When Data Drives Decisions: Why Bias & Data Quality Matter in Healthcare AI

How EHI Guarantees Fair, Accurate, and Responsible AI for all Patients

AI has become integral to various clinical decision-making, ranging from identifying health issues, assisting in documentation and managing care across various locations. But with AI functioning in additional roles, a crucial element comes to the forefront: how AI performs its tasks is reliant on the quality of information it has.

Deficient, unbalanced, and biased information leads to adverse outcomes. Predictions become futile. An algorithm can systematically exclude some cohorts from its predictions, even those populations that have been overlooked by standard healthcare practices.

At Enable Healthcare (EHI), and for all products in our line of Aria One (including Lumina AI Scribe and RevQ AI Modeling), it is not a hypothetical concern. AI can only be dependable if the data on which it is built is accurate, equitable, and managed in an ethically sustainable manner.

 When Data Fails, Patients Are Affected 

There is a plethora of documented proof that shows healthcare data is biased. It is a known fact that AI models trained on unbalanced datasets will misread or misinterpret the needs of certain demographic groups. This has been documented in the literature for tools used for sepsis diagnosis, chronic condition modeling, imaging analytics, and risk assessment.

Some industry observations include:

• Pulse oximeters that read artificially high oxygen levels in individuals with higher levels of melanin.

• Algorithms built on prior claim patterns may misinterpret little use with low demand.

• Risk models that misclassify chronic illness because historical data did not include the populations most affected, those that were underdiagnosed and therefore underrepresented. 

These aren't minor mistakes. When bias sneaks into an algorithm, it becomes part of the model's reasoning. This creates a feedback loop that further entrenches the unfairness that is the focus of the healthcare inequities. 

Due to this, EHI believes that data quality must be a healthcare responsibility and a healthcare injustice.

 Why Data Quality Is Even More Important in AI-Driven Care 

Even one false data point can alter the entire care plan within RPM, CCM, TCM, AWVs, claims intelligence, and predictive modeling. A value that is skewed, missing, or unread can impact:

• risk scores

• alerts and thresholds

• documentation accuracy

• care coordination

• coding integrity

• quality measure performance

• revenue projections 

Everyone within the workflow is adversely affected by bad data.  

Providers experience a loss of efficiency due to unnecessary redirection of their efforts and noncompliant tasks. 

Patients face the risks of diagnostic delays, loss of trust in digital health, and the risk of misdiagnosis. 

To mitigate these risks, EHI constructs its AI ecosystems from the ground up.

 How EHI Builds AI That Is Fair, Transparent, and Clinically Sound 

EHI enriches the data by combining technology and clinical management to ensure the data quality is correct. 

1. Rigorous Cross-Checking of Information Across Multiple Sources 

For the sake of early detection of inconsistencies, we analyze both structured and unstructured data from EMRs, claims, RPM devices, care coordination notes, provider documentation, and social data.

2. Continuous Bias Scanning and Algorithm Auditing 

Demographic imbalances, performance drifts, outliers, and inconsistencies about labels and reviews are identified to ensure reliability across different categories of patients.

3. Training Data Made for Representation 

In order to eliminate the potential of underrepresentation of patients, the modeling frameworks are designed to comply with CMS, AMA, and ONC’s equity regulations.

4. AI That Can Explain Itself 

Aria One provides tools like Lumina and RevQ that enable clinicians to understand and question the models proposed to them.

5. Clinical Humans Always in the Loop 

Nurses, coders, and clinicians examine AI-augmented documentation, risk scoring, revenue cycle management, and care coordination insights. AI assists them in their tasks, enhances their value, and preserves their jobs.

 AI That Closes the Gaps Instead of Opening Them 

When developed with care, AI can enable practices to identify mentally detached, overlooked patients, and deteriorating patients easily, enhance outcomes of chronic care, streamline tasks, and ensure precision in coding and revenue generation.

 EHI’s Commitment to Responsible, Patient-Centered AI 

Unfortunately, this is not technology-related; however, bias and lack of data quality can compromise the safety of the patient. These factors determine whether a patient will receive follow-up care or if they are offered appropriate diagnoses or relevant preventive services. 

EHI’s mission is to advance AI for clinicians, patients, and trust — transparent, equitable, accurate, and humane. Our safety measures adapt to new technologies, and this is how we build trust.

Given proper data integrity, fairness in algorithms, and sufficient clinical supervision, AI has the ability to ensure that all patients receive the level of care equitable to their individual needs.