Order Entry System Research Articles

Nurses' Attitudes Toward Computerized Provider Order Entry (CPOE) System: A Feasibility Study Prior to Implementation

Objectives: The main objective of this study was to understand the nurses' attitudes in implementation and adoption of CPOE system through Rogers’ innovation diffusion theory. Methods: This study was cross-sectional and performed in four months (from March to June, 2017). In order to understand the acceptance of the CPOE investigated level of nursing staff’s computer literacy. The major factors influencing the nursing staff’s acceptance and use of the CPOE system were examined through Rogers’ innovation diffusion theory. Data were collected using a researcher. Results: Majority of the nursing staff adopt this innovation. The level of nursing staff’s computer literacy was high (71.32%). Highly educated nursing staff were more likely to accept computer work. The acceptance or rejection of the use of a computer is related to experiencing the HIS system in nursing. Our finding showed that the received relative advantages, compatibility, the complexity of the system were 66.29%, 71.94% and 40.36% for the nursing staff. Conclusions: Majority of the nursing staff had a good degree of computer literacy. The analysis of the finding shows that CPOE systems improve the quality and safety of healthcare. CPOE increases the security and quality of health care and reduces errors.

Assessing inpatient platelet ordering practice: evaluation of computer provider order entry overrides.

Judicious utilization of platelet products protects a limited resource and mitigates risks of transfusion. At many institutions, computer physician order entry systems provide prompts to guide transfusion decisions; many capture the indication for transfusion, and generate metadata when orders are dissonant with guidelines. We conducted a retrospective review to examine adherence to and overrides of hospital guidelines for platelet transfusion to identify opportunities for improved transfusion practice. Physician override reports (1/1/2018-3/31/2019) were examined and physician-entered justification comments accompanying override orders were extracted, in addition to patient-specific data (clinical service, age, sex, and pretransfusion platelet count). Two transfusion medicine physicians independently assessed comments in context of patient data and institutional guidelines and categorized as: indicated, protocol driven, or not indicated. Following adjudication, consensus was reached between the two reviewers. Override keyword frequencies were also determined. Over 15-months, 1373 override orders were placed for 558 unique patients (25% of all adult inpatient platelet transfusions). haematology/oncology providers placed 573 (42%) override orders (261 unique patients), 46% of which were categorized as "not indicated", based on consensus review. Overall, 470 (34%) override orders were categorized as "not indicated". Examples of recurring key words included "bleeding/risk of bleeding", "falling platelet count", "platelet goal of XX". A large percentage of override orders for platelet transfusions were determined to be "not indicated" and out of compliance with institutional guidelines. The metadata captured identified concerns regarding clinical transfusion practice and opportunities for revised indications (e.g. threshold for retinal haemorrhage).

Impact of clinical decision support on oritavancin prescribing in the emergency department

BackgroundEmergency department (ED) visits and hospitalizations from skin and soft tissue infections (SSTIs) have been on the rise and have led to an increased clinical and economic burden. Owing to their single-dose regimen, recently approved lipoglycopeptides such as oritavancin have the potential to shift the management of SSTIs from an inpatient to outpatient setting. Limited data exist regarding the use of these drugs in the ED setting. ObjectivesThe purpose of this study was to describe the impact that clinical decision support (CDS) incorporation into the computerized physician order entry (CPOE) system had on oritavancin use and to assess compliance with appropriate use guidelines in the ED. MethodsThis was a retrospective cohort study evaluating patients who received oritavancin from September 2016 to May 2018. The patients were assigned to the pre-CDS–implementation group if oritavancin was used between September 13, 2016, and June 28, 2017 and to the post-CDS–implementation group if oritavancin was used between August 28, 2017, and May 25, 2018. There was a 2-month transition period between the 2 study time periods. Patients were excluded if the administration occurred outside of the ED or during the transition period. The primary endpoints were oritavancin use and compliance with the appropriate use guidelines after the implementation. ResultsThere were 169 oritavancin orders in total, of which 119 met the inclusion criteria. There was a marked decrease in use post-CDS implementation (9.2 orders/mo vs. 3 orders/mo). Among those who were prescribed oritavancin, compliance with the appropriate use guidelines increased; however, this did not reach statistical significance. ConclusionThe implementation of the appropriate use guidelines with CDS integration into the CPOE system decreased overall oritavancin use but did not have an impact on compliance with the appropriate use guidelines.

Risk factors associated with medication ordering errors.

We utilized a computerized order entry system-integrated function referred to as "void" to identify erroneous orders (ie, a "void" order). Using voided orders, we aimed to (1) identify the nature and characteristics of medication ordering errors, (2) investigate the risk factors associated with medication ordering errors, and (3) explore potential strategies to mitigate these risk factors. We collected data on voided orders using clinician interviews and surveys within 24 hours of the voided order and using chart reviews. Interviews were informed by the human factors-based SEIPS (Systems Engineering Initiative for Patient Safety) model to characterize the work systems-based risk factors contributing to ordering errors; chart reviews were used to establish whether a voided order was a true medication ordering error and ascertain its impact on patient safety. During the 16-month study period (August 25, 2017, to December 31, 2018), 1074 medication orders were voided; 842 voided orders were true medication errors (positive predictive value = 78.3 ± 1.2%). A total of 22% (n = 190) of the medication ordering errors reached the patient, with at least a single administration, without causing patient harm. Interviews were conducted on 355 voided orders (33% response). Errors were not uniquely associated with a single risk factor, but the causal contributors of medication ordering errors were multifactorial, arising from a combination of technological-, cognitive-, environmental-, social-, and organizational-level factors. The void function offers a practical, standardized method to create a rich database of medication ordering errors. We highlight implications for utilizing the void function for future research, practice and learning opportunities.

Using the clinical information system and self-supervision to rationalize the need for antibiotic stewardship: An interventional study in a 2000-bed university hospital

To describe the usefulness of electronic medical records (EMRs) and a computerized physician order entry (CPOE) system to support and assess an antimicrobial stewardship programme (ASP). At the study hospital, infectious diseases specialists supervise antimicrobial prescription when solicited by physicians in charge of patients. From January to October 2015, treatment days of antibiotic prescription, supervised or unsupervised by infectious disease specialists (SAP or UAP, respectively) in all wards, except intensive care units emergency department, bone marrow transplantation units, and paediatric units, were calculated. Embedding recommendations on carbapenem indications as a checklist into the CPOE system, a self-administered ASP was implemented in 2017. EMRs were reviewed to determine global compliance with carbapenem prescription guidelines (combining introduction of therapy and 72-h assessment) before and after implementation of a self-administered ASP in departments with a low SAP rate for these antibiotics. Among 16 090 prescriptions extracted, 19.9% were SAPs. Three patterns of prescription were identified. The first pattern (amoxicillin-clavulanate, ceftriaxone) was characterized by a high UAP rate in every department, the second pattern (cloxacillin, rifampin) was characterized by a high SAP rate in every department, and the third pattern (broad-spectrum beta-lactams) was characterized by heterogeneous distribution of SAP/UAP among departments. Carbapenem prescription was reviewed in five departments with a low SAP rate for carbapenems over 6 months: 94 before and 107 after implementation of the self-administered ASP. Global compliance with guidelines increased significantly from 22% to 37% (risk difference 15%, 95% confidence interval 2.3-28.5%; P=0.02). A clinical information system may help to rationalize antibiotic stewardship in a context of scarce medical resources. Mapping of antibiotic prescriptions and self-supervision are efficient, complementary and easy-to-implement tools.

Machine Learning Approach to Reduce Alert Fatigue Using a Disease Medication-Related Clinical Decision Support System: Model Development and Validation.

BackgroundComputerized physician order entry (CPOE) systems are incorporated into clinical decision support systems (CDSSs) to reduce medication errors and improve patient safety. Automatic alerts generated from CDSSs can directly assist physicians in making useful clinical decisions and can help shape prescribing behavior. Multiple studies reported that approximately 90%-96% of alerts are overridden by physicians, which raises questions about the effectiveness of CDSSs. There is intense interest in developing sophisticated methods to combat alert fatigue, but there is no consensus on the optimal approaches so far.ObjectiveOur objective was to develop machine learning prediction models to predict physicians’ responses in order to reduce alert fatigue from disease medication–related CDSSs.MethodsWe collected data from a disease medication–related CDSS from a university teaching hospital in Taiwan. We considered prescriptions that triggered alerts in the CDSS between August 2018 and May 2019. Machine learning models, such as artificial neural network (ANN), random forest (RF), naïve Bayes (NB), gradient boosting (GB), and support vector machine (SVM), were used to develop prediction models. The data were randomly split into training (80%) and testing (20%) datasets.ResultsA total of 6453 prescriptions were used in our model. The ANN machine learning prediction model demonstrated excellent discrimination (area under the receiver operating characteristic curve [AUROC] 0.94; accuracy 0.85), whereas the RF, NB, GB, and SVM models had AUROCs of 0.93, 0.91, 0.91, and 0.80, respectively. The sensitivity and specificity of the ANN model were 0.87 and 0.83, respectively.ConclusionsIn this study, ANN showed substantially better performance in predicting individual physician responses to an alert from a disease medication–related CDSS, as compared to the other models. To our knowledge, this is the first study to use machine learning models to predict physician responses to alerts; furthermore, it can help to develop sophisticated CDSSs in real-world clinical settings.

Use of ICD-10-CM T codes in hospital claims data to identify adverse drug events in Taiwan.

Adverse drug events (ADEs) are a major public health concern worldwide and may prolong hospital stays, causing a burden on the healthcare system and increasing the associated costs. Therefore, optimizing medication use and reducing ADEs are priorities for public health. Medication safety can be monitored and improved by identifying ADEs. The utilization of diagnoses coded according to the International Statistical Classification of Diseases and Related Health Problems (ICD) system for the identification of ADEs has been firmly established. In Taiwan, however, the validity of recording ADEs on the basis of inpatient ICD-10-CM T codes has not been evaluated. Therefore, this study investigated the potential usefulness of ICD-10-CM T codes in routine hospital data for the identification of ADEs and for increasing the rate of reporting. We use hospital claims data of hospitalized patients from one medical centre in northern Taiwan between 1 July 2016 and 30 June 2018. We defined an ADE to have taken place if an ICD-10-CM T code was present among the primary or secondary diagnosis codes. The inpatients who were discharged with T codes in a primary or secondary diagnosis were identified by the computerized T code information platform, and the retrospective review of the medical charts was performed by pharmacists to confirm the ADEs. 1384 inpatients who were discharged with the relevant T codes in a primary or secondary diagnosis were identified during the study period. Code T36 (poisoning by, adverse effect of or underdosing of systemic antibiotics) was the most common code, accounting for 56.6%, followed by T42 (17.7%; poisoning by, adverse effect of or underdosing of antiepileptic, sedative-hypnotic or antiparkinsonism drug). Overall, 789 clinically significant ADEs were identified after medical chart review. The dermatologic system was the most commonly involved. The overall positive predictive value for a flagged code representing an ADE was 57%. Furthermore, the use of T codes to confirm the number of ADE cases increased the ADE reporting rate by 9.17%. The PPV of ICD-10-CM T codes analysed in our study was insufficient for identifying ADEs during hospitalization. The sensitivity and specificity of this were inadequate. However, the T code system can be used as an auxiliary resource for pharmacists to identify potential ADEs and report the information as prompts on the physician order entry system. When a physician prescribes a drug that may cause an ADE in a patient, an alert is issued to ensure medication safety. In conclusion, the T codes did not perform well in our study and caution should be exercised in their use to identify ADEs on their own.

Abstract 15056: Machine Learning Prediction of Long QT Syndrome

Introduction: Drug-induced QT prolongation causes significant morbidity and mortality but could be preventable with prediction of susceptible individuals. Machine learning (ML) algorithms applied to electronic health record (EHR) data may provide a method for identifying these individuals and could be automated to directly alert providers of risk. This study applies ML techniques to EHR data to identify an integrated model that can be deployed to predict risk of drug-induced QT prolongation. Methods: We examined data from the UCHealth EHR that has been harmonized to the Observational Medical Outcomes Partnership common data model and identified inpatients who had received a medication known to prolong the QT interval and had an electrocardiogram (ECG) performed within 24 hours. We used a binary outcome of the development of a QTc interval > 500 ms by ECG within 24 hours of medication initiation or no ECG with a QTc interval > 500 ms throughout the encounter. We compared multiple machine learning methods (logistic regression, random forest, naïve Bayes, and deep neural network (DNN)) by classification accuracy as assessed by AUC and F 1 score. We performed calibration and scaling of the final model. Results: We identified 35639 inpatients who received a known QT-prolonging medication and had an ECG within 24 hours. Of those, 4558 patients developed a QTc > 500 ms and 31081 patients did not. The DNN model provided reasonable classification accuracy (F1 score 0.404; AUC 0.71) and was the most accurate method tested. A range of decision cutpoints were plotted with respective sensitivity/specificity (Figure 1). Conclusions: We found that by applying a DNN to EHR data, we could reasonably predict individuals susceptible to drug-induced QT prolongation. Varying cutpoints can be used to tailor the model to the desired sensitivity. Future studies are needed to validate this model in novel EHRs and within the physician order entry system to assess ability to improve patient safety.

Extended Half-Life Factor VIII/Factor IX Products: Assay Discrepancies and Implications for Hemophilia Management.

Due to structural differences between extended half-life (EHL) factor VIII (FVIII) or FIX products and equivalent plasma wild-type molecules used for assay calibration, reagent-dependent discrepancies during monitoring of FVIII- and FIX-replacement therapies with EHL products have been described. To assess the performance of available one-stage clotting and chromogenic substrate assays on the Siemens Atellica COAG 360 analyzer, an in vitro study using spiked plasma samples was performed. The described results confirm previously described findings and allowed allocation of each EHL product to an appropriate assay. In addition, corresponding EHL product-specific analytes were defined within the order entry system of the University Hospital Bonn. The requirement of product-specific FVIII and FIX assays complicates patient monitoring and demonstrates the need for both continuous education and communication between treating physicians and the coagulation laboratory.

Role of clinical pharmacists in telemonitoring for patients with Coronavirus Disease 2019 (COVID-19).

Clinical pharmacists actively participate in patient care via patients' medication use. Yet the setting of Coronavirus Disease 2019 (COVID-19) limits patient contact with healthcare personnel. We aimed to review the services provided and drug-related problems detected using telemonitoring methods to guide clinical pharmacists in providing service in treating COVID-19 patients. At a tertiary care hospital in Thailand, clinical pharmacists provided pharmaceutical care services for COVID-19 patients via telemonitoring using the hospital's computerized physician order entry system. The pharmacists were able to provide therapeutic drug monitoring services, especially for anticoagulants. Many patients were considered special populations, with individualized requirements for drug dosing. Some adverse drug reactions were observed. Drug-related problems were mostly related to medication use in critically ill patients. Telemonitoring is a viable method for clinical pharmacists to provide pharmaceutical care and meet the challenges posed by treating patients with COVID-19.

Results of a pilot study to increase adherence to ASCO G-CSF recommendations at community clinics.

19 Background: An ASCO 2012 Choosing Wisely recommendation cautions against the use of primary prophylactic colony stimulating factors (PP-CSF) for chemotherapy regimens with <20% risk of febrile neutropenia (FN). Our pilot aimed to test the feasibility and impact of using academic detailing and an automated CSF ordering system on CSF prescribing at 6 regional community oncology clinics in Washington State. Methods: The intervention was 1) academic detailing for oncologists during a regular staff meeting and 2) reconfiguring ordering systems to show FN risk and PP-CSF recommendation in a passive alert and include or exclude PP-CSF standing orders with >20% or ≤20% FN risk regimens, respectively. Clinic tumor registries were queried for patients with stage II-IV breast, non-small cell lung, or colorectal cancer starting first-line chemotherapy pre- or post-intervention. PP-CSF use, FN risk factors, and chemotherapy regimens matching the study protocol were manually abstracted. A regimen order was coded as adherent if it is low (<10%) FN risk without PP-CSF use; intermediate (10-20%) FN risk without PP-CSF use or FN risk factors are present; or high (>20%) FN risk with PP-CSF use. Results: The intervention was successfully implemented at 4 out of 6 participating clinics. The remaining 2 clinics were transitioning from paper to electronic orders in 1-2 years or using a system managed by a non-participating hospital. Adherence across the four implementing clinics increased after the intervention. *=p<0.05 Implementation of the CSF order-entry intervention was successful across a variety of ordering systems, including paper-based systems. Overall, while adherence prior to the intervention was high for these clinics, the order entry systems significantly improved adherence. Population characteristics and data availability may account for variation in adherence. Conclusions: An intervention with both academic detailing and ordering system presets may help increase adherence to Choosing Wisely recommendations. [Table: see text]

Effect of Computerized Physician Order Entry and Clinical Decision Support System on Adverse Drug Events Prevention in the Emergency Department: A Systematic Review.

Objective: An adverse drug event (ADE) is an injury resulting from a medical intervention related to a drug. The emergency department (ED) is a ward vulnerable to more ADEs because of overcrowding. Information technologies such as computerized physician order entry (CPOE) and clinical decision support system (CDSS) may decrease the occurrence of ADEs. This study aims to review research that reported the evaluation of the effectiveness of CPOE and CDSS on lowering the occurrence of ADEs in the ED. Data Sources: PubMed, EMBASE, and Web of Science databases were used to find studies published from 2003 to 2018. The search was conducted in November 2018. Study Selection and Data Extraction: The search resulted in 1700 retrieved articles. After applying inclusion and exclusion criteria, 11 articles were included. Data on the date, country, type of system, medication process stages, study design, participants, sample size, and outcomes were extracted. Data Synthesis: Results showed that CPOE and CDSS may prevent ADEs in the ED through significantly decreasing the rate of errors, ADEs, excessive dose, and inappropriate prescribing (in 54.5% of articles); furthermore, CPOE and CDSS may significantly increase the rate of appropriate prescribing and dosing in compliance with established guidelines (45.5% of articles). Conclusion: This study revealed that the use of CPOE and CDSS can lower the occurrence of ADEs in the ED; however, further randomized controlled trials are needed to address the effect of a CDSS, with basic or advanced features, on the occurrence of ADEs in the ED.

The Effect of Communication and EMR Meaningful Use Technologies on Patient Outcomes

The adoption of health IT transforms communication between care providers and patients. Unfortunately, research on the transformation of communication has produced conflicting results, creating tension regarding its efficacy among healthcare professionals. Upon closer examination of prior research models, we find that nurse and physician communication performance mediate the relationship between health IT implementation and patient outcomes. We test the mediating role of communication with a hospital-level data set spanning 2011 through 2015. The specific health information technologies we investigate include EMR documentation, computerized physician order entry (CPOE) systems, clinical decision support (CDS) systems, and health information exchanges (HIE). Our results provide that EMR documentation, CPOE, and HIE directly improve communication between care providers and patients as well as patient outcomes. Further, nurse-patient and physician-patient communication mediates the relationship between health IT implementation and patient outcomes. The mediating effect extends the positive benefits to patient outcomes following technology implementation. We also find that increasing nurses and physicians’ communication with patients directly improves mortality, satisfaction, and loyalty. Surprisingly, CDS negatively affects communication and patient outcomes. Our findings contribute to the information systems and healthcare literatures by demonstrating the need to account for the multidimensional nature of healthcare and by providing context for the positive and negative effects previously discovered. Furthermore, the results offer practical and theoretical implications for leveraging specific health IT adoption and for realigning federal incentive structures for hospitals.

Development of a Mortality Risk Model in Elderly Hip Fracture Patients by Different Analytical Approaches

Hip fracture is a major health issue that accompanies community aging. The most critical time after a hip fracture should be the first year. Care systems and surgical techniques for hip fractures have improved, so the trend of mortality in elderly hip fracture could be changed with them. Therefore, we observed the changes in the trend and critical factors for first-year mortality for the hip fractures in an elderly population in Taiwan, and mortality of prognosis prediction model was developed for the early diagnosis using a population-based database in Taiwan (National Health Insurance Research Database, NHIRD). A total of 166,274 elderly subjects with an age greater than 60-years-old from 2001 to 2010 were collected for this study. Cox proportional-hazards (PH) regression and logistic regression were calculated to odds ratio and hazard ratio for mortality of those patients and compared it. Data mining algorithms were also used to generate a risk stratification prediction model. The first-year mortality rate of the overall study group was 21.5% in 2001 and 15.0% in 2010 (p for trend < 0.001). In the male subgroup, the first-year mortality rate was 29.3% in 2001 and decreased to 17.3% in 2010; the trend of standardized mortality ratio was significantly decreased from 4.4 to 2.6 (p for trend < 0.001). By logistic regression, mortality significantly increased with age and male gender. Furthermore, gender, age, patients with diabetes mellitus (DM), cardiovascular (CV), and renal comorbidity, and surgical intervention can be variables for constructing the risk stratification model. The findings of the study will be used for helping related field physicians to predict the prognosis risk of hip fracture patients, and provide evidence-based tailored treatment recommendations for those patients. It may consider to build various models for predicting the prognosis of hip fracture or integrating prediction algorithms into the computerized physician order entry system, thus creating a practical clinical decision support system with warning functions.

Identifying End Users' Preferences about Structuring Pharmacogenetic Test Orders in an Electronic Health Record System

Pharmacogenetics (PGx) testing can be used for detecting genetic variations that may affect an individual's anticipated metabolism of, or response to, medications. Although several studies have focused on developing tools for delivering results from PGx testing, there is a relative dearth of information about how to design provider-friendly electronic order-entry systems for PGx. The U.S. Department of Veterans Affairs (VA) is preparing to implement a new electronic health records system. In this study, VA PGx test end users were surveyed about their preferences for how electronic test orders for PGx should be structured, including the nomenclature that should be used to search for and identify PGx-test orders, whether to offer single- versus multigene tests, and whether information about test methodology should be included in the order name. Responses were analyzed systematically to identify areas of agreement and disagreement with the survey options, and areas where respondents' opinions diverged. End users endorsed preferences for flexible ways to identify and order PGx tests and multigene panel tests; opinions on whether test methodology should be included in the test name were divergent. The results could be used for both informing the VA's new electronic health records implementation (including how PGx tests are searched for and ordered) and for providing insights for other health systems implementing PGx-testing programs.

Rethinking the Drug Distribution and Medication Management Model: How a New York City Hospital Pharmacy Department Responded to COVID-19.

Beginning in March 2020, New York City began the fight against coronavirus disease 2019. Health care workers were faced with a disease that led to significant morbidity and mortality with no proven therapies. As hospitals became inundated with patients and underwent rapid expansion of capacity, resources such as drugs, protective and medical equipment, and hospital staff became limited. Pharmacists played a critical role in the management of clinical care and drug delivery during the pandemic. As members of the department of pharmacy within NewYork‐Presbyterian Hospital, we describe our experiences and processes to overcome challenges faced during the pandemic. Strict inventory management through the use of daily usage reports, frequent communication, and minimization of waste was critical for the management of drug shortages. The creation of guidelines, protocols, and restrictions were not only used to mitigate drug shortages, but also helped educate health care providers and guided medication use. Managing technology through setting up new automatic dispensing cabinets to address hospital expansions and modifying the electronic order entry system to include new protocols and drug shortage information were also vital. Additional key pharmacist functions included provision of investigational drug service support and training of pharmacists, prescribers, nurses, and respiratory therapists to educate and standardize medication use. Through implementation of operational and clinical processes, pharmacists managed critical drug inventory and guided patient treatment. As the pandemic continues, pharmacists will remain vital members of the multidisciplinary team dedicated to the fight against the virus.

Computerized prescription order entry and adverse drug events in pediatrics: a systematic review

Abstract Background Potentially harmful errors occur in pediatric patients at a rate three times higher than in adults, mainly due to dosage calculations. Objective. To evaluate the effect of Computerized Prescription Order Entry (CPOE) and Decision Support Systems (DSS) on Adverse Drug Events (ADE) in pediatrics. Method. Systematic review updated up to December 2019 in the PubMed, Scopus, Web of Science and Embase, Lilacs and cross-references, recorded in Prospero CRD 42019126590. Observational and pre- and post-implementation CPOE studies were included. in any language. A scale was used for methodological evaluation of the studies. Relative risk (RR), attributable risk (AR) or number needed to treat (NNT) with 95% CIs were computed for comparing the incidence of total, actual, preventable and potentially harmful ADE. Results Starting with 203 retrieved abstracts, 12 articles were included - eight interventional and four observational studies. Five articles evaluated CPOE and seven CPOE plus DSS. Settings for eleven studies were hospitals and one an outpatient facility. Sample sizes ranged from 514 to 45,615 totaling 112,187 patients. The number of prescriptions ranged from 724 to 92,398, totaling 182,803. A reduction in total ADE was observed in two studies. The risk reduction of preventable ADE ranged from 24% RR 0.76 (0.59 - 0.97) to 47% RR 0.53 (0.29-0.91) in four articles. Potentially harmful ADE (ADE ph) in five studies were reduced from 76% RR = 0.24 (0.09-0.68) to 44% (RR 0.56 (0.46 - 0.70), with an AR variyng from 1.8 to 0.9%. One of the articles stated that 64 (95% CI 25-100) patients would need to be using CPOE in order to avoid one potentially harmful ADE. Studies identified reductions in ADE in various settings- two in wards, one in wards and Pediatric Intensive Care Unit (PICU), the others in PICU and Neonatal Intensive Care Units. Conclusions Implementation of CPOE plus DSS reduces EADs in pediatric settings. Key messages Accumulated evidence favors implementation of CPOE plus DSS as important for prevention of ADE in pediatric settings. Potentially harmful and preventable errors can be avoided by implementing electronic prescribing systems in pediatrics. Its use should be encouraged in countries in low-income countries.

Clinical Staff Attitude Toward the Computerized Physician Order Entry System in Cardiac Care Unit Based on a Model by the Name of “Technology Acceptance Model in Health Care Centers of Iran”

Background: Currently, many physicians use the computerized physician order entry (CPOE) system, and the adoption of this system is increasing worldwide. The technology acceptance model has been implemented and evaluated. It has become one of the most recognized empirical models. Besides, recent studies showed that this model well anticipates users’ behavioral tendencies in accepting technology in healthcare centers. The use of the technology acceptance model in the implementation of health care systems such as CPOE, which is effective in reducing medical errors, is necessary. Objectives: This applied research has used a descriptive-analytical approach. In the current study the technology acceptance model in Iranian health care centers, which was proposed by Safdari and colleagues aimed to investigate the attitudes of clinical staff toward the computerized physician orders entry system, was used in the Cardiac Care Unit of Alzahra’s Heart Hospital in Shiraz to determine the importance and effectiveness of each dimension on attitudes toward using CPOE by 140 of physicians and nurses. Methods: Three main dimensions, including human, monitoring-management, and organizational strategies factors, were evaluated using a researcher-made questionnaire. Data were analyzed by SPSS version 25 using the descriptive and analytical statistics (i.e., Pearson correlation test and t-test). Results: The results showed that organizational strategies dimension with an average of 3.85 (out of 5) had the highest users’ attention, followed by monitoring-management and human dimensions. The overall average of all dimensions was 3.78. All dimensions had a positive and direct effect on the attitudes toward using the CPOE. Demographic characteristics did not have a meaningful relationship with any dimensions. Conclusions: Finally, the average above 3 in the mean of the total dimensions, as well as each dimension, shows views of the users are appropriate to using the CPOE., the views of the users are appropriated toward using CPOE. It should be noted that the difference in the dimension means is a small amount. The centered focus on organizational strategies with the highest average should not cause to neglect other dimensions.

Electronic ordering and the management of treatment interdependencies: a qualitative study of paediatric chemotherapy

BackgroundThere are serious safety risks associated with chemotherapy, often associated with interdependencies in regimens administered over months or years. Various strategies are used to manage these risks. Computerized provider order entry (CPOE) systems are also implemented to improve medication safety. Little is known regarding the effect of CPOE on how clinicians manage chemotherapy interdependencies and their associated safety strategies.MethodsWe conducted a multi-method qualitative study in a paediatric hospital. We analysed 827 oncology incidents reported following CPOE implementation and carried out semi-structured interviews with doctors (n = 10), nurses (n = 6), a pharmacist, and oncology CPOE team members (n = 2). Results were interpreted according to safety models (ultra-safe, high-reliability organisations [HROs], or ultra-adaptive).ResultsIncident reports highlighted two interrelated types of interdependencies: those within organisation of clinical activities and those inherent in chemotherapy regimens. Clinicians reported strategies to address chemotherapy risks and interdependencies. These included rigid rules and ‘no go’ contexts for treatment to proceed, typical of the ultra-safe model; use of time (e.g. planning only so far ahead) and sensitivity to operations, typical of HROs. We identified three different time horizons in CPOE use in relation to patients’ treatments: life-long, the whole regimen, and the ‘here and now’. CPOE supported ultra-safe strategies through automation and access to rules/standardisation, but also created difficulties and contributed to incidents. It supported the ‘here and now’ better than a life-long or whole regimen view of a patient treatment. Sensitivity to operations was essential to anticipate and resolve uncertainties, hazards, CPOE limitations, and mismatches between CPOE processes and workflow in practice.ConclusionsWithin oncology, CPOE appears to move the ‘mix’ of risk strategies towards ultra-safe models of safety and protocol-mandated care. However, in order to operate ultra-safe strategies embedded in CPOE and stay on protocol it is essential for clinicians to be thoughtful and show sensitivity to operations in CPOE use. CPOE design can be advanced by better consideration of mechanisms to support interdependencies.

Pragmatic Considerations on Clinical Decision Support from the 2019 Literature.

Summary Objectives : To summarize significant research contributions published in 2019 in the field of computerized clinical decision support and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook. Methods : Two bibliographic databases were searched for papers referring to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by external reviewers. The IMIA Yearbook editorial committee finally selected the best papers on the basis of all reviews including the section editors’ evaluation. Results : A total of 1,378 articles were retrieved. Fifteen best paper candidates were selected, the reviews of which resulted in the selection of three best papers. One paper reports on a guideline modeling approach based on clinical decision trees, both clinically interpretable and suitable for implementation in CDSSs. In another paper, authors promote the use of extended Timed Transition Diagrams in CDSSs to formalize consistently recurrent medical processes for chronic diseases management. The third paper proposes a conceptual framework and a grid for assessing the performance of predictive tools based on the critical appraisal of published evidence. Conclusions : As showed by the number and the variety of works related to decision support, research in the field is very active. This year’s selection highlighted pragmatic works that promote transparency and trust required by decision support tools.