Transforming Rapid Diagnostic Tests for Precision Public Health: Open Guidelines for Manufacturers and Users

Abstract

Background: Precision public health (PPH) can maximize impact by targeting surveillance and interventions by temporal, spatial, and epidemiological characteristics. Although rapid diagnostic tests (RDTs) have enabled ubiquitous point-of-care testing in low-resource settings, their impact has been less than anticipated, owing in part to lack of features to streamline data capture and analysis.

Objective: We aimed to transform the RDT into a tool for PPH by defining information and data axioms and an information utilization index (IUI); identifying design features to maximize the IUI; and producing open guidelines (OGs) for modular RDT features that enable links with digital health tools to create an RDT-OG system.

Methods: We reviewed published papers and conducted a survey with experts or users of RDTs in the sectors of technology, manufacturing, and deployment to define features and axioms for information utilization. We developed an IUI, ranging from 0% to 100%, and calculated this index for 33 World Health Organization-prequalified RDTs. RDT-OG specifications were developed to maximize the IUI; the feasibility and specifications were assessed through developing malaria and COVID-19 RDTs based on OGs for use in Kenya and Indonesia.

Results: The survey respondents (n=33) included 16 researchers, 7 technologists, 3 manufacturers, 2 doctors or nurses, and 5 other users. They were most concerned about the proper use of RDTs (30/33, 91%), their interpretation (28/33, 85%), and reliability (26/33, 79%), and were confident that smartphone-based RDT readers could address some reliability concerns (28/33, 85%), and that readers were more important for complex or multiplex RDTs (33/33, 100%). The IUI of prequalified RDTs ranged from 13% to 75% (median 33%). In contrast, the IUI for an RDT-OG prototype was 91%. The RDT open guideline system that was developed was shown to be feasible by (1) creating a reference RDT-OG prototype; (2) implementing its features and capabilities on a smartphone RDT reader, cloud information system, and Fast Healthcare Interoperability Resources; and (3) analyzing the potential public health impact of RDT-OG integration with laboratory, surveillance, and vital statistics systems.

Conclusions: Policy makers and manufacturers can define, adopt, and synergize with RDT-OGs and digital health initiatives. The RDT-OG approach could enable real-time diagnostic and epidemiological monitoring with adaptive interventions to facilitate control or elimination of current and emerging diseases through PPH.

Keywords: FHIR; Fast Healthcare Interoperability Resources; diagnostic; digital health; guideline; manufacture; precision public health; rapid diagnostic test; surveillance; testing.

Figure 1

Current rapid diagnostic test processes (left) use undefined or proprietary standards, which lead to multiple incompatible protocols (ie, incompatible apps and devices). The rapid diagnostic test open guideline process (right) uses standard guidelines to produce modular reference rapid diagnostic tests that are compatible and can be read by a well-defined protocol for devices and apps. ID: identifier code or number for each device; ML: machine learning; POC: point-of-care; RDT: rapid diagnostic test.

Figure 2

The trajectory of information use in response to rapid diagnostic test technology innovation, which shows the introduction of rapid diagnostic tests and their initial impact (black line) followed by subsequent challenges (red line) and improvements (green line). Potential future paths are also shown: a lower growth in information utilization under the current incremental improvements (gray dashed line) or an accelerating trajectory enabled with rapid diagnostic test open guidelines (blue dashed line). GTIN: global trade item number; RDT: rapid diagnostic test.

Figure 3

Conceptual framework of gaps in information use through the rapid diagnostic test life cycle. Information utilization (vertical axis) is quantified relative to 5 distinct phases of the rapid diagnostic test life cycle (horizontal axis): Manufacturing, Shipping, Use, Interpretation, and Disposal. Over the life cycle, the information utilization of the current process increases (black line), but with the use of rapid diagnostic test open guidelines, information utilization would increase (blue line) as a result of several features (list at bottom).

Figure 4

Information utilization index for WHO prequalified rapid diagnostic tests (RDTs). Scores were calculated for 33 WHO prequalified devices that had accessible information (blue, names listed below), as well as an RDT based on the RDT Open Guidelines (grey). The median information utilization score was 0.30 (magenta line), in contrast to 0.91, the score for an Open Guidelines RDT. 70% (top bracket) of the Open Guidelines RDT score can be attributed to non-physical changes, while the remaining 30% (bottom bracket) requires physical changes to the RDT.

Figure 5

Unifying rapid diagnostic test functionalities based on formal guidelines. A rapid diagnostic test based on the rapid diagnostic test open guidelines (left) should have certain functional components, as indicated by the color-coded overlay. In contrast, 3 RDTs currently on the World Health Organization–prequalified list have only some of these components (right, with color-coded overlays).

Figure 6

In a system that incorporates rapid diagnostic test open guidelines, data are captured and digitized data from rapid diagnostic tests using a smartphone app that is compatible with the rapid diagnostic test open guidelines. These data are transmitted to a health information system platform and integrated with health system data, laboratory data, and other relevant data, then used to build machine learning models that both feed upstream, to smartphone apps to model symptoms and to be used to better interpret results, as well as downstream, for monitoring. Planners, managers, and researchers can use the real-time data to decide on modifications to existing programs and plan new programs. The color of the lines identifies the primary participant in that portion of the workflow, and the badges depict where to apply the features of the rapid diagnostic test open guideline. ML: machine learning; RDT: rapid diagnostic tests.

Figure 7

A Fast Healthcare Interoperability Resources–based workflow using the Device Definition resource for Open Guidelines based rapid diagnostic tests connected to Device, Observation, and Patient resources. Medical devices are defined using the DeviceDefinition resource (to specify their physical characteristics and links to external information systems). Each rapid diagnostic test used corresponds to a Device resource linked to the appropriate DeviceDefinition resource, as well as to Patient and Observation resources that store patient information and test results, respectively.