Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.

BACKGROUND: Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited. OBJECTIVE: This meta-analysis identified predictors influencing health care practitioners' intention to use AI-CDSSs based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Additional predictors were examined based on existing empirical evidence. METHODS: The literature search using electronic databases, forward searches, conference programs, and personal correspondence yielded 7731 results, of which 17 (0.22%) studies met the inclusion criteria. Random-effects meta-analysis, relative weight analyses, and meta-analytic moderation and mediation analyses were used to examine the relationships between relevant predictor variables and the intention to use AI-CDSSs. RESULTS: The meta-analysis results supported the application of the UTAUT to the context of the intention to use AI-CDSSs. The results showed that performance expectancy (r=0.66), effort expectancy (r=0.55), social influence (r=0.66), and facilitating conditions (r=0.66) were positively associated with the intention to use AI-CDSSs, in line with the predictions of the UTAUT. The meta-analysis further identified positive attitude (r=0.63), trust (r=0.73), anxiety (r=-0.41), perceived risk (r=-0.21), and innovativeness (r=0.54) as additional relevant predictors. Trust emerged as the most influential predictor overall. The results of the moderation analyses show that the relationship between social influence and use intention becomes weaker with increasing age. In addition, the relationship between effort expectancy and use intention was stronger for diagnostic AI-CDSSs than for devices that combined diagnostic and treatment recommendations. Finally, the relationship between facilitating conditions and use intention was mediated through performance and effort expectancy. CONCLUSIONS: This meta-analysis contributes to the understanding of the predictors of intention to use AI-CDSSs based on an extended UTAUT model. More research is needed to substantiate the identified relationships and explain the observed variations in effect sizes by identifying relevant moderating factors. The research findings bear important implications for the design and implementation of training programs for health care practitioners to ease the adoption of AI-CDSSs into their practice.

Override rate of drug-drug interaction alerts in clinical decision support systems: A brief systematic review and meta-analysis.

Primary studies have demonstrated that despite being useful, most of the drug-drug interaction (DDI) alerts generated by clinical decision support systems are overridden by prescribers. To provide more information about this issue, we conducted a systematic review and meta-analysis on the prevalence of DDI alerts generated by CDSS and alert overrides by physicians. The search strategy was implemented by applying the terms and MeSH headings and conducted in the MEDLINE/PubMed, EMBASE, Web of Science, Scopus, LILACS, and Google Scholar databases. Blinded reviewers screened 1873 records and 86 full studies, and 16 articles were included for analysis. The overall prevalence of alert generated by CDSS was 13% (CI95% 5-24%, p-value <0.0001, I^2 = 100%), and the overall prevalence of alert override by physicians was 90% (CI95% 85-95%, p-value <0.0001, I^2 = 100%). This systematic review and meta-analysis presents a high rate of alert overrides, even after CDSS adjustments that significantly reduced the number of alerts. After analyzing the articles included in this review, it was clear that the CDSS alerts physicians about potential DDI should be developed with a focus on the user experience, thus increasing their confidence and satisfaction, which may increase patient clinical safety.

A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services.

OBJECTIVE: Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful. MATERIALS AND METHODS: We searched related studies in Science Direct and PubMed from 2018 to 2023(Jun), and also in ACM (Association for Computing Machinery) Digital Library, DBLP (Database systems and Logic Programming), and Google Scholar from 2018 to 2021. We have reviewed 39 studies and extracted types of analytical methods, information content, and information sources for decision-making. RESULTS: In order to compare studies, we developed a framework for characterizing health services, functions, and data features. Most data sets in reviewed studies were small, with a median of 1,176 patients and 46,503 record entries. Structured data was used for all studies except two that used textual clinical notes. Most studies used supervised classification and regression. Service and situation-specific data analysis dominated among the studies, only two studies used temporal, or process features from the patient data. This paper presents and summarizes the utility, but not quality, of the studies according to the care situations and care providers to identify service functions where data-driven decision aids may be relevant. CONCLUSIONS: Frameworks identifying services, functions, and care processes are necessary for characterizing and comparing electronic health record (EHR) data analysis studies. The majority of studies use features related to diagnosis and assessment and correspondingly have utility for intervention planning and follow-up. Profiling the disease severity of referred patients is also an important application area.

Digital Medication Management in Polypharmacy.

BACKGROUND: Inappropriate drug prescriptions for patients with polypharmacy can have avoidable adverse consequences. We studied the effects of a clinical decision-support system (CDSS) for medication management on hospitalizations and mortality. METHODS: This stepped-wedge, cluster-randomized, controlled trial involved an open cohort of adult patients with polypharmacy in primary care practices (=clusters) in Westphalia-Lippe, Germany. During the period of the intervention, their medication lists were checked annually using the CDSS. The CDSS warns against inappropriate prescriptions on the basis of patient-related health insurance data. The combined primary endpoint consisted of overall mortality and hospitalization for any reason. The secondary endpoints were mortality, hospitalizations, and high-risk prescription. We analyzed the quarterly health insurance data of the intention- to-treat population with a mixed logistic model taking account of clustering and repeated measurements. Sensitivity analyses addressed effects of the COVID-19 pandemic and other effects. RESULTS: 688 primary care practices were randomized, and data were obtained on 42 700 patients over 391 994 quarter years. No significant reduction was found in either the primary endpoint (odds ratio [OR] 1.00; 95% confidence interval [0.95; 1.04]; p = 0.8716) or the secondary endpoints (hospitalizations: OR 1.00 [0.95; 1.05]; mortality: OR 1.04 [0.92; 1.17]; high-risk prescription: OR 0.98 [0.92; 1.04]). CONCLUSION: The planned analyses did not reveal any significant effect of the intervention. Pandemicadjusted analyses yielded evidence that the mortality of adult patients with polypharmacy might potentially be lowered by the CDSS. Controlled trials with appropriate follow-up are needed to prove that a CDSS has significant effects on mortality in patients with polypharmacy.

Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease: A Cluster Randomized Clinical Trial.

Importance: Management of cardiovascular disease (CVD) risk in socioeconomically vulnerable patients is suboptimal; better risk factor control could improve CVD outcomes. Objective: To evaluate the impact of a clinical decision support system (CDSS) targeting CVD risk in community health centers (CHCs). Design, Setting, and Participants: This cluster randomized clinical trial included 70 CHC clinics randomized to an intervention group (42 clinics; 8 organizations) or a control group that received no intervention (28 clinics; 7 organizations) from September 20, 2018, to March 15, 2020. Randomization was by CHC organization accounting for organization size. Patients aged 40 to 75 years with (1) diabetes or atherosclerotic CVD and at least 1 uncontrolled major risk factor for CVD or (2) total reversible CVD risk of at least 10% were the population targeted by the CDSS intervention. Interventions: A point-of-care CDSS displaying real-time CVD risk factor control data and personalized, prioritized evidence-based care recommendations. Main Outcomes and Measures: One-year change in total CVD risk and reversible CVD risk (ie, the reduction in 10-year CVD risk that was considered achievable if 6 key risk factors reached evidence-based levels of control). Results: Among the 18 578 eligible patients (9490 [51.1%] women; mean [SD] age, 58.7 [8.8] years), patients seen in control clinics (n = 7419) had higher mean (SD) baseline CVD risk (16.6% [12.8%]) than patients seen in intervention clinics (n = 11 159) (15.6% [12.3%]; P < .001); baseline reversible CVD risk was similarly higher among patients seen in control clinics. The CDSS was used at 19.8% of 91 988 eligible intervention clinic encounters. No population-level reduction in CVD risk was seen in patients in control or intervention clinics; mean reversible risk improved significantly more among patients in control (-0.1% [95% CI, -0.3% to -0.02%]) than intervention clinics (0.4% [95% CI, 0.3% to 0.5%]; P < .001). However, when the CDSS was used, both risk measures decreased more among patients with high baseline risk in intervention than control clinics; notably, mean reversible risk decreased by an absolute 4.4% (95% CI, -5.2% to -3.7%) among patients in intervention clinics compared with 2.7% (95% CI, -3.4% to -1.9%) among patients in control clinics (P = .001). Conclusions and Relevance: The CDSS had low use rates and failed to improve CVD risk in the overall population but appeared to have a benefit on CVD risk when it was consistently used for patients with high baseline risk treated in CHCs. Despite some limitations, these results provide preliminary evidence that this technology has the potential to improve clinical care in socioeconomically vulnerable patients with high CVD risk. Trial Registration: ClinicalTrials.gov Identifier: NCT03001713.

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.

Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.

A transfer learning approach to facilitate ComBat-based harmonization of multicentre radiomic features in new datasets.

PURPOSE: To facilitate the demonstration of the prognostic value of radiomics, multicenter radiomics studies are needed. Pooling radiomic features of such data in a statistical analysis is however challenging, as they are sensitive to the variability in scanner models, acquisition protocols and reconstruction settings, which is often unavoidable in a multicentre retrospective analysis. A statistical harmonization strategy called ComBat was utilized in radiomics studies to deal with the "center-effect". The goal of the present work was to integrate a transfer learning (TL) technique within ComBat-and recently developed alternate versions of ComBat with improved flexibility (M-ComBat) and robustness (B-ComBat)-to allow the use of a previously determined harmonization transform to the radiomic feature values of new patients from an already known center. MATERIAL AND METHODS: The proposed TL approach were incorporated in the four versions of ComBat (standard, B, M, and B-M ComBat). The proposed approach was evaluated using a dataset of 189 locally advanced cervical cancer patients from 3 centers, with magnetic resonance imaging (MRI) and positron emission tomography (PET) images, with the clinical endpoint of predicting local failure. The impact performance of the TL approach was evaluated by comparing the harmonization achieved using only parts of the data to the reference (harmonization achieved using all the available data). It was performed through three different machine learning pipelines. RESULTS: The proposed TL technique was successful in harmonizing features of new patients from a known center in all versions of ComBat, leading to predictive models reaching similar performance as the ones developed using the features harmonized with all the data available. CONCLUSION: The proposed TL approach enables applying a previously determined ComBat transform to new, previously unseen data.

Predicting Malignancy in Pediatric Thyroid Nodules: Early Experience With Machine Learning for Clinical Decision Support.

OBJECTIVE: To develop a machine learning tool to integrate clinical data for the prediction of non-benign thyroid cytology and histology. CONTEXT: Papillary thyroid carcinoma is the most common endocrine malignancy. Since most nodules are benign, the challenge for the clinician is to identify those most likely to harbor malignancy while limiting exposure to surgical risks among those with benign nodules. METHODS: Random forests (augmented to select features based on our clinical measure of interest), in conjunction with interpretable rule sets, were used on demographic, ultrasound, and biopsy data of thyroid nodules from children younger than 18 years at a tertiary pediatric hospital. Accuracy, false-positive rate (FPR), false-negative rate (FNR), and area under the receiver operator curve (AUROC) are reported. RESULTS: Our models predict nonbenign cytology and malignant histology better than historical outcomes. Specifically, we expect a 68.04% improvement in the FPR, 11.90% increase in accuracy, and 24.85% increase in AUROC for biopsy predictions in 67 patients (28 with benign and 39 with nonbenign histology). We expect a 23.22% decrease in FPR, 32.19% increase in accuracy, and 3.84% decrease in AUROC for surgery prediction in 53 patients (42 with benign and 11 with nonbenign histology). This improvement comes at the expense of the FNR, for which we expect 10.27% with malignancy would be discouraged from performing biopsy, and 11.67% from surgery. Given the small number of patients, these improvements are estimates and are not tested on an independent test set. CONCLUSION: This work presents a first attempt at developing an interpretable machine learning based clinical tool to aid clinicians. Future work will involve sourcing more data and developing probabilistic estimates for predictions.

The role of digital clinical decision support tool in improving quality of intrapartum and postpartum care: experiences from two states of India.

BACKGROUND: Computerized clinical decision support (CDSS) -digital information systems designed to improve clinical decision making by providers - is a promising tool for improving quality of care. This study aims to understand the uptake of ASMAN application (defined as completeness of electronic case sheets), the role of CDSS in improving adherence to key clinical practices and delivery outcomes. METHODS: We have conducted secondary analysis of program data (government data) collected from 81 public facilities across four districts each in two sates of Madhya Pradesh and Rajasthan. The data collected between August -October 2017 (baseline) and the data collected between December 2019 - March 2020 (latest) was analysed. The data sources included: digitized labour room registers, case sheets, referral and discharge summary forms, observation checklist and complication format. Descriptive, univariate and multivariate and interrupted time series regression analyses were conducted. RESULTS: The completeness of electronic case sheets was low at postpartum period (40.5%), and in facilities with more than 300 deliveries a month (20.9%). In multivariate logistic regression analysis, the introduction of technology yielded significant improvement in adherence to key clinical practices. We have observed reduction in fresh still births rates and asphyxia, but these results were not statistically significant in interrupted time series analysis. However, our analysis showed that identification of maternal complications has increased over the period of program implementation and at the same time referral outs decreased. CONCLUSIONS: Our study indicates CDSS has a potential to improve quality of intrapartum care and delivery outcome. Future studies with rigorous study design is required to understand the impact of technology in improving quality of maternity care.

Optimizing pharmacists' detection of prescribing errors: Comparison of on-ward and central pharmacy services.

WHAT IS KNOWN AND OBJECTIVE: Prescribing errors are the leading cause of adverse drug events in hospitalized patients. Pharmaceutical validation, defined as the review of drug orders by a pharmacist, associated with clinical decision support (CDS) systems, significantly reduces these errors and adverse drug events. In Belgium, because clinical pharmacy services have limited public financial support, most pharmaceutical validations are performed at the central pharmacy instead of on-ward, by hospital pharmacists doing dispensing activities. In that context, we aimed at evaluating whether the strategy of CDS-guided central validation was the most appropriate method to improve the quality and safety of medicines' use compared to an on-ward pharmaceutical validation. METHODS: Our retrospective observational study was conducted in a Belgian tertiary care hospital, in 2018-2019. Data were extracted from our validation software and pharmacists' charts. The outcomes of the study were the number of pharmaceutical interventions due to the detection of prescribing errors, reasons for interventions, their acceptance rate and their potential clinical impact (according to two blinded experts) in the central pharmacy and on-ward validation groups. RESULTS AND DISCUSSION: Despite the use of the same CDS, a pharmaceutical intervention following the detection of a prescribing error was made for 2.9% (20/698) of central group patients and 13.3% (93/701) of on-ward patients (χ2  = 49.97, p < 0.001). Interventions made at the central pharmacy (n = 20) mostly relied on CDS-alerts (i.e. drug-drug interaction [25%] or overdosing [20%]) while interventions made on-ward (n = 93) were also for pharmacotherapy optimization (i.e. no valid indication [25%] or inappropriate drug's choice [11%]). The on-ward validation group showed a higher acceptance rate compared to the central group (84% and 65%, respectively [Fisher's test, p = 0.053]). Proportions of interventions with significant or very significant clinical impact were similar between the two groups but as fewer interventions were made centrally, a significant proportion of errors were probably not detected by the central validation. WHAT IS NEW AND CONCLUSION: On-ward pharmaceutical validation leads to a higher rate of prescribing error detection. Pharmaceutical interventions made by on-ward pharmacists are also better accepted and more relevant, going further than CDS-alerts.

Serial use of existing clinical decisions aids can reduce computed tomography pulmonary angiography for pulmonary embolism.

Pulmonary embolism (PE) remains a diagnostic challenge in emergency medicine. Clinical decision aids (CDAs) like the Pulmonary Embolism Rule-Out Criteria (PERC) are sensitive but poorly specific; serial CDA use may improve specificity. The goal of this before-and-after study was to determine if serial use of existing CDAs in a novel diagnostic algorithm safely decreases the use of CT pulmonary angiograms (CTPA). This was a retrospective before-and-after study conducted at an urban ED with 105,000 annual visits. Our algorithm uses PERC, Wells' score, and D-dimer in series, before moving to CTPA. The algorithm was introduced in January, 2017. Use of CDAs and D-dimer in the 24 months pre- and 12 months post-intervention were obtained by chart review. The algorithm's effect on CTPA ordering was assessed by comparing volume 5 years pre- and 3 years post-intervention, adjusted for ED volume. Mean CTPAs per 1000 adult ED visits was 11.1 in the 5 pre-intervention years and 9.9 in the 3 post-intervention years (p < 0.0001). Use of PERC, Wells' score and D-dimer increased from 1.1%, 1.1%, and 28% to 8.8% (p = 0.0002) 8.1% (p = 0.0005), and 35% (p = 0.0066), respectively. Pre-intervention, there were six potentially missed PEs compared to three in the post-intervention period. Introduction of our serial CDA diagnostic algorithm was associated with increased use of CDAs and D-dimer and reduced CTPA rate without an apparent increase in the number of missed PEs. Prospective validation is needed to confirm these results.

Environmental Scan on Canadian Interactive Knowledge Translation Tools to Prevent Diabetes Complications in Patients With Diabetes.

In this study, we identify existing interactive knowledge translation tools that could help patients and health-care professionals to prevent diabetes complications in the Canadian context. We conducted an environmental scan in collaboration with researchers and 4 patient partners across Canada. We conducted searches among the research team members, their networks and Twitter, and through searches in databases and Google. To be included, interactive knowledge translation tools had to meet the following criteria: used to prevent diabetes complications; used in a real-life setting; used any instructional method or material; had relevance in the Canadian context, written in English or French; developed and/or published by experts in diabetes complications or by a recognized organization; created in 2013 or after; and accessibility online or on paper. Two reviewers independently screened each record for selection and extracted the following data: authorship, objective(s), patients' characteristics, type of diabetes complications targeted, type of knowledge users targeted and tool characteristics. We used simple descriptive statistics to summarize our results. Thirty-one of the 1,700 potentially eligible interactive knowledge translation tools were included in the scan. Tool formats included personal notebook, interactive case study, risk assessment tool, clinical pathway, decision support tool, knowledge quiz and checklist. Diabetes complications targeted by the tools included foot-related neuropathy, cardiovascular diseases, mental disorders and distress and any complications related to diabetes and kidney disease. Our results inform Canadian stakeholders interested in the prevention of diabetes complications to avoid unnecessary duplication, identify gaps in knowledge and support implementation of these tools in clinical and patients' decision-making.

Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study.

BACKGROUND: Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI. METHODS: The study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI. RESULTS: Each phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05. CONCLUSIONS: This observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.

Evaluation and cost estimation of laboratory test overuse in 43 commonly ordered parameters through a Computerized Clinical Decision Support System (CCDSS) in a large university hospital.

BACKGROUND: Computerized Clinical Decision Support Systems (CCDSS) have become increasingly important in ensuring patient safety and supporting all phases of clinical decision making. The aim of this study is to evaluate, through a CCDSS, the rate of the laboratory tests overuse and to estimate the cost of the inappropriate requests in a large university hospital. METHOD: In this observational study, hospital physicians submitted the examination requests for the inpatients through a Computerized Physician Order Entry. Violations of the rules in tests requests were intercepted and counted by a CCDSS, over a period of 20 months. Descriptive and inferential statistics (Student's t-test and ANOVA) were made. Finally, the monthly comprehensive cost of the laboratory tests was calculated. RESULTS: During the observation period a total of 5,716,370 requests were analyzed and 809,245 violations were counted. The global rate of overuse was 14.2% ± 3.0%. The most inappropriate exams were Alpha Fetoprotein (85.8% ± 30.5%), Chlamydia trachomatis Nucleic Acid Amplification (48.7% ± 8.8%) and Alkaline Phosphatase (20.3% ± 6.5%). The monthly cost of over-utilization was 56,534€ for basic panel, 14,421€ for coagulation, 4,758€ for microbiology, 432€ for immunology exams. All the exams, generated an estimated avoidable cost of 1,719,337€ (85,967€ per month) for the hospital. CONCLUSIONS: The study confirms the wide variability in over-utilization rates of laboratory tests. For these reasons, the real impact of inappropriateness is difficult to assess, but the generated costs for patients, hospitals and health systems are certainly high and not negligible. It would be desirable for international medical communities to produce a complete panel of prescriptive rules for all the most common laboratory exams that is useful not only to reduce costs, but also to ensure standardization and high-quality care.

Informatics Experience Can Help Predict Doctor of Nursing Practice Student Mastery of Informatics Competencies.

With information technology increasingly guiding nursing practice, Doctor of Nursing Practice students must be prepared to use informatics to optimize patient outcomes despite their varied experience and education. Understanding how students' baseline experience affects their mastery of informatics competencies could help faculty design Doctor of Nursing Practice course content. Therefore, the aim of this retrospective descriptive study was to evaluate whether Doctor of Nursing Practice students' baseline informatics experience affected their mastery of four competencies: meaningful use, datasets, e-health, and clinical support systems. Participants were Doctor of Nursing Practice students (n = 55) enrolled in an online informatics course. Participant experience was compared to competency mastery using χ tests. Logistic regression was performed to assess the effect of experience and highest degree obtained on competency mastery. Analysis revealed that participants with meaningful use experience were significantly more likely to master the meaningful use competency than were those without it. Relevant experience did not predict mastery of dataset competencies. Participants with e-health experience were significantly more likely to master the e-health competency (applying e-health resources to vulnerable patients' learning needs). While not significant, a greater percentage of students with clinical support systems experience mastered the clinical support systems competency. Informatics courses might need to be designed to address students' needs based on their experience.

Evaluation of the prescribing decision support system Synonyms in a primary care setting: a mixed-method study.

BACKGROUND: Primary care prescribers must cope with an increasing number and complexity of considerations. Prescribing decision support systems (DSS) have therefore been developed to assist prescribers. Previous studies have shown that although there is wide variance in the different DSS available within primary care, barriers and facilitators to uptake remain. The Drug Synonyms function ('Synonyms') is a DSS inherent in the commercial electronic medical record system EMIS. Synonyms functionality has been further developed by the NHS Greater Glasgow and Clyde (GG&C) Central Prescribing Team to promote safe and cost-effective prescribing; however, it does not support the collection of usage data. As there is no knowledge on the uptake nor on the perceived effect of using Synonyms on prescribing, quantitative and qualitative analyses of Synonyms usage are required to ascertain the impact Synonyms has on primary care prescribers, which will influence the continued maintenance and/or future development of this prescribing DSS. AIM: To determine the uptake of Synonyms and explore users' perceptions of its usefulness and future development. DESIGN AND SETTING: An exploratory sequential mixed-method observational study using quantitative questionnaires, followed by semi-structured interviews with primary care prescribers within NHS GG&C. METHOD: An electronic questionnaire (Questionnaire 1) accessible across 218 EMIS-compliant NHS GG&C GP practices ascertained Synonyms uptake by determining whether prescribers were aware of the DSS, whether they were aware of it and whether they used it. Prescribers who were aware of and used Synonyms were asked to opt in to participating further. This involved answering a second electronic questionnaire (Questionnaire 2), with the option of taking part in an additional one-to-one interview, to investigate their use and perceptions of Synonyms. RESULTS: Questionnaire 1 was completed by 201 respondents from 43.1% of eligible GP practices: 186 (92.5%) respondents were aware of Synonyms, of whom 163 (87.6%) had used it and 155 (83.3%) continued to use it. Questionnaire 2 was completed by 104 respondents: 90 (86.5%) indicated that Synonyms informed or influenced their choice of drug prescribed; 94 (90.4%) reported that Synonyms changed their prescribing choice towards medication on NHS GG&C formulary, and 104 (100%) reported that they trust Synonyms. Six interviews generated suggestions for improvements, mainly extending the clinical conditions listed. CONCLUSION: Most respondents were aware of and continued to use Synonyms. Respondents perceived Synonyms to influence prescribing choices towards local formulary medicines and improve adherence to local prescribing guidelines. Respondents trusted the DSS, but there is potential to increase awareness and training amongst non-users to encourage usage. Potentially, the NHS GG&C Synonyms function could be utilised by other health boards with supportive clinical systems.