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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.

Large-scale multi-ethnic cohorts offer unprecedented opportunities to elucidate the genetic factors influencing complex traits related to health and disease among minority populations. At the same time, the genetic diversity in these cohorts presents new challenges for analysis and interpretation. We consider the utility of race and/or ethnicity categories in genome-wide association studies (GWASs) of multi-ethnic cohorts. We demonstrate that race/ethnicity information enhances the ability to understand population-specific genetic architecture. To address the practical issue that self-identified racial/ethnic information may be incomplete, we propose a machine learning algorithm that produces a surrogate variable, termed HARE. We use height as a model trait to demonstrate the utility of HARE and ethnicity-specific GWASs.

ObjectiveTo develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions.Materials and MethodsWe identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions.ResultsWe analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common.DiscussionAcross organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS.ConclusionCDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.

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.

Early B cell factor 3 (EBF3) is a member of the highly evolutionarily conserved Collier/Olf/EBF (COE) family of transcription factors. Prior studies on invertebrate and vertebrate animals have shown that EBF3 homologs are essential for survival and that loss-of-function mutations are associated with a range of nervous system developmental defects, including perturbation of neuronal development and migration. Interestingly, aristaless-related homeobox (ARX), a homeobox-containing transcription factor critical for the regulation of nervous system development, transcriptionally represses EBF3 expression. However, human neurodevelopmental disorders related to EBF3 have not been reported. Here, we describe three individuals who are affected by global developmental delay, intellectual disability, and expressive speech disorder and carry de novo variants in EBF3. Associated features seen in these individuals include congenital hypotonia, structural CNS malformations, ataxia, and genitourinary abnormalities. The de novo variants affect a single conserved residue in a zinc finger motif crucial for DNA binding and are deleterious in a fly model. Our findings indicate that mutations in EBF3 cause a genetic neurodevelopmental syndrome and suggest that loss of EBF3 function might mediate a subset of neurologic phenotypes shared by ARX-related disorders, including intellectual disability, abnormal genitalia, and structural CNS malformations.

s 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.

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.

Objective To illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions.Materials and Methods We identified and investigated several CDSS malfunctions at Brigham and Women’s Hospital and present them as a case series. We also conducted a preliminary survey of Chief Medical Information Officers to assess the frequency of such malfunctions.Results We identified four CDSS malfunctions at Brigham and Women’s Hospital: (1) an alert for monitoring thyroid function in patients receiving amiodarone stopped working when an internal identifier for amiodarone was changed in another system; (2) an alert for lead screening for children stopped working when the rule was inadvertently edited; (3) a software upgrade of the electronic health record software caused numerous spurious alerts to fire; and (4) a malfunction in an external drug classification system caused an alert to inappropriately suggest antiplatelet drugs, such as aspirin, for patients already taking one. We found that 93% of the Chief Medical Information Officers who responded to our survey had experienced at least one CDSS malfunction, and two-thirds experienced malfunctions at least annually.Discussion CDSS malfunctions are widespread and often persist for long periods. The failure of alerts to fire is particularly difficult to detect. A range of causes, including changes in codes and fields, software upgrades, inadvertent disabling or editing of rules, and malfunctions of external systems commonly contribute to CDSS malfunctions, and current approaches for preventing and detecting such malfunctions are inadequate.Conclusion CDSS malfunctions occur commonly and often go undetected. Better methods are needed to prevent and detect these malfunctions.

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.

Background Quality improvement requires using quality measures that can be implemented in a valid manner. Using guidelines set forth by the Meaningful Use portion of the Health Information Technology for Economic and Clinical Health Act, the authors assessed the feasibility and performance of an automated electronic Meaningful Use dental clinical quality measure to determine the percentage of children who received fluoride varnish. Methods The authors defined how to implement the automated measure queries in a dental electronic health record. Within records identified through automated query, the authors manually reviewed a subsample to assess the performance of the query. Results The automated query results revealed that 71.0% of patients had fluoride varnish compared with the manual chart review results that indicated 77.6% of patients had fluoride varnish. The automated quality measure performance results indicated 90.5% sensitivity, 90.8% specificity, 96.9% positive predictive value, and 75.2% negative predictive value. Conclusions The authors’ findings support the feasibility of using automated dental quality measure queries in the context of sufficient structured data. Information noted only in free text rather than in structured data would require using natural language processing approaches to effectively query electronic health records. Practical Implications To participate in self-directed quality improvement, dental clinicians must embrace the accountability era. Commitment to quality will require enhanced documentation to support near-term automated calculation of quality measures.

In recent years, there has been an increase in research studies highlighting patients' experiences of adverse events (AEs) as well as the role of patients in promoting safety. The primary goal of the study was to assess the prevalence of dental AEs (DAEs) among dental patients in South Africa and its associated factors. The integration of the patient perspective into dental patient safety research will enhance our collective understanding of DAEs.We conducted a cross-sectional study of adult patients at a large dental academic institution in South Africa from May to June 2015, evaluating their previous experiences of DAEs at any dental clinic in South Africa. Descriptive statistics and bivariate and multivariate analyses were performed to identify the factors associated with an increased likelihood of experiencing a DAE.A total of 440 questionnaires were returned during the 6-week study period (response rate = 97.8%). Overall, 45.5% of participants reported experiencing one or more DAEs. Two hundred participants reported a total of 717 DAEs giving us a lifetime prevalence of 1.6 DAEs per respondent. Our results suggest that respondents who were younger (18-24 y), from high-income families (R150,000 or US $9200), dissatisfied with their last dental visit and oral health had an increased likelihood of reporting a previous experience of a DAE.This study provides an insight into the nature of information that can be gleaned from dental patients regarding safety and helps lay the foundation for patient involvement in patient safety reporting.

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

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.

Objective: Secondary data are a significant resource for in‐depth epidemiologic and public health research. It also allows for effective quality control and clinical outcomes measurement. To illustrate the value of structured diagnostic entry, a use case was developed to quantify adherence to current practice guidelines for managing chronic moderate periodontitis (CMP).Methods: Six dental schools using the same electronic health record (EHR) contribute data to a dental data repository (BigMouth) based on the i2b2 data‐warehousing platform. Participating institutions are able to query across the full repository without being able to back trace specific data to its originating institution. At each of the three sites whose data are included in this analysis, the Dental Diagnostic System (DDS) terminology was used to document diagnoses in the clinics. We ran multiple queries against this multi‐institutional database, and the output was validated by manually reviewing a subset of patient charts.Results: Over the period under study, 1,866 patients were diagnosed with CMP. Of these, 15 percent received only periodontal prophylaxis treatment, 20 percent received only periodontal maintenance treatment, and only 41 percent received periodontal maintenance treatment in combination with other AAP guideline treatments.Conclusions: Our results showed that most patients with CMP were not treated according to the AAP guidelines. On the basis of this use case, we conclude that the availability and habitual use of a structured diagnosis in an EHR allow for the aggregation and secondary analyses of clinical data to support downstream analyses for quality improvement and epidemiological assessments.

Author(s): Kalenderian, E; Maramaldi, P; Kim, S; Etolue, J; McClellan, L; Simmons, K; Yansane, A; White, JM; Walji, MF; Ramoni, RB | Abstract: 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.

Few oral health databases are available for research and the advancement of evidence-based dentistry. In this work we developed a centralized data repository derived from electronic health records (EHRs) at four dental schools participating in the Consortium of Oral Health Research and Informatics. A multi-stakeholder committee developed a data governance framework that encouraged data sharing while allowing control of contributed data. We adopted the i2b2 data warehousing platform and mapped data from each institution to a common reference terminology. We realized that dental EHRs urgently need to adopt common terminologies. While all used the same treatment code set, only three of the four sites used a common diagnostic terminology, and there were wide discrepancies in how medical and dental histories were documented. BigMouth was successfully launched in August 2012 with data on 1.1 million patients, and made available to users at the contributing institutions.

BackgroundNon-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. ObjectiveThe 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. MethodsThe 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. ResultsThe 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. ConclusionsThe 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.