Dashboard Prototype for Improved HIV Monitoring and Reporting for Indiana

ObjectiveThe objective was to design and develop a dashboard prototype (DP) that integrates HIV data from disparate sources to improve monitoring and reporting of HIV care continuum metrics in Indiana. The tool aimed to support Indiana State Department of Health (ISDH) to monitor key HIV performance indicators, more fully understand populations served, more quickly identify and respond to crucial needs, and assist in planning and decision-making.IntroductionIn 2015, ISDH responded to an HIV outbreak among persons using injection drugs in Scott County [1]. Information to manage the public health response to this event and aftermath included data from multiple sources (e.g., HIV testing, surveillance, contact tracing, medical care, and HIV prevention activities). During the outbreak, access to timely and accurate data for program monitoring and reporting was difficult for health department staff. Each dataset was managed separately and tailored to the relevant HIV program area’s needs. Our challenge was to create a platform that allowed separate systems to communicate with each other and design a DP that offered a consolidated view of data.ISDH initiated efforts to integrate these HIV data sources to better track HIV prevention, diagnosis, and care metrics statewide, support decision-making and policies, and facilitate a more rapid response to future HIV-related investigations. The Centers for Disease Control and Prevention (CDC) through its Info-Aid program provided technical assistance to support ISDH’s data integration process and develop a DP that could aggregate these data and improve reporting of crucial statewide metrics.After an initial assessment phase, an in-depth analysis of requirements resulted in several design principles and lessons learned that later translated into standardization of data formats and design of the data integration process [2].MethodsSpecific design principles and prototyping methods were applied during the 9 months that lasted the DP design and development process starting from June 2017.Requirements elicitation, analysis, and validationThe elicitation and analysis of the requirements were done using a dashboard content inventory tool to gather and analyze HIV reporting needs and dashboard requirements from stakeholders. Results of this analysis allowed us to validate project goals, list required functionalities, prioritize features, and design the initial dashboard architecture. The initial scope was Scott County.Design mappingThe design mapping exercise reviewed different scenarios involving data visualization using DP, clarified associations among data from different programs and determined how best to capture and present them in the DP. For example, we linked data in separate datasets using unique identifier or county name. This step’s output was to refine DP architecture.Parallel designIn a parallel design session, we drew dashboard mockups on paper with end users. These mockups helped illustrate how information captured during design mapping would be translated into visual design before prototype implementation. Drawings were converted to PowerPoint mockups for validation and modifications. The mockup helped testers and future users, interact and rapidly understand the DP architecture. The model can be used for designing other DP.IntegrationData integration was conducted in SAS by merging datasets from different program areas iteratively. Next, we cleaned (e.g., deleted records missing crucial information) and validated data. The integration step solved certain challenges with ISDH data (e.g. linking data across systems while automating data cleaning was planned for later), increased data consistency and reduced redundancy, and resulted in a consolidated view of the data.PrototypingAfter data integration, we extracted a reduced dataset to implement and test different DP features. The first prototype was in Excel. We applied a modular design that allowed frequent feedback and input from ISDH program managers. Developers of the first prototype were in two locations, but team members kept in close contact and further refined the DP through weekly communications. We expanded the DP scope from Scott County to include all counties in Indiana.Beta VersionTo enable advanced analysis and ease collaboration of the final tool across users, we moved to Tableau Desktop Professional version 10. All Excel screens were redeveloped and integrated into a unique dashboard for a consolidated view of ISDH programs. After beta version completion, usability tests were conducted to guide the DP production version.Technical requirementsAll users were provided Tableau Reader to interact with the tool. DP is not online, but shared by ISDH through a protected shared drive. Provisions are made for the DP to use a relational database that will provide greater data storage flexibility, management, and retrieval. DP benefits from the existing security infrastructure at ISDH that allows for safeguarding personal identifiable information, secured access, backup and restoration.ResultsSystem contentISDH’s data generated at the county and state level were used to assess the following domains: HIV Testing, HIV Surveillance, Contact Tracing, HIV Care Coordination, and Syringe Exchange. The DP was populated through an offline extract of the integrated datasets. This approach sped up the Tableau workbook and allowed monthly update to the uploaded datasets. The system also included reporting features to display aggregate information for multiple population groups.Stakeholders’ feedbackTo improve users’ experience, the development team trained and offered stakeholders multiple opportunities to provide feedback, which was collected informally from ISDH program directors to guide DP enhancements. The initial feedback was collected through demonstration to CDC domain experts and ISDH staff. They were led through different scenarios and provided comments on overall design and suggestions for improvement. The goal of the demos was to assess ease of use and benefits and determine how it could be used to engage with stakeholders inside and outside of ISDH.DP Action ReportingThe DP reporting function will allow users to download spreadsheets and graphs. Some reports will be automatically generated and some will be ad-hoc. All users, including the ISDH Quality Manager and grant writers, can use the tool to guide program evaluations and justifications for funding. The tool will provide a way for ISDH staff to stay current about work of grantees, document key interactions with each community, and track related next steps. In addition, through an extract of the integrated dataset (e.g., out-of-care HIV positives), DP could support another ISDH program area, Linkage to Care.ConclusionsWe describe the process to design and develop a DP to improve monitoring and reporting of statewide HIV-related data. The solution from this technical assistance project was a useful and innovative tool that allows for capture of time-crucial information about populations at high risk. The system is expected to help ISDH improves HIV surveillance and prevention in Indiana. Our approach could be adapted to similar public health areas in Indiana.References1. Peters PJ et al. HIV infection linked to injection use of oxymorphone in Indiana, 2014–2015. N Engl J Med. 2016;375(3):229-39.2. Ahmed K et al. Integrating data from disparate data systems for improved HIV reporting: Lessons learned. OJPHI. 2018 May 17;10 (1).

Integrating data from disparate data systems for improved HIV reporting: Lessons learned

Objective: To assess the integration process of HIV data from disparate sources for reporting HIV prevention metrics in Scott County, IndianaIntroduction: In 2015, the Indiana State Department of Health (ISDH) responded to a large HIV outbreak among persons who inject drugs (PWID) in Scott County1. Information to manage the public health response to this event and its aftermath included data from multiple sources such as surveillance, HIV testing, contact tracing, medical care, and HIV prevention activities. Each dataset was managed separately and had been tailored to the relevant HIV program area’s needs, which is a typical practice for health departments. Currently, integrating these disparate data sources is managed manually, which makes this dataset susceptible to inconsistent and redundant data. During the outbreak investigation, access to data to monitor and report progress was difficult to obtain in a timely and accurate manner for local and state health department staff. ISDH initiated efforts to integrate these disparate HIV data sources to better track HIV prevention metrics statewide, to support decision making and policies, and to facilitate a more rapid response to future HIV-related investigations. The Centers for Disease Control and Prevention (CDC) through its Info-Aid mechanism is providing technical assistance to support assessment of the ISDH data integration process. The project is expected to lead to the development of a dashboard prototype that will aggregate and improve critical data reporting to monitor the status of HIV prevention in Scott County.Methods: We assessed six different HIV-related datasets in addition to the state-level integrated HIV dataset developed to report HIV monitoring and prevention metrics. We conducted site visits to the ISDH and Scott County to assess the integration process. We also conducted key informant interviews and focus group discussions with data managers, analysts, program managers, and epidemiologists using HIV data systems at ISDH, Scott County and CDC. We also conducted a documentation review of summary reports of the HIV outbreak, workflow, a business process analysis, and information gathered during the site visit on operations, processes and attributes of HIV data sources. We, then, summarized the information flow, including the data collection process, reporting, and analysis at federal, state and county levels.Results: We have developed a list of lessons learned that can be translated for use in any state-level jurisdiction engaged in HIV prevention monitoring and reporting:Standardization of data formats: The disparate data sources storing HIV-related information were developed independently on different platforms using different architectures; they were not necessarily designed to link and exchange data. Hence, these systems could not seamlessly interact with each other, posing challenges when rapid data linkage was needed.To better manage unstructured data coming from disparate data sources and improve data integration efforts, we recommend standardization of data formats, unique identifiers for registered individuals, and coding across data systems. Use of standard operating procedures can streamline data flow and facilitate automated creation of integrated datasets. This approach may be helpful for future integration efforts in other healthcare domains.Data integration process: Manually integrating data is time intensive, increases workload, and poses significant risk of human error in data compilation. Hence, it may compromise data quality and the accuracy of HIV prevention metrics used by decision-makers.We propose an automated integration process using an extract, transform and load (ETL) method to extract HIV-related data from disparate data sources, transforming it to fit the prevention metrics reporting needs and loading it into a state-level integrated HIV dataset or database. This approach can drastically decrease dependency on manual methods and help avoid data compilation errors.Dashboard development: Major challenges in the process of integrating HIV-related data included disparate data sources, compromised data quality, and the lack of standard metrics for some of the HIV-related metrics of interest. Despite these challenges to data integration, creation of a dashboard to track HIV prevention metrics is feasible. Integrating data is a critical part of developing an HIV dashboard that can generate real-time metrics without creating additional burden for the health department staff, if manual integration is no longer needed.                                                                                      Stakeholder participation: Due to the immediate need for outbreak response, involvement of stakeholders at all levels was limited. Active stakeholder engagement in this process is essential. The stakeholders’ interest and participation can be improved by helping them understand the value of each other’s data, and providing regular feedback about their data and its best use in public health interventions.Conclusions: This assessment highlighted the importance of standardizing data formats, coding across systems for HIV data, and the use of unique identifiers to store individuals’ information across data systems. Promoting stakeholder understanding of the value and best use of their data is also essential in improving data integration efforts. The results of this assessment offer an opportunity to learn and apply these lessons to improve future public health informatics initiatives, including HIV (but not limited to HIV), at any state-level jurisdictio