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Building Next Generation EHR Data Models

March 29, 2022 - Parul Saini, Webmedy Team


Patient care today is disjointed, and most collaborators don't know what they are doing for a patient in real-time. Keeping track of the patient's entire care team (providers, payers, family members, etc.) as well as coordinating and integrating their electronic activities is what successful EHRs will need to handle with ease as they move from retrospective documentation to modern patient collaboration platforms. As of today, EHR apps are usually restricted to "legal entities" (e.g., a single hospital or hospital system or a single practice). To provide integrated and coordinated health care, EHR systems must open themselves up to outside boundaries, but few have done so.

This is due to a lack of understanding that tomorrow's shared savings programs, capitated payment models, ACOs, and PCHMs would need a level of coordination and quantity of quality metrics measurements that are difficult to establish, implement, and secure. Future EHRs must be viewed as comprehensive care coordination platforms with dynamic economic models that can handle a significant deal of volatility and flexibility, particularly in terms of legal boundaries. When working with the uncertainty of multi-organizational partnerships and user communities, application designs and data models must enable more fluid processes that can change daily or weekly based on the demands of new players.

Data modeling is not merely a technical exercise, which is what leads to bad designs that don't embrace next-generation business models, according to the healthcare IT application development community. A group of engineers and other nerds sitting around a table cannot define a data model. Understanding all of the data's applications, the relationships and attributes involved in the data, and, most crucially, how the data management strategy will grow and change in the future are all part of data modeling. When most systems are designed, the last part (database extensibility) is frequently overlooked. All of this necessitates direct contact with end-users, stakeholders, and other non-technical individuals. Too often, databases are considered like a filing cabinet: just throw anything you need in there and deal with the organization afterward. That will not be conceivable in the emerging world of ACOs and PCMH.

Required Attributes in the Next Generation EHR Data Model

  • Adaptable patient-driven "individual" models. Present-day and extensible information bases model patient (buyer), doctor, nurse, staff, overseer, contact, insurance contract holders, and related information as Person records. Rather than having a different table for each kind of individual (for instance, an alternate table for a patient versus a doctor), you ought to attempt to show the different individual sorts in a solitary inheritable and related table.
  • Similarly, organizations should have flexible multi-facility "organization" models. Facilities, tenants, hospitals, insurance providers, departments, clinics, administration, and related data should be organized in something called an organization. Any entity that isn't a person will likely fall into the Organization record type category. Therefore, a single table with appropriate attributes should do.
  • Patient identification and de-duplication are supported when working across multiple legal entities. There will be no single identifier that governs all systems in a multi-entity legal framework. There are a variety of mappings that can be used for any entity, ranging from a primary key for internal consistency to several external identifiers. A person's records should be able to accommodate a set of identification values that can be used for both ID lookups and deduplication requirements when integrating multiple systems.
  • Separation of PHI from clinical and transactional attributes. A good design is to move PHI data into one database (configured with proper security), and place clinical, business, and other attributes in another.
  • Multi-role support. Each entity in the database, such as a person or organization, should be capable of supporting multiple roles at once.
  • Long-term storage and management (revision control) of entity attributes. Data is subject to changes after having been stored for a long time. An extensible database allows for long-term archiving of data and change management of the structure, as well as revision control of data.
  • Support of multiple users and devices within the same database. Always assume that multiple applications will write to the same database when creating a database. Therefore, you should keep track of what application wrote or changed the record, as well as which device did so.

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