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.

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:

• family and friends
• primary care provider
• mental health provider
• suicide hot-line responder
• emergency provider
• psychiatric hospital provider

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

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.

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.

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.

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.

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:

• Having problems with confirming identity.
• Not being enrolled with payer.
• Claiming coverage under a relative’s name.
• Having a fraudulent intent.
• Having an uncertain diagnosis.

Potential Provider related issues:

• Provider not being enrolled with payer.
• Submission of incomplete information.
• Request for coverage of a service that is essentially research.
• Request for coverage of a service that has some evidence but not yet “standard of care”.
• Inadequate trial of alternative therapies that may be cheaper or safer.
• Request for coverage of a service that is inappropriate for the patient.
• Repeat request from multiple providers.

Potential Payer related issues:

• Insufficient number of reviewing personnel.
• Poorly trained personnel.
• “No, no, no” directives, with reviewers given incentives to deny everything.
• “Delay, delay, delay” directives, with barriers at all stages, hoping that the Provider gives up or the Patient no longer needs it (e.g. dies).
• Use of post-facto information to deny a request.
• Use of complicated and non-transparent processes.

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:

• Lack sufficient personnel to manage the paperwork.
• Lack office processes to track an appeal’s status.
• Lack information about what the Payer is thinking.

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:

• Improved Patient identification.
• Complete and accessible Patient health records.
• Improved communications between Providers, Payers and Patients.
• Transparency.
• A level playing field between stakeholders.
• A monitoring mechanism to ensure compliance.
• A means of orchestrating the entire patient journey.

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.

Examples of choreography include scheduling an examination with a patient, making a referral with a consultant, or obtaining preauthorization from an insurance company.

Comparison of Orchestration versus Choreography

Both orchestration and choreography have their pros and cons. The choice of using one or the other depends on your needs and goals.

Incorporating these into a notional model may be simple. However, actually getting them to work properly in an automated process can be challenging, especially for novices.

For healthcare a common approach is to use orchestration implemented with BPMN. These orchestrations may need to extend beyond interactions between BPMN processes and pools. They may also need to control data acquisition and connections to outside systems impacting the patient’s care, such as generative AI.

Conclusion

When dealing with different processes or actor pools that need to interact, modelers use orchestration or choreography. The particular choice depends upon the conditions and the goals. Having choices allows for flexibility in designing a solution that can serve both current needs as well as evolve over time as conditions change.

One challenge with microservices is the user experience. Having a lot of microservices means that a lot of messages could be generated and cause alarm fatigue. It is important to develop a strategy that will allow essential information to get through to the user.

Conclusions

The decision to automate or not can be challenging. Several things need to be considered such as cost, liability, acceptability, and care quality. However, considering the economic challenges faced in healthcare today, automation is an attractive idea. Some processes can and should be automated.

Modelling software offers a more formal representation of the process, with rules of how each shape interacts with others. The availability of validation tools can alert the modeler that violations have occurred and where they are located.

Third, process model software like Trisotech DES include an animator, which allows you to directly interact with the model by stepping through the model to observe its behavior under different circumstances. Developers can show this to end-users to confirm performance before it is accepted. Hidden problems with a process can be identified and resolved before going into production.

Finally, the organization eventually wants to automate all or parts of a guideline process. With the Trisotech modeling software the notional model is the foundation for automating the content. If validation and animation steps have been used properly, then you can feel comfortable that the final model will execute as expected.

Making the Choice

If you have something simple, are unsure about your goals, or have cost constraints, then Visio can work to some degree and Trisotech offers some free Visio templates. However, if you have a project that is complex, critical or challenging, then process modeling may be the better choice. You can request a demo and see why Trisotech is the leading low-code/no-code platform for healthcare.

If you are a payer faced with preauthorizing drugs or services, then this one pattern can be used over and over again with minor variations. Using patterns can speed development when compared to treating each situation as a unique problem. In addition, users can better understand what you are trying to do.

Reuse

Once a model has been created, it can be used repeatedly. One goal of process and decision modelers is to create a library of models that can be re-used as building blocks in future projects.

When copying a model into another, the copy can occur in 2 ways:

• reuse by reference: the model is locked, with any changes to the source model propagated to all instances
• reuse by copy: model is unlocked, with changes kept local and not propagated back through all of the instances

Each approach has their pros and cons. Reuse by reference has many benefits since you do not have to go to each model that uses a particular decision to make any changes. However, to achieve this a good deal of standardization is needed.

Other ways to reuse a previously created knowledge include services or business knowledge models (BKMs).

Conclusions

Several strategies can be used to reduce the burden of programming burden without compromising quality. These require some careful thought and planning upfront, but they pay dividends over the long haul, speeding development and simplifying maintenance.

Figure 1

What happens in an attended task?

As mentioned above, when execution of a process comes to an attended task or decision, it stops and allows the provider to interact with it in ways that have been configured by the model developer. The settings for the attended task are shown in Figure 2.

Figure 2

The users able to make changes can be restricted. This allows a provider who is familiar with the patient to individualize the patient’s care based upon information known or observed about the patient. For example, the significance of a hemoglobin value may vary depending on whether or not the patient was transfused prior to the specimen being collected. Similarly, a certain pattern of clinical findings may not fully capture the patient’s current state, while a clinician at the bedside can observe it. Things in life may look different than they do on paper.

Since data and decisions are all recorded, retrospective analysis of decisions relative to outcomes can be performed. This gives insights into care and interventions, supporting the development of a learning health system.

Caveats in Using Attended Tasks

Attended tasks are useful at key decision points that can significantly impact the patient. Not every task in a process should be an attended task, since an attended task requires interaction with a user, thereby slowing the process. Deciding which tasks should be treated as an attended task requires weighing the pros and cons of the choice.

Conclusion

Healthcare process models may seem like a black box to users. An attended task can shed light on the process and allows clinicians to interact with a model at key decision points. If used judiciously they can improve healthcare, as well as provide insights into how clinical decisions impact outcomes.