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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.

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.

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.

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

“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.

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

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.

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.

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

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.