A suite of data and clinical interventions was identified and implemented as part of the Siyenza program in Ehlanzeni, South Africa and daily data review meetings resulting in improved ART initiation and retention in care compared to the period immediately preceding Siyenza. Interventions included improved facility data management (file audits, workflow improvements and same day data entry, and standardized filing system) including the application of clinical best practices (increased multi-month scripting and dispensing, enhanced track, and trace for lost to follow-up patients, extended clinic hours, and appointment reminders and follow-up for early missed appointments). The program has since established a tracking system to better link these interventions with facility outcomes.
The availability and actioning of almost real-time data are critical to demonstrating the impact of the rapid program improvements described above and targeting those interventions to the right facilities. Program data in large HIV care and treatment programs are currently relegated to providing retrospective program evaluation at an aggregated level. However, to meet increasingly ambitious program performance targets, care, and treatment partners with authorized users must be able to access, process and act on daily data from multiple integrated data sources, preferably at patient level to avoid double data entry.
As the Siyenza intervention began, program managers identified several data gaps related to human re-sources, timely procurement, supply chain management, data flow and quality assurance issues. These data are often captured in other systems (e.g., Workload Indicators for Staffing Needs, Synch, Rx Solution, etc.) supporting the need for further system integration. The data should be at the lowest level possible, bearing in mind program efficiency, feasibility, quality, and cost (10). Any mHealth or e-Health programs should consider evaluation from the outset, including collection of baseline data prior to implementation.
Leadership promoting daily data review of progress against targets for key indicators at priority facilities worked to reinforce a culture of data utilization for Right to Care as has been demonstrated to be effective elsewhere (11). Multi-disciplinary data review meetings should allow rapid review of the data and decision-making, including tapping into the tacit knowledge of a diverse team. Data managers and program managers are then cooperatively able to interrogate the trends and interpret real-time data (aware of inconsistencies and quality implications of this type of data) to adjust resources and propose tailored interventions. The rapidity of the data and review allow for quick evaluation of the interventions that are being implemented at struggling facilities.
All care and treatment partners are expected to submit similar data to funders; however, during the intervention the process was previously overly manual and required multiple points of data capture, categorization, and entry. This time-consuming process limited any further analysis that could have been performed by our staff to focus on program improvement. Automating certain steps in the reporting and analytic process with Right to Care’s Knowledge Centre will allow for more time to interpret and act on the data, which is essential when using daily data. Automated reporting templates ensure standardized data from every facility with reports mimicking the required reporting forms and simplifying transcription; and automated visualizations provide up-to-date data with every form submission.
If the granularity and frequency of data collection is overly burdensome then reporting compliance and data quality can suffer. A near-real time system can expect to have some data quality trade-offs: when data is reported more frequently, an invalid data point is diluted and mitigated as an outlier. Focusing on data recency (the time elapsed between event and reporting) allows for very timely correlation between intervention and program outcome.
Improving the efficiency of the data management and analysis provide time to interpret and act on the data. The latter a driving factor to improved program performance and sustaining daily review meetings. Data review meetings allow program managers to select evidence-based interventions based on the facility need. The interventions’ scopes vary from clinical technical assistance, increased human resources, file management and data quality, and supply chain efficiencies. The recency of the data and frequency of review also enable rapid evaluation of the chosen interventions.
The process that we developed and implemented for our programs in South Africa still relies on an inter-mediate data transcription process - from clinical registers, paper files, and electronic medical records into the Knowledge Centre data collection forms. Right to Care has demonstrated the ability to integrate testing, clinical, and laboratory databases leveraging the investments made to capture clinical data in electronic systems. All care and treatment partners and the Department of Health could benefit by further providing governance on how electronic clinical data can be accessed, integrated, and used. Standards for application programming interfaces and interoperability of systems (12)(13)(14) could further create a data eco-system that is broader than government facilities and truly tracks patients across systems to ensure their outcomes are achieved as programs meet performance targets more efficiently and effectively.
Democratizing the ethical use and integration of electronic disaggregated patient-level clinical data can potentially propel HIV care and treatment into new frontiers with segmentation to truly understand patient needs as well as predictive modelling. We can then start to strengthen the governmental support at the district and provincial level through regular access to analytics covering the care and treatment cascade. We can also begin to tailor patient treatment protocols (inclusive of psychosocial support) at the point of testing based on their potential for poor outcomes like being lost from care. Providing advanced analytics as a service to government and supporting partners puts greater focus on effective and precise interventions. Data managers can then focus on ensuring that quality data is captured on time for every patient into the primary data source rather than a myriad of alternative data collection systems. The integration of systems will result in less duplication and better efficiency after innovations and legacy risks have been assessed.
The positive results from the study indicate that accessibility and utilization of near to real-time data within HIV programs allow for rapid responses to program performance, needs and improved patient outcomes. Daily situation rooms and reviews, facilitates precision programming, and contributes to the behavior change necessary to institute routine data use for decision making and evaluations within programs. Continued investment in real-time data sources and automated analytic and visualization systems are essential to ensuring programs meet their aggressive goals and the individual needs of patients to stay in care.
Integration or a (15) Health Information Exchange (HIE) has the potential to close many of the gaps that lead to sub-optimal care for people with HIV by, for example, improving the referral and tracking of patients among services, identifying patients who have fallen out of care in a health system, and allowing providers to coordinate services to ensure that every individual receives the optimal services needed.
Information provision and communication of non-conflicting messages, facilitated through workshops to inform or update on the synthesized evidence and subsequent recommendations, result in successful implementation (11) as demonstrated in the Siyenza results.