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.

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.

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.

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.

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.

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.

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.

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.

A Clinical Informatics Program Directors' Proposal to the American Board of Preventive Medicine.

In 2013, the American Board of Preventive Medicine (ABPM) and the American Board of Pathology (ABPath) offered the first board certification examination in Clinical Informatics to eligible physicians in the United States. In 2022, the Practice Pathway will expire and in 2023 only candidates eligible through the Fellowship Pathway will be eligible for the board certification. To date, Clinical Informatics as a specialty has not had a regular match process and used a controlled offer-acceptance process that does not meet candidates' or programs' needs. Fellows may not be offered a position with their top choice program initially, and they may accept offers from other programs to avoid risk by ensuring that they have a fellowship position. Programs have to consider losing an applicant in the first round in the ranking of applicants. The process is open to manipulation including early agreements between program directors and candidates. In this open letter to the ABPM, program directors make the case for a third-party match and are calling on the ABPM to leverage its status as the Clinical Informatics certifying body and its existing infrastructure to implement a Clinical Informatics match.

Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.

OBJECTIVE: Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers. MATERIALS AND METHODS: All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes. RESULTS: Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days. DISCUSSION: NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis. CONCLUSION: For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.

Rationale and design of the regimen education and messaging in diabetes (REMinD) trial.

BACKGROUND: Individuals with type 2 diabetes manage complex multi-drug regimens, but nearly half of all patients do not consistently take the dose prescribed for them, and therefore may not receive the full potential therapeutic benefit. Both patient and health system factors contribute to achieving and maintaining medication adherence. To determine if patients with type 2 diabetes who are concurrently managing other chronic conditions could improve their adherence, we devised and are testing a multifaceted, primary care-based strategy to provide health literacy-appropriate and language concordant regimen information, guidance and reminders. OBJECTIVE: We are testing the effectiveness of an electronic health record (EHR) based strategy and short message service (SMS) text reminders to help patients with type 2 diabetes adhere to their medications. METHODS: We are conducting a 3-arm, multi-site trial to test and compare the effectiveness of the EHR, and EHR + SMS text reminder strategies compared to usual care on medication adherence. Our goal is to enroll a total of 900 English or Spanish-speaking patients with type 2 diabetes and hemoglobin A1C value ≥7.5%. Enrolled participants will complete interviews at baseline and 3 and 6 months following their baseline interview. CONCLUSIONS: Our trial is evaluating practical, clinic-based, scalable, evidence-based solutions for patients with type 2 diabetes managing complex medication regimens. Our findings will evaluate strategies that can be implemented broadly in primary care practices, and programmed as defaults within EHRs to support appropriate medication adherence to allow the widespread implementation of those strategies.

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.

Development of a Video Coding Scheme Focused on Socio-Technical Aspects of Human-Computer Interaction in Healthcare.

The objective of the work described in this paper was to develop a coding scheme focused on socio-technical issues and considerations for the analysis of video data collected in usability studies of health information systems. The usability and safety of health information systems such as electronic health records, decision support and patient facing applications has become a critical issue. Although a variety of studies and methods have appeared in the literature involving video-based analysis of human factors data from healthcare, few have reported the coding scheme used to analyze the data. In this paper we describe how we have developed and continue to refine a video coding scheme that extends basic usability engineering by considering socio-technical aspects of system use that have become critical to assess in evaluating the effectiveness and efficacy of health information systems in real use.

Approaches to Demonstrating the Effectiveness and Impact of Usability Testing of Healthcare Information Technology.

In recent years the usability of health information systems has come to the fore as a major issue, with many reported examples of problems with the usability of systems such as electronic health records and other health information technologies (HIT). In response a range of usability engineering methods have emerged to help in the design and evaluation of HIT. Many studies have shown the importance of usability testing methods that include full video recording of user interactions, such as the method known as low-cost rapid usability testing. However, such approaches have been considered by many as being too costly to carry out and some have argued that they may take too long to be used for practical input into improving applications and systems. In this paper we demonstrate several approaches we have taken for proving the cost-effectiveness and benefit of conducting principled usability testing. It is argued that such studies are needed to inform system design and evaluation and for proving to healthcare management the need for properly conducting such studies before releasing HIT.

Reimagining the research-practice relationship: policy recommendations for informatics-enabled evidence-generation across the US health system.

The widespread adoption and use of electronic health records and their use to enable learning health systems (LHS) holds great promise to accelerate both evidence-generating medicine (EGM) and evidence-based medicine (EBM), thereby enabling a LHS. In 2016, AMIA convened its 10th annual Policy Invitational to discuss issues key to facilitating the EGM-EBM paradigm at points-of-care (nodes), across organizations (networks), and to ensure viability of this model at scale (sustainability). In this article, we synthesize discussions from the conference and supplements those deliberations with relevant context to inform ongoing policy development. Specifically, we explore and suggest public policies needed to facilitate EGM-EBM activities on a national scale, particularly those policies that can enable and improve clinical and health services research at the point-of-care, accelerate biomedical discovery, and facilitate translation of findings to improve the health of individuals and populations.

Challenges optimizing the after visit summary.

BACKGROUND: The after visit summary (AVS) is a paper or electronic document given to patients after a medical appointment, which is intended to summarize patients' health and guide future care, including self-management tasks. OBJECTIVE: To describe experiences of health systems implementing a redesigned outpatient AVS in commercially available electronic health record (EHR) systems to inform future optimization. MATERIALS AND METHODS: We conducted semi-structured interviews with information technology and clinical leaders at 12 hospital and community-based healthcare institutions across the continental United States focusing on the process of AVS redesign and implementation. We also report our experience implementing a redesigned AVS in the Epic EHR at the Mount Sinai Hospital in New York City, NY. RESULTS: Health systems experienced many challenges implementing the redesigned AVS. While many IT leaders noted that the redesigned AVS is easier to understand and the document is better organized, they claim the effort is time-consuming, Epic system upgrades render AVS modifications non-functional, and primary care and specialty practices have different needs in regards to content and formatting. Our team was able to modify the document by changing the order of print groups, modifying the font size, bolding section headers, and inserting page breaks. Similar to other health systems, our team found that it is difficult to achieve some desired features due to limitations in the EHR platform. CONCLUSION: Health IT leaders view the AVS as a valuable source of information for patients. However, limitations to AVS modifications in EHR systems present challenges to optimizing the tool. EHR vendors should incorporate learning from healthcare systems innovation efforts and consider building more flexibility into their product development.

Barriers and facilitators affecting the implementation of substance use screening in primary care clinics: a qualitative study of patients, providers, and staff.

BACKGROUND: Alcohol and drug use are leading causes of morbidity and mortality that frequently go unidentified in medical settings. As part of a multi-phase study to implement electronic health record-integrated substance use screening in primary care clinics, we interviewed key clinical stakeholders to identify current substance use screening practices, barriers to screening, and recommendations for its implementation. METHODS: Focus groups and individual interviews were conducted with 67 stakeholders, including patients, primary care providers (faculty and resident physicians), nurses, and medical assistants, in two urban academic health systems. Themes were identified using an inductive approach, revised through an iterative process, and mapped to the Knowledge to Action (KTA) framework, which guides the implementation of new clinical practices (Graham et al. in J Contin Educ Health Prof 26(1):13-24, 2006). RESULTS: Factors affecting implementation based on KTA elements were identified from participant narratives. Identifying the problem: Participants consistently agreed that having knowledge of a patient's substance use is important because of its impacts on health and medical care, that substance use is not properly identified in medical settings currently, and that universal screening is the best approach. Assessing barriers: Patients expressed concerns about consequences of disclosing substance use, confidentiality, and the individual's own reluctance to acknowledge a substance use problem. Barriers identified by providers included individual-level factors such as lack of clinical knowledge and training, as well as systems-level factors including time pressure, resources, lack of space, and difficulty accessing addiction treatment. Adapting to the local context: Most patients and providers stated that the primary care provider should play a key role in substance use screening and interventions. Opinions diverged regarding the optimal approach to delivering screening, although most preferred a patient self-administered approach. Many providers reported that taking effective action once unhealthy substance use is identified is crucial. CONCLUSIONS: Participants expressed support for substance use screening as a valuable part of medical care, and identified individual-level as well as systems-level barriers to its implementation. These findings suggest that screening programs should clearly communicate the goals of screening to patients and proactively counteract stigma, address staff concerns regarding time and workflow, and provide education as well as treatment resources to primary care providers.

Evaluation of a patient-centered after visit summary in primary care.

OBJECTIVE: To test the impact of a redesigned, patient-centered after visit summary (AVS) on patients' and clinicians' ratings of and experience with the document. METHODS: We conducted a difference-in-differences (DiD) evaluation of the impact of the redesigned AVS before and after its introduction in an academic primary care practice compared to a concurrent control practice. Outcomes included ratings of the features of the AVS. RESULTS: The intervention site had 118 and 98 patients in the pre- and post-intervention periods and the control site had 99 and 105, respectively. In adjusted DiD analysis, introduction of the patient-centered AVS in the intervention site increased patient reports that the AVS was an effective reminder for taking medications (p = .004) and of receipt of the AVS from clinicians (p = .002). However, they were more likely to perceive it as too long (p = .04). There were no significant changes in overall rating of the AVS by clinicians or their likelihood of providing it to patients. CONCLUSIONS: A patient-centered AVS increased the number of patients receiving it and reporting that it would help them remember to take their medications. PRACTICE IMPLICATIONS: Improvements in the patient-centeredness of the AVS may improve its usefulness as a document to support self-management in primary care.