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The potential value of rapid, cloud-enabled onsite testing for the diagnosis of rheumatoid arthritis in the United States

Abstract Aims: Improvements in information technology have granted the recent development of rapid, cloud-enabled, onsite laboratory testing for rheumatoid arthritis (RA). This study aims to quantify the value to payers of such technologies. Materials and methods: To calculate the value of rapid, cloud-enabled, onsite laboratory testing to diagnose RA relative to traditional, centralized laboratory testing, an Excel-based decision tree model was created that simulated potential cost-savings to payers who cover routine evaluations of RA patients in the US. First, a conceptual framework was created to identify the value components of rapid, cloud-enabled onsite testing. Second, value associated with patient time savings, savings on visit fees, change in treatment costs, and QALY improvements was measured, leveraging existing literature and information from an observational study. Lastly, these value components were combined to estimate the total incremental value accruing to payers per patient-year relative to centralized laboratory testing. Results: Rapid, cloud-enabled, onsite testing is estimated to save one office and 1.81 laboratory visits during the evaluation period for the average patient. Results from an observational study found that rapid, cloud-enabled testing increased the likelihood of completing diagnostic orders from 84.5% to 97%, resulting in an increased probability of early treatment (3.5 percentage points) with disease-modifying anti-rheumatic drugs among patients eligible for treatment. The combined total value was $5,648 per evaluated patient-year. This value is primarily attributed to health benefits of early treatment ($5,048), fewer visit payments ($459), and patient time savings due to fewer office ($216) and laboratory visits ($255). Limitations and conclusions: Data on the impact of rapid, cloud-enabled, onsite testing on patient health, care delivery, and clinical decision-making is scarce. More robust real-world data would confirm the validity of our model. Rapid, cloud-enabled, onsite testing has the potential to generate significant value to payers.