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This is Part 2 of our blog series on 2023 Mental Health Trends.
As I think about the direction mental healthcare is heading this year, one thing that stands out is the ongoing need for mental health support—and the fact that therapists continue to be in short supply.
There was an existing shortage before the pandemic, which has only become worse. This dynamic makes finding effective treatment important, especially as recession fears grow, layoffs keep happening, and budgets are squeezed.
Organizations are trying to figure out how to use limited funds efficiently and ensure a positive return on their health spend. In mental health, this begins with tracking clinical outcomes to ensure quality of care.
Improving quality through measurement-based care
There’s no blood test, vital sign data, or set of x-rays for mental health, so it can be challenging to figure out what’s working and what’s not. It is possible to show the effectiveness and value of mental health treatments—but you can’t do it without tracking clinical outcomes.
There’s been a call to action in mental health literature and among professionals to improve the quality of our care delivery. I believe this best starts with implementing precision mental healthcare, using data and machine learning to:
- Eliminate trial-and-error in care
- Match people to the right care from the beginning
- Measure clinical outcomes as a routine part of care
- Use data to determine treatment effectiveness
Here are six areas that will become increasingly important in 2023, to ensure higher quality of care delivery and both clinical and financial outcomes for all stakeholders.
Collecting data must be a routine part of care
The only feasible “clinical tests” we have for mental health are called standard clinical scales. These are a set of tools, in the form of standard questions administered to clients, which measure mental health symptoms and functioning in different parts of a person’s life.
The way people answer the questionnaires correlates with the severity of specific mental health conditions. So, changes in the scores over time can show who is getting better and who is not improving.
This is important because research has also shown that without measuring outcomes regularly, providers can’t easily detect which of their clients are reliably improving or not. Therefore, if we are not routinely tracking outcomes, we are delaying effective care for a large proportion of those in care.
Using computers for routine tasks
Clinical scales can be administered by a human or a computer, and research shows that both methods are effectively equivalent in most situations.
There are three compelling reasons why it is likely better to use computers than humans in collecting this data. How standard clinical scales are administered is just as important as what is being asked, if your goal is to get accurate data about the efficacy of mental health treatment.
First, humans can impact the accuracy of the data collected by asking questions in different ways, or unknowingly using an off putting tone of voice that can impact how a question is answered. This variability in question asking can lead to less accurate data collection, and problems in mental health need detection or accurately tracking outcomes.
Second, there can also be a shame factor with admitting mental health struggles to a human that isn’t present when a computer is asking the questions. We should be doing everything we can to decrease stigma so we can help as many people in need as possible.
Finally, using computers for routine tasks like standard question asking gives providers more time to connect with a person and their unique story during treatment sessions. Providers have more time to develop the strong therapeutic alliance that we know is a key predictor of clinical outcomes.
Tracking care throughout treatment
Using clinical scales at the beginning of treatment helps a provider narrow down which mental health conditions the person might need care for, allowing the provider to determine an accurate diagnosis and focus efforts on the greatest needs quickly.
Then, by re-administering the clinical scales throughout care (and not just at the end of care), the provider is able to routinely track how the person is doing, so it’s clearer earlier in the process how they’re responding to care.
More evidence is emerging that early response to care efforts may predict longer-term outcomes. We can only use this new evidence to adapt quickly if we are tracking outcomes on a regular basis.
Using big data for precision mental health
Machine learning is a tool that is just beginning to be used in mental healthcare. It involves using computers to find predictors of outcomes that we may not be able to fully see in the course of caring for individual patients day to day.
Machine learning technology will allow the mental health field to better understand the factors that influence how people respond to treatment, so we can design better interventions that are tailored to each person’s needs.
For mental health treatment, machine learning can be used for:
- Finding who may need mental health support
- Which questions to ask for more accurate results in less time
- Matching someone with the right provider
- Tracking clinical outcomes effectively and efficiently
Another trend I’m seeing are companies that are trying to solve not just the access issue to any provider, but truly offering choice to members from a selection of providers who offer different types of evidence-based therapies.
For example, cognitive behavioral therapy (CBT), although widely used in therapy, might not work for everyone. Many other therapies also have a strong evidence base of effectiveness, including ones like Acceptance and Commitment Therapy (ACT), Dialectic Behavioral Therapy (DBT), and many others.
There’s also emerging evidence showing that providing therapy choice for people can improve engagement in treatment and eventual outcomes.
There are several things we should take into account when trying to find the right provider match for someone, including:
- Personal preferences of the member. For example, it might be important for a member to find a provider of a specific gender, similar race/ethnicity, or past lived experience to help make initial engagement easier for the member.
- Clinical needs of the member. For example, if someone screens positive for PTSD, they should likely be matched with a provider who is trained in evidence-based therapies for PTSD.
Although the form of therapy used is important, the biggest predictor of better mental health outcomes is still therapeutic alliance, which is the relationship between the provider and their client.
There are three core components of this:
- Do they have a relationship or connection?
- Is there a shared understanding of the issue to be addressed?
- Is there a shared understanding of the treatment procedures?
For example, if the provider wants to use cognitive behavioral therapy, but the client isn’t interested, then the treatment won’t be very effective.
About 45% of clinical outcomes are based on the strength of therapeutic alliance, and about 15% come from the specific therapeutic techniques—showing that we need to pay attention to alliance along with providers’ use of evidence-based therapies.
3 areas for HR leaders to address this year
It’s useful to know where the mental health treatment field is moving. At the same time, there are also mental health-related areas within the workplace that HR leaders should consider addressing in the upcoming year.
There’s still a lot of work to be done around lessening mental health stigma in the workplace. We’ve seen that mental health-related disability claims are trending upward, especially after over three years of COVID-19, and we need employees to be comfortable seeking care.
Addressing stigma is something HR leaders can do to help employees seek help sooner.
When people put off getting care, which happens for a variety of reasons, outcomes tend to worsen. For example, if someone is struggling with a substance use disorder, and doesn’t seek help until something happens, like a DUI or performance issues at work, that’s not a great outcome.
Studies show that untreated mental health conditions cause other medical issues to be three to six times more expensive to manage. The earlier employees are able to find mental healthcare, the easier it is to support them, drive health spend lower, and achieve better overall outcomes.
Mental health support during layoffs
With layoffs happening across multiple industries, supporting the mental health of employees is becoming even more important.
Remaining employees may deal with workplace survival syndrome after layoffs, which can include a range of emotional responses, such as grief from losing coworkers, anxiety about job security, guilt about still having a job, anger at the layoffs, and stress due to a higher workload.
Precision mental healthcare is the future of care
As I think about the upcoming year and where mental healthcare is headed, I believe that precision mental health will become the new standard of care. It helps us understand what’s working and not working, it helps employees feel better faster, and it’s key for driving a consistent ROI for everyone.
It is truly the best way to drive toward the highest quality of care, while equipping organizations to build an effective mental health support program for their employees.
See the results that employees at General Mills are experiencing from this approach, and how their leaders are transforming the mental health stigma.