How Digital Health Data from EHRs and Wearables Fuels AI in Patient Care

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How Digital Health Data from EHRs and Wearables Fuels AI in Patient Care

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May 2025 | Source: News-Medical

The healthcare sector is going through digital transformation and Artificial Intelligence (AI) is the primary agent of change. AI technologies which are being applied in healthcare require substantial amounts of high-quality data to be reliable and efficient. Two of the largest sources of data for AI in healthcare are Electronic Health Records (EHRs) and Patient-Reported Outcomes (PROs) obtained from wearables. Employing these data sources is allowing organizations to better the quality of patient care and improve decision-making and overall management and operation.

1. Electronic Health Records (EHRs) and Electronic Medical Records (EMRs) Overview:

EMRs and EHRs are electronic accounts of a patient’s medical history that are kept by a health care provider. They typically include diagnoses, treatment plans, medications, laboratory results, and patient demographic information. Since EHRs are automatically updated at every visit, they provide real-time, structured data that will be required to create AI healthcare models. [1]

Why EHRs Matter for AI Healthcare Applications

Comprehensive Data: EHRs contain information about a patient’s health over time, including every diagnosis, treatment, and outcome. This is important longitudinal data for AI models to analyze health patterns and identify areas of risk such as chronic disease or reactions to past treatments.
Structured Data: EHRs use structured data fields (e.g. diagnosis codes and lab test results) so that algorithms may identify and sort the data.
Real-Time Updates: The EHR data is updated during patient visits, making it current when used with AI models. This is important as provision of diagnoses, risks, etc. can change in critical situations for care provision (e.g. emergencies). [2]

Applications in AI-Based Healthcare

Predictive Analytics: AI evaluates historical health information to promote possible health risks in the future, such as the risk of developing a chronic disease or risk of have a significant complication for surgery.
Personalized Treatment Plans: AI will evaluate a patient’s medical journey (medical history) to develop the personalized treatment plan that most closely fits the patient, looking to treatments and diagnosis from others from the past. The objective is to develop a better treatment.
Clinical Decision Support: AI tools may offer recommended treatment plans using evidence-based guidelines subject to a patient’s health data (population health strategy) or individual health.[3]

Challenges:

Privacy and Security: EHRs store sensitive medical information – Therefore, data security and patient privacy are always forefront.
Interoperability: When different health systems use different EHR platforms, it can be proficiently complex when providing data where needed for interdisciplinary health in real-time.
Data Accuracy: EHRs have most often contained inaccuracies or stale information which included things about patients who had previously benefitted from the AI diagnostics. [4]

2. Patient-Reported Outcomes (PROs) and Wearable Devices

Overview:

Patient-reported outcomes (PROs) are types of health data provided directly by the patient as self-reports of symptoms, satisfaction with treatments, and the quality of life. Additionally, wearable devices (e.g., fitness trackers) and medical grade sensors that collect real time data are a new source of health care data that include physical activity, heart rates, and sleep. PROs and other patient-centered data is an important source of data for artificial intelligence (AI) health care solutions seeking to customize patient care.[5]

Significance of PROs and Wearables in AI Healthcare Applications

Real-Time Monitoring: Wearables and other devices can provide ongoing information regarding a health metric, enabling AI to dynamically monitor an individual’s health, e.g., a wearable heart monitor can notify either a individual (or a person’s healthcare provider) of an atrial fibrillation episode.
Patient-Centred Data: Patient-reported outcomes and wearable data provide insights into a patient’s symptoms, mental state, and quality of life—information that may be recorded only in limited contexts in clinical settings. Such data enables AI to develop personalized health recommendations.
Longitudinal Insights: Patient-reported outcomes (PROs) and wearables can collect data over extended periods of time which provides contextualization for chronic conditions, and also the ability to detect early signals of changes in health.[6]

Applications of AI-Based Healthcare

Chronic Disease Management: Wearables support tracking of chronic disease, such as hypertension, and diabetes. Ongoing tracking of the health condition will recognize health changes as they occur and will allow a timely response from healthcare providers.
Remote Patient Monitoring: PROs and wearables can allow healthcare providers to monitor patients remotely and could reduce the need for patients to travel for a in-person visit, particularly important for patients in rural or other underserved populations.
Behavioral Health: Wearable technology can provide users with key metrics related to mental health and specifically sleep and sleep disturbances, which lead to AI or machine learning suggesting specific interventions or differences in therapy.[7]

Challenges:

Data Quality: Wearables depend on user engagement and device accuracy which can differ, possibly affecting the overall consistency of the data.
Patient Engagement: For PROs to be effective, patients must be accurately and regularly report out their symptoms and experiences.
Data Integration: There are many challenges in aggregating wearable and PRO tools data with clinical data (EHRs), principally due to format differences between platforms. [8]

Conclusion

AI-driven healthcare applications rely on the health data — diverse and plentiful — to improve the quality of care and predict health outcomes. Electronic Health Records (EHRs) provide many insights into a patient’s clinical status while Patient-Reported Outcomes (PROs) can provide real-time data collection from wearable devices with a patient-centric approach. Together, these forms of data allow AI to augment decision making, personalizing care, and providing better health outcomes.

For an organization considering using AI solutions, data security, privacy, and interoperability are vital issues. Without interoperability it will be hard to create AI ecosystems with integrated data collection that advance equitable effective care.

Are You Ready to Use AI for Health Innovation?

At Statswork, we help healthcare organizations—both public and private—leverage AI with advanced data analytics. By leveraging electronic health records (EHRs), patient-reported outcomes (PROs), and wearable data, we facilitate the creation of intelligent, efficient healthcare applications that revolutionize patient care and operational performance.

References

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Uslu, A., & Stausberg, J. (2021). Value of the electronic medical record for hospital care: update from the literature. Journal of medical Internet research, 23(12), e26323. https://www.jmir.org/2021/12/e26323
Kristiadi, D. P., Hasanudin, M., & Sutrisno, S. (2021). Mobile application of electronic medical record (EMR) systems using near field communication (NFC) technology. International Journal of Open Information Technologies, 9(10), 68-72. https://www.injoit.org/index.php/j1/article/view/1174
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Stradford, L., Curtis, J. R., Zueger, P., Xie, F., Curtis, D., Gavigan, K., … & Nowell, W. B. (2024). Wearable activity tracker study exploring rheumatoid arthritis patients’ disease activity using patient-reported outcome measures, clinical measures, and biometric sensor data (the wear study). Contemporary Clinical Trials Communications, 38, 101272. https://www.sciencedirect.com/science/article/pii/S245186542400019X
Griffiths, E. A., Min, J. S., Lee, W. N., Yu, J. C., Patel, Y., Myren, K. J., & Dingli, D. (2024). Patient-reported outcomes and daily activity assessed with a digital wearable device in patients with paroxysmal nocturnal hemoglobinuria treated with