Abstract 119 - Building an Open Access Software Tool to allow countries to design patient-centered and cost-effective Diagnostic Networks Ryan Purcell, LLamasoft Heidi Albert, FIND November 21st, 2019

Gaps in access and efficiency of diagnostic services 24% patients with TB initiated care seeking in a health facility with diagnostic capacity1 66% patients sought care in private sector where diagnostic capacity is even lower1 Where testing capacity is available, it is often under- utilized and of variable quality. Significant investments made, but largely focused on device procurement. Patient pathway data from Ethiopia, Indonesia, Kenya, Pakistan and Philippines How do we increase patient access to quality diagnosis while improving service delivery efficiency? Hanson, J Infect Dis. 2017.

Diagnostic network planning today Diagnostics are essential to guide patient care and treatment, but diagnostic services are often lacking where patients first seek care Network design that is better aligned with patient health seeking behavior is expected to reduce barriers to care and improve efficiency of investments and service delivery Improved access and efficiency are both key to achieving national and global disease control targets. Currently, decisions are often based on MS Excel work with current testing and case-detection data (usually for individual disease programs), possibly with simple visualization tools like Google Maps

Diagnostics network design and optimization Objectives Map TB burden and current demand for TB diagnostic services Map current TB diagnostic network structure Identify the extent and distribution of gaps in services Develop a set of diagnostic network designs defined by NTP and partners that better and more efficiently reach “missing” TB cases (unmet demand) using existing infrastructure Model a set of new network paradigms, including new product and/or services investment, to inform government strategic planning and budgeting processes Example questions: What proportion of presumptive TB cases are reached by the existing TB diagnostic network? What is the capacity for expansion? Where is the trade-off between increasing point of care and establishing a better sample referral mechanism? What would be the most efficient design for sample referral to minimize turnaround times and cost? When rolling out new instruments, which sites will show greatest impact in terms of case detection, and thus should be prioritized for early implementation?

Increase network efficiency Greater visibility of network Expected outputs Improve access Reduce loss to follow up and diagnostic delay More people diagnosed and treated Increase network efficiency Reduce procurement and operating costs Easier-to-manage device footprint Greater visibility of network More efficient funding allocation Country empowerment & better decision-making

Vision for diagnostic network optimization tool A standardized methodology that is accepted as a key component of a data-driven approach to national strategic planning (across diseases), as well as the design and optimization of diagnostic networks using existing data sources. To reduce barriers to uptake and facilitate sustainable use, it is imperative that a user friendly tool to facilitate this analysis is openly accessible, freely available to national programs and partners, and whose use is endorsed and supported by major donors and global organizations.

What’s next for laboratory network optimization? Purpose-built point solution app for Diagnostic Networks

Supply Chain Guru® (SCG) Open Access tool: bringing together network optimization capabilities with access and usability in LMICs Proprietary software requiring license; desktop and web versions Leading supply chain management software in corporate sector and significant experience in global health applications Multi-disease Mapping and visualization of current and future demand and network structure, incl. multi-echelon Flexible input tables Robust performance for large data sets Customizable outputs/ data dashboards Ability to link to external data systems Supply Chain Guru® (SCG) LabEQIP Open Access tool Open access, web-based tool Multi-disease (TB/HIV and other diseases) Designed for national programs Includes key SCG functionalities – flexible inputs, customizable, robust with large datasets Set up to be able to link to external data systems, e.g. ForLab (not part of initial build) Open source, desktop software Mapping and visualization of current and future demand and network structure (no multi-echelon) Network optimization and EQA functionality Simple to use Primarily HIV Limited demand inputs No costing functionality Side-to-side comparison of multiple scenario outputs not available Performance not guaranteed beyond 3000 sites Costing of different network options (including site costs and sample transport costs) Multi-echelon network optimization to incorporate referrals and sample transportation (e.g. hubs) Data-size limitations –performance is not guaranteed past 3000+ sites More detailed inputs required for demand calculations Side-by-side comparison of multiple scenario outputs for network optimization

Open Access Tool Development Overview Strategic partnership established between FIND, PSM/Chemonics and LLamasoft for open access tool development and roll out, including: Joint product requirements for open access tool including TB and HIV Web-based tool, available free of cost to end users in LMICs Tool development funded by a Bill & Melinda Gates Foundation grant to FIND Hosting/maintenance supported by PSM/Chemonics with USAID/PEPFAR funding Steering committee mechanism established to guide tool development and roll out with provision of ad hoc members for advice from various stakeholders Tool build started Sept 2019 and expected to be completed by mid 2020 Country pilots of open access tool planned for late 2020 Who is on the steering committee? Gates, USAID (TB + HIV), CDC, FIND, PSM. Monthly/bimonthly meetings Technical Sub-Committee: LL, FIND, PSM. Every two weeks Country Pilot options: Kenya, PHL, India

Key functions Demand: How many tests do we expect at each health facility in the future? What about as we change testing algorithm and demand assumptions? Options for simplified and detailed demand estimation  Network Optimization: how can a full diagnostic network be organized to most efficiently offer services? Example questions include: How many machines should we have? Where should they be located? How should samples be referred from health facilities to testing locations? Flows could be multi-echelon (e.g. Health Facility to Hub to Lab to Higher Level Lab)  Vehicle Route Optimization (VRO) (basic costing estimate for multi-stop routes) If the samples are transported using multi-stop routes, what will be the expected cost? Three options for demand data: Upload demand data directly to the template – Simple Demand Calculation Scale up/down current demand using a scaling factor (only available where current demand data has already been provided for baseline model) – Simple Demand Calculation Generate demand from parameters – Detailed Demand Calculation (based on developed algorithmic approach for TB)

Illustrative example of workflow and screens: Inputs Access via username/login based on user profile Customized dashboard for different users Ability to share results, link to external data sources Models can be created and analyzed separately for each country Repeatable workflow guides the users through data inputs and model build

Illustrative example of workflow and screens: Outputs Multiple data visualization formats Customizable dashboard Customized view for different users

Thank you! Questions? Sidharth Rupani, Neelima Ramaraju, LLamasoft Zachary Katz, Kekeletso Kao, FIND Lillian Gu, Clement Ndongmo, Matthew C Wattleworth, Andrew Inglis, Shadrack Were, PSM Christy Hanson, BMGF NTLP & partners, Kenya NTP & partners, Philippines Financial support: The Bill & Melinda Gates Foundation USAID/PEPFAR USAID GLOBAL HEALTH SUPPLY CHAIN PROGRAM Procurement and Supply Management

Thanks to our generous sponsors 2019 Global Health Supply Chain Summit, Johannesburg, S. Africa