Informação na judicialização da saúde: o protótipo JUDJe / Information in the judicialization of health: the JUDJe prototype / Información en la judicialización de la salud: el prototipo JUDJe

O fenômeno da judicialização da saúde carece de dados organizados e comparáveis entre estudos sobre o tema. Diversas fontes, recortes prévios e intermediários geram resultados conflitantes e de difícil repro-dução. Esta nota argumenta a necessidade de definir um padrão/elemento comum nos processos judiciais em saúde, propondo o sistema JUDJe, que utiliza o Diário de Justiça Eletrônico para extrair, organizar e classificar esses dados. O JUDJe gerou um banco de dados aberto com 100 mil movimentações processuais sobre casos de câncer. Defende mais qualidade e conexão dos dados, e mais acesso a esses últimos, pro-movendo equidade e visão multidimensional. Propõe a "judicialização 2.0" com dados em rede conectando saúde e direito.

The phenomenon of health judicialisation lacks organised and comparable data between studies on the subject. Different sources, previous and intermediate pieces of information generate conflicting results that are difficult to reproduce. This note argues the need to define a common standard/element in health lawsuits and proposes the JUDJe system, using the online Official Gazette to extract, organize and classify such data. JUDJe generated an open geo-referenced database with 100 thousand legal proceedings on cancer cases. It advocates more quality and connection of data, and more access to them, promoting equity and a multidimensional vision. It proposes a "judicialization 2.0" connecting the health and law domains.

El fenómeno de la judicialización de la salud carece de datos organizables y comparables entre los estudios sobre el tema. Diferentes fuentes, cortes previos y intermedios generan resultados contradictorios y dificiles de reproduzir. Esta nota argumenta la necesidad de definir un elemento común/estándar en los procesos judiciales de salud, proponiendo el sistema JUDJe, que utiliza el Diario Oficial Electrónico de Justicia para extraer, organizar y clasificar esos datos. El JUDJe generó una base de datos abiertos georreferenciada con 100 mil actuaciones judiciales sobre casos de cáncer. Defiende más calidad y conexión de datos, y más acceso a esos últimos, promoviendo la equidad y una visión multidimensional. Propone la "judicialización 2.0" con datos en red que conecten salud y derecho.

The implications of data aggregation on propagating racial and ethnic disparities within the health care landscape: Actionable recommendations and considerations for pharmacists.

As the U.S. population becomes more racially and ethnically diverse, it is increasingly important to characterize health inequities for targeted intervention. As it stands, demographic data regarding race and ethnicity for patients and pharmacy trainees alike are aggregated into heterogenous population groups, resulting in findings that may inaccurately reflect the experiences of smaller subgroups. Disaggregation of patient outcomes data can serve to better inform public health interventions for the most vulnerable populations. In pharmacy, disaggregation can allow for better identification of racial and ethnic subgroups who have been traditionally excluded from funding support among other opportunities. In this commentary, we provide historical context and actionable recommendations to better describe our patient and pharmacy trainee populations, with the objectives of improving pharmacist representation and health equity.

Addressing Data Aggregation and Data Inequity in Race and Ethnicity Reporting and the Impact on Breast Cancer Disparities.

Collecting and reporting data on race and ethnicity is vital to understanding and addressing health disparities in the United States. These health disparities can include increased prevalence and severity of disease, poorer health outcomes, decreased access to healthcare, etc., in disadvantaged populations compared with advantaged groups. Without these data, researchers, administrators, public health practitioners, and policymakers are unable to identify the need for targeted interventions and assistance. When researching or reporting on race and ethnicity, typically broad racial categories are used. These include White or Caucasian, Black or African American, Asian American, Native Hawaiian or Other Pacific Islander, or American Indian and Alaska Native, as well as categories for ethnicity such as Latino or Hispanic or not Latino or Hispanic. These categories, defined by the Office of Management and Budget, are the minimum standards for collecting and reporting race and ethnicity data across federal agencies. Of note, these categories have not been updated since 1997. The lack of accurate and comprehensive data on marginalized racial and ethnic groups limits our understanding of and ability to address health disparities. This has implications for breast cancer outcomes in various populations in this country. In this paper, we examine the impact data inequity and the lack of data equity centered processes have in providing appropriate prevention and intervention efforts and resource allocations.

Disaggregation of Race and Ethnicity Group Data: Research-to-Practice Issues in Clinical Environments.

This Viewpoint discusses the importance of accurately categorizing and collecting race and ethnicity data, matching self-identity with race and ethnicity labels, in an effort to quantify the extent of health disparities.

Considerations When Aggregating Data to Measure Performance Across Levels of the Health Care System.

BACKGROUND: Measuring quality at varying levels of the health care system requires attribution, a process of determining the patients and services for which each level is responsible. However, it is important to ensure that attribution approaches are equitable; otherwise, individuals may be assigned differentially based upon social determinants of health. METHODS: First, we used Medicaid claims (2010-2018) from Michigan to assess the proportion of children with sickle cell anemia who had less than 12 months enrollment within a single Medicaid health plan and could therefore not be attributed to a specific health plan. Second, we used the Medicaid Analytic eXtract data (2008-2009) from 26 states to simulate adapting the 30-Day Pediatric All-Condition Readmission measure to the Accountable Care Organization (ACO) level and examined the proportion of readmissions that could not be attributed. RESULTS: For the sickle cell measure, an average of 300 children with sickle cell anemia were enrolled in Michigan Medicaid each year. The proportion of children that could not be attributed to a Medicaid health plan ranged from 12.2% to 89.0% across years. For the readmissions measure, of the 1,051,365 index admissions, 22% were excluded in the ACO-level analysis because of being unable to attribute the patient to a health plan for the 30 days post discharge. CONCLUSIONS: When applying attribution models, it is essential to consider the potential to induce health disparities. Differential attribution may have unintentional consequences that deepen health disparities, particularly when considering incentive programs for health plans to improve the quality of care.

Feasibility for aggregation of commutable external quality assessment results to evaluate metrological traceability and agreement among results.

Objectives: External quality assessment (EQA) with commutable samples is used for assessing agreement of results for patients' samples. We investigated the feasibility to aggregate results from four different EQA schemes to determine the bias between different measurement procedures and a reference target value. Methods: We aggregated EQA results for creatinine from programs that used commutable EQA material by calculating the relative difference between individual participant results and the reference target value for each sample. The means and standard errors of the means were calculated for the relative differences. Results were partitioned by methods, manufacturers and instrument platforms to evaluate the biases for the measurement procedures. Results: Data aggregated for enzymatic methods had biases that varied from -8.2 to 3.8% among seven instrument platforms for creatinine at normal concentrations (61-85 µmol/L). EQA schemes differed in the evidence provided about the commutability of their samples, and in the amount of detail collected from participants regarding the measurement procedures which limited the ability to sub-divide aggregated data by instrument platforms and models. Conclusions: EQA data could be aggregated from four different programs using different commutable samples to determine bias among different measurement procedures. Criteria for commutability for EQA samples as well as standardization of reporting the measurement methods, reagents, instrument platforms and models used by participants are needed to improve the ability to aggregate the results for optimal assessment of performance of measurement procedures. Aggregating data from a larger number of EQA schemes is feasible to assess trueness on a global scale.

IoMT Platform for Pervasive Healthcare Data Aggregation, Processing, and Sharing Based on OneM2M and OpenEHR.

Pervasive healthcare services have undergone a great evolution in recent years. The technological development of communication networks, including the Internet, sensor networks, and M2M (Machine-to-Machine) have given rise to new architectures, applications, and standards related to addressing almost all current e-health challenges. Among the standards, the importance of OpenEHR has been recognized, since it enables the separation of medical semantics from data representation of electronic health records. However, it does not meet the requirements related to interoperability of e-health devices in M2M networks, or in the Internet of Things (IoT) scenarios. Moreover, the lack of interoperability hampers the application of new data-processing techniques, such as data mining and online analytical processing, due to the heterogeneity of the data and the sources. This article proposes an Internet of Medical Things (IoMT) platform for pervasive healthcare that ensures interoperability, quality of the detection process, and scalability in an M2M-based architecture, and provides functionalities for the processing of high volumes of data, knowledge extraction, and common healthcare services. The platform uses the semantics described in OpenEHR for both data quality evaluation and standardization of healthcare data stored by the association of IoMT devices and observations defined in OpenEHR. Moreover, it enables the application of big data techniques and online analytic processing (OLAP) through Hadoop Map/Reduce and content-sharing through fast healthcare interoperability resource (FHIR) application programming interfaces (APIs).

Accounting for accountable care: Value-based population health management.

Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients' health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Population wide assessment of antimicrobial use in dogs and cats using a novel data source - A cohort study using pet insurance data.

Antimicrobial use in veterinary practice is under increasing scrutiny as a contributor to the rising risk of multidrug resistant bacterial pathogens. Surveillance of antimicrobial use in food animals is extensive globally, but population level data is lacking for companion animals. Lack of census data means cohorts are usually restricted to those attending veterinary practices, which precludes aggregating data from large cohorts of animals, independent of their need for veterinary intervention. The objective of this study was to investigate the exposure of dogs and cats to antimicrobials at a population level. A retrospective cohort study was performed using a novel data source; a pet insurance database. The rate of antimicrobial prescribing, and the rate of prescribing of critically important antimicrobials, was measured in a large population of dogs (813,172 dog-years) and cats (129,232 cat-years) from 2013 - 2017. The incidence rate of antimicrobial prescribing was 5.8 prescriptions per 10 dog years (95% CI 5.8-5.9 per 10 dog years) and 3.1 prescriptions per 10 cat years (95% CI 3.1-3.2 per 10 cat years). Critically important antimicrobials accounted for 8% of all the antimicrobials prescribed over the 4-year study. Cats were 4.8-fold more likely than dogs to be prescribed 3rd-generation cephalosporins. The level of antimicrobial exposure in dogs and cats was less than half that for the coincident human community. Data such as this provides a unique opportunity to monitor antimicrobial prescribing in veterinary medicine, which is a critical component of optimal antimicrobial stewardship.

Big Data and the Opioid Crisis: Balancing Patient Privacy with Public Health.

Parts I through III of this paper will examine several, increasingly comprehensive forms of aggregation, ranging from insurance reimbursement "lock-in" programs to PDMPs to completely unified electronic medical records (EMRs). Each part will advocate for the adoption of these aggregation systems and provide suggestions for effective implementation in the fight against opioid misuse. All PDMPs are not made equal, however, and Part II will, therefore, focus on several elements - mandating prescriber usage, streamlining the user interface, ensuring timely data uploads, creating a national data repository, mitigating privacy concerns, and training doctors on how to respond to perceived doctor-shopping - that can make these systems more effective. In each part, we will also discuss the privacy concerns of aggregating data, ranging from minimal to significant, and highlight the unique role of stigma in motivating these concerns. In Part IV, we will conclude by suggesting remedial steps to offset this loss of privacy and to combat the stigma around SUDs and mental health disorders in general.