Transplant has evolved significantly since the first successful kidney transplant in 1954 and the first organ matching computer program in 1977. Like many healthcare organizations, organ procurement organizations (OPOs), transplant centers, and matching agencies continue to face many challenges associated with legacy software.
Manual Efforts: The greatest challenge with most legacy software is the manual workarounds and intervention required to accommodate of functionality, including duplication of functions like data entry, phone calls to provide additional details/confirm system data when data integrity and quality is an issue, as well as the accommodation of edge cases/new functionality not required in the initial system design.
Lack of Data Quality and Integrity: duplicate data entry for items such as HLA typing and manual efforts are both a symptom of and can contribute to lack of data quality and integrity. It can be a sign of standardization issues and a lack of integration with lab systems or EHRs. It can also cause further data silos, quality issues, and errors as knowledge remains tacit and is stored in note fields, shared over phone calls, and otherwise not sufficiently captured.
Poor Design Leading to Further Data Issues: if transplant and donor systems are not designed with a strong understanding of the business and clinical processes, fields that are required may not be appropriately flagged as such or data may not be validated effectively at the field-level. Alternatively, the system may require data that is not always available in every use case, leading users to use placeholder data which impacts quality.
Lack of Integration Capabilities: Lack of integration capabilities can lead organizations to hold on to legacy systems, despite their limitations, because these systems may still contain critical data stores it would be costly and challenging to migrate.
Inability to Own Data: Many legacy systems and legacy COTS products may have licensing agreements that do not allow customers to own, access, and use their own data freely, creating dependency on the vendor.
Inability to Update at the Pace of Research: New proteins are discovered each month relevant to antibodies and the compatibility of specific donors and potential recipients. Many legacy systems cannot easily codify these values as fields and the information ends up being unstructured data.
Out-of-date Organ Allocations: Match runs may not be reliable and require manual confirmation and checking. Some systems even offer a high percentage of offers to deceased waitlisted patients or patients who have already accepted an organ offer.
Complexity due to Hard-Coding: Software development and practices have evolved and code is now much more flexible and easy to update, maintain, and test. Improved coding practices and discipline also mean cycles to improve are faster, easier, and more robust.
Downtime and Performance Issues: In systems that have been updated incrementally over a long period of time, technical debt can accumulate resulting in the system becoming unstable, slow, and prone to downtime. Integrating and testing new code becomes complicated, particularly if the system was not designed with loosely coupled architecture. These issues compound like debt and make it increasingly difficult to make changes.