WHAT IS ARTIFICIAL INTELLIGENCE?
Artificial Intelligence (AI) combines robust data, computers, and machines to mimic the problem-solving and decision-making capabilities of the human mind. The benefits of AI include the automation of repetitive tasks, improved decision-making, and a better customer experience. Machine learning can be used to process and analyze large sets of clinical data more quickly, efficiently, and accurately than we can. We should look to machines to continually learn how to improve the human element in clinical work.
DELIVERING THE VALUE OF CLINICAL DECISION SUPPORT AT THE POINT OF CLINICAL INTERVENTION
Through research and interviewing our own AI Subject Matter Expert, Grayson Kelso, Product Director at Qualifacts, we should not think of AI as “robo-clinicians,” set out to replace human clinical work, but instead as clinical suggestion and support tools, allowing clinicians more time to provide human care and empathy to clients; think of it like “Clinical Decision Support (CDS) 2.0.”
Over time, through research and learning, your care teams and your leadership have adopted best practices and protocols. These protocols have been trained on and communicated agency wide for general adoption. An AI supported electronic health record (EHR) will aggregate and analyze the assessments, screenings, and interventions used to recommend protocols during an enhanced intake process, based on its analysis of past processes followed and your population’s demographics, diagnoses, living circumstances, social determinants of health (SDoH), outcomes, and other relevant criteria. The goal of the machine and human partnership is to create more personalized treatment plans faster, to enhance care and drive better outcomes for clients.
When AI is compared to the current CDS functionality, we see CDS as being manual while AI sees a CDS engine with aggregated data across all clients from all time on the EHR that is going to allow, in one click, generate-output of all suggested supports and interventions for a client with these specific characteristics. It’s efficient and automated, saving the provider time to do more face to face with the client; a powerful addition to value-based care.
A next level AI feature would be integrated with a third party, research based, “evidence-based practice“ (EBP) app that the EHR could automatically query for the most current research based, recommended practices pulling that information back into the EHR so that it could now be acted upon real time. This would be a combination of CDS and the use of EBPs, but in real time and automated.
AI data analysis can also be used to illustrate patterns of events and risks, based on past occurrences, including risk of suicide, risk of substance use relapse, and risk of a recurrence of depression, for example. Tracking whether people are on a path to crises and another trip to ED or a repeated inpatient hospitalization can alert providers to mitigate risks, thereby improving the quality of life of the people served. Mitigating risk is key in value-based care, from automating outreach calls after missed appointments to compiling outreach actions based on trending outcomes.
BENEFITS OF AI DRIVEN TOOLS IN BEHAVIORAL HEALTH
- Greater efficiency – Saving the provider time with research and documentation
- Population Management – More consistent use of EBPs across like populations
- Improved clinical outcomes – Improved clinical outcomes in perhaps a shorter time span, meaning improved quality of life for the people served sooner
- Ability to predict cost- “length of episode” and “cost of episode” at the onset of the episode giving us a benchmark of care to aim for
Grayson Kelso provided a presentation at our 2022 Annual Community Conference where he talked about how AI and Machine Learning are used for behavioral health in the EHR today:
- Wearables and Client devices
- Chatbots and personal care
- Behavior and mood trackers
- Data transformation and analytics
- Treatment support
According to the World Economic Forum, AI is improving mental health therapy by:
- Keeping therapy standards high with quality control- The mental health clinic is using AI to analyze the language used in its therapy sessions through natural-language processing (NLP) – a technique where machines process transcripts. The clinic aims to provide therapists with a better insight into their work to ensure the delivery of high standards of care and to help trainees improve.
- Refining diagnosis and assigning the right therapist – AI is helping doctors to spot mental illness earlier and to make more accurate choices in treatment plans.
- Monitoring patient progress and altering treatment where necessary – AI can help identify when a treatment change needs to take place or if it’s time for a different therapist.
- Justifying cognitive behavioral therapy (CBT) instead of medication
In the Behavioral and Mental Care space, AI can be seen in:
- Predicting the Risk of Relapse
- Eliminating Negative Influences
- Treatment Tracking
- Finding Moral Support
- Starting Treatment
THE FUTURE DIRECTION OF AI IN BEHAVIORAL HEALTH
Many behavioral health providers are not yet ready to accept AI and machine learning as the great resource that it can be when delivering behavioral health services. Some see it as removing some of the human element from care, which is just not founded. AI takes advantage of all the data that has been collected over time and analyzes it and elevates it to a point where it is useful by demonstrating patterns and predictive outcomes that allow us to learn from our past.
As with all data, what you get out is only as good as what you put in so if your data entered is biased or subjective, the AI data suggestions would be as well. The same can be said for “manually built” clinical decision support rules. Although we can’t reach complete objectivity, there is a lot to be learned from experience captured in our databases. AI is a tool in a toolbox. One of many. It doesn’t take the place of provider experience and intuition-it aides it with objective, real time data for better clinical decision making.
If your organization is a CCBHC or is looking to become one in the future, you can schedule a free consultation with our CCBHC Program Manager to find the technology that is right for you. Please contact us today at info@qualifacts.com to learn more.
WHAT IS ARTIFICIAL INTELLIGENCE?
Artificial Intelligence (AI) combines robust data, computers, and machines to mimic the problem-solving and decision-making capabilities of the human mind. The benefits of AI include the automation of repetitive tasks, improved decision-making, and a better customer experience. Machine learning can be used to process and analyze large sets of clinical data more quickly, efficiently, and accurately than we can. We should look to machines to continually learn how to improve the human element in clinical work.
DELIVERING THE VALUE OF CLINICAL DECISION SUPPORT AT THE POINT OF CLINICAL INTERVENTION
Through research and interviewing our own AI Subject Matter Expert, Grayson Kelso, Product Director at Qualifacts, we should not think of AI as “robo-clinicians,” set out to replace human clinical work, but instead as clinical suggestion and support tools, allowing clinicians more time to provide human care and empathy to clients; think of it like “Clinical Decision Support (CDS) 2.0.”
Over time, through research and learning, your care teams and your leadership have adopted best practices and protocols. These protocols have been trained on and communicated agency wide for general adoption. An AI supported electronic health record (EHR) will aggregate and analyze the assessments, screenings, and interventions used to recommend protocols during an enhanced intake process, based on its analysis of past processes followed and your population’s demographics, diagnoses, living circumstances, social determinants of health (SDoH), outcomes, and other relevant criteria. The goal of the machine and human partnership is to create more personalized treatment plans faster, to enhance care and drive better outcomes for clients.
When AI is compared to the current CDS functionality, we see CDS as being manual while AI sees a CDS engine with aggregated data across all clients from all time on the EHR that is going to allow, in one click, generate-output of all suggested supports and interventions for a client with these specific characteristics. It’s efficient and automated, saving the provider time to do more face to face with the client; a powerful addition to value-based care.
A next level AI feature would be integrated with a third party, research based, “evidence-based practice“ (EBP) app that the EHR could automatically query for the most current research based, recommended practices pulling that information back into the EHR so that it could now be acted upon real time. This would be a combination of CDS and the use of EBPs, but in real time and automated.
AI data analysis can also be used to illustrate patterns of events and risks, based on past occurrences, including risk of suicide, risk of substance use relapse, and risk of a recurrence of depression, for example. Tracking whether people are on a path to crises and another trip to ED or a repeated inpatient hospitalization can alert providers to mitigate risks, thereby improving the quality of life of the people served. Mitigating risk is key in value-based care, from automating outreach calls after missed appointments to compiling outreach actions based on trending outcomes.
BENEFITS OF AI DRIVEN TOOLS IN BEHAVIORAL HEALTH
- Greater efficiency – Saving the provider time with research and documentation
- Population Management – More consistent use of EBPs across like populations
- Improved clinical outcomes – Improved clinical outcomes in perhaps a shorter time span, meaning improved quality of life for the people served sooner
- Ability to predict cost- “length of episode” and “cost of episode” at the onset of the episode giving us a benchmark of care to aim for
Grayson Kelso provided a presentation at our 2022 Annual Community Conference where he talked about how AI and Machine Learning are used for behavioral health in the EHR today:
- Wearables and Client devices
- Chatbots and personal care
- Behavior and mood trackers
- Data transformation and analytics
- Treatment support
According to the World Economic Forum, AI is improving mental health therapy by:
- Keeping therapy standards high with quality control- The mental health clinic is using AI to analyze the language used in its therapy sessions through natural-language processing (NLP) – a technique where machines process transcripts. The clinic aims to provide therapists with a better insight into their work to ensure the delivery of high standards of care and to help trainees improve.
- Refining diagnosis and assigning the right therapist – AI is helping doctors to spot mental illness earlier and to make more accurate choices in treatment plans.
- Monitoring patient progress and altering treatment where necessary – AI can help identify when a treatment change needs to take place or if it’s time for a different therapist.
- Justifying cognitive behavioral therapy (CBT) instead of medication
In the Behavioral and Mental Care space, AI can be seen in:
- Predicting the Risk of Relapse
- Eliminating Negative Influences
- Treatment Tracking
- Finding Moral Support
- Starting Treatment
THE FUTURE DIRECTION OF AI IN BEHAVIORAL HEALTH
Many behavioral health providers are not yet ready to accept AI and machine learning as the great resource that it can be when delivering behavioral health services. Some see it as removing some of the human element from care, which is just not founded. AI takes advantage of all the data that has been collected over time and analyzes it and elevates it to a point where it is useful by demonstrating patterns and predictive outcomes that allow us to learn from our past.
As with all data, what you get out is only as good as what you put in so if your data entered is biased or subjective, the AI data suggestions would be as well. The same can be said for “manually built” clinical decision support rules. Although we can’t reach complete objectivity, there is a lot to be learned from experience captured in our databases. AI is a tool in a toolbox. One of many. It doesn’t take the place of provider experience and intuition-it aides it with objective, real time data for better clinical decision making.
If your organization is a CCBHC or is looking to become one in the future, you can schedule a free consultation with our CCBHC Program Manager to find the technology that is right for you. Please contact us today at BDRteam@qualifacts.com to learn more.
