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Background American Indian/Alaska Native (AI/AN) populations have been disproportionately affected by chronic respiratory diseases for reasons incompletely understood. Past research into disease disparity using population-based surveys mostly focused on state-specific factors. The present study investigates the independent contributions of AI/AN racial status and other socioeconomic/demographic variables to chronic respiratory disease disparity in an 11-state region with historically high AI/AN representation. Using data from the Behavioral Risk Factor Surveillance System (BRFSS) spanning years 2011–2018, this work provides an updated assessment of disease disparity and potential determinants of respiratory health in AI/AN populations. Methods This cross-sectional study used data from the BRFSS survey, 2011–2018. The study population included AI/AN and non-Hispanic white individuals resident in 11 states with increased proportion of AI/AN individuals. The yearly number of respondents averaged 75,029 (62878–87,350) which included approximately 5% AI/AN respondents (4.5–6.3%). We compared the yearly adjusted prevalence for chronic respiratory disease, where disease status was defined by self-reported history of having asthma and/or chronic obstructive pulmonary disease (COPD). Multivariable logistic regression was performed to determine if being AI/AN was independently associated with chronic respiratory disease. Covariates included demographic (age, sex), socioeconomic (marital status, education level, annual household income), and behavioral (smoking, weight morbidity) variables. Results The AI/AN population consistently displayed higher adjusted prevalence of chronic respiratory disease compared to the non-Hispanic white population. However, the AI/AN race/ethnicity characteristic was not independently associated with chronic respiratory disease (OR, 0.93; 95% CI, 0.79–1.10 in 2017). In contrast, indicators of low socioeconomic status such as annual household income of Conclusions This study highlighted that AI/AN socioeconomic burdens are key determinants of chronic respiratory disease, in addition to well-established risk factors such as smoking and weight morbidity. Disease disparity experienced by the AI/AN population is therefore likely a symptom of disproportionate socioeconomic challenges they face. Further promotion of public health and social service efforts may be able to improve AI/AN health and decrease this disease disparity.

Multidisciplinary tumor boards (TBs) is an integral part of cancer care. Emerging evidence shows that effective TB implementation is crucial. It remains largely unknown how digital solutions can assist effective TB conduction. This study aimed to evaluate the impact of a digital solution on case discussion during TB meetings in four cancer types: Breast, Gastrointestinal (GI), Ear, Nose & Throat (ENT), and Hematopathology. A prospective study was performed to evaluate case discussion time during TB meetings pre- and post-solution implementation, at an US academic healthcare cancer center. Data were recorded by a Nurse Navigator for each case during TB meetings. Case discussion times were recorded for 2312 patients, at a total of 286 TB meetings. Significant decreases were observed in the average case discussion time for the breast and GI TBs. We observed a trend for reduction in discussion time variance for all TBs, suggesting the potential of the digital solution to standardize case discussion via provision of uniform case presentation and data access. Postponement rate decreased from 23 to 10% for ENT TB. This study demonstrated that the digital solution enhanced effective TB implementation, with heterogeneity across cancer types.

Health Information Exchange (HIE) exhibits remarkable benefits for patient care such as improving healthcare quality and expediting coordinated care. The Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric HIE designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric HIE in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of HIE, this paper provides a feasible solution to these challenges by utilizing the unique features of blockchain, a distributed ledger technology which is considered "unhackable". Utilizing the smart contract feature, which is a programmable self-executing protocol running on a blockchain, we developed a blockchain model to protect data security and patients' privacy, ensure data provenance, and provide patients full control of their health records. By personalizing data segmentation and an "allowed list" for clinicians to access their data, this design achieves patient-centric HIE. We conducted a large-scale simulation of this patient-centric HIE process and quantitatively evaluated the model's feasibility, stability, security, and robustness.

A prevalent challenge in underdeveloped countries is the mapping and accounting of certain sub-populations for public health matters. This is especially true in sub-Saharan African countries that lack specific resources and technologies. Often, international non-profit groups provide targeted support for populations' health on specific matters, such as clean water initiatives and basic health clinics. This is especially critical in various ethnic populations that are not well integrated into a country's existing public health services. In countries such as Tanzania, one such ethnic population is the Maasai people. In this paper, we demonstrate how machine learning algorithms, combined with remote sensing data, can aid non-profit health delivery organizations to map Maasailand boma homesteads in northern Tanzania. Our study area encompasses over 3900 square kilometers of Massailand, where we have manually identified 635 bomas. We have evaluated four different deep neural network architectures for their ability to recognize boma, ResNet50, Xception, Inception-ResNet-V2, and ProxylessNAS, with cross-validation F1 scores of 95.32%, 95.45%, 96.78%, and 97.28%, respectively.

The use of telemedicine continues to grow as more patients are receptive to this innovative way of providing health care. Multiple publications in telemedicine indicated high satisfaction for this service. This study focuses on the use of telemedicine in a pediatric urology clinic and examines 3 research questions: (1) How did patients’ parents/guardians feel about their video appointments? (2) What were the experiences of novice telemedicine providers conducting postsurgical appointments via video? and (3) How did novice telemedicine providers’ experiences compare to those of expert telemedicine providers?

UNSTRUCTURED This is a peer review report.

Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. “Blockchain,” a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable “Smart Contract” feature makes blockchain more suitable and feasible for VCT by solving computational issues. Our previous work has shown the power of applying blockchain to clinical trial recruitment. This work develops a comprehensive blockchain framework, with simulations and case studies, including patient recruitment, patient engagement, and persistent monitoring modules.

Opioid use disorder (OUD) is a medical condition that has evolved into a serious and deadly epidemic in the United States. Both medical and psychological interventions are called for to end this growing epidemic, but too few health care professionals are trained to treat OUD. One proven model of training physicians and cross-disciplinary teams in treating a variety of disorders is exemplified by Project ECHO (Extension for Community Healthcare Outcomes), a collaborative tele-mentoring program in which specialists train health-care workers to treat medical conditions, especially those that affect underserved populations. This systematic review found that Project ECHO has the potential to effectively extend current services to patients suffering from OUD, but that there is also a gap in knowledge regarding this type of training. The articles that we reviewed all presented evidence that Project ECHO improves healthcare provider preparedness to treat OUD, especially in regard to improving knowledge and self-efficacy.

Telehealth has been used for decades to improve access to care for rural and underserved patients. The adoption of telehealth in orthopedic oncology is novel and expected to positively impact patient access and compliance. However, no previous evaluation has been published of this expected impact. The objective of this pilot project was to evaluate patients’ perceptions regarding orthopedic oncology telehealth services. A 13-question satisfaction survey was distributed to patients who used tele-orthopedic oncology. Fifteen respondents (a response rate of 42%) reported satisfaction with services at 9.7 of 10. Median travel distance to the nearest in-person orthopedic oncologist was greater than 150 miles (241 km). These results are consistent with the previous findings of high satisfaction with telehealth in other specialties. Health care organizations are likely to benefit from offering telehealth to orthopedic oncology patients with limited access.

Background Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions. Objective Identify existing approaches to inclusion of a broad set of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions. Methods A systematic review of scientific literature published and indexed in PubMed, Web of Science, Association of Computing Machinery (ACM) and SCOPUS from 2010 through October 2020 was performed. To be included, articles had to include search terms related to Electronic Health Record (EHR) data Neighborhood-Level Risk Factors (NLRFs), and Machine Learning (ML) Methods. Citations of relevant articles were also reviewed for additional articles for inclusion. Articles were reviewed and coded by two independent reviewers to capture key information including data sources, linkage of EHR to NRLFs, methods, and results. Articles were assessed for quality using a modified Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO). Results A total of 334 articles were identified for abstract review. 36 articles were identified for full review with 19 articles included in the final analysis. All but two of the articles included socio-demographic data derived from the U.S. Census and we found great variability in sources of NLRFs beyond the Census. The majority or the articles (14 of 19) included broader clinical (e.g. medications, labs and co-morbidities) and demographic information about the individual from the EHR in addition to the clinical outcome variable. Half of the articles (10) had a stated goal to predict the outcome(s) of interest. While results of the studies reinforced the correlative association of NLRFs to clinical outcomes, only one article found that adding NLRFs into a model with other data added predictive power with the remainder concluding either that NLRFs were of mixed value depending on the model and outcome or that NLRFs added no predictive power over other data in the model. Only one article scored high on the quality assessment with 13 scoring moderate and 4 scoring low. Conclusions In spite of growing interest in combining NLRFs with EHR data for clinical prediction, we found limited evidence that NLRFs improve predictive power in clinical risk models. We found these data and methods are being used in four ways. First, early approaches to include broad NLRFs to predict clinical risk primarily focused on dimension reduction for feature selection or as a data preparation step to input into regression analysis. Second, more recent work incorporates NLRFs into more advanced predictive models, such as Neural Networks, Random Forest, and Penalized Lasso to predict clinical outcomes or predict value of interventions. Third, studies that test how inclusion of NLRFs predict clinical risk have shown mixed results regarding the value of these data over EHR or claims data alone and this review surfaced evidence of potential quality challenges and biases inherent to this approach. Finally, NLRFs were used with unsupervised learning to identify underlying patterns in patient populations to recommend targeted interventions. Further access to computable, high quality data is needed along with careful study design, including sub-group analysis, to better determine how these data and methods can be used to support decision making in a clinical setting.

PURPOSE Multidisciplinary tumor boards (TBs) are the gold standard for decision-making in cancer care. Variability in preparation, conduction, and impact is widely reported. The benefit of digital technologies to support TBs is unknown. This study evaluated the impact of the NAVIFY Tumor Board solution (NTB) on TB preparation time across multiple user groups in 4 cancer categories: breast, GI, head and neck (ie, ear, nose, and throat, or ENT), and hematopathology. METHODS This prospective study evaluated TB preparation time in multiple phases pre- and post-NTB implementation at an academic health care center. TB preparation times were recorded for multiple weeks using a digital time tracker. RESULTS Preparation times for 59 breast, 61 GI, 36 ENT, and 71 hematopathology cancer TBs comparing a pre-NTB phase to 3 phases of NTB implementation were evaluated between February 2018 and July 2019. NTB resulted in significant reductions in overall preparation time (30%) across 3 TBs pre-NTB compared with the final post-NTB implementation phase. In the breast TB, NTB reduced overall preparation time by 28%, with a 76% decrease in standard deviation (SD). In the GI TB, a 23% reduction in average preparation time was observed for all users, with a 48% decrease in SD. In the ENT TB, a 33% reduction in average preparation time was observed for all users, with a 73% decrease in SD. The hematopathology TB, which was the cocreation partner and initial adopter of the solution, showed variable results. CONCLUSION This study showed a significant impact of a digital solution on time preparation for TBs across multiple users and different TBs, reflecting the generalizability of the NTB. Adoption of such a solution could improve the efficiency of TBs and have a direct economic impact on hospitals.

Background Cancer clinical trials (CCT) offer significant potential benefit, not only for future patients but also for enrolled participants, yet a very small minority of cancer patients participate, resulting in low levels of enrollment that have stalled clinical trials dramatically. Though many have endeavored to study this phenomenon, relatively little research has explored the demographic factors which may affect CCT enrollment. Understanding patient demographics is critical to optimizing enrollment, evaluating generalizability, and ensuring equity of CCT. Methods To better understand the effect of social determinants of health on CCT enrollment, the authors constructed a multivariable logistic regression model to analyze data collected in the last ten years in the CDC Behavioral Risk Factor Surveillance System (BRFSS) Survey, an annual national survey conducted among the non-institutionalized adult population of the U.S. Results In multivariable regression analysis, enrollment varied significantly with sociodemographic factors. Individuals of higher income, Hispanic ethnicity, and younger age were most likely to participate in CCTs. Enrollment did not vary significantly by educational attainment. Conclusion Our multivariable analysis indicated people of color are more likely to participate in CCT, perhaps demonstrating that structural barriers shape participation more than race alone. Efforts to improve CCT enrollment may benefit from a shift in focus towards access to care by alleviating structural and financial barriers to enrollment.

SummaryBackground: Because 5% of patients incur 50% of healthcare expenses, population health managers need to be able to focus preventive and longitudinal care on those patients who are at highest risk of increased utilization. Predictive analytics can be used to identify these patients and to better manage their care. Data mining permits the development of models that surpass the size restrictions of traditional statistical methods and take advantage of the rich data available in the electronic health record (EHR), without limiting predictions to specific chronic conditions.Objective: The objective was to demonstrate the usefulness of unrestricted EHR data for predictive analytics in managed healthcare.Methods: In a population of 9,568 Medicare and Medicaid beneficiaries, patients in the highest 5% of charges were compared to equal numbers of patients with the lowest charges. Contrast mining was used to discover the combinations of clinical attributes frequently associated with high utilization and infrequently associated with low utilization. The attributes found in these combinations were then tested by multiple logistic regression, and the discrimination of the model was evaluated by the c-statistic.Results: Of 19,014 potential EHR patient attributes, 67 were found in combinations frequently associated with high utilization, but not with low utilization (support>20%). Eleven of these attributes were significantly associated with high utilization (pConclusions: EHR mining reduced an unusably high number of patient attributes to a manageable set of potential healthcare utilization predictors, without conjecturing on which attributes would be useful. Treating these results as hypotheses to be tested by conventional methods yielded a highly accurate predictive model. This novel, two-step methodology can assist population health managers to focus preventive and longitudinal care on those patients who are at highest risk for increased utilization.Citation: Sheets L, Petroski GF, Zhuang Y, Phinney MA, Ge B, Parker JC, Shyu C-R. Combining contrast mining with logistic regression to predict healthcare Appl Clin Inform 2017; 8: 430–446 https://doi.org/10.4338/ACI-2016-05-RA-0078

SettingObesity, once considered an epidemic of the developed world, is now becoming an even more prominent problem than underweight in low and lower middle income countries (LLMICs). Ample literature has shown that as a country’s income increases, the burden of obesity shifts from the rich to the poor. This is known as the “Reversal Hypothesis.” Many studies have explored the effects of various social determinants of health on obesity, but few have studied education as an independent variable on female obesity across LLMICs.ObjectiveGlobally, adult females have a higher prevalence of obesity and the obesity shift occurs more quickly for women than for men. We aim to address this disparity and contribute towards the reversal hypothesis by exploring the association of education and obesity in women in LLMICs.DesignIn this cross-sectional study, we used a multi-national and multi-year database from the publicly available Demographic and Health Surveys program with data from 34 LLMICs. Education levels are standardized across countries during survey collection.ResultsOur age-adjusted prevalence ratio (AA-PR) analysis shows that women in LLMICs with higher education have a significantly greater prevalence of obesity than women with no education. We analyzed this phenomenon by individual nations, continents, and income classifications. Educated women living in low income countries are 5.12 times more obese than uneducated women (AA-PR, 95% CI=4.75, 5.53) and 3.42 times more obese in lower middle income countries (AA-PR, 95% CI=3.31, 3.54).ConclusionThese findings highlight a need for more studies and policy attention focusing on female education levels, among other factors, to understand, predict, and prevent obesity in LLMICs.ARTICLE SUMMARYStrengths and limitations of this studyA rigorous sample size of 943,947 adult females in 34 LLMIC countries was utilized to study the association between adult female obesity and education level.Age-adjusted and age-and-wealth-adjusted prevalence ratios of obesity were analyzed based on 34 individual nations, three continents, and two major income categories.This study includes the most recent data available through the Demographic and Health Surveys program, which standardizes education levels during data collection, allowing for comparison between all surveyed countries.This study is limited by the relatively small number of countries for which data is available through the DHS dataset, and thus, further research will be needed to show whether these results are generalizable to other LLMICs.

“Blockchain” is a distributed ledger technology originally applied in the financial sector. This technology ensures the integrity of transactions without third-party validation. Its functions of decentralized transaction validation, data provenance, data sharing, and data integration are a good fit for the needs of health information exchange and clinical trials. We investigated the current workflow of Health Information Exchange and clinical trials; conducted design thinking processes with clinicians, trial managers, informaticians, and blockchain professionals; and implemented a private blockchain model to tackle known issues. We used coded Smart Contract regulations to simulate several scenarios in healthcare processes. This proof-of-concept work provides a feasible simulation for potential solutions to monitor clinical trials across different census regions persistently. Various levels of data access privileges have been designed to utilize a suite of customized Smart Contract settings. These settings emulate the workflow protocols for the monitoring entities, trial sponsors, clinical sponsors and participating subjects. Keywords: Blockchain, Smart Contract, Health Information Exchange, Clinical Trial, Persistent Monitoring.

Neighborhood context impacts health. Using an index of geospatial disadvantage measures to predict neighborhood socioeconomic disparities would support area-based allocation of preventative resources, as well as the use of location as a clinical risk factor in care of individual patients. This study tested the association of the Area Deprivation Index (ADI), a neighborhood-based index of socioeconomic contextual disadvantage, with elderly obesity risk. We sampled 5066 Medicare beneficiaries at the University of Missouri between September 1, 2013 and September 1, 2014. We excluded patients with unknown street addresses, excluded body mass index (BMI) lower than 18 or higher than 62 as probable errors, and excluded patients with missing BMI data. We used a plot of simple proportions to examine the association between ADI and prevalence of obesity, defined as BMI of 30 and over. We found that obesity was significantly less prevalent in the least-disadvantaged ADI decile (decile 1) than in all other deciles (p

Patient socioeconomic data is not usually included in medical records nor easily accessible to clinicians, yet socioeconomic disadvantage can be an important guide to disease management. This study evaluated the neighborhood-level Area Deprivation Index (ADI), a measure of neighborhood socioeconomic disadvantage, as a factor in diabetes mellitus prevalence. Electronic health records at an academic hospital system identified 4,770 Medicare beneficiaries. Logistic regression of diabetes diagnosis (ICD9=250.x) against ADI quintile, age, gender, and race/ethnicity found all these patient characteristics to be significantly associated. Diabetes prevalence was lowest in the least disadvantaged quintile of neighborhoods after adjusting for age, gender, and race/ethnicity. The positive non-linear association of diabetes prevalence with ADI demonstrates the power of this index to practically quantify socioeconomic disadvantage. The ADI may be suitable for clinical decision support, and for informing the policy changes which are needed to reduce socioeconomic disparities in diabetes prevalence and other health outcomes.

The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data. By using distributed Association Rule Mining and Contrast Mining in a big data ecosystem, it is possible to discover discrepancies within large, complex populations which are inaccessible using traditional methods. These discrepancies, when used as hypotheses, can help improve patient care through decision support, population health analytics, and other areas of healthcare.

The LIGHT

Effective care coordination requires risk stratification, but little evidence has been collected about how it impacts clinicians. This care coordination pilot project created a unique opportunity to observe care coordination activities for 10,000 patients over 18 months, before and after risk stratification. Risk stratification feedback increased care coordination contacts with high-risk patients, without decreasing contacts with low-risk patients. The results of this study provide quantitative evidence of the importance of risk stratification in care coordination.

Objective. To develop a systematic and reproducible way to identify patients at increased risk for higher healthcare costs. Methods. Medical records were analyzed for 9,581 adults who were primary care patients in the University of Missouri Health System and who were enrolled in Medicare or Medicaid. Patients were categorized into one of four risk tiers as of October 1, 2013, and the four tiers were compared on demographic characteristics, number of healthcare episodes, and healthcare charges in the year before and the year after cohort formation. Results. The mean number of healthcare episodes and the sum of healthcare charges in the year following cohort formation were higher for patients in the higher-risk tiers. Conclusions. Retrospective information that is easily extracted from medical records can be used to create risk tiers that provide highly useful information about the prospective risk of healthcare utilization and costs.

308 Background: A multidisciplinary tumor board (MTB) provides an interdisciplinary approach for decision-making in cancer care. Efficient conduction of MTBs is importantfor optimal patient management. It is, however, often observed that prepared patient cases are not discussed during tumor boards due to limited time or incomplete information, which could cause delaytocaredecisions and/or the initiation of treatments. It remains unknown whether digital technologies canreduce the rate of failure to discuss during MTBs. Methods: A prospective cohort study was undertaken to evaluate the preparation & conduction of MTBs pre- & post-implementation of the NAVIFY Tumor Board (NTB) solution at Missouri University Health Care (MU), including the Ear, Nose & Throat (ENT) MTB. The NTB is a cloud-based workflow product, integrated with the hospital EMR, that aggregates and displays relevant clinical information. NTB was introduced to theMUENT MTB on Oct 10, 2018. Results: Pre-NTB implementation, data was collected from 42 ENT MTBs. A total of 551 patient cases were prepared for MTBs, but only 423 patient cases were discussed. This was an average “failure-to-discuss” rate of 19.4% per meeting (SD = 15.6%). After NTB implementation, data was collected from 7 MTBs where a total of 70 patient cases were prepared and discussed. There were no instances of failure to discuss, and as such, was significantlyreduced after the implementation of NTB (Mann-Whitney U test, p = 0.0004). The average number of patient cases discussed per meeting pre- and post-NTB implementation did not change (Mann-Whitney U test, p > 0.1) and meeting duration was the same. Conclusions: Introduction of the NTB did not change the weekly number of cases discussed, but did significantly reduce the failure to discuss rates for ENT MTB cases. Reducing failure to discuss rates could decrease the overall time to clinical decision and the initiation of treatment, which could potentially improve patient outcomes. Additional studies are needed to examine the impact of digital solutions on the quality of clinical care.

e18028 Background: A multidisciplinary tumor board (MTB) provides an interdisciplinary approach for decision-making in cancer care. Information factors such as, multiple data sources, incomplete or missing information and teleconferencing failures, have been identified as issues contributing to variability in MTB conduction and impact. Little is known about how digital tumor board solutions can optimize MTB meeting conduction. Methods: A prospective IRB approved cohort study was undertaken to evaluate the time for patient case discussions,before and after the implementation of the NAVIFY Tumor Board (NTB) solution, at University of Missouri Health Care. Data was collected using a digital time-tracking application. The NTB manual version was implemented via a phased roll-out (Breast May 18, 2018; Gastrointestinal (GI) Jul 11, 2018; ENT Oct 30, 2018 – no manual version phase). Subsequently, the integrated version was introduced (Oct 4, 2018) whereby automated electronic medical record (EMR) data extraction was enabled. Results: Patient case discussion time was recorded at 138 MTBs (1109 patient cases) during 2018 (Breast 40 MTB; 236cases / GI 49 MTB; 389 cases / ENT 49 MTBs; 484 cases). Case discussion time significantly reduced at the Breast MTB (6.6mins to 5.3 mins; p-value = 0.01). Case discussion time at ENT MTB and GI MTB showed no significant change (Table 1). Time variance in case discussions significantly decreased post-NTB implementation at the Breast MTB (p-value = 0.008). For the GI & ENT MTBs, there was no significant difference post-NTB implementation (GI p-value =0.199; ENT p-value = 0.511), however, variance was already sma Conclusions: Introduction of the NTB reduced the time spent discussing cases for the Breast MTB, but showed no change forGI MTB and ENT MTB. Interestingly, case discussion times converged to between 5.5 - 6.5 minutesirrespective of the MTB type. Furthermore, variance decreased or was already small, for all MTB types. Both observations suggest that NTB standardized the case discussion process, perhaps because it created a common format for case presentation. A common workflow tool for MTB meeting conduction could ensure availability of all required data, increase efficiency of therapy decision-making, and lead to higher throughput of cases resulting in shorter time-to-treatment.

e18020 Background: A multidisciplinary tumor board (MTB) provides an interdisciplinary approach for decision-making in cancer care. Oncology nurse navigators (NN) have been introduced to facilitate patient access to services and resources, and to improve continuity and coordination of care. Little is known about how digital technologies support this role. Methods: A prospective IRB approved cohort study was undertaken to evaluate NN time preparation for MTBs before and after the implementation of the NAVIFY Tumor Board (NTB) solution at University of Missouri Health Care (MU). Data was collected using a digital time-tracking application. The NTB manual version was implemented via a phased roll-out (Hematology – Apr 9, 2018; Breast May 18, 2018; Gastrointestinal Jul 11, 2018). Subsequently, the integrated version was introduced (Oct 4, 2018). Results: Time preparation for 101 MTBs (828 cases) were evaluated throughout 2018 (35 Breast; 213 cases / 32 GI; 302 cases / 34 Hematology; 313 cases). Data showed NN meeting preparation time decreased for each MTB after the introduction of NTB; further time saving was achieved with NTB integrated version (Table 1). For the Breast MTB there was statistical significance between time preparation pre- & post-NTB (p-value = 0.011) but not between the manual and integrated versions (p-value = 0.118). For the Gastrointestinal MTB, there was no statistical significance between pre- & post-NTB (p-value = 0.214). For Hematology MTB there was statistical significance between the change from the NTB manual version vs. integration (p-value = 0.004). Note before NTB implementation Hematology MTB was not supported by NN. Conclusions: Introduction of the NTB reduced time spent by NNs preparing for 3 different MTBs, EMR integration delivered further time-savings. No discernable learning curve was observed suggesting user-training was sufficient and NTB was easy to learn. A reduction in variance (SD & IQR) for time preparation across all MTBs was seen. This suggests the NTB solution standardized the process for MTB preparation. Less time spent preparing for MTBs gives NNs more time to support patients.

Background: Combination oral contraceptives are prescribed 10 million times a year in the United States alone. Prescribers have more than 70 oral-contraceptive choices, but individual women's medical conditions may make some contraceptives inappropriate or dangerous. Traditional sources of prescribing information are impractical at the point of care. Mobile apps are revolutionizing clinical workflow, but none have been deployed for oral-contraceptive selection. Objective/Purpose: to design of an evidence-based interactive tool for selecting oral contraceptives, as a model for dissemination of clinical practice guidelines to clinical settings via mobile applications. Methods: I indexed the practice guidelines for hormonal contraception from the World Health Organization and the American Congress of Obstetricians and Gynecologists, and expert recommendations from two contraceptive textbooks. I gathered the rules into IF-THEN use cases, annotated with evidence quality and prioritized by likelihood and importance, and classified contraceptive choices into specific recommendation groups. Results: The sources yielded 32 guidelines and 24 expert recommendations using a total of 73 data elements, mostly TRUE/FALSE choices or numeric values. Organizing these into 22 use cases evaluating one to eight data elements each, few enough to fit on a smartphone screen, permits the entire questionnaire to be navigated in 22 or fewer screens. Discussion/Conclusions: Handheld clinical applications can facilitate evidence-based medicine, and reduce medical errors, by moving the best available medical knowledge from laborintensive traditional resources to prescribers' fingertips. They may create privacy and data-security risks, but limiting apps to anonymous single-use queries obviates these. Potential enhancements include additional object-oriented treatment domains, automatically updating rules engines using the Guideline Elements Model, and allowing user communities to add rules.

The University of Missouri's population health management pilot employs 22 Nurse Care Managers (NCM) to manage medical casework for approximately 10,000 patients. We studied the NCMs' documentation system to identify interruptions to their clinical workflow, and identify missing functionality. We used an interview script and Morae software to observe and record five NCMs at work, measuring time on task, click counts, and task completion. We also documented quantitative and qualitative responses to a directed interview. All tasks were completed satisfactorily, with completion times of 443±275 seconds and click counts of 58±23. Surveys on a scale of 1 (worst) to 5 (best) scored "ease of use" at 3.4±1.1, "integration of functions" at 3.0±1.2, and "ease of learning" at 2.6±1.5. Overall, the system is functional and reliable but could be improved to support workflow.

Smartphones provide both advanced mobile communications and portable computing in a single handheld device. The number of smartphone users is growing rapidly, including among healthcare professionals. Many medical applications for smartphones have been developed and are widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients. In this article, the authors present healthcare applications for smartphones in three user groups: healthcare professionals, medical or nursing students, and patients.

Summary Background: Increased usage of MedlinePlus by Spanish-speakers was observed after introduction of MedlinePlus in Spanish. This probably reflects increased usage of MEDLINE and PubMed by those with greater fluency in the language in which it is presented; but this has never been demonstrated in English speakers. Evidence that lack of English fluency deters international healthcare personnel from using PubMed could support the use of multi-language search tools like Babel-MeSH. Objectives: This study aims to measure the effects of language fluency and other socioeconomic factors on PubMed MEDLINE and MedlinePlus access by international users. Methods: We retrospectively reviewed server pageviews of PubMed and MedlinePlus from various periods of time, and analyzed them against country statistics on language fluency, GDP, literacy rate, Internet usage, medical schools, and physicians per capita, to determine whether they were associated. Results: We found fluency in English to be positively associated with pageviews of PubMed and MedlinePlus in countries with high literacy rates. Spanish was generally found to be positively associated with pageviews of MedlinePlus en Español. The other parameters also showed varying degrees of association with pageviews. Conclusions: After adjusting for the other factors investigated in this study, language fluency was a consistently significant predictor of the use of PubMed, MedlinePlus English and MedlinePlus en Español. This study may support the need for multi-language search tools and may increase access of health information resources from non-English speaking countries. Citation: Sheets L, Gavino A, Callaghan F, Fontelo P. Do language fluency and other socioeconomic factors influence the use of PubMed and MedlinePlus? Appl Clin Inf 2013; 4: 170–184http://dx.doi.org/10.4338/ACI-2013-01-RA-0006

Summary Background: Smartphones are increasingly important for clinical decision support, but smartphone and Internet use are limited by cost or coverage in many settings. txt2MEDLINE provides access to published medical evidence by text messaging. Previous studies have evaluated this approach, but we found no comparisons with other tools in this format. Objectives: To compare txt2MEDLINE with other databases for answering clinical queries by text messaging in low-resource settings. Methods: Using varied formats, we searched txt2MEDLINE and five other search portals (askMED-LINE, Cochrane, DynaMed, PubMed PICO, and UpToDate) to develop optimal strategies for each. We then searched each database again with five benchmark queries, using the customized search-optimization formats. We truncated the results to less than 480 characters each to simulate delivering them to a maximum of three text messages. Clinicians with practice experience in low-resource areas scored the results on a 5-point Likert scale. Results: Median scores and standard deviations from 17 reviewers were: txt2MEDLINE, 3.2±0.82 (control); askMEDLINE, 3.2±0.90 (p = 0.918); Cochrane, 3.8±0.58 (p = 0.073); DynaMed, 3.6±0.65 (p = 0.105); PubMed PICO, 3.6±0.82 (p = 0.005); and UpToDate, 4.0±0.52 (p = 0.002). Our sample size was sufficiently powered to find differences of 1.0 point. Conclusions: Comparing several possible sources for texting-based clinical-decision-support information, our results did not demonstrate one-point differences in usefulness on a scale of 1 to 5. PubMed PICO and UpToDate were significantly better than txt2MEDLINE, but with relatively small improvements in Likert score (0.4 and 0.8, respectively). In a texting-only setting, txt2MEDLINE is comparable to simulated alternatives based on established reference sources.

Background Advanced mobile communications and portable computation are now combined in handheld devices called “smartphones”, which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category. Methods In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches. Results A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students. Conclusions Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

Purpose Confounding of racial, socioeconomic, and behavioral characteristics may lead to the misidentification of race as a risk factor for certain diseases. The authors evaluated the validity of attributing race as a risk factor in a widely used pathology textbook. Method In 2009-2010, the authors searched Robbins and Cotran Pathologic Basis of Disease, Professional Edition (8th ed) for assertions that African Americans have a different disease profile than do individuals of other races. They evaluated the references cited in the textbook, assessing the quality of the sources and whether those authors controlled for confounding variables, such as socioeconomic status and behavioral characteristics. For statements that remained unconfirmed, the authors searched the literature for evidence supporting or contradicting the claims made in the textbook. The authors classified each statement from the textbook as supported, unconfirmed, or contradicted. Results Of the 31 relevant statements, 11 were supported by the literature review, 17 remained unconfirmed, and three were contradicted. Conclusions About two-thirds of the assertions that different risk factors exist for African Americans found in a widely used pathology textbook could not be supported by the published literature. Failure to separate race from other socioeconomic and behavioral characteristics as risk factors for certain diseases may contribute to the misdiagnosis of patients and interfere with efforts to identify and remove causes of health disparities.