ObjectivesTo compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps.DesignVignettes study.Setting200 primary care vignettes.Intervention/comparatorFor eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes’ gold-standard.Primary outcome measures(1) Proportion of conditions ‘covered’ by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of ‘safe’ urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative).ResultsCondition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs—Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs—Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs—Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10-3).ConclusionsThe utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.

Background Crowding can negatively affect patient and staff experience, and consequently the performance of health care facilities. Crowding can potentially be eased through streamlining and the reduction of duplication in patient history-taking through the use of a digital symptom-taking app. Objective We simulated the introduction of a digital symptom-taking app on patient flow. We hypothesized that waiting times and crowding in an urgent care center (UCC) could be reduced, and that this would be more efficient than simply adding more staff. Methods A discrete-event approach was used to simulate patient flow in a UCC during a 4-hour time frame. The baseline scenario was a small UCC with 2 triage nurses, 2 doctors, 1 treatment/examination nurse, and 1 discharge administrator in service. We simulated 33 scenarios with different staff numbers or different potential time savings through the app. We explored average queue length, waiting time, idle time, and staff utilization for each scenario. Results Discrete-event simulation showed that even a few minutes saved through patient app-based self-history recording during triage could result in significantly increased efficiency. A modest estimated time saving per patient of 2.5 minutes decreased the average patient wait time for triage by 26.17%, whereas a time saving of 5 minutes led to a 54.88% reduction in patient wait times. Alternatively, adding an additional triage nurse was less efficient, as the additional staff were only required at the busiest times. Conclusions Small time savings in the history-taking process have potential to result in substantial reductions in total patient waiting time for triage nurses, with likely effects of reduced patient anxiety, staff anxiety, and improved patient care. Patient self-history recording could be carried out at home or in the waiting room via a check-in kiosk or a portable tablet computer. This formative simulation study has potential to impact service provision and approaches to digitalization at scale.

BACKGROUND Early efforts to control the COVID-19 pandemic have been focused on Non-Pharmaceutical Interventions (NPIs) in the absence of effective treatments or sufficient vaccine supply. While retrospective analyses and modeling studies confirmed that severe restrictions of social contacts, i.e., lockdowns, are most effective in reducing transmission of SARS-CoV-2, they incur large economic costs and mental health risks. Earlier detection of cases has also been proposed as an effective method of control, but studies have so far only considered enhanced laboratory testing. Digital applications have been developed which aim to identify possible cases of COVID-19 based on reported symptoms and risk factors. OBJECTIVE The aim of this study is to explore the effects of digital screening applications for COVID-19 on the transmission of SARS-CoV-2. METHODS Using an established epidemiological Susceptible-Exposed-Infectious-Recovered (SEIR) model for infectious disease transmission, we simulate the transmission of SARS-CoV-2 in Germany, the UK, and the USA for 366 days after the virus was introduced in the population. We study 4 scenarios: 1) no interventions (base case), 2) symptom-based self-isolation after consulting healthcare providers, 3) self-isolation using digital screening applications, and 4) severe social contact limitations (lockdown). We included sensitivity analyses for different ratios of infectiousness of pre-symptomatic cases compared to symptomatic cases, and different rates of adoption of digital screening tools. RESULTS Without any intervention, 74% of the German population would be infected with SARS-CoV-2 within the simulation period (UK: 76%, USA: 77%). Self-isolation of symptomatic cases would already slow the spread of the virus significantly and lead to only 18% of the German population being infected (UK: 17%, USA: 17%). Using a digital application could further reduce the infected population to 10% (UK: 9%, USA: 9%), compared to 3% under lockdown conditions. While the effectiveness of digital screening applications varies with the adoption rate, even a low adoption rate could significantly reduce transmission. In the case that pre-symptomatic cases are less infectious than symptomatic cases, the overall proportion of infected individuals in the population decreases, and the effectiveness of different interventions converges. CONCLUSIONS Digital symptom-based screening tools can substantially impact the transmission of SARS-CoV-2 and might be a viable element in strategies to control COVID-19 through NPIs.