Hospital Data Mapping Scenario.

<p><i>First</i>, existing clinical data are extracted into a locally controlled database for research. <i>Second</i>, each local code is mapped to one or more standard concept codes, and vice versa. <i>Third</i>, related medical concepts are grouped using standard hierarchies curated by medical experts. The bipartite graphs produced by this process enable bidirectional translation between concept systems. <i>Fourth</i>, adapt the incoming query to use the local concept codes.</p

Quadratic growth in the number of edges in a communication network.

<p>Each edge incurs administrative overhead to maintain a list of peer locations and trust relationships. Fully meshed peer-to-peer (P2P) topologies have N*(N-1)/2 edges shown in red. Edge growth of hub-spoke topologies are shown with an average hub size of 3 (size of the first deployments of east and west coast networks). A simple hub-spoke topology requires one additional link per hub, shown in green. A fault tolerant topology requires two additional links per hub, shown in purple. With 60 peers, the number of p2p edges is administratively infeasible with 1,770 firewall rules and trust relationships.</p

Percentage of Diagnosis and Medication concepts mapped for SHRINE queries at participating Harvard affiliated teaching hospitals.

<p><b><i>Left</i></b>: Percentage of ICD9-CM diagnoses concepts mapped to at least one diagnosis concept at the hospital. <b><i>Right</i></b>: Percentage of RxNorm medication concepts mapped to at least one patient medication concept at the hospital.</p

Investigator's perspective of the SHRINE Webclient.

<p><i>Group 1</i> defines searches for patients with Acute Lymphoid Leukemia (ALL). <i>Group 2</i> refines the search result to only those patients having one of the medications listed. The medications shown are all chemotherapeutic agents administered during intensive phase. <i>Group 3</i> further refines the result to require a lab test administered during diagnosis. Lab test values can be set directly or flagged as ‘abnormally high/low’. In the Query Status window, patient counts are displayed with a Gaussian blur to provide additional privacy safeguards of small patient populations. Results are shown for each hospital and the aggregated patient set size.</p

Constructing Bipartite graphs to map concept systems.

<p><b><i>Left</i></b><i>: Medications</i> are mapped between Children's Hospital Boston (blue) and the RxNorm standard (green) if they share a drug ingredient. The hospital concept code for Acetaminophen is mapped to the RxNorm concept code for Acetaminophen. Codeine also has one mapping. ‘Acetaminophen with Codeine’ has a mapping to RxNorm for each of its ingredients. Patients recorded with the local concept ‘Acetaminophen with Codeine’ will match standard queries using any of the mapped RxNorm drug ingredients. <b><i>Right</i></b><i>: Lab Test concepts</i> are mapped between Children's Hospital Boston (blue) and the LOINC standard (green). Bicarbonate and Blood Urea Nitrogen are each mapped once. Other lab tests require a one-to-many mapping, for example, there are at least four different metabolic tests for sodium (Na+) levels recorded in the Children's Hospital Boston clinical systems.</p

Query Expansion in the Core Ontology.

<p><i>Selected Example</i>: ‘Cardiovascular medications’ is selected and the child contents are shown. At runtime, the query is expanded to include every concept in the cardiovascular medication group, recursively.</p

SHRINE Core Ontology.

<p><i>Left column</i>: categories supported in the core ontology include diagnoses, medications, lab tests, and demographics. <i>Middle column</i>: coding system used for each category. The demographics category uses multiple coding systems to handle the relevant sub-categories such as gender and language. <i>Right column</i>: hierarchy used to group medically related concepts. Standard hierarchies were adopted where possible, which was the case for diagnoses and medications.</p