case study

Intermountain® Health’s

New Interoperability Platform Saves Lives and Reduces Costs

Intermountain Health is widely recognized as a leader in transforming healthcare by using evidence-based best practices to consistently deliver high-quality outcomes at sustainable costs. A non-profit organization headquartered in Utah, Intermountain Health has locations in 8 western states including 33 hospitals, 385 clinics, and more than 1 million members.

Saving lives icon

Saving lives
more then 100 lives annually

Saving money icon

Saving money
financial burden decreased by more than $3M per year

Deployment speed icon

Deployment speed
from months to days

Lessons Learned - Part 4: Decision Modeling in DMN 1.3++ for Credit Risk Rating

Download the full case study

Content

Articles

View all

case study

Mayo Clinic

Mayo Expert Decision Advisor:
Revolutionizing Health IT with a Model-Driven Strategy, Enabling Rapid EHR Workflow Updates in Days, Not Months

The Mayo Clinic, a world-renowned nonprofit healthcare organization founded in Rochester, Minnesota, specializes in clinical practice, education, and research, employing physicians scientists, and staff across campuses in Minnesota, Florida, and Arizona, with additional affiliated facilities nationwide. It consistently ranks as the top hospital by U.S. News & World Report. Its knowledge management program focuses on consolidating evidence-based best practices for enterprisewide application.

The Mayo Expert Decision Advisor, as detailed in Mayo Clinic Proceedings, integrates Mayo-vetted knowledge with patient data in Electronic Health Records. This tool streamlines patient data analysis, offering clinician-like interpretation, thereby reducing clinician cognitive load, and enhancing patient care efficiency.

Mayo-vetted knowledge icon

Mayo-vetted knowledge
dynamically integrated with patient data.

Reduces cognitive load icon

Reduces cognitive load
of clinicians.

Dissemination of changes icon

Dissemination of changes
to knowledge within days rather than months.

Case Study - Mayo Clinic

Download the full case study

Content

Articles

View all

case study

Dana-Farber Cancer Institute

Preemptively managing the side effects of cancer treatment through model-driven clinical decision support

Located in Boston and the surrounding communities, Dana-Farber Cancer Institute brings together world renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS, and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, they provide compassionate and comprehensive care to patients of all ages; they conduct research that advances treatment; they educate tomorrow’s physicians and researchers; they reach out to underserved members of their community; and they work with amazing partners, including other Harvard Medical School-affiliated hospitals.

Cancer care is complex. The treatment landscape is constantly changing, and it will soon become impossible for oncology providers to appropriately manage their patients without decision support. To address this need for a new cancer care delivery model, Dana-Farber launched Dana-Farber Pathways in 2012. This multidisciplinary program brought together a dedicated group of clinicians, informaticists, and analysts with the common goal of developing an electronic roadmap for quality cancer care. To date, Dana-Farber has built a portfolio of over 70 clinical pathways, providing treatment recommendations for almost all solid tumor and hematologic malignancies.

Cancer treatment has side effects. Regardless of their diagnosis, all cancer patients have the potential to experience a wide range of symptoms related to therapy or the progression of their disease. Given that the current approach to symptom management is often fragmented and reactive, Dana-Farber Cancer Institute has launched an innovative initiative to preemptively manage the side effects of cancer therapy by leveraging digital technologies. This includes the development of a portfolio of symptom management pathways by Dana-Farber Pathways. By implementing decision support at point of care, Dana-Farber hopes to:

Enhance patient outcomes icon

Enhance patient outcomes

Increase patient and caregiver engagement icon

Increase patient and caregiver engagement

Streamline clinical workflows icon

Streamline clinical workflows

Extend the impact of its best practices icon

Extend the impact of its best practices

Case Study - Dana-Farber Cancer Institute

Download the full case study

Content

Articles

View all

HL7 Field Guide to Sharable Clinical Pathways

HL7 Field Guide to Sharable Clinical Pathways White Paper

Clinical Guide Lines are the knowledge sources of modern evidence-based medicine and Clinical Decision Support (CDS) systems. But what if the clinical guideline document could not only be consumable by practitioners but by computers systems as well? The same document, offering clear interpretation and course of actions to both care providers and the computer systems supporting them.

This version is being published by the HL7 BPM Community of Practice (previously BPM+ Health at OMG). This is the latest version of the “Field Guide” aimed at organizations producing, consuming and deploying such “Sharable Clinical Guidelines.”

Download White Paper

Content

Articles

View all

Unleashing Innovation: HL7 AI Challenge Winners Transforming Healthcare with Standards-Based AI

Presented By
Denis Gagne
Description

This on-demand session highlights the inaugural HL7 AI Challenge winners, organizations proving that healthcare AI can be powerful and trustworthy when built on open standards. Moderated by HL7 Chief AI Officer Dan Vreeman, DPT, the webinar features three teams delivering practical, standards-aligned solutions that move beyond prototypes and into real-world impact.

The program includes:

You will see how HL7 standards are enabling scalable, interoperable and verifiable AI solutions that improve clinical workflows, enhance data usability and support responsible adoption across the health ecosystem. This session is relevant for developers, clinicians, policymakers and health IT leaders who want a clear view of where healthcare AI is heading and what “good” looks like in practice.

View the slides Download the full case study
Videos

Recorded webinars

View all videos
Attend a live webinar

case study

HL7 International Excellence in AI Transparency and Trust Award

Trisotech capabilities deliver a trusted foundation for AI-powered clinical orchestration, combining the creativity of AI with the governance of BPM+ and HL7 standards.

Case Study - Setting the world on FHIR
Download the full case study Watch the recorded webinar
Content

Articles

View all

Dr. John Svirbely's blog post - Why Bring Your Own AI (BYOAI)?
Dr. John Svirbely, MD
Blog

Why Bring Your Own AI (BYOAI)?

By Dr. John Svirbely, MD

Read Time: 3 Minutes

Recently Trisotech posted a position paper entitled “Bring Your Own AI (BYOAI)“. Its observations are relevant to modeling and automation in healthcare. Personally, I think this is the way to make use of AI – at least for now.

Chasing the Next Shiny Thing

AI models are evolving right before your eyes, with many companies appearing then disappearing on a monthly basis. The environment is chaotic, and it is hard to know what to do, and it is tempting to try the newest platforms. This can paralyze project development as you struggle with deciding what tool to use, afraid that it will be obsolete next week. Often you look to see what others are doing rather than forging your own strategy.

Constantly changing environments are plagued by problems with version control and testing. Users become confused and may push back, especially after they hear about bias in algorithms or incorrect responses. They may feel forced to use something that they have no confidence in.

Overreaching

The natural tendency when using a powerful tool is to apply to a large problem all at once. For many projects this can result in subpar performance. Anyone using the current AI platforms can see a deterioration in performance once a certain task size is reached. Often you have no idea exactly what changed or why. Has a seemingly minor change in a prompt caused a vast change in output? Could you have run out of tokens? Are you competing against other users for access? When faced with something outside of your control you can hold a rabbit’s foot and pray for the best.

Who Is Going to Be Liable?

At some point something is going to go wrong – it always does. So just who is going to be left holding the bag? The lawyers are already lining up.

Do you think the AI companies are going to step up? The fine print of their user agreement tries as hard as possible to deflect responsibility. Traditionally clinicians have been liable and so carry liability insurance. What person is going to accept responsibility for a black box that they have no control over, do not understand, and offers no evidence for its actions. How can using a novel AI platform be considered standard of care? Will malpractice insurance cover this? You can be sure that your already expensive cyber insurance is going to cost a whole lot more.

So Why Does BYOAI Work in Healthcare Modeling?

Here are some reasons why I like the idea of BYOAI for modeling in healthcare.

1

You are not tied to a single AI platform.

Some platforms perform better in some tasks than others. A specific task can call whatever service works better. If a better one comes along then you can swap it in without changing the rest of your model.

2

You can control when and how the platform gets called.

You can limit its scope so that it returns focused responses. This makes the system easier to test.

3

You can make use of attended tasks.

These allow physicians to review and modify the model as it progresses in light of the patient’s clinical context. They can accept, reject or modify any suggestion based on the current situation and so can act responsibly in caring for a specific individual. Here AI functions as a useful ally with the clinician in control.

BPM+ Modeling with BYOAI gives you the best of both worlds. No one knows where AI will be going in the future, but modeling should be able to evolve along with the technology. You can confidently get started solving your problems now, rather than waiting for a future that is unpredictable.

Blog Articles

John Svirbely

View all

All Blog Articles

Read our experts’ blog

View all

BPM+

What is BPM+?

BPM+ (BPM Plus) is a powerful framework that combines industry-standard modeling languages to help organizations clearly define, automate, and improve their decisions, processes, and data flows. Instead of relying on ambiguous natural language documents, BPM+ enables clear, shareable, and executable visual models that bridge the gap between human intent and digital automation.

At its core, BPM+ combines the strengths of the following open standards from the Object Management Group, Inc. (OMG®):

Together, these standards form a cohesive modeling ecosystem that supports end-to-end business transformation, governance, and automation.

Why BPM+?

Most organizations rely on written documents to define processes and rules, but natural language often introduces ambiguity and inconsistency. BPM+ replaces this with precise, visual models that are easier to understand, validate, and automate.

Whether you’re managing a hospital, onboarding a customer, processing a loan, or running DevOps pipelines, BPM+ gives you:

Key Benefits

With BPM+, your models are more than just documentation, they are living assets that drive execution and transformation.

Who Uses BPM+?

BPM+ is widely adopted by organizations that operate in complex, regulated environments and require clarity, consistency, and automation across their decisions, processes, and data. It’s especially valuable for teams looking to align business operations with digital transformation goals while ensuring regulatory compliance.

Used Across Industries

BPM+ appeals to a diverse range of medium to large enterprises, particularly in sectors where auditability, standardization, and agility are critical:

Typical users include product owners, solution architects, business process teams, compliance departments, and low-code developers who need to orchestrate business decisions and workflows, manage cases, and standardize data.

BPM+ in Financial Services

BPM+ in Financial Services

The financial services sector is a leading adopter of BPM+, driven by the need to navigate complex regulations and maintain standardized, auditable business processes.

More info on Finance

Banks, insurers, investment firms, credit unions, and GSEs use BPM+ to manage critical functions like loan origination, customer onboarding, complaint resolution, and regulatory reporting. Financial institutions rely on BPM+ to model complex decision logic for credit scoring, fraud detection, and compliance, while maintaining the transparency required for audits and regulatory reviews. With BPMN and DMN, organizations codify risk and compliance rules; with CMMN, they manage investigative and exception-driven cases; and with SDMN, they standardize data definitions across processes. BPM+ helps ensure consistency, traceability, and agility, key to staying competitive and compliant in a fast-evolving financial landscape.

As the MISMO-approved standard for expressing business rules and decisions, DMN supports end-to-end lifecycle management, from authoring and validation, to execution and exchange, across systems and platforms.

BPM+ is being used to document and automate hundreds of types of financial processes including:

With BPM+, financial institutions can model and orchestrate business decisions, ensure auditability, and demonstrate governance across systems and geographies.

BPM+ in Healthcare

BPM+ is transforming healthcare by enabling organizations to model, standardize, and automate complex clinical and administrative workflows.

More info on Healthcare

BPM+ in Healthcare

By integrating BPMN, CMMN, DMN, and SDMN standards, BPM+ supports both structured care protocols and dynamic case management, making it ideal for environments where patient care must be individualized yet evidence-based. Healthcare providers use BPM+ to improve care coordination, reduce administrative burdens, and enhance regulatory compliance. The result is greater operational efficiency, better patient outcomes, and a data-driven foundation for continuous improvement across the care continuum.

BPM+ is being used to create healthcare process flows, manage cases and orchestrate DMN decision services in hundreds of ways. Here are some representative examples:

Trisotech: A Leader in BPM+

Trisotech is a global leader in business automation and a key contributor to all four BPM+ standards: DMN, BPMN, CMMN, and SDMN. Its tools, DMN Decision Modeler, BPMN Workflow Modeler, CMMN Case Modeler, and SDMN Shared Data Modeler are recognized as the reference implementations for these standards. Together, they form the foundation of the Trisotech Digital Enterprise Suite (DES), a definitive platform for standards-based business modeling and automation.

The suite offers a visual, browser-based environment for creating and deploying BPM+ models across public or private cloud infrastructures. It includes advanced capabilities such as AI and machine learning integration via Predictive Model Markup Language (PMML) and Clinical Quality Language (CQL), as well as Attended Tasks that support human-in-the-loop validation during automation. Additionally, Trisotech’s Knowledge Entity Modeler (KEM) allows organizations to manage business vocabularies and concept models based on the Semantics of Business Vocabulary and Business Rules™ (SVBR™) standard, supporting rich, domain-specific applications in industries like healthcare and finance.

Trisotech
in Financial Services:

Trisotech
in Healthcare:

Why Choose Trisotech for BPM+?

Trisotech delivers a scalable, cloud-ready, standards-based automation platform trusted by enterprises and governments worldwide. With full support for BPM+, Trisotech enables:

Start Your BPM+ Journey

Transform your business operations with Trisotech’s BPM+ platform. Contact us today to schedule a demo or explore how BPM+ can help your organization drive clarity, automation, and compliance.

Request Demo
BPM+ icon
Trisotech

the
Innovator

View all

Decision Intelligence

Model, Automate, and Govern every Decision

What is Decision Intelligence?

Decision Intelligence (DI) is defined by Gartner as:

A practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.

It blends data science, AI, decision modeling, and domain expertise to support, augment, or automate business decisions. It treats decisions as strategic enterprise assets, ensuring that human expertise, business rules, AI insights, and organizational context are orchestrated into high-quality, auditable outcomes. Unlike traditional analytics, which stop at insight, DI connects the dots between data, rules, processes, and people, creating a closed-loop system where decisions are explicitly modeled, executed, monitored, and improved over time.

This decision-centric approach is rapidly gaining adoption across sectors, from finance and healthcare to government and public services.

How Trisotech Addresses
Decision Intelligence (DI)

Trisotech Digital Enterprise Suite (DES) is a cloud-native platform that treats decisions as first-class assets, combining decision models based on open-standards with AI and knowledge graphs to orchestrate processes, data, and human judgment. Business value is delivered by orchestrating decisions rather than just tasks or workflows. Every decision, process (workflow), case, or API is managed as a governed semantic asset, meaning it’s explainable and auditable. The platform is built on BPM+ standards (DMN for decisions, BPMN for processes, CMMN for cases, SDMN for data) to ensure model-driven interoperability and avoid vendor lock-in. A Digital Enterprise Graph (DEG) links these models with data and business vocabulary, providing rich context and reuse across the enterprise. Trisotech Decision Centric Orchestration (DCO) technology also emphasizes AI augmentation with governance: it can embed AI services (e.g. GenAI prompts, machine learning classifiers) directly into workflows while applying “TRUST” principles (Traceability, Reflectiveness, Understanding/Oversight, Separation of duty, Transparency) for safe, explainable AI use. In short, Trisotech DES is an integrated decision-centric platform combining decision automation, process orchestration, case management, and knowledge management with AI, all under strong governance. This comprehensive capability set aligns closely with Gartner’s vision of decision-centric solutions and Decision Intelligence Platforms (DIP).

What are Decision Intelligence Platforms (DIPs)?

A Decision Intelligence Platform (DIP) is a software environment that empowers organizations to model, automate, monitor, and optimize complex decisions. According to Gartner a DIP must support capabilities such as decision modeling, orchestration, composability, collaboration, execution, governance, and learning from outcomes.

Unlike standalone AI or analytics tools, a DIP delivers an end-to-end lifecycle:

This unified approach accelerates agility and ensures trust, transparency, and accountability in automated decisions.

How Trisotech Addresses
Decision Intelligence Platform (DIP)

Trisotech Decision-Centric Orchestration (DCO) technology aligns strongly with Gartner’s criteria for Decision Intelligence Platforms (DIPs) by offering explicit decision modeling and orchestration through BPM+ standards (DMN, BPMN, CMMN, SDMN). In fact, Trisotech is recognized by Gartner as a representative vendor in the DIP category. Trisotech modular, API-driven architecture supports composability and microservice deployment, while its cloud-native execution environment enables governed, traceable decision services at scale. The platform fosters collaboration among business and technical stakeholders and integrates human oversight into AI-augmented decisions via its TRUST (Traceability, Reflectiveness, Understanding, Separation, Transparency) framework. Trisotech also emphasizes governance, policy enforcement, and real-time observability to meet strict monitoring and compliance demands. Through modeling and continuous monitoring, it supports decision refinement and learning over time. Conceptually, Trisotech neuro-symbolic approach, combining business rules, knowledge graphs, and AI, embodies the principles of decision intelligence, positioning it as a fully realized and future-ready DIP.

In short, the Trisotech Digital Enterprise (DES) Suite offers:

Decision Intelligence in Financial Services

Decision Intelligence in Financial Services

In financial services, DI is transforming how firms manage risk, ensure compliance, personalize customer experiences, and detect fraud.

More info on Finance

By treating decisions as governed, repeatable assets, financial institutions can:

Decision Intelligence in financial services improves “decision quality and explainability while reducing time-to-insight and operational costs”.

How Trisotech Addresses
Decision Intelligence in Financial Services

Trisotech for Financial Services enables institutions to:

Through semantic modeling and governed orchestration, Trisotech helps financial organizations operationalize DI to accelerate innovation while maintaining compliance and trust.

Decision Intelligence in Healthcare

In healthcare, DI supports more consistent, explainable, and patient-centered decisions, from clinical pathways to administrative approvals.

More info on Healthcare

Decision Intelligence in Healthcare

DI enables:

Decision intelligence in healthcare is entering the early mainstream and is key for next-generation care orchestration and utilization management.

How Trisotech Addresses
Decision Intelligence in Healthcare

Trisotech for Healthcare provides:

By embedding decision intelligence into clinical and operational pathways, Trisotech empowers health systems to improve outcomes and reduce variability, all while maintaining transparency and trust.

Conclusion

Decision Intelligence is more than a buzzword; it’s the future of responsible automation. Whether in finance, healthcare, or any data-driven industry, the ability to model, govern, and optimize decisions is the key to agility and trust.

Trisotech leads this transformation with its Decision-Centric Orchestration (DCO) technology: a complete decision intelligence platform rooted in open standards, AI augmentation, and human-AI collaboration. It’s not just automation; it’s orchestration of intelligence. A statement that could serve as a manifesto for decision intelligence.

Decision Intelligence

Trisotech provides decision-centric augmented intelligence, where humans and AI systems collaborate seamlessly to make trusted, explainable, orchestrated decisions.

Request Demo
Decision Intelligence icon
Trisotech

the
Innovator

View all

Fast-Track CMS-57 Compliance: Wrap, Comply, and Iteratively Modernize with BPM+

Presented By
Melanie Gauthier, Solution Architect, Trisotech
Denis Gagne, CEO and CTO, Trisotech
Description

Achieving CMS-57 compliance doesn’t have to mean a costly system overhaul. Learn how Trisotech’s BPM+ approach enables organizations to quickly wrap existing legacy systems for immediate FHIR compliance—while progressively modernizing capabilities at their own pace. Reduce disruption, streamline prior authorization, and future-proof your IT investments with a flexible, standards-based strategy.

Join us to see how compliance can be a catalyst for transformation.

Watch the video

Content

Presentation

View all

Fast-Track CMS-57 Compliance: Wrap, Comply, and Iteratively Modernize with BPM+

Presented By
Melanie Gauthier, Solution Architect, Trisotech
Denis Gagne, CEO and CTO, Trisotech
Description

Achieving CMS-57 compliance doesn’t have to mean a costly system overhaul. Learn how Trisotech’s BPM+ approach enables organizations to quickly wrap existing legacy systems for immediate FHIR compliance—while progressively modernizing capabilities at their own pace. Reduce disruption, streamline prior authorization, and future-proof your IT investments with a flexible, standards-based strategy.

Join us to see how compliance can be a catalyst for transformation.

View the slides

Videos

Recorded webinars

View all videos
Attend a live webinar

AI, FHIR, and BPM+ in Suicide Prevention: From Early Detection to Coordinated Care

Presented By
Dr. John Svirbely, CMIO, Trisotech
Denis Gagne, CEO and CTO, Trisotech
Description

Suicide prevention requires more than early detection—it demands seamless, patient-centered care. This session explores how AI-powered monitoring, BPM+ visual standards, and FHIR interoperability work together to detect suicidal ideation early and coordinate interventions across emergency departments, mental health specialists, and primary care.

Learn how technology-driven, standards-based orchestration enhances care continuity, reduces gaps, and ensures timely, effective support for at-risk patients.

Watch the video

Content

Presentation

View all

Dr. John Svirbely's blog post - Suicide Prevention with Modeling Tools
Dr. John Svirbely, MD
Blog

Suicide Prevention with Modeling Tools

By Dr. John Svirbely, MD

Read Time: 3 Minutes

Suicide is an important problem around the world, causing significant morbidity and mortality. It impacts family and friends, causing long-lasting wounds. Many people are interested in finding effective solutions to the problem, often looking to technology for answers.

Facts about Suicide

Before we can find effective solutions, we need to understand the problem. Figure 1 from the Centers for Disease Control and Prevention (CDC) Suicide Prevention website shows some of the relevant numbers related to suicide. The number of people committing suicide may be undercounted since there is a tendency to avoid calling a death at suicide. In addition, some forms of suicide such as death by cop can be missed.

Over 49,000 people died by suicide in 2022. That is one death every 11 minutes.

When we look at the numbers, the number of people seriously thinking about suicide is 4-5% of the population. Of this group, only 0.4% die from suicide. This suggests a heterogenous problem of varying severity that may require a complex strategy for different subpopulations.

It is often tempting when crafting a solution to make assumptions that simplify the task. In the case of suicide, it is very easy to recommend that everyone at risk be sent to the Emergency Department (ED) for assessment. However, this strategy introduces several problems. The number of people seriously thinking about suicide could overload the system. Many people who attempt suicide may have little or no health insurance, causing financial distress. Finally, because of EMTALA, an Emergency Department cannot discharge a person unless it is reasonably safe to do so. If no psychiatric hospital is willing to take a patient unable to pay, then the patient may be held in the ED for a long period, reducing the ability to see other emergencies. Any solution for the problem of suicide must address the entire spectrum of the disorder, only sending a patient to the ED when appropriate.

Screening for Suicide Using Natural Language Analysis

Several investigators have taken the approach of early detection of suicidal ideation, using natural language processing. They look for words and phrases in a patient’s communications that suggest depression or suicidal ideation. Figure 2 shows such a model that does monitoring a patient’s personal journal using Generative AI.

A model using natural language monitoring to detect suicidal thoughts.

More sophisticated systems can analyze responses over time, looking for trends and patterns. Once triggered, the model decides whether the risk is low, intermediate or high and triages the patient accordingly.

Orchestration

The patient spends the vast majority of her/his time out of touch with healthcare providers. Events that may trigger suicidal thoughts and the resources that can pull the patient back are in the home, workplace, and community. What the patient needs are interventions that prevent an escalation to crisis by optimizing personal resources. At the same time the patient needs to be able to access healthcare providers when necessary.

The patient and the patient’s care may need to be coordinated over months or years between multiple actors:

With orchestration modeling it is possible to control the interaction of all these participants over time, as shown in Figure 3.

Model showing interactions for the patient with mental health provider, emergency provider, crisis hot line and psychiatric hospital.

Hopefully, the patient can defuse the situation through interactions with community providers, family, and friends. However, if the issues escalate towards a crisis, then it is important to escalate the care according to need.

Conclusions

Suicide is an important problem around the world. Finding its solution is not simple. However, with the appropriate use of technology and modeling tools we should be able to find appropriate care for this emotionally vulnerable population.

Check out a webinar we did on that topic.

Blog Articles

John Svirbely

View all

All Blog Articles

Read our experts’ blog

View all

AI, FHIR, and BPM+ in Suicide Prevention: From Early Detection to Coordinated Care

Presented By
Dr. John Svirbely, CMIO, Trisotech
Denis Gagne, CEO and CTO, Trisotech
Description

Suicide prevention requires more than early detection—it demands seamless, patient-centered care. This session explores how AI-powered monitoring, BPM+ visual standards, and FHIR interoperability work together to detect suicidal ideation early and coordinate interventions across emergency departments, mental health specialists, and primary care.

Learn how technology-driven, standards-based orchestration enhances care continuity, reduces gaps, and ensures timely, effective support for at-risk patients.

View the slides

Videos

Recorded webinars

View all videos
Attend a live webinar

Dr. John Svirbely's blog post - Getting FHIRed Up with SDMN
Dr. John Svirbely, MD
Blog

Getting FHIRed Up with SDMN

By Dr. John Svirbely, MD

Read Time: 2 Minutes

People unfamiliar with BPM+ Health often ask if the models can work with FHIR. The answer of course is “Yes”. This can be done in several ways, but probably the best is through the use of the Shared Data Model and Notation (SDMN) standard.

What Is SDMN?

SDMN stands for the Shared Data Model and Notation, which is an open standard of the Object Management Group (OMG). Its specifications are freely accessible at www.omg.org/spec/SDMN.

SDMN offers a graphical environment where data structures can be explicitly defined. The various data types generated can then be used in the BPMN, CMMN and DMN models.

Where Does SDMN Fit In?

When creating a clinical practice guideline, you normally progress through a sequence of steps using BPMN, CMMN and DMN to capture the information.

Narrative Elicitation
Concept Model
Computational Independent Model (CIM)
Shared Data Model
Platform Independent Model (PIM)
Platform Specific Model (PSM)

The final step in the sequence is the Platform Specific Model (PSM), which stage where system integration takes place. This typically involves a bidirectional interface for importing data and exporting information. This is the step where models conforming to the SDMN can be interfaced with FHIR.

What Does SDMN Look Like?

With SDMN you can document the required data structures visually. An example of a data structure is shown in Figure 2.

SDMN Data Structure

Sex is addressed as Patient Health Record.Demographics.Sex with type tSex. To make our knowledge about sex richer, all of the relevant information about sex can be documented in the Knowledge Entity Modeler, as shown in Figure 3.

Entry for Sex in the Knowledge Entity Modeler

Is There Extra Work to Use SDMN?

At the start of a project you need to create all of the data structures that will be needed. This can take thinking and careful planning. However, the amount of work is usually quite manageable for several reasons.

First, this is work that needs to get done. Doing in an organized manner significantly reduces the rework encountered when you just jump in building models.

Second, the FHIR standard specifies the template for each datum being exchanged. This template can be implemented into SDMN. Once created it can be reused repeatedly (build once, use often).

Third, when looking at what data is used in modeling, only a relatively small number of data items are used frequently (like age or weight). Once these items have been built they can be also be used over and over again. The need to create brand new data structures is low and these will usually have a FIHR/SDMN template to start from.

If your software vendor has implemented the modeling tools properly, then you can use SDMN seamlessly with BPMN. CMMN, DMN, and the Knowledge Entity Model (KEM). Sharing the data items reduces variation, improving the quality of the models and easing maintenance.

If you would like to learn more about SDMN and are coming to HIMSS this year, just stop by one of the Trisotech booths and we will be glad to answer your questions.

Blog Articles

John Svirbely

View all

All Blog Articles

Read our experts’ blog

View all

Dr. John Svirbely's blog post - Modelling Preauthorization Part I: The Problem
Dr. John Svirbely, MD
Blog

Modelling Preauthorization Part I

The Problem

By Dr. John Svirbely, MD

Read Time: 3 Minutes

Preauthorization is the process by which a Payer determines whether it will provide coverage for a future service (drug, imaging study, surgery, etc). Each Payer provides a list of the requirements for each condition that must be met to obtain approval. The whole process is simple in theory, but it has proven to be complex in practice.

Because of perceived problems around preauthorization, the Centers for Medicare and Medicaid Services (CMS) has issued a mandate (CMS-0057) that must be met in the next few years by Providers and Payers. The goal is to improve patient care by removing some of the barriers that Patients experience in their care.

If It Were an Ideal World

In theory preauthorization should not be a problem. There are 4 core validations to be made:

1

Does the Patient have a contraindication, making the request unsuitable?

2

Does the Patient have an approved indication?

3

Is the indication significant (based on severity, stage or some other measure)?

4

Have alternative therapies that may be cheaper or less hazardous been tried?

As a rule, this can be stated as: IF the Patient does not have a contraindication AND if the Patient has an approved indication AND if the condition is significant enough AND if other options have failed, THEN the request should be approved ELSE denied.

This can be depicted in BPMN as:

BPMN Template for Pre-Authorization

This is all very straightforward. So why are there perceived problems?

Nothing Is Perfect

Unfortunately, assumptions about an ideal world tend to fail in the real world. Failures may be due to a range of factors, such as:

Potential Patient-related issues:

Potential Provider related issues:

Potential Payer related issues:

While denial of a request can always be appealed, the whole process of responding to a denial is a major pain point for Providers. Failing to appeal may mean that a Patient does not get the care that the Provider believes is necessary. On the other hand, appealing a denial can be a long and painful experience. The denial process is not standardized between Payers and can appear to be somewhat arbitrary. Providers often:

A Provider can always refer denials to a third party to manage, but the costs of doing so may become an issue when reimbursements are low. This leaves many Providers feeling trapped by a system that does not listen to them.

What Might Be an Effective Solution?

To solve these problems there is a need for:

Whether CMS will be able to provide an effective solution will depend on several factors, including any unexpected consequences of the mandates. The goals are commendable, and now it is up to the stakeholders to work together to make it a success. In the next part we will discuss how BPM+ can provide solutions to these problems.

Blog Articles

John Svirbely

View all

All Blog Articles

Read our experts’ blog

View all

Learn how it works

Request Demo

Confirm your budget

Request Pricing

Discuss your project

Request Meeting
Graph