Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network.

PURPOSE: A critical gap in the adoption of genomic medicine into medical practice is the need for the rigorous evaluation of the utility of genomic medicine interventions. METHODS: The Implementing Genomics in Practice Pragmatic Trials Network (IGNITE PTN) was formed in 2018 to measure the clinical utility and cost-effectiveness of genomic medicine interventions, to assess approaches for real-world application of genomic medicine in diverse clinical settings, and to produce generalizable knowledge on clinical trials using genomic interventions. Five clinical sites and a coordinating center evaluated trial proposals and developed working groups to enable their implementation. RESULTS: Two pragmatic clinical trials (PCTs) have been initiated, one evaluating genetic risk APOL1 variants in African Americans in the management of their hypertension, and the other to evaluate the use of pharmacogenetic testing for medications to manage acute and chronic pain as well as depression. CONCLUSION: IGNITE PTN is a network that carries out PCTs in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.

Effect of Clinical Decision Support on Appropriateness of Advanced Imaging Use Among Physicians-in-Training.

OBJECTIVE: Clinical decision support (CDS) tools have been shown to reduce inappropriate imaging orders. We hypothesized that CDS may be especially effective for house staff physicians who are prone to overuse of resources. MATERIALS AND METHODS: Our hospital implemented CDS for CT and MRI orders in the emergency department with scores based on the American College of Radiology's Appropriateness Criteria (range, 1-9; higher scores represent more-appropriate orders). Data on CT and MRI orders from April 2013 through June 2016 were categorized as pre-CDS or baseline, post-CDS period 1 (i.e., intervention with active feedback for scores of ≤ 4), and post-CDS period 2 (i.e., intervention with active feedback for scores of ≤ 6). Segmented regression analysis with interrupted time series data estimated changes in scores stratified by house staff and non-house staff. Generalized linear models further estimated the modifying effect of the house staff variable. RESULTS: Mean scores were 6.2, 6.2, and 6.7 in the pre-CDS, post-CDS 1, and post-CDS 2 periods, respectively (p < 0.05). In the segmented regression analysis, mean scores significantly (p < 0.05) increased when comparing pre-CDS versus post-CDS 2 periods for both house staff (baseline increase, 0.41; 95% CI, 0.17-0.64) and non-house staff (baseline increase, 0.58; 95% CI, 0.34-0.81), showing no differences in effect between the cohorts. The generalized linear model showed significantly higher scores, particularly in the post-CDS 2 period compared with the pre-CDS period (0.44 increase in scores; p < 0.05). The house staff variable did not significantly change estimates in the post-CDS 2 period. CONCLUSION: Implementation of active CDS increased overall scores of CT and MRI orders. However, there was no significant difference in effect on scores between house staff and non-house staff.

Uptake of hepatitis C screening, characteristics of patients tested, and intervention costs in the BEST-C study.

From December 2012 to March 2014, three randomized trials, each implementing a unique intervention in primary care settings (repeated mailing, an electronic health record best practice alert [BPA], and patient solicitation), evaluated hepatitis C virus (HCV) antibody testing, diagnosis, and costs for each of the interventions compared with standard-of-care testing. Multilevel multivariable models were used to estimate the adjusted risk ratio (aRR) for receiving an HCV antibody test, and costs were estimated using activity-based costing. The goal of this study was to estimate the effects of interventions conducted as part of the Birth-Cohort Evaluation to Advance Screening and Testing for Hepatitis C study on HCV testing and costs among persons of the 1945-1965 birth cohort (BC). Intervention resulted in substantially higher HCV testing rates compared with standard-of-care testing (26.9% versus 1.4% for repeated mailing, 30.9% versus 3.6% for BPA, and 63.5% versus 2.0% for patient solicitation) and significantly higher aRR for testing after controlling for sex, birth year, race, insurance type, and median household income (19.2 [95% confidence interval (CI), 9.7-38.2] for repeated mailing, 13.2 [95% CI, 3.6-48.6] for BPA, and 32.9 [95% CI, 19.3-56.1] for patient solicitation). The BPA intervention had the lowest incremental cost per completed test ($24 with fixed startup costs, $3 without) and also the lowest incremental cost per new case identified after omitting fixed startup costs ($1691). CONCLUSION: HCV testing interventions resulted in an increase in BC testing compared with standard-of-care testing but also increased costs. The effect size and incremental costs of BPA intervention (excluding startup costs) support more widespread adoption compared with the other interventions. (Hepatology 2017;65:44-53).

An Electronic Health Record-based Intervention to Promote Hepatitis C Virus Testing Among Adults Born Between 1945 and 1965: A Cluster-randomized Trial.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) recommends one-time hepatitis C virus (HCV) antibody testing for "Birth Cohort" adults born during 1945-1965. OBJECTIVE: To examine the impact of an electronic health record (EHR)-embedded best practice alert (BPA) for HCV testing among Birth Cohort adults. DESIGN: Cluster-randomized trial was conducted from April 29, 2013 to March 29, 2014. SUBJECTS AND SETTING: Ten community and hospital-based primary care practices. Participants were attending physicians and medical residents during 25,620 study-eligible visits. INTERVENTION: Physicians in all practices received a brief introduction to the CDC testing recommendations. At visits for eligible patients at intervention sites, physicians received a BPA through the EHR to order HCV testing or medical assistants were prompted to post a testing order for the physician. Physicians in control sites did not receive the BPA. MAIN OUTCOMES: HCV testing; the incidence of HCV antibody positive tests was a secondary outcome. RESULTS: Testing rates were greater among Birth Cohort patients in intervention sites (20.2% vs. 1.8%, P<0.0001) and the odds of testing were greater in intervention sites after controlling for imbalances of patient and visit characteristics between comparison groups [odds ratio (OR), 9.0; 95% confidence interval, 7.6-10.7). The adjusted OR of identifying HCV antibody positive patients was also greater in intervention sites (OR, 2.1; 95% confidence interval, 1.3-11.2). CONCLUSIONS: An EHR-embedded BPA markedly increased HCV testing among Birth Cohort patients, but the majority of eligible patients did not receive testing indicating a need for more effective methods to promote uptake.

Longitudinal adoption rates of complex decision support tools in primary care.

Translating research findings into practice promises to standardise care. Translation includes the integration of evidence-based guidelines at the point of care, discerning the best methods to disseminate research findings and models to sustain the implementation of best practices.By applying usability testing to clinical decision support(CDS) design, overall adoption rates of 60% can be realised.What has not been examined is how long adoption rates are sustained and the characteristics associated with long-term use. We conducted secondary analysis to decipher the factors impacting sustained use of CD Stools. This study was a secondary data analysis from a clinical trial conducted at an academic institution in New York City. Study data was identified patients electronic health records (EHR). The trial was to test the implementation of an integrated clinical prediction rule(iCPR) into the EHR. The primary outcome variable was iCPR tool acceptance of the tool. iCPR tool completion and iCPR smartest completion were additional outcome variables of interest. The secondary aim was to examine user characteristics associated with iCPR tool use in later time periods. Characteristics of interest included age, resident year, use of electronic health records (yes/no) and use of best practice alerts (BPA) (yes/no). Generalised linear mixed models (GLiMM) were used to compare iCPR use over time for each outcome of interest: namely, iCPR acceptance, iCPR completion and iCPR smartset completion.GLiMM was also used to examine resident characteristics associated with iCPR tool use in later time periods; specifically, intermediate and long-term (ie, 90+days). The tool was accepted, on average, 82.18% in the first 90 days (short-term period). The use decreases to 56.07% and 45.61% in intermediate and long-term time periods, respectively. There was a significant association between iCPR tool completion and time periods(p<0.0001). There was no significant difference in iCPR tool completion between resident encounters in the intermediate and long-term periods (p<0.6627). There was a significant association between iCPR smartset completion and time periods (p<0.0021). There were no significant associations between iCPR smartest completion and any of the four predictors of interest. We examined the frequencies of components of the iCPR tool being accepted over time by individual clinicians. Rates of adoption of the different components of the tool decreased substantially over time. The data suggest that over time and prolonged exposure to CDS tools, providers are less likely to utilise the tool. It is not clear if it is fatigue with the CDS tool, acquired knowledge of the clinical prediction rule, or gained clinical experience and gestalt that are influencing adoption rates. Further analysis of individual adoption rates over time and the impact it has on clinical outcomes should be conducted.

Toward a responsible future: recommendations for AI-enabled clinical decision support.

BACKGROUND: Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. OBJECTIVES: This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. MATERIALS AND METHODS: In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. RESULTS: Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided. DISCUSSION: AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow. CONCLUSIONS: Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.

Structure and Funding of Clinical Informatics Fellowships: A National Survey of Program Directors.

BACKGROUND: In 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures. OBJECTIVES: The aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses. METHODS: We created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021. RESULTS: We invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities. CONCLUSION: CI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.

The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.

The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute-funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and health-care informatics, particularly for electronic phenotyping, genome-wide association studies, genomic medicine implementation, and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here, we describe the evolution, accomplishments, opportunities, and challenges of the network from its inception as a five-group consortium focused on genotype-phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting toward the implementation of genomic medicine.

Commercial versus in-situ usability testing of healthcare information systems: towards "public" usability testing in healthcare organizations.

The need for improved usability in healthcare IT has been widely recognized. In addition, methods from usability engineering, including usability testing and usability inspection have received greater attention. Many vendors of healthcare software are now employing usability testing methods in the design and development of their products. However, despite this, the usability of healthcare IT is still considered to be problematic and many healthcare organizations that have purchased systems that have been tested at vendor testing sites are still reporting a range of usability and safety issues. In this paper we explore the distinction between commercial usability testing (conducted at centralized vendor usability laboratories and limited beta test sites) and usability testing that is carried out locally within healthcare organizations that have purchased vendor systems and products (i.e. public "in-situ" usability testing). In this paper it will be argued that both types of testing (i.e. commercial vendor-based testing) and in-situ testing are needed to ensure system usability and safety.

Creation and Evaluation of a Clinical Informatics Match: Initial Findings.

BACKGROUND: Clinical Informatics (CI) fellowship programs utilize the Electronic Residency Application Service (ERAS) to gather applications but until recently used an American Medical Informatics Association (AMIA) member-developed, simultaneous offer-acceptance process to match fellowship applicants to programs. In 2021, program directors collaborated with the AMIA to develop a new match to improve the process. OBJECTIVE: Describe the results of the first 2 years of the match and address opportunities for improvement. METHODS: We obtained applicant data for fellowship applicants in 2021 and 2022 from the ERAS and match data for the same years from the AMIA. We analyzed our data using descriptive statistics. RESULTS: There were 159 unique applicants over the 2-year period. Applicants submitted 2,178 applications with a median of 10 per applicant (interquartile range [IQR] 3-20). One hundred and four applicants (65.4%) participated in the match and ranked a median of seven programs (2-12). Forty-two programs in 2021 and 47 programs in 2022 offered a combined total 153 positions in the match. Participating programs ranked a median of eight applicants per year (IQR 5-11). Of participating applicants, 95 (91.3%) successfully matched and of those 66 (69.5%) received their top choice. Thirty-two programs (76.2%) matched at least one candidate in 2021 and 33 programs (70.2%) matched at least one candidate in 2022. In both years, 24 programs filled all available slots (57.1% in 2021 and 51.1% in 2022). CONCLUSION: Applicants were extremely successful in the new match, which successfully addressed most of the challenges of the simultaneous offer-acceptance process identified by program directors. However, applicant attrition resulted in a quarter of programs going unmatched. Although many programs still filled slots outside the match, fellowship slots may remain unfilled while the CI practice pathway remains open.

Defining AMIA's artificial intelligence principles.

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.

Implementing a pragmatic clinical trial to tailor opioids for acute pain on behalf of the IGNITE ADOPT PGx investigators.

Opioid prescribing for postoperative pain management is challenging because of inter-patient variability in opioid response and concern about opioid addiction. Tramadol, hydrocodone, and codeine depend on the cytochrome P450 2D6 (CYP2D6) enzyme for formation of highly potent metabolites. Individuals with reduced or absent CYP2D6 activity (i.e., intermediate metabolizers [IMs] or poor metabolizers [PMs], respectively) have lower concentrations of potent opioid metabolites and potentially inadequate pain control. The primary objective of this prospective, multicenter, randomized pragmatic trial is to determine the effect of postoperative CYP2D6-guided opioid prescribing on pain control and opioid usage. Up to 2020 participants, age ≥8 years, scheduled to undergo a surgical procedure will be enrolled and randomized to immediate pharmacogenetic testing with clinical decision support (CDS) for CYP2D6 phenotype-guided postoperative pain management (intervention arm) or delayed testing without CDS (control arm). CDS is provided through medical record alerts and/or a pharmacist consult note. For IMs and PM in the intervention arm, CDS includes recommendations to avoid hydrocodone, tramadol, and codeine. Patient-reported pain-related outcomes are collected 10 days and 1, 3, and 6 months after surgery. The primary outcome, a composite of pain intensity and opioid usage at 10 days postsurgery, will be compared in the subgroup of IMs and PMs in the intervention (n = 152) versus the control (n = 152) arm. Secondary end points include prescription pain medication misuse scores and opioid persistence at 6 months. This trial will provide data on the clinical utility of CYP2D6 phenotype-guided opioid selection for improving postoperative pain control and reducing opioid-related risks.

The SignOut Discharge Summary System: using workflow byproducts to pre-populate and assemble discharge summaries.

At the time of hospital discharge, communication between inpatient and outpatient physicians is poor. Multiple studies demonstrate that discharge summaries, a means of improving information exchange between inpatient and outpatient providers, are frequently not available to the outpatient provider at the time of the post discharge visit. We have constructed a web-based solution for generating discharge summaries, SignOut Discharge Summary System (SDSS) which uses the workflow byproduct of SignOut data to pre-populate summaries, a post-discharge preparation module to ensure quality, a discharge edit module to designate accurate discharge summary assignment, and integration with HIM. SDSS had 1130 unique users in a recent period and captured signout information for 75% of hospitalized patients. The system has generated 78740 D/C summaries for 17 specialties since going live July 2005. Overall SDSS is responsible for 69% of all hospital discharge summaries and SDSS discharge summaries on average are available 1.91 days after discharge.

Emerging approaches to usability evaluation of health information systems: towards in-situ analysis of complex healthcare systems and environments.

The effective evaluation of health information technology (HIT) is currently a major challenge. It is essential that applications we develop are usable, meet user information needs and are shown to be safe. Furthermore, to provide appropriate feedback to designers of systems new methods for both formative and summative evaluation are needed as applications become more complex and distributed. To ensure system usability a variety of methods have emerged from the area of usability engineering that have been adapted to healthcare. The authors have applied methods of usability engineering, working with hospitals and other healthcare organizations designing and evaluating a range of HIT applications. We describe how our approach to doing portable low-cost usability testing has evolved to the use of clinical simulations conducted in-situ, within real hospital and clinical units to rapidly evaluate the usability and safety of healthcare information systems both before and after system release. We discuss how this approach was extended to development of methods for conducting in-situ clinical simulations in a range of clinical settings.

Engineering the electronic health record for safety: a multi-level video-based approach to diagnosing and preventing technology-induced error arising from usability problems.

Electronic health records (EHRs) promise to improve and streamline healthcare through electronic entry and retrieval of patient data. Furthermore, based on a number of studies showing their positive benefits, they promise to reduce medical error and make healthcare safer. However, a growing body of literature has clearly documented that if EHRS are not designed properly and with usability as an important goal in their design, rather than reducing error, EHR deployment has the potential to actually increase medical error. In this paper we describe our approach to engineering (and reengineering) EHRs in order to increase their beneficial potential while at the same time improving their safety. The approach described in this paper involves an integration of the methods of usability analysis with video analysis of end users interacting with EHR systems and extends the evaluation of the usability of EHRs to include the assessment of the impact of these systems on work practices. Using clinical simulations, we analyze human-computer interaction in real healthcare settings (in a portable, low-cost and high fidelity manner) and include both artificial and naturalistic data collection to identify potential usability problems and sources of technology-induced error prior to widespread system release. Two case studies where the methods we have developed and refined have been applied at different levels of user-computer interaction are described.

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper.

The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data-here referred to as Adaptive CDS-present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist. In this AMIA position paper, the authors describe current and emerging challenges to the safe use of Adaptive CDS and lay out recommendations for the effective management and monitoring of Adaptive CDS.

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.

Comparison of Methods for Alcohol and Drug Screening in Primary Care Clinics.

Importance: Guidelines recommend that adult patients receive screening for alcohol and drug use during primary care visits, but the adoption of screening in routine practice remains low. Clinics frequently struggle to choose a screening approach that is best suited to their resources, workflows, and patient populations. Objective: To evaluate how to best implement electronic health record (EHR)-integrated screening for substance use by comparing commonly used screening methods and examining their association with implementation outcomes. Design, Setting, and Participants: This article presents the outcomes of phases 3 and 4 of a 4-phase quality improvement, implementation feasibility study in which researchers worked with stakeholders at 6 primary care clinics in 2 large urban academic health care systems to define and implement their optimal screening approach. Site A was located in New York City and comprised 2 clinics, and site B was located in Boston, Massachusetts, and comprised 4 clinics. Clinics initiated screening between January 2017 and October 2018, and 93 114 patients were eligible for screening for alcohol and drug use. Data used in the analysis were collected between January 2017 and October 2019, and analysis was performed from July 13, 2018, to March 23, 2021. Interventions: Clinics integrated validated screening questions and a brief counseling script into the EHR, with implementation supported by the use of clinical champions (ie, clinicians who advocate for change, motivate others, and use their expertise to facilitate the adoption of an intervention) and the training of clinic staff. Clinics varied in their screening approaches, including the type of visit targeted for screening (any visit vs annual examinations only), the mode of administration (staff-administered vs self-administered by the patient), and the extent to which they used practice facilitation and EHR usability testing. Main Outcomes and Measures: Data from the EHRs were extracted quarterly for 12 months to measure implementation outcomes. The primary outcome was screening rate for alcohol and drug use. Secondary outcomes were the prevalence of unhealthy alcohol and drug use detected via screening, and clinician adoption of a brief counseling script. Results: Patients of the 6 clinics had a mean (SD) age ranging from 48.9 (17.3) years at clinic B2 to 59.1 (16.7) years at clinic B3, were predominantly female (52.4% at clinic A1 to 64.6% at clinic A2), and were English speaking. Racial diversity varied by location. Of the 93,114 patients with primary care visits, 71.8% received screening for alcohol use, and 70.5% received screening for drug use. Screening at any visit (implemented at site A) in comparison with screening at annual examinations only (implemented at site B) was associated with higher screening rates for alcohol use (90.3%-94.7% vs 24.2%-72.0%, respectively) and drug use (89.6%-93.9% vs 24.6%-69.8%). The 5 clinics that used a self-administered screening approach had a higher detection rate for moderate- to high-risk alcohol use (14.7%-36.6%) compared with the 1 clinic that used a staff-administered screening approach (1.6%). The detection of moderate- to high-risk drug use was low across all clinics (0.5%-1.0%). Clinics with more robust practice facilitation and EHR usability testing had somewhat greater adoption of the counseling script for patients with moderate-high risk alcohol or drug use (1.4%-12.5% vs 0.1%-1.1%). Conclusions and Relevance: In this quality improvement study, EHR-integrated screening was feasible to implement in all clinics and unhealthy alcohol use was detected more frequently when self-administered screening was used at any primary care visit. The detection of drug use was low at all clinics, as was clinician adoption of counseling. These findings can be used to inform the decision-making of health care systems that are seeking to implement screening for substance use. Trial Registration: ClinicalTrials.gov Identifier: NCT02963948.

Increasing the safety of healthcare information systems through improved procurement: toward a framework for selection of safe healthcare systems.

In this article, we describe a framework that we have developed for improving the effectiveness of critical decision-making in selecting information systems. In our framework, we consider system selection in terms of strength of evidence obtained from the testing of candidate systems in order to reduce risk and increase the likelihood of selection and implementation of an effective and safe system. Two case studies, one from a major North American hospital and one from a major European hospital, are presented to illustrate how methods such as usability testing can be applied to improve system selection as well as customization (through early identification of system-organization mismatches and error-prone system features). It is argued that technology-organization fit and consideration of the potential for technology-induced error should be important selection criteria in the procurement process. Here, implications are discussed for the development of improved procurement processes to lead to safer healthcare systems.

Policy Statement on Clinical Informatics Fellowships and the Future of Informatics-Driven Medicine.

Board certified clinical informaticians provide expertise in leveraging health IT (HIT) and health data for patient care and quality improvement. Clinical Informatics experts possess the requisite skills and competencies to make systems-level improvements in care delivery using HIT, workflow and data analytics, knowledge acquisition, clinical decision support, data visualization, and related informatics tools. However, these physicians lack structured and sustained funding because they have no billing codes. The sustainability and growth of this new and promising medical subspecialty is threatened by outdated and inconsistent funding models that fail to support the education and professional growth of clinical informaticians. The Clinical Informatics Program Directors' Community is calling upon the Centers for Medicare and Medicaid Services to consider novel funding structures and programs through its Innovation Center for Clinical Informatics Fellowship training. Only through structural and sustained funding for Clinical Informatics fellows will be able to fully develop the potential of electronic health records to improve the quality, safety, and cost of clinical care.