Dataset Information

Evaluation of symptom checkers for self diagnosis and triage: audit study.

ABSTRACT:

Objective

To determine the diagnostic and triage accuracy of online symptom checkers (tools that use computer algorithms to help patients with self diagnosis or self triage).

Design

Audit study.

Setting

Publicly available, free symptom checkers.

Participants

23 symptom checkers that were in English and provided advice across a range of conditions. 45 standardized patient vignettes were compiled and equally divided into three categories of triage urgency: emergent care required (for example, pulmonary embolism), non-emergent care reasonable (for example, otitis media), and self care reasonable (for example, viral upper respiratory tract infection).

Main outcome measures

For symptom checkers that provided a diagnosis, our main outcomes were whether the symptom checker listed the correct diagnosis first or within the first 20 potential diagnoses (n=770 standardized patient evaluations). For symptom checkers that provided a triage recommendation, our main outcomes were whether the symptom checker correctly recommended emergent care, non-emergent care, or self care (n=532 standardized patient evaluations).

Results

The 23 symptom checkers provided the correct diagnosis first in 34% (95% confidence interval 31% to 37%) of standardized patient evaluations, listed the correct diagnosis within the top 20 diagnoses given in 58% (55% to 62%) of standardized patient evaluations, and provided the appropriate triage advice in 57% (52% to 61%) of standardized patient evaluations. Triage performance varied by urgency of condition, with appropriate triage advice provided in 80% (95% confidence interval 75% to 86%) of emergent cases, 55% (47% to 63%) of non-emergent cases, and 33% (26% to 40%) of self care cases (P<0.001). Performance on appropriate triage advice across the 23 individual symptom checkers ranged from 33% (95% confidence interval 19% to 48%) to 78% (64% to 91%) of standardized patient evaluations.

Conclusions

Symptom checkers had deficits in both triage and diagnosis. Triage advice from symptom checkers is generally risk averse, encouraging users to seek care for conditions where self care is reasonable.

SUBMITTER: Semigran HL

PROVIDER: S-EPMC4496786 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundYoung adults often browse the internet for self-triage and diagnosis. More sophisticated digital platforms such as symptom checkers have recently become pervasive; however, little is known about their use.ObjectiveThe aim of this study was to understand young adults' (18-34 years old) perspectives on the use of the Google search engine versus a symptom checker, as well as to identify the barriers and enablers for using a symptom checker for self-triage and self-diagnosis.MethodsA qualitative descriptive case study research design was used. Semistructured interviews were conducted with 24 young adults enrolled in a university in Ontario, Canada. All participants were given a clinical vignette and were asked to use a symptom checker (WebMD Symptom Checker or Babylon Health) while thinking out loud, and were asked questions regarding their experience. Interviews were audio-recorded, transcribed, and imported into the NVivo software program. Inductive thematic analysis was conducted independently by two researchers.ResultsUsing the Google search engine was perceived to be faster and more customizable (ie, ability to enter symptoms freely in the search engine) than a symptom checker; however, a symptom checker was perceived to be useful for a more personalized assessment. After having used a symptom checker, most of the participants believed that the platform needed improvement in the areas of accuracy, security and privacy, and medical jargon used. Given these limitations, most participants believed that symptom checkers could be more useful for self-triage than for self-diagnosis. Interestingly, more than half of the participants were not aware of symptom checkers prior to this study and most believed that this lack of awareness about the existence of symptom checkers hindered their use.ConclusionsAwareness related to the existence of symptom checkers and their integration into the health care system are required to maximize benefits related to these platforms. Addressing the barriers identified in this study is likely to increase the acceptance and use of symptom checkers by young adults.

Project description:BackgroundSymptom checkers (SCs) are tools developed to provide clinical decision support to laypersons. Apart from suggesting probable diagnoses, they commonly advise when users should seek care (triage advice). SCs have become increasingly popular despite prior studies rating their performance as mediocre. To date, it is unclear whether SCs can triage better than those who might choose to use them.ObjectiveThis study aims to compare triage accuracy between SCs and their potential users (ie, laypersons).MethodsOn Amazon Mechanical Turk, we recruited 91 adults from the United States who had no professional medical background. In a web-based survey, the participants evaluated 45 fictitious clinical case vignettes. Data for 15 SCs that had processed the same vignettes were obtained from a previous study. As main outcome measures, we assessed the accuracy of the triage assessments made by participants and SCs for each of the three triage levels (ie, emergency care, nonemergency care, self-care) and overall, the proportion of participants outperforming each SC in terms of accuracy, and the risk aversion of participants and SCs by comparing the proportion of cases that were overtriaged.ResultsThe mean overall triage accuracy was similar for participants (60.9%, SD 6.8%; 95% CI 59.5%-62.3%) and SCs (58%, SD 12.8%). Most participants outperformed all but 5 SCs. On average, SCs more reliably detected emergencies (80.6%, SD 17.9%) than laypersons did (67.5%, SD 16.4%; 95% CI 64.1%-70.8%). Although both SCs and participants struggled with cases requiring self-care (the least urgent triage category), SCs more often wrongly classified these cases as emergencies (43/174, 24.7%) compared with laypersons (56/1365, 4.10%).ConclusionsMost SCs had no greater triage capability than an average layperson, although the triage accuracy of the five best SCs was superior to the accuracy of most participants. SCs might improve early detection of emergencies but might also needlessly increase resource utilization in health care. Laypersons sometimes require support in deciding when to rely on self-care but it is in that very situation where SCs perform the worst. Further research is needed to determine how to best combine the strengths of humans and SCs.

Project description:BackgroundWeb-based symptom checkers are promising tools that provide help to patients seeking guidance on health problems. Many health organizations have started using them to enhance triage. Patients use the symptom checker to report their symptoms online and submit the report to the health care center through the system. Health care professionals (registered nurse, practical nurse, general physician, physiotherapist, etc) receive patient inquiries with urgency rating, decide on actions to be taken, and communicate these to the patients. The success of the adoption, however, depends on whether the tools can efficiently support health care professionals' workflow and achieve their support.ObjectiveThis study explores the factors influencing health care professionals' support for a web-based symptom checker for triage.MethodsData were collected through a web-based survey of 639 health care professionals using either of the two most used web-based symptom checkers in the Finnish public primary care. Linear regression models were fitted to study the associations between the study variables and health care professionals' support for the symptom checkers. In addition, the health care professionals' comments collected via survey were qualitatively analyzed to elicit additional insights about the benefits and challenges of the clinical use of symptom checkers.ResultsResults show that the perceived beneficial influence of the symptom checkers on health care professionals' work and the perceived usability of the tools were positively associated with professionals' support. The perceived benefits to patients and organizational support for use were positively associated, and threat to professionals' autonomy was negatively associated with health care professionals' support. These associations were, however, not independent of other factors included in the models. The influences on professionals' work were both positive and negative; the tools streamlined work by providing preliminary information on patients and reduced the number of phone calls, but they also created extra work as the professionals needed to call patients and ask clarifying questions. Managing time between the use of symptom checkers and other tasks was also challenging. Meanwhile, according to health care professionals' experience, the symptom checkers benefited patients as they received help quickly with a lower threshold for care.ConclusionsThe efficient use of symptom checkers for triage requires usable solutions that support health care professionals' work. High-quality information about the patients' conditions and an efficient way of communicating with patients are needed. Using a new eHealth tool also requires that health organizations and teams reorganize their workflows and work distributions to support clinical processes.

Project description:ObjectivesIn England, the NHS111 service provides assessment and triage by telephone for urgent health problems. A digital version of this service has recently been introduced. We aimed to systematically review the evidence on digital and online symptom checkers and similar services.DesignSystematic review.Data sourcesWe searched Medline, Embase, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Health Management Information Consortium, Web of Science and ACM Digital Library up to April 2018, supplemented by phrase searches for known symptom checkers and citation searching of key studies.Eligibility criteriaStudies of any design that evaluated a digital or online symptom checker or health assessment service for people seeking advice about an urgent health problem.Data extraction and synthesisData extraction and quality assessment (using the Cochrane Collaboration version of QUADAS for diagnostic accuracy studies and the National Heart, Lung and Blood Institute tool for observational studies) were done by one reviewer with a sample checked for accuracy and consistency. We performed a narrative synthesis of the included studies structured around pre-defined research questions and key outcomes.ResultsWe included 29 publications (27 studies). Evidence on patient safety was weak. Diagnostic accuracy varied between different systems but was generally low. Algorithm-based triage tended to be more risk averse than that of health professionals. There was very limited evidence on patients' compliance with online triage advice. Study participants generally expressed high levels of satisfaction, although in mainly uncontrolled studies. Younger and more highly educated people were more likely to use these services.ConclusionsThe English 'digital 111' service has been implemented against a background of uncertainty around the likely impact on important outcomes. The health system may need to respond to short-term changes and/or shifts in demand. The popularity of online and digital services with younger and more educated people has implications for health equity.Prospero registration numberCRD42018093564.

Project description:Online symptom checkers are increasingly popular health technologies that enable patients to input their symptoms to produce diagnoses and triage advice. However, there is concern regarding the performance and safety of symptom checkers in diagnosing and triaging patients with life-threatening conditions. This retrospective cross-sectional study aimed to evaluate and compare commercially available symptom checkers for performance in diagnosing and triaging myocardial infarctions (MI). Symptoms and biodata of MI patients were inputted into 8 symptom checkers identified through a systematic search. Anonymised clinical data of 100 consecutive MI patients were collected from a tertiary coronary intervention centre between 1st January 2020 to 31st December 2020. Outcomes included (1) diagnostic sensitivity as defined by symptom checkers outputting MI as the primary diagnosis (D1), or one of the top three (D3), or top five diagnoses (D5); and (2) triage sensitivity as defined by symptom checkers outputting urgent treatment recommendations. Overall D1 sensitivity was 48±31% and varied between symptom checkers (range: 6-85%). Overall D3 and D5 sensitivity were 73±20% (34-92%) and 79±14% (63-94%), respectively. Overall triage sensitivity was 83±13% (55-91%). 24±16% of atypical cases had a correct D1 though for female atypical cases D1 sensitivity was only 10%. Atypical MI D3 and D5 sensitivity were 44±21% and 48±24% respectively and were significantly lower than typical MI cases (p<0.01). Atypical MI triage sensitivity was significantly lower than typical cases (53±20% versus 84±15%, p<0.01). Female atypical cases had significantly lower diagnostic and triage sensitivity than typical female MI cases (p<0.01).Given the severity of the pathology, the diagnostic performance of symptom checkers for correctly diagnosing an MI is concerningly low. Moreover, there is considerable inter-symptom checker performance variation. Patients presenting with atypical symptoms were under-diagnosed and under-triaged, especially if female. This study highlights the need for improved clinical performance, equity and transparency associated with these technologies.

Project description:BackgroundA large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner.ObjectiveThe aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers.MethodsWe identified 10 web-based COVID-19 symptom checkers, all of which were included in the study. We evaluated the COVID-19 symptom checkers by assessing 50 COVID-19 case reports alongside 410 non-COVID-19 control cases. A bootstrapping method was used to counter the unbalanced sample sizes and obtain confidence intervals (CIs). Results are reported as sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC).ResultsThe classification task between COVID-19-positive and COVID-19-negative for "high risk" cases among the 460 test cases yielded (sorted by F1 score): Symptoma (F1=0.92, MCC=0.85), Infermedica (F1=0.80, MCC=0.61), US Centers for Disease Control and Prevention (CDC) (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Cleveland Clinic (F1=0.40, MCC=0.07), Providence (F1=0.40, MCC=0.05), Apple (F1=0.29, MCC=-0.10), Docyet (F1=0.27, MCC=0.29), Ada (F1=0.24, MCC=0.27) and Your.MD (F1=0.24, MCC=0.27). For "high risk" and "medium risk" combined the performance was: Symptoma (F1=0.91, MCC=0.83) Infermedica (F1=0.80, MCC=0.61), Cleveland Clinic (F1=0.76, MCC=0.47), Providence (F1=0.75, MCC=0.45), Your.MD (F1=0.72, MCC=0.33), CDC (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Apple (F1=0.70, MCC=0.25), Ada (F1=0.42, MCC=0.03), and Docyet (F1=0.27, MCC=0.29).ConclusionsWe found that the number of correctly assessed COVID-19 and control cases varies considerably between symptom checkers, with different symptom checkers showing different strengths with respect to sensitivity and specificity. A good balance between sensitivity and specificity was only achieved by two symptom checkers.

Project description:BackgroundDiagnosis is a core component of effective health care, but misdiagnosis is common and can put patients at risk. Diagnostic decision support systems can play a role in improving diagnosis by physicians and other health care workers. Symptom checkers (SCs) have been designed to improve diagnosis and triage (ie, which level of care to seek) by patients.ObjectiveThe aim of this study was to evaluate the performance of the new large language model ChatGPT (versions 3.5 and 4.0), the widely used WebMD SC, and an SC developed by Ada Health in the diagnosis and triage of patients with urgent or emergent clinical problems compared with the final emergency department (ED) diagnoses and physician reviews.MethodsWe used previously collected, deidentified, self-report data from 40 patients presenting to an ED for care who used the Ada SC to record their symptoms prior to seeing the ED physician. Deidentified data were entered into ChatGPT versions 3.5 and 4.0 and WebMD by a research assistant blinded to diagnoses and triage. Diagnoses from all 4 systems were compared with the previously abstracted final diagnoses in the ED as well as with diagnoses and triage recommendations from three independent board-certified ED physicians who had blindly reviewed the self-report clinical data from Ada. Diagnostic accuracy was calculated as the proportion of the diagnoses from ChatGPT, Ada SC, WebMD SC, and the independent physicians that matched at least one ED diagnosis (stratified as top 1 or top 3). Triage accuracy was calculated as the number of recommendations from ChatGPT, WebMD, or Ada that agreed with at least 2 of the independent physicians or were rated "unsafe" or "too cautious."ResultsOverall, 30 and 37 cases had sufficient data for diagnostic and triage analysis, respectively. The rate of top-1 diagnosis matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 9 (30%), 12 (40%), 10 (33%), and 12 (40%), respectively, with a mean rate of 47% for the physicians. The rate of top-3 diagnostic matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 19 (63%), 19 (63%), 15 (50%), and 17 (57%), respectively, with a mean rate of 69% for physicians. The distribution of triage results for Ada was 62% (n=23) agree, 14% unsafe (n=5), and 24% (n=9) too cautious; that for ChatGPT 3.5 was 59% (n=22) agree, 41% (n=15) unsafe, and 0% (n=0) too cautious; that for ChatGPT 4.0 was 76% (n=28) agree, 22% (n=8) unsafe, and 3% (n=1) too cautious; and that for WebMD was 70% (n=26) agree, 19% (n=7) unsafe, and 11% (n=4) too cautious. The unsafe triage rate for ChatGPT 3.5 (41%) was significantly higher (P=.009) than that of Ada (14%).ConclusionsChatGPT 3.5 had high diagnostic accuracy but a high unsafe triage rate. ChatGPT 4.0 had the poorest diagnostic accuracy, but a lower unsafe triage rate and the highest triage agreement with the physicians. The Ada and WebMD SCs performed better overall than ChatGPT. Unsupervised patient use of ChatGPT for diagnosis and triage is not recommended without improvements to triage accuracy and extensive clinical evaluation.

Project description:BackgroundSymptom checkers are digital tools assisting laypersons in self-assessing the urgency and potential causes of their medical complaints. They are widely used but face concerns from both patients and health care professionals, especially regarding their accuracy. A 2015 landmark study substantiated these concerns using case vignettes to demonstrate that symptom checkers commonly err in their triage assessment.ObjectiveThis study aims to revisit the landmark index study to investigate whether and how symptom checkers' capabilities have evolved since 2015 and how they currently compare with laypersons' stand-alone triage appraisal.MethodsIn early 2020, we searched for smartphone and web-based applications providing triage advice. We evaluated these apps on the same 45 case vignettes as the index study. Using descriptive statistics, we compared our findings with those of the index study and with publicly available data on laypersons' triage capability.ResultsWe retrieved 22 symptom checkers providing triage advice. The median triage accuracy in 2020 (55.8%, IQR 15.1%) was close to that in 2015 (59.1%, IQR 15.5%). The apps in 2020 were less risk averse (odds 1.11:1, the ratio of overtriage errors to undertriage errors) than those in 2015 (odds 2.82:1), missing >40% of emergencies. Few apps outperformed laypersons in either deciding whether emergency care was required or whether self-care was sufficient. No apps outperformed the laypersons on both decisions.ConclusionsTriage performance of symptom checkers has, on average, not improved over the course of 5 years. It decreased in 2 use cases (advice on when emergency care is required and when no health care is needed for the moment). However, triage capability varies widely within the sample of symptom checkers. Whether it is beneficial to seek advice from symptom checkers depends on the app chosen and on the specific question to be answered. Future research should develop resources (eg, case vignette repositories) to audit the capabilities of symptom checkers continuously and independently and provide guidance on when and to whom they should be recommended.