corrected terms with italics

2.0_how_to_use_mtl.md

@@ -64,7 +64,7 @@ Hi, I'm Lindsey and this is David. If you're watching this video, you want to kn
 
 [<img src="https://raw.githubusercontent.com/lzim/teampsd/master/resources/mtl_consult_thumbnails/script_14_how_do_five_key_variables_drive.png" width="350" style="float: left; margin: 10px">](https://bcove.video/3A3sBiG)
 
-Hi, I'm Lindsey and this is David. How do five key variables drive care quality? _Modeling to Learn_ emphasizes the dynamics of care over time, which can be accurately simplified to the key time-based variables that drive care quality. But be careful, the important principle is that these variables operate together over time to define an episode of care. That means care quality cannot be improved without understanding how these variables influence one another. If you want to see what Lindsey's talking about, navigate to the _Modeling to Learn_ Data User Interface at [mtl.how/data](https://app.powerbigov.us/groups/me/apps/b9686a29-6857-46c9-bdf9-043ca2b29138/reports/05dd8dbd-313f-4993-b406-6feea2fdb060/ReportSection?ctid=e95f1b23-abaf-45ee-821d-b7ab251ab3bf) and review each care problem—care coordination, medication management, psychotherapy, team care, and team flow. Our partners across VA describe how challenging it is to review data in one information system and then in another and end up unsure how to reconcile them, especially when you think that they indicate a different course of action. Folks are extremely busy and any new data resources must really add value to be worth learning. Let's see if these _Modeling to Learn_ variables meet the commonsense test of value added. Well, what do you think the five time-based variables are that make up an evidence-based episode of care? All outpatient care is defined by whether you can get an appointment when you need help. We focus on this all the time in VA. But then, and this is critical, you must be able to be seen again to complete a therapeutic course of care adequate to meet your need. Clinicians told us that a week was the way they think clinically. So, in _Modeling to Learn_, teams make their clinic selections to obtain an estimate of their local new patient start rate in patients per week and their appointment supply in appointments per week. Then we define evidence-based engagement as the new patient wait time in weeks, time between visits and weeks, and the engagement duration over time, again in weeks. This is a simplified definition of an evidence-based episode of care that is accurate for time. Why is it wise to focus on the dynamics of care over time? That's why _Modeling to Learn_ Blue is useful. So watch that video to find out.
+Hi, I'm Lindsey and this is David. How do five key variables drive care quality? _Modeling to Learn_ emphasizes the dynamics of care over time, which can be accurately simplified to the key time-based variables that drive care quality. But be careful, the important principle is that these variables operate together over time to define an episode of care. That means care quality cannot be improved without understanding how these variables influence one another. If you want to see what Lindsey's talking about, navigate to the _Modeling to Learn_ Data User Interface at [mtl.how/data](https://app.powerbigov.us/groups/me/apps/b9686a29-6857-46c9-bdf9-043ca2b29138/reports/05dd8dbd-313f-4993-b406-6feea2fdb060/ReportSection?ctid=e95f1b23-abaf-45ee-821d-b7ab251ab3bf) and review each care problem—care coordination, medication management, psychotherapy, team care, and team flow. Our partners across VA describe how challenging it is to review data in one information system and then in another and end up unsure how to reconcile them, especially when you think that they indicate a different course of action. Folks are extremely busy and any new data resources must really add value to be worth learning. Let's see if these _Modeling to Learn_ variables meet the commonsense test of value added. Well, what do you think the five time-based variables are that make up an evidence-based episode of care? All outpatient care is defined by whether you can get an appointment when you need help. We focus on this all the time in VA. But then, and this is critical, you must be able to be seen again to complete a therapeutic course of care adequate to meet your need. Clinicians told us that a week was the way they think clinically. So, in _Modeling to Learn_, teams make their clinic selections to obtain an estimate of their local new patient start rate in patients per week and their appointment supply in appointments per week. Then we define evidence-based engagement as the new patient wait time in weeks, time between visits and weeks, and the engagement duration over time, again in weeks. This is a simplified definition of an evidence-based episode of care that is accurate for time. Why is it wise to focus on the dynamics of care over time? That's why _Modeling to Learn Blue_ is useful. So watch that video to find out.
 
 ## How does _Modeling to Learn_ help improve medication management?
 <!-- video 19 -->
@@ -96,7 +96,7 @@ Hi, I'm Lindsey and this is David. How does an appointment backlog extend the we
 
 [<img src="https://raw.githubusercontent.com/lzim/teampsd/master/resources/mtl_consult_thumbnails/script_23_how_can_we_better_balance_needs.png" width="350" style="float: left; margin: 10px">](https://bcove.video/3yaXy41)
 
-Hi, I'm Lindsey and this is Debbie. How can we better balance the needs of new and existing patients? Should we prioritize new patient start rate or weeks between visits? These trade-offs are challenging for clinicians when you know there is a whole community of patients who need help. When teams are struggling with the limits of their available time in the day to see patients, it can feel like a clinical, ethical, and even moral quandary about how to best balance patients’ needs for services with the staff resources available. Of course, staff resources are always changing. We know many teams that are excelling in providing the highest quality addiction and mental health care available. But the behavioral health workforce shortage in the US is much bigger than VA. And VAs and teams need the right tools to make sure Veterans get the care they need for recovery. Challenges balancing the new patient start rate and the weeks between visits for existing patients apply systems thinking insights that we've talked about in other _MTL_ videos, such as the physics of conserving staff time in order to ensure a clinically beneficial, realistic approach to care decisions and quality improvement that meets VA quality standards _and_ meets Veterans needs for evidence-based episodes of care. The _Modeling to Learn_ Blue Simulation User Interface available at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) enables a site or team to evaluate these two balancing system stories as a function of their data for the last two years exported from _Modeling to Learn_ Red Data User Interface at [mtl.how/data](https://app.powerbigov.us/groups/me/apps/b9686a29-6857-46c9-bdf9-043ca2b29138/reports/05dd8dbd-313f-4993-b406-6feea2fdb060/ReportSection?ctid=e95f1b23-abaf-45ee-821d-b7ab251ab3bf). As we talked about in the _How does an appointment backlog extend the weeks between visits?_ video and _What if we keep making the same care decisions, will things get better or worse?_ video, balancing feedbacks occur in any system that has a goal, including our healthcare and clinical systems. Balancing feedbacks occur in systems that have constraints of resources, staff, and time. And as a result, the trends that occur over time tend to reset to a status quo or oscillate around the status quo. A brief _Modeling to Learn_ consult uses the site or team reviewed local data plus simulation to efficiently find local improvements that account for all these balancing trade-offs. We aim to help clinical and improvement teams find a couple empowering clinical heuristics, or rules of thumb, that are more effective for ensuring evidence-based care to get more Veterans better. When we partner with a VA or a team, we aim to find the lightest lift we can, like increase groups by 10% or adjust the return-to-clinic order for three weeks for these presenting concerns. And we often find something small that has a big payoff for Veterans. Do you want to be empowered to leverage the feedback, flow, and volume of your local care system? Watch that video to find out.
+Hi, I'm Lindsey and this is Debbie. How can we better balance the needs of new and existing patients? Should we prioritize new patient start rate or weeks between visits? These trade-offs are challenging for clinicians when you know there is a whole community of patients who need help. When teams are struggling with the limits of their available time in the day to see patients, it can feel like a clinical, ethical, and even moral quandary about how to best balance patients’ needs for services with the staff resources available. Of course, staff resources are always changing. We know many teams that are excelling in providing the highest quality addiction and mental health care available. But the behavioral health workforce shortage in the US is much bigger than VA. And VAs and teams need the right tools to make sure Veterans get the care they need for recovery. Challenges balancing the new patient start rate and the weeks between visits for existing patients apply systems thinking insights that we've talked about in other _MTL_ videos, such as the physics of conserving staff time in order to ensure a clinically beneficial, realistic approach to care decisions and quality improvement that meets VA quality standards _and_ meets Veterans needs for evidence-based episodes of care. The _Modeling to Learn Blue_ Simulation User Interface available at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) enables a site or team to evaluate these two balancing system stories as a function of their data for the last two years exported from _Modeling to Learn Red_ Data User Interface at [mtl.how/data](https://app.powerbigov.us/groups/me/apps/b9686a29-6857-46c9-bdf9-043ca2b29138/reports/05dd8dbd-313f-4993-b406-6feea2fdb060/ReportSection?ctid=e95f1b23-abaf-45ee-821d-b7ab251ab3bf). As we talked about in the _How does an appointment backlog extend the weeks between visits?_ video and _What if we keep making the same care decisions, will things get better or worse?_ video, balancing feedbacks occur in any system that has a goal, including our healthcare and clinical systems. Balancing feedbacks occur in systems that have constraints of resources, staff, and time. And as a result, the trends that occur over time tend to reset to a status quo or oscillate around the status quo. A brief _Modeling to Learn_ consult uses the site or team reviewed local data plus simulation to efficiently find local improvements that account for all these balancing trade-offs. We aim to help clinical and improvement teams find a couple empowering clinical heuristics, or rules of thumb, that are more effective for ensuring evidence-based care to get more Veterans better. When we partner with a VA or a team, we aim to find the lightest lift we can, like increase groups by 10% or adjust the return-to-clinic order for three weeks for these presenting concerns. And we often find something small that has a big payoff for Veterans. Do you want to be empowered to leverage the feedback, flow, and volume of your local care system? Watch that video to find out.
 
 ## How can we leverage the feedback, rates, and volume of our local care system?
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