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Background: Addressing the opioid epidemic requires timely insights into population-level factors, such as trends in prevalence of legal and illegal substances, overdoses, and deaths., Objective: This study aimed to examine whether toxicology test results of living individuals from a variety of sources could be useful in surveilling the opioid epidemic., Methods: A retrospective analysis standardized, merged, and linked toxicology results from 24 laboratories in Marion County, Indiana, United States, from September 1, 2018, to August 31, 2019. The data set consisted of 33,787 Marion County residents and their 746,681 results. We related the data to general Marion County demographics and compared alerts generated by toxicology results to opioid overdose-related emergency department visits. Nineteen domain experts helped prototype analytical visualizations. Main outcome measures included test positivity in the county and by ZIP code; selected demographics of individuals with toxicology results; and correlation of toxicology results with opioid overdose-related emergency department visits., Results: Four percent of Marion County residents had at least 1 toxicology result. Test positivity rates ranged from 3% to 19% across ZIP codes. Males were underrepresented in the data set. Age distribution resembled that of Marion County. Alerts for opioid toxicology results were not correlated with opioid overdose-related emergency department visits., Conclusions: Analyzing toxicology results at scale was impeded by varying data formats, completeness, and representativeness; changes in data feeds; and patient matching difficulties. In this study, toxicology results did not predict spikes in opioid overdoses. Larger, more rigorous and well-controlled studies are needed to assess the utility of toxicology tests in predicting opioid overdose spikes., Competing Interests: Conflicts of Interest: None declared., (©Titus Schleyer, Bill Robinson, Samir Parmar, Diane Janowiak, P Joseph Gibson, Val Spangler. Originally published in the Online Journal of Public Health Informatics (https://ojphi.jmir.org/), 28.09.2023.)

Introduction: Implementation of research findings in clinical practice often is not realized or only partially achieved, and if so, with a significant delay. Learning health systems (LHSs) hold promise to overcome this problem by embedding clinical research and evidence-based best practices into care delivery, enabling innovation and continuous improvement. Implementing an LHS is a complex process that requires participation and resources of a wide range of stakeholders, including healthcare leaders, clinical providers, patients and families, payers, and researchers. Engaging these stakeholders requires communicating clear, tangible value propositions. Existing models identify broad categories of benefits but do not explicate the full range of benefits or ways they can manifest in different organizations., Methods: To develop such a framework, a working group with representatives from six Clinical and Translational Science Award (CTSA) hubs reviewed existing literature on LHS characteristics, models, and goals; solicited expert input; and applied the framework to their local LHS experiences., Results: The Framework of LHS Benefits includes six categories of benefits (quality, safety, equity, patient satisfaction, reputation, and value) relevant for a range of stakeholders and defines key concepts within each benefit. Applying the framework to five LHS case examples indicated preliminary face validity across varied LHS approaches and revealed three dimensions in which the framework is relevant: defining goals of individual LHS projects, facilitating collaboration based on shared values, and establishing guiding tenets of an LHS program or mission., Conclusion: The framework can be used to communicate the value of an LHS to different stakeholders across varied contexts and purposes, and to identify future organizational priorities. Further validation will contribute to the framework's evolution and support its potential to inform the development of tools to evaluate LHS impact., Competing Interests: The authors declare no conflicts of interest., (© 2023 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Background: Opioid use disorder (OUD) contributes to rising morbidity and mortality. Life-saving OUD treatments can be provided in primary care but most patients with OUD don't receive treatment. Comorbid depression and other conditions complicate OUD management, especially in primary care. The MI-CARE trial is a pragmatic randomized encouragement (Zelen) trial testing whether offering collaborative care (CC) to patients with OUD and clinically-significant depressive symptoms increases OUD medication treatment with buprenorphine and improves depression outcomes compared to usual care., Methods: Adult primary care patients with OUD and depressive symptoms (n ≥ 800) from two statewide health systems: Kaiser Permanente Washington and Indiana University Health are identified with computer algorithms from electronic Health record (EHR) data and automatically enrolled. A random sub-sample (50%) of eligible patients is offered the MI-CARE intervention: a 12-month nurse-driven CC intervention that includes motivational interviewing and behavioral activation. The remaining 50% of the study cohort comprise the usual care comparison group and is never contacted. The primary outcome is days of buprenorphine treatment provided during the intervention period. The powered secondary outcome is change in Patient Health Questionnaire (PHQ)-9 depression scores. Both outcomes are obtained from secondary electronic healthcare sources and compared in "intent-to-treat" analyses., Conclusion: MI-CARE addresses the need for rigorous encouragement trials to evaluate benefits of offering CC to generalizable samples of patients with OUD and mental health conditions identified from EHRs, as they would be in practice, and comparing outcomes to usual primary care. We describe the design and implementation of the trial, currently underway., Trial Registration: ClinicalTrials.gov Identifier: NCT05122676. Clinical trial registration date: November 17, 2021., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Using a diabetes app can improve glycemic control; however, the use of diabetes apps is low, possibly due to design issues that affect patient motivation., Objective: This study aimed to describes how adults with diabetes requiring insulin perceive diabetes apps based on 3 key psychological needs (competence, autonomy, and connectivity) described by the Self-Determination Theory (SDT) on motivation., Methods: This was a qualitative analysis of data collected during a crossover randomized laboratory trial (N=92) testing 2 diabetes apps. Data sources included (1) observations during app testing and (2) survey responses on desired app features. Guided by the SDT, coding categories included app functions that could address psychological needs for motivation in self-management: competence, autonomy, and connectivity., Results: Patients described design features that addressed needs for competence, autonomy, and connectivity. To promote competence, electronic data recording and analysis should help patients track and understand blood glucose (BG) results necessary for planning behavior changes. To promote autonomy, BG trend analysis should empower patients to set safe and practical personalized behavioral goals based on time and the day of the week. To promote connectivity, app email or messaging function could share data reports and communicate with others on self-management advice. Additional themes that emerged are the top general app designs to promote positive user experience: patient-friendly; automatic features of data upload; voice recognition to eliminate typing data; alert or reminder on self-management activities; and app interactivity of a sound, message, or emoji change in response to keeping or not keeping BG in the target range., Conclusions: The application of the SDT was useful in identifying motivational app designs that address the psychological needs of competence, autonomy, and connectivity. User-centered design concepts, such as being patient-friendly, differ from the SDT because patients need a positive user experience (ie, a technology need). Patients want engaging diabetes apps that go beyond data input and output. Apps should be easy to use, provide personalized analysis reports, be interactive to affirm positive behaviors, facilitate data sharing, and support patient-clinician communication., (©Helen N C Fu, Jean F Wyman, Cynthia J Peden-McAlpine, Claire Burke Draucker, Titus Schleyer, Terrence J Adam. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 24.02.2023.)

Objective: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity., Materials and Methods: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity., Results: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively., Discussion: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors., Conclusion: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Understanding the current state of real-world Fast Healthcare Interoperability Resources (FHIR) applications (apps) will benefit biomedical research and clinical care and facilitate advancement of the standard. This study aimed to provide a preliminary assessment of these apps' clinical, technical, and implementation characteristics., Materials and Methods: We searched public repositories for potentially eligible FHIR apps and surveyed app implementers and other stakeholders., Results: Of the 112 apps surveyed, most focused on clinical care (74) or research (45); were implemented across multiple sites (56); and used SMART-on-FHIR (55) and FHIR version R4 (69). Apps were primarily stand-alone web-based (67) or electronic health record (EHR)-embedded (51), although 49 were not listed in an EHR app gallery., Discussion: Though limited in scope, our results show FHIR apps encompass various domains and characteristics., Conclusion: As FHIR use expands, this study-one of the first to characterize FHIR apps at large-highlights the need for systematic, comprehensive methods to assess their characteristics., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Heated humidified high-flow nasal cannula (HFNC) is a respiratory support device historically used in pediatrics for infants with bronchiolitis. No large-scale analysis has determined the current frequency or demographic distribution of HFNC use in children. The objective of this study was to determine the frequency and correlates of HFNC use in children presenting to the hospital for asthma, bronchiolitis, or pneumonia., Methods: This longitudinal observational study was based on electronic health record data from a large regional health information exchange, the Indiana Network for Patient Care (INPC). Subjects were age 0-18 y with recorded hospital encounters at an INPC hospital between 2010-2019 with International Classification of Diseases codes for bronchiolitis, asthma, or pneumonia. Annual proportions of HFNC use among all hospital encounters were assessed using generalized additive models. Log-binomial regression models were used to identify correlates of incident HFNC use and determine risk ratios of specific subjects receiving HFNC., Results: The study sample included 242,381 unique subjects with 412,712 hospital encounters between 2010-2019. The 10-y period prevalence of HFNC use was 2.54% (6,155/242,381) involving 7,974 encounters. Hospital encounters utilizing HFNC increased by 400%, from 326 in 2010 to 1,310 in 2019. This increase was evenly distributed across all 3 diagnostic categories (bronchiolitis, asthma, and pneumonia). Sex, race, age, and ethnicity all significantly influenced the risk of HFNC use. Over the 10-y period, the percentage of all hospital encounters using HFNC increased from 1.11% in 2010 to 3.15% in 2018. Subjects with multiple diagnoses had significantly higher risk of receiving HFNC., Conclusions: The use of HFNC in children presenting to the hospital with common respiratory diseases has increased substantially over the past decade and is no longer confined to treating infants with bronchiolitis. Demographic and diagnostic factors significantly influenced the frequency of HFNC use., Competing Interests: The authors have disclosed no conflicts of interest., (Copyright © 2022 by Daedalus Enterprises.)

Electronic health record (EHR) data are increasingly used to develop prediction models to support clinical care, including the care of patients with common chronic conditions. A key challenge for individual healthcare systems in developing such models is that they may not be able to achieve the desired degree of robustness using only their own data. A potential solution-combining data from multiple sources-faces barriers such as the need for data normalization and concerns about sharing patient information across institutions. To address these challenges, we evaluated three alternative approaches to using EHR data from multiple healthcare systems in predicting the outcome of pharmacotherapy for type 2 diabetes mellitus(T2DM). Two of the three approaches, named Selecting Better (SB) and Weighted Average(WA), allowed the data to remain within institutional boundaries by using pre-built prediction models; the third, named Combining Data (CD), aggregated raw patient data into a single dataset. The prediction performance and prediction coverage of the resulting models were compared to single-institution models to help judge the relative value of adding external data and to determine the best method to generate optimal models for clinical decision support. The results showed that models using WA and CD achieved higher prediction performance than single-institution models for common treatment patterns. CD outperformed the other two approaches in prediction coverage, which we defined as the number of treatment patterns predicted with an Area Under Curve of 0.70 or more. We concluded that 1) WA is an effective option for improving prediction performance for common treatment patterns when data cannot be shared across institutional boundaries and 2) CD is the most effective approach when such sharing is possible, especially for increasing the range of treatment patterns that can be predicted to support clinical decision making., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Access to up-to-date patient medical history is essential for dental clinicians (DCs) to avoid potential harm to patients and to improve dental treatment outcomes. The predominant approach for dental clinicians (DCs) to gather patients' medical history is through patient-reported medical histories and medical consults. However, studies reported varied concordance and reliability of patient-reported medical conditions and medication histories compared to the patient medical records and this process also places a significant burden on patients. Information technology tools/platforms such as an integrated electronic health record containing an electronic dental record module may address these issues. However, these integrated systems are expensive and technically complex and may not be easily adopted by DCs in solo and small group practice who provide the most dental care. The recent expansion of regional healthcare information exchange (HIE) provides another approach, but to date, studies on connecting DCs with HIE are very limited. Our study objectives were to model different aspects of the current approaches to identify the strengths and weaknesses, and then model the HIE approach that addresses the weaknesses and retain the strengths of current approaches. The models of current approaches identified the people, resources, organizational aspects, workflow, and areas for improvement; while models of the HIE approach identified system requirements, functions, and processes that may be shared with software developers and other stakeholders for future development., Methods: There are three phases in this study. In Phase 1, we retrieved peer-reviewed PubMed indexed manuscripts published between January 2013 and November 2020 and extracted modeling related data from selected manuscripts. In Phase 2, we built models for the current approaches by using the Integrated DEFinition Method 0 function modeling method (IDEF0), the Unified Modeling Language (UML) Use Case Diagram, and Business Process Model and Notation (BPMN) methods. In Phase 3, we created three conceptual models for the HIE approach., Results: From the 47 manuscripts identified, three themes emerged: 1) medical consult process following patient-reported medical history, 2) integrated electronic dental record-electronic health record (EDR-EHR), and 3) HIE. Three models were built for each of the three themes. The use case diagrams described the actions of the dental patients, DCs, medical providers and the use of information systems (EDR-EHR/HIE). The IDEF0 models presented the major functions involved. The BPMN models depicted the detailed steps of the process and showed how the patient's medical history information flowed through different steps. The strengths and weaknesses revealed by the models of the three approaches were also compared., Conclusions: We successfully modeled the DCs' current approaches of accessing patient medical history and designed an HIE approach that addressed the current approaches' weaknesses as well as leveraged their strengths. Organizational management and end-users can use this information to decide the optimum approach to integrate dental and medical care. The illustrated models are comprehensive and can also be adopted by EHR and EDR vendors to develop a connection between dental systems and HIEs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li, Rajapuri, Felix Gomez, Schleyer, Mendonca and Thyvalikakath.)

Computerized clinical decision support (CDS) will be essential to ensuring the safety and efficiency of new care delivery models, such as the patient-centered medical home. CDS will help empower non-physician team members, coordinate overall team efforts, and facilitate physician oversight. In this article, we discuss common clinical scenarios that could benefit from CDS optimized for team-based healthcare, including (1) low-acuity episodic illness, (2) diagnostic workup of new onset symptoms, (3) chronic care, (4) preventive care, and (5) care coordination. CDS that maximally supports teams may be one of biomedical informatics' best opportunities to decrease health care costs, improve quality, and increase clinical capacity., (©2021 AMIA - All rights reserved.)

Introduction: Learning health systems (LHSs) are usually created and maintained by single institutions or healthcare systems. The Indiana Learning Health System Initiative (ILHSI) is a new multi-institutional, collaborative regional LHS initiative led by the Regenstrief Institute (RI) and developed in partnership with five additional organizations: two Indiana-based health systems, two schools at Indiana University, and our state-wide health information exchange. We report our experiences and lessons learned during the initial 2-year phase of developing and implementing the ILHSI., Methods: The initial goals of the ILHSI were to instantiate the concept, establish partnerships, and perform LHS pilot projects to inform expansion. We established shared governance and technical capabilities, conducted a literature review-based and regional environmental scan, and convened key stakeholders to iteratively identify focus areas, and select and implement six initial joint projects., Results: The ILHSI successfully collaborated with its partner organizations to establish a foundational governance structure, set goals and strategies, and prioritize projects and training activities. We developed and deployed strategies to effectively use health system and regional HIE infrastructure and minimize information silos, a frequent challenge for multi-organizational LHSs. Successful projects were diverse and included deploying a Fast Healthcare Interoperability Standards (FHIR)-based tool across emergency departments state-wide, analyzing free-text elements of cross-hospital surveys, and developing models to provide clinical decision support based on clinical and social determinants of health. We also experienced organizational challenges, including changes in key leadership personnel and varying levels of engagement with health system partners, which impacted initial ILHSI efforts and structures. Reflecting on these early experiences, we identified lessons learned and next steps., Conclusions: Multi-organizational LHSs can be challenging to develop but present the opportunity to leverage learning across multiple organizations and systems to benefit the general population. Attention to governance decisions, shared goal setting and monitoring, and careful selection of projects are important for early success., Competing Interests: Dr Michele Saysana serves as the Board Chair for the Indianapolis Coalition for Patient Safety (ICPS), which is a paid position, but the money is paid to IU Health Physicians for her time. ICPS is a non‐profit entity that the Indianapolis hospitals serve as members; their mission is to bring the hospitals and health systems together to work together to improve patient safety. Dr Randall Grout has received institutional grant funding from McKinsey and Company in the last 12 months. Dr Linda Williams receives grant support as co‐Principal Investigator from VHA HSR&D for the Expanding Expertise through e‐Health Network Development (EXTEND) QUERI Program., (© 2021 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Objective: Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient's record in clinical settings to characterize the current state of research on the topic and identify areas for future study., Methods: We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme., Results: Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient's record., Conclusion: Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering., Competing Interests: J.R.H. has nothing to disclose. S.V. and X.N. report grants from NIH/NLM during the conduct of the study. T.K.S. reports grants from NIH/NLM and AHRQ during the conduct of the study. In addition, T.K.S. and X.N. have a patent related to collaborative filtering pending., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Objectives: The aim of this study is to determine the feasibility of conducting clinical research using electronic dental record (EDR) data from U.S. solo and small-group general dental practices in the National Dental Practice-Based Research Network (network) and evaluate the data completeness and correctness before performing survival analyses of root canal treatment (RCT) and posterior composite restorations (PCR)., Methods: Ninety-nine network general dentistry practices that used Dentrix or EagleSoft EDR shared de-identified data of patients who received PCR and/or RCT on permanent teeth through October 31, 2015. We evaluated the data completeness and correctness, summarized practice, and patient characteristics and summarized the two treatments by tooth type and arch location., Results: Eighty-two percent of practitioners were male, with a mean age of 49 and 22.4 years of clinical experience. The final dataset comprised 217,887 patients and 11,289,594 observations, with the observation period ranging from 0 to 37 years. Most patients (73%) were 18 to 64 years old; 56% were female. The data were nearly 100% complete. Eight percent of observations had incorrect data, such as incorrect tooth number or surface, primary teeth, supernumerary teeth, and tooth ranges, indicating multitooth procedures instead of PCR or RCT. Seventy-three percent of patients had dental insurance information; 27% lacked any insurance information. While gender was documented for all patients, race/ethnicity was missing in the dataset., Conclusion: This study established the feasibility of using EDR data integrated from multiple distinct solo and small-group network practices for longitudinal studies to assess treatment outcomes. The results laid the groundwork for a learning health system that enables practitioners to learn about their patients' outcomes by using data from their own practice., Competing Interests: None declared., (Georg Thieme Verlag KG Stuttgart · New York.)

Despite efforts aimed at improving the integration of clinical data from health information exchanges (HIE) and electronic health records (EHR), interoperability remains limited. Barriers due to inefficiencies and workflow interruptions make using HIE data during care delivery difficult. Capitalizing on the development of the Fast Healthcare Interoperability Resource (FHIR) specification, we designed and developed a Chest Pain Dashboard that integrates HIE data into EHRs. This Dashboard was implemented in one emergency department (ED) of Indiana University Health in Indiana. In this paper, we present the preliminary findings from a mixed-methods evaluation of the Dashboard. A difference-in-difference analysis suggests that the ED with the Dashboard implementation resulted in a significant increase in HIE use compared to EDs without. This finding was supported by qualitative interviews. While these results are encouraging, we also identified areas for improvement. FHIR-based solutions may offer promising approaches to encourage greater accessibility and use of HIE data.

Diagnostic procedures, therapeutic recommendations, and medical risk stratifications are based on dedicated, strictly controlled clinical trials. However, a plethora of real-world medical data exists, whereupon the increase in data volume comes at the expense of completeness, uniformity, and control. Here, a case-by-case comparison shows that the predictive power of our real world data-based model for diabetes-related chronic kidney disease outperforms published algorithms, which were derived from clinical study data.

Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd's) and directional DDI relations (DDI-d's), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its stochastic algorithm SD 2 ID 2 S to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs., Competing Interests: Conflict of interestsThe authors declare that they have no conflict of interest., (© Springer International Publishing AG, part of Springer Nature 2018.)

Personal health records available to patients today suffer from multiple limitations, such as information fragmentation, a one-size-fits-all approach and a focus on data gathered over time and by institution rather than health conditions. This makes it difficult for patients to effectively manage their health, for these data to be enriched with relevant information from external sources and for clinicians to support them in that endeavor. We propose a novel conceptual architecture for person-centered health record information systems that transcends many of these limitations and capitalizes on the emerging trend of socially-driven information systems. Our proposed personal health record system is personalized on demand to the conditions of each individual patient; organized to facilitate the tracking and review of the patient's conditions; and able to support patient-community interactions, thereby promoting community engagement in scientific studies, facilitating preventive medicine, and accelerating the translation of research findings.

The United States' decade-long transition from a paper- to technology-based information infrastructure has always been recognized as an initial step-a laying of the foundation-for future changes to the delivery of care. An increasingly important focal area for improvement is population health. Numerous policies and programs now require healthcare organizations to manage the risks, outcomes, utilization, and health of entire groups of individuals. Nonetheless, current health information technology (IT) systems are not ready to support population health improvements effectively and efficiently. Existing health IT systems were designed for organizations that are structurally, operationally, and culturally focused on individual care delivery, rather than improving health for a population. Opportunities exist to align health IT resources and population health management strategies to fill the gaps among technological capabilities, use and the emerging demands of population health. To realize this alignment, healthcare leaders must think differently about the types of data their organizations need, the types of partners with whom they share information, and how they can leverage new information and partnerships for evidence-based action.

Objectives: The reuse of electronic patient data collected during clinical care has received increased attention as a way to increase our evidence base. The purpose of this paper was to review studies reusing electronic patient data for dental research., Data Sources: 1527 citations obtained by searching MEDLINE and Embase databases, hand-searching seven dental and informatics journals, and snowball sampling., Study Selection: We included studies reusing electronic patient data for research on dental and craniofacial topics, alone or in combination with medical conditions, medications and outcomes. Studies using administrative or research databases and systematic reviews were excluded. Three reviewers extracted data independently and performed analysis jointly, Results: The 60 studies reviewed covered epidemiological (32 studies), outcomes (16), health services research (10) and other (2) topics; were primarily retrospective (58 studies); varied significantly in sample size (9-153,619 patients) and follow-up period (1-12 years); often drew on other data sources in addition to electronic ones (25); but rarely tapped electronic dental record (EDR) data in private practices (3). Type of research was not associated with data sources used, but research topics/questions were. The most commonly reported advantages of reusing electronic data were being able to study large samples and saving time, while data quality and the inability to capture study-specific data were identified as major limitations., Conclusions: Dental research reusing electronic patient data is nascent but accelerating. Future EDR design should focus on enhancing data quality, begin to integrate research data collection and implement interoperability with electronic medical records to facilitate oral-systemic investigations., Clinical Significance: Measuring and improving the quality of dental care requires that we begin to reuse electronic patient data collected in practice for clinical research. Practice data can potentially serve as a useful complement to data collected in traditional research studies., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

A key question for healthcare is how to operationalize the vision of the Learning Healthcare System, in which electronic health record data become a continuous information source for quality assurance and research. This project presents an initial, ontology-based, method for secondary use of electronic dental record (EDR) data. We defined a set of dental clinical research questions; constructed the Oral Health and Disease Ontology (OHD); analyzed data from a commercial EDR database; and created a knowledge base, with the OHD used to represent clinical data about 4,500 patients from a single dental practice. Currently, the OHD includes 213 classes and reuses 1,658 classes from other ontologies. We have developed an initial set of SPARQL queries to allow extraction of data about patients, teeth, surfaces, restorations and findings. Further work will establish a complete, open and reproducible workflow for extracting and aggregating data from a variety of EDRs for research and quality assurance.

Information overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank.

Natural language processing applications that extract information from text rely on semantic representations. The objective of this paper is to describe a methodology for creating a semantic representation for information that will be automatically extracted from textual clinical records. We illustrate two of the four steps of the methodology in this paper using the case study of encoding information from dictated dental exams: (1) develop an initial representation from a set of training documents and (2) iteratively evaluate and evolve the representation while developing annotation guidelines. Our approach for developing and evaluating a semantic representation is based on standard principles and approaches that are not dependent on any particular domain or type of semantic representation.

We evaluated the effectiveness of modeling principles intended to harmonize the information representation between terminology-ontology models and information models. Our study utilized dental clinical statements and sample dental record questions. We asked experts to define the equivalency (mapping) of these elements and measured their agreement. We modified the data elements and asked the experts to conduct subsequent mappings. We measured the agreement and compared the levels of agreement before and after changes, expecting that agreement would increase. The level of agreement (Kappa) before modeling was 0.3 to 0.4 and after was 0.5 (p<0.05). The difference was small but statistically significant. Our results suggest that the modeling principles improve information representation since agreement increased.

Usability methods, such as heuristic evaluation, cognitive walk-throughs and user testing, are increasingly used to evaluate and improve the design of clinical software applications. There is still some uncertainty, however, as to how those methods can be used to support the development process and evaluation in the most meaningful manner. In this study, we compared the results of a heuristic evaluation with those of formal user tests in order to determine which usability problems were detected by both methods. We conducted heuristic evaluation and usability testing on four major commercial dental computer-based patient records (CPRs), which together cover 80% of the market for chairside computer systems among general dentists. Both methods yielded strong evidence that the dental CPRs have significant usability problems. An average of 50% of empirically-determined usability problems were identified by the preceding heuristic evaluation. Some statements of heuristic violations were specific enough to precisely identify the actual usability problem that study participants encountered. Other violations were less specific, but still manifested themselves in usability problems and poor task outcomes. In this study, heuristic evaluation identified a significant portion of problems found during usability testing. While we make no assumptions about the generalizability of the results to other domains and software systems, heuristic evaluation may, under certain circumstances, be a useful tool to determine design problems early in the development cycle.

Background: The usability of dental computer-based patient record (CPR) systems has not been studied, despite early evidence that poor usability is a problem for dental CPR system users at multiple levels., Methods: The authors conducted formal usability tests of four dental CPR systems by using a purposive sample of four groups of five novice users. The authors measured task outcomes (correctly completed, incorrectly completed and incomplete) in each CPR system while the participants performed nine clinical documentation tasks, as well as the number of usability problems identified in each CPR system and their potential relationship to task outcomes. The authors reviewed the software application design aspects responsible for these usability problems., Results: The range for correctly completed tasks was 16 to 64 percent, for incorrectly completed tasks 18 to 38 percent and for incomplete tasks 9 to 47 percent. The authors identified 286 usability problems. The main types were three unsuccessful attempts, negative affect and task incorrectly completed. They also identified six problematic interface and interaction designs that led to usability problems., Conclusion: The four dental CPR systems studied have significant usability problems for novice users, resulting in a steep learning curve and potentially reduced system adoption., Clinical Implications: The significant number of data entry errors raises concerns about the quality of documentation in clinical practice.

In this study, we examined speech recognition functionality in four leading dental practice management systems. Twenty dental students used voice to chart a simulated patient with 18 findings in each system. Results show it can take over a minute to chart one finding and that users frequently have to repeat commands. Limited functionality, poor usability and a high error rate appear to retard adoption of speech recognition in dentistry.

To improve patient care in general dentistry it is important to understand the information needs of general dentists and the barriers that prevent timely access to information at the point of care. We interviewed a convenience sample of 14 general dentists during routine patient care encounters. All dentists experienced information needs (M = 55% of cases). Grounded theory methodology was used to analyze interview transcripts for emerging themes regarding their information needs.

Based on the morbidity and mortality due to oral cancer, it is essential that oral cancer information available on the Internet be usable, organized and credible. We evaluated the information quality of 24 English-language and 25 Spanish-language oral cancer websites. English-language sites scored 74.7 out of 100 points on the Information Quality Tool scale, while Spanish-language sites scored 48.8. Developers of oral cancer websites should improve the design, organization and credibility of the information presented.

Background: Most current paper- and computer-based formats for patient documentation use a two-dimensional dental chart, a design that originated almost 150 years ago in the United States. No studies have investigated the inclusion of a three-dimensional (3-D) charting interface in a general dental record., Methods: A multidisciplinary research team with expertise in human-computer interaction, dental informatics and computer science conducted a 14-week project to develop and evaluate a proof of concept for a 3-D dental record. Through several iterations of paper- and computer-based prototypes, the project produced a high-fidelity (hi-fi) prototype that was evaluated by two dentists and two dental students., Results: The project implemented a prototypical patient record built around a 3-D model of a patient's maxillofacial structures. Novel features include automatic retrieval of images and radiographs; a flexible view of teeth, soft tissue and bone; access to historical patient data through a timeline; and the ability to focus on a single tooth., Conclusions: Users tests demonstrated acceptance for the basic design of the prototype, but also identified several challenges in developing intuitive, easy-to-use navigation methods and hi-fi representations in a 3-D record., Clinical Implications: Test participants in this project accepted the preliminary design of a 3-D dental record. Significant further research must be conducted before the concept can be applied and evaluated in clinical practice.

Objective: Approximately 25% of all general dentists practicing in the United States use a computer in the dental operatory. Only 1.8% maintain completely electronic records. Anecdotal evidence suggests that dental computer-based patient records (CPR) do not represent clinical information with the same degree of completeness and fidelity as paper records. The objective of this study was to develop a basic content model for clinical information in paper-based records and examine its degree of coverage by CPRs., Design: We compiled a baseline dental record (BDR) from a purposive sample of 10 paper record formats (two from dental schools and four each from dental practices and commercial sources). We extracted all clinical data fields, removed duplicates, and organized the resulting collection in categories/subcategories. We then mapped the fields in four market-leading dental CPRs to the BDR., Measurements: We calculated frequency counts of BDR categories and data fields for all paper-based and computer-based record formats, and cross-mapped information coverage at both the category and the data field level., Results: The BDR had 20 categories and 363 data fields. On average, paper records and CPRs contained 14 categories, and 210 and 174 fields, respectively. Only 72, or 20%, of the BDR fields occurred in five or more paper records. Categories related to diagnosis were missing from most paper-based and computer-based record formats. The CPRs rarely used the category names and groupings of data fields common in paper formats., Conclusion: Existing paper records exhibit limited agreement on what information dental records should contain. The CPRs only cover this information partially, and may thus impede the adoption of electronic patient records.

Speech recognition allows clinicians a hands-free option for interacting with computers, which is important for dentists who have difficulty using a keyboard and a mouse when working with patients. While roughly 13% of all general dentists with computers at chairside use speech recognition for data entry, 16% have tried and discontinued using this technology. In this study, researches explored the speech recognition features and functionality of four dental software applications. For each system, the documentation as well as the working program was evaluated to determine speech recognition capabilities. A comparison checklist was created to highlight each program's speech functionality. Next, after the development of charting scripts, feasibility user tests were conducted to determine if performance comparisons could be made across systems. While four systems were evaluated in the feature comparison, only two of the systems were reviewed during the feasibility user tests. Results show that current speech functionality, instead of being intuitive, is directly comparable to using a mouse. Further, systems require memorizing an enormous amount of specific terminology opposed to using natural language. User testing is a feasible way to measure the performance of speech recognition across systems and will be conducted in the near future. Overall, limited speech functionality reduces the ability of clinicians to interact directly with the computer during clinical care. This can hinder the benefits of electronic patient records and clinical decision support systems.

Objective: Measure the adoption and utilization of, opinions about, and attitudes toward clinical computing among general dentists in the United States., Design: Telephone survey of a random sample of 256 general dentists in active practice in the United States., Measurements: A 39-item telephone interview measuring practice characteristics and information technology infrastructure; clinical information storage; data entry and access; attitudes toward and opinions about clinical computing (features of practice management systems, barriers, advantages, disadvantages, and potential improvements); clinical Internet use; and attitudes toward the National Health Information Infrastructure., Results: The authors successfully screened 1,039 of 1,159 randomly sampled U.S. general dentists in active practice (89.6% response rate). Two hundred fifty-six (24.6%) respondents had computers at chairside and thus were eligible for this study. The authors successfully interviewed 102 respondents (39.8%). Clinical information associated with administration and billing, such as appointments and treatment plans, was stored predominantly on the computer; other information, such as the medical history and progress notes, primarily resided on paper. Nineteen respondents, or 1.8% of all general dentists, were completely paperless. Auxiliary personnel, such as dental assistants and hygienists, entered most data. Respondents adopted clinical computing to improve office efficiency and operations, support diagnosis and treatment, and enhance patient communication and perception. Barriers included insufficient operational reliability, program limitations, a steep learning curve, cost, and infection control issues., Conclusion: Clinical computing is being increasingly adopted in general dentistry. However, future research must address usefulness and ease of use, workflow support, infection control, integration, and implementation issues.

The Systematized Nomenclature of Dentistry (SNODENT) is an effort of the American Dental Association (ADA) to develop a controlled terminology that addresses the needs of clinical dentistry. The ADA, collaborating with the College of American Pathologists, developed and incorporated SNODENT as a microglossary of SNOMED. However, little evidence exists of the effectiveness of its clinical coverage. We extracted diagnostic terms from clinical case reports and evaluated SNODENT's and other medical terminologies' coverage of those terms.

Background: Mandatory continuing education (CE) is an almost universal requirement for relicensure of dentists in the United States. In recent years, computer-based methods have become more widely adopted as a vehicle for earning CE credits. To obtain a comprehensive overview of CE requirements in the United States, the authors conducted a survey on how computer-based CE methods are regulated., Methods: The authors reviewed the dental statutes of 50 states and the District of Columbia regarding the license renewal period, required number of CE hours, limitations on clinical and nonclinical subjects, on-site versus independent-study courses, and other requirements regarding CE., Results: The authors found that 45 states and the District of Columbia mandate CE for relicensure at this time. Most dentists were required to complete approximately 20 hours per year. Ten states specified a minimum number of clinical hours, 17 states limited nonclinical hours, and seven states placed constraints on both clinical and nonclinical CE. Sixty-five percent of states limited the number of CE credits that could be accumulated through independent study. Specific requirements for computer-based methods of earning CE credits were absent in general., Conclusions: State licensing boards across the United States have implemented comprehensive requirements for CE. Few, if any, provisions addressed computer-based methods of earning CE credits., Practice Implications: Given the increasing adoption of computer-based methods of earning CE credits, state dental boards and accrediting agencies may want to consider steps to improve the quality of computer-based methods of earning CE credits.

Historically, an anatomic dental chart is a compilation of color-coded symbols and numbers used within a template, either paper or computerized, to create a graphic record of a patient's oral health status. This poster depicts how Geospatial Information System (GIS) technology can be used to create an accurate, current anatomic dental chart that contains detailed information not present in current charting systems.

Objective: Currently, no comprehensive, controlled vocabulary for dentistry is available. The objective of this study was to determine how well the Unified Medical Language System, the largest repository of concepts and terms in biomedicine, represents dental concepts., Method and Materials: The dental subset of concepts was extracted from Unified Medical Language System using the software program APEX (APplication for the EXtraction of domain-specific concepts). The relationships contained in the Unified Medical Language System Metathesaurus were used to locate the concepts related to 12 seed terms. The Encyclopédie Médico-Chirurgicale (513 dental terms) and the Diagnostic Codes developed by Leake et al (124 terms) were compared to the dental subset. Terms were classified as exact match, related term, or no match. The resultant matching characteristics were compared to those determined by the National Library of Medicine/Agency for Health Care Policy and Research Large Scale Vocabulary Test., Results: The dental subset of Unified Medical Language System contained 948 concepts. The Encyclopédie Médico-Chirurgicale and the Diagnostic Codes exhibited similar matching characteristics for exact match (61.6% and 58.9%, respectively) and related term (38.0% and 32.2%, respectively). For no match, the matching frequencies were significantly different (P < .001)., Conclusion: The Unified Medical Language System may be a comprehensive source of terms suitable for various representation requirements in dentistry.