## 3 Results
### Applications of the Phenotypic Query Approach  
1.1 Use of HL7 FHIR Search in Smart Medical Information Technology for Healthcare to Enable Location-Independent Searches in Hospital Information Systems  
The main occupation of university hospitals in Germany is the treatment of patients, but they are also involved in clinical research, and thus they must be able to provide data to clinical researchers. A local hospital information system typically consists of various specialised applications which result in very different IT architectures for each healthcare provider. The applications usually do not support a common transfer or use of data in electronic medical records [34; 35]. To overcome difficulties (like heterogeneous data and structures) in using data from multiple hospital information systems for clinical research, the project Smart Medical Information Technology for Healthcare (SMITH) [26] was funded by the German Federal Ministry of Education and Research as part of the German Medical Information Initiative [36; 37]. The objective of the SMITH consortium is to establish so-called Data Integration Centers (DICs) at all participating university hospitals, which will serve as common data access units. The DIC will maintain Health Data Storage (HDS), where a common core data set [38] of patient data will be stored and provided as standardised and interoperable EHRs. The EHRs are made accessible via the HL7 Fast Healthcare Interoperability Resources (FHIR) standard [39]. This is accomplished by incorporating an FHIR server into the local set-up. By using HL7 FHIR, SMITH will enable researchers to access available healthcare data in a standardised manner and use them for innovative research and treatment optimisations. In SMITH, FHIR Search [40] is used to query the HDS. Searches are performed by sending GET requests to a RESTful FHIR server (like HAPI FHIR [41], VONK [42], or Blaze [43]). Those requests contain query parameters which specify the inclusion and exclusion criteria. Values like blood pressure can be encoded with codes of external terminologies like Logical Observation Identifiers Names and Codes (LOINC) and embedded into requests (e.g., 8480-6 for \"blood pressure\" in LOINC). The following example request would result in a response with all observations of blood pressure, where the measured values were greater or equal to 130 mmHg. Matching observations are returned in a so-called \"bundle\", a set of FHIR-formatted resources. GET [base]/Observation?component-code-value-quantity=  
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[[http://unitsoftmeasure.org](http://unitsoftmeasure.org)]([http://unitsoftmeasure.org](http://unitsoftmeasure.org))\(\text{\text{\text{T1}}{|}}\text{text{text}}{|}\)text{text}}{|}\)  
The mapping of PheSO phenotype classes to phenotypic queries and FHIR Search queries was achieved with the software suite PhenoMan [23; 44]. To specify the FHIR-related conditions for the desired result set, domain experts can add additional properties to the phenotype classes in the PheSOs. The mandatory property \"code\" must be specified to map phenotypes to one or more external terminologies (e.g., LOINC). These codes are used to query specific observation types (e.g., blood pressure observation) and medical conditions (e.g., myocardial infarction). Specific units of measurement (formatted according to UCUM [45]) may also be added as properties so that units can be assigned to a numeric restriction and thus transmitted to the FHIR server (an example query with the unit \"mmHG\" is shown above). When FHIR queries are executed by the FHIR server, all matching resources (e.g., observations) are returned regardless of when they were acquired. Therefore, it may be useful to specify periods of time where relevant observations took place (e.g., last year).