• Insufficient use of external data sets. When combined with transactional and clinical data sets, external data (such as those shared by community-based organizations, and the ones available from WHO, CDC, Johns Hopkins, etc.) can provide actionable insights for providers and health plans to proactively address SDoH and other factors that impact patient access to care and clinical outcomes
• No real-time or near real-time data. It isn’t possible today to access a patient longitudinal whole health record across systems or payment information in real time. This complicates point-of-care decision-making and creates delays in the revenue cycle
• Infrastructure inadequacies. Most healthcare organizations lack an IT infrastructure that can support a “network of networks” model
• Data redundancy. Data duplication problems are far too common – even within the same data store – and different EMPI (Enterprise Master Patient Index) algorithms are used to tie those records together


Sources of patient data sets are complex
Patient data falls into 18 different categories or sets. These data sets are:

1. Immunity tests
2. Disease registry
3. Demographics data
4. Employment
5. Claims & remittance data
6. Pharmacy data
7. Eligibility & benefits data
8. Clinical (Notes, Charts, X-Ray, MRI, Radiographs, etc.)
9. Financial (from Banks for HC loans, AR Reports, Patient Responsibility from Claims)
10. IOT/Wearables/Sensors data
11. DNA/Genome data
12. Schedule/Referral data
13. SDoH data
14. Surveys
15. Consent management processes
16. Notifications and alerts – Communication protocols
17. Clinical trials data
18. External summary data sources for analysis (WHO, CDC, Johns Hopkins, etc.)

Patient data sets are spread across different systems. Some need to be digitized and combined with structured and external data sets to complete the whole health picture for patients.

Applying AI/ML to digitize data
Artificial intelligence (AI) and machine learning (ML) are essential to data digitization, prediction analytics, and interoperability of digital healthcare data. For example, AI and ML facilitate better automation of tasks and decision-making processes since data-driven insights are needed to automate processes and require digitized data. 

Data digitization and integration of that data with structured and external data sets that offer a 360-degree view of the patient can provide actionable insights to providers, payers, and patients. AI coupled with ML algorithms in a well-designed data engineering framework that supports bidirectional integration between systems are necessary to make this a reality. 

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