Evaluating diagnostic decision support systems for patients requiring urgent primary or emergency care or with stroke

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Medical diagnosis is a critical component of effective health care but misdiagnosis, delayed diagnosis and incorrect triage is common especially in urgent or emergency care settings, and a major contributor to adverse clinical events. While improvements in health care are important in addressing misdiagnosis, patients have a key role by recognizing potentially serious symptoms and seeking care in a timely manner. These concerns are of particular importance for patients requiring urgent or emergency care with potentially life-threatening diseases, such as transient ischemic attacks (TIA), stroke or myocardial infarction. They often fail to recognize the seriousness of their symptoms and may fail to seek care promptly, resulting in missed treatments and poorer outcomes. Patients with stroke typically must be treated within 4 hours to achieve a good response, and public education campaigns have not significantly helped. Smartphone apps for medical diagnosis termed Symptom Checkers (SCs) are widely available to patients in the US and worldwide. They have been shown to be usable by patients, and can affect patient decision making and care seeking behavior. Evidence from our work and others has shown that, if used correctly, SCs can achieve accuracy of diagnosis and triage close to that of physicians (relying on symptom data). However, most studies are based on case summaries created by physicians, SC apps are not used by patients, and lack evidence on the effects of patient characteristics, or SCs influence on patient decision making. To address these gaps we will evaluate the safety, usability, diagnostic and triage accuracy of a leading symptom checker in use by patients in an emergency department or urgent primary care, and the effect of SC outputs on patients’ decisions to seek care. In aim 1 we will recruit 700 patients to use a SC app from Ada Health when they are seen in urgent primary care or the emergency department at Rhode Island Hospital (RIH), including those with possible symptoms of TIA or stroke. This builds on our previous studies of the Ada SC with 241 patients recruited in these locations. The level of urgency of care they intend to seek will be assessed before and after use of Ada, along with a questionnaire on app usability. Diagnostic and triage accuracy will compared to the assessment of the physician who saw the patient, and the Ada results both compared with, and critiqued by, independent physicians viewing the symptom data collected by Ada. We will also evaluate the effects of different presentations of diagnosis and triage data on patient decision making. In aim 2 we will utilize 2 unique data sets of 2300 patients with possible TIA or stroke seen in the ED at RIH, and use machine learning techniques to create new algorithms to improve early diagnosis and risk stratification. Performance will be compared with existing algorithms and guidelines on accurate diagnosis and effective management of these conditions. We will also analyze a data set of 158,000 patients with possible TIA or stroke who used the Ada app in a community setting, and evaluate the influence of patient characteristics, including age, sex, race, ethnicity, country, and socio- economic group on their symptoms, comparing this to the RIH data set results. The results of these studies will improve our understanding of symptom checker safety, ability to recognize high risk patients and direct them to seek care, and the potential impact on health service use, for a broad range of patients including those with TIA or stroke.