GuLiNav lets you infer context-specific representations of computerized clinical practice guidelines. It follows the 3-Layer approach as described HERE. It focuses on implementing the Guideline Procedures Layer (second Layer) using BPMN-based procedure models and provides a simple variant of the Medical Criteria Layer (first layer). It can be connected to an implementation of the Institution Workflow Layer (third layer) directly in code by using it as a software library. More sophisticated interpreters for the Medical Criteria Layer (e.g. an ARDEN Syntax Engine) can be registered and used instead of (or in addition to) the aforementioned simplistic variant that is included in GuLiNav.
The implementation also provides a simplistic REST-based interface to interpret previously defined practice guidelines. The code comes with an artificial mini-example (adapted from a clinical algorithm for the treatment of specific bloodstream infections see Here), but custom guidelines can also be uploaded into the backend using the REST interface.
The REST Interface is mainly for demonstration
purposes, since the software is meant to be used as a library in combination with an implementation
of the other two layers.
GuLiNav is a software library, but if you want to have a peek at its capabilities, you can use a simple demonstrator frontend for a preview.
You can start the demonstrator-frontend using Gradle's bootRun task of the gulinav-rest module:
./gradlew build gulinav-rest:bootRun
The system will be available on localhost:8080.
In case you want to use the dockerized version of GuLiNav (e.g. because you do not have Java 11 installed), you can just build the docker image and then run it afterwards. Make sure to map the correct port:
docker build -t gulinav .
docker container run -p 8080:8080 gulinav
Here the functionality of GuLiNav can be accessed via a simplistic demonstrator user-interface.
GuLiNav currently provides two modules that can be used as a software library: core and mks.
The gulinav-cli is an example project uses GuLiNav as a software library to make GuLiNav usable
from the command-line. It is the most basic example to show how GuLiNav can be used that way:
- Create an input-stream to the CIG
final InputStream bpmnIs = // Input Stream to BPMN File.
- Create a repository to store the CIGs
final GuidelineProcessorRepository repo = new GuidelineProcessorRepository();
- Add the CIG to the repository
// Add the guideline to the repo under a random, yet known UUID
final String randomUUID = UUID.randomUUID().toString();
repo.addGuideline(randomUUID, bpmnIs);
- Read the patient data / context data
// TODO: Here we would need to read the patient-data from the command line or define another way
// to provide the patient-data to the processor. For now, we pretend we know nothing about the
// patient (thus usually the very first step of the guideline should be active etc.)
final SimplePatient patientData = new SimplePatient();
- Process the guideline
final List<GuidelineItem> contextualizedGuideline = repo.process(randomUUID, patientData);
- Do something with the result
System.out.println("The resulting guideline-items are: ");
System.out.println("#########################################################################");
contextualizedGuideline.forEach(System.out::println);
The output of this procedure looks like this for the included demo file:
The resulting guideline-items are:
#########################################################################
GuidelineItem(id=null, externalId=null, taskId=Task_1vc7eqj, description=read microbiology report, detailText=null, timePhase=PRESENT, handledProperties={AUREUS_OR_LUGDUNENSIS=MISC:AUREUS_OR_LUGDUNENSIS}, properties={AUREUS_OR_LUGDUNENSIS=null}, performer=HUMAN, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=Task_0p2sfeg, description=consult an infectiologist, detailText=null, timePhase=FUTURE, handledProperties={INFECTIOLOGIST=MISC:INFECTIOLOGIST}, properties={INFECTIOLOGIST=null}, performer=HUMAN, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=tInvestigate, description=investigate if more than one sample positive, detailText=null, timePhase=FUTURE, handledProperties={MULTIPLE_SAMPLES=MISC:MULTIPLE_SAMPLES}, properties={MULTIPLE_SAMPLES=null}, performer=DEVICE, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=tDecideSampleIndependence, description=decide if samples are sufficiently independent , detailText=null, timePhase=FUTURE, handledProperties={SUFFICIENTLY_INDEPENDENT=MKM:SUFFICIENTLY_INDEPENDENT}, properties={SUFFICIENTLY_INDEPENDENT=null}, performer=DEVICE, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=tTakeBloodsample, description=take another blood sample, detailText=null, timePhase=FUTURE, handledProperties={ANOTHER_SAMPLE_TAKEN=MISC:ANOTHER_SAMPLE_TAKEN}, properties={ANOTHER_SAMPLE_TAKEN=null}, performer=HUMAN, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=Task_05tug1u, description=decide if independent samples are both positive, detailText=null, timePhase=FUTURE, handledProperties={BOTH_POSITIVE=MISC:BOTH_POSITIVE}, properties={BOTH_POSITIVE=null}, performer=DEVICE, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=tSearchFoci, description=search for potential infection foci, detailText=null, timePhase=FUTURE, handledProperties={FOCI_EXIST=MISC:FOCI_EXIST}, properties={FOCI_EXIST=null}, performer=HUMAN, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=tRemoveFoci, description=treat foci, detailText=null, timePhase=FUTURE, handledProperties={REMOVE_FOCI=MISC:REMOVE_FOCI}, properties={REMOVE_FOCI=null}, performer=HUMAN, basedOnExternalEntity=null)
GuidelineItem(id=null, externalId=null, taskId=tAntibioticTreatment, description=antibiotic treatment, detailText=null, timePhase=FUTURE, handledProperties={ANTIBIOTIC_TREATMENT=MISC:ANTIBIOTIC_TREATMENT}, properties={ANTIBIOTIC_TREATMENT=null}, performer=HUMAN, basedOnExternalEntity=null)
Note that this is purely for the sake of demonstrating how to use the GuLiNav library. There is no real use-case for such a command-line tool and also the usage of fat-jars should generally be discouraged.
The core module contains the most relevant part of GuLiNav and can be used by adding a guideline to the repository:
processorRepository.addGuideline("demo", inputStreamToBpmnModel);
For a more details consult the example above (GuLiNav via CLI).
The mks module (mks: medical knowledge service) acts as a simple java framework to encode
medical knowledge (as could otherwise, e.g. be encoded as an ARDEN Syntax MLM).
The MKS is a technical service which requires the knowledge to be defined in natural language
first, since it is inaccessible for non-technicians. It is a simplistic implementation of the
medical criteria layer which is registered into GuLiNav core by default.
The mks implements the PatientEvaluator-Interface of the core module and is consequently
registered into the core module for "MKM" system.
You should implement the PatientEvaluator-interface for an ARDEN Engine (or any other
knowledge-evaluator) and register it in the core module using another system.
The interface provides two simple POST Resources for the sake of demonstrating GuLiNav's mode of operation.
This resource allows it to upload new (versions of) guidelines to GuLiNav. The exact constraints for
the structure of this BPMN-based guideline definition are omitted in this README. An example is
already uploaded by default. It is contained in the core module's resources (demo.bpmn)
Example Payload:
{
"id": "some_id_1234",
"guideline": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\r\n........ REPR. OF BPMN"
}This resource allows it to combine a previously uploaded guideline with patient data . In addition, a guideline with id demo is already present by default (and used as an example within this readme ).
{
"guidelineId": "demo",
"patientPayload": [
{
"code": "MISC:AUREUS_OR_LUGDUNENSIS",
"values": [
{
"time": "2020-12-02T09:49:49.401Z",
"value": false
}
]
},
{
"code": "MISC:MULTIPLE_SAMPLES",
"values": [
{
"time": "2020-12-02T09:49:49.401Z",
"value": false
}
]
},
{
"code": "MISC:ANOTHER_SAMPLE_TAKEN",
"values": [
{
"time": "2020-12-02T09:49:49.401Z",
"value": true
}
]
}
]
}The evaluation is done while taking any patient data that is provided into account.