Patterns and Influencing Factors of eHealth Tools Adoption Among Medicaid and Non-Medicaid Populations From the Health Information National Trends Survey (HINTS) 2017-2019: Questionnaire Study.

BACKGROUND: Evidence suggests that eHealth tools adoption is associated with better health outcomes among various populations. The patterns and factors influencing eHealth adoption among the US Medicaid population remain obscure. OBJECTIVE: The objective of this study is to explore patterns of eHealth tools adoption among the Medicaid population and examine factors associated with eHealth adoption. METHODS: Data from the Health Information National Trends Survey from 2017 to 2019 were used to estimate the patterns of eHealth tools adoption among Medicaid and non-Medicaid populations. The effects of Medicaid insurance status and other influencing factors were assessed with logistic regression models. RESULTS: Compared with the non-Medicaid population, the Medicaid beneficiaries had significantly lower eHealth tools adoption rates for health information management (11.2% to 17.5% less) and mobile health for self-regulation (0.8% to 9.7% less). Conversely, the Medicaid population had significantly higher adoption rates for using social media for health information than their counterpart (8% higher in 2018, P=.01; 10.1% higher in 2019, P=.01). Internet access diversity, education, and cardiovascular diseases were positively associated with health information management and mobile health for self-regulation among the Medicaid population. Internet access diversity is the only factor significantly associated with social media adoption for acquisition of health information (OR 1.98, 95% CI 1.26-3.11). CONCLUSIONS: Our results suggest digital disparities in eHealth tools adoption between the Medicaid and non-Medicaid populations. Future research should investigate behavioral correlates and develop interventions to improve eHealth adoption and use among underserved communities.

Consolidating Emergency Department-specific Data to Enable Linkage with Large Administrative Datasets.

INTRODUCTION: The American Hospital Association (AHA) has hospital-level data, while the Centers for Medicare & Medicaid Services (CMS) has patient-level data. Merging these with other distinct databases would permit analyses of hospital-based specialties, units, or departments, and patient outcomes. One distinct database is the National Emergency Department Inventory (NEDI), which contains information about all EDs in the United States. However, a challenge with merging these databases is that NEDI lists all US EDs individually, while the AHA and CMS group some EDs by hospital network. Consolidating data for this merge may be preferential to excluding grouped EDs. Our objectives were to consolidate ED data to enable linkage with administrative datasets and to determine the effect of excluding grouped EDs on ED-level summary results. METHODS: Using the 2014 NEDI-USA database, we surveyed all New England EDs. We individually matched NEDI EDs with corresponding EDs in the AHA and CMS. A "group match" was assigned when more than one NEDI ED was matched to a single AHA or CMS facility identification number. Within each group, we consolidated individual ED data to create a single observation based on sums or weighted averages of responses as appropriate. RESULTS: Of the 195 EDs in New England, 169 (87%) completed the NEDI survey. Among these, 130 (77%) EDs were individually listed in AHA and CMS, while 39 were part of groups consisting of 2-3 EDs but represented by one facility ID. Compared to the individually listed EDs, the 39 EDs included in a "group match" had a larger number of annual visits and beds, were more likely to be freestanding, and were less likely to be rural (all P<0.05). Two grouped EDs were excluded because the listed ED did not respond to the NEDI survey; the remaining 37 EDs were consolidated into 19 observations. Thus, the consolidated dataset contained 149 observations representing 171 EDs; this consolidated dataset yielded summary results that were similar to those of the 169 responding EDs. CONCLUSION: Excluding grouped EDs would have resulted in a non-representative dataset. The original vs consolidated NEDI datasets yielded similar results and enabled linkage with large administrative datasets. This approach presents a novel opportunity to use characteristics of hospital-based specialties, units, and departments in studies of patient-level outcomes, to advance health services research.

Digitalización de historias clínicas: análisis de un proyecto en un contexto real en el Servicio Extremeño de Salud en España / Medical record digitalization: analysis of a real life project involving the Extremadura Health Service in Spain

Se presenta un estudio de caso en el contexto de la gestión de la información clínica sanitaria, en el que, a partir de la revisión de la literatura científica, se detectó que las tecnologías de la información se han ido introduciendo de manera desigual e irregular en la sanidad pública española. El estudio se planteó como objetivo analizar cómo se realiza un proyecto de digitalización de historias clínicas, el cual condujo a las siguientes preguntas de investigación: ¿Cómo se gestiona la información sanitaria? ¿Cómo se realiza un proyecto de digitalización de historias clínicas en un contexto real? ¿Qué formación tienen los profesionales que participan en el proceso y ¿cuál es el papel de los documentalistas? Se siguió una metodología de investigación cualitativa con perspectiva naturalista, además del análisis de la literatura científica, en la que se presentaron los resultados del estudio basados en la dotación de equipamiento, el servicio de consultoría, la gestión documental y de archivo, además del perfil profesional que se plantea para la ejecución de un proyecto de digitalización de historias clínicas. Se concluye que la gestión de la información no satisface las necesidades del personal sanitario; que no existe un procedimiento documentado para la digitalización de las historias clínicas y que no se requiere la participación de profesionales sanitarios ni de documentalistas, a pesar de que la literatura científica resalta la inestimable participación de estos profesionales(AU)

A health information management case study is presented in a which scientific literature review revealed that information technologies have been incorporated into Spanish public health in an uneven, irregular manner. To corroborate this fact we set ourselves the objective of analyzing the way in which a medical record digitalization project is conducted, which led to the following research questions: How is health information managed? How is a medical record digitalization project conducted in a real life context? What is the background of professionals involved in the process and what is the role of documentalists? Besides examination of the scientific literature, a naturalist qualitative analysis was performed in which the results of the study were presented, based on equipment provision, advisory service, document management and archiving, and the professional profile established for the conduct of a medical record digitalization project. It is concluded that information management does not meet the needs of the health personnel, that a documented procedure is not available for medical record digitalization, and that participation of health professionals and documentalists is not requested, despite the scientific literature highlighting the invaluable participation of these professionals(AU)

Insights from Adopting a Data Commons Approach for Large-scale Observational Cohort Studies: The California Teachers Study.

BACKGROUND: Large-scale cancer epidemiology cohorts (CEC) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more findable, accessible, interoperable, and reusable, or FAIR. How CECs should approach this transformation is unclear. METHODS: The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a data warehouse to integrate and manage data and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses. RESULTS: Our new CTS infrastructure includes a data warehouse and data marts, which are focused subsets from the data warehouse designed for efficiency. The secure CTS workspace utilizes a remote desktop service that operates within a Health Insurance Portability and Accountability Act (HIPAA)- and Federal Information Security Management Act (FISMA)-compliant platform. Our infrastructure offers broad access to CTS data, includes statistical analysis and data visualization software and tools, flexibly manages other key data activities (e.g., cleaning, updates, and data sharing), and will continue to evolve to advance FAIR principles. CONCLUSIONS: Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework. IMPACT: The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.See all articles in this CEBP Focus section, "Modernizing Population Science."

The impact of an integrated electronic immunization registry and logistics management information system (EIR-eLMIS) on vaccine availability in three regions in Tanzania: A pre-post and time-series analysis.

BACKGROUND: Since 2016, the Government of Tanzania has been implementing TImR, an integrated Electronic Immunization registry-logistics management information system (EIR-LMIS) that includes stock notifications. The objective of this study is to estimate the impact of this intervention on vaccine availability. METHODS: Monthly stock-out data were collected from paper registers at facilities, an Excel-based system at districts, and the new system (TImR) across all 924 health facilities in Arusha, Tanga and Kilimanjaro Regions. Six months of stockout rates pre- and post-introduction, by antigen, were compared via a two-way analysis of variance (ANOVA). A mixed-effects logistic regression model with the TImR data identified predictors of vaccine availability across antigens. FINDINGS: Post-introduction, ANOVA models estimated that overall stock-out rates declined from a monthly average of 7.1% to 2.1% (p < 0.01). Three specific vaccines had fewer stock-outs; OPV's monthly average dropped from 12.5% to 2.1% (p < 0.01), MR from 9.4% to 1.0% (p < 0.01) and DTP-HepB-HiB from 8.1% to 1.7% (p < 0.01). In the mixed-effects logistic regression model, controlling for antigen, odds of stock-out were 4.1% (95% CI: 3.3 - 4.9) lower for each week of tenure. Compared to DTP-HepB-HiB vaccine, odds of BCG vaccine being stocked out were 4.31 as high (95% CI: 3.1 - 5.0). The odds of being stocked-out were 29.7% lower for PCV (95% CI: 8.8 - 45.8) and 26.6% (95% CI: 3.4 - 44.1) lower for rotavirus vaccines compared to DTP-HepB-HiB. The odds of stock out were 37.7% lower for MR vaccine than DTP-HepB-HiB (95% CI: 18.1 - 52.6). CONCLUSIONS: Tanzania's integrated EIR-eLMIS may increase vaccine availability compared to its paper and Excel based system. Post-introduction of an eLMIS, the odds of a vaccine stock-out reduced over time. Further research could determine the impact of this intervention on vaccine wastage and replenishment response times.

Diabetes Care Editors' Expert Forum 2018: Managing Big Data for Diabetes Research and Care.

Technological progress in the past half century has greatly increased our ability to collect, store, and transmit vast quantities of information, giving rise to the term "big data." This term refers to very large data sets that can be analyzed to identify patterns, trends, and associations. In medicine-including diabetes care and research-big data come from three main sources: electronic medical records (EMRs), surveys and registries, and randomized controlled trials (RCTs). These systems have evolved in different ways, each with strengths and limitations. EMRs continuously accumulate information about patients and make it readily accessible but are limited by missing data or data that are not quality assured. Because EMRs vary in structure and management, comparisons of data between health systems may be difficult. Registries and surveys provide data that are consistently collected and representative of broad populations but are limited in scope and may be updated only intermittently. RCT databases excel in the specificity, completeness, and accuracy of their data, but rarely include a fully representative sample of the general population. Also, they are costly to build and seldom maintained after a trial's end. To consider these issues, and the challenges and opportunities they present, the editors of Diabetes Care convened a group of experts in management of diabetes-related data on 21 June 2018, in conjunction with the American Diabetes Association's 78th Scientific Sessions in Orlando, FL. This article summarizes the discussion and conclusions of that forum, offering a vision of benefits that might be realized from prospectively designed and unified data-management systems to support the collective needs of clinical, surveillance, and research activities related to diabetes.

A semi-automated approach for analyzing collages to inform the design of a family health information management system for Hispanic dementia caregivers.

Culturally- and linguistically-tailored health communication is needed for vulnerable populations to manage their health and the health of their families. This presents a significant design challenge. The use of collages is an increasingly popular technique with the flexibility to capture the needs and experiences of individuals with various cultural and language preferences. Collage analysis has typically remained qualitative in nature. We introduce a novel, objective, semi-automated approach that enhances collage analysis to elucidate pattern differences that may not be detectable by natural perception. We present a case scenario of collage analysis based on the expressed experience and self-management needs of Hispanic dementia caregivers (n = 24). We demonstrate how our innovative approach may reveal cultural differences between language groups that could have otherwise been missed using traditional techniques.

Considerations for Genomic Data Privacy and Security when Working in the Cloud.

Next-generation sequencing produces large amounts of data. The complexity and data management issues associated with next-generation sequencing have led many laboratories to turn to cloud services, especially when internal information technology infrastructure is inadequate to support data requirements. In addition, public cloud repositories of variants are being increasingly utilized, and their data sets are being populated through crowdsourcing submissions of human genetic variation identified within laboratories. The purpose of this review is to describe the challenges of managing genomic data in the cloud and to discuss potential strategies to surmount these challenges in a compliant manner. The definitions and advantages of cloud systems are outlined. Special regulatory considerations for laboratories are included, and strategies for compliance in the US regulatory environment for genetic information in clinical patient care as well as in research and public databases are also discussed.

Gestión de la información como herramienta para la toma de decisiones en salud: escenarios más probables / Information management as a decision-making tool in health: most probable scenarios

Introducción: La gestión de la información constituye una de las herramientas modernas para facilitar los procesos al interior de las organizaciones, en especial la toma de decisiones. En el sector de la salud, dada la importancia de dominar los más recientes avances en la práctica asistencial, es fundamental para garantizar la proactividad en la satisfacción de las necesidades de pacientes, familiares y otros grupos de interés. Objetivo: El objetivo de la investigación es determinar, a partir del empleo del método SMIC, los escenarios más probables en relación al empleo de la gestión de la información como herramienta de la toma de decisiones en organizaciones de salud pública. Métodos: Se aplicó el método prospectivo SMIC, basado en la consulta a 12 expertos de seis países iberoamericanos, con el fin de determinar las combinaciones de hipótesis de futuro (escenarios) más probables en relación al tema de estudio. Resultados: El procesamiento realizado mediante el software SMIC permitió determinar que la hipótesis de futuro más probable e influyente sobre el resto se relaciona con que existan herramientas y plataformas tecnológicas adecuadas (H4), mientras que la referida a que la información se maneje como un recurso crítico de las organizaciones de salud y facilite la innovación (H1) es la menos probable y dependiente. El escenario donde no se cumplen a futuro ninguna de las hipótesis resultó ser el más probable (P= 0,503). Conclusiones: El estudio permitió evidenciar la necesidad de dinamizar las políticas del sector salud de cara a que la gestión de la información cuente con las bases necesarias y adecuadas (sistemas, plataformas, recursos humanos capacitados, entre otros) para convertirse en una efectiva herramienta para la toma de decisiones(AU)

ACT Introduction: The management of information is one of the modern tools to facilitate processes within organizations, especially decision making. In the health sector, given the importance of mastering the most recent advances in healthcare practice, it is essential to guarantee proactivity in satisfying the needs of patients, family members and other interest groups. Objective: The objective of the article is to determine, from the use of the SMIC method, the most probable scenarios in relation to the use of information management as a tool of decision making in public health organizations. Methods: The prospective SMIC method was applied, based on the consultation of 12 experts from six Ibero-American countries, in order to determine the combinations of future hypotheses (scenarios) most likely in relation to the study topic. Results: The processing done by the SMIC software allowed to determine that the most probable and influential future hypothesis about the rest is related to the existence of adequate technological tools and platforms (H4), while that related to the fact that the information is handled as a critical resource of Health organizations and facilitate innovation (H1) is the least likely and dependent. The scenario where none of the hypotheses were met in the future was the most probable (P = 0.503). Conclusions: The study made it possible to highlight the need to streamline the health sector policies so that information management has the necessary and appropriate bases (systems, platforms, trained human resources, among others) to become an effective tool for the decision making process. of decisions(AU)

Making the pediatric perioperative surgical home come to life by leveraging existing health information technology.

PURPOSE OF REVIEW: To design a patient data dashboard for the Department of Anesthesiology, Perioperative and Pain Medicine at Boston Children's Hospital that supports care integration across the healthcare system as described by the pediatric perioperative surgical home (PPSH) initiative. RECENT FINDINGS: By using 360 Technology, patient data was automatically pulled from all available Electronic Health Record sources from 2005 to the present. The PPSH dashboard described in this report provides a guide for implementation of PPSH Clinical Care Pathways. The dashboard integrates several databases to allow for visual longitudinal tracking of patient care, outcomes, and cost. The integration of electronic information provided the ability to display, compare, and analyze selected PPSH metrics in real time. By utilizing the PPSH dashboard format the use of an automated, integrated clinical, and financial health data profile for a specific patient population may improve clinicians' ability to have a comprehensive assessment of all care elements. This more global clinical thinking has the potential to produce bottom-up, evidence-based healthcare reform. SUMMARY: The experience with the PPSH dashboard provides solid evidence for the use of integrated Electronic Health Record to improve patient outcomes and decrease cost.

[Experiences in construction, dissemination and use of the health situation analysis in Colombia 2016]. / Experiencias departamentales en la construcción, divulgación y uso del análisis de situación de salud, Colombia 2016.

OBJETIVE: The Health Situation Analysis (ASIS in Spanish) is a methodology that has been implemented recently in Colombia. This study aims at understanding the experience of building, disseminating and using ASIS for decision-making in some territorial entities. METHODS: Semistructured interviews were applied to officials of the departmental health entities. The information was analyzed according to a set of categories previously established. RESULTS: The territorial entities implement ASIS by incorporating the Social Determinants of Health approach; however, the technical, economic and human capacities for the elaboration of this type of analysis are not equitable. Intersectoral and social participation is still weak and the results do not guide the decision making at territorial level yet. CONCLUSIONS: The ASIS methodology seeks to position itself as one of the official mechanisms to generate evidence that guides health policy and decision making at national, regional and local levels. There are economic, institutional and political challenges for its consolidation as a useful strategy in health planning. ASIS is a methodology of great relevance for the territorial entities and its implementation should be further strengthened.

OBJETIVO: El Análisis de Situación de Salud (ASIS) es una metodología que se encuentra en implementación recientemente en Colombia. Este estudio buscó comprender la experiencia de construcción, divulgación y uso del ASIS para la toma de decisiones en algunas entidades territoriales (ET). MÉTODOS: Entrevistas semiestructuradas a funcionarios de las entidades departamentales de salud; la información fue analizada de acuerdo a un conjunto de categorías establecidas previamente. RESULTADOS: Las ET implementan el ASIS incorporando el enfoque de los Determinantes Sociales de la Salud; sin embargo, las capacidades técnicas, económicas y humanas son desiguales para la elaboración de este tipo de análisis; la participación intersectorial y social aún es débil y los resultados generados todavía no orientan la toma de decisiones a nivel territorial. CONCLUSIONES: La metodología ASIS aspira a posicionarse como uno de los mecanismos oficiales para generar evidencia que oriente las políticas y la toma de decisiones en salud a nivel nacional, regional y local; existen desafíos a nivel económico, institucional y político para su consolidación como estrategia de útil en la planificación en salud. El ASIS es una metodología de gran relevancia para las ET y debe seguir fortaleciéndose su implementación.

Information needs and information seeking behaviors of patients and families in acute healthcare settings: a scoping review protocol.

REVIEW QUESTION/OBJECTIVE: The objective of this scoping review is to explore the information needs and information seeking behaviors of patients and families from healthcare providers in acute healthcare settings.More specifically the review questions are.

Integration of healthcare and financial information: Evaluation in a public hospital using a comprehensive approach.

Public healthcare organisations are moving towards the use of new technologies to automate and improve their internal processes in order to increase the effectiveness and efficiency of their use of resources. The aim of this research is to tackle the systematic evaluation of an experience of integrating information in a healthcare organisation, paying attention to the implications that this entails. The results show that the integration of the information in the hospital results in higher levels of quality. This study contributes a vision of interrelated work, in which tasks are shared and aims are jointly established.

Adoption of online health management tools among healthy older adults: An exploratory study.

As the population ages and chronic diseases abound, overburdened healthcare systems will increasingly require individuals to manage their own health. Online health management tools, quickly increasing in popularity, have the potential to diminish or even replace in-person contact with health professionals, but overall efficacy and usage trends are unknown. The current study explored perceptions and usage patterns among users of online health management tools, and identified barriers and barrier-breakers among non-users. An online survey was completed by 169 computer users (aged 50+). Analyses revealed that a sizable minority (37%) of participants use online health management tools and most users (89%) are satisfied with these tools, but a limited range of tools are being used and usage occurs in relatively limited domains. Improved awareness and education for online health management tools could enhance people's abilities to remain at home as they age, reducing the financial burden on formal assistance programs.

Data Collection and Management in Community Engaged Research: Lessons Learned From Two Community-Based Participatory Research Partnerships.

BACKGROUND: Data collection and data management (DCDM) within community-engaged research (CEnR) requires special considerations that differ from those of traditional research. To date, little guidance exists to preemptively help community-academic partnerships anticipate and resolve DCDM issues that may arise. OBJECTIVE: We sought to provide recommendations for DCDM based on two CEnR research projects. METHODS: We used a case study design to describe the DCDM process and lessons learned that were generated through formal and informal discussions between community and academic partners. LESSONS LEARNED: We identified 4 key lessons: 1) CEnR requires a flexible, iterative approach to DCDM, 2) there are trade-offs to having a flexible DCDM approach, 3) responsibilities for DCDM should consider the skill sets and priorities of all partners, and 4) nuances of DC within CEnR has important implications for human subjects and ethics training. CONCLUSIONS: Based on our lessons learned, we provide recommendations for how to approach DCDM within CEnR.

Using 'big data' to validate claims made in the pharmaceutical approval process.

Big Data in the healthcare setting refers to the storage, assimilation, and analysis of large quantities of information regarding patient care. These data can be collected and stored in a wide variety of ways including electronic medical records collected at the patient bedside, or through medical records that are coded and passed to insurance companies for reimbursement. When these data are processed it is possible to validate claims as a part of the regulatory review process regarding the anticipated performance of medications and devices. In order to analyze properly claims by manufacturers and others, there is a need to express claims in terms that are testable in a timeframe that is useful and meaningful to formulary committees. Claims for the comparative benefits and costs, including budget impact, of products and devices need to be expressed in measurable terms, ideally in the context of submission or validation protocols. Claims should be either consistent with accessible Big Data or able to support observational studies where Big Data identifies target populations. Protocols should identify, in disaggregated terms, key variables that would lead to direct or proxy validation. Once these variables are identified, Big Data can be used to query massive quantities of data in the validation process. Research can be passive or active in nature. Passive, where the data are collected retrospectively; active where the researcher is prospectively looking for indicators of co-morbid conditions, side-effects or adverse events, testing these indicators to determine if claims are within desired ranges set forth by the manufacturer. Additionally, Big Data can be used to assess the effectiveness of therapy through health insurance records. This, for example, could indicate that disease or co-morbid conditions cease to be treated. Understanding the basic strengths and weaknesses of Big Data in the claim validation process provides a glimpse of the value that this research can provide to industry. Big Data can support a research agenda that focuses on the process of claims validation to support formulary submissions as well as inputs to ongoing disease area and therapeutic class reviews.