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BackgroundDocumenting standardized dental diagnostic terms represents an emerging change for how dentistry is practiced. We focused on a mid-sized dental group practice as it shifted to a policy of documenting patients' diagnoses using standardized terms in the electronic health record.MethodsKotter's change framework was translated into interview questions posed to the senior leadership in a mid-size dental group practice. In addition, quantitative content analyses were conducted on the written policies and forms before and after the implementation of standardized diagnosis documentation to assess the extent to which the forms and policies reflected the shift. Three reviewers analyzed the data individually and reached consensuses where needed.ResultsKotter's guiding change framework explained the steps taken to 97 percent utilization rate of the Electronic Health Record and Dental Diagnostic Code. Of the 96 documents included in the forms and policy analysis, 31 documents were officially updated but only two added a diagnostic element.ConclusionChange strategies established in the business literature hold utility for dental practices seeking diagnosis-centered care.Practical implicationsA practice that shifts to a diagnosis-driven care philosophy would be best served by ensuring that the change process follows a leadership framework that is calibrated to the organization's culture.

Objective: Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. We describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research., Materials and Methods: The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site., Results: The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development., Discussion: The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping., Conclusion: CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

Background: Within a year of the start of the COVID-19 pandemic, the US Department of Veterans Affairs (VA) was managing about 300 COVID-19-related research projects across roughly 100 facilities, which has since grown to more than 900 projects. This robust set of activities arose from an existing enterprise strategy and aimed at identifying needs for supporting the clinical care mission, more rapidly leveraging resources, and coordinating research across the VA. The VA's efforts to implement an enterprise strategy before March 2020 positioned its research community to dynamically partner with other federal agencies, academic institutions, and industry in addressing a national public health emergency., Observations: The VA research enterprise involves a broad range of functions, scientific and clinical leaders, and organizational resources to enhance the health and care of veterans and the nation. The scope of research activities enables it to support its priorities while also partnering with others who share in mutual commitments to veteran health. Moving toward being the nation's learning health care system, the VA's leadership support, staff, patient volunteers, and partners were key contributors to a national response to COVID-19. Swift action and consistent communication helped address the complexities of the pandemic and strengthened the VA's ability to prepare and mobilize for emergencies and other potential disease outbreaks. Documenting strategies and practices can enhance future opportunities aimed at addressing the most challenging health care needs while also focusing on the primary mission to serve veterans., Conclusions: The COVID-19 pandemic contributed to critical knowledge and lessons that enabled the VA to advance enterprise goals, particularly in the context of its health care system. Sharing these unique processes and experiences will inform current and future partnerships among research, clinical, and public health communities oriented to serve veterans and the nation through scientific innovation., Competing Interests: Author disclosures: The authors report no actual or potential conflicts of interest or outside sources of funding with regard to this article., (Copyright © 2023 Frontline Medical Communications Inc., Parsippany, NJ, USA.)

The development of phenotypes using electronic health records is a resource-intensive process. Therefore, the cataloging of phenotype algorithm metadata for reuse is critical to accelerate clinical research. The Department of Veterans Affairs (VA) has developed a standard for phenotype metadata collection which is currently used in the VA phenomics knowledgebase library, CIPHER (Centralized Interactive Phenomics Resource), to capture over 5000 phenotypes. The CIPHER standard improves upon existing phenotype library metadata collection by capturing the context of algorithm development, phenotyping method used, and approach to validation. While the standard was iteratively developed with VA phenomics experts, it is applicable to the capture of phenotypes across healthcare systems. We describe the framework of the CIPHER standard for phenotype metadata collection, the rationale for its development, and its current application to the largest healthcare system in the United States., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2023.)

Background: The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has demonstrated the need to share data and biospecimens broadly to optimize clinical outcomes for US military Veterans., Methods: In response, the Veterans Health Administration established VA SHIELD (Science and Health Initiative to Combat Infectious and Emerging Life-threatening Diseases), a comprehensive biorepository of specimens and clinical data from affected Veterans to advance research and public health surveillance and to improve diagnostic and therapeutic capabilities., Results: VA SHIELD now comprises 12 sites collecting de-identified biospecimens from US Veterans affected by SARS-CoV-2. In addition, 2 biorepository sites, a data processing center, and a coordinating center have been established under the direction of the Veterans Affairs Office of Research and Development. Phase 1 of VA SHIELD comprises 34 157 samples. Of these, 83.8% had positive tests for SARS-CoV-2, with the remainder serving as contemporaneous controls. The samples include nasopharyngeal swabs (57.9%), plasma (27.9%), and sera (12.5%). The associated clinical and demographic information available permits the evaluation of biological data in the context of patient demographics, clinical experience and management, vaccinations, and comorbidities., Conclusions: VA SHIELD is representative of US national diversity with a significant potential to impact national healthcare. VA SHIELD will support future projects designed to better understand SARS-CoV-2 and other emergent healthcare crises. To the extent possible, VA SHIELD will facilitate the discovery of diagnostics and therapeutics intended to diminish COVID-19 morbidity and mortality and to reduce the impact of new emerging threats to the health of US Veterans and populations worldwide., Competing Interests: Potential conflicts of interest. The authors have noo reported conflicts of interest., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach., (© 2022. The Author(s).)

Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ among patients hospitalized with COVID-19. We examined the effects of HCQ alone and in combination with azithromycin in a hospitalized population of US veterans with COVID-19, using a propensity score-adjusted survival analysis with imputation of missing data. According to electronic health record data from the US Department of Veterans Affairs health care system, 64,055 US Veterans were tested for the virus that causes COVID-19 between March 1, 2020 and April 30, 2020. Of the 7,193 veterans who tested positive, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and there was an increased risk of intubation when HCQ was used in combination with azithromycin (hazard ratio = 1.55; 95% confidence interval: 1.07, 2.24). In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treatment of patients hospitalized with COVID-19, using a national sample of the US veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2021.)

Background: Dentists strive to provide safe and effective oral healthcare. However, some patients may encounter an adverse event (AE) defined as "unnecessary harm due to dental treatment." In this research, we propose and evaluate two systems for categorizing the type and severity of AEs encountered at the dental office., Methods: Several existing medical AE type and severity classification systems were reviewed and adapted for dentistry. Using data collected in previous work, two initial dental AE type and severity classification systems were developed. Eight independent reviewers performed focused chart reviews, and AEs identified were used to evaluate and modify these newly developed classifications., Results: A total of 958 charts were independently reviewed. Among the reviewed charts, 118 prospective AEs were found and 101 (85.6%) were verified as AEs through a consensus process. At the end of the study, a final AE type classification comprising 12 categories, and an AE severity classification comprising 7 categories emerged. Pain and infection were the most common AE types representing 73% of the cases reviewed (56% and 17%, respectively) and 88% were found to cause temporary, moderate to severe harm to the patient., Conclusions: Adverse events found during the chart review process were successfully classified using the novel dental AE type and severity classifications. Understanding the type of AEs and their severity are important steps if we are to learn from and prevent patient harm in the dental office., Competing Interests: The authors disclose no conflict of interest., (Copyright © 2017 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Standardized dental diagnostic terminologies (SDDxTs) were introduced decades ago. Their use has been on the rise, accompanying the adoption of electronic health records (EHRs). One of the most broadly used terminologies is the Dental Diagnostic System (DDS). Our aim was to assess the adoption of SDDxTs by US dental schools by using the Rogers diffusion of innovations framework, focusing on the DDS., Methods: The authors electronically surveyed clinic deans in all US dental schools (n = 61) to determine use of an EHR and SDDxT, perceived barriers to adoption of an SDDxT, and the effect of implementing an SDDxT on clinical productivity., Results: The response rate was 57%. Of the 35 responses, 91% reported using an EHR to document patient care, with 84% using axiUm; 41% used the DDS. Fifty-four percent of those who did not use an SDDxT had considered adopting the DDS, but 38% had not, citing barriers such as complexity and compatibility., Conclusions: Adoption of an SDDxT, particularly the DDS, is on the rise. Nevertheless, a large number of institutions are in the Rogers late majority and laggards categories with respect to adoption. Several factors may discourage adoption, including the inability to try out the terminology on a small scale, poor usability within the EHR, the fact that it would be a cultural shift in practice, and a perception of unclear benefits. However, the consolidation of the DDS and American Dental Association terminology efforts stands to encourage adoption., Practical Implications: The successful adoption of dental innovation depends not only on the intrinsic merit of the innovation, as some useful innovations do not achieve widespread traction. As such, it is important for health care providers to understand how to disseminate their ideas in order to ensure traction and widespread adoption., (Copyright © 2017 American Dental Association. Published by Elsevier Inc. All rights reserved.)

Diagnosis at the edges of our knowledge calls upon clinicians to be data driven, cross-disciplinary, and collaborative in unprecedented ways. Exact disease recognition, an element of the concept of precision in medicine, requires new infrastructure that spans geography, institutional boundaries, and the divide between clinical care and research. The National Institutes of Health (NIH) Common Fund supports the Undiagnosed Diseases Network (UDN) as an exemplar of this model of precise diagnosis. Its goals are to forge a strategy to accelerate the diagnosis of rare or previously unrecognized diseases, to improve recommendations for clinical management, and to advance research, especially into disease mechanisms. The network will achieve these objectives by evaluating patients with undiagnosed diseases, fostering a breadth of expert collaborations, determining best practices for translating the strategy into medical centers nationwide, and sharing findings, data, specimens, and approaches with the scientific and medical communities. Building the UDN has already brought insights to human and medical geneticists. The initial focus has been on data sharing, establishing common protocols for institutional review boards and data sharing, creating protocols for referring and evaluating patients, and providing DNA sequencing, metabolomic analysis, and functional studies in model organisms. By extending this precision diagnostic model nationally, we strive to meld clinical and research objectives, improve patient outcomes, and contribute to medical science., (Copyright © 2017 American Society of Human Genetics. All rights reserved.)

Background: Although some patients experience adverse events (AEs) resulting in harm caused by treatments in dentistry, few published reports have detailed how dental providers describe these events. Understanding how dental treatment professionals view AEs is essential to building a safer environment in dental practice., Methods: The authors interviewed dental professionals and domain experts through focus groups and in-depth interviews and asked them to identify the types of AEs that may occur in dental settings., Results: The initial interview and focus group findings yielded 1,514 items that included both causes and AEs. In total, 632 causes were coded into 1 of the 8 categories of the Eindhoven classification, and 882 AEs were coded into 12 categories of a newly developed dental AE classification. Interrater reliability was moderate among coders. The list was reanalyzed, and duplicate items were removed leaving a total of 747 unique AEs and 540 causes. The most frequently identified AE types were "aspiration and ingestion" at 14% (n = 142), "wrong-site, wrong-procedure, wrong-patient errors" at 13%, "hard-tissue damage" at 13%, and "soft-tissue damage" at 12%., Conclusions: Dental providers identified a large and diverse list of AEs. These events ranged from "death due to cardiac arrest" to "jaw fatigue from lengthy procedures.", Practical Implications: Identifying threats to patient safety is a key element of improving dental patient safety. An inventory of dental AEs underpins efforts to track, prevent, and mitigate these events., Competing Interests: None of the authors declare any conflict of interest, financial or other., (Copyright © 2016 American Dental Association. Published by Elsevier Inc. All rights reserved.)

Background: Although complete and accurate clinical records do not guarantee the provision of excellent dental care, they do provide an opportunity to evaluate the quality of care provided. However, a lack of universally accepted documentation standards, incomplete record-keeping practices, and unfriendly electronic health care record (EHR) user interfaces are factors that have allowed for persistent poor dental patient record keeping., Methods: Using 2 different methods-a validated survey, and a 2-round Delphi process-involving 2 appropriately different sets of participants, we explored what a dental clinical record should contain and the frequency of update of each clinical entry., Results: For both the closed-ended survey questions and the open-ended Delphi process questions, respondents had a significant degree of agreement on the "clinical entry" components of an adequate clinical record. There was, however, variance on how frequently each of those clinical entries should be updated., Summary: Dental providers agree that complete and accurate record keeping is essential and that items such as histories, examination findings, diagnosis, radiographs, treatment plans, consents, and clinic notes should be documented. There, however, does not seem to be universal agreement how frequently such items should be recorded., Clinical Implications: As the dental profession moves towards prevalent use of electronic health care records, the issue of standardization and interoperability becomes ever more pressing. Settling issues of standardization, including record documentation, must begin with guideline-creating dental professional bodies, who need to clearly define and disseminate what these standards should be and everyday dentists who will ultimately ensure that these standards are met and kept., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objective: In early 2010, Harvard Medical School and Boston Children's Hospital began an interoperability project with the distinctive goal of developing a platform to enable medical applications to be written once and run unmodified across different healthcare IT systems. The project was called Substitutable Medical Applications and Reusable Technologies (SMART)., Methods: We adopted contemporary web standards for application programming interface transport, authorization, and user interface, and standard medical terminologies for coded data. In our initial design, we created our own openly licensed clinical data models to enforce consistency and simplicity. During the second half of 2013, we updated SMART to take advantage of the clinical data models and the application-programming interface described in a new, openly licensed Health Level Seven draft standard called Fast Health Interoperability Resources (FHIR). Signaling our adoption of the emerging FHIR standard, we called the new platform SMART on FHIR., Results: We introduced the SMART on FHIR platform with a demonstration that included several commercial healthcare IT vendors and app developers showcasing prototypes at the Health Information Management Systems Society conference in February 2014. This established the feasibility of SMART on FHIR, while highlighting the need for commonly accepted pragmatic constraints on the base FHIR specification., Conclusion: In this paper, we describe the creation of SMART on FHIR, relate the experience of the vendors and developers who built SMART on FHIR prototypes, and discuss some challenges in going from early industry prototyping to industry-wide production use., (© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

A person's right to access his or her protected health information is a core feature of the U.S. Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. If the information is stored electronically, covered entities must be able to provide patients with some type of machine-readable, electronic copy of their data. The aim of this study was to understand how academic dental institutions execute the Privacy Rule's right of access in the context of electronic health records (EHRs). A validated electronic survey was distributed to the clinical deans of 62 U.S. dental schools during a two-month period in 2014. The response rate to the survey was 53.2% (N=33). However, three surveys were partially completed, and of the 30 completed surveys, the 24 respondents who reported using axiUm as the EHR at their dental school clinic were the ones on which the results were based (38.7% of total schools at the time). Of the responses analyzed, 86% agreed that clinical modules should be considered part of a patient's dental record, and all agreed that student teaching-related modules should not. Great variability existed among these clinical deans as to whether administrative and financial modules should be considered part of a patient record. When patients request their records, close to 50% of responding schools provide the information exclusively on paper. This study found variation among dental schools in their implementation of the Privacy Rule right of access, and although all the respondents had adopted EHRs, a large number return records in paper format.

Background: Attitudes and views are critical to the adoption of innovation. Although there have been broadening calls for a standardized dental diagnostic terminology, little is known about the views of private practice dental team members regarding the adoption of such a terminology., Methods: The authors developed a survey by using validated questions identified through literature review. Domain experts' input allowed for further modifications. The authors administered the final survey electronically to 814 team members at a multioffice practice based in the US Pacific Northwest., Results: Response proportion was 92%. The survey had excellent reliability (Cronbach α coefficient = 0.87). Results suggested that participants showed, in general, positive attitudes and beliefs about using a standardized diagnostic terminology in their practices. Additional written comments by participants highlighted the potential for improved communication with use of the terminology., Conclusions: Dental care providers and staff in 1 multioffice practice showed positive attitudes about the use of a diagnostic terminology; specifically, they believed it would improve communication between the dentist and patient, as well as among providers, while expressing some concerns about whether using standardized dental diagnostic terms helps clinicians to deliver better dental care., Practical Implications: As the dental profession is advancing toward the use of standardized diagnostic terminology, successful implementation will require that dental team leaders prepare their teams by gauging their attitude about the use of such a terminology., (Copyright © 2015 American Dental Association. Published by Elsevier Inc. All rights reserved.)

Background: Errors are commonplace in health care, including dentistry. It is imperative for dental professionals to intercept errors before they lead to an adverse event and to mitigate their effects when an adverse event occurs. This requires a systematic approach at both the profession level, encapsulated in the Agency for Healthcare Research and Quality's patient safety initiative framework, as well as at the practice level, in which crew resource management is a tested paradigm. Supporting patient safety at both the profession and dental practice levels relies on understanding the types and causes of errors, which have not been well studied., Methods: The authors performed a retrospective review of dental adverse events reported in the literature. Electronic bibliographic databases were searched, and data were extracted on background characteristics, incident description, case characteristics, clinic setting where adverse event originated, phase of patient care that adverse event was detected, proximal cause, type of patient harm, degree of harm, and recovery actions., Results: The authors identified 182 publications (containing 270 cases) through their search. Delayed treatment, unnecessary treatment, or disease progression after misdiagnosis was the largest type of harm reported. Of the reviewed cases, 24.4% of those patients involved in an adverse event experienced permanent harm. One of every 10 case reports reviewed (11.1%) reported that the adverse event resulted in the death of the affected patient., Conclusions: Published case reports provide a window into understanding the nature and extent of dental adverse events; however, the overall dearth of publications on adverse events in the dental literature points to the need for more study., Practical Implications: Siloed and incomplete contributions to dentistry's understanding of adverse events in the dental office are threats to dental patients' safety. Publishing more, and more comprehensive, case reports on adverse events is recommended for dental practitioners., (Copyright © 2015 American Dental Association. Published by Elsevier Inc. All rights reserved.)

Objectives: To document how race and ethnicity are identified, categorized, and utilized in contemporary dental public health literature., Methods: Two researchers independently performed a literature review of all articles in Community Dentistry and Oral Epidemiology and the Journal of Public Health Dentistry over a 5-year period (2004-2009). Articles pertaining to the study of US-based populations with any mention of race or ethnicity were included. The following data were abstracted from each article:( a) how each article broadly described race and/or ethnicity; (b) the terms used to specifically define the races and/or ethnicities captured; (c) the location of any mention of the concept of race and/or ethnicity; (d) the stated purpose for including race and/or ethnicity concepts; (e) the stated analytic use of race and/or ethnicity concepts; and (f) the stated method used to assess race and/or ethnicity concepts., Results: Overall, race and/or ethnicity concepts were most commonly referred to within the text of the results section. Fifty percent of articles did not state their purpose for including race and/or ethnicity concepts within their studies, while 34.3 percent omitted stating their analytic use of these concepts. When assessing these concepts, 41.4 percent relied upon subject self-report., Conclusion: These data showed that there was inconsistent documentation of how race and ethnicity was measured. While race and ethnicity are important measures for public health studies and are frequently reported in dental public health research, there is no clear system for classifying these measures., (© 2012 American Association of Public Health Dentistry.)

Background and Objective: Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors., Materials and Methods: We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements., Results: We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents., Conclusions: We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Purpose: Whether and how to return individual genetic results to study participants is among the most contentious policy issues in contemporary genomic research., Methods: We surveyed corresponding authors of genome-wide association studies, identified through the National Human Genome Research Institute's Catalog of Published Genome-Wide Association Studies, to describe the experiences and attitudes of these stakeholders., Results: Of 357 corresponding authors, 200 (56%) responded. One hundred twenty-six (63%) had been responsible for primary data and sample collection, whereas 74 (37%) had performed secondary analyses. Only 7 (4%) had returned individual results within their index genome-wide association studies. Most (69%) believed that return of results to individual participants was warranted under at least some circumstances. Most respondents identified a desire to benefit participants' health (63%) and respect for participants' desire for information (57%) as major motivations for returning results. Most also identified uncertain clinical utility (76%), the possibility that participants will misunderstand results (74%), the potential for emotional harm (61%), the need to ensure access to trained clinicians (59%), and the potential for loss of confidentiality (51%) as major barriers to return of results., Conclusion: Investigators have limited experience returning individual results from genome-scale research, yet most are motivated to do so in at least some circumstances.

Background: Non-adherence to prescribed medications is a serious health problem in the United States, costing an estimated $100 billion per year. While poor adherence should be addressable with point of care health information technology, integrating new solutions with existing electronic health records (EHR) systems require customization within each organization, which is difficult because of the monolithic software design of most EHR products., Objective: The objective of this study was to create a published algorithm for predicting medication adherence problems easily accessible at the point of care through a Web application that runs on the Substitutable Medical Apps, Reusuable Technologies (SMART) platform. The SMART platform is an emerging framework that enables EHR systems to behave as "iPhone like platforms" by exhibiting an application programming interface for easy addition and deletion of third party apps. The app is presented as a point of care solution to monitoring medication adherence as well as a sufficiently general, modular application that may serve as an example and template for other SMART apps., Methods: The widely used, open source Django framework was used together with the SMART platform to create the interoperable components of this app. Django uses Python as its core programming language. This allows statistical and mathematical modules to be created from a large array of Python numerical libraries and assembled together with the core app to create flexible and sophisticated EHR functionality. Algorithms that predict individual adherence are derived from a retrospective study of dispensed medication claims from a large private insurance plan. Patients' prescription fill information is accessed through the SMART framework and the embedded algorithms compute adherence information, including predicted adherence one year after the first prescription fill. Open source graphing software is used to display patient medication information and the results of statistical prediction of future adherence on a clinician-facing Web interface., Results: The user interface allows the physician to quickly review all medications in a patient record for potential non-adherence problems. A gap-check and current medication possession ratio (MPR) threshold test are applied to all medications in the record to test for current non-adherence. Predictions of 1-year non-adherence are made for certain drug classes for which external data was available. Information is presented graphically to indicate present non-adherence, or predicted non-adherence at one year, based on early prescription fulfillment patterns. The MPR Monitor app is installed in the SMART reference container as the "MPR Monitor", where it is publically available for use and testing. MPR is an acronym for Medication Possession Ratio, a commonly used measure of adherence to a prescribed medication regime. This app may be used as an example for creating additional functionality by replacing statistical and display algorithms with new code in a cycle of rapid prototyping and implementation or as a framework for a new SMART app., Conclusions: The MPR Monitor app is a useful pilot project for monitoring medication adherence. It also provides an example that integrates several open source software components, including the Python-based Django Web framework and python-based graphics, to build a SMART app that allows complex decision support methods to be encapsulated to enhance EHR functionality.

Background: There is a dearth of knowledge about the type and frequency of adverse events (AEs) in dentistry. Current approaches to obtaining information rely on reviews of randomly selected records, which may not be the most efficient or effective methodology., Methods: Inspired by the Institute for Healthcare Improvement's (IHI) global and outpatient trigger tools, which identifies records with characteristics ("triggers") that are associated with AEs, the authors created the dental clinic trigger tool. The triggers included procedures for incision and drainage, failed implants and selected treatment patterns. The authors ran the trigger tool against six months of electronic health records data and compared its performance with that of a review of 50 randomly selected patient records., Results: In total, 315 records were triggered, 158 (50 percent) of which were positive for one or more AEs; 17 (34 percent) of the 50 randomly selected records were positive for at least one AE. The authors assigned each AE an IHI severity ranking. Most AEs caused temporary harm, but nine were considered to have caused permanent harm according to a modified IHI severity ranking., Conclusions: The study results demonstrate the promise of a directed records review approach, as the dental clinic trigger tool was more effective in identifying AEs than was a review of randomly selected records., Practical Implications: All dental practices should proactively monitor the safety of the care they provide. Use of the trigger tool will help make this process more efficient and effective.

Through the 2009 HITECH (Health Information Technology for Economic and Clinical Health) Act, the U.S. government committed $27 billion to incentivize the adoption and "meaningful use" of certified electronic health records (EHRs) by providers, including dentists. Given their patient profiles, dental school clinics are in a position to benefit from this time-delimited commitment to support the adoption and use of certified EHR technology under the Medicaid-based incentive. The benefits are not merely financial: rather, the meaningful use objectives and clinical quality measures can drive quality improvement initiatives within dental practices and help develop a community of medical and dental professionals focused on quality. This article describes how dentists can qualify as eligible providers and the set of activities that must be undertaken and attested to in order to obtain this incentive. Two case studies describe the approaches that can be used to meet the Medicaid threshold necessary to be eligible for the incentive. Dentists can and have successfully applied for meaningful use incentive payments. Given the diverse set of patients who are treated at dental schools, these dental practices are among those most likely to benefit from the incentive programs.

Aim: The return of individual genetic research results has been identified as one of the most pressing ethical challenges warranting immediate policy attention. We explored the practices and perspectives of genome-wide association studies (GWAS) investigators on this topic., Materials & Methods: Corresponding authors of published GWAS were invited to participate in a semistructured interview. Interviews (n = 35) were transcribed and analyzed using conventional content analysis., Results: Most investigators had not returned GWAS results. Several had experience returning results in the context of linkage/family studies, and many felt that it will become a larger issue in whole-genome/-exome sequencing., Conclusions: Research context and nature of the study are important considerations in the decision to return results. More nuanced ethical guidelines should take these contextual factors into account.

Individuals' dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.