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To determine whether preventive dental visits are associated with fewer subsequent nonpreventive dental visits and lower dental expenditures.Indiana Medicaid enrollment and claims data (2015-2018) and the Area Health Resource File.A repeated measures design with individual and year fixed effects examining the relationship between preventive dental visits (PDVs) and nonpreventive dental visits (NPVs) and dental expenditures.Not applicable.Of 28,152 adults (108,349 observation-years) meeting inclusion criteria, 36.0% had a dental visit, 27.8% a PDV, and 22.1% a NPV. Compared to no PDV in the prior year, at least one was associated with fewer NPVs (β = -0.13; 95% CI -0.12, -0.11), lower NPV expenditures (β = -$29.12.53; 95% CI -28.07, -21.05), and lower total dental expenditures (-$70.12; 95% -74.92, -65.31), as well as fewer PDVs (β = -0.24; 95% CI -0.26, -0.23).Our findings suggest that prior year PDVs are associated with fewer subsequent NPVs and lower dental expenditures among Medicaid-enrolled adults. Thus, from a public insurance program standpoint, supporting preventive dental care use may translate into improved population oral health outcomes and lower dental costs among certain low-income adult populations, but barriers to consistent utilization of PDV prohibit definitive findings.

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.

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.

The purpose of this study is to test whether race is associated with longitudinal success in glycemic control or consecutive years of A1c testing. A retrospective cohort study of 531,995 adults with type 2 diabetes who received care between 20 and 2018 was conducted. The independent variable was race (white, black, Hispanics, and other) . Dependent variables were (1) success rate for glycemic control (A1c =65 y/o]) , defined as (number of A1c tests meeting target) / (number of A1c tests) ; and (2) the number of three consecutive years of A1c testing. Confounder variables were age, gender, first year, last year, and health service utilization (yearly rate of outpatient, emergency department, hospitalization and home care visits) . We used generalized linear regression (GLM) to analyze the success rate of glycemic control, and GLM with negative binomial to analyze the number of years of A1c testing. Ten-year level observations (N=531,995) summarized from 3,044,247 year-level electronic medical-record observations showed mean observed years of 5.2, median years of A1c tested of 3.5, and mean fraction of years tested A1c over observed of 69%. Mean success rate of glycemic control was 67% in a 10-year period (IQR of 67%) . The success rate of glycemic control differed by race (P Disclosure H.N.Fu: None. T.Schleyer: None. J.Wolfson: None. T.S.Hannon: Advisory Panel; Eli Lilly and Company. P.Balius: None. M.Weiner: Stock/Shareholder; AbbVie Inc., Accuray, Inc., Allscripts Healthcare Solutions, Amgen Inc., Boston Scientific Corporation, Bristol-Myers Squibb Company, Crispr Therapeutics Ag Com, Express Scripts Hdlg Co, General Electric Co., Globus Med, Inc., IBM, Integer Hldgs Corp Com, Integra Lifesciences Holdings Corp, Johnson & Johnson, Mallinckrodt PLC, Mead Johnson & Company, LLC., Medtronic, Metlife Inc Com, Mylan N.V., Novo Nordisk A/S, Nuvasive, Inc., Orthofix Medical Inc., Perspecta Inc Com, Pfizer Inc., Resmed, Inc., Roche Pharmaceuticals, Seaspine Hldgs, Inc., Senseonics, Stryker Corp., Teva Pharmaceutical Industries Ltd., Varex Imaging Corp., Varian Med Sys, Inc., Walgreens Boots Alliance, Inc. Funding T15LM012502, UL1TR002529, the Lilly Endowment Physician Scientist Initiative

The purpose of this study was to assess success rates of long-term glycemic control and diabetes drug prescriptions by gender, age and race in a large, state-wide, integrated, electronic health record database in Indiana. Descriptive analysis of a retrospective cohort of 308,4adults with type 2 diabetes observed from 20to 2018 was conducted. The success rate for glycemic control (A1c =65 y/o]) was defined as (number of A1c tests meeting target) / (number of A1c tests) in any given year. Sample characteristics included gender, age, race, and diabetes drug prescription rate for twelve classes of diabetes drugs. Analysis of 876,656 year-level observations showed the success rate of glycemic control was steady at 60% with year-to-year change of 0-1% since 2009. It was greater for females (62-64%) than males (57-59%) ; patients aged 65 or above (75-89%) than those below (43-62%) ; and whites (61-63%) than blacks (51-54%) . Biguanides (56-63% = subject n with orders of biguandes in x year/ population n in x year) were most frequently prescribed, followed by sulfonylurea (29-33%) , fast-acting and long-acting insulin (29-32%) . Blacks had more insulin prescription (47-53%) regardless of age group. Costlier drugs were more frequently prescribed among whites vs. non-white: 0.6-1.2% vs. 0.5-0.8 for meglitinides; 10.3-16.7% vs. 1.3-17.3% for DPP-4; 0.5-9.3% vs. 0.2-8.0 for SGLT2i; and 5.3-12.15% vs. 1-11.5% for GLP-1. For blacks who were prescribed insulin (44-53%) , the success rate for glycemic control was lower than for whites (38-43% vs. 46-50%) . The success of longitudinal glycemic control did not improve over a 10-year period among 308,4adults with type 2 diabetes in Indiana. Racial minorities received notably fewer prescriptions for higher-priced drugs such as meglitinide, DPP4, SGLT2, and GLP-1. Disclosure H.N.Fu: None. M.Weiner: Stock/Shareholder; AbbVie Inc., Accuray, Inc., Allscripts Healthcare Solutions, Amgen Inc., Boston Scientific Corporation, Bristol-Myers Squibb Company, Crispr Therapeutics Ag Com, Express Scripts Hdlg Co, General Electric Co., Globus Med, Inc., IBM, Integer Hldgs Corp Com, Integra Lifesciences Holdings Corp, Johnson & Johnson, Mallinckrodt PLC, Mead Johnson & Company, LLC., Medtronic, Metlife Inc Com, Mylan N.V., Novo Nordisk A/S, Nuvasive, Inc., Orthofix Medical Inc., Perspecta Inc Com, Pfizer Inc., Resmed, Inc., Roche Pharmaceuticals, Seaspine Hldgs, Inc., Senseonics, Stryker Corp., Teva Pharmaceutical Industries Ltd., Varex Imaging Corp., Varian Med Sys, Inc., Walgreens Boots Alliance, Inc. J.Wolfson: None. T.S.Hannon: Advisory Panel; Eli Lilly and Company. P.Balius: None. T.Schleyer: None. Funding T15LM012502, UL1TR002529, the Lilly Endowment Physician Scientist Initiative

Despite advances in therapies, many patients with diabetes worldwide are not meeting glycemic control targets. The objective of this study was to characterize adult patients with T2D in Indiana with respect to glycemic control over a 10-year period. We retrospectively analyzed a cohort of 531,995 patients with T2D and generated summary statistics. The success rate for achieving glycemic control (A1c =65 y/o]) was defined as (number of A1c tests meeting target) / (number of A1c tests) . Uncontrolled diabetes was defined as (number of A1c tests >8) / (number of A1c tests) . We summarized 1,868,1year-level A1c observations from 20to 2018 by gender, age, and race. In four years within the 10-year period, rates of glycemic control decreased from 68% to 65% with inconsistent year-to-year change of 0.4-1.8%. The rate of uncontrolled diabetes increased from 19% to 23%. Females were more successful than males (67-71% vs. 62-66%) in achieving control.t4 Patients >=65 y/o had the highest success rate (82-88%) , while 49% to 60% of younger patients were controlled. Whites had higher success (67-70%) and less uncontrolled diabetes (17-22%) than other races.t7 Blacks’ rates of success and uncontrolled diabetes were stable at ∼46%t6 and ∼27%, respectively.t7 Black males 18-44 y/o had the lowest success rate (40-45%) . Long-term rates of achieving glycemic control in adults with T2D in Indiana declined over a 10-year period. However, one third to one half of those aged >=65 y/o achieved a high success rate for glycemic control (>80%) . Achieving longitudinal glycemic control is least successful for male blacks aged 18-44. There is a need for diabetes health service research to study population health interventions tailored by age and race. Disclosure H.N.Fu: None. T.S.Hannon: Advisory Panel; Eli Lilly and Company. M.Weiner: Stock/Shareholder; AbbVie Inc., Accuray, Inc., Allscripts Healthcare Solutions, Amgen Inc., Boston Scientific Corporation, Bristol-Myers Squibb Company, Crispr Therapeutics Ag Com, Express Scripts Hdlg Co, General Electric Co., Globus Med, Inc., IBM, Integer Hldgs Corp Com, Integra Lifesciences Holdings Corp, Johnson & Johnson, Mallinckrodt PLC, Mead Johnson & Company, LLC., Medtronic, Metlife Inc Com, Mylan N.V., Novo Nordisk A/S, Nuvasive, Inc., Orthofix Medical Inc., Perspecta Inc Com, Pfizer Inc., Resmed, Inc., Roche Pharmaceuticals, Seaspine Hldgs, Inc., Senseonics, Stryker Corp., Teva Pharmaceutical Industries Ltd., Varex Imaging Corp., Varian Med Sys, Inc., Walgreens Boots Alliance, Inc. P.Balius: None. J.Wolfson: None. T.Schleyer: None. Funding T15LM012502, UL1TR002529, the Lilly Endowment

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.

To conduct a systematic review to identify studies that assessed the association between CYP2C19 polymorphisms and clinical outcomes in peripheral artery disease (PAD) patients who took clopidogrel.We systematically searched Ovid EMBASE, PubMed, and Web of Science from November 1997 (inception) to September 2020. We included observational studies evaluating how CYP2C19 polymorphism is associated with clopidogrel's effectiveness and safety among patients with PAD. We extracted relevant information details from eligible studies (e.g., study type, patient population, study outcomes). We used the Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) Tool to assess the risk of bias for included observational studies.The outcomes of interest were the effectiveness and safety of clopidogrel. The effectiveness outcomes included clinical ineffectiveness (e.g., restenosis). The safety outcomes included bleeding and death related to the use of clopidogrel. We identified four observational studies with a sample size ranging from 50 to 278. Outcomes and comparison groups of the studies varied. Three studies (75%) had an overall low risk of bias. All included studies demonstrated that carrying CYP2C19 loss of function (LOF) alleles was significantly associated with reduced clinical effectiveness and safety of clopidogrel.Our systematic review showed an association between CYP2C19 LOF alleles and reduced functions of clopidogrel. The use of CYP2C19 testing in PAD patients prescribed clopidogrel may help improve the clinical outcomes. However, based on the limited evidence, there is a need for randomized clinical trials in PAD patients to test both the effectiveness and safety outcomes of clopidogrel.

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.

Background A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care exposes many of the challenges that confront a wider scale approach. For example, to improve the quality of dental care, we must be able to collect and analyze data about dental procedures from multiple practices. However, a number of challenges make doing so difficult. First, dental electronic health record (EHR) information is often stored in complex relational databases that are poorly documented. Second, there is not a commonly accepted and implemented database schema for dental EHR systems. Third, integrative work that attempts to bridge dentistry and other settings in healthcare is made difficult by the disconnect between representations of medical information within dental and other disciplines’ EHR systems. As dentistry increasingly concerns itself with the general health of a patient, for example in increased efforts to monitor heart health and systemic disease, the impact of this disconnect becomes more and more severe. To demonstrate how to address these problems, we have developed the open-source Oral Health and Disease Ontology (OHD) and our instance-based representation as a framework for dental and medical health care information. We envision a time when medical record systems use a common data back end that would make interoperating trivial and obviate the need for a dedicated messaging framework to move data between systems. The OHD is not yet complete. It includes enough to be useful and to demonstrate how it is constructed. We demonstrate its utility in an analysis of longevity of dental restorations. Our first narrow use case provides a prototype, and is intended demonstrate a prospective design for a principled data backend that can be used consistently and encompass both dental and medical information in a single framework. Results The OHD contains over 1900 classes and 59 relationships. Most of the classes and relationships were imported from existing OBO Foundry ontologies. Using the LSW2 (LISP Semantic Web) software library, we translated data from a dental practice’s EHR system into a corresponding Web Ontology Language (OWL) representation based on the OHD framework. The OWL representation was then loaded into a triple store, and as a proof of concept, we addressed a question of clinical relevance – a survival analysis of the longevity of resin filling restorations. We provide queries using SPARQL and statistical analysis code in R to demonstrate how to perform clinical research using a framework such as the OHD, and we compare our results with previous studies. Conclusions This proof-of-concept project translated data from a single practice. By using dental practice data, we demonstrate that the OHD and the instance-based approach are sufficient to represent data generated in real-world, routine clinical settings. While the OHD is applicable to integration of data from multiple practices with different dental EHR systems, we intend our work to be understood as a prospective design for EHR data storage that would simplify medical informatics. The system has well-understood semantics because of our use of BFO-based realist ontology and its representation in OWL. The data model is a well-defined web standard.

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.