription Errors and Factors of Clinical Relevance in 314 Hospitalized Patients for Improved Multidimensional Clinical Decision Support Algorithms.

Potential medication errors and related adverse drug events (ADE) pose major challenges in clinical medicine. Clinical decision support systems (CDSSs) help identify preventable prescription errors leading to ADEs but are typically characterized by high sensitivity and low specificity, resulting in poor acceptance and alert-overriding. With this cross-sectional study we aimed to analyze CDSS performance, and to identify factors that may increase CDSS specificity. Clinical pharmacology services evaluated current pharmacotherapy of 314 patients during hospitalization across three units of two Swiss tertiary care hospitals. We used two CDSSs (pharmaVISTA and MediQ), primarily for the evaluation of drug-drug interactions (DDI). Additionally, we evaluated potential drug-disease, drug-age, drug-food, and drug-gene interactions. Recommendations for change of therapy were forwarded without delay to treating physicians. Among 314 patients, automated analyses by both CDSSs produced an average of 15.5 alerts per patient. In contrast, additional expert evaluation resulted in only 0.8 recommendations per patient to change pharmacotherapy. For clinical pharmacology experts, co-factors such as comorbidities and laboratory results were decisive for the classification of CDSS alerts as clinically relevant in individual patients in about 70% of all decisions. Such co-factors should therefore be used for the development of multidimensional CDSS alert algorithms with improved specificity. In combination with local expert services, this poses a promising approach to improve drug safety in clinical practice.

An Ontology-Based Approach to Improving Medication Appropriateness in Older Patients: Algorithm Development and Validation Study.

Background: Inappropriate medication in older patients with multimorbidity results in a greater risk of adverse drug events. Clinical decision support systems (CDSSs) are intended to improve medication appropriateness. One approach to improving CDSSs is to use ontologies instead of relational databases. Previously, we developed OntoPharma-an ontology-based CDSS for reducing medication prescribing errors. Objective: The primary aim was to model a domain for improving medication appropriateness in older patients (chronic patient domain). The secondary aim was to implement the version of OntoPharma containing the chronic patient domain in a hospital setting. Methods: A 4-step process was proposed. The first step was defining the domain scope. The chronic patient domain focused on improving medication appropriateness in older patients. A group of experts selected the following three use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events. The second step was domain model representation. The implementation was conducted by medical informatics specialists and clinical pharmacists using Protégé-OWL (Stanford Center for Biomedical Informatics Research). The third step was OntoPharma-driven alert module adaptation. We reused the existing framework based on SPARQL to query ontologies. The fourth step was implementing the version of OntoPharma containing the chronic patient domain in a hospital setting. Alerts generated from July to September 2022 were analyzed. Results: We proposed 6 new classes and 5 new properties, introducing the necessary changes in the ontologies previously created. An alert is shown if the Medication Regimen Complexity Index is ≥40, if the Drug Burden Index is ≥1, or if there is a trigger based on an abnormal laboratory value. A total of 364 alerts were generated for 107 patients; 154 (42.3%) alerts were accepted. Conclusions: We proposed an ontology-based approach to provide support for improving medication appropriateness in older patients with multimorbidity in a scalable, sustainable, and reusable way. The chronic patient domain was built based on our previous research, reusing the existing framework. OntoPharma has been implemented in clinical practice and generates alerts, considering the following use cases: medication regimen complexity, anticholinergic and sedative drug burden, and the presence of triggers for identifying possible adverse events.

A Review of the Role of Artificial Intelligence in Healthcare.

Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs' belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.

Prescribing errors in a Brazilian teaching hospital: Causes and underlying factors from the perspective of junior doctors.

In university hospital settings most prescriptions are written by junior doctors, who are more likely to make prescribing errors than experienced doctors. Prescribing errors can cause serious harm to patients and drug harm differs among low, middle and high-income countries. In Brazil, few studies have investigated the causes of these errors. Our aim was to explore medication prescribing errors in a teaching hospital, their causes, and underlying factors from the perspective of junior doctors. Qualitative, descriptive and exploratory study that used a semi-structured individual interview with questions related to the planning and execution of prescriptions. It was conducted with 34 junior doctors who graduated from twelve different universities located in six Brazilian states. The data were analyzed according to the Reason's Accident Causation model. Among the 105 errors reported, medication omission stood out. Most errors resulted from unsafe acts during execution, followed by mistakes and violations. Many errors reached the patients; unsafe acts of rule violations and slips accounted for the majority. Work overload and time pressure were the most frequently reported causes. Difficulties faced by the National Health System and organizational problems were identified as latent conditions. The results reaffirm international findings about the severity of prescribing errors and the multifactorial aspect of their causes. Unlike other studies, we found a large number of violations, which, from the interviewees' perspectives, are related to socioeconomic and cultural patterns. The violations were not seen or mentioned by the interviewees as violations, but as difficulties in accomplishing their tasks on time. Knowing these patterns and perspectives is important for implementing strategies to improve the safety of both patients and professionals involved in the medication process. It is suggested that the exploitation culture of junior doctors' work be discouraged and that their training be improved and prioritized.

Impacto de los «daily huddle» en la seguridad del paciente pediátrico hospitalizado

IntroductionIn 2017, the Joint Commission proposed daily meetings called “huddle” as an indicator of quality of care. They are brief daily meetings of the multidisciplinary team, where security problems of the last 24h are shared and risks are anticipated. The objectives were to describe the most frequent safety events in Pediatric wards, implement improvements in patient safety, improve team communication, implement international safety protocols, and measure the satisfaction of the staff involved. Material and methodsProspective, longitudinal and analytical design (June 2020–February 2022), with previous educational intervention. Safety incidents, data related to unequivocal identification, allergy and pain records, data from the Scale for the Early Detection of Deficiencies (SAPI) and the Scale for the Secure Transmission of Information (SBAR) were collected. The degree of satisfaction of the professionals was evaluated. ResultsThree hundred forty-eight security incidents were recorded. Medication prescription or administration errors stood out (n=103). Drug prescription or administration errors stood out (n=103), especially those related to high-risk medication: acetaminophen (n=14) (×10 doses of acetaminophen; n=6), insulin (n=6), potassium (n=5) and morphic (n=5). An improvement was observed in the pain record; 5% versus 80% (P<.01), in the SAPI registry 5% versus 70% (P<.01), in SBAER scale 40% vs 100% (P<.01), in unequivocal identification of the patient 80% versus 100%; (P<.01) and in the application of analgesic techniques 60% versus 85% (P=.01). In the survey of professionals, a degree of satisfaction of 8 (7–9.5)/10 was obtained. ConclusionsHuddles made it possible to learn about security events in our environment and increase the safety of hospitalized patients, and improved communication and the relationship of the multidisciplinary team.

OntoPharma: ontology based clinical decision support system to reduce medication prescribing errors

BackgroundClinical decision support systems (CDSS) have been shown to reduce medication errors. However, they are underused because of different challenges. One approach to improve CDSS is to use ontologies instead of relational databases. The primary aim was to design and develop OntoPharma, an ontology based CDSS to reduce medication prescribing errors. Secondary aim was to implement OntoPharma in a hospital setting.MethodsA four-step process was proposed. (1) Defining the ontology domain. The ontology scope was the medication domain. An advisory board selected four use cases: maximum dosage alert, drug-drug interaction checker, renal failure adjustment, and drug allergy checker. (2) Implementing the ontology in a formal representation. The implementation was conducted by Medical Informatics specialists and Clinical Pharmacists using Protégé-OWL. (3) Developing an ontology-driven alert module. Computerised Physician Order Entry (CPOE) integration was performed through a REST API. SPARQL was used to query ontologies. (4) Implementing OntoPharma in a hospital setting. Alerts generated between July 2020/ November 2021 were analysed.ResultsThe three ontologies developed included 34,938 classes, 16,672 individuals and 82 properties. The domains addressed by ontologies were identification data of medicinal products, appropriateness drug data, and local concepts from CPOE. When a medication prescribing error is identified an alert is shown. OntoPharma generated 823 alerts in 1046 patients. 401 (48.7%) of them were accepted.ConclusionsOntoPharma is an ontology based CDSS implemented in clinical practice which generates alerts when a prescribing medication error is identified. To gain user acceptance OntoPharma has been designed and developed by a multidisciplinary team. Compared to CDSS based on relational databases, OntoPharma represents medication knowledge in a more intuitive, extensible and maintainable manner.

Medication Prescription Errors in the Intensive Care Unit: Prospective Observational Study.

IntroductionThe WHO launched a 5-year global initiative to address the problem of medication errors on March 29, 2017, targeting a decrease in severe and avoidable medication-related harm by 50% in all the countries. Since prescription errors are preventable, this study was conducted to determine incidence and severity of medication prescription errors (MPEs).Settings and designIntensive care unit of a tertiary care academic hospital, prospective observational study.Methods and materialsFor all patients admitted in a medical ICU, baseline data (demographic, APACHE II, length of ICU stay, and days of mechanical ventilation) were noted. Treatment charts were reviewed daily, and each prescription was compared against a master chart prepared using standardized references to study the incidence of prescription errors. Severity classification was done using National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) classification. Mean and median, along with standard deviation and interquartile range, were calculated for all quantitative variables. Multivariate linear regression analysis model was used.ResultsOut of the total 24,572 medication orders, 2,624 had prescription errors, an error rate of 10.7% (95% CI, 10.3–11.1). When analyzed for severity, 1,757 (7.15%) (95% CI, 6.8–7.5) MPEs did not result in patient harm and 867 (3.52%) (95% CI, 3.3–3.8) MPEs required interventions and/or resulted in patient harm. Patients with deranged creatinine (p <0.001) and INR (p = 0.024) had higher number of severe MPEs.ConclusionThe incidence of MPEs in the medical ICU at the tertiary care hospital was 10.7%, 3.52% being severe errors.How to cite this articleKumar M, Sahni N, Shafiq N, Yaddanapudi LN. Medication Prescription Errors in the Intensive Care Unit: Prospective Observational Study. Indian J Crit Care Med 2022;26(5):555–559.

Identifying rates and risk factors for medication errors during hospitalization in the Australian Parkinson's disease population: A 3-year, multi-center study.

Admission to hospital introduces risks for people with Parkinson's disease in maintaining continuity of their highly individualized medication regimens, which increases their risk of medication errors. This is of particular concern as omitted medications and irregular dosing can cause an immediate increase in an individual's symptoms as well as other adverse outcomes such as swallowing difficulties, aspiration pneumonia, frozen gait and even potentially fatal neuroleptic malignant type syndrome. To determine the occurrence and identify factors that contribute to Parkinson's medication errors in Australian hospitals. A retrospective discharge diagnosis code search identified all admissions for people with Parkinson's disease to three tertiary metropolitan hospitals in South Australia, Australia over a 3-year period. Of the 405 case notes reviewed 351 admissions met our inclusion criteria. Medication prescribing (30.5%) and administration (85%) errors during admission were extremely common, with the most frequent errors related to administration of levodopa preparations (83%). A higher levodopa equivalent dosage, patients with a modified swallowing status or nil by mouth order during admission, and patients who did not have a pharmacist led medication history within 24 hours of admission had significantly higher rates of medication errors. This study identified 3 major independent factors that increased the risk of errors during medication management for people with Parkinson's disease during hospitalization. Thus, targeting these areas for preventative interventions have the greatest chance of producing a clinically meaningful impact on the number of hospital medication errors occurring in the Parkinson's population.

PERBANDINGAN MEDICATION ERROR PADA PERESEPAN ELEKTRONIK DAN PERESEPAN MANUAL PADA TAHAP PRESCRIBING DI APOTEK KOTA PALU

Prescribing drugs is now growing, as evidenced by the existence of electronic prescribing that have been applied in several hospitals and pharmacies. The development of this prescribing method does not rule out the possibility of increasing the incidence of medication errors in prescribing. So, it is necessary to identify the prescribing that has been implemented, especially in drugstore at Palu City. The purpose of this study was to look at the comparison of medication errors at the prescribing stage which included aspects of prescribing completeness and therapeutic errors in electronic prescribing at Drugstore X and manual prescribing at Drugstore Y in Palu city in 2018. This type of research is a descriptive study with retrospective data collection. Samples taken in the form of secondary data are prescribing using purposive sampling method. The results obtained in this study are, the prevalence of medication error at the prescribing stage of electronic prescribing in drugstore at Palu City includes data, patient's body weight is 2%, doctor's license 100%, doctor's telephone number 100%, doctor's initial 100%, dosage form 97% and 90% strength of the drug. While the prevalence of medication error at the prescribing stage of manual prescribing in Drugstore Palu City is data, the patient's age is 2%, the patient's body weight is 99.1%, the patient's sex is 100%, the doctor's license is 91.6%, the doctor's telephone number is 80% , initial doctor 42.7%, 88% dosage form and 65% drug strength. Based on these results, it can be concluded that the use of electronic prescribing is better than manual prescribing.

PERBANDINGAN MEDICATION ERROR PADA PERESEPAN ELEKTRONIK DAN PERESEPAN MANUAL PADA TAHAP PRESCRIBING DI APOTEK KOTA PALU

Prescribing drugs is now growing, as evidenced by the existence of electronic prescribing that have been applied in several hospitals and pharmacies. The development of this prescribing method does not rule out the possibility of increasing the incidence of medication errors in prescribing. So, it is necessary to identify the prescribing that has been implemented, especially in drugstore at Palu City. The purpose of this study was to look at the comparison of medication errors at the prescribing stage which included aspects of prescribing completeness and therapeutic errors in electronic prescribing at Drugstore X and manual prescribing at Drugstore Y in Palu city in 2018. This type of research is a descriptive study with retrospective data collection. Samples taken in the form of secondary data are prescribing using purposive sampling method. The results obtained in this study are, the prevalence of medication error at the prescribing stage of electronic prescribing in drugstore at Palu City includes data, patient's body weight is 2%, doctor's license 100%, doctor's telephone number 100%, doctor's initial 100%, dosage form 97% and 90% strength of the drug. While the prevalence of medication error at the prescribing stage of manual prescribing in Drugstore Palu City is data, the patient's age is 2%, the patient's body weight is 99.1%, the patient's sex is 100%, the doctor's license is 91.6%, the doctor's telephone number is 80%, initial doctor 42.7%, 88% dosage form and 65% drug strength. Based on these results, it can be concluded that the use of electronic prescribing is better than manual prescribing.

Comparison of Prescribing Errors in Different Wards of Tertiary Care Hospital before and after Implementation of CPOE System

Background: Medication prescribing errors were always known as inevitable errors in health care system which mainly includes physician’s writing error which then leads to wrong dispensing error. Manual Prescribing has now shifted to CPOE System that seems to be more efficient. Through this system, it is believed that quality and patient security is improved by lessening medicine and different mistakes at various phases of the request the board procedure and by maintaining a strategic reserve from repetitive testing. This Study was conducted to compare the prescribing errors in different hospital wards of a tertiary care hospital for in-patients by using two approaches; Manual Prescribing and CPOE System Prescribing.&#x0D; Methods: This cross sectional study was conducted on manual prescriptions for three months and on CPOE prescriptions for three months. In this way a total of 4102 prescriptions were evaluated for different types of prescribing errors occurred in different wards. Whole study was conducted on In-patients from all the wards in the hospital except Emergency and OPD patients.&#x0D; Results and Discussion: The results showed that the maximum number of manual prescription errors were found in Medical ICU ward, while prescribing errors for CPOE setup were found comparatively at higher rate in Medicinal ward. It was also found that maximum frequency of prescribing errors were found in Lower Respiratory Tract Infections in both the settings; CPOE System and Manual Prescribing&#x0D; Conclusion: It was concluded that CPOE system is helpful for reducing prescribing errors but it must be supervised by pharmacists to overcome potential errors.

Medication Errors in Surgery: A Classification Taxonomy and a Pilot Study in Postcall Residents

IntroductionThe post-call state in postgraduate medical trainees is associated with impaired decision-making and increased medical errors. An association between post-call state and medication prescription errors for surgery residents is yet to be established. Our objective was to determine whether post-call state is associated with increased proportion of medication prescription errors committed by surgery residents in an academic hospital without a computerized physician order entry (CPOE) system. MethodsThis prospective observational study was conducted at a tertiary academic hospital between June 28 and August 31, 2017. It compared the proportion of medication prescription errors committed by surgery residents in their post-call (PC) and no-call (NC) states. A novel taxonomy was developed to classify medication prescription errors. ResultsSixteen of twenty-one eligible residents (76%) participated in this study. Self-reported hours of sleep per night was significantly higher in the NC group compared to the PC group (6(4-8) vs 2(0-4) hours, P < 0.01). PC residents committed a significantly higher proportion of medication prescription errors versus NC residents (9.2% vs 3.2%; p=0.04). Decision-making and prescription-writing errors comprised 33% and 67% of errors, respectively. ConclusionsThe post-call state in surgery residents is associated with a significantly higher proportion of medication prescription errors in a hospital without a CPOE system. Decision-making and prescription-writing errors could potentially be addressed by additional educational interventions.