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Terms & Keywords

IHE – Integrating the Healthcare Enterprise.
A standards-setting organization; Integrates standards such as DICOM, HL7 to provide solutions for real-world needs.

RSNA –  Radiology Society of North America.
An international society of Radiologists who promote advances in the field of Radiology and provide Software Solutions to support Electronic Health Records including Imaging of all modalities.

NIBIB National Institute of Biomedical Imaging and Bioengineering.
An innovating arm of the Department of Health and Human Services dedicated to advancing research and technologies to improve and innovate healthcare. Plays an important role in advancing Imaging with different modalities and promoting early detection.

DICOM – Digital Imaging and Communication in Medicine.
An Internationally recognized standard to transmit, store, retrieve, print, process and display medical imaging information. Standards fully incorporated in image-acquisition devices, PACS, and workstations throughout healthcare infrastructure.

HL7 – Health Level Seven is a standards-developing body.
Provides standards for the exchange, integration, and sharing and retrieval of electronic health information. ANSI Accredited – founded in 1987.

PACS – Picture Archiving & Communications System technology.
Utilizes DICOM standards for storage & retrieval of images.

RIS – Radiology Information System.
Integrates workflows in Radiology from initial order through to billing and sharing of exams within the radiology department. Utilizes HL7 and DICOM standards for integration of various devices.

EMPI – Enterprise Master Patient Index.
Database of patient records maintained across the healthcare organization, it maintains a consistent and unique patient identifier by merging and linking patient records. Provides a rich querying mechanism.

PIX – Patient Identifier Cross Referencing.
IHE Actor uses services provided by the EMPI. PIX API including Query API is defined by IHE.

 

Introduction/Overview

Healthcare professionals have a need to store and access relevant medical documents for any patient undercare. Document creation and submission, query & retrieval functions, and secure access must span the healthcare enterprise to provide timely care in the most efficient manner.

Patient exams that include imaging of varying types of modalities and diagnostic reports ultimately belong to the patient, and only the patient has the right to share this information with other caregiving facilities. Strict HIPAA compliance requires that any PHR must always be treated with utmost security and never leave the designated secure areas.

In Radiology, the process begins when an order for an exam is placed by a physician. As part of this process, a local patient-identifier is generated for the order. However, for the lifetime of the patient, there is a global, unique patient identifier that spans the healthcare enterprise. Any Electronic Medical Record must then be tracked and accessible via this global patient ID.

An EMPI or Enterprise Master Patient Index system that is deployed across the healthcare organization helps maintain a consistent and accurate view of a patient’s identity. This database identifies the patient by use of a Global Unique Patient Identifier and accesses the patient record. The patient record contains patient demographics such as date-of-birth, place-of-birth, current address, and numerous other fields. Any variances or multiple records result in the merging or linking of extant records. Tolerance for error is close to zero and is a huge challenge in maintaining integrity across the healthcare enterprise.

IHE (Integrating Healthcare Enterprise) specifications pull together many industry standards such as DICOM, DICOMWeb, MTOM/XOP and more to provide a technical framework (TF) for integrating healthcare systems. This technical-framework specifies IHE-Actors and their Transactions which, when combined together, define a specific integration profile. IHE Profiles can cover various aspects of Healthcare Integration are specified in the following documents:

  • IHE ITI TF-1
  • IHE ITI TF-2a and IHE ITI TF-2b
  • IHE ITI TF-2x
  • IHE ITI TF-3

The Radiology specific profiles are provided in the specifications listed below. The radiology profiles are based on the general specifications but provide additional protocols and fields that apply to Radiology. These sets of specifications also detail the RSNA Image Sharing Network requirements.

  • IHE RAD TF-1
  • IHE RAD TF-2
  • IHE RAD TF-3
  • TF Supplement for Cross-enterprise Document Reliable Interchange of Images – XDR-I
  • TF Supplement for Cross-enterprise Document Sharing for Imaging – XDS-I.b

There are numerous subfields of Radiology that are specified in IHE and IHE-RAD specifications, but the rest of this post mainly focuses on describing Integration Profiles required for the implementation of an Image Sharing Network.

 

Image-Shared Network

The reference graph below shows a combination of actors and the SOAP transaction used to carry out the workflow.

There is a distinct Client / Server relationship where the ISN provides services that allow the clients to perform the following:

  • Document submissions on behalf of a patient
  • Query and Retrieve transactions for the retrieval of
    • Patient information
    • Image-manifests associated with an exam
    • Diagnostic reports
    • Images associated with the exams

Within the ISN, various actors implement integration profiles in order to

  • Register a patient when a new order is generated
  • Persist received images to a permanent store
  • Register diagnostic reports and Image Manifest to support future query
  • Retrieve documents from a permanent store and serve them out to the external clients

Fig.1 ISN Reference Model

ISN Implementation

Sponsored by a grant from NIBIB, RSNA, Mayo Clinic, and research facilities associated with universities were given a charter to build a patient-centric image sharing network (ISN). This network is designed to automate and improve Radiology workflows by facilitating storage of patient documents to the cloud.

Rather than provide a copy of the exam on a CD, the patient can now choose to receive the exam electronically. This has significant implications, such as

  • Patient exams are centrally available

    • Patient documents can easily be accessed by any clinician providing care to the patien
  • The study does not have to be repeated if the patient has traveled to another region and needs care

lifeIMAGE, a startup company based in Newton, MA, provided the ISN solution to RSNA based workflows. lifeIMAGE was part of the original research group that pioneered the end to end solution strictly based on IHE & IHE-RAD specifications.  

The engineering team from BigR.io participated in the ISN Architecture evolution, its implementation, and subsequently validation and interoperability testing at Connectathons where vendors are required to perform live tests against other vendors in order to show full conformance. BigR.io, in collaboration with lifeIMAGE resources, demonstrated pure excellence in showing conformance and also assisted other teams to meet their objectives.

BigR.io’s knowledge in navigating a plethora of standards, such as DICOM and HL7, and its ability to innovate has proven to be a great asset towards providing a sound and robust solution.

The remainder of this post briefly provides the architecture and workflow details, specifically the RSNA Workflow.

ISN Reference Model and Workflow

The ISN Reference Model as shown in Fig.1 comprises three major functions:

  • The Edge Server Function
  • lifeIMAGE Registry as a Service
  • Patient Health Record Account Access
The Edge Server Function

The edge server Function is an application that integrates with RIS and PACS and is deployed on-premise in a healthcare facility. Participating healthcare enterprises are designated an Affinity Domain. The ISN Service itself is multi-tenant and is capable of supporting multiple Affinity Domains.

The workflow begins when an order is entered in the RIS. At this time, a local patient identifier is generated and registered with the PIX-Manager. The PIX associates the patient identifier to a global patient-identifier, and a new global patient identifier is created if one is not found.

On completion of the exam, the edge server constructs an XDR-i-based Provide & Register SOAP Request and sends the submission request to the lifeIMAGE Registry. The request can include the following:

  • Diagnostic Reports – HL7 Service is used to obtain diagnostic reports
  • Images (different modalities) – DICOM Service is used to obtain all images and MTOM/XOP is used to attach images to the SOAP Request
  • Metadata describing the request and patient information
    • Both local and global patient identifiers are sent with the Request

Note that XDR-i does not require the Image Manifest (KOS) as this is built by the XDR-Imaging Recipient component in the Clearinghouse.

The lifeIMAGE Registry or Clearinghouse

The Clearinghouse is a hosted service. Any number of Healthcare Enterprises may subscribe to this service to conduct their desired workflows.

The service is made up of various IHE-specified Actors and these Actors implement the IHE/RAD specific Integration Profiles. Actors & their transactions are as follows:

  1. XDR.Imaging Document Recipient (IDR)

    • The recipient can receive incoming SOAP Requests over any affinity-domain.
    • On receiving a valid request, the recipient retrieves all attachments and persists the data as necessary
  2. XDS Imaging Document Source (IDS)
    • IDR & IDS are grouped actors, and as such, they collaborate in processing of Provide and Register SOAP Requests
    • The IDS digests the received request to produce Image Manifest. This is metadata that describes the Images and Diagnostic Reports
    • The image-manifest and the diagnostic reports are then registered with the Repository Actor
      • To this effect, IDS acts as a Client. It generates a Register Imaging Document Set SOAP Messages and sends it to the XDS Repository Actor
  3. XDS Repository
    • XDS Repository is the keeper of Image Manifests (KOS) and Diagnostic Reports. It provides an ITI-43 Retrieve Document Set service to external clients
    • The Repository acts as a Client and sends an ITI-41 Register Document Set-b to the XDS Registry
  4. XDS Registry
    • XDS Registry maintains a database of all registered exams. The metadata received in the ITI-41 is persisted and is made available for future queries
    • The Registry provides an ITI-18 Registry Stored Query Service to the external clients
  5. PIX manager
    • The PIX Manager mainly tracks all Patient Identifiers and its API is used by the Edge Server and the PHR Access Points
    • The PIX maintains a single Global Unique Patient Identifier for a given patient. It associates all local patient-identifiers to a single global patient identifier as required by the ISN
The Patient Health Record (PHR) Account Access

PHR is an Edge Application that allows the end-users such as clinicians to access documents such as diagnostic reports and images submitted for a given patient.

In the RSNA Workflow, the patient is given an access key to access their exam electronically. To perform these actions the following Actors are implemented in the PHR:

  1. Document Consumer function

    • The document consumer first checks in with the PIX Manager to obtain a global patient-identifier associated with the patient
    • This identifier is then used to perform a Registry Stored Query, anITI-18 SOAP Message to XDS Registry Service
    • The Registry returns metadata in the Response. This metadata is then used by the Imaging Document Consumer for image and report retrieval
  2. Imaging Document Consumer function
    • The Imaging Document Consumer uses RAD-69 Retrieve Imaging Document Set – SOAP Message to retrieve desired set of images from the XDS Imaging Document Source Service.

In Conclusion

Integrating Healthcare Enterprise is actively working to bring necessary modernization and efficiencies to the healthcare industry. The patient-centric workflow to share diagnostic exams is just one example of the integration profiles. There is a fair amount of innovation that lies ahead of us to modernize and make healthcare enterprises IT Infrastructure secure, robust, and efficient.

About the author

Sushil is a Principal Architect at BigR.io. He leads a team of engineers from lifeIMAGE and BigR.io to deliver a robust, conformant solution for ISN.

NLP evolved to be an important way to track and categorize viewership in the age of cookie-less ad targeting. While users resist being identified by a single user ID, they are much less sensitive to and even welcome the chance for advertisers to personalize media content based on discovered preferences. This personalization comes from improvements made upon the original LDA algorithm and incorporate word2vec concepts.

The classic LDA algorithm developed at Columbia University raised industry-wide interest in computerized understanding of documents. It incidentally also launched variational inference as a major research direction in Bayesian modeling. The ability of LDA to process massive amounts of documents, extract their main theme based on a manageable set of topics and compute with relative high efficiency (compared to the more traditional Monte Carlo methods which sometimes run for months) made LDA the de facto standard in document classification.

However, the original LDA approach left the door open on certain desirable properties. It is, at the end, fundamentally just a word counting technique. Consider these two statements:

“His next idea will be the breakthrough the industry has been waiting for.”

“He is praying that his next idea will be the breakthrough the industry has been waiting for.”

After removal of common stop words, these two semantically opposite sentences have almost identical word count features. It would be unreasonable to expect a classifier to tell them apart if that’s all you provide it as inputs.

The latest advances in the field improve upon the original algorithm on several fronts. Many of them incorporate the word2vec concept where an embedded vector is used to represent each word in a way that reflects its semantic meaning. E.g. king – man + woman = queen

Autoencoder variational inference (AVITM) speeds up inference on new documents that are not part of the training set. It’s variant prodLDA uses product of experts to achieve higher topic coherence. Topic-based classification can potentially perform better as a result.

Doc2vec – generates semantically meaningful vectors to represent a paragraph or entire document in a word order preserving manner.

LDA2vec – derives embedded vectors for the entire document in the same semantic space as the word vectors.

Both Doc2vec and LDA2vec provide document vectors ideal for classification applications.

All these new techniques achieve scalability using either GPU or parallel computing. Although research results demonstrate a significant improvement in topic coherence, many investigators now choose to deemphasize topic distribution as the means of document interpretation. Instead, the unique numerical representation of the individual documents became the primary concern when it comes to classification accuracy. The derived topics are often treated as simply intermediate factors, not unlike the filtered partial image features in a convolutional neural network.

With all this talk of the bright future of Artificial Intelligence (AI), it’s no surprise that almost every industry is looking into how they will reap the benefits from the forthcoming (dare I say already existing?) AI technologies. For some, AI will merely enhance the technologies already being used. For others, AI is becoming a crucial component to keeping the industry alive. Healthcare is one such industry.

The Problem: Diminishing Labor Force

Part of the need for AI-based Healthcare stems from the concern that one-third of nurses are baby boomers, who will retire by 2030, taking their knowledge with them. This drastic shortage in healthcare workers poses the imminent need for replacements and, while the enrollment numbers in nursing school stay stable, the demand for experienced workers will continue to increase. This need for additional clinical support is one area where AI comes into play. In fact, these emerging technologies will not only help serve as a multiplier force for experienced nurses, but for doctors and clinical staff support as well.

Healthcare-AI Automation Applications to the Rescue

One of the most notable solutions for this shortage will be automating processes for determining whether or not a patient actually needs to visit a doctor in-person. Doctors’ offices are currently inundated with appointments and patients who’s lower-level questions and concerns could be addressed without a face-to-face consultation via mobile applications. Usually in the from of chatbots, these AI-powered applications can provide basic healthcare support by “bringing the doctor to the patient” and alleviating the need for the patient to leave the comfort of their home, let alone scheduling an appointment to go in-office and visit a doctor (saving time and resources for all parties involved).

Should a patient need to see a doctor,  these applications also contain schedulers capable of determining appointment type, length, urgency, and available dates/times, foregoing the need for constant human-based clinical support and interaction. With these AI schedulers also comes AI-based Physician’s Assistants that provide additional in-office support like scheduling follow-up appointments, taking comprehensive notes for doctors, ordering specific prescriptions and lab testing, providing drug interaction information for current prescriptions, etc. And this is just one high-level AI-based Healthcare solution (albeit with many components).

With these advancements, Healthcare stands to gain significant ground with the help of domain-specific AI capabilities that were historically powered by humans. As a result, the next generation of healthcare has already begun, and it’s being revolutionized by AI.