The following is a guest article by Jane Hite-Syed, VP and COO of National Government Services.
Intro
Recent government initiatives and legislation, such as the Omnibus Bill and Executive Order on Transforming Federal Customer Experience and Service Delivery include provisions intended to bolster federal healthcare IT programs.
Digitization efforts have resulted in tons of healthcare-related data, and the key to capitalizing on the revolutionary benefits that data has to offer is comprehensive healthcare data management (HCDM). The technology driving federal healthcare systems must continually develop to address changing needs, a fact that has become increasingly evident considering the coronavirus pandemic. Cloud applications and tools, for instance, offer federal health agencies a flexible and compliant option for storing and managing troves of sensitive data.
Utilizing HCDM solutions to ease the process of data integration allows for more organized and secure systems, which alleviates the strain on people and enables faster and more streamlined data intelligence, insights, and innovation.
These innovations have the potential to unlock dramatic improvements in the healthcare landscape at scale.
A Data Problem
A common challenge that has arisen due to the shift to remote work across the federal space is that health organizations are becoming inundated with data and information. Making sense of this abundance of data from disparate systems is extremely taxing for federal health agencies. Moreover, when data records are maintained improperly, they may not be indicative of the true state of a given patient’s health, which can result in improper care, diagnosis, and treatment.
Considerations for HCDM Success
Security, Integrity, and Accuracy
Security and data integrity is vital to any HCDM strategy and should be embedded at the beginning of every data management solution. Federal healthcare data is highly sensitive and valuable to threat actors and must therefore be protected.
The accuracy of beneficiary data is also paramount, which is why HCDM platforms maintain updated essential health information to enable informed decision making.
Additionally, if providers don’t have to worry about data errors, they are free to dedicate their time to better serving those in medical need.
Interoperability
Once an organization has confidence in the accuracy and integrity of their data, the second hurdle to be addressed with data management is interoperability. The primary benefit of interoperability is real-time data sharing between patients and care providers.
There are many stakeholders involved in the healthcare industry, between federal agencies, pharmacies, providers, patients, and medical labs, so it stands to reason that when all these institutions can seamlessly access the patient data and materials they need, the time saved on administrative and logistical coordination is tremendous.
However, proprietary systems struggle to communicate with contemporary applications or software, which is why the implementation of data standards, like Fast Healthcare Interoperability Resources (FHIR) developed by Health Level 7, would help to ensure greater interoperability. Effective data management organizes data efficiently, which simplifies and streamlines information that is otherwise difficult to navigate.
Data Governance
Another key component of data management is data governance, the process of managing the availability, usability, and security of the data in enterprise systems. Data governance is intended to ensure the integrity of the lifecycle of patient data and information.
This lifecycle includes all aspects of the healthcare process, from payments to medications to care notes. To ensure data remains a strategic asset rather than a source of confusion, data cataloguing and governance are vital.
If the lifecycle of patient information contains inconsistencies, critical health information can be misinterpreted, which, at best, forces providers to spend time interpreting records, and, at worst, can result in consequential errors to patient care.
Data management strategies can help with the governance process by gathering data that is similar but referenced differently and sorting it accordingly. Data management platforms with interoperability enable cohesion across organizations and systems, effectively speeding up and providing accurate patient care.
Tangible Benefits
Once mature data management strategies and policies are adopted, the possibilities for improvements in the healthcare landscape are extensive.
Accurate, real-time data about a patient can prompt a faster diagnosis, resulting in quicker treatments. When it comes to one’s health, time is often of the essence. Furthermore, a robust data management system can free up valuable time for healthcare workers, allowing them to prioritize more complex tasks.
A strong HCDM strategy can enable AI, NLP, ML technologies, which can complete predictive modeling to predict future outcomes. Predictive modeling can estimate health trends for individual patients, and even populations at scale.
The more data collected, the better predictive modeling and artificial intelligence will become, but without robust healthcare data management practices, this is impossible.
Conclusion
When discussing healthcare data management, it is important to acknowledge that the healthcare industry is constantly evolving. Agility and flexibility will be crucial for any organization striving to utilize data as well as possible.
Agencies and organizations looking to bolster their data management practices should seek counsel from experts in the healthcare data management space. Every organization has distinct needs and should therefore create plans suited for those specific needs.
Implementing HCDM offers immediate, tangible improvements for organizations, as well as far reaching potential benefits in the future.
While all healthcare organizations are on their own unique paths towards modernization, it is evident that strong data management practices are imperative for the future of healthcare.
