Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma

ObjectiveTo evaluate the architecture, integration requirements, and execution characteristics of a remote clinical decision support (CDS) service used in a multicenter clinical trial. The trial tested the efficacy of implementing brain injury prediction rules for children with minor blunt head trauma.Materials and MethodsWe integrated the Epic^® electronic health record (EHR) with the Enterprise Clinical Rules Service (ECRS), a web-based CDS service, at two emergency departments. Patterns of CDS review included either a delayed, near-real-time review, where the physician viewed CDS recommendations generated by the nursing assessment, or a real-time review, where the physician viewed recommendations generated by their own documentation. A backstopping, vendor-based CDS triggered with zero delay when no recommendation was available in the EHR from the web-service. We assessed the execution characteristics of the integrated system and the source of the generated recommendations viewed by physicians.ResultsThe ECRS mean execution time was 0.74  ± 0.72 s. Overall execution time was substantially different at the two sites, with mean total transaction times of 19.67 and 3.99 s. Of 1930 analyzed transactions from the two sites, 60% (310/521) of all physician documentation-initiated recommendations and 99% (1390/1409) of all nurse documentation-initiated recommendations originated from the remote web service.DiscussionThe remote CDS system was the source of recommendations in more than half of the real-time cases and virtually all the near-real-time cases. Comparisons are limited by allowable variation in user workflow and resolution of the EHR clock.ConclusionWith maturation and adoption of standards for CDS services, remote CDS shows promise to decrease time-to-trial for multicenter evaluations of candidate decision support interventions.     

Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: A randomized clinical trial

ObjectiveThe use of evidence-based guidelines can improve the care for asthma patients. We implemented a computerized asthma management system in a pediatric emergency department (ED) to integrate national guidelines. Our objective was to determine whether patient eligibility identification by a probabilistic disease detection system (Bayesian network) combined with an asthma management system embedded in the workflow decreases time to disposition decision.MethodsWe performed a prospective, randomized controlled trial in an urban, tertiary care pediatric ED. All patients 2–18 years of age presenting to the ED between October 2010 and February 2011 were screened for inclusion by the disease detection system. Patients identified to have an asthma exacerbation were randomized to intervention or control. For intervention patients, asthma management was computer-driven and workflow-integrated including computer-based asthma scoring in triage, and time-driven display of asthma-related reminders for re-scoring on the electronic patient status board combined with guideline-compliant order sets. Control patients received standard asthma management. The primary outcome measure was the time from triage to disposition decision.ResultsThe Bayesian network identified 1339 patients with asthma exacerbations, of which 788 had an asthma diagnosis determined by an ED physician-established reference standard (positive predictive value 69.9%). The median time to disposition decision did not differ among the intervention (228 min; IQR = (141, 326)) and control group (223 min; IQR = (129, 316)); (p = 0.362). The hospital admission rate was unchanged between intervention (25%) and control groups (26%); (p = 0.867). ED length of stay did not differ among intervention (262 min; IQR = (165, 410)) and control group (247 min; IQR = (163, 379)); (p = 0.818).ConclusionsThe control and intervention groups were similar in regards to time to disposition; the computerized management system did not add additional wait time. The time to disposition decision did not change; however the management system integrated several different information systems to support clinicians’ communication.     

Knowledge discovery in clinical decision support systems for pain management: A systematic review

ObjectiveThe occurrence of pain accounts for billions of dollars in annual medical expenditures; loss of quality of life and decreased worker productivity contribute to indirect costs. As pain is highly subjective, clinical decision support systems (CDSSs) can be critical for improving the accuracy of pain assessment and offering better support for clinical decision-making. This review is focused on computer technologies for pain management that allow CDSSs to obtain knowledge from the clinical data produced by either patients or health care professionals.Methods and materialsA comprehensive literature search was conducted in several electronic databases to identify relevant articles focused on computerised systems that constituted CDSSs and include data or results related to pain symptoms from patients with acute or chronic pain, published between 1992 and 2011 in the English language. In total, thirty-nine studies were analysed; thirty-two were selected from 1245 citations, and seven were obtained from reference tracking.ResultsThe results highlighted the following clusters of computer technologies: rule-based algorithms, artificial neural networks, nonstandard set theory, and statistical learning algorithms. In addition, several methodologies were found for content processing such as terminologies, questionnaires, and scores. The median accuracy ranged from 53% to 87.5%.ConclusionsComputer technologies that have been applied in CDSSs are important but not determinant in improving the systems’ accuracy and the clinical practice, as evidenced by the moderate correlation among the studies. However, these systems play an important role in the design of computerised systems oriented to a patient's symptoms as is required for pain management. Several limitations related to CDSSs were observed: the lack of integration with mobile devices, the reduced use of web-based interfaces, and scarce capabilities for data to be inserted by patients.     

Implementation of a health-literate patient decision aid for chest pain in the emergency department

ObjectiveThe aim of this study was to investigate the implementation of a new health-literacy-tested patient decision aid for chest pain in Emergency Department (ED) patients. Outcomes included disposition, knowledge, decisional conflict and satisfaction prior to discharge. Patient health literacy was explored as a factor that may explain disparities in sub-group analysis of all outcomes.MethodsA health-literacy adapted tool was deployed using a pre/post intervention design. Patients enrolled during the intervention period were given the adapted chest pain decision aid that was used in conversation with their emergency medicine physician to decide on their course of action prior to being discharged.ResultsA total of 169 participants were surveyed and used in the final analysis. Patients in the usual care group were 2.6 times more likely to be admitted for chest pain than patients in the intervention group. Knowledge scores were higher in the intervention group, while no significant differences were observed in decisional conflict and patient satisfaction, or by patient health literacy level.Conclusion and practice implicationsUsing the adapted chest pain decision tool in emergency medicine may improve knowledge and reduce admissions, while addressing known barriers to understanding related to patient health literacy.     

Usability evaluation of pharmacogenomics clinical decision support aids and clinical knowledge resources in a computerized provider order entry system: A mixed methods approach

BackgroundPharmacogenomics (PGx) is positioned to have a widespread impact on the practice of medicine, yet physician acceptance is low. The presentation of context-specific PGx information, in the form of clinical decision support (CDS) alerts embedded in a computerized provider order entry (CPOE) system, can aid uptake. Usability evaluations can inform optimal design, which, in turn, can spur adoption.ObjectivesThe study objectives were to: (1) evaluate an early prototype, commercial CPOE system with PGx-CDS alerts in a simulated environment, (2) identify potential improvements to the system user interface, and (3) understand the contexts under which PGx knowledge embedded in an electronic health record is useful to prescribers.MethodsUsing a mixed methods approach, we presented seven cardiologists and three oncologists with five hypothetical clinical case scenarios. Each scenario featured a drug for which a gene encoding drug metabolizing enzyme required consideration of dosage adjustment. We used Morae^® to capture comments and on-screen movements as participants prescribed each drug. In addition to PGx-CDS alerts, ‘Infobutton^®’ and ‘Evidence’ icons provided participants with clinical knowledge resources to aid decision-making.ResultsNine themes emerged. Five suggested minor improvements to the CPOE user interface; two suggested presenting PGx information through PGx-CDS alerts using an ‘Infobutton’ or ‘Evidence’ icon. The remaining themes were strong recommendations to provide succinct, relevant guidelines and dosing recommendations of phenotypic information from credible and trustworthy sources; any more information was overwhelming. Participants’ median rating of PGx-CDS system usability was 2 on a Likert scale ranging from 1 (strongly agree) to 7 (strongly disagree).ConclusionsUsability evaluation results suggest that participants considered PGx information important for improving prescribing decisions; and that they would incorporate PGx-CDS when information is presented in relevant and useful ways.     

Interventions for rapid recognition and treatment of sepsis in the emergency department: a narrative review

BackgroundSepsis is a major cause of morbidity and mortality worldwide. Early recognition and treatment of sepsis is associated with improved outcome. The emergency department (ED) is the department where patients with sepsis seek care. However, recognition of sepsis in the ED remains difficult. Different alert and triage systems, screening scores and intervention strategies have been developed to assist clinicians in early recognition of sepsis and to optimize management.ObjectivesThis narrative review describes currently applied interventions or interventions we can start using today, such as screening scores, (automated) triage systems, sepsis teams and clinical pathways in sepsis care; and it summarizes evidence for the effect of implementation of these interventions in the ED on patient management and outcomes.SourcesA systematic literature search was conducted in PubMed, resulting in 39 eligible studies.ContentThe main sepsis interventions in the ED are (automated) triage systems, sepsis teams and clinical pathways, the most integrative being a clinical pathway. Implementation of any of these interventions in sepsis care will generally lead to increased protocol adherence. Presumably increased adherence to sepsis guidelines and bundles will lead to better patient outcomes, but the level of evidence to support this improvement is low, whereas implementation of interventions is often complex and costly. No studies comparing different interventions were identified. Two essential factors for success of interventions in the ED are obtaining the support from all professionals and providing ongoing education. The vulnerability of these interventions lies in the lack of accurate tools to identify sepsis; diagnosing sepsis ultimately still relies on clinical assessments. A lack of specificity or sepsis alerts may lead to alert fatigue and/or overtreatment.ImplicationsThe severity and poor outcome of sepsis as well as the frequency of its presentation in EDs make a structured, protocol-based approach towards these patients essential, preferably as part of a clinical pathway.     

Renal safety of a single dose of gentamicin in patients with sepsis in the emergency department

ObjectivesTo determine the effect of a single dose of gentamicin on the incidence and persistence of acute kidney injury (AKI) in patients with sepsis in the emergency department (ED).MethodsWe retrospectively studied patients with sepsis in the ED in three hospitals. Local antibiotic guidelines recommended a single dose of gentamicin as part of empirical therapy in selected patients in one hospital, whereas the other two hospitals did not. Multivariate analysis was used to evaluate the effect of gentamicin and other potential risk factors on the incidence and persistence of AKI after admission. AKI was defined according to the KDIGO (Kidney Disease Improving Global Outcomes) criteria.ResultsOf 1573 patients, 571 (32.9%) received a single dose of gentamicin. At admission, 181 (31.7%) of 571 of the gentamicin-treated and 228 (22.8%) of 1002 of the non–gentamicin-treated patients had AKI (p < 0.001). After admission, AKI occurred in 64 (12.0%) of 571 patients who received gentamicin and in 82 (8.9%) of 1002 people in the control group (p 0.06). Multivariate analysis showed that shock (odds ratio (OR), 2.72; 95% CI, 1.31–5.67), diabetes mellitus (OR, 1.49; 95% CI, 1.001–2.23) and higher baseline (i.e. before admission) serum creatinine levels (OR, 1.007; 95% CI, 1.005–1.009) were associated with the development of AKI after admission, but not receipt of gentamicin (OR, 1.29; 95% CI, 0.89–1.86). Persistent AKI was rare in both the group that received gentamicin (16/260, 6.2%) and the group that did not (15/454, 3.3%, p 0.09).ConclusionsWith regard to renal function, a single dose of gentamicin in patients with sepsis in the ED is safe. The development of AKI after admission was associated with shock, diabetes mellitus and higher baseline creatinine level.     

Integrating computerized clinical decision support systems into clinical work: A meta-synthesis of qualitative research

PurposeComputerized clinical decision support systems (CDSS) are an emerging means for improving healthcare safety, quality and efficiency, but meta-analyses findings are mixed. This meta-synthesis aggregates qualitative research findings as possible explanations for variable quantitative research outcomes.Inclusion criteriaQualitative studies published between 2000 and 2013 in English, involving physicians, registered and advanced practice nurses’ experience of CDSS use in clinical practice were included.Search strategyPubMed and CINAHL databases were searched. Study titles and abstracts were screened against inclusion criteria. Retained studies were appraised against quality criteria. Findings were extracted iteratively from studies in the 4th quartile of quality scores. Two reviewers constructed themes inductively. A third reviewer applied the defined themes deductively achieving 92% agreement.Results3798 unique records were returned; 56 met inclusion criteria and were reviewed against quality criteria. 9 studies were of sufficiently high quality for synthetic analysis. Five major themes (clinician-patient-system integration; user interface usability; the need for better ‘algorithms’; system maturity; patient safety) were defined.ConclusionsDespite ongoing development, CDSS remains an emerging technology. Lack of understanding about and lack of consideration for the interaction between human decision makers and CDSS is a major reason for poor system adoption and use. Further high-quality qualitative research is needed to better understand human—system interaction issues. These issues may continue to confound quantitative study results if not addressed.     

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

BackgroundMachine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).ObjectivesWe aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.SourcesReferences for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.ContentWe found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).ImplicationsConsidering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.     

SANDS: a service-oriented architecture for clinical decision support in a National Health Information Network

In this paper, we describe and evaluate a new distributed architecture for clinical decision support called SANDS (Service-oriented Architecture for NHIN Decision Support), which leverages current health information exchange efforts and is based on the principles of a service-oriented architecture. The architecture allows disparate clinical information systems and clinical decision support systems to be seamlessly integrated over a network according to a set of interfaces and protocols described in this paper. The architecture described is fully defined and developed, and six use cases have been developed and tested using a prototype electronic health record which links to one of the existing prototype National Health Information Networks (NHIN): drug interaction checking, syndromic surveillance, diagnostic decision support, inappropriate prescribing in older adults, information at the point of care and a simple personal health record. Some of these use cases utilize existing decision support systems, which are either commercially or freely available at present, and developed outside of the SANDS project, while other use cases are based on decision support systems developed specifically for the project. Open source code for many of these components is available, and an open source reference parser is also available for comparison and testing of other clinical information systems and clinical decision support systems that wish to implement the SANDS architecture.The SANDS architecture for decision support has several significant advantages over other architectures for clinical decision support. The most salient of these are:

1. Greater modularity than other architectures, allowing for work to be distributed.

2. The potential for creating and sustaining a commercial market for clinical decision support.

3. Reduced cost and risk of trying new decision support systems because of its ability to easily integrate a variety of decision support services, and to easily remove them, if desired, as well.

4. Significant freedom for developers of clinical decision support systems to choose the way they represent knowledge and internally implement their system, in comparison to other approaches which constrain such developers to a particular knowledge representation formalism.

5. Unification of the direction and agenda of decision support research and development with promising near-term efforts to improve interoperability of clinical systems.

Antimicrobial stewardship in the emergency department: characteristics and evidence for effectiveness of interventions

BackgroundEmergency departments (EDs) are the entrance gates for patients presenting with infectious diseases into the hospital, yet most antimicrobial stewardship programmes are primarily focused on inpatient management. With equally high rates of inappropriate antibiotic use, the ED is a frequently overlooked yet important unit for targeted antimicrobial stewardship (AMS) interventions.ObjectivesWe aimed to (a) describe the specific aspects of antimicrobial stewardship in the ED and (b) summarize the findings from improvement studies that have investigated the effectiveness of antimicrobial stewardship interventions in the ED setting.Sources: (a) a PubMed search for ‘antimicrobial stewardship’ and ‘emergency department’, and (b) published reviews on effectiveness combined with publications from the first source.Content: (a) An in depth analysis of selected publications provided four key antimicrobial use processes typically performed by front-line healthcare professionals in the ED: making a (tentative) clinical diagnosis, starting empirical therapy based on that diagnosis, performing microbiological tests before starting that therapy and following up patients who are discharged from the ED. (b) Further, we discuss the literature on improvement strategies in the ED focusing on guidelines and clinical pathways and multifaceted improvement strategies. We also summarize the evidence of microbiologic culture review.Implications: Based on our review of the literature, we describe four essential elements of antimicrobial use in the ED. Studying the various interventions targeting these care processes, we have found them to be of a variable degree of success. Nonetheless, while there is a paucity of AS studies specifically targeting the ED, there is a growing body of evidence that AS programmes in the ED are effective with modifications to the ED setting. We present key questions for future research.     

Using multi-perspective methodologies to study users’ interactions with the prototype front end of a guideline-based decision support system for diabetic foot care

Purpose

Clinical practice guidelines are important instruments for improving the quality of care; in paper form, however, they are not used as effectively as possible. In order to develop a guideline-based decision support system (DSS) prototype to help clinicians deal with diabetic patients’ foot problems, we drew on methodologies from qualitative research, cognitive science, and information systems. This multi-perspective approach was intended to facilitate user-centered design and evaluation.

Methods

We employed field observations, structured interviews, and document analyses to collect and analyze users’ workflow patterns, decision support goals, and preferences regarding interactions with a DSS. Next, we aligned their requirements with sequence diagrams and followed Nielsen's heuristics to develop a DSS prototype. We then performed think-aloud analyses and used the technology acceptance model to direct our evaluation of users’ perceptions of the prototype.

Results

Users had a positive response to the DSS prototype in terms of its clarity of design and ease of use. They expressed a high intention of using the system in the future.

Conclusion

Applying multi-perspective methodologies is an effective way to study and design user interactions with the front end of a guideline-based DSS.     

The GRAIDS Trial: The development and evaluation of computer decision support for cancer genetic risk assessment in primary care

The development and evaluation of computer decision support for the assessment of cancer genetic risk in primary care is reported with two series of studies described: the RAGs (Risk Assessment in Genetics) studies and the GRAIDS (Genetic Risk Assessment in an Intranet and Decision Support) Trial. In the GRAIDS Trial, 45 general practices in Eastern England have been recruited and randomised. Comparison practices attend an educational session and receive clinical guidelines about familial breast and colorectal cancer. In the intervention practices a lead clinician is trained in cancer genetics and use of the GRAIDS software. The GRAIDS software is a simple pedigree-drawing program that implements clinical guidelines for familial breast and colorectal cancer and presents individualised information about breast cancer risk in a range of formats. Outcome measures of the trial include: frequency of software use, practitioners’ attitudes towards the software, total number of referrals to secondary care about familial cancer and the proportion that meet regional referral criteria, and a patient-centred measure of informed decision making. The family history will become an increasingly important tool in primary care to assess genetic risk. This research evaluates an approach to support high-quality advice about cancer genetics in primary care which could be applied more broadly as our understanding of complex disease genetics increases.     

Impact of errors in paper-based and computerized diabetes management with decision support for hospitalized patients with type 2 diabetes. A post-hoc analysis of a before and after study

ObjectiveMost preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support.MethodsSeventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab^®—a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated.ResultsThe BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4–6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1–4.6).DiscussionManual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab^®, user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant.ConclusionInsulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia.     

Identifying patients with bacterial infections using a combination of C-reactive protein,procalcitonin, TRAIL,and IP-10 in the emergency department: a prospective observational cohort study

ObjectivesThe aim was to effectively reduce the unnecessary use of broad spectrum antibiotics in the emergency department (ED), patients with bacterial infections need to be identified accurately. We investigated the diagnostic value of a combination of biomarkers for bacterial infections, C-reactive protein (CRP), and procalcitonin (PCT), together with biomarkers for viral infections, tumour necrosis factor-related apoptosis-inducing ligand (TRAIL), and interferon-gamma-induced protein-10 (IP-10), in identifying suspected and confirmed bacterial infections in a general ED population with fever.MethodsThis is a sub-study in the HiTEMP cohort. Patients with fever were included during ED triage, and blood samples were obtained. Using both diagnostics and expert panel analysis, all patients were classified as having either suspected or confirmed bacterial infections, or non-bacterial disease. Using multivariable logistic regression analysis, three biomarker models were analysed: model 1, CRP, TRAIL, IP-10; model 2, PCT, TRAIL, IP-10; and model 3, CRP, PCT, TRAIL, IP-10.ResultsA total of 315 patients were included, of whom 228 patients had a suspected or confirmed bacterial infection. The areas under the curve for the combined models were the following: model 1, 0.730 (95% CI 0.665–0.795); model 2, 0.748 (95% CI 0.685–0.811); and model 3, 0.767(95% CI 0.704–0.829).ConclusionsThese findings show that a combination of CRP, PCT, TRAIL and IP-10 can identify bacterial infections with higher accuracy than single biomarkers and combinations of a single bacterial biomarkers combined with TRAIL and IP-10.     

The effectiveness of pharmacist- led discharge medication counselling in the emergency department (ExPLAIN): A pilot quasi-experimental study

ObjectiveTo evaluate the effectiveness of pharmacist-led discharge medication counselling using a structured, multimodal educational strategy with teach-back (intervention) against standard care.MethodsThis was a quasi-experimental study in a public, metropolitan ED. Participants discharged home with new medications were allocated to receive the intervention or standard care using convenience sampling. Participant characteristics (i.e. age, sex, socio-economic status, medications) and health literacy were collected. The outcomes measured were satisfaction with information, ED re-presentation and length of stay.ResultsThere were 51 participants: 14 received intervention, 37 had standard care. Overall, 12% had inadequate health literacy. Group characteristics and health literacy were similar. Participants who received the intervention were significantly reported higher satisfaction with information about their new medications compared to standard care (p = 0.009). Specifically, the intervention was associated with a 98% increase in satisfaction with information relating to side-effects. There were no differences in re-presentation and length of stay.ConclusionPharmacist-led discharge medication counselling incorporating a structured, multimodal educational strategy and teach-back was effective in improving patient satisfaction with medication information in the ED.Practice implicationsA similar intervention could be trialled in other EDs, but outcomes other beyond satisfaction should be considered.     

Impact of time to antibiotic therapy on clinical outcome in patients with bacterial infections in the emergency department: implications for antimicrobial stewardship

BackgroundRapid initiation of antibiotic treatment is considered crucial in patients with severe infections such as septic shock and bacterial meningitis, but may not be as important for other infectious syndromes. A better understanding of which patients can tolerate a delay in start of therapy is important for antibiotic stewardship purposes.ObjectivesTo explore the existing evidence on the impact of time to antibiotics on clinical outcomes in patients presenting to the emergency department (ED) with bacterial infections of different severity of illness and source of infection.SourcesA literature search was performed in the PubMed/MEDLINE database using combined search terms for various infectious syndromes (sepsis/septic shock, bacterial meningitis, lower respiratory tract infections, urinary tract infections, intra-abdominal infections and skin and soft tissue infections), time to antibiotic treatment, and clinical outcome.ContentThe literature search generated 8828 hits. After screening titles and abstracts and assessing potentially relevant full-text papers, 60 original articles (four randomized controlled trials, 43 observational studies) were included. Most articles addressed sepsis/septic shock, while few studies evaluated early initiation of therapy in mild to moderate disease. The lack of randomized trials and the risk of confounding factors and biases in observational studies warrant caution in the interpretation of results. We conclude that the literature supports prompt administration of effective antibiotics for septic shock and bacterial meningitis, but there is no clear evidence showing that a delayed start of therapy is associated with worse outcome for less severe infectious syndromes.ImplicationsFor patients presenting with suspected bacterial infections, withholding antibiotic therapy until diagnostic results are available and a diagnosis has been established (e.g. by 4–8 h) seems acceptable in most cases unless septic shock or bacterial meningitis are suspected. This approach promotes the use of ecologically favourable antibiotics in the ED, reducing the risks of side effects and selection of resistance.     

A tale of two studies: the importance of setting, subjects and context in two randomized, controlled trials of a web-based decision support for perimenopausal and postmenopausal health decisions

OBJECTIVE: Knowledge relevant to women's peri- and postmenopausal health decisions has been evolving rapidly. Web-based decision supports can be rapidly updated and have the potential to improve the quality of patients' decisions. We developed and tested a web-based decision support for peri- and postmenopausal health decisionmaking. METHODS: We recruited 409 women aged 45-75 for one randomized, controlled trial and 54 women with an upcoming clinic appointment for a subsequent trial. Women were randomized to use the web-based decision support versus a printed brochure (first trial) and usual care (second trial). Outcomes were changes in decisional satisfaction, decisional conflict, and knowledge, both within each trial and compared across the trials. RESULTS: Intervention subjects had greater increases in decisional satisfaction in the second trial and knowledge in both trials. A high dropout rate among women randomized to the website in the first trial effectively negated benefits in that trial, but not in the second. CONCLUSIONS: The utility of this web-based decision support in two trials depended on a number of factors that appear related to the urgency of making a decision. PRACTICE IMPLICATIONS: Decision aids should be targeted to patients actively trying to make a decision.