Computerized clinical decision support systems for therapeutic drug monitoring and dosing: a decision-maker-researcher partnership systematic review

Clinical decision support systems in addiction and concurrent disorders: A systematic review and meta-analysis

INTRODUCTION

This review aims to synthesise the literature on the efficacy, evolution, and challenges of implementing Clincian Decision Support Systems (CDSS) in the realm of mental health, addiction, and concurrent disorders.

METHODS

Following PRISMA guidelines, a systematic review and meta-analysis were performed. Searches conducted in databases such as MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science through 25 May 2023, yielded 27,344 records. After necessary exclusions, 69 records were allocated for detailed synthesis. In the examination of patient outcomes with a focus on metrics such as therapeutic efficacy, patient satisfaction, and treatment acceptance, meta-analytic techniques were employed to synthesise data from randomised controlled trials.

RESULTS

A total of 69 studies were included, revealing a shift from knowledge-based models pre-2017 to a rise in data-driven models post-2017. The majority of models were found to be in Stage 2 or 4 of maturity. The meta-analysis showed an effect size of -0.11 for addiction-related outcomes and a stronger effect size of -0.50 for patient satisfaction and acceptance of CDSS.

DISCUSSION

The results indicate a shift from knowledge-based to data-driven CDSS approaches, aligned with advances in machine learning and big data. Although the immediate impact on addiction outcomes is modest, higher patient satisfaction suggests promise for wider CDSS use. Identified challenges include alert fatigue and opaque AI models.

CONCLUSION

CDSS shows promise in mental health and addiction treatment but requires a nuanced approach for effective and ethical implementation. The results emphasise the need for continued research to ensure optimised and equitable use in healthcare settings.

How intervention studies measure the effectiveness of medication safety-related clinical decision support systems in primary and long-term care: a systematic review

BACKGROUND

Medication errors and associated adverse drug events (ADE) are a major cause of morbidity and mortality worldwide. In recent years, the prevention of medication errors has become a high priority in healthcare systems. In order to improve medication safety, computerized Clinical Decision Support Systems (CDSS) are increasingly being integrated into the medication process. Accordingly, a growing number of studies have investigated the medication safety-related effectiveness of CDSS. However, the outcome measures used are heterogeneous, leading to unclear evidence. The primary aim of this study is to summarize and categorize the outcomes used in interventional studies evaluating the effects of CDSS on medication safety in primary and long-term care.

METHODS

We systematically searched PubMed, Embase, CINAHL, and Cochrane Library for interventional studies evaluating the effects of CDSS targeting medication safety and patient-related outcomes. We extracted methodological characteristics, outcomes and empirical findings from the included studies. Outcomes were assigned to three main categories: process-related, harm-related, and cost-related. Risk of bias was assessed using the Evidence Project risk of bias tool.

RESULTS

Thirty-two studies met the inclusion criteria. Almost all studies (n = 31) used process-related outcomes, followed by harm-related outcomes (n = 11). Only three studies used cost-related outcomes. Most studies used outcomes from only one category and no study used outcomes from all three categories. The definition and operationalization of outcomes varied widely between the included studies, even within outcome categories. Overall, evidence on CDSS effectiveness was mixed. A significant intervention effect was demonstrated by nine of fifteen studies with process-related primary outcomes (60%) but only one out of five studies with harm-related primary outcomes (20%). The included studies faced a number of methodological problems that limit the comparability and generalizability of their results.

CONCLUSIONS

Evidence on the effectiveness of CDSS is currently inconclusive due in part to inconsistent outcome definitions and methodological problems in the literature. Additional high-quality studies are therefore needed to provide a comprehensive account of CDSS effectiveness. These studies should follow established methodological guidelines and recommendations and use a comprehensive set of harm-, process- and cost-related outcomes with agreed-upon and consistent definitions.

PROSPERO REGISTRATION

CRD42023464746.

A Randomized Trial Comparing Standard of Care to Bayesian Warfarin Dose Individualization

The quality of warfarin treatment may be improved if management is guided by the use of models based upon pharmacokinetic-pharmacodynamic theory. A prospective, two-armed, single-blind, randomized controlled trial compared management aided by a web-based dose calculator (NextDose) with standard clinical care. Participants were 240 adults receiving warfarin therapy following cardiac surgery, followed up until the first outpatient appointment at least 3 months after warfarin initiation. We compared the percentage of time spent in the international normalized ratio acceptable range (%TIR) during the first 28 days following warfarin initiation, and %TIR and count of bleeding events over the entire follow-up period. Two hundred thirty-four participants were followed up to day 28 (NextDose: 116 and standard of care: 118), and 228 participants (114 per arm) were followed up to the final study visit. Median %TIR tended to be higher for participants receiving NextDose guided warfarin management during the first 28 days (63 vs. 56%, P = 0.13) and over the entire follow-up period (74 vs. 71%, P = 0.04). The hazard of clinically relevant minor bleeding events was lower for participants in the NextDose arm (hazard ratio: 0.21, P = 0.041). In NextDose, there were 89.3% of proposed doses accepted by prescribers. NextDose guided dose management in cardiac surgery patients requiring warfarin was associated with an increase in %TIR across the full follow-up period and fewer hemorrhagic events. A theory-based, pharmacologically guided approach facilitates higher quality warfarin anticoagulation. An important practical benefit is a reduced requirement for clinical experience of warfarin management.

Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates

BACKGROUND AND OBJECTIVE

Pharmacokinetic models can inform drug dosing of vancomycin in neonates to optimize therapy. However, the model selected needs to describe the intended population to provide appropriate dose recommendations. Our study aims to identify the population pharmacokinetic (PopPK) model(s) with the best performance to predict vancomycin exposure in neonates in our hospital.

METHODS

Relevant published PopPK models for vancomycin in neonates were selected based on demographics and vancomycin dosing strategy. The predictive performance of the models was evaluated in Tucuxi using a local cohort of 69 neonates. Mean absolute error (MAE), relative bias (rBias) and relative root mean square error (rRMSE) were used to quantify the accuracy and precision of the predictive performance of each model for three different approaches: a priori, a posteriori, and Bayesian forecasting for the next course of therapy based on the previous course predictions. A PopPK model was considered clinically acceptable if rBias was between ± 20 and 95% confidence intervals included zero.

RESULTS

A total of 25 PopPK models were identified and nine were considered suitable for further evaluation. The model of De Cock et al. 2014 was the only clinically acceptable model based on a priori [MAE 0.35 mg/L, rBias 0.8 % (95% confidence interval (CI) - 7.5, 9.1%), and rRMSE 8.9%], a posteriori [MAE 0.037 mg/L, rBias - 0.23% (95% CI - 1.3, 0.88%), and rRMSE 6.02%] and Bayesian forecasting for the next courses [MAE 0.89 mg/L, rBias 5.45% (95% CI - 8.2, 19.1%), and rRMSE 38.3%) approaches.

CONCLUSIONS

The De Cock model was selected based on a comprehensive approach of model selection to individualize vancomycin dosing in our neonates.

The Learning Electronic Health Record

Electronic medical records (EMRs) constitute the electronic version of all medical information included in a patient's paper chart. The electronic health record (EHR) technology has witnessed massive expansion in developed countries and to a lesser extent in underresourced countries during the last 2 decades. We will review factors leading to this expansion, how the emergence of EHRs is affecting several health-care stakeholders; some of the growing pains associated with EHRs with a particular emphasis on the delivery of care to the critically ill; and ongoing developments on the path to improve the quality of research, health-care delivery, and stakeholder satisfaction.

Anticoagulation-Associated Adverse Drug Events in Hospitalized Patients Across Two Time Periods

PURPOSE

Anticoagulants often cause adverse drug events (ADEs), comprised of medication errors and adverse drug reactions, in patients. Our study objective was to determine the clinical characteristics, types, severity, cause, and outcomes of anticoagulation-associated ADEs from 2015-2020 (a contemporary period following implementation of an electronic health record, infusion device technology, and anticoagulant dosing nomograms) and to compare them with those of a historical period (2004-2009).

METHODS

We reviewed all anticoagulant-associated ADEs reported as part of our hospital-wide safety system. Reviewers classified type, severity, root cause, and outcomes for each ADE according to standard definitions. Reviewers also assessed events for patient harm. Patients were followed up to 30 days after the event.

RESULTS

Despite implementation of enhanced patient safety technology and procedure, ADEs increased in the contemporary period. In the contemporary period, we found 925 patients who had 984 anticoagulation-associated ADEs, including 811 isolated medication errors (82.4%); 13 isolated adverse drug reactions (1.4%); and 160 combined medication errors, adverse drug reactions, or both (16.2%). Unfractionated heparin was the most frequent ADE-related anticoagulant (77.7%, contemporary period vs 58.3%, historical period). The most frequent anticoagulation-associated medication error in the contemporary period was wrong rate or frequency of administration (26.1%, n = 253), with the most frequent root cause being prescribing errors (21.3%, n = 207). The type, root cause, and harm from ADEs were similar between periods.

CONCLUSIONS

We found that anticoagulation-associated ADEs occurred despite advances in patient safety technologies and practices. Events were common, suggesting marginal improvements in anticoagulant safety over time and ample opportunities for improvement.

Effects and characteristics of clinical decision support systems on the outcomes of patients with kidney disease: a systematic review

OBJECTIVES

This systematic review was conducted to investigate the characteristics and effects of clinical decision support systems (CDSSs) on clinical and process-of-care outcomes of patients with kidney disease.

METHODS

A comprehensive systematic search was conducted in electronic databases to identify relevant studies published until November 2020. Randomized clinical trials evaluating the effects of using electronic CDSS on at least one clinical or process-of-care outcome in patients with kidney disease were included in this study. The characteristics of the included studies, features of CDSSs, and effects of the interventions on the outcomes were extracted. Studies were appraised for quality using the Cochrane risk-of-bias assessment tool.

RESULTS

Out of 8722 retrieved records, 11 eligible studies measured 32 outcomes, including 10 clinical outcomes and 22 process-of-care outcomes. The effects of CDSSs on 45.5% of the process-of-care outcomes were statistically significant, and all the clinical outcomes were not statistically significant. Medication-related process-of-care outcomes were the most frequently measured (54.5%), and CDSSs had the most effective and positive effect on medication appropriateness (18.2%). The characteristics of CDSSs investigated in the included studies comprised automatic data entry, real-time feedback, providing recommendations, and CDSS integration with the Computerized Provider Order Entry system.

CONCLUSION

Although CDSS may potentially be able to improve processes of care for patients with kidney disease, particularly with regard to medication appropriateness, no evidence was found that CDSS affects clinical outcomes in these patients. Further research is thus required to determine the effects of CDSSs on clinical outcomes in patients with kidney diseases.

Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness

Background

Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented.

Methods

We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications.

Results

The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321).

Conclusion

This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Knowledge and Attitude of Healthcare Practitioners Toward Therapeutic Drug Monitoring Practices in the Najran Region, Kingdom of Saudi Arabia

BACKGROUND

Therapeutic drug monitoring (TDM) is one of the useful clinical tools that aim to improve and ensure the best therapeutic effects while avoiding drug-related toxicity. We aimed to evaluate the knowledge and attitude toward TDM practices among healthcare practitioners in the Najran region, Saudi Arabia.

METHODOLOGY

A cross-sectional study was conducted between May 2021 and October 2021 for assessing the awareness and attitude of licensed doctors and pharmacists working in Saudi Arabia regarding TDM practices. A 31-item questionnaire was distributed to the healthcare professionals via an electronic link. Descriptive and inferential statistics were used to report the data.

RESULTS

A total of 392 participants submitted questionnaires. More than half of the professionals (55.9%) had an overall awareness of the TDM, while only 3.1% did not. Only 16% and 23% of those who were surveyed indicated that TDM is used at the beginning of medication administration and when shifting from one drug to another, respectively. The majority of the professionals responded that TDM is revealed with laboratory changes in liver and kidney function (81%), and TDM is specified with suspected therapeutic failure (93%). Only half of the respondents claimed that they had ever requested or suggested a TDM in their practice. More than 90% of the respondents claimed that they were aware of the indications for vancomycin (n = 381), gentamicin (n = 375), lithium (n = 369), digoxin (n = 380), and theophylline (n = 369).

CONCLUSION

This study found that the majority of the healthcare professionals in Najran were aware of the TDM, particularly those with more than 10 years of professional experience. In addition, TDM service is not widely available in smaller hospitals in Najran. There is a need to conduct customized training programs for junior healthcare professionals, thus increasing the levels of awareness toward TDM. Future studies in Saudi Arabia can explore the role of clinical pharmacists in the provision of TDM services by assessing the clinical and economic outcomes of pharmacist interventions in TDM.

Clinical decision support in cardiovascular medicine

Despite considerable progress in tackling cardiovascular disease over the past 50 years, many gaps in the quality of care for cardiovascular disease remain. Multiple missed opportunities have been identified at every step in the prevention and treatment of cardiovascular disease, such as failure to make risk factor modifications, failure to diagnose cardiovascular disease, and failure to use proper evidence based treatments. With the digital transformation of medicine and advances in health information technology, clinical decision support (CDS) tools offer promise to enhance the efficiency and effectiveness of delivery of cardiovascular care. However, to date, the promise of CDS delivering scalable and sustained value for patient care in clinical practice has not been realized. This article reviews the evidence on key emerging questions around the development, implementation, and regulation of CDS with a focus on cardiovascular disease. It first reviews evidence on the effectiveness of CDS on healthcare process and clinical outcomes related to cardiovascular disease and design features associated with CDS effectiveness. It then reviews the barriers encountered during implementation of CDS in cardiovascular care, with a focus on unintended consequences and strategies to promote successful implementation. Finally, it reviews the legal and regulatory environment of CDS with specific examples for cardiovascular disease.

Development of a Fluorescent Microfluidic Device Based on Antibody Microarray Read-Out for Therapeutic Drug Monitoring of Acenocoumarol

The development of a proof-of-concept point-of-care (PoC) device for the determination of oral anticoagulants determination is presented. Acenocoumarol (ACL) is prescribed to prevent certain cardiovascular diseases related to the prevention of deep vein thrombosis, pulmonary embolism, myocardial infarction, and stroke. Oral anticoagualant treatment (OAT) represents a population of 2% under treatment which has expenditures about $ 144 million in 2011. The main drawback for OAT is the associated narrow therapeutic window and the unpredictable dose-response relationship, which is one of the main causes for visiting the emergency room at the hospitals. In a previous work, family antibodies were produced for the simultaneous detection of ACL and warfarin (W) depending on the area of application. It was developed in different formats, indirect and direct, either with similar detectabilities and both assays quantifying the oral anticoagulants with high accuracy and reproducibility. We present the implementation of the already developed immunochemical method to a point-of-care (PoC) device to assist on the patient compliance assessment programs. In order to achieve this goal, a first development was performed implementing ACL ELISA assay into a microarray format with fluorescent read-out. The assay was successfully implemented achieving a LOD of 1.23 nM of ACL directly measured in human plasma. Then, a fully integrated microfluidic system is developed which incorporates the specific immunoreagents for the detection of ACL. The immunoreagents were attached onto the glass slide in a microarray format. The system is automatic, rapid, sensitive, and disposable that could help clinicians monitor patients under OAT. According to the fluorescent label of the ACL binding, the chip can be easily read with a scanner. The microfluidic system performed good according to the robust and reproducible signals, and subsequently yielded an accurate result.

Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance

Background

Recent advances in machine and deep learning based on an increased availability of clinical data have fueled renewed interest in computerized clinical decision support systems (CDSSs). CDSSs have shown great potential to improve healthcare, increase patient safety and reduce costs. However, the use of CDSSs is not without pitfalls, as an inadequate or faulty CDSS can potentially deteriorate the quality of healthcare and put patients at risk. In addition, the adoption of a CDSS might fail because its intended users ignore the output of the CDSS due to lack of trust, relevancy or actionability.

Aim

In this article, we provide guidance based on literature for the different aspects involved in the adoption of a CDSS with a special focus on machine and deep learning based systems: selection, acceptance testing, commissioning, implementation and quality assurance.

Results

A rigorous selection process will help identify the CDSS that best fits the preferences and requirements of the local site. Acceptance testing will make sure that the selected CDSS fulfills the defined specifications and satisfies the safety requirements. The commissioning process will prepare the CDSS for safe clinical use at the local site. An effective implementation phase should result in an orderly roll out of the CDSS to the well‐trained end‐users whose expectations have been managed. And finally, quality assurance will make sure that the performance of the CDSS is maintained and that any issues are promptly identified and solved.

Conclusion

We conclude that a systematic approach to the adoption of a CDSS will help avoid pitfalls, improve patient safety and increase the chances of success.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis

Background

Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts.

Objective

In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators.

Methods

A mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation.

Results

Quantitative analysis revealed that physicians’ expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability.

Conclusions

This study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future.

Designing, Conducting, and Reporting Clinical Decision Support Studies: Recommendations and Call to Action

By enabling more efficient and effective medical decision making, computer-based clinical decision support (CDS) could unlock widespread benefits from the significant investment in electronic health record (EHR) systems in the United States. Evidence from high-quality CDS studies is needed to enable and support this vision of CDS-facilitated care optimization, but limited guidance is available in the literature for designing and reporting CDS studies. To address this research gap, this article provides recommendations for designing, conducting, and reporting CDS studies to: 1) ensure that EHR data to inform the CDS are available; 2) choose decision rules that are consistent with local care processes; 3) target the right users and workflows; 4) make the CDS easy to access and use; 5) minimize the burden placed on users; 6) incorporate CDS success factors identified in the literature, in particular the automatic provision of CDS as a part of clinician workflow; 7) ensure that the CDS rules are adequately tested; 8) select meaningful evaluation measures; 9) use as rigorous a study design as is feasible; 10) think about how to deploy the CDS beyond the original host organization; 11) report the study in context; 12) help the audience understand why the intervention succeeded or failed; and 13) consider the financial implications. If adopted, these recommendations should help advance the vision of more efficient, effective care facilitated by useful and widely available CDS.

Design and Usability of an Electronic Health Record-Integrated, Point-of-Care, Clinical Decision Support Tool for Modeling and Simulation of Antihemophilic Factors

BACKGROUND

With the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers.

OBJECTIVES

The aim of this study is to develop and examine the usability of a CDS tool for antihemophilic factor dose individualization.

METHODS

Our development strategy was based on the features associated with successful CDS tools and driven by a formal requirements analysis. The back-end code was based on algorithms developed for manual individualization and unit tested with 23,000 simulated patient profiles created from the range of patient-derived pharmacokinetic parameter estimates defined in children and adults. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users were recruited to participate in traditional usability testing under an institutional review board approved protocol.

RESULTS

CDS software was developed to systematically walk the point-of-care clinician through dose individualization after seamlessly importing the requisite patient data from the EHR. Classical and population pharmacokinetic approaches were incorporated with clearly displayed estimates of reliability and uncertainty. Users can perform simulations for prophylaxis and acute bleeds by providing two of four therapeutic targets. Testers were highly satisfied with our CDS and quickly became proficient with the tool.

CONCLUSION

With early and broad stakeholder engagement, we developed a CDS tool for hematology provider that affords seamless transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent dose selection.

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial

Background

The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care.

Methods

A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate.

Results

Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system.

Conclusions

The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system.

Trial registration

ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

3D Printing Methods for Pharmaceutical Manufacturing: Opportunity and Challenges

BACKGROUND

A recently FDA approved 3D printed drug is paving a path for new pharmaceutical manufacturing era. The 3D printing is a novel approach of producing 3D pharmaceuticals from digital designs, in a layer-by-layer fashion. However, traditional manufacturing of drug products is being carried out from decades with well-established manufacturing processes and with well approved regulatory guidelines but these processes are too obsolete in concern of process aptitude and manufacturing flexibility. On the other hand, 3D printing provides a competitive flexibility in terms of personalized drug dosage forms with complex geometries that will be made on-demand with desired drug release kinetics, hence providing the formulator a substantial provision of improvising the safety and efficacy of the drugs. Furthermore, this novel 3D technology allows tailoring of composite tissue scaffolds and sample models for characterization that closely mimic in-vivo simulations. Nevertheless, certain limitations are there in terms of regulatory aspects hindering the launch of 3DP products in the market.

METHODS

Exhaustive search were made on Google Scholar and PubMed databases concerning 3-D printing methods, drug delivery applications, and past to present evolution of personalized medicine.

RESULTS

Although a high magnitude of progress have been made on 3-D printing techniques in a short span of time, still inkjet, nozzle-based deposition, stereolithography and selective laser sintering techniques are the most popular ones. Their application is adapted in the fabrication of tablets, implants, polypills and nanoparticles.

CONCLUSION

3D printing is revolutionizing the pharma expectations towards customized medicines but still there is a need to explore the aspects of cost, flexibility and bioequivalence. The present review provides a comprehensive account of various 3D printing technologies and highlights the opportunities and key challenges of 3D printing relevant to pharmaceuticals.

Electronic Medical Record Use and Satisfaction Among Pediatric Orthopaedic Surgeons

INTRODUCTION

Electronic medical record (EMR) use among pediatric orthopaedic surgeons has evolved substantially within the past decade. In response to the Patient Protection and Affordable Care Act, large hospitals and tertiary pediatric medical centers rapidly acquired and implemented EMRs with uncertainty as to the potential impact on patient care and operational efficiency of subspecialists. This study reviews the background and regulatory framework for Meaningful Use of EMR and assesses the current landscape of EMR utilization by pediatric orthopaedic surgeons.

METHODS

In 2015, the Practice Management Committee distributed a survey regarding EMR use and satisfaction to members of the Pediatric Orthopaedic Society of North America. Survey responses from 324 members were used to analyze levels of satisfaction by EMR platform and practice type and to consider drivers of satisfaction or dissatisfaction of end users.

RESULTS

Although there were no differences in overall satisfaction based on vendor or practice type, significant differences were noted for 5 specific parameters of satisfaction, including: usefulness of templates, efficiency of practice workflow, information services support, number of logon events, and speed of the system. A user/vendor map is provided to facilitate networking among providers and groups utilizing common EMR platforms to help bring about rational improvements in EMR functionality for the future.

CONCLUSIONS

Substantial effort needs to be made to improve subspecialty-specific EMR documentation, order entry, research tools, and clinical workflows to enhance the processes of care for children with orthopaedic conditions in the era of EMR.

LEVEL OF EVIDENCE

Level IV.

Towards a Maturity Model for Clinical Decision Support Operations

Clinical decision support (CDS) systems delivered through the electronic health record are an important element of quality and safety initiatives within a health care system. However, managing a large CDS knowledge base can be an overwhelming task for informatics teams. Additionally, it can be difficult for these informatics teams to communicate their goals with external operational stakeholders and define concrete steps for improvement. We aimed to develop a maturity model that describes a roadmap toward organizational functions and processes that help health care systems use CDS more effectively to drive better outcomes. We developed a maturity model for CDS operations through discussions with health care leaders at 80 organizations, iterative model development by four clinical informaticists, and subsequent review with 19 health care organizations. We ceased iterations when feedback from three organizations did not result in any changes to the model. The proposed CDS maturity model includes three main "pillars": "Content Creation," "Analytics and Reporting," and "Governance and Management." Each pillar contains five levels-advancing along each pillar provides CDS teams a deeper understanding of the processes CDS systems are intended to improve. A "roof" represents the CDS functions that become attainable after advancing along each of the pillars. Organizations are not required to advance in order and can develop in one pillar separately from another. However, we hypothesize that optimal deployment of preceding levels and advancing in tandem along the pillars increase the value of organizational investment in higher levels of CDS maturity. In addition to describing the maturity model and its development, we also provide three case studies of health care organizations using the model for self-assessment and determine next steps in CDS development.

Systematic review of interventions to improve safety and quality of anticoagulant prescribing for therapeutic indications for hospital inpatients

PURPOSE

Anticoagulation-associated adverse drug events are common in hospitalised patients and result in morbidity, mortality, increased length of hospital stay and higher costs of care. Many are preventable. We reviewed the literature to identify and assess interventions intended to improve safety or quality anticoagulant prescribing.

METHODS

A systematic search of EMBASE, MEDLINE, the Cochrane Library, Pretty Darn Quick-Evidence and Health Systems Evidence was undertaken to identify controlled studies assessing system-level interventions to improve prescribing of oral or parenteral therapeutic anticoagulation for any indication in hospitalised adults. Data were extracted for safety and quality outcomes, with studies grouped by intervention type for meta-analysis and narrative review.

RESULTS

Of 10,640 records screened, 19 trials evaluating 12,742 participants were included for analysis. No study specifically evaluated prescribing of low molecular weight heparins (LMWHs) or direct acting oral anticoagulants (DOACs). Our findings suggest that physician-led anticoagulation consultation services may reduce bleeding rates in high-risk patients. On meta-analysis, decision supported warfarin dosing resulted in higher proportion of time with international normalised ratio in therapeutic range (p = 0.0007). Studies of other clinical decision support systems and heparin monitoring systems did not demonstrate improved safety, and quality findings were inconsistent. Systematic education and feedback programs were not efficacious.

CONCLUSIONS

There is currently insufficient high-quality evidence to recommend any reviewed intervention, though several warrant closer evaluation. Adequately powered controlled trials assessing safety outcomes and evidence-based quality markers in high-risk patient groups and studies of interventions to improve safety of LMWH and DOAC prescribing are needed.

Development of Decision Support System to Predict Neurofeedback Response in ADHD: an Artificial Neural Network Approach

INTRODUCTION

Clinical decision support system (CDSS) is an analytical tool that converts raw data into useful information to help clinicians make better decisions for patients.

AIM

The purpose of this study was to investigate the efficacy of neurofeedback (NF), in Attention Deficit Hyperactivity Disorder (ADHD) by the development of CDSS based on artificial neural network (ANN).

METHODS

This study analyzed 122 patients with ADHD who underwent NF in the Parand-Human Potential Empowerment Institute in Tehran. The patients were divided into two groups according to the effects of NF: effective and non-effective groups. The patients' record information was mined by data mining techniques to identify effective features. Based on unsaturated condition of data and imbalanced classes between the patient groups (patients with successful NF response and those without it), the SMOTE technique was applied on dataset. Using MATLAB 2014a, a modular program was designed to test both multiple architectures of neural networks and their performance. Selected architecture of the neural networks was then applied in the procedure.

RESULTS

Eleven features from 28 features of the initial dataset were selected as effective features. Using the SMOTE technique, number of the samples rose to around 300 samples. Based on the multiple neural networks architecture testing, a network by 11-20-16-2 neurons was selected (specify>00.91%, sensivity=100%) and applied in the software.

CONCLUSION

The ANN used in this study has led to good results in sensivity, specificity, and AUC. The ANN and other intelligent techniques can be used as supportive tools for decision making by healthcare providers.

Efficacy of CPP-ACP and CPP-ACPF for Prevention and Remineralization of White Spot Lesions in Orthodontic Patients: a Systematic Review of Randomized Controlled Clinical Trials

INTRODUCTION

Enamel subsurface lesions or white spot lesions (WSLs) are commonly found in orthodontic patients with a prevalence of 5% to 97%.

AIM

This systematic review aimed to evaluate the efficacy of casein phosphopeptide amorphous calcium phosphate (CPP-ACP) and casein phosphopeptide amorphous calcium phosphate fluoride (CPP-ACPF) for prevention and remineralization of WSLs in orthodontic patients in human randomized controlled clinical trials (RCTs).

METHODS

Relevant articles were retrieved by searching the Web of Science, Scopus, PubMed, and Cochrane Library databases up to November 2018 with no language or date restriction. The collected data included examination method, groups included in each study with number of patients in each group, study design, follow-up period and summary of important findings of each study. The risk of bias of each study was assessed according to the guidelines of the Cochrane Collaboration's tool.

RESULTS

Of 213 articles retrieved, 13 RCTs were included in this systematic review (none of them were included in the meta-analysis). Three articles showed superior efficacy of CPP-ACP for remineralization of WSLs while four studies reported the superior clinical efficacy of CPP-ACPF for this purpose.

CONCLUSION

Both CPP-ACP and CPP-ACPF can decrease the prevalence and increase the remineralization of WSLs during/after orthodontic treatment.

Evaluation of clinical decision support systems for diabetes care: An overview of current evidence

BACKGROUND

Systematic reviews (SRs) have shown that clinical decision support systems (CDSSs) have the potential to improve diabetes care. However, methods of measuring and presenting outcomes are varied, and conclusions have been inconsistent. In addition, the reporting and methodological quality in this field is unknown, which could affect the integrity and accuracy of research. Therefore, it is difficult to confirm whether CDSSs are effective in improving diabetes care.

OBJECTIVE

To comprehensively evaluate the effects of CDSS on diabetes care and to examine the methodological and reporting qualities.

METHODS

We searched PubMed, EMBASE, and Cochrane Library from their inception to February 2017. Systematic reviews investigating the effects of CDSS on diabetes care were included. Outcomes were determined in advance and assessed separately for process of care and patient outcomes. Methodological and reporting qualities were assessed by AMSTAR and PRISMA, respectively.

RESULTS

Seventeen SRs, consisting of 222 unique randomized controlled trials and 102 nonrandomized controlled trials, were included. Evidence that CDDS significantly impacted patient outcomes was found in 32 of 102 unique studies of the 15 SRs that examined this effect (31%). A significant impact of CDSS on process of care was found in 117 out of 143 unique studies of the 11 SRs that examined this effect (82%). Ratings for overall scores of AMSTAR resulted in a mean score of 6.5 with a range of scores from 3.5 to 10.0. Reporting quality related to methodological domains was particularly incomplete.

CONCLUSIONS

Clinical decision support systems improved the quality of diabetes care by inconsistently improving process of care or patient outcomes. There is evidence that CDSS for providing alerts, reminders, or feedback to participants were most likely to impact diabetes care. Poor reporting of methodological domains, together with qualitative or narrative methods to combine findings, may limit the confidence in research evidence.

Optimizing primary care management of atrial fibrillation: The rationale and methods of the Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) study

The Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) is an investigator designed, prospective, randomized, un-blinded, cluster design clinical trial, conducted in the primary care setting of Nova Scotia, Canada. Its aim is to evaluate whether an electronic Clinical Decision Support System (CDSS) designed to assist both practitioners and patients with evidence-based management strategies for Atrial Fibrillation (AF) can improve process of care and outcomes in a cost-efficient manner as compared to usual AF care. At least 200 primary care providers are being recruited and randomized at the level of the practice to control (usual care) or intervention (eligible to access to CDSS) cohorts. Over 1,000 patients of participating providers with confirmed AF will be managed per their provider's respective assignment. The targeted primary clinical outcome is a reduction in the composite of unplanned cardiovascular (CV) or major bleeding hospitalizations and AF-related emergency department visits. Secondary clinical outcomes, process of care, patient and provider satisfaction as well as economic costs at the system and patient levels are being examined. The trial is anticipated to report in 2018.

Engaging policy-makers, health system managers, and policy analysts in the knowledge synthesis process: a scoping review

BACKGROUND

It is unclear how to engage a wide range of knowledge users in research. We aimed to map the evidence on engaging knowledge users with an emphasis on policy-makers, health system managers, and policy analysts in the knowledge synthesis process through a scoping review.

METHODS

We used the Joanna Briggs Institute guidance for scoping reviews. Nine electronic databases (e.g., MEDLINE), two grey literature sources (e.g., OpenSIGLE), and reference lists of relevant systematic reviews were searched from 1996 to August 2016. We included any type of study describing strategies, barriers and facilitators, or assessing the impact of engaging policy-makers, health system managers, and policy analysts in the knowledge synthesis process. Screening and data abstraction were conducted by two reviewers independently with a third reviewer resolving discrepancies. Frequency and thematic analyses were conducted.

RESULTS

After screening 8395 titles and abstracts followed by 394 full-texts, 84 unique documents and 7 companion reports fulfilled our eligibility criteria. All 84 documents were published in the last 10 years, and half were prepared in North America. The most common type of knowledge synthesis with knowledge user engagement was a systematic review (36%). The knowledge synthesis most commonly addressed an issue at the level of national healthcare system (48%) and focused on health services delivery (17%) in high-income countries (86%). Policy-makers were the most common (64%) knowledge users, followed by healthcare professionals (49%) and government agencies as well as patients and caregivers (34%). Knowledge users were engaged in conceptualization and design (49%), literature search and data collection (52%), data synthesis and interpretation (71%), and knowledge dissemination and application (44%). Knowledge users were most commonly engaged as key informants through meetings and workshops as well as surveys, focus groups, and interviews either in-person or by telephone and emails. Knowledge user content expertise/awareness was a common facilitator (18%), while lack of time or opportunity to participate was a common barrier (12%).

CONCLUSIONS

Knowledge users were most commonly engaged during the data synthesis and interpretation phases of the knowledge synthesis conduct. Researchers should document and evaluate knowledge user engagement in knowledge synthesis.

REGISTRATION DETAILS

Open Science Framework ( https://osf.io/4dy53/ ).

Integration of Hospital Information and Clinical Decision Support Systems to Enable the Reuse of Electronic Health Record Data

Background: The efficiency and acceptance of clinical decision support systems (CDSS) can increase if they reuse medical data captured during health care delivery. High heterogeneity of the existing legacy data formats has become the main barrier for the reuse of data. Thus, we need to apply data modeling mechanisms that provide standardization, transformation, accumulation and querying medical data to allow its reuse.

Objectives: In this paper, we focus on the interoperability issues of the hospital information systems (HIS) and CDSS data integration.

Materials and Methods: Our study is based on the approach proposed by Marcos et al. where archetypes are used as a standardized mechanism for the interaction of a CDSS with an electronic health record (EHR). We build an integration tool to enable CDSSs collect data from various institutions without a need for modifications in the implementation. The approach implies development of a conceptual level as a set of archetypes representing concepts required by a CDSS.

Results: Treatment case data from Regional Clinical Hospital in Tomsk, Russia was extracted, transformed and loaded to the archetype database of a clinical decision support system. Test records’ normalization has been performed by defining transformation and aggregation rules between the EHR data and the archetypes. These mapping rules were used to automatically generate openEHR compliant data. After the transformation, archetype data instances were loaded into the CDSS archetype based data storage. The performance times showed acceptable performance for the extraction stage with a mean of 17.428 s per year (3436 case records). The transformation times were also acceptable with 136.954 s per year (0.039 s per one instance). The accuracy evaluation showed the correctness and applicability of the method for the wide range of HISes. These operations were performed without interrupting the HIS workflow to prevent the HISes from disturbing the service provision to the users.

Conclusions: The project results have proven that archetype based technologies are mature enough to be applied in routine operations that require extraction, transformation, loading and querying medical data from heterogeneous EHR systems. Inference models in clinical research and CDSS can benefit from this by defining queries to a valid data set with known structure and constraints. The standard based nature of the archetype approach allows an easy integration of CDSSs with existing EHR systems.

Impact on process results of clinical decision support systems (CDSSs) applied to medication use: overview of systematic reviews

Objective

The purpose of this overview (systematic review of systematic reviews) is to evaluate the impact of clinical decision support systems (CDSS) applied to medication use in the care process.

Methods

A search for systematic reviews that address CDSS was performed on Medline following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Cochrane recommendations. Terms related to CDSS and systematic reviews were used in combination with Boolean operators and search field tags to build the electronic search strategy. There was no limitation of date or language for inclusion. We included revisions that investigated, as a main or secondary objective, changes in process outcomes. The Revised Assessment of Multiple Systematic Reviews (R-AMSTAR) score was used to evaluate the quality of the studies.

Results

The search retrieved 954 articles. Five articles were added through manual search, totaling an initial sample of 959 articles. After screening and reading in full, 44 systematic reviews met the inclusion criteria. In the medication-use processes where CDSS was used, the most common stages were prescribing (n=38 (86.36%) and administering (n=12 (27.27%)). Most of the systematic reviews demonstrated improvement in the health care process (30/44 - 68.2%). The main positive results were related to improvement of the quality of prescription by the physicians (14/30 - 46.6%) and reduction of errors in prescribing (5/30 - 16.6%). However, the quality of the studies was poor, according to the score used.

Conclusion

CDSSs represent a promising technology to optimize the medication-use process, especially related to improvement in the quality of prescriptions and reduction of prescribing errors, although higher quality studies are needed to establish the predictors of success in these systems.

Randomised comparison of a simple warfarin dosing algorithm versus a computerised anticoagulation management system for control of warfarin maintenance therapy

Excellent control of the international normalised ratio (INR) is associated with improved clinical outcomes in patients receiving warfarin, and can be achieved by anticoagulation clinics but is difficult in general practice. Anticoagulation clinics have often used validated commercial computer systems to manage the INR, but these are not usually available to general practitioners. It was the objective of this study to perform a randomised trial of a simple one-step warfarin dosing algorithm against a widely used computerised dosing system. During the period of introduction of a commercial computerised warfarin dosing system (DAWN AC) to an anticoagulation clinic, patients were randomised to have warfarin dose adjustment done according to recommendations of the existing warfarin dosing algorithm or to those of the computerised system. The study tested if the computerised system was non-inferior to the existing algorithm for the primary outcome of time in therapeutic INR range of 2.0–3.0 (TTR), with a one-sided non-inferiority margin of 4.5%. There were 541 patients randomised to commercial computerised system and 527 to the algorithm. Median follow-up was 159 days. A dose recommendation was provided and followed in 91% of occasions for the computerised system and in 90% for the algorithm (p=0.03). The mean TTR was 71.0% (standard deviation [SD] 23.2) for the computerised system and 71.9% (SD 22.9) for the algorithm (difference 0.9% [95% confidence interval: –1.4% to 4.1%];p-value for noninferiority=0.002;p-value for superiority=0.34). In conclusion, similar maintenance control of the INR was achieved with a simple one-step dosing algorithm and a commercial computerised management system.

Notification of international normalized ratio test in atrial fibrillation patients treated with warfarin via short message service: Study protocol for a randomized controlled trial

BACKGROUND

Atrial Fibrillation (AF) is the most common abnormal heart rhythm. AF patients usually use warfarin therapy. Safety and efficacy of warfarin are dependent on maintaining the International Normalized Ratio (INR) within the therapeutic range.

OBJECTIVE

We will use a Short Message Service (SMS) to evaluate the effect of a reminder on carrying out the INR laboratory test in a timely manner.

METHODS

This study (a Randomized Controlled Trial) will be done in Loghman hospital Tehran, Iran. Convenience sampling will be done and 400 AF patients that have inclusion criteria will be randomized equally to an intervention or control group. Patients in the intervention group will receive an SMS that will remind them of the INR test date. The SMS will be sent at 6 PM on the day before and 8 AM on the scheduled date but the patients of the control group will receive usual care without any SMS reminders. We will evaluate the effect of reminders on carrying out the INR test in a timely manner and maintaining the INR in the therapeutic range. Patients' follow-up will be done via telephone conversation to identify thromboembolic events, bleeding and mortality. The data will be analyzed by IBM SPSS version 24. We will use independent samples t-test or Mann-Whitney and Chi-square tests for the analyses of outcomes.

DISCUSSION

This protocol describes the randomized control trial to study the effects of the SMS reminder system on adherence to the timing of INR test in AF patients treated with warfarin. The research will also form the basis for future decision support systems for monitoring of patients who receive oral anticoagulants.

TRIAL REGISTRATION

Iranian Registry of Clinical Trials (http://www.irct.ir) was used to register the trial and IRCT ID was IRCT2016052528070N1.

FUNDING

This research was supported by Mashhad University of Medical Sciences.

Combining alcohol interventions with tobacco addictions treatment in primary care-the COMBAT study: a pragmatic cluster randomized trial

BACKGROUND

Tobacco and alcohol use present multiplicative risk for aerodigestive cancers. Reducing alcohol consumption improves smoking cessation outcomes and reduces cancer risk. Risky alcohol consumption and smoking are often treated separately despite concurrent treatment potentially leading to better outcomes for each. However, no rapidly scalable program exists for combined interventions in primary care clinics spread across wide geographic areas. This cluster randomized trial aims to report on the effects of a novel clinical decision support system (CDSS) on intervention rates by primary care practitioners addressing risky alcohol use in a smoking cessation program.

METHODS/DESIGN

We will be implementing a clinical decision support system (CDSS) in 221 primary care sites participating in the Smoking Treatment for Ontario Patients (STOP) program across Ontario, Canada. Sites will be blindly allocated to one of two clinical decision support systems guiding practitioners to provide a risky alcohol use intervention to smokers attempting to quit using nicotine replacement therapy (NRT). Risky alcohol use is defined as drinking above the Canadian Cancer Society's low-risk drinking guidelines. Primary analysis will measure the proportion of risky drinkers offered an alcohol intervention in each CDSS arm at baseline. Patients will be contacted by phone or email to track smoking cessation and alcohol consumption rates at 6- and 12-month follow-up.

DISCUSSION

Upon completion of the trial, the effect of different clinical decision support systems on practitioner behaviour, and on client tobacco and alcohol use, will be discussed. If the CDSS successfully promotes SBIRT for risky alcohol use in a primary care setting and/or improves patient-level outcomes, including smoking cessation rates and alcohol use reduction, this tool can be used as a model for other web-based behaviour change interventions integrated into primary care practice.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03108144.

The use of computerized clinical decision support systems in emergency care: a substantive review of the literature

OBJECTIVES

: This paper provides a substantive review of international literature evaluating the impact of computerized clinical decision support systems (CCDSSs) on the care of emergency department (ED) patients.

MATERIAL AND METHODS

: A literature search was conducted using Medline, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase electronic resources, and gray literature. Studies were selected if they compared the use of a CCDSS with usual care in a face-to-face clinical interaction in an ED.

RESULTS

: Of the 23 studies included, approximately half demonstrated a statistically significant positive impact on aspects of clinical care with the use of CCDSSs. The remaining studies showed small improvements, mainly around documentation. However, the methodological quality of the studies was poor, with few or no controls to mitigate against confounding variables. The risk of bias was high in all but 6 studies.

DISCUSSION

: The ED environment is complex and does not lend itself to robust quantitative designs such as randomized controlled trials. The quality of the research in ∼75% of the studies was poor, and therefore conclusions cannot be drawn from these results. However, the studies with a more robust design show evidence of the positive impact of CCDSSs on ED patient care.

CONCLUSION

This is the first review to consider the role of CCDSSs in emergency care and expose the research in this area. The role of CCDSSs in emergency care may provide some solutions to the current challenges in EDs, but further high-quality research is needed to better understand what technological solutions can offer clinicians and patients.

Impact of Information and Communication Technologies on Nursing Care: Results of an Overview of Systematic Reviews

Background

Information and communication technologies (ICTs) are becoming an impetus for quality health care delivery by nurses. The use of ICTs by nurses can impact their practice, modifying the ways in which they plan, provide, document, and review clinical care.

Objective

An overview of systematic reviews was conducted to develop a broad picture of the dimensions and indicators of nursing care that have the potential to be influenced by the use of ICTs.

Methods

Quantitative, mixed-method, and qualitative reviews that aimed to evaluate the influence of four eHealth domains (eg, management, computerized decision support systems [CDSSs], communication, and information systems) on nursing care were included. We used the nursing care performance framework (NCPF) as an extraction grid and analytical tool. This model illustrates how the interplay between nursing resources and the nursing services can produce changes in patient conditions. The primary outcomes included nurses’ practice environment, nursing processes, professional satisfaction, and nursing-sensitive outcomes. The secondary outcomes included satisfaction or dissatisfaction with ICTs according to nurses’ and patients’ perspectives. Reviews published in English, French, or Spanish from January 1, 1995 to January 15, 2015, were considered.

Results

A total of 5515 titles or abstracts were assessed for eligibility and full-text papers of 72 articles were retrieved for detailed evaluation. It was found that 22 reviews published between 2002 and 2015 met the eligibility criteria. Many nursing care themes (ie, indicators) were influenced by the use of ICTs, including time management; time spent on patient care; documentation time; information quality and access; quality of documentation; knowledge updating and utilization; nurse autonomy; intra and interprofessional collaboration; nurses’ competencies and skills; nurse-patient relationship; assessment, care planning, and evaluation; teaching of patients and families; communication and care coordination; perspectives of the quality of care provided; nurses and patients satisfaction or dissatisfaction with ICTs; patient comfort and quality of life related to care; empowerment; and functional status.

Conclusions

The findings led to the identification of 19 indicators related to nursing care that are impacted by the use of ICTs. To the best of our knowledge, this was the first attempt to apply NCPF in the ICTs’ context. This broad representation could be kept in mind when it will be the time to plan and to implement emerging ICTs in health care settings.

Trial Registration

PROSPERO International Prospective Register of Systematic Reviews: CRD42014014762; http://www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42014014762 (Archived by WebCite at http://www.webcitation.org/6pIhMLBZh)

Development of a clinical decision support system for diabetes care: A pilot study

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

ACC/AHA Special Report: Clinical Practice Guideline Implementation Strategies: A Summary of Systematic Reviews by the NHLBI Implementation Science Work Group: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines

BACKGROUND

In 2008, the National Heart, Lung, and Blood Institute convened an Implementation Science Work Group to assess evidence-based strategies for effectively implementing clinical practice guidelines. This was part of a larger effort to update existing clinical practice guidelines on cholesterol, blood pressure, and overweight/obesity.

OBJECTIVES

Review evidence from the published implementation science literature and identify effective or promising strategies to enhance the adoption and implementation of clinical practice guidelines.

METHODS

This systematic review was conducted on 4 critical questions, each focusing on the adoption and effectiveness of 4 intervention strategies: (1) reminders, (2) educational outreach visits, (3) audit and feedback, and (4) provider incentives. A scoping review of the Rx for Change database of systematic reviews was used to identify promising guideline implementation interventions aimed at providers. Inclusion and exclusion criteria were developed a priori for each question, and the published literature was initially searched up to 2012, and then updated with a supplemental search to 2015. Two independent reviewers screened the returned citations to identify relevant reviews and rated the quality of each included review.

RESULTS

Audit and feedback and educational outreach visits were generally effective in improving both process of care (15 of 21 reviews and 12 of 13 reviews, respectively) and clinical outcomes (7 of 12 reviews and 3 of 5 reviews, respectively). Provider incentives showed mixed effectiveness for improving both process of care (3 of 4 reviews) and clinical outcomes (3 reviews equally distributed between generally effective, mixed, and generally ineffective). Reminders showed mixed effectiveness for improving process of care outcomes (27 reviews with 11 mixed and 3 generally ineffective results) and were generally ineffective for clinical outcomes (18 reviews with 6 mixed and 9 generally ineffective results). Educational outreach visits (2 of 2 reviews), reminders (3 of 4 reviews), and provider incentives (1 of 1 review) were generally effective for cost reduction. Educational outreach visits (1 of 1 review) and provider incentives (1 of 1 review) were also generally effective for cost-effectiveness outcomes. Barriers to clinician adoption or adherence to guidelines included time constraints (8 reviews/overviews); limited staffing resources (2 overviews); timing (5 reviews/overviews); clinician skepticism (5 reviews/overviews); clinician knowledge of guidelines (4 reviews/overviews); and higher age of the clinician (1 overview). Facilitating factors included guideline characteristics such as format, resources, and end-user involvement (6 reviews/overviews); involving stakeholders (5 reviews/overviews); leadership support (5 reviews/overviews); scope of implementation (5 reviews/overviews); organizational culture such as multidisciplinary teams and low-baseline adherence (9 reviews/overviews); and electronic guidelines systems (3 reviews).

CONCLUSION

The strategies of audit and feedback and educational outreach visits were generally effective in improving both process of care and clinical outcomes. Reminders and provider incentives showed mixed effectiveness, or were generally ineffective. No general conclusion could be reached about cost effectiveness, because of limitations in the evidence. Important gaps exist in the evidence on effectiveness of implementation interventions, especially regarding clinical outcomes, cost effectiveness and contextual issues affecting successful implementation.

Going digital: a narrative overview of the clinical and organisational impacts of eHealth technologies in hospital practice

Objective The aim of the present study was to determine the effects of hospital-based eHealth technologies on quality, safety and efficiency of care and clinical outcomes. Methods Systematic reviews and reviews of systematic reviews of eHealth technologies published in PubMed/Medline/Cochrane Library between January 2010 and October 2015 were evaluated. Reviews of implementation issues, non-hospital settings or remote care or patient-focused technologies were excluded from analysis. Methodological quality was assessed using a validated appraisal tool. Outcome measures were benefits and harms relating to electronic medical records (EMRs), computerised physician order entry (CPOE), electronic prescribing (ePrescribing) and computerised decision support systems (CDSS). Results are presented as a narrative overview given marked study heterogeneity. Results Nineteen systematic reviews and two reviews of systematic reviews were included from 1197 abstracts, nine rated as high quality. For EMR functions, there was moderate-quality evidence of reduced hospitalisations and length of stay and low-quality evidence of improved organisational efficiency, greater accuracy of information and reduced documentation and process turnaround times. For CPOE functions, there was moderate-quality evidence of reductions in turnaround times and resource utilisation. For ePrescribing, there was moderate-quality evidence of substantially fewer medications errors and adverse drug events, greater guideline adherence, improved disease control and decreased dispensing turnaround times. For CDSS, there was moderate-quality evidence of increased use of preventive care and drug interaction reminders and alerts, increased use of diagnostic aids, more appropriate test ordering with fewer tests per patient, greater guideline adherence, improved processes of care and less disease morbidity. There was conflicting evidence regarding effects on in-patient mortality and overall costs. Reported harms were alert fatigue, increased technology interaction time, creation of disruptive workarounds and new prescribing errors. Conclusion eHealth technologies in hospital settings appear to improve efficiency and appropriateness of care, prescribing safety and disease control. Effects on mortality, readmissions, total costs and patient and provider experience remain uncertain. What is known about the topic? Healthcare systems internationally are undertaking large-scale digitisation programs with hospitals being a major focus. Although predictive analyses suggest that eHealth technologies have the potential to markedly transform health care delivery, contemporary peer-reviewed research evidence detailing their benefits and harms is limited. What does this paper add? This narrative overview of 19 systematic reviews and two reviews of systematic reviews published over the past 5 years provides a summary of cumulative evidence of clinical and organisational effects of contemporary eHealth technologies in hospital practice. EMRs have the potential to increase accuracy and completeness of clinical information, reduce documentation time and enhance information transfer and organisational efficiency. CPOE appears to improve laboratory turnaround times and decrease resource utilisation. ePrescribing significantly reduces medication errors and adverse drug events. CDSS, especially those used at the point of care and integrated into workflows, attract the strongest evidence for substantially increasing clinician adherence to guidelines, appropriateness of disease and treatment monitoring and optimal medication use. Evidence of effects of eHealth technologies on discrete clinical outcomes, such as morbid events, mortality and readmissions, is currently limited and conflicting. What are the implications for practitioners? eHealth technologies confer benefits in improving quality and safety of care with little evidence of major hazards. Whether EMRs and CPOE can affect clinical outcomes or overall costs in the absence of auxiliary support systems, such as ePrescribing and CDSS, remains unclear. eHealth technologies are evolving rapidly and the evidence base used to inform clinician and managerial decisions to invest in these technologies must be updated continually. More rigorous field research using appropriate evaluation methods is needed to better define real-world benefits and harms. Customisation of eHealth applications to the context of patient-centred care and management of highly complex patients with multimorbidity will be an ongoing challenge.

The Effects of Clinical Decision Support Systems on Medication Safety: An Overview

BACKGROUND

The clinical decision support system(CDSS) has potential to improving medication safety. However, the effects of the intervention were conflicting and uncertain. Meanwhile, the reporting and methodological quality of this field were unknown.

OBJECTIVE

The aim of this overview is to evaluate the effects of CDSS on medication safety and to examine the methodological and reporting quality.

METHODS

PubMed, Embase and Cochrane Library were searched to August 2015. Systematic reviews (SRs) investigating the effects of CDSS on medication safety were included. Outcomes were determined in advance and assessed separately for process of care and patient outcomes. The methodological quality was assessed by Assessment of Multiple Systematic Reviews (AMSTAR) and the reporting quality was examined by Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

RESULTS

Twenty systematic reviews, consisting of 237 unique randomized controlled trials(RCTs) and 176 non-RCTs were included. Evidence that CDSS significantly impacted process of care was found in 108 out of 143 unique studies of the 16 SRs examining this effect (75%). Only 18 out of 90 unique studies of the 13 SRs reported significantly evidence that CDSS positively impacted patient outcomes (20%). Ratings for the overall scores of AMSTAR resulted in a mean score of 8.3 with a range of scores from 7.5 to 10.5. The reporting quality was varied. Some contents were particularly strong. However, some contents were poor.

CONCLUSIONS

CDSS reduces medication error by obviously improving process of care and inconsistently improving patient outcomes. Larger samples and longer-term studies are required to ensure more reliable evidence base on the effects of CDSS on patient outcomes. The methodological and reporting quality were varied and some realms need to be improved.

Clinical Decision Support Tools for Selecting Interventions for Patients with Disabling Musculoskeletal Disorders: A Scoping Review

Purpose We aimed to identify and inventory clinical decision support (CDS) tools for helping front-line staff select interventions for patients with musculoskeletal (MSK) disorders. Methods We used Arksey and O'Malley's scoping review framework which progresses through five stages: (1) identifying the research question; (2) identifying relevant studies; (3) selecting studies for analysis; (4) charting the data; and (5) collating, summarizing and reporting results. We considered computer-based, and other available tools, such as algorithms, care pathways, rules and models. Since this research crosses multiple disciplines, we searched health care, computing science and business databases. Results Our search resulted in 4605 manuscripts. Titles and abstracts were screened for relevance. The reliability of the screening process was high with an average percentage of agreement of 92.3 %. Of the located articles, 123 were considered relevant. Within this literature, there were 43 CDS tools located. These were classified into 3 main areas: computer-based tools/questionnaires (n = 8, 19 %), treatment algorithms/models (n = 14, 33 %), and clinical prediction rules/classification systems (n = 21, 49 %). Each of these areas and the associated evidence are described. The state of evidentiary support for CDS tools is still preliminary and lacks external validation, head-to-head comparisons, or evidence of generalizability across different populations and settings. Conclusions CDS tools, especially those employing rapidly advancing computer technologies, are under development and of potential interest to health care providers, case management organizations and funders of care. Based on the results of this scoping review, we conclude that these tools, models and systems should be subjected to further validation before they can be recommended for large-scale implementation for managing patients with MSK disorders.

Design and Testing of an EHR-Integrated, Busulfan Pharmacokinetic Decision Support Tool for the Point-of-Care Clinician

Background: Busulfan demonstrates a narrow therapeutic index for which clinicians routinely employ therapeutic drug monitoring (TDM). However, operationalizing TDM can be fraught with inefficiency. We developed and tested software encoding a clinical decision support tool (DST) that is embedded into our electronic health record (EHR) and designed to streamline the TDM process for our oncology partners.

Methods: Our development strategy was modeled based on the features associated with successful DSTs. An initial Requirements Analysis was performed to characterize tasks, information flow, user needs, and system requirements to enable push/pull from the EHR. Back-end development was coded based on the algorithm used when manually performing busulfan TDM. The code was independently validated in MATLAB using 10,000 simulated patient profiles. A 296-item heuristic checklist was used to guide design of the front-end user interface. Content experts and end-users (n = 28) were recruited to participate in traditional usability testing under an IRB approved protocol.

Results: Decision support software was developed to systematically walk the point-of-care clinician through the TDM process. The system is accessed through the EHR which transparently imports all of the requisite patient data. Data are visually inspected and then curve fit using a model-dependent approach. Quantitative goodness-of-fit are converted to single tachometer where “green” alerts the user that the model is strong, “yellow” signals caution and “red” indicates that there may be a problem with the fitting. Override features are embedded to permit application of a model-independent approach where appropriate. Simulations are performed to target a desired exposure or dose as entered by the clinician and the DST pushes the user approved recommendation back into the EHR. Usability testers were highly satisfied with our DST and quickly became proficient with the software.

Conclusions: With early and broad stake-holder engagement we developed a clinical DST for the non-pharmacologist. This tools affords our clinicians the ability to seamlessly transition from patient assessment, to pharmacokinetic modeling and simulation, and subsequent prescription order entry.

Optimization of vitamin K antagonist drug dose finding by replacement of the international normalized ratio by a bidirectional factor: validation of a new algorithm

ESSENTIALS: We developed a new algorithm to optimize vitamin K antagonist dose finding. Validation was by comparing actual dosing to algorithm predictions. Predicted and actual dosing of well performing centers were highly associated. The method is promising and should be tested in a randomized trial.

SUMMARY

BACKGROUND

Oral vitamin K antagonists (VKAs) have a narrow therapeutic window and thus require frequent monitoring of its intensity by the international normalized ratio (INR). Improvement of VKA dosing defined as more time in therapeutic range (TTR) can reduce thrombotic disease and bleeding. Computerized decision support programs (CDSs) are used to optimize VKA dosing, but the effects are heterogeneous. CDSs significantly improve the proportion of time in the therapeutic INR range for initiation therapy but not the quality of anticoagulant management in an outpatient setting. One of the major problems of VKA dose finding is that the INR is a ratio and does not present linearity. We developed a new dose-finding algorithm, based on a novel bidirectional factor (BF). This BF is linear transformation of the nonlinear INR.

METHODS

We compared the outcomes of the new algorithm, called BF-N, with dose finding performed at three highly ranked Dutch anticoagulation centers, using both acenocoumarol and phenprocoumon.

RESULTS

The outcomes of the BF-N algorithm showed a linear correlation with VKA doses of the three centers (y = 1.001x, r(2) 0.999 for acenocoumarol and y = 0.999x, r(2) 0.999 for phenprocoumon), with a standard deviation of 3.83%. The rate of automated dosage proposals increased to 100%.

CONCLUSION

The BF-N algorithm performs well in real-life settings and increases the rate of automated dosage proposals. The algorithm can be easily built into existing CDSs. Experienced staff remains necessary for complicated situations. The new algorithm needs to be evaluated in a prospective trial.

Outcomes of clinical decision support (CDS) and correlates of CDS use for home care patients with high medication regimen complexity: a randomized trial

RATIONALE, AIMS AND OBJECTIVES

To assess the outcomes of a clinical decision support (CDS) intervention designed for home care patients with high medication regimen complexity (MRC) and to examine correlates of CDS use.

METHOD

The CDS consisted of a computerized algorithm that identified high MRC patients, electronic alerts and a care management module. Nurses were randomized upon identification of an eligible patient. Full intention to treat and intervention group-only analyses were completed. Regression-adjusted outcomes were hospitalization, emergency department use and reduction in MRC.

RESULTS

Five hundred nurses were randomized with 7919 of their patients. Approximately 20% of the intervention group was hospitalized versus 21% in the control group; 16.5% versus 16.7% had an emergency department visit; and 6% in each group dropped below the high MRC threshold. No statistically significant differences were found in the intention to treat analysis. Eighty-two percent of intervention nurses used the CDS but for only 42% of their patients. Among intervention patients, CDS use (vs. non-use) was associated with reduced MRC and hospitalization. CDS use was associated with various clinician and patient characteristics.

CONCLUSION

CDS use was limited, negating the impact of the intervention overall. Findings on correlates of CDS use and the relationship between CDS use and positive outcomes suggest that CDS use and outcomes could be enhanced by avoiding short patient lengths of stay, improving continuity of care, increasing reliance on salaried nurses and/or increasing per diem nurses' incentives to use CDS.

User perspectives on an electronic decision-support tool performing comprehensive medication reviews - a focus group study with physicians and nurses

Background

Although a number of studies have evaluated the effectiveness of computerized decision-support systems (CDSS), there is lack of data on user perspectives, barriers, and facilitators to the implementation of CDSSs in real-life surroundings. The aim of this study was to assess individually perceived barriers, facilitators and ideas influencing the CDSS implementation and usability.

Methods

In this qualitative study, five focus groups were carried out with physicians and nurses separately at the Tampere City Health Center, Finland. The participants were end-users of the EBMeDS computerized decision support system. An explorative data content analysis was applied.

Results

The most important barrier to benefitting from CDSS was the lack of structured and coded diagnosis documentation and outdated medication information in the electronic health records. This led to false alerts and distrust towards the system. Among the major facilitators found were e.g. the beneficial reminders that helped practitioners take into account matters otherwise ignored; automatic glomerular filtration rate (GFR) calculations; medication safety checks; and the summaries in the single medication review at a glance.

Conclusions

Physicians’ and nurses’ are keen to use the CDSS and it may enhance their inter-professional collaboration. Documenting patient information in a structured, uniform and easy manner is the essential starting point for electronic decision support. When implementing CDSS, managers need to focus on common practices in documenting structured data in their organizations in order to prevent undermining trust in the system.

Study protocol for "Study of Practices Enabling Implementation and Adaptation in the Safety Net (SPREAD-NET)": a pragmatic trial comparing implementation strategies

BACKGROUND

Little research has directly compared the effectiveness of implementation strategies in any setting, and we know of no prior trials directly comparing how effectively different combinations of strategies support implementation in community health centers. This paper outlines the protocol of the Study of Practices Enabling Implementation and Adaptation in the Safety Net (SPREAD-NET), a trial designed to compare the effectiveness of several common strategies for supporting implementation of an intervention and explore contextual factors that impact the strategies' effectiveness in the community health center setting.

METHODS/DESIGN

This cluster-randomized trial compares how three increasingly hands-on implementation strategies support adoption of an evidence-based diabetes quality improvement intervention in 29 community health centers, managed by 12 healthcare organizations. The strategies are as follows: (arm 1) a toolkit, presented in paper and electronic form, which includes a training webinar; (arm 2) toolkit plus in-person training with a focus on practice change and change management strategies; and (arm 3) toolkit, in-person training, plus practice facilitation with on-site visits. We use a mixed methods approach to data collection and analysis: (i) baseline surveys on study clinic characteristics, to explore how these characteristics impact the clinics' ability to implement the tools and the effectiveness of each implementation strategy; (ii) quantitative data on change in rates of guideline-concordant prescribing; and (iii) qualitative data on the "how" and "why" underlying the quantitative results. The outcomes of interest are clinic-level results, categorized using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework, within an interrupted time-series design with segmented regression models. This pragmatic trial will compare how well each implementation strategy works in "real-world" practices.

DISCUSSION

Having a better understanding of how different strategies support implementation efforts could positively impact the field of implementation science, by comparing practical, generalizable methods for implementing clinical innovations in community health centers. Bridging this gap in the literature is a critical step towards the national long-term goal of effectively disseminating and implementing effective interventions into community health centers.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02325531.

Implementation of multiple-domain covering computerized decision support systems in primary care: a focus group study on perceived barriers

BACKGROUND

Despite the widespread availability of computerized decision support systems (CDSSs) in various healthcare settings, evidence on their uptake and effectiveness is still limited. Most barrier studies focus on CDSSs that are aimed at a limited number of decision points within selected small-scale academic settings. The aim of this study was to identify the perceived barriers to using large-scale implemented CDSSs covering multiple disease areas in primary care.

METHODS

Three focus group sessions were conducted in which 24 primary care practitioners (PCPs) participated (general practitioners, general practitioners in training and practice nurses), varying from 7 to 9 per session. In each focus group, barriers to using CDSSs were discussed using a semi-structured literature-based topic list. Focus group discussions were audio-taped and transcribed verbatim. Two researchers independently performed thematic content analysis using the software program Atlas.ti 7.0.

RESULTS

Three groups of barriers emerged, related to 1) the users' knowledge of the system, 2) the users' evaluation of features of the system (source and content, format/lay out, and functionality), and 3) the interaction of the system with external factors (patient-related and environmental factors). Commonly perceived barriers were insufficient knowledge of the CDSS, irrelevant alerts, too high intensity of alerts, a lack of flexibility and learning capacity of the CDSS, a negative effect on patient communication, and the additional time and work it requires to use the CDSS.

CONCLUSIONS

Multiple types of barriers may hinder the use of large-scale implemented CDSSs covering multiple disease areas in primary care. Lack of knowledge of the system is an important barrier, emphasizing the importance of a proper introduction of the system to the target group. Furthermore, barriers related to a lack of integration into daily practice seem to be of primary concern, suggesting that increasing the system's flexibility and learning capacity in order to be able to adapt the decision support to meet the varying needs of different users should be the main target of CDSS interventions.

An Evaluation of the Effects of Human Factors and Ergonomics on Health Care and Patient Safety Practices: A Systematic Review

Background

From the viewpoint of human factors and ergonomics (HFE), errors often occur because of the mismatch between the system, technique and characteristics of the human body. HFE is a scientific discipline concerned with understanding interactions between human behavior, system design and safety.

Objective

To evaluate the effectiveness of HFE interventions in improving health care workers’ outcomes and patient safety and to assess the quality of the available evidence.

Methods

We searched databases, including MEDLINE, EMBASE, BIOSIS Previews and the CBM (Chinese BioMedical Literature Database), for articles published from 1996 to Mar.2015. The quality assessment tool was based on the risk of bias criteria developed by the Cochrane Effective Practice and Organization of Care (EPOC) Group. The interventions of the included studies were categorized into four relevant domains, as defined by the International Ergonomics Association.

Results

For this descriptive study, we identified 8, 949 studies based on our initial search. Finally, 28 studies with 3,227 participants were included. Among the 28 included studies, 20 studies were controlled studies, two of which were randomized controlled trials. The other eight studies were before/after surveys, without controls. Most of the studies were of moderate or low quality. Five broad categories of outcomes were identified in this study: 1) medical errors or patient safety, 2) health care workers’ quality of working life (e.g. reduced fatigue, discomfort, workload, pain and injury), 3) user performance (e.g., efficiency or accuracy), 4) health care workers’ attitudes towards the interventions(e.g., satisfaction and preference), and 5) economic evaluations.

Conclusion

The results showed that the interventions positively affected the outcomes of health care workers. Few studies considered the financial merits of these interventions. Most of the included studies were of moderate quality. This review highlights the need for scientific and standardized guidelines regarding how HFE should be implemented in health care.

To what extent is clinical and laboratory information used to perform medication reviews in the nursing home setting? the CLEAR study

BACKGROUND

The aim of this study was to evaluate to what extent laboratory data, actual medication, medical history, and/or drug indication influence the quality of medication reviews for nursing home patients.

METHODS

Forty-six health care professionals from different fields were requested to perform medication reviews for three different cases. Per case, the amount of information provided varied in three subsequent stages: stage 1, medication list only; stage 2, adding laboratory data and reason for hospital admission; and stage 3, adding medical history/drug indication. Following a slightly modified Delphi method, a multidisciplinary team performed the medication review for each case and stage. The results of these medication reviews were used as reference reviews (gold standard). The remarks from the participants were scored, according to their potential clinical impact, from relevant to harmful on a scale of 3 to -1. A total score per case and stage was calculated and expressed as a percentage of the total score from the expert panel for the same case and stage.

RESULTS

The overall mean percentage over all cases, stages, and groups was 37.0% when compared with the reference reviews. For one of the cases, the average score decreased significantly from 40.0% in stage 1, to 30.9% in stage 2, and 27.9% in stage 3; no significant differences between stages was found for the other cases.

CONCLUSION

The low performance, against the gold standard, of medication reviews found in the present study highlights that information is incorrectly used or wrongly interpreted, irrespective of the available information. Performing medication reviews without using the available information in an optimal way can have potential implications for patient safety.