case study

Dana-Farber Cancer Institute

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

Cancer patients face a myriad of distressing symptoms and side effects from their disease and treatments. However, current approaches to symptom management are often fragmented and inconsistent.

Dana-Farber Cancer Institute (DFCI) has launched an innovative Symptom Management Pathways initiative to preemptively manage the side effects of cancer treatment by leveraging digital technologies, DFCI expertise, and the latest clinical evidence. By standardizing care pathways for managing cancer-related symptoms, DFCI is enhancing patient outcomes, increasing patient and caregiver engagement, streamlining clinical workflows, and extending the impact of its expertise.

Central to this effort is DFCI’s partnership with Trisotech to integrate clinical decision support automation into providers’ existing Epic® EHR workflows. DFCI has developed symptom management pathways through standardized clinical decision support models and workflows that provide evidencebased recommendations to patients and providers at the point of care. This promotes the delivery of effective clinical, educational, and community-based interventions that help prevent, mitigate, and treat symptoms in a consistent way – ultimately improving patient outcomes, increasing satisfaction, streamlining operations, and reducing costs.

This case study exemplifies how digital transformation, through the strategic application of Trisotech’s Digital Enterprise Suite, can significantly improve cancer symptom treatment thus setting a benchmark for other healthcare institutions to follow.

Generative AI is spreading fast and constantly becoming more powerful. Its uses and roles in healthcare are still uncertain. Although it will be disruptive, it is unclear what it will change or what will be replaced as the technology evolves.

The use of Generative AI poses several challenges, at least for now. In some respects, it behaves like a black box. It may be unable to give the sources for what it produces, so it is hard to judge the reliability of its sources. It can be hard to validate depending on how it is used. These factors may make doctors, patients, and regulators nervous about its use in a sensitive area like healthcare. If a claim of malpractice is made involving it, then it may be hard to defend its mysterious behavior.

Generative AI and Business Process Models

A business process model can access Generative AI simply by adding a connector to a task, which is done by a simple drag and drop. Because it is now part of a process, you can control when and how it is called.

Since there may be several possible paths through the model, you can have different calls that are appropriate for each path. Orchestrating the output provides an opportunity to give an individualized solution for a specific situation. Orchestration of Generative AI can make it less of a black box.

Since the calls to Generative AI can be tightly constrained and since you know exactly where it is being used and what the inputs are, the appropriateness of its explanation can be judged in context. This can make validation a bit less daunting.

Illustrative Example

A common problem in healthcare is the need to communicate health information to patients. Not only may the patient and family not understand what the provider is saying, but also the provider may misunderstand the patient. The need to communicate better has created a need for access to human translators around the clock. This raises other problems, as the translator may not understand the nuances of medical terms. It can also be quite expensive since you need to have multiple translators on call.

In Figure 1 there is a portion of a BPMN model for the diagnosis of anemia. A DMN decision model first determines whether a patient has anemia, and, if so, its severity. It may be desirable to inform the patient quickly and easily about these findings. The problem of translation can be approached by taking the outputs of the decision and sending them as inputs to Generative AI (in this case OpenAI, indicated by the icon in the top left corner), along with the patient’s preferred language and education level. The Generative AI then takes these inputs and instructions and generates a letter tailored to the patient.

Welcome to the third installment of this three-part series providing an overview of the resources and steps required to achieve success when automating your first clinical guideline using the BPM+ family of open standards on the Trisotech platform.

In Part I we discussed how long it will take you to reach cruising speed for creating BPM+ visual models. In Part II, we discuss the critical step of grasping the knowledge presented in the guideline and standardizing your approach to deal with the various pitfalls you may encounter in doing so. Now we will delve more into the details of how to develop an automated guideline. While the Trisotech modeling tools provide low-code programming that is easily comprehended by novices, there are many details “under the hood” that need to be specified to achieve automation.

Stages of Development

The entire process of automating a guideline starts from a written guideline and proceeds through a sequence of stages to the final automated clinical model, as outlined in the following diagram. There is some flexibility in the process; however, it is not recommended to complete a stage without completing preceding one.

Welcome to the second installment of this three-part series providing an overview of the resources, steps and the success factors required to achieve success with your first clinical guideline automation project using the BPM+ family of open standards on the Trisotech platform. In Part I we discussed how long it will take you to reach cruising speed for creating BPM+ visual models. In Part II, we discuss the critical step of grasping the knowledge presented in the guideline under study and standardizing your approach to deal with the various pitfalls you may encounter in doing so.

A common project for someone starting with BPM+ for Healthcare is the implementation of a clinical guideline (or similar structured knowledge). Guidelines are commonly accepted as an authority and “source of truth”. Guidelines vary in their complexity and no two are exactly alike. To implement a guideline requires a methodical approach. There are no rigid rules on how to do this, but there are best practices that can be followed.

Prep Work

The process of implementing a guideline starts with becoming intimately familiar with its contents and to gather the important source documents. Once the guideline is understood you can then start to dissect it apart. One approach is to identify the decisions that are being made in the guideline and the decision tools being used to achieve them. Once these are identified, then the different task flows are identified, as these will be the basis for process models. It is important during this phase to identify those decisions and processes that are high value to clinicians and outcomes. Identifying processes that follow a common pattern (triage, staging, etc) can help to speed later development.

Problems with Clinical Guidelines

When you start to dissect guidelines, you will often find that most guidelines have problems, some minor and some major. Anything put together by a committee may have hidden biases, and many guidelines have some form of baggage. The fact that two societies can publish conflicting recommendations on the same topic indicates that the process is not perfect.

Most guidelines do a good job of discussing the core topics, but they often become blurry around the edges. For example, a surgical guideline may provide only cursory details on topics like nutritional support or handling of complications. These may seem minor to a casual reader but still need to be handled when modeling the guideline for some automation. As an aside, using BPM+ models to capture and deliver a guideline is a great way to identify problems that otherwise be masked.

Standardizing Your Personal Approach

Since there are many sources of variation, it is important to determine your goals and to standardize your approach to building models. Do you rigidly adhere to the guideline verbatim, or do you allow flexibility? If you favor flexibility, can you demonstrate that the changes do not negatively impact outcomes? Does everyone on the team share the same philosophy, or is everyone doing their own thing with little coordination?

One of the foundations of BPM+ modelling is the use of standards-based languages such as BPMN, DMN and CMMN. If a team is uncoordinated when developing the guideline BPM+ models, then personal variation creeps in. A common problem is the naming and constraining of entities such as data inputs. If two programmers use the same name for data inputs constrained differently, then software will merge them. This can negatively affect any models using these as inputs.

Narrative Elicitation

To analyze and structure information and knowledge from existing evidence-based guidelines, I recommend using the Knowledge Entity Modeler (KEM) to get control on terminology from the start. The KEM can be used to create a central repository of terms, definitions, clinical codes, and rules as presented in the guideline narrative. If properly built, it can capture the core knowledge of the guideline, providing a valuable resource for documenting the models later. It provides a solid foundation and helps to orient people to the information being used. I find that it takes me about a month working for a couple hours a day to build a complete KEM model for a moderately complex topic.

In the next part of this series, we will discuss how to proceed from here to a series of notional models and then on to automation.

Welcome to the first installment of this informative three-part series providing an overview of the resources and the success factors required to develop innovative, interoperable healthcare workflow and decision applications using the BPM+ family of open standards. This series will unravel the complexities and necessities for achieving success with your first clinical guideline automation project. Part I focuses on how long it will take you to reach cruising speed for creating BPM+ visual models.

When starting something new, people often ask some common questions. One is how long will it take to learn the new skills required. This impacts how long it will take to complete a project and therefore costs. Learning something new can also be somewhat painful when we are set in our old ways.

Asking such questions is important, since there is often a disconnect between what is promoted online and the reality. I can give my perspective based on using the Trisotech tools for several years, starting essentially from scratch.

How long does it take to learn?

The simple answer – it depends. A small project can be tackled by a single person quite rapidly. That is how I got started. Major projects using these tools should be approached as team projects rather than something an individual can do. Sure, there are people who can master a wide range of skills, but in general most people are better at some things than others. Focusing on a few things is more productive than trying to do everything. A person can become familiar with the range of tools, but they need to realize that they may only be able to unlock a part of what is needed to automate a clinical guideline.

The roles that need to be filled to automate a clinical guideline with BPM+ include:

1 subject matter expert (SME)

2 medical informaticist

3 visual model builder

4 hospital programmer/system integrator

5 project manager

6 and of course, tester

A team may need to be composed of various people who bring a range of skills and fill various roles. A larger project may need more than one person in some of these roles.

The amount of time needed to bring a subject matter expert (SME) up to speed is relatively short. Most modeling diagrams can be understood and followed after a few days. I personally use a tool called the Knowledge Entity Modeler (KEM) to document domain knowledge; this allows specification of term definitions, clinical coding, concepts maps and rule definitions. The KEM is based on the SVBR standard, but its visual interface makes everything simple to grasp. Other comparable visual tools are available. The time spent is quickly compensated for by greater efficiency in knowledge transfer.

The medical informaticist has a number of essential tasks such as controlling terminology, standardizing data, and assigning code terms. The person must understand the nuances of how clinical data is acquired including FHIR. These services cannot be underestimated since failures here can cause many problems later as the number of models increase or as models from different sources are installed.

The model builder uses the various visual modelling languages (DMN, BPMN, CMMN) according to the processes and decisions specified by the SME. These tools can be learned quickly to some extent, but there are nuances that may take years to master. While some people can teach themselves from books or videos, the benefits of taking a formal course vastly outweigh the cost and time spent. Trsiotech offers eLearning modules that you can learn from at your own pace.

When building models, there is a world of difference between a notional model and one that is automatable. Notional models are good for knowledge capture and transfer. A notional model may look good on paper only to fail when one tries to automate it. The reasons for this will be discussed in Part 3 of this blog series.

The hospital programmer or system integrator is the person who connects the models with the local EHR or FHIR server so that the necessary data is available. Tools based on CDS Hooks or SMART on FHIR can integrate the models into the clinical workflow so that they can be used by clinicians. This person may not need to learn the modeling tools to perform these tasks.

The job of the project manager is primarily standard project management. Some knowledge of the technologies is helpful for understanding the problems that arise. This person’s main task is to orchestrate the entire project so that it keeps focused and on schedule. In addition, the person keeps chief administrators up to date and tries to get adequate resources.

The final player is the tester. Testing prior to release is best done independently of other team members to maintain objectivity. There is potential for liability with any medical software, and these tools are no exception. This person also oversees other quality measures such as bug reports and complaints. Knowing the modeling languages is helpful but understanding how to test software is more important.

My journey

I am a retired pathologist and not a programmer. While having used computers for many years, my career was spent working in community hospitals. When I first encountered the BPM+ standards, it took several months and a lot of prodding before I was convinced to take formal training. I have never regretted that decision and wish that I had taken training sooner.

I started with DMN. On-line training takes about a month. After an additional month I had enough familiarity to become productive. In the following 12 months I was able to generate over 1,000 DMN models while doing many other things. It was not uncommon to generate 4 models in one day.

I learned BPMN next. Training online again took a month. This takes a bit longer to learn because it requires an appreciation of how to design a process so that it executes optimally. Initially a model would take me 2-3 days to complete, but later this dropped to less than a day. Complex models can take longer, especially when multiple people need to be orchestrated and exception handling is introduced.

CMMN, although offering great promise for healthcare, is a tough nut to crack. Training is harder to arrange, and few vendors offer automatable versions. This standard is better saved until the other standards have been mastered.

What are the barriers?

Most of the difficulties that I have encountered have not been related to using the standards. They usually arise from organizational or operational issues. Some common barriers that I have encountered include:

1 lack of clear objectives, or objectives that constantly change.

2 lack of commitment from management, with insufficient resources.

3 unrealistic expectations.

4 rushing into models before adequate preparations are made.

If these can be avoided, then most projects can be completed in a satisfactory manner. How long it takes to implement a clinical guideline will be discussed in the next blog.

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.

Trisotech
The Pioneer Streamlining Clinical Workflow
and Decision Automation

In the healthcare sector, a realm characterised by its resource-intensity and stringent compliance standards, the management of data is pivotal along every step of the clinical journey. Healthcare business process management (BPM) tools have emerged as a transformative force, offering a comprehensive solution to address these complex challenges.

These BPM tools serve as a beacon of efficiency, dismantling data silos that often obstruct the seamless flow of critical information. By creating a unified and secure environment, healthcare providers can transcend the barriers of data isolation, enabling the sharing of patient records, inventory levels, and other vital data with ease. Furthermore, BPM tools extend their impact beyond data integration. They optimise internal processes within healthcare facilities by automating routine and timeconsuming tasks, liberating healthcare professionals to focus on more value-added activities. Additionally, these tools provide the essential capability to generate insightful reports, empowering healthcare administrators with the information needed for effective decision-making.

Pioneering this transformative journey in healthcare is Trisotech, a global leader in enterprise software. Trisotech empowers end-to-end Digital Transformation, catering to both business and IT needs. Its Digital Enterprise Suite – a cloud-based powerhouse – combines standard-based lowcode with intelligent automation, serving as a versatile, user-friendly solution. With a rich history spanning over two decades, Trisotech has been instrumental in shaping the landscape of BPM standards worldwide. Today, their unwavering commitment to innovation is reshaping healthcare operations, heralding a future where efficiency, transparency, and excellence converge to benefit healthcare…

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.

What is Low-Code/No-Code/Pro-Code?

Low-Code No-Code (LCNC), or low code no code as it is sometimes written, refers to how to make an app – computer and mobile applications – that doesn’t require traditional programming skills (Pro-Code.)

Conceptually, using no code platforms, anyone can figure out how to create an app using a few clicks. With low-code solutions, power users and subject matter experts (citizen developers) can build applications using a business-friendly expression language, and, of course, pro-code solutions utilize programming languages like JavaScript, Java, Python and C# where professional development teams are the app creator.

Healthcare automation needs have become so critical and pervasive that to address these concerns head-on, a community of practice, BPM+ Health, has been created. BPM+ Health was established based on the use of open, standards-based notations including BPMN™ (workflow management) DMN™ (decision management), CMMN™ (case management), and other open IT standards which allows for all types of health organizations, professional societies, and vendors to document their care pathways and workflows, so they are sharable, discoverable, and automatable.

In this age of digital transformations, software is becoming increasingly strategic ad pervasive. Both new care settings and continuously changing medical and pharmaceutical advances are driving an explosion in the demand for new and updated applications yet professional programmers who can build and maintain this software has reached a critical shortage. Other industries are already adopting lowcode and nocode platforms to speed the creation of applications, reduce the backlog, and make application development more affordable. Because professional software developers are an increasingly scarce and expensive resource, everyday employees are stepping up as “citizen developers.” These power users and SMEs already have the domain knowledge needed to rapidly compose their needed applications using low-code tools. The healthcare software revolution is behind the curve and low-code development is rapidly becoming the best option to catch up. However, even as low-code platforms advance, pro-code professional developers will still be needed to create the more complex features and integrations required in healthcare.

Most current Electronic Health Record systems (EHRs) are increasing the burden of work on doctors and other care team members primarily because they are neither agile nor provide for innovation capabilities. Surprisingly, while virtually every other business sector is utilizing these new technologies, little is developed with low-code or no code in healthcare. Low-code solutions would seem to lend themselves to patient portal apps, common patient care apps or even complex back-office systems. Using low-code, care givers and other subject matter experts (SMEs) don’t need to learn a professional programming language to create apps but only need to learn an app where they set configurations in a graphical user environment and sometimes a simple expression language. Low-code app development is therefore faster and far less expensive. Using low-code platforms, developers can use agile methods to test the new and changing needs of providers and patients directly during the development cycle. Since low-code platforms provide simple ways to invoke other applications or components via RESTful API calls, apps can easily integrate with existing IT systems and standards such as HL7®, FHIR®, CDS Hooks™, SMART™, and SMART on FHIR allowing new functionalities to be added to existing systems with little or no disruption to current operations.

A hallmark of the healthcare industry is the diversity and volume of data needed to provide the vast array of clinical, administrative, and insurance services patients and caregivers have come to expect while controlling the costs of those services. Just creating and maintaining a comprehensive data layer accessible to all is a major and ongoing undertaking. Add to that the hundreds, if not thousands, of applications, APIs, and interdependencies, the complexity is nearly overwhelming. Emerging standards like FHIR may help. Still, by using low-code composable apps along with FHIR and other data sources, organizations and their citizen developers may be able to modernize faster and more affordably by assembling their own vendor-neutral digital platforms.

Trisotech LCNC
in Healthcare

Trisotech is a founding member of BPM + Health which includes BPMN, as well as DMN and CMMN, as an integral standards technology. Trisotech healthcare clientele include international and U.S. acute care hospitals, healthcare insurance organizations, HMOs, renowned teaching hospitals, PPOs, and healthcare professional organizations.

The Trisotech Healthcare Feature Set (HFS) is an optional set of advanced low code development functionalities extending the Trisotech Digital Enterprise Suite with healthcare-specific additions. Through a combination of these new features and functions, healthcare organizations can now access FHIR®, SMART™, and SMART on FHIR capabilities as well as AI and Machine Learning (ML) in their modeling and automation of model-driven applications. Predefined FHIR Data types (simple, complex, and special purpose) are provided as no-code reusable drag and drop data structures that can be assigned as data objects in model-driven applications and autogenerated SMART on FHIR webapps can be created from automations stored in the Digital Enterprise Suite (DES) Service Library.

Building low-code healthcare apps requires access to advanced computational and logical capabilities. Trisotech supports the only international standard expression language perfect for low-code developers – FEEL (Friendly Enough Expression Language) published as part of the DMN (Decision Model and Notation™) from OMG, an international standards body.

Another no-code feature is the Attended Tasks extension. The Trisotech Attended Task feature allows for validation and confirmation of the inputs and/or outputs of any automated task by the care provider user or any other designated performer. This feature ensures that a knowledgeable human expert can correct/modify information in real time during apps execution.

Trisotech also provides nearly 1,000 pre-built evidence-based workflow and decision models including care pathways, clinical guidelines, and healthcare calculators using the the BPM+ Health standard. These models can be quickly and easily modified to fit the exact nature of anorganization’s policies and procedures. Healthcare organizations can also create their own apps from scratch with the easily understood visual Workflow Modeler (BPMN) that can be shared by IT, practitioners, SMEs, and business people – LCNC citizen developers.