A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial

Impact of model-informed precision dosing on achievement of vancomycin exposure targets in pediatric patients with cystic fibrosis

BACKGROUND

Vancomycin is commonly used to treat acute pulmonary exacerbations in pediatric patients with cystic fibrosis (CF) and a history of methicillin-resistant Staphylococcus aureus. Optimizing vancomycin exposure during therapy is essential and area under-the-curve (AUC)-guided dosing is now recommended. Model-informed precision dosing (MIPD) utilizing Bayesian forecasting is a powerful approach that can support AUC-guided dose individualization. The objective of the current study was to examine the impact of implementing an AUC-guided dose individualization approach supported via a MIPD clinical decision support (CDS) tool on vancomycin exposure, target attainment rate, and safety in pediatric patients with CF treated with vancomycin during clinical care.

METHODS

A retrospective chart review was performed in patients with CF at a single children's hospital comparing pre- and post-implementation of a MIPD approach for vancomycin supported by a cloud-based, CDS tool integrated into the electronic health record (EHR). In the pre-MIPD period, vancomycin starting doses of 60 mg/kg/day (<13 years) or 45 mg/kg/day (≥13 years) were used. Dose adjustment was guided by therapeutic drug monitoring (TDM) with a target trough 10-20 mg/L. In the post-MIPD period, starting dose and dose adjustment were based on the MIPD CDS tool predictions with a target 24 h AUC (AUC24 ) 400-600 mg*h/L. Exposure and target achievement rates were retrospectively calculated and compared. Rates of acute kidney injury (AKI) were also compared.

RESULTS

Overall, 23 patient courses were included in the pre-MIPD period and 21 patient courses in the post-MIPD period. In the post-MIPD period, an individualized MIPD starting dose resulted in 71% of patients achieving target AUC24 compared to 39% in the pre-MIPD period (p < 0.05). After the first TDM and dose adjustment, target AUC24 achievement was also higher post-MIPD versus pre-MIPD (86% vs. 57%; p < 0.05). AKI rates were low and similar between periods (pre-MIPD 8.7% vs. post-MIPD 9.5%; p = 0.9).

CONCLUSION

An MIPD approach implemented within a cloud-based, EHR-integrated CDS tool safely supported vancomycin AUC-guided dosing and resulted in high rates of target achievement.

A roadmap for the development and evaluation of the eHealthResp online course

Background

Inappropriate antibiotic use constitutes one of the most concerning public health issues, being one of the main causes of antibiotic resistance. Hence, to tackle this issue, it is important to encourage the development of educational interventions for health practitioners, namely by using digital health tools. This study focuses on the description of the development and validation process of the eHealthResp online course, a web platform directed to physicians and pharmacists, with the overall goal of improving antibiotic use for respiratory tract infections, along with the assessment of its usability.

Methods

The eHealthResp platform and the courses, developed with a user-centered design and based on Wordpress and MySQL, were based on a previously developed online course. A questionnaire to assess the usability was distributed among physicians (n = 6) and pharmacists (n = 6). Based on the obtained results, statistical analyses were conducted to calculate the usability score and appraise the design of the online course, as well as to compare the overall scores attributed by both groups. Further qualitative comments provided by the participants have also been analyzed.

Results

The eHealthResp contains two online courses directed to physicians and pharmacists aiming to aid in the management of respiratory tract infections. The average usability score of the eHealthResp online courses for physicians and pharmacists was of 78.33 (±11.57, 95%CI), and 83.75 (±15.90, 95%CI), respectively. Qualitative feedback emphasized the usefulness of the course, including overall positive reviews regarding user-friendliness and consistency.

Conclusions

This study led us to conclude that the eHealthResp online course is not recognized as a complex web platform, as both qualitative and quantitative feedback obtained were globally positive.

Multi-Institutional Implementation of Clinical Decision Support for APOL1, NAT2, and YEATS4 Genotyping in Antihypertensive Management

(1) Background: Clinical decision support (CDS) is a vitally important adjunct to the implementation of pharmacogenomic-guided prescribing in clinical practice. A novel CDS was sought for the APOL1, NAT2, and YEATS4 genes to guide optimal selection of antihypertensive medications among the African American population cared for at multiple participating institutions in a clinical trial. (2) Methods: The CDS committee, made up of clinical content and CDS experts, developed a framework and contributed to the creation of the CDS using the following guiding principles: 1. medical algorithm consensus; 2. actionability; 3. context-sensitive triggers; 4. workflow integration; 5. feasibility; 6. interpretability; 7. portability; and 8. discrete reporting of lab results. (3) Results: Utilizing the principle of discrete patient laboratory and vital information, a novel CDS for APOL1, NAT2, and YEATS4 was created for use in a multi-institutional trial based on a medical algorithm consensus. The alerts are actionable and easily interpretable, clearly displaying the purpose and recommendations with pertinent laboratory results, vitals and links to ordersets with suggested antihypertensive dosages. Alerts were either triggered immediately once a provider starts to order relevant antihypertensive agents or strategically placed in workflow-appropriate general CDS sections in the electronic health record (EHR). Detailed implementation instructions were shared across institutions to achieve maximum portability. (4) Conclusions: Using sound principles, the created genetic algorithms were applied across multiple institutions. The framework outlined in this study should apply to other disease-gene and pharmacogenomic projects employing CDS.

Using a Systems Engineering Framework to Evaluate Proton Pump Inhibitor Prescribing in Critically Ill Patients

Proton pump inhibitors (PPIs) are a risk factor for hospital-acquired Clostridium difficile infection (CDI). Much PPI use is inappropriate, and interventions to reduce PPI use, such as for stress ulcer prophylaxis in all critically ill patients, are essential to reduce CDI rates. This mixed-methods study in a combined medical-surgical intensive care unit at a tertiary academic medical center used a human factors engineering approach to understand barriers and facilitators to optimizing PPI prescribing in these patients. We performed chart review of patients for whom PPIs were prescribed to evaluate prescribing practices. Semistructured provider interviews were conducted to determine barriers and facilitators to reducing unnecessary PPI use. Emergent themes from provider interviews were classified according to the Systems Engineering Initiative for Patient Safety model. In our intensive care unit, 25% of PPI days were not clinically indicated. Barriers to optimizing PPI prescribing included inadequate provider education, lack of institutional guidelines for stress ulcer prophylaxis, and strong institutional culture favoring PPI use. Potential facilitators included increased pharmacy oversight, provider education, and embedded decision support in the electronic medical record. Interventions addressing barriers noted by front line providers are needed to reduce unnecessary PPI use, and future studies should assess the impact of such interventions on CDI rates.

Integrating the Practical Robust Implementation and Sustainability Model With Best Practices in Clinical Decision Support Design: Implementation Science Approach

Background

Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles.

Objective

This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations.

Methods

We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach.

Results

Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise.

Conclusions

Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.

E-Health Tools to Improve Antibiotic Use and Resistances: A Systematic Review

(1) Background: e-Health tools, especially in the form of clinical decision support systems (CDSSs), have been emerging more quickly than ever before. The main objective of this systematic review is to assess the influence of these tools on antibiotic use for respiratory tract infections. (2) Methods: The scientific databases, MEDLINE-PubMed and EMBASE, were searched. The search was conducted by two independent researchers. The search strategy was mainly designed to identify relevant studies on the effectiveness of CDSSs in improving antibiotic use, as a primary outcome, and on the acceptability and usability of CDSSs, as a secondary outcome. (3) Results: After the selection, 22 articles were included. The outcomes were grouped either into antibiotics prescription practices or adherence to guidelines concerning antibiotics prescription. Overall, 15 out of the 22 studies had statistically significant outcomes related to the interventions. (4) Conclusions: Overall, the results show a positive impact on the prescription and conscientious use of antibiotics for respiratory tract infections, both with respect to patients and prescribing healthcare professionals. CDSSs have been shown to have great potential as powerful tools for improving both clinical care and patient outcomes.

Model-Informed Precision Dosing of Vancomycin in Hospitalized Children: Implementation and Adoption at an Academic Children's Hospital

Background

Model-informed precision dosing (MIPD) can serve as a powerful tool during therapeutic drug monitoring (TDM) to help individualize dosing in populations with large pharmacokinetic variation. Yet, adoption of MIPD in the clinical setting has been limited. Overcoming technologic hurdles that allow access to MIPD at the point-of-care and placing it in the hands of clinical specialists focused on medication dosing may encourage adoption.

Objective

To describe the hospital implementation and usage of a MIPD clinical decision support (CDS) tool for vancomycin in a pediatric population.

Methods

Within an academic children’s hospital, MIPD for vancomycin was implemented via a commercial cloud-based CDS tool that utilized Bayesian forecasting. Clinical pharmacists were recognized as local champions to facilitate adoption of the tool and operated as end-users. Integration within the electronic health record (EHR) and automatic transmission of patient data to the tool were identified as important requirements. A web-link icon was developed within the EHR which when clicked sends users and needed patient-level clinical data to the CDS platform. Individualized pharmacokinetic predictions and exposure metrics for vancomycin are then presented in the form of a web-based dashboard. Use of the CDS tool as part of TDM was tracked and users were surveyed on their experience.

Results

After a successful pilot phase in the neonatal intensive care unit, implementation of MIPD was expanded to the pediatric intensive care unit, followed by availability to the entire hospital. During the first 2+ years since implementation, a total of 853 patient-courses (n = 96 neonates, n = 757 children) and 2,148 TDM levels were evaluated using the CDS tool. For the most recent 6 months, the CDS tool was utilized to support 79% (181/230) of patient-courses in which TDM was performed. Of 26 users surveyed, > 96% agreed or strongly agreed that automatic transmission of patient data to the tool was a feature that helped them complete tasks more efficiently; 81% agreed or strongly agreed that they were satisfied with the CDS tool.

Conclusions

Integration of a vancomycin CDS tool within the EHR, along with leveraging the expertise of clinical pharmacists, allowed for successful adoption of MIPD in clinical care.

Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care

BACKGROUND

For complex patients with chronic conditions, electronic health records (EHRs) contain large amounts of relevant historical patient data. To use this information effectively, clinicians may benefit from visual information displays that organize and help them make sense of information on past and current treatments, outcomes, and new treatment options. Unfortunately, few clinical decision support tools are designed to support clinical sensemaking.

OBJECTIVE

The objective of this study was to describe a decision-centered design process, and resultant interactive patient information displays, to support key clinical decision requirements in chronic noncancer pain care.

METHODS

To identify key clinical decision requirements, we conducted critical decision method interviews with 10 adult primary care clinicians. Next, to identify key information needs and decision support design seeds, we conducted a half-day multidisciplinary design workshop. Finally, we designed an interactive prototype to support the key clinical decision requirements and information needs uncovered during the previous research activities.

RESULTS

The resulting Chronic Pain Treatment Tracker prototype summarizes the current treatment plan, past treatment history, potential future treatments, and treatment options to be cautious about. Clinicians can access additional details about each treatment, current or past, through modal views. Additional decision support for potential future treatments and treatments to be cautious about is also provided through modal views.

CONCLUSION

This study designed the Chronic Pain Treatment Tracker, a novel approach to decision support that presents clinicians with the information they need in a structure that promotes quick uptake, understanding, and action.

Clinical Decision Support Systems and Their Role in Antibiotic Stewardship: a Systematic Review

PURPOSE OF REVIEW

The purpose of this article is to perform a systematic review over the past 5 years on the role and effectiveness of clinical decision support systems (CDSSs) on antibiotic stewardship.

RECENT FINDINGS

CDDS interventions found a significant impact on multiple outcomes relevant to antibiotic stewardship. There are various types of CDSS implementations, both active and passive (provider initiated). Passive interventions were associated with more significant outcomes; however, both interventions appeared effective. In the reviewed literature, CDSSs were consistently associated with decreasing antibiotic consumption and narrowing the spectrum of antibiotic usage. Generally, guideline adherence was improved with CDSS, although this was not universal. The effect on other outcomes, such as mortality, Clostridiodes difficile infections, length of stay, and cost, inconsistently showed a significant difference. Overall, CDDS implementation has effectively decreased antibiotic consumption and improved guideline adherence across the various types of CDSS. Other positive outcomes were noted in certain settings, but were not universal. When creating a new intervention, it is important to identify the optimal structure and deployment of a CDSS for a specific setting.

A Quality Improvement Initiative to Decrease Platelet Ordering Errors and a Proposed Model for Evaluating Clinical Decision Support Effectiveness

BACKGROUND AND OBJECTIVES

Clinical decision support (CDS) and computerized provider order entry have been shown to improve health care quality and safety, but may also generate previously unanticipated errors. We identified multiple CDS tools for platelet transfusion orders. In this study, we sought to evaluate and improve the effectiveness of those CDS tools while creating and testing a framework for future evaluation of other CDS tools.

METHODS

Using a query of an enterprise data warehouse at a tertiary care pediatric hospital, we conducted a retrospective analysis to assess baseline use and performance of existing CDS for platelet transfusion orders. Our outcome measure was the percentage of platelet undertransfusion ordering errors. Errors were defined as platelet transfusion volumes ordered which were less than the amount recommended by the order set used. We then redesigned our CDS and measured the impact of our intervention prospectively using statistical process control methodology.

RESULTS

We identified that 62% of all platelet transfusion orders were placed with one of two order sets (Inpatient Service 1 and Inpatient Service 2). The Inpatient Service 1 order set had a significantly higher occurrence of ordering errors (3.10% compared with 1.20%). After our interventions, platelet transfusion order error occurrence on Inpatient Service 1 decreased from 3.10 to 0.33%.

CONCLUSION

We successfully reduced platelet transfusion ordering errors by redesigning our CDS tools. We suggest that the use of collections of clinical data may help identify patterns in erroneous ordering, which could otherwise go undetected. We have created a framework which can be used to evaluate the effectiveness of other similar CDS tools.

An Electronic Clinical Decision Support System for the Assessment and Management of Suicidality in Primary Care: Protocol for a Mixed-Methods Study

Background

Suicide is a global public health concern, but it is preventable. Increased contact with primary care before the suicide or attempted suicide raises opportunities for intervention and prevention. However, suicide assessment and management are areas that many general practitioners (GPs) find particularly challenging. Previous research has indicated significant variability in how GPs understand, operationalize, and assess suicide risk, which subsequently has an impact on clinical decision making. Clinical decision support systems (CDSS) have been widely implemented across different health care settings, including primary care to support practitioners in clinical decision making. A CDSS may reduce inconsistencies in the identification, assessment, and management of suicide risk by GPs by guiding them through the consultation and generating a risk assessment plan that can be shared with a service user or with specialized mental health services.

Objective

Our aim is to co-develop and test with end users (eg, GPs, primary care attendees, mental health professionals) an electronic clinical decision support system (e-CDSS) to support GPs in the identification, assessment, and management of suicidality in primary care.

Methods

Ours is an ongoing embedded mixed-methods study with four phases: (1) qualitative interviews with GPs to explore their views on the content, format, and use of the e-CDSS, as well as consultation with two service-user advisory groups (people aged ≤25 and people aged ≥25) to inform the content of the e-CDSS including phrasing of items and clarity; (2) participatory co-production workshops with GPs, service users, and clinical experts in suicidality to determine the content and format of the e-CDDS; gain consensus of the relevance of items; establish content validity and identify pathways to implementation, using the Consolidated Framework for Implementation Research; (3) building the e-CDSS so that it guides the GP through a consultation; and (4) usability testing of the e-CDSS with GPs and service users in one primary care practice involving a nonlive and a live stage.

Results

The study was funded for four years, to take place between 2015 and 2019, and is currently completing phase 4 data collection. The first results are expected to be submitted for publication in June 2019. The findings will enable us to evaluate the feasibility, acceptability, and usability of a suicide-specific, electronic, guided decision support system in primary care.

Conclusions

This study will be the first to explore the feasibility, acceptability, and usability of an electronic, guided decision support system for use in primary care consultations for the improved assessment and management of suicidality.

International Registered Report Identifier (IRRID)

RR1-10.2196/11135

Bridging the Gap Between Academic Research and Pragmatic Needs in Usability: A Hybrid Approach to Usability Evaluation of Health Care Information Systems

BACKGROUND

Technology is increasingly embedded into the full spectrum of health care. This movement has benefited from the application of software development practices such as usability testing and agile development processes. These practices are frequently applied in both commercial or operational and academic settings. However, the relative importance placed on rapid iteration, validity, reproducibility, generalizability, and efficiency differs between the 2 settings and the needs and objectives of academic versus pragmatic usability evaluations.

OBJECTIVE

This paper explores how usability evaluation typically varies on key dimensions in pragmatic versus academic settings that impact the rapidity, validity, and reproducibility of findings and proposes a hybrid approach aimed at satisfying both pragmatic and academic objectives.

METHODS

We outline the characteristics of pragmatic versus academically oriented usability testing in health care, describe the tensions and gaps resulting from differing contexts and goals, and present a model of this hybrid process along with 2 case studies of digital development projects in which we demonstrate this integrated approach to usability evaluation.

RESULTS

The case studies presented illustrate design choices characteristic of our hybrid approach to usability evaluation.

CONCLUSIONS

Designed to leverage the strengths of both pragmatically and academically focused usability studies, a hybrid approach allows new development projects to efficiently iterate and optimize from usability data as well as preserves the ability of these projects to produce deeper insights via thorough qualitative analysis to inform further tool development and usability research by way of academically focused dissemination.

Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software

Background

Moving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test–driven development and automated regression testing promotes reliability. Test–driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a “safety net” for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and “living” design documentation. Rapid-cycle development or “agile” methods are being successfully applied to CDS development. The agile practice of automated test–driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as “executable requirements.”

Objective

We aimed to establish feasibility of acceptance test–driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse).

Methods

Acceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory’s expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test suite.

Results

We used test–driven development to construct a new CDS tool advising Emergency Department nurses to perform a swallowing assessment prior to administering oral medication to a patient with suspected stroke. Test tables specified desired behavior for (1) applicable clinical settings, (2) triggering action, (3) rule logic, (4) user interface, and (5) system actions in response to user input. Automated test suite results for the “executable requirements” are shown prior to building the CDS alert, during build, and after successful build.

Conclusions

Automated acceptance test–driven development and continuous regression testing of CDS configuration in a commercial EHR proves feasible with open source software. Automated test–driven development offers one potential contribution to achieving high-reliability EHR configuration. Vetting acceptance tests with clinicians elicits their input on crucial configuration details early during initial CDS design and iteratively during rapid-cycle optimization.

Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations

OBJECTIVE

Critically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations.

DESIGN

Systematic review.

DATA SOURCES

Medline, Embase and Web of Science until November 2016.

ELIGIBILITY

Studies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal.

RESULTS

14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71-0.80 in derivation (reported in 8/11 studies), 0.66-0.80 for internal validation studies (n=7) and 0.65-0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found.

CONCLUSIONS

AKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI.

Integrating data from an online diabetes prevention program into an electronic health record and clinical workflow, a design phase usability study

Background

Health information is increasingly being digitally stored and exchanged. The public is regularly collecting and storing health-related data on their own electronic devices and in the cloud. Diabetes prevention is an increasingly important preventive health measure, and diet and exercise are key components of this. Patients are turning to online programs to help them lose weight. Despite primary care physicians being important in patients’ weight loss success, there is no exchange of information between the primary care provider (PCP) and these online weight loss programs. There is an emerging opportunity to integrate this data directly into the electronic health record (EHR), but little is known about what information to share or how to share it most effectively. This study aims to characterize the preferences of providers concerning the integration of externally generated lifestyle modification data into a primary care EHR workflow.

Methods

We performed a qualitative study using two rounds of semi-structured interviews with primary care providers. We used an iterative design process involving primary care providers, health information technology software developers and health services researchers to develop the interface.

Results

Using grounded-theory thematic analysis 4 themes emerged from the interviews: 1) barriers to establishing healthy lifestyles, 2) features of a lifestyle modification program, 3) reporting of outcomes to the primary care provider, and 4) integration with primary care. These themes guided the rapid-cycle agile design process of an interface of data from an online diabetes prevention program into the primary care EHR workflow.

Conclusions

The integration of external health-related data into the EHR must be embedded into the provider workflow in order to be useful to the provider and beneficial for the patient. Accomplishing this requires evaluation of that clinical workflow during software design. The development of this novel interface used rapid cycle iterative design, early involvement by providers, and usability testing methodology. This provides a framework for how to integrate external data into provider workflow in efficient and effective ways. There is now the potential to realize the importance of having this data available in the clinical setting for patient engagement and health outcomes.

CDS, UX, and System Redesign - Promising Techniques and Tools to Bridge the Evidence Gap

Introduction:

In this special issue of eGEMs, we explore the struggles related to bringing evidence into day-to-day practice, what I define as the “evidence gap.” We are all aware of high quality evidence in the form of guidelines, randomized clinical trials for treatments and diagnostic tests, and clinical prediction rules, which are all readily available online. We also know that electronic health records (EHRs) are now ubiquitous in health care and in most practices across the country. How we marry this high quality evidence and the practice of medicine through effective decision support is a major challenge.

About the Issue:

All of the articles in this issue explore, in some fashion, CDS systems and how we can best bring providers and their work environment to the evidence. We are at the very early stages of the science of usability. Much more research and funding is needed in this area if we hope to improve the dissemination and implementation of evidence in practice. While the featured examples, techniques, and tools in the special issue are a promising start to improving usability and CDS, many of the papers highlight current gaps in knowledge and a great need for generalizable approaches. The great promise is for “learning” approaches to generate new evidence and to integrate this evidence in reliable, patient-centered ways at scale using new technology. Closing the evidence gap is a real possibility, but only if the community works together to innovate and invest in research on the best ways to disseminate, communicate, and implement evidence in practice.