Community Vulnerability Index Dashboard

A community vulnerability index dashboard includes a data ingestion logic module configured to automatically receive real-time and non-real-time data from a variety of sources including education agencies, law enforcement, health services agencies, healthcare agencies, medical insurance agencies, housing and transportation agencies, childcare licensing agencies, and non-emergency citywide services. The dashboard includes a data processing module that extracts and process the ingested data, and a data analysis logic module that analyzes the processed data to determine values for indicators and an overall vulnerability index value based on the values of the plurality of indicators to provide insight into the lives of residents living in a community on a block group level. The dashboard includes a data presentation dashboard interface to display an interactive choropleth map of the overall vulnerability index and indicator values on a block group level for the community of interest.

FIELD

This disclosure relates to a dashboard analytic logic module for a computing device graphical user interface that interactively presents data insight into the quality of life and health-related vulnerabilities of a population at a block group granularity level.

BACKGROUND

Every day, huge volumes of data are generated in various forms by disparate types of different sources. For instance, the retail giant, Wal-Mart, handles 1 million customer transactions every day, thereby adding 2.5 petabytes of information to the database. Such enormous volume of digital information is the storehouse of meaningful insights such as valuable business trends, shifting consumer behavior, onset of an epidemic, changing weather patterns and rising crime rate. When managed well, this data can provide businesses and governments an opportunity to unlock new business avenues and provide insightful solutions for better governance to improve people's lives.

Most existing data discovery platforms used to achieve insight in the collected data are either too broad and simply end up as data aggregation technical solutions, or aim too narrowly and result in static reports and dashboards for a single domain. These conventional tools fall short of providing actionable data needed by various entities to truly make significant improvements.

DETAILED DESCRIPTION

The community vulnerability index dashboard described herein is a data analytics and presentation system and method10that enable the users to truly understand the factors that impact the quality of life and health of various communities. The community vulnerability index dashboard system and method10generate highly specific, block group-level indicators from key indicator data received from a variety of publicly available data sources, and present the data analysis via a highly interactive and user-friendly geospatial graphical user interface that are adaptable for a variety of computing platforms. The dashboard system and method uses an overall community vulnerability index (CVI) and four sub-indices: 1) Household Essentials, 2) Empowered People, 3) Equitable Communities, and 4) Good Health. Each sub-index is made up of key indicators. The indices are created on the block group level and are designed to reflect both individual and neighborhood-level characteristics. The dashboard system and method provide actionable insights that enable community-based organizations, local civic leaders, and philanthropic funders to assess community needs, evaluate program effectiveness, redirect funding, apply for grants, inform key stakeholders, make and track goals, and monitor and forecast trends. The dashboard can also be incorporated into other use cases such as predictive models for health services utilization, neighborhood quality index, scenario planning, and impact analyses.

Referring toFIG. 1, The dashboard10is hosted, for example, on the Microsoft Azure Cloud12. By hosting everything on a single platform in an exemplary embodiment, the dashboard10is a streamlined process for ingesting, cleaning, analyzing, and presenting the data. The architecture20includes a data ingestion layer22, a backend and front-end design layer24, and a storage layer26that sits on top of a predictive analytics platform28that may be hosted in the Azure infrastructure, as shown inFIG. 2.

Referring also toFIG. 3, through an automated data ingestion engine30using, for example, Apache NiFi, raw data is received from a variety of data sources14using, for example, File Transfer Protocol (FTP), Simple Object Database Access (SODA)/Application Program Interface (API), client URL (cURL), and other methods. Data is automatically pulled from the data sources14on a regular or periodic basis to ensure that the current data is the up to date. Real-time data, if available, may also be received and used for analysis. The data sources14may include (for data related to, for example, Dallas County, Tex.) Texas Education Agency, the Centers for Disease Control and Prevention, the Census, Feeding America, Department of State Health Services, Dallas Independent School District, Dallas Police Department, Texas Department of Family and Protective Services, Neighborhood Atlas, County Health Rankings & Roadmaps, Centers for Medicare and Medicaid Services, Housing and Transportation Affordability Index, Texas Health and Human Services, U.S. Department of Housing and Urban Development, Dallas County Votes, etc. The data is cleaned for quality and accuracy according to predefined scripts. Cleaned data are then moved within the Azure environment to a HIPAA-compliant database32, such as a PostgreSQL database management system, and stored in a tabular format.

The dashboard10may use a data presentation/interface tool34, for example, the Power BI dashboard tool, to pull data from the database management system32via a gateway36. Power BI is a Microsoft product that can be easily integrated with the Azure platform. An overall community vulnerability index is based on five sub-indices for five main SDOH (Social Determinants of Health) categories. These sub-indices include: household essentials (indicators: food insecurity, paycheck predictability, household structure, health insurance coverage, and median income), empowered people (indicators: educational attainment, internet connectivity, literacy, and mobility), equitable communities (indicators: employment, affordable housing, neighborhood safety, neighborhood stability, clean air, and green space), good health (indicators: life expectancy, alcohol abuse, mental health, cancer, chronic diseases: coronary heart disease, diabetes, chronic obstructive pulmonary disease (COPD), kidney disease, and asthma), and access to vital services (indicators: childcare, elder care, healthcare, social services, utilities, and food). These five categories are described in more detail below. Additionally, a master community vulnerability index is available to provide users the opportunity to look at how these indicators interact across these categories. The dashboard10also uses a mapping application that presents data as actionable insights into the community in question covered by the data. Users may access the dashboard10by using a variety of user devices16, including and not limited to, mobile phones, tablet computers, laptop computers, and desktop computers.

Referring toFIG. 4, a simplified flowchart of the dashboard process is shown. Through the automated pipeline, raw data are obtained electronically from various data sources14. The data ingestion is automated and occurs periodically and/or in real-time. A data integration logic40includes a data extraction module/process42, data cleansing module/process44, and data manipulation module/process46. The data extraction process42may extract data using various technologies and protocols. The data cleansing process44“cleans” or pre-processes the data, putting structured data in a standardized format and preparing unstructured text for natural language processing (NLP) to be performed in the predictive analysis logic module50described below. This logic module may also convert the data into desired formats (e.g., text date field converted to numeric for calculation purposes). The data manipulation module/process46may analyze the representation of a particular data feed against a meta-data dictionary and determine if a particular data feed should be re-configured or replaced by alternative data feeds.

The predictive analysis logic module/process50receives the data from the data integration logic module/process40and analyzes the data. The predictive analysis logic module/process50includes a natural language processing logic52. During natural language processing, raw unstructured data, for example, physicians' notes and reports, first go through a process called tokenization. The tokenization process divides the text into basic units of information in the form of single words or short phrases by using defined separators such as punctuation marks, spaces, or capitalizations. Using the rule-based model, these basic units of information are identified in a meta-data dictionary and assessed according to predefined rules that determine meaning. Using the statistical-based learning model, the disease identification process44quantifies the relationship and frequency of word and phrase patterns and then processes them using statistical algorithms. Using machine learning, the statistical-based learning model develops inferences based on repeated patterns and relationships. A number of complex natural language processing functions including text pre-processing, lexical analysis, syntactic parsing, semantic analysis, handling multi-word expression, word sense disambiguation, and other functions are performed.

The predictive analysis logic module/process50includes a predictive model process54that is adapted to analyze the data and predict the risk of occurrence of particular conditions of interest according to one or more predictive models. It may be used to assess the vulnerability of a certain population with respect to certain diseases, such as, for example, alcohol abuse, mental health, cancer, coronary heart disease, diabetes, COPD, kidney disease, and asthma. The predictive model analysis takes into account of the values of risk factors or variables (weighed or unweighed) and compare them against setpoints and thresholds to determine the amount of risk certain residents in a population of a community is subject to or suffering from certain diseases. One or more predictive models may be incorporated to analyze the data and calculate risk scores associated with particular members of a certain block group in order to determine the best course of action to take with respect to those members or that block group.

Artificial Intelligence (AI)58may also be used to analyze the ingested data. The artificial intelligence model tuning module/process58utilizes adaptive self-learning capabilities using machine learning technologies. The capacity for self-reconfiguration enables the system and method to be sufficiently flexible and adaptable to detect and incorporate trends or differences in the underlying patient data or population that may affect the predictive accuracy of a given algorithm. The artificial intelligence model tuning module/process58may periodically retrain a selected predictive model for improved accurate outcome to allow for selection of the most accurate statistical methodology, variable count, variable selection, interaction terms, weights, and intercept for a local health system or clinic. The artificial intelligence model tuning module/process may automatically modify or improve a predictive model in three exemplary ways. First, it may adjust the predictive weights of the variables without human supervision. Second, it may adjust the threshold values of specific variables without human supervision. Third, the artificial intelligence model tuning process may, without human supervision, evaluate new variables present in the data feed but not presently used in the predictive model, which may result in improved accuracy. The artificial intelligence model tuning module/process may compare the actual observed outcome of the event to the predicted outcome then separately analyze the variables within the model that contributed to the incorrect outcome. It may then re-weigh the variables that contributed to this incorrect outcome, so that in the next reiteration those variables are less likely to contribute to a false prediction. In this manner, the artificial intelligence model tuning module/process is adapted to reconfigure or adjust the predictive model based on the specific clinical setting or population in which it is applied. Further, no manual reconfiguration or modification of the predictive model is necessary. The artificial intelligence model tuning module/process may also be useful to scale the predictive model to different populations, communities, and geographical areas in a rapid timeframe.

The community vulnerability index dashboard system and method10further includes a graphical user interface60that includes a data presentation and configuration logic module/process62. The dashboard interface60is an interactive and user-friendly visualization tool that is designed to enable the user to understand neighborhood characteristics of a selected region or a default geopolitical region on a block group level in a holistic manner. The dashboard displays60+ sub-indicators grouped into five categories that measure the resiliency, commitment and amenities in the neighborhoods on a block group level.FIGS. 5-12 and 14-17show exemplary screenshots of the dashboard graphical user interface60, which has three main components: an indicator right panel, a choropleth or heatmap, and a right panel showing average summary of the selected index for the selected area, which may be a neighborhood, street, city, county, state, multi-state regions.

FIG. 5is an exemplary landing page for the community vulnerability dashboard. On the left sidebar100, users can choose to display either the community vulnerability index (CVI) or any of the sub-indices (only four shown in this example). In the center is a choropleth map or heat map102of the community vulnerability index across a certain geographical region, such as geopolitical area of interest, colored by the CVI quintile for each of the block groups. When the user clicks on a particular category, the heat map is updated to reflect the vulnerability index for the region on a group block level. On the right sidebar104, the average values for the community vulnerability indices are displayed for the region of interest. The dashboard home page also displays a rating of the vulnerability level based on quintiles on the block group level in the geopolitical area of interest in the five categories (as shown on right sidebar) as lowest, low, average, high, and highest with respective color coding on a block group level. A menu button bar106across the top of the page provides a quick way for the user to obtain insight into each category: household essentials, empowered people, good health, equitable communities.

FIG. 6is an exemplary screenshot of an exemplary page for the household essentials index. The house essentials index page captures broader measures of economic stability and access to health insurance coverage. The left sidebar110shows the buttons for the five indicators that make up the household essentials sub-index. The distribution of the household essentials sub-index is shown on the choropleth map112by block group. If users choose a different indicator on the left sidebar, the map will update to show the distribution of that indicator across the geopolitical area of interest. The right sidebar114provides an overall average for the sub-index and indicators for the geopolitical area of interest.

When the user clicks on the “Tabular” button on the screen shown inFIG. 7, the screen shown inFIG. 7is displayed. The tabular panel on the right116displays information for the top five highest vulnerability block groups, in addition to the top five lowest vulnerability block groups. These tables contain information about the selected indicator, in addition to a cross street within the block group and corresponding zip code, county, and state to allow for better identification of the block group on the map.

When the user hovers the cursor over a block group on the map112, information for that specific block group118is displayed, as shown inFIG. 8.

As shown inFIG. 9, after the user clicks on a specific block group, the right sidebar120is repopulated to show the sub-index and indicator information for the selected block group of interest.

As shown inFIG. 10, users can select a particular year from a drop-down menu122to show vulnerability index data for a specific year of interest. As shown inFIG. 11, a lasso tool124is incorporated into the dashboard, which allows users to select multiple block groups. The right sidebar updates to show the average values of the sub-index and indicators for all of the block groups selected by using the lasso tool124.

FIG. 12shows the home page for the empowered people sub-index. The empowered people sub-index captures information related to factors that help residents live their healthiest lives possible, including education, access to internet, literacy, and mobility access. The empowered people page also features a left panel130with buttons for the indicators make up the empowered people index. The distribution of the empowered people sub-index is shown on the choropleth map132by block group. If users choose a different indicator on the left sidebar, the map will update to show the distribution of that indicator across the geopolitical area of interest. The right sidebar134provides an overall average for the empowered people sub-index and indicators for the geopolitical area of interest.

FIG. 13shows the home page for the good health sub-index. The good health sub-index captures information related to mental health, physical health, and life expectancy. The good health page also features a left panel140with buttons for the indicators make up the good health index: a disease burden index and life expectancy. The distribution of the good health sub-index is shown on the choropleth map142by block group. If users choose a different indicator on the left sidebar, the map will update to show the distribution of that indicator across the geopolitical area of interest. The right sidebar144provides an overall average for the good health sub-index and indicators for the geopolitical area of interest. As shown inFIG. 14, the good health index150has a hierarchical structure, where a disease burden index151includes a number of indicators: alcohol abuse153, mental health154, cancer155, and a chronic disease index156, which in turn includes coronary heart disease157, diabetes158, COPD159, kidney disease160, and asthma161.FIG. 15shows an exemplary landing page for the disease burden index, which includes a chronic disease index, cancer, mental health, and alcohol abuse. The right panel displays the prevalence of cancer, poor mental health, and alcohol abuse.

FIG. 16shows the landing page for the chronic disease index, which shows indicators coronary heart disease, diabetes, COPD, kidney disease, and asthma. The prevalence for these chronic diseases is displayed in the right panel144.

FIG. 17is the landing page for the equitable communities sub-index. The equitable communities sub-index captures information related to the neighborhoods in which the residents live, ranging from economic, safety, and environmental factors. The left panel170shows the indicators that relate to equitable communities sub-index: employment, affordable housing, neighborhood safety, neighborhood stability, clean air, and green space. The distribution of the equitable communities sub-index is shown on the choropleth map172by block group. If users choose a different indicator on the left sidebar, the map will update to show the distribution of that indicator across the geopolitical area of interest. The right sidebar174provides an overall average for the equitable communities sub-index and indicators for the geopolitical area of interest.

The dashboard system and method use various key indicators that have been selected and used to track measures of resiliency, commitment, and amenities in a region. Table A lists the sub-indices, the indicators for each sub-index, and the data source for the indicators.

The features of the present invention which are believed to be novel are set forth below with particularity in the appended claims. However, modifications, variations, and changes to the exemplary embodiments described above will be apparent to those skilled in the art, and the community vulnerability index dashboard described herein thus encompasses such modifications, variations, and changes and are not limited to the specific embodiments described herein.