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Research · March 1, 2026

Effortless Problem List Management

Effortless Problem List Management

Within the electronic health record (EHR), the problem list plays a central role. Since the introduction of the problem-oriented medical record (POMR) by Dr. Lawrence Weed in the 1960s [1], the concept has been clear: a structured overview of active and inactive problems, complaints, and diagnoses forms the backbone of high-quality clinical documentation.

In practice, however, problem lists are often incomplete, unstructured, or contain duplicate and outdated information [2]. Diagnoses are not consistently recorded or maintained, leading healthcare professionals to rely more heavily on free-text notes and correspondence. This results in additional searching and increases the risk of interpretation or medication errors [3]. Medication errors are a global concern, with inadequate or inaccurate system documentation playing a significant role [4]. A 2025 survey by the Federation of Medical Specialists (FMS) found that 95% of medical specialists experience barriers due to insufficient available patient information [5]. For example, medications may be re-prescribed that were previously discontinued because of side effects, or surgical risks may be incorrectly assessed. To keep healthcare accessible, affordable, and of high quality, improved digital support is essential.

A coded and structured problem list supports clinical reasoning by explicitly presenting and organizing relevant, up-to-date problems. This:

  • Accelerates insight into the most important health issues
  • Improves coordination among involved healthcare professionals
  • Reduces the risk of missing critical information, especially during handovers or acute care
  • Leads to better and faster clinical decision-making. Research shows that an individual medical specialist’s likelihood of making the correct medical decision increases from 60% with an unstructured list to 81% with a correctly structured problem list [7]
  • Provides input for secondary use, such as clinical decision support (e.g., medication interaction alerts [8]), submission to quality registries, cohort selection for research, and data exchange with other healthcare institutions

At the same time, manually updating a problem list requires time and effort, and as current problem list data quality shows, this is rarely perceived as feasible in daily practice. The question is therefore not whether the problem list is necessary, but how we can keep it accurate and complete in practice — without adding administrative burden.

Why action is needed now: legislation

This urgency is reinforced by national and European developments. With the upcoming European Health Data Space (EHDS) [9] and the existing Dutch Act on Electronic Data Exchange in Healthcare (Wegiz) [10], electronic and standardized data exchange has become mandatory. Both work with prioritized datasets that must be made interoperable first [11]. In the Netherlands, the “Basisgegevensset Zorg” (BgZ) forms the foundation for this. Current problems and diagnoses play a central role within the BgZ. Crucially, problems must also be coded using a standardized terminology. In the Netherlands and Belgium, SNOMED CT is the designated standard [12]. In Belgium, SNOMED CT is expected to become the primary source terminology for registration in 2027 [13].

To meet the requirements, reduce administrative burden, enable clinical decision support and data-driven healthcare, problem lists must be complete, up to date, and standardized — without increasing the administrative workload for healthcare professionals. This is precisely where Natural Language Processing (NLP) and Large Language Models (LLMs) offer the opportunity to automatically convert free-text EHR documentation into structured and coded problem list data.

The Smart Problem List Agent

Coforix has developed the Smart Problem List Agent to address this challenge.

The agent automatically analyzes clinical documentation and compares it with the existing problem list in the EHR. All clinically valid problems are automatically linked with high accuracy and completeness to a SNOMED CT concept or to a reference set (such as the Dutch Diagnosis Thesaurus or Flemish reference sets).

In conclusion, with the Smart Problem List Agent

  • Lower administrative burden
  • Reduced search time in the problem list through a user-friendly interface — and therefore greater job satisfaction
  • Higher data quality
  • Improved clinical decision support
  • Fewer medication errors
  • Compliance with legislation — and therefore lower costs

References

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