The Longitudinal Record, defined as a patient summary and one encounter summary per encounter, is a valuable artifact for understanding the chronology of a patient’s care journey. The default content, however, can contain more information than is applicable to the clinical goals of the requestor. The quantity and quality of content can make it difficult to understand the context around particular pieces of data that are of interest and the connection between pieces of information in different sections of the document.
Reply to this post with your answers to these questions:
1. What important types of information are easily available, searchable and filterable in your current EMR but you find hard to find or understand when looking at data from other systems?
2. Is there data that you would like to be able to temporarily filter out when looking at Patient Summaries or Encounter Documents?
3. How would you envision optimal filtering and searching of external summary data to incorporate and improve clinical usability at the point of care?
Perhaps another way of asking this question is would providers want to see data in a Problem Oriented View as pioneered by Dr. Larry Weed to reduce their clinical burden? What data is preferred in POV?
My team is providing freely available Problem Concept Maps that can be utilized in EHRs globally to produce POV and reduce clinical burden. Initially maps are problem centric and listing relevant labs and meds for each. Epic currently has functionality to import PCM content to generate the POV. PCM framework permits addition of radiology/imaging results, procedures (pulmonary function, cardiology testing), Social Determinants of Health (SDOH) and other data desired by clinicians.
-Relevant labs and meds can be imported across providers/organizations and include historic results (especially those like genetics, tumor markers, etc. that may be done once across a patient’s timeline.
-HL7 Reducing Clinical Burden Workgroup is working on specifications to generate global standards/implementation guide for POV.
APHL does not work on summary documents, which are hopefully using CDA, especially the structured part, so that filtering etc can be accomplished.
From a lab perspective LOINC and SNOMED (for ordinal and nominal results) should be helpful in finding specific tests, though if the results are comparable oer time, when tests were performed at different institutions, or even over time at the same institution requires more infomratione, that may not be currently captured. SHIELD (Systemic Harmonization and Interoperability Enhancement for Laboratory Data) – a public Private partnership group is currentl working on a strategic plan with the goal of identifying the same test the saem way across the healthcare continuum.
Often Labs (speifically public health labs) are not patient centric, so knowing the specimen ID = accession number is important, when getting data fro the source.