Objective and peer-reviewed research can raise the industry bar for quality, safety, and performance. Here's some research that looks at the performance and efficacy of Ada's technology.
Scroll through or navigate to a topic of interest below.
Ada was significantly more accurate in the recognition of inflammatory rheumatic diseases (IRD) than physicians (70% vs physicians' 54%), which included experienced rheumatology physicians.
Ada was more accurate than physicians in suggesting the correct final diagnosis (54% of cases vs physicians' 32%).
Highlights the potential of using symptom checkers early in the patient journey and the advantages of considering complete patient information to establish a correct diagnosis.
Shows the potential of symptom checkers to make accurate triage and diagnostic decisions in rheumatology.
Real-world hospital ED study of 378 patients, comparing the safety of Ada’s urgency advice to that of the Manchester Triage System (MTS).
Ada demonstrated a high rate of safety (94.7%) over all medical specialties of the Emergency Department, with a focus on Internal Medicine, Orthopedics and Trauma, and Neurology.
From the 3 lowest MTS categories, 43.4% could have safely accessed lower urgency care such as seeing a GP or managing their symptoms at home.
Ada has the potential to relieve pressure on Emergency Departments by guiding patients to lower urgency care if used at home.
When used in combination with ER physicians assessments, Ada significantly increased diagnostic accuracy (87.3%) compared with an ER physician alone (80.9%).
Patients with early diagnosis and rapid treatment allocation exhibited significantly reduced complications and length of hospital stay.
AI tools have the potential to benefit the diagnostic efficacy of clinicians and improve quality of care.
Using a dataset from 464,547 patients, Ada's CVD risk prediction tool achieved superior performance to several established general CVD risk prediction models.
Ada could be used to direct individuals to app-based behavior change recommendations for risk reduction and guidance.
Ada identified the correct condition in the top 3 suggestions in 83% of cases, compared to the next best of 77%, including Australia-specific scenarios.
63% of Ada’s urgency advice matched the gold standard, compared to the next highest app’s score of 52%
93% of Ada’s assessments provided useful information prior to consultation with 83% of Ada’s top condition suggestions matching the physician’s primary diagnosis.
In 52% of appointments, Ada saved more than 1 minute of primary care consultation time.
Most physicians agreed that Ada is safe and effective, guiding patients to the correct care, providing useful information, and saving time in consultations.
Healthcare Information and Management Systems Society (HIMSS), 2023
Protocol for a clinical study set in a Tanzanian district hospital outpatient clinic to investigate the usefulness of a prototype diagnosis decision support system (DDSS) in the hands of a mid-level healthcare worker.
The diagnostic accuracy and qualitative data on the usability, usefulness, and acceptance of the prototype DDSS will provide insights on the appropriateness of the prototype DDSS interface.
Protocol for first-of-type clinical study set in a Tanzanian district hospital outpatient clinic to investigate the usefulness of symptom assessment applications in LMICs.
Evaluation of Ada's compared to gold-standard differential diagnoses and triage advice for participants with various conditions and age groups, including children.
Ada’s knowledge base was adapted to account for differences in incidence and disease presentation in South Africa, including optimizing 25 maternal and 25 pediatric conditions.
Ada’s readability was lowered from grade 11 level to below grade 8 level (7.4) while still maintaining medical accuracy to improve accessibility and uptake.
White-box AI like Ada, based on interpretable models that can be explained and easily understood by a human, can control and reduce data bias.
AI solutions can only improve health outcomes in LMICs if they are designed and adapted with the needs of the local population in mind.
To account for regional differences in incidence and disease presentation, 51 maternal and pediatric health conditions were optimized in Ada for South Africa.
Ada’s content readability score has been reduced from grade 11 to below grade 8 (7.4±0.8) while still maintaining medical accuracy.
White-box AI-approach like Ada’s makes it possible to capture the differences in underrepresented populations and low-income countriea.
A new approach of conducting expert interviews to create 11 clinical vignettes on lysosomal storage diseases (LSDs) which were then used to update Ada’s condition models.
15 LSD patients and 9 LSD experts will rate both new and old disease models in Ada to compare efficacy.
This novel approach can potentially shorten the ‘time to diagnosis’ for rare diseases.
Retrospective analysis of the diagnostic costs of rare inflammatory systemic diseases and the impact of a prototype Ada as a diagnostic decision support system (DDSS).
If this Ada DDSS prototype were released, it has the potential to lead to earlier diagnosis and could save between 50% and 70% of diagnostic costs.
Ada has the potential to help surface users who should seek care, including possible early cases and users who do not participate in screening programs.
Correlations between Ada’s cancer frequency in Germany and the German cancer registry incidence data were statistically significant.
Between 23% and 94% of Ada assessments where cancer was suggested reported no advanced signs, and between 19% and 41% reported first signs and no advanced signs.
AI in Cancer Diagnostics: from research to clinical practice, European Association for Cancer Research (EACR), 2021
Ada uncovered similar ILI trends to an official German surveillance system and could help identify health trends in countries without population-based monitoring systems.
Proposals by the EU and FDA have set the regulatory groundwork for algorithm change protocols. The FDA proposals are detailed and actionable but are yet to be finalized. The EU has not yet detailed its plan. The authors recommended the EU set out an approach that is coordinated, actionable, and consultative.
The public, manufacturers, and medical community should be engaged to inform requirements of algorithm change, with a focus on post market surveillance, RWP measurement, clinical evaluation, and labeling.
Ada Health successfully demonstrates the feasibility of a pathophysiology-based DDSS with Medical Deep Reasoning (MDR).
MDR extends current DDSS capabilities by utilizing pathophysiology to describe the dynamic components of disease pathogenesis to create an individualized, high-resolution model for personal healthcare.
Facilitating the integration of this kind of health-related data will significantly advance personalized medicine.
MedInfo, World Congress of Medical and Health Informatics, 2019
