This article reviews the rapid and unprecedented development, scaleup and deployment of POC devices in response to the COVID-19 pandemic and its implications for the future of diagnostics and digital health.

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Single multiplexed assays could replace the standard 2-tiered (STT) algorithm recommended for the laboratory diagnosis of Lyme disease if they perform with a specificity and a sensitivity superior or equal to those of the STT algorithm. We used human serum rigorously characterized to be sera from patients with acute- and convalescent-phase early Lyme disease, Lyme arthritis, and posttreatment Lyme disease syndrome, as well as the necessary controls (n = 241 samples), to select the best of 12 Borrelia burgdorferi proteins to improve our microfluidic assay (mChip-Ld). We then evaluated its serodiagnostic performance in comparison to that of a first-tier enzyme immunoassay and the STT algorithm. We observed that more antigens became positive as Lyme disease progressed from early to late stages. We selected three antigens (3Ag) to include in the mChip-Ld: VlsE and a proprietary synthetic 33-mer peptide (PepVF) to capture sensitivity in all disease stages and OspC for early Lyme disease. With the specificity set at 95%, the sensitivity of the mChip-Ld with 3Ag ranged from 80% (95% confidence interval [CI], 56% to 94%) and 85% (95% CI, 74% to 96%) for two panels of serum from patients with early Lyme disease and was 100% (95% CI, 83% to 100%) for serum from patients with Lyme arthritis; the STT algorithm detected early Lyme disease in the same two panels of serum from patients with early Lyme disease with a sensitivity of 48.5% and 75% and Lyme arthritis in serum from patients with Lyme arthritis with a sensitivity of 100%, and the specificity was 97.5% to 100%. The mChip-Ld platform outperformed the STT algorithm according to sensitivity. These results open the door for the development of a single, rapid, multiplexed diagnostic test for point-of-care use that can be designed to identify the Lyme disease stage.

As point-of-care diagnostic devices become increasingly available to untrained users, it will be critical to understand how user behavior can best guide the engineering design process.