@azure-rest/health-insights-radiologyinsights v1.0.0-beta.1

Azure Cognitive Services Health Insights Radiology Insights REST client library for JavaScript

Health Insights is an Azure Applied AI Service built with the Azure Cognitive Services Framework, that leverages multiple Cognitive Services, Healthcare API services and other Azure resources.

Radiology Insights is a model that aims to provide quality checks as feedback on errors and inconsistencies (mismatches) and ensures critical findings are identified and communicated using the full context of the report. Follow-up recommendations and clinical findings with measurements (sizes) documented by the radiologist are also identified.

Please rely heavily on our REST client docs to use this library

Key links:

Key links:

• Source code
• Package (NPM)
• API reference documentation
• Product Information
• Samples

Getting started

Currently supported environments

• LTS versions of Node.js
• Latest versions of Safari, Chrome, Edge and Firefox.

Prerequisites

• LTS versions of Node.js
• You must have an Azure subscription to use this package.
• An existing Cognitive Services Health Insights instance.

Install the @azure-rest/health-insights-radiologyinsights package

Install the RadiologyInsights REST client library for JavaScript with npm:

npm install @azure-rest/health-insights-radiologyinsights

Create and authenticate a RadiologyInsightsClient

To use an Azure Active Directory (AAD) token credential, provide an instance of the desired credential type obtained from the Azure Identity library.

To authenticate with AAD, you must first npm install @azure/identity

After setup, you can choose which type of credential from @azure/identity to use. As an example, DefaultAzureCredential can be used to authenticate the client.

Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET

Examples

Create a RadiologyInsights asynchronous client

const apiKey = process.env["HEALTH_INSIGHTS_API_KEY"] || "";
const endpoint = process.env["HEALTH_INSIGHTS_ENDPOINT"] || "";
const credential = new AzureKeyCredential(apiKey);
const client = RadiologyInsightsRestClient(endpoint, credential);

Build a request, send it to the client and print the description of a Critical Result Inference

export async function main() {
  const credential = new AzureKeyCredential(apiKey);
  const client = AzureHealthInsightsClient(endpoint, credential);

  // Create request body
  const radiologyInsightsParameter = createRequestBody();

  // Initiate radiology insights job and retrieve results
  const initialResponse = await client.path("/radiology-insights/jobs").post(radiologyInsightsParameter);
  if (isUnexpected(initialResponse)) {
    throw initialResponse;
  }
  const poller = await getLongRunningPoller(client, initialResponse);
  const RadiologyInsightsResult = await poller.pollUntilDone();
  if (isUnexpected(RadiologyInsightsResult)) {
    throw RadiologyInsightsResult;
  }
  const resultBody = RadiologyInsightsResult.body;
  printResults(resultBody);
}

function createRequestBody(): CreateJobParameters {

  const codingData = {
    system: "Http://hl7.org/fhir/ValueSet/cpt-all",
    code: "USPELVIS",
    display: "US PELVIS COMPLETE"
  };

  const code = {
    coding: [codingData]
  };

  const patientInfo = {
    sex: "female",
    birthDate: new Date("1959-11-11T19:00:00+00:00"),
  };

  const encounterData = {
    id: "encounterid1",
    period: {
      "start": "2021-8-28T00:00:00",
      "end": "2021-8-28T00:00:00"
    },
    class: "inpatient"
  };

  const authorData = {
    "id": "authorid1",
    "name": "authorname1"
  };

  const orderedProceduresData = {
    code: code,
    description: "US PELVIS COMPLETE"
  };

  const administrativeMetadata = {
    orderedProcedures: [orderedProceduresData],
    encounterId: "encounterid1"
  };

  const content = {
    sourceType: "inline",
    value: "CLINICAL HISTORY:   "
      + "\r\n20-year-old female presenting with abdominal pain. Surgical history significant for appendectomy."
      + "\r\n "
      + "\r\nCOMPARISON:   "
      + "\r\nRight upper quadrant sonographic performed 1 day prior."
      + "\r\n "
      + "\r\nTECHNIQUE:   "
      + "\r\nTransabdominal grayscale pelvic sonography with duplex color Doppler "
      + "\r\nand spectral waveform analysis of the ovaries."
      + "\r\n "
      + "\r\nFINDINGS:   "
      + "\r\nThe uterus is unremarkable given the transabdominal technique with "
      + "\r\nendometrial echo complex within physiologic normal limits. The "
      + "\r\novaries are symmetric in size, measuring 2.5 x 1.2 x 3.0 cm and the "
      + "\r\nleft measuring 2.8 x 1.5 x 1.9 cm.\n \r\nOn duplex imaging, Doppler signal is symmetric."
      + "\r\n "
      + "\r\nIMPRESSION:   "
      + "\r\n1. Normal pelvic sonography. Findings of testicular torsion."
      + "\r\n\nA new US pelvis within the next 6 months is recommended."
      + "\n\nThese results have been discussed with Dr. Jones at 3 PM on November 5 2020.\n "
      + "\r\n"
  };

  const patientDocumentData = {
    type: "note",
    clinicalType: "radiologyReport",
    id: "docid1",
    language: "en",
    authors: [authorData],
    specialtyType: "radiology",
    administrativeMetadata: administrativeMetadata,
    content: content,
    createdDateTime: new Date("2021-06-01T00:00:00.000"),
    orderedProceduresAsCsv: "US PELVIS COMPLETE"
  };


  const patientData = {
    id: "Samantha Jones",
    info: patientInfo,
    encounters: [encounterData],
    patientDocuments: [patientDocumentData]
  };

  const inferenceTypes = [
    "finding",
    "ageMismatch",
    "lateralityDiscrepancy",
    "sexMismatch",
    "completeOrderDiscrepancy",
    "limitedOrderDiscrepancy",
    "criticalResult",
    "criticalRecommendation",
    "followupRecommendation",
    "followupCommunication",
    "radiologyProcedure"];

  const followupRecommendationOptions = {
    includeRecommendationsWithNoSpecifiedModality: true,
    includeRecommendationsInReferences: true,
    provideFocusedSentenceEvidence: true
  };

  const findingOptions = {
    provideFocusedSentenceEvidence: true
  };

  const inferenceOptions = {
    followupRecommendationOptions: followupRecommendationOptions,
    findingOptions: findingOptions
  };

  const configuration = {
    inferenceOptions: inferenceOptions,
    inferenceTypes: inferenceTypes,
    locale: "en-US",
    verbose: false,
    includeEvidence: true
  };

  const radiologyInsightsData = {
    patients: [patientData],
    configuration: configuration
  };

  return {
    body: radiologyInsightsData
  }

}

function printResults(radiologyInsightsResult: RadiologyInsightsResultOutput): void {
  if (radiologyInsightsResult.status === "succeeded") {
    const results = radiologyInsightsResult.result;
    if (results !== undefined) {
      results.patientResults.forEach((patientResult: { inferences: any[]; }) => {
        if (patientResult.inferences) {
          patientResult.inferences.forEach((inference) => {
            if (inference.kind === "criticalResult") {
              if ("result" in inference) {
                console.log("Critical Result Inference found: " + inference.result.description);
              }
            }
          });
        }
      });
    }
  } else {
    const error = radiologyInsightsResult.error;
    if (error) {
      console.log(error.code, ":", error.message);
    }
  }
}

Troubleshooting

Logging

Enabling logging may help uncover useful information about failures. In order to see a log of HTTP requests and responses, set the AZURE_LOG_LEVEL environment variable to info. Alternatively, logging can be enabled at runtime by calling setLogLevel in the @azure/logger:

const { setLogLevel } = require("@azure/logger");

setLogLevel("info");

For more detailed instructions on how to enable logs, you can look at the @azure/logger package docs.