diff --git a/2.0_how_to_use_mtl.md b/2.0_how_to_use_mtl.md index b9d29df1..e3a44c62 100644 --- a/2.0_how_to_use_mtl.md +++ b/2.0_how_to_use_mtl.md @@ -8,7 +8,7 @@ Click on the hyperlinks or video thumbnails to watch the videos. Transcripts of [](https://bcove.video/4bIhtF6) -I'm Lindsey, and this is Debbie. How can we get more Veterans better without adding hours in our day? To get Veterans better, we have to account for the number of hours available in the day. Yet because we don't have the right information about how we manage our time, we can pursue unrealistic treatment plans or improvement strategies that violate the laws of physics. _Modeling to Learn_ encourages us to account for clinician time differently, to make our plans feasible locally. Most VA data systems offer snapshots of cross-sectional data, but this kind of information doesn't help us account for the dynamics of evidence-based care over time. While many VA data systems are available, most display either quarterly lags from a high-level view or an in the weeds patient by patient view. _Modeling to Learn_ helps to bridge these two views. _Modeling to Learn_ introduces trends over time based on the clinic selections carefully reviewed by the staff on site. Trends help us zoom out to understand how we're doing for most of the Veterans we serve. _Modeling to Learn_ also introduces time-based definitions of care in the units of patients per week, appointments per week, or episodes of care per week. We use a week because over the last several years clinicians told us this is how they think in the clinic. There often is no typical day and a lot has happened by the end of a month. Defining care by week is more beneficial. High quality care in the clinic is always constrained by the available staffing capacity and the hours in the day to see patients, the number of weeks between visits, and the weeks of engagement over time. But do you have time to run these equations in your head or calculate them from the other dashboards you use? As an alternative, _Modeling to Learn_ puts these components of care together to understand their interdependence over time. How can _Modeling to Learn_ consultation help? Watch that video to find out. +How can we get more Veterans better without adding hours in our day? To get Veterans better, we have to account for the number of hours available in the day. Yet because we don't have the right information about how we manage our time, we can pursue unrealistic treatment plans or improvement strategies that violate the laws of physics. _Modeling to Learn_ encourages us to account for clinician time differently, to make our plans feasible locally. Most VA data systems offer snapshots of cross-sectional data, but this kind of information doesn't help us account for the dynamics of evidence-based care over time. While many VA data systems are available, most display either quarterly lags from a high-level view or an in the weeds patient by patient view. _Modeling to Learn_ helps to bridge these two views. _Modeling to Learn_ introduces trends over time based on the clinic selections carefully reviewed by the staff on site. Trends help us zoom out to understand how we're doing for most of the Veterans we serve. _Modeling to Learn_ also introduces time-based definitions of care in the units of patients per week, appointments per week, or episodes of care per week. We use a week because over the last several years clinicians told us this is how they think in the clinic. There often is no typical day and a lot has happened by the end of a month. Defining care by week is more beneficial. High quality care in the clinic is always constrained by the available staffing capacity and the hours in the day to see patients, the number of weeks between visits, and the weeks of engagement over time. But do you have time to run these equations in your head or calculate them from the other dashboards you use? As an alternative, _Modeling to Learn_ puts these components of care together to understand their interdependence over time. How can _Modeling to Learn_ consultation help? Watch that video to find out. ## How does _Modeling to Learn_ benefit Substance Use Disorder or SUD programs? @@ -16,7 +16,7 @@ I'm Lindsey, and this is Debbie. How can we get more Veterans better without add [](https://bcove.video/46dSKHt) -Hi, I'm Lindsey and this is David. How does _Modeling to Learn_ benefit substance use disorder, or SUD programs? When _Modeling to Learn_ began almost 10 years ago, as a partnership among patients, providers and policy makers across VA, we worked iteratively to define the dynamics of common care problems for the primary reasons Veterans seek addiction and mental health care. We found that if we focused on improving timely, high-quality evidence-based psychotherapy and pharmacotherapy for alcohol use disorder, depression, opioid use disorder and PTSD, we could support VA staff in meeting around 80% of Veterans needs for behavioral healthcare. We work with frontline teams and partners from the VA Academic Detailing Program and the Psychotropic Drug Safety Initiative or PDSI and developed the Medication Management Module of _Modeling to Learn_. This module helps manage the dynamics of evidence-based pharmacotherapies for alcohol use disorder and opioid use disorder based on their effectiveness in reducing craving, relapse, and overdose. You can find the Medication Management module in _Modeling to Learn_ data [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 Simulation User Interface [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html). The _Modeling to Learn_ Medication Management module supports improvement in the AlcTop or Alcohol Topiramate indicator from the Mental Health Information System or MIS and the SUD 16 indicator from the Strategic Analytics for Improvement in Learning or SAIL. How does _Modeling to Learn_ help? Prescribers describe the need to better visualize how to locally optimize their medication management appointment supply, including top of license care from staff with and without a Drug Enforcement Agency X-waiver for OUD medication. _Modeling to Learn_ helps a team assess this appointment supply, new patient start rate, no show or missed appointment rate, and the clinically appropriate return-to-clinic visit interval needed for a therapeutic response to alcohol use disorder and opioid use disorder medications. The Medication Management module helps teams and sites find the optimal number of patients who can be engaged in AUD and OUD therapies over time and helps them distinguish the flow of their patients who require antidepressants or other medication needs. Given that community needs and team staffing is dynamic and can change over time, these resources can empower teams to find local improvements that otherwise may be hard to find. Another common module for addressing Veterans SUD needs for group and other therapies is the Team Care module, which helps teams assess and optimize their overall mix of local multidisciplinary services as a function of staffing and patient needs. We are evaluating how _Modeling to Learn_ improves Veterans who start and complete a therapeutic course of medication for AUD and OUD with research funding from the VA and the National Institute on Drug Abuse or NIDA. What about other common SUD comorbidities and presenting concerns? For example, how does _Modeling to Learn_ benefit PTSD, clinical teams or PCTs? Watch that video to find out. +How does _Modeling to Learn_ benefit substance use disorder, or SUD programs? When _Modeling to Learn_ began almost 10 years ago, as a partnership among patients, providers and policy makers across VA, we worked iteratively to define the dynamics of common care problems for the primary reasons Veterans seek addiction and mental health care. We found that if we focused on improving timely, high-quality evidence-based psychotherapy and pharmacotherapy for alcohol use disorder, depression, opioid use disorder and PTSD, we could support VA staff in meeting around 80% of Veterans needs for behavioral healthcare. We work with frontline teams and partners from the VA Academic Detailing Program and the Psychotropic Drug Safety Initiative or PDSI and developed the Medication Management Module of _Modeling to Learn_. This module helps manage the dynamics of evidence-based pharmacotherapies for alcohol use disorder and opioid use disorder based on their effectiveness in reducing craving, relapse, and overdose. You can find the Medication Management module in _Modeling to Learn_ data [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 Simulation User Interface [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html). The _Modeling to Learn_ Medication Management module supports improvement in the AlcTop or Alcohol Topiramate indicator from the Mental Health Information System or MIS and the SUD 16 indicator from the Strategic Analytics for Improvement in Learning or SAIL. How does _Modeling to Learn_ help? Prescribers describe the need to better visualize how to locally optimize their medication management appointment supply, including top of license care from staff with and without a Drug Enforcement Agency X-waiver for OUD medication. _Modeling to Learn_ helps a team assess this appointment supply, new patient start rate, no show or missed appointment rate, and the clinically appropriate return-to-clinic visit interval needed for a therapeutic response to alcohol use disorder and opioid use disorder medications. The Medication Management module helps teams and sites find the optimal number of patients who can be engaged in AUD and OUD therapies over time and helps them distinguish the flow of their patients who require antidepressants or other medication needs. Given that community needs and team staffing is dynamic and can change over time, these resources can empower teams to find local improvements that otherwise may be hard to find. Another common module for addressing Veterans SUD needs for group and other therapies is the Team Care module, which helps teams assess and optimize their overall mix of local multidisciplinary services as a function of staffing and patient needs. We are evaluating how _Modeling to Learn_ improves Veterans who start and complete a therapeutic course of medication for AUD and OUD with research funding from the VA and the National Institute on Drug Abuse or NIDA. What about other common SUD comorbidities and presenting concerns? For example, how does _Modeling to Learn_ benefit PTSD, clinical teams or PCTs? Watch that video to find out. ## How does _Modeling to Learn_ benefit PTSD Clinical Teams or PCTs? @@ -24,7 +24,7 @@ Hi, I'm Lindsey and this is David. How does _Modeling to Learn_ benefit substanc [](https://bcove.video/3zNR6An) -Hi, I'm Lindsey and this is Debbie. How does _Modeling to Learn_ benefit PTSD clinical teams, or PCTs? Since I'm at the National Center for PTSD, this priority is very close to my heart. When _Modeling to Learn_ began almost 10 years ago as a partnership among patients, providers and policy makers across VA, we worked iteratively to define the dynamics of common care problems for the primary reasons Veterans seek addiction and mental health care. We found that if we focused on improving timely, high-quality evidence-based psychotherapy and pharmacotherapy for alcohol use disorder, depression, opioid use disorder and PTSD, we could support VA staff in meeting around 80% of Veterans needs for behavioral healthcare. _Modeling to Learn_ helps teams better evaluate the local dynamics of balancing the needs of new and existing patients for evidence-based psychotherapies, particularly cognitive processing therapy and prolonged exposure. Teams described the challenges of starting new patients who had never had their needs met without compromising care for Veterans already engaged in therapy. _Modeling to Learn_ enables teams to see where their Veterans get stuck, to decide when to graduate patients, or to recognize when the weeks between return to clinic visits start to get too long. Have you been trying to improve PTSD 56 in the Strategic Analytics for Improvement and Learning, or SAIL? We worked with local PTSD clinical teams from the beginning and have supported many more teams over the years who have engaged with the evidence-based psychotherapy programs or the PTSD mentorship program to meet these needs. The Psychotherapy module of the _Modeling to Learn_ data user interface, and simulation user interface helps teams to see how they're doing for patients who are starting psychotherapy, how many flow through to complete a therapeutic dose, and whether Veterans are getting near weekly therapy consistent with the PTSD treatment evidence base. There's also the ability to assess emerging approaches such as masked or intensive outpatient psychotherapy and other scenarios that are commonly considered and implemented in PCTs. You may also be wondering about the flow from primary care or from primary care mental health integration, or PCMHI, teams into general mental health and specialty mental health programs like PCTs. Stepped care up and down the mental health continuum of care can also be optimized to local needs using the _Modeling to Learn_ Team Flow module. Speaking of the care continuum, you may be wondering _How does _Modeling to Learn_ benefit Behavioral Health Integration program or BHIP teams?_ Watch that video to find out. +How does _Modeling to Learn_ benefit PTSD clinical teams, or PCTs? Since I'm at the National Center for PTSD, this priority is very close to my heart. When _Modeling to Learn_ began almost 10 years ago as a partnership among patients, providers and policy makers across VA, we worked iteratively to define the dynamics of common care problems for the primary reasons Veterans seek addiction and mental health care. We found that if we focused on improving timely, high-quality evidence-based psychotherapy and pharmacotherapy for alcohol use disorder, depression, opioid use disorder and PTSD, we could support VA staff in meeting around 80% of Veterans needs for behavioral healthcare. _Modeling to Learn_ helps teams better evaluate the local dynamics of balancing the needs of new and existing patients for evidence-based psychotherapies, particularly cognitive processing therapy and prolonged exposure. Teams described the challenges of starting new patients who had never had their needs met without compromising care for Veterans already engaged in therapy. _Modeling to Learn_ enables teams to see where their Veterans get stuck, to decide when to graduate patients, or to recognize when the weeks between return to clinic visits start to get too long. Have you been trying to improve PTSD 56 in the Strategic Analytics for Improvement and Learning, or SAIL? We worked with local PTSD clinical teams from the beginning and have supported many more teams over the years who have engaged with the evidence-based psychotherapy programs or the PTSD mentorship program to meet these needs. The Psychotherapy module of the _Modeling to Learn_ data user interface, and simulation user interface helps teams to see how they're doing for patients who are starting psychotherapy, how many flow through to complete a therapeutic dose, and whether Veterans are getting near weekly therapy consistent with the PTSD treatment evidence base. There's also the ability to assess emerging approaches such as masked or intensive outpatient psychotherapy and other scenarios that are commonly considered and implemented in PCTs. You may also be wondering about the flow from primary care or from primary care mental health integration, or PCMHI, teams into general mental health and specialty mental health programs like PCTs. Stepped care up and down the mental health continuum of care can also be optimized to local needs using the _Modeling to Learn_ Team Flow module. Speaking of the care continuum, you may be wondering _How does _Modeling to Learn_ benefit Behavioral Health Integration program or BHIP teams?_ Watch that video to find out. ## Can _Modeling to Learn_ benefit Behavioral Health Integration Program or BHIP teams? @@ -32,7 +32,7 @@ Hi, I'm Lindsey and this is Debbie. How does _Modeling to Learn_ benefit PTSD cl [](https://bcove.video/4bQfDSH) -Hi, I'm Lindsey and this is David. How does modeling to learn benefit Behavioral Health Integration Program or BHIP teams? What are the challenges with providing excellent team care? One challenge is the data sources to support team decision making jump from the individual patient and clinician counter all the way to the entire clinic or facility. It can mean that teams are flying blind to how they're coordinating care for their Veterans. This can be especially hard when teams are short staffed or when staff, patient needs, and local policies continually change. It's hard to know what improvements you might be able to find in your own team for coordinating evidence-based psychotherapies and pharmacotherapies over time. One way that _Modeling to Learn_ can complement other BHIP resources is through the Data User Interface available 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). The Clinic Selection and Team Flow Selection tabs empower teams to carefully review the clinics that comprise their data views. Data are never all good or all bad. _Modeling to Learn_ emphasizes transparency. Teams need to know what data they're getting and its strengths and weaknesses for guiding a particular decision. The Clinic Selection tabs update every day to reflect all the Mental Health 500 series Stop Code, Clinic, or Grid updates that may be going on locally. Teams can save bookmarks that reflect their specific clinic selections, for example to include trainees or to select any of the clinics they have used within the last two years. This can be helpful when new BHIP teams are being formed or remapped. The team Patient Data tabs update daily too, so teams can view the patient diagnosis, encounter, health factor, measurement-based care, and high-risk flag information all in one place, and then the team can zoom out to filter and view these data as team trends. Coordinating care within a team is challenging. No single clinician can offer all the services a Veteran may need, so treatment plans must be coordinated in real time, over time, to support Veteran improvement. Clinicians may not have visibility on exactly how appointment supplies divvied up into intake evaluations, individual and group psychotherapy, medication management, or other adjunctive supportive therapies. What is particularly important about the _Modeling to Learn_ simulation user interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) is the ability to assess not only the appointment supply, but even more important, the service proportions of patients that flow through to these services after intake or treatment plan review. One of the most common scenarios teams assess is how to respond to patient needs when they may not have the perfect staffing mix. Even when you know patient flow through care is divvied up into a mix of services, the dynamics over time are too much to understand in your head. The _Modeling to Learn_ simulation user interface enables teams to quickly assess the dynamics of available appointment supply, service proportions, and the other two major factors that govern patient improvement, the return-to-clinic visit interval in weeks and the overall duration in care. We've often used _Modeling to Learn_ to help teams find a way to ensure evidence-based care through the decisions that they make all day and find improvements in quality of care and quality of work life that they didn't think was possible due to limited staffing. You might wonder _How does _Modeling to Learn_ support stepped care up to BHIP from primary care or primary care mental health integration, or from general mental health up or down into specialty mental health programs such as substance use disorder programs or PTSD clinical teams?_ Watch that video to find out. +How does modeling to learn benefit Behavioral Health Integration Program or BHIP teams? What are the challenges with providing excellent team care? One challenge is the data sources to support team decision making jump from the individual patient and clinician counter all the way to the entire clinic or facility. It can mean that teams are flying blind to how they're coordinating care for their Veterans. This can be especially hard when teams are short staffed or when staff, patient needs, and local policies continually change. It's hard to know what improvements you might be able to find in your own team for coordinating evidence-based psychotherapies and pharmacotherapies over time. One way that _Modeling to Learn_ can complement other BHIP resources is through the Data User Interface available 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). The Clinic Selection and Team Flow Selection tabs empower teams to carefully review the clinics that comprise their data views. Data are never all good or all bad. _Modeling to Learn_ emphasizes transparency. Teams need to know what data they're getting and its strengths and weaknesses for guiding a particular decision. The Clinic Selection tabs update every day to reflect all the Mental Health 500 series Stop Code, Clinic, or Grid updates that may be going on locally. Teams can save bookmarks that reflect their specific clinic selections, for example to include trainees or to select any of the clinics they have used within the last two years. This can be helpful when new BHIP teams are being formed or remapped. The team Patient Data tabs update daily too, so teams can view the patient diagnosis, encounter, health factor, measurement-based care, and high-risk flag information all in one place, and then the team can zoom out to filter and view these data as team trends. Coordinating care within a team is challenging. No single clinician can offer all the services a Veteran may need, so treatment plans must be coordinated in real time, over time, to support Veteran improvement. Clinicians may not have visibility on exactly how appointment supplies divvied up into intake evaluations, individual and group psychotherapy, medication management, or other adjunctive supportive therapies. What is particularly important about the _Modeling to Learn_ simulation user interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) is the ability to assess not only the appointment supply, but even more important, the service proportions of patients that flow through to these services after intake or treatment plan review. One of the most common scenarios teams assess is how to respond to patient needs when they may not have the perfect staffing mix. Even when you know patient flow through care is divvied up into a mix of services, the dynamics over time are too much to understand in your head. The _Modeling to Learn_ simulation user interface enables teams to quickly assess the dynamics of available appointment supply, service proportions, and the other two major factors that govern patient improvement, the return-to-clinic visit interval in weeks and the overall duration in care. We've often used _Modeling to Learn_ to help teams find a way to ensure evidence-based care through the decisions that they make all day and find improvements in quality of care and quality of work life that they didn't think was possible due to limited staffing. You might wonder _How does _Modeling to Learn_ support stepped care up to BHIP from primary care or primary care mental health integration, or from general mental health up or down into specialty mental health programs such as substance use disorder programs or PTSD clinical teams?_ Watch that video to find out. ## How does _Modeling to Learn_ support stepped care? @@ -40,7 +40,7 @@ Hi, I'm Lindsey and this is David. How does modeling to learn benefit Behavioral [](https://bcove.video/4d6F2Ja) -Hi, I'm Lindsey and this is Debbie. How does _Modeling to Learn_ support stepped care? We developed the measurement-based stepped care module of _Modeling to Learn_ to help teams assess episodes of care when stepping patients up or down across the continuum of care. General mental health or behavioral health integration program, or BHIP teams, and specialty mental health teams such as substance use disorder, or SUD, programs and PTSD clinical teams, or PCTs, use the team flow selection tab in the _Modeling to Learn_ Data User Interface to define flow from their care up or down to another team. Teams make these team flow selections 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). Based on years of testing the Data User Interface, we found that teams are most accurate in defining who they refer Veterans to after completing an episode of care within their team, rather than where they receive referrals from. And the teams also define the gap between visits that they believe is locally most reasonable for defining a new episode of care in each setting. The primary evaluations in the Team Flow module of the _Modeling to Learn_ Simulation User Interface include assessing the flow through care of Veterans with high-risk flags as well as the proportions of high- and low-symptom patients as they flow through care to recovery and step down or discharge. We know that higher care quality improves recovery among Veterans, but how does that vary as a function of the total patients a team is serving? Team Flow can be used to assess the total manageable patients in a team and the impact of the patient load on care quality. Teams can also account for use of community care, which reduces some of the patients actively managed in the team. But as teams know, it does not reduce this care management to zero as Veterans using community care may still trigger an emergency care response or coordination of other services within a team. Perhaps most helpful is the view at a glance of where all Veterans are accumulating, waiting to step up or down across the continuum of care. This can help leadership and teams understand where there may need to be improved referral flows or service agreements between these settings. Teams can evaluate the flows through care when implementing measurement-based care to reduce the time it takes to detect when Veterans may be getting better or worse, which can also help improve agreements or decisions about when transitions across service settings are warranted. Would you like to know more about using measurement-based care to improve the time to detect patient improvement or risk? Watch that video to find out. +How does _Modeling to Learn_ support stepped care? We developed the measurement-based stepped care module of _Modeling to Learn_ to help teams assess episodes of care when stepping patients up or down across the continuum of care. General mental health or behavioral health integration program, or BHIP teams, and specialty mental health teams such as substance use disorder, or SUD, programs and PTSD clinical teams, or PCTs, use the team flow selection tab in the _Modeling to Learn_ Data User Interface to define flow from their care up or down to another team. Teams make these team flow selections 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). Based on years of testing the Data User Interface, we found that teams are most accurate in defining who they refer Veterans to after completing an episode of care within their team, rather than where they receive referrals from. And the teams also define the gap between visits that they believe is locally most reasonable for defining a new episode of care in each setting. The primary evaluations in the Team Flow module of the _Modeling to Learn_ Simulation User Interface include assessing the flow through care of Veterans with high-risk flags as well as the proportions of high- and low-symptom patients as they flow through care to recovery and step down or discharge. We know that higher care quality improves recovery among Veterans, but how does that vary as a function of the total patients a team is serving? Team Flow can be used to assess the total manageable patients in a team and the impact of the patient load on care quality. Teams can also account for use of community care, which reduces some of the patients actively managed in the team. But as teams know, it does not reduce this care management to zero as Veterans using community care may still trigger an emergency care response or coordination of other services within a team. Perhaps most helpful is the view at a glance of where all Veterans are accumulating, waiting to step up or down across the continuum of care. This can help leadership and teams understand where there may need to be improved referral flows or service agreements between these settings. Teams can evaluate the flows through care when implementing measurement-based care to reduce the time it takes to detect when Veterans may be getting better or worse, which can also help improve agreements or decisions about when transitions across service settings are warranted. Would you like to know more about using measurement-based care to improve the time to detect patient improvement or risk? Watch that video to find out. ## How does measurement-based care detect patient improvement or risk? @@ -48,7 +48,7 @@ Hi, I'm Lindsey and this is Debbie. How does _Modeling to Learn_ support stepped [](https://bcove.video/3yl8FqY) -Hi, I'm Lindsey and this is David. Why use measurement-based care to improve the time to detect patient improvement or risk? To improve care quality in the flow of patients through care to recovery, the major value of measurement-based care is ensuring Veterans are getting appropriate care and are responding to the care they receive. Clinicians are likely familiar with the emphasis on measurement-based care, but from the perspective of care flow, what is powerful is reducing the time it takes for a team to detect whether a patient is getting better or worse. In 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), the dataMeas tab provides tables of all the patient measures recorded at the VA Corporate Data Warehouse, whether they were captured via an evidence-based psychotherapy template, mental health assistant, or other approach. These include measures such as the GAD-7 for anxiety, PHQ-2 or PHQ-9 for depression, PCL-5 for PTSD, and the Brief Addiction Monitor or BAM. As a clinician, I like to use the patient-level views to sort scores from high to low to identify patients who may need to step up to a higher level of care or might benefit from referral to a specialty substance use disorder or PTSD service. The lowest score may indicate Veterans who can graduate from outpatient mental health care and step back down to primary care, which is an amazing day for the Veteran, their loved ones, and their treatment team. The vizMeas tab helps teams see trends in these measures to confirm when their efforts to implement measurement-based care are paying off. Teams can toggle to see what proportion of services include measurement-based care by comparing the vizMeas and vizEnc views. The _Modeling to Learn_ Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) also empowers teams to assess the impacts of improved implementation of measurement-based care and how it can drive improvements in care quality. Do you want to learn more about the key drivers of higher care quality to improve recovery and prevent suicide? Watch that video to find out. +Why use measurement-based care to improve the time to detect patient improvement or risk? To improve care quality in the flow of patients through care to recovery, the major value of measurement-based care is ensuring Veterans are getting appropriate care and are responding to the care they receive. Clinicians are likely familiar with the emphasis on measurement-based care, but from the perspective of care flow, what is powerful is reducing the time it takes for a team to detect whether a patient is getting better or worse. In 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), the dataMeas tab provides tables of all the patient measures recorded at the VA Corporate Data Warehouse, whether they were captured via an evidence-based psychotherapy template, mental health assistant, or other approach. These include measures such as the GAD-7 for anxiety, PHQ-2 or PHQ-9 for depression, PCL-5 for PTSD, and the Brief Addiction Monitor or BAM. As a clinician, I like to use the patient-level views to sort scores from high to low to identify patients who may need to step up to a higher level of care or might benefit from referral to a specialty substance use disorder or PTSD service. The lowest score may indicate Veterans who can graduate from outpatient mental health care and step back down to primary care, which is an amazing day for the Veteran, their loved ones, and their treatment team. The vizMeas tab helps teams see trends in these measures to confirm when their efforts to implement measurement-based care are paying off. Teams can toggle to see what proportion of services include measurement-based care by comparing the vizMeas and vizEnc views. The _Modeling to Learn_ Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) also empowers teams to assess the impacts of improved implementation of measurement-based care and how it can drive improvements in care quality. Do you want to learn more about the key drivers of higher care quality to improve recovery and prevent suicide? Watch that video to find out. ## How can we improve SAIL with _Modeling to Learn_? @@ -56,7 +56,7 @@ Hi, I'm Lindsey and this is David. Why use measurement-based care to improve the [](https://bcove.video/4dmPVX5) -Hi, I'm Lindsey and this is David. If you're watching this video, you want to know about strategic analytics improvement in learning, or SAIL metrics, with _Modeling to Learn_. _Modeling to Learn_ was developed to complement existing VA priorities. One important benchmark is SAIL. Some of the more frustrating aspects of SAIL are the lag in data to support daily decision-making in the clinic and emphasis on high-level facility views and, of course, how challenging it can be to improve sale metrics over time, especially when you're short-staffed. In fact, when SAIL metrics change over time, either in a positive or negative direction, it is difficult to know what's driving the change. Facility leadership and clinical teams may not know how to interpret these changes or what improvement strategy to try next. _Modeling to Learn_ supports two primary dimensions of the SAIL mental health quality domain—care coverage and care continuity. But importantly, _Modeling to Learn_ recognizes that these two dimensions may work against each other in a dynamic tension over time. If you're expanding population coverage measures, such as PSY 32 for depression, PSY 38, or PTSD 56 for PTSD, or SUD 16 for opioid use disorder, then it may be more challenging to ensure continuity of care measures such as MDD43h, MDD47h, and PSY 33 for depression and PSY 39 for PTSD. Of course, the reverse is also true. If you're doing great with care continuity on those measures, then it may be challenging to expand population coverage at the same time. I'm fond of calling this the physics of our care quality problems. What I mean is that we cannot change the laws of physics, such as the law of conservation, which states that matter and energy in a physical process cannot be created or destroyed, but can only be transformed. In _Modeling to Learn_, staff time is neither created nor destroyed. This means that when data is exported from the _MTL_ Data User Interface and uploaded to the _MTL_ Simulation User Interface, the scenarios a team or VA explores to find improvements in care quality will always account for or conserve the total local staff time available. This helps to avoid fixes that fail, such as something that improves population coverage in the short term but reduces care continuity in the long term. Have you ever felt like you were playing Whac-A-Mole® with these metrics, where you improve one indicator only to have a problem later somewhere else? _Modeling to Learn_ can help. Let's start with why _Modeling to Learn_ Red is useful. And why does the _Modeling to Learn_ Data User Interface provide new insights? Watch that video to find out. +If you're watching this video, you want to know about strategic analytics improvement in learning, or SAIL metrics, with _Modeling to Learn_. _Modeling to Learn_ was developed to complement existing VA priorities. One important benchmark is SAIL. Some of the more frustrating aspects of SAIL are the lag in data to support daily decision-making in the clinic and emphasis on high-level facility views and, of course, how challenging it can be to improve sale metrics over time, especially when you're short-staffed. In fact, when SAIL metrics change over time, either in a positive or negative direction, it is difficult to know what's driving the change. Facility leadership and clinical teams may not know how to interpret these changes or what improvement strategy to try next. _Modeling to Learn_ supports two primary dimensions of the SAIL mental health quality domain—care coverage and care continuity. But importantly, _Modeling to Learn_ recognizes that these two dimensions may work against each other in a dynamic tension over time. If you're expanding population coverage measures, such as PSY 32 for depression, PSY 38, or PTSD 56 for PTSD, or SUD 16 for opioid use disorder, then it may be more challenging to ensure continuity of care measures such as MDD43h, MDD47h, and PSY 33 for depression and PSY 39 for PTSD. Of course, the reverse is also true. If you're doing great with care continuity on those measures, then it may be challenging to expand population coverage at the same time. I'm fond of calling this the physics of our care quality problems. What I mean is that we cannot change the laws of physics, such as the law of conservation, which states that matter and energy in a physical process cannot be created or destroyed, but can only be transformed. In _Modeling to Learn_, staff time is neither created nor destroyed. This means that when data is exported from the _MTL_ Data User Interface and uploaded to the _MTL_ Simulation User Interface, the scenarios a team or VA explores to find improvements in care quality will always account for or conserve the total local staff time available. This helps to avoid fixes that fail, such as something that improves population coverage in the short term but reduces care continuity in the long term. Have you ever felt like you were playing Whac-A-Mole® with these metrics, where you improve one indicator only to have a problem later somewhere else? _Modeling to Learn_ can help. Let's start with why _Modeling to Learn_ Red is useful. And why does the _Modeling to Learn_ Data User Interface provide new insights? Watch that video to find out. ## How do five key variables drive care quality over time? @@ -64,7 +64,7 @@ Hi, I'm Lindsey and this is David. If you're watching this video, you want to kn [](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. +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? @@ -72,7 +72,7 @@ Hi, I'm Lindsey and this is David. How do five key variables drive care quality? [](https://bcove.video/3To3mP4) -Hi, I'm Lindsey and this is Debbie. How does _Modeling to Learn_ improve medication management? Medication management requires identifying a patient need, starting a medication, and evaluating for a therapeutic response over time. Some of the challenges and effective medication management over time include the realities that not all clinicians can prescribe. And of course, it requires follow up care to ensure that a patient is benefiting from their medication. So, when should the patient be seen again? One thing that can be so frustrating to prescribers is that a scheduler tells them when a patient can be seen again, rather than them telling the scheduler when the patient should be seen based on top of licensed care. The frustration is that medication management return-to-clinic visits may not occur at the appropriate interval to balance patient needs and risks. The VA has guidelines for some patient cohorts such as SAIL, MDD43h, and MDD47h, which define high quality continuity of follow up care as a valuation once every 12 weeks for Veterans diagnosed with depression who are using antidepressants. Other needs may reflect a different follow-up interval. For example, many addiction psychiatrists have described the appropriate medication management return-to-clinic visit interval for buprenorphine or methadone for opioid use disorder as every four weeks. These differences in medication needs, and the evidence-based pharmacotherapy standards that go with them, make it hard for prescribers to evaluate at a patient population level in a real time, especially when you're completing another medication management encounter every 20 minutes. And of course, depending on the community at any given time, the staffing mix may change and the size or volume of patient needs can change too. Some SUD teams may have a high volume of patients receiving evidence-based pharmacotherapy for alcohol use disorder, whereas the modal presenting concern in a BHIP may be more likely to be depression. And the decisions the prescribers are making using current rules of thumb may be boxing them in to a pattern of care that makes it even harder to be responsive to crises or reduce future wait times. The Medication Management module of _Modeling to Learn_ helps a team evaluate the trade-offs among all these factors as compared to the status quo over the last two years in the clinic. Comparing to the base case of no new decisions, the team can evaluate changes in their X-waivered MOUD appointment supply or evaluate and tailor the return-to-clinic visit interval by patient cohort. The goal is to help prescribers see what daily medication management decisions are adding up to over time and find feasible and effective local rules of thumb for clinically appropriate return-to-clinic orders in weeks that reduce wait times and ensure Veterans are getting better. For coordinating medication management in a multidisciplinary team such as a BHIP or SUD program, the Team Care module of _Modeling to Learn_ can also help. If you want to know how _Modeling to Learn_ helps to improve psychotherapy, watch that video to find out. +How does _Modeling to Learn_ improve medication management? Medication management requires identifying a patient need, starting a medication, and evaluating for a therapeutic response over time. Some of the challenges and effective medication management over time include the realities that not all clinicians can prescribe. And of course, it requires follow up care to ensure that a patient is benefiting from their medication. So, when should the patient be seen again? One thing that can be so frustrating to prescribers is that a scheduler tells them when a patient can be seen again, rather than them telling the scheduler when the patient should be seen based on top of licensed care. The frustration is that medication management return-to-clinic visits may not occur at the appropriate interval to balance patient needs and risks. The VA has guidelines for some patient cohorts such as SAIL, MDD43h, and MDD47h, which define high quality continuity of follow up care as a valuation once every 12 weeks for Veterans diagnosed with depression who are using antidepressants. Other needs may reflect a different follow-up interval. For example, many addiction psychiatrists have described the appropriate medication management return-to-clinic visit interval for buprenorphine or methadone for opioid use disorder as every four weeks. These differences in medication needs, and the evidence-based pharmacotherapy standards that go with them, make it hard for prescribers to evaluate at a patient population level in a real time, especially when you're completing another medication management encounter every 20 minutes. And of course, depending on the community at any given time, the staffing mix may change and the size or volume of patient needs can change too. Some SUD teams may have a high volume of patients receiving evidence-based pharmacotherapy for alcohol use disorder, whereas the modal presenting concern in a BHIP may be more likely to be depression. And the decisions the prescribers are making using current rules of thumb may be boxing them in to a pattern of care that makes it even harder to be responsive to crises or reduce future wait times. The Medication Management module of _Modeling to Learn_ helps a team evaluate the trade-offs among all these factors as compared to the status quo over the last two years in the clinic. Comparing to the base case of no new decisions, the team can evaluate changes in their X-waivered MOUD appointment supply or evaluate and tailor the return-to-clinic visit interval by patient cohort. The goal is to help prescribers see what daily medication management decisions are adding up to over time and find feasible and effective local rules of thumb for clinically appropriate return-to-clinic orders in weeks that reduce wait times and ensure Veterans are getting better. For coordinating medication management in a multidisciplinary team such as a BHIP or SUD program, the Team Care module of _Modeling to Learn_ can also help. If you want to know how _Modeling to Learn_ helps to improve psychotherapy, watch that video to find out. ## How does _Modeling_ to Learn help improve psychotherapy? @@ -80,7 +80,7 @@ Hi, I'm Lindsey and this is Debbie. How does _Modeling to Learn_ improve medicat [](https://bcove.video/4gjSdbG) -Hi, I'm Lindsey and this is David. How does _Modeling to Learn_ help improve psychotherapy? VA led the nation in dissemination and training for evidence-based psychotherapy. Many clinicians are ready to offer Veterans the highest quality care available. So, what makes it hard to get Veterans through an evidence-based episode of care when they start? What I've learned from working with clinical psychologists like Lindsey and other VA therapists from around the country is that the dynamics of psychotherapy in the clinic are challenging for the same reasons most care problems persist over time. Clinical teams typically do not have good visibility of where all their Veterans are in care. You may know that you plan to do CPT session six today with an individual Veteran, but how many Veterans are currently in the middle of therapy now? How many appointments are on the books? How many weeks are there before they return to clinic for follow-up care and how many graduate when they complete a course of an evidence-based psychotherapy? And how many drop out of therapy after one session or early in care but return to the team later? Evidence-based psychotherapy is typically defined by getting near weekly psychotherapy and having clinically meaningful improvement, which usually starts around session 8 to 12. Many VA data systems provide useful counts and proportions of evidence-based psychotherapy templates and the SAIL continuity of care metrics to find quality based on the number of sessions Veterans get within a specific time period. But the SAIL metric itself is a quarterly snapshot, and we know Veterans don't all start therapy at the beginning of a new quarter. In the _Modeling to Learn_ Data User Interface, a one-year patient cohort is identified that includes all Veterans seen from 18 months to six months ago based on the clinic selections made with the 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). Then, for the Veterans seen within those 12 months, we look back to see when those individual Veterans began therapy and how they were engaged over time. Once this psychotherapy cohort is defined and uploaded to the Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), we can look at the stocks and see where all the Veterans have accumulated, where they are in care in a typical week, and how many patients per week are progressing through key therapy milestones. Finally, at a local team level, psychotherapists can see how they are doing for most of the Veterans they serve. And even better, they can run experiments to see what the impact will be of making new decisions over time. You might wonder _What if we keep making the same care decisions? Will things get better or worse?_ Watch that video to find out. +How does _Modeling to Learn_ help improve psychotherapy? VA led the nation in dissemination and training for evidence-based psychotherapy. Many clinicians are ready to offer Veterans the highest quality care available. So, what makes it hard to get Veterans through an evidence-based episode of care when they start? What I've learned from working with clinical psychologists like Lindsey and other VA therapists from around the country is that the dynamics of psychotherapy in the clinic are challenging for the same reasons most care problems persist over time. Clinical teams typically do not have good visibility of where all their Veterans are in care. You may know that you plan to do CPT session six today with an individual Veteran, but how many Veterans are currently in the middle of therapy now? How many appointments are on the books? How many weeks are there before they return to clinic for follow-up care and how many graduate when they complete a course of an evidence-based psychotherapy? And how many drop out of therapy after one session or early in care but return to the team later? Evidence-based psychotherapy is typically defined by getting near weekly psychotherapy and having clinically meaningful improvement, which usually starts around session 8 to 12. Many VA data systems provide useful counts and proportions of evidence-based psychotherapy templates and the SAIL continuity of care metrics to find quality based on the number of sessions Veterans get within a specific time period. But the SAIL metric itself is a quarterly snapshot, and we know Veterans don't all start therapy at the beginning of a new quarter. In the _Modeling to Learn_ Data User Interface, a one-year patient cohort is identified that includes all Veterans seen from 18 months to six months ago based on the clinic selections made with the 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). Then, for the Veterans seen within those 12 months, we look back to see when those individual Veterans began therapy and how they were engaged over time. Once this psychotherapy cohort is defined and uploaded to the Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), we can look at the stocks and see where all the Veterans have accumulated, where they are in care in a typical week, and how many patients per week are progressing through key therapy milestones. Finally, at a local team level, psychotherapists can see how they are doing for most of the Veterans they serve. And even better, they can run experiments to see what the impact will be of making new decisions over time. You might wonder _What if we keep making the same care decisions? Will things get better or worse?_ Watch that video to find out. ## How does our appointment backlog extend weeks between visits? @@ -88,7 +88,7 @@ Hi, I'm Lindsey and this is David. How does _Modeling to Learn_ help improve psy [](https://bcove.video/4d3jVGG) -Hi, I'm Lindsey and this is David. How does an appointment backlog extend the weeks between visits? At first, this sounds like a simple system story that many clinicians describe as we work together to co-develop _Modeling to Learn_. But they also told us when an appointment backlog extends the week between visits, a long interval between visits effects care too. That's right. As the number of appointments for a specific service grows on the books, then the clinic has no choice but to increase the number of weeks or push out the time between visits to get everyone seen. We call this the return-to-clinic visit interval. This quantifies the return-to-clinic order in a unit of time—one week—which is critical for finding realistic local improvements in our partnerships with sites. Okay, so the balancing feedback principle operating here is that if you have a clinic calendar filled up with appointments, it's harder for new patients to start care _and_ it becomes harder for all existing patients to be seen with appropriate therapeutic follow-up standards for psychotherapy and pharmacotherapy. Lindsey, you and Debbie also talked about balancing feedback in the video, _What if we keep making the same care decisions, will things get better or worse?_ For an explanation of how start delays affect treatment decisions over time, watch that video. As I said at the beginning of this video, it's not just that a growing appointment backlog forces teams and clinics to extend the number of weeks between visits to get Veterans seen. It's that long return-to-clinic visit intervals impact treatment decisions and care quality. None of these effects occur in isolation. All these components of care interact over time. It's why system scientists will often point out there are no side effects and systems, there are only effects. Some we want and some we don't. As clinicians identify Veterans who might benefit from an evidence-based course of psychotherapy for depression or PTSD, they may find that many patients are waiting to start, or they know that those 90833 or 90834 psychotherapy encounters are occurring 12 weeks apart—or both. Clinicians recognize that they cannot see patients in a way that is consistent with an evidence-based episode of care and will have to start finding alternatives. _Modeling to Learn_ can help. What a relief that hundreds of clinicians, managers, data experts, quality improvement leads, and patients have already worked with us over the last decade to develop scalable _Modeling to Learn_ resources for clinical and improvement teams to use today. The _Modeling to Learn_ Simulation User Interface accounts for all these feedback effects so the team can upload their data, review what they see, and get immediate feedback about what the most feasible alternatives are to ensure quality of care for patients and quality of work life for providers and local staff. Based on this discussion, you may wonder _How can we better balance the needs of new and existing patients? Should we prioritize new patient start rate or weeks between visits for quality?_ Watch that video to find out. +How does an appointment backlog extend the weeks between visits? At first, this sounds like a simple system story that many clinicians describe as we work together to co-develop _Modeling to Learn_. But they also told us when an appointment backlog extends the week between visits, a long interval between visits effects care too. That's right. As the number of appointments for a specific service grows on the books, then the clinic has no choice but to increase the number of weeks or push out the time between visits to get everyone seen. We call this the return-to-clinic visit interval. This quantifies the return-to-clinic order in a unit of time—one week—which is critical for finding realistic local improvements in our partnerships with sites. Okay, so the balancing feedback principle operating here is that if you have a clinic calendar filled up with appointments, it's harder for new patients to start care _and_ it becomes harder for all existing patients to be seen with appropriate therapeutic follow-up standards for psychotherapy and pharmacotherapy. Lindsey, you and Debbie also talked about balancing feedback in the video, _What if we keep making the same care decisions, will things get better or worse?_ For an explanation of how start delays affect treatment decisions over time, watch that video. As I said at the beginning of this video, it's not just that a growing appointment backlog forces teams and clinics to extend the number of weeks between visits to get Veterans seen. It's that long return-to-clinic visit intervals impact treatment decisions and care quality. None of these effects occur in isolation. All these components of care interact over time. It's why system scientists will often point out there are no side effects and systems, there are only effects. Some we want and some we don't. As clinicians identify Veterans who might benefit from an evidence-based course of psychotherapy for depression or PTSD, they may find that many patients are waiting to start, or they know that those 90833 or 90834 psychotherapy encounters are occurring 12 weeks apart—or both. Clinicians recognize that they cannot see patients in a way that is consistent with an evidence-based episode of care and will have to start finding alternatives. _Modeling to Learn_ can help. What a relief that hundreds of clinicians, managers, data experts, quality improvement leads, and patients have already worked with us over the last decade to develop scalable _Modeling to Learn_ resources for clinical and improvement teams to use today. The _Modeling to Learn_ Simulation User Interface accounts for all these feedback effects so the team can upload their data, review what they see, and get immediate feedback about what the most feasible alternatives are to ensure quality of care for patients and quality of work life for providers and local staff. Based on this discussion, you may wonder _How can we better balance the needs of new and existing patients? Should we prioritize new patient start rate or weeks between visits for quality?_ Watch that video to find out. ## How can we better balance needs of new and existing patients? @@ -96,7 +96,7 @@ Hi, I'm Lindsey and this is David. How does an appointment backlog extend the we [](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. +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? @@ -104,7 +104,7 @@ Hi, I'm Lindsey and this is Debbie. How can we better balance the needs of new a [](https://bcove.video/4fonk5v) -Hi, I'm Lindsey and this is David. Do you want to be empowered to leverage the feedback, flow, and volume of your local care system? In any system, leverage describes the places where a small shift in one thing can produce big changes in everything. For a big benefit for Veterans in their care teams, the primary places to focus are where the greatest volume is, the fastest flower rates are, and of course to account for feedback dynamics over time. When we partner with the VA or team, we aim to find the lightest lift we can, like finding for one BHIP that increasing group psychotherapy by 10% eliminates wait times and gets five times the number of Veterans near weekly therapy. Or, adjusting the return-to-clinic order by three weeks for some presenting concerns can provide the same medication management supply as hiring another prescriber. We can often find small care decisions that when implemented frequently over time, have a big payoff for Veterans because they add to more than the sum of the parts in the interdependent system over time. The last thing we want to do is make big changes that are difficult for Veterans and clinical teams that produce limited benefit over time. In my experience over many years now, finding something clinically powerful that leverages the dynamics of the system can be a huge boost to morale, productivity, and staff empowerment. You might wonder _How can you find powerful insights that you are sure will work locally?_ We do this by reviewing the _Modeling to Learn_ Simulation User Interface stocks to see where all the Veterans are accumulating the most. We review the rates to see where the floodwaters are rising fastest and require action. Or where things are trending in the right direction and our attention could be better focused elsewhere. _Modeling to Learn_ consultation offers clinical and improvement teams real-time efficient support. We can do this quickly because we've partnered with patients, providers, policy makers, data experts, evaluators, and quality improvement consultants from the frontline to VA Central Office for nearly 10 years now to define the primary drivers of care problems and care quality using system dynamics. _Modeling to Learn_ consultation involves empowering clinicians, managers, improvers, data leads, and all of VA to leverage the system for getting more Veterans better. How can _Modeling to Learn_ consultation help? Watch that video to find out. +Do you want to be empowered to leverage the feedback, flow, and volume of your local care system? In any system, leverage describes the places where a small shift in one thing can produce big changes in everything. For a big benefit for Veterans in their care teams, the primary places to focus are where the greatest volume is, the fastest flower rates are, and of course to account for feedback dynamics over time. When we partner with the VA or team, we aim to find the lightest lift we can, like finding for one BHIP that increasing group psychotherapy by 10% eliminates wait times and gets five times the number of Veterans near weekly therapy. Or, adjusting the return-to-clinic order by three weeks for some presenting concerns can provide the same medication management supply as hiring another prescriber. We can often find small care decisions that when implemented frequently over time, have a big payoff for Veterans because they add to more than the sum of the parts in the interdependent system over time. The last thing we want to do is make big changes that are difficult for Veterans and clinical teams that produce limited benefit over time. In my experience over many years now, finding something clinically powerful that leverages the dynamics of the system can be a huge boost to morale, productivity, and staff empowerment. You might wonder _How can you find powerful insights that you are sure will work locally?_ We do this by reviewing the _Modeling to Learn_ Simulation User Interface stocks to see where all the Veterans are accumulating the most. We review the rates to see where the floodwaters are rising fastest and require action. Or where things are trending in the right direction and our attention could be better focused elsewhere. _Modeling to Learn_ consultation offers clinical and improvement teams real-time efficient support. We can do this quickly because we've partnered with patients, providers, policy makers, data experts, evaluators, and quality improvement consultants from the frontline to VA Central Office for nearly 10 years now to define the primary drivers of care problems and care quality using system dynamics. _Modeling to Learn_ consultation involves empowering clinicians, managers, improvers, data leads, and all of VA to leverage the system for getting more Veterans better. How can _Modeling to Learn_ consultation help? Watch that video to find out. ## What key drivers of higher care quality improve recovery and prevent suicide? @@ -112,4 +112,4 @@ Hi, I'm Lindsey and this is David. Do you want to be empowered to leverage the f [](https://bcove.video/4c1YgOQ) -Hi, I'm Lindsey and this is Debbie. What are key drivers of higher care quality to improve recovery and prevent suicide? A central idea of _Modeling to Learn_ resources is that delivering consistent, timely, high-quality care is challenging for teams because we don't accurately account for the primary dynamics driving care quality over time. You might be thinking _Care dynamics, what does that mean?_ We literally mean that due to time pressures and cognitive limits, we don't effectively understand relationships among key care decisions that we make every day over time, especially decisions made in the aggregate across a clinical team. For example, we may try to break down problems by looking at referrals or wait times, but we don't look at how clinical teams’ knowledge of wait times influence referrals to a specific service or team. In the _Modeling to Learn_ Data User Interface, we define care dynamics in terms of rates over time, like new patients per week, so that we can understand the flow of Veterans through care. As another example in terms of care quality, recovery and suicide prevention, the _Modeling to Learn_ Team Flow module looks not only at the number of patients served by a team or site, or the number of patients with a high-risk flag for suicide, but also the time it takes to unflag high-risk patients in a team and the typical time to improve within the team over the last two years. The _Modeling to Learn_ Simulation User Interface uses an interactive structure fed by local team data from the Data User Interface. The _Modeling to Learn_ Simulation User Interface visually depicts accumulation of patients in desirable or undesirable states of care, like waiting to start a new service. These states are defined by their inflows and outflows. A lot of clinical teams and mental health leadership wonder whether they can get any new insights when they are already doing everything that they can to support the Veterans in their care. Would you like to know how to improve SAIL with _Modeling to Learn_? Watch that video to find out. +What are key drivers of higher care quality to improve recovery and prevent suicide? A central idea of _Modeling to Learn_ resources is that delivering consistent, timely, high-quality care is challenging for teams because we don't accurately account for the primary dynamics driving care quality over time. You might be thinking _Care dynamics, what does that mean?_ We literally mean that due to time pressures and cognitive limits, we don't effectively understand relationships among key care decisions that we make every day over time, especially decisions made in the aggregate across a clinical team. For example, we may try to break down problems by looking at referrals or wait times, but we don't look at how clinical teams’ knowledge of wait times influence referrals to a specific service or team. In the _Modeling to Learn_ Data User Interface, we define care dynamics in terms of rates over time, like new patients per week, so that we can understand the flow of Veterans through care. As another example in terms of care quality, recovery and suicide prevention, the _Modeling to Learn_ Team Flow module looks not only at the number of patients served by a team or site, or the number of patients with a high-risk flag for suicide, but also the time it takes to unflag high-risk patients in a team and the typical time to improve within the team over the last two years. The _Modeling to Learn_ Simulation User Interface uses an interactive structure fed by local team data from the Data User Interface. The _Modeling to Learn_ Simulation User Interface visually depicts accumulation of patients in desirable or undesirable states of care, like waiting to start a new service. These states are defined by their inflows and outflows. A lot of clinical teams and mental health leadership wonder whether they can get any new insights when they are already doing everything that they can to support the Veterans in their care. Would you like to know how to improve SAIL with _Modeling to Learn_? Watch that video to find out. diff --git a/index.Rmd b/index.Rmd index f0a6eaae..255da36f 100644 --- a/index.Rmd +++ b/index.Rmd @@ -17,7 +17,7 @@ Click on the hyperlinks or video thumbnails to watch the videos. Transcripts of [](https://bcove.video/465KOIt) -Hi, I'm Lindsey and this is Debbie. You might wonder _What is _Modeling to Learn_, why use it, and how might it help?_ We often don't understand what is driving our care problems over time. _Modeling to Learn_ helps to upgrade the decisions you make all day, every day, because the decisions made most frequently will be the most powerful for improving our care. We're introducing _Modeling to Learn_, which has been developed and evaluated in the VA for over 9 years. _Modeling to Learn_ is a way to upgrade care decisions by understanding the dynamics of common care problems over time. Are you very, very busy? _Modeling to Learn_ includes step by step guides with gifs showing each click. These guides are available at [mtl.how](https://github.com/lzim/mtl). Are you unsure what data to use, how it's defined, and are you wishing it was current enough to guide your care decisions? _Modeling to Learn_ includes a transparent, real-time locally defined data user interface available 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), where you specify the clinics that define your data views. Are you unsure what is realistic for improving care locally, given your patients’ needs and your existing staff and resources? _Modeling to Learn_ also includes a simulation modeling interface available at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), so you can try out new decisions before you implement them in the clinic. We all want to meet Veterans needs for timely, high-quality care. So, what gets in our way? Watch that video to find out. +You might wonder _What is _Modeling to Learn_, why use it, and how might it help?_ We often don't understand what is driving our care problems over time. _Modeling to Learn_ helps to upgrade the decisions you make all day, every day, because the decisions made most frequently will be the most powerful for improving our care. We're introducing _Modeling to Learn_, which has been developed and evaluated in the VA for over 9 years. _Modeling to Learn_ is a way to upgrade care decisions by understanding the dynamics of common care problems over time. Are you very, very busy? _Modeling to Learn_ includes step by step guides with gifs showing each click. These guides are available at [mtl.how](https://github.com/lzim/mtl). Are you unsure what data to use, how it's defined, and are you wishing it was current enough to guide your care decisions? _Modeling to Learn_ includes a transparent, real-time locally defined data user interface available 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), where you specify the clinics that define your data views. Are you unsure what is realistic for improving care locally, given your patients’ needs and your existing staff and resources? _Modeling to Learn_ also includes a simulation modeling interface available at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), so you can try out new decisions before you implement them in the clinic. We all want to meet Veterans needs for timely, high-quality care. So, what gets in our way? Watch that video to find out. ## How can a _Modeling to Learn_ consult help? @@ -25,7 +25,7 @@ Hi, I'm Lindsey and this is Debbie. You might wonder _What is _Modeling to Learn [](https://bcove.video/3WuJ7kI) -Hi, I'm Lindsey and this is David. _Modeling to Learn_ improves visibility and provides new insights into how common care problems persist over time. How? _Modeling to Learn_ is based on over 60 years of scholarship known as participatory system dynamics. For this reason, we call ourselves Team PSD for Team Participatory System Dynamics. The Data User Interface and Simulation User Interface comprise two versions of _Modeling to Learn_. The data-only version is known as _Modeling to Learn Red_; _Modeling to Learn Blue_ ads participatory learning from simulation. Team PSD supports _MTL Red_ and _MTL Blue_ and is carefully evaluating how each works to support VA in meeting Veterans needs. Learning from simulation can help us to place a better initial bet on what is likely to work locally by evaluating alternative decisions via simulation before we implement them in the real world. _MTL Red_ tells us where we've been over the last two years based on the clinic selections made to produce the patient data reports and team trends or visualizations. Many staff report that viewing the Data UI real-time patient data tabs or the team trends is efficient and encouraging. The data tabs help with clinical decision making. The visualizations of team trends provide leading indicators that improvement efforts are paying off, which can be validating. Why is _Modeling to Learn_ useful when we have critical staffing and hiring needs? Watch that video to find out. +_Modeling to Learn_ improves visibility and provides new insights into how common care problems persist over time. How? _Modeling to Learn_ is based on over 60 years of scholarship known as participatory system dynamics. For this reason, we call ourselves Team PSD for Team Participatory System Dynamics. The Data User Interface and Simulation User Interface comprise two versions of _Modeling to Learn_. The data-only version is known as _Modeling to Learn Red_; _Modeling to Learn Blue_ ads participatory learning from simulation. Team PSD supports _MTL Red_ and _MTL Blue_ and is carefully evaluating how each works to support VA in meeting Veterans needs. Learning from simulation can help us to place a better initial bet on what is likely to work locally by evaluating alternative decisions via simulation before we implement them in the real world. _MTL Red_ tells us where we've been over the last two years based on the clinic selections made to produce the patient data reports and team trends or visualizations. Many staff report that viewing the Data UI real-time patient data tabs or the team trends is efficient and encouraging. The data tabs help with clinical decision making. The visualizations of team trends provide leading indicators that improvement efforts are paying off, which can be validating. Why is _Modeling to Learn_ useful when we have critical staffing and hiring needs? Watch that video to find out. ## What gets in the way of meeting patients' needs? @@ -33,7 +33,7 @@ Hi, I'm Lindsey and this is David. _Modeling to Learn_ improves visibility and p [](https://bcove.video/4d4TmRZ) -Hi, I'm Lindsey and this is David. We all want to meet Veterans’ needs for timely, high-quality care. So, what gets in our way? _Modeling to Learn_ is a way to upgrade care decisions by understanding the dynamics of common care problems over time. Demanding clinical days mean we don't often have bandwidth to gain new insights, particularly in our understanding of interdependent clinic dynamics over time. We're often flying blind to the impacts that our daily decisions have on the overall community that relies on us. As clinicians, we look at the patient in front of us and decide when we think they should be seen again. But have you ever been told you cannot see a patient as soon as you would like due to the constraints of the clinic? For example, an evidence-based course of care requires starting treatment without delay and keeping the Veteran engaged in a therapeutic dose of care over time to meet their needs. If we only emphasize Veterans starting care, but the time between visits extends way out, this interferes with evidence-based care and Veterans getting better. How can we get more Veterans better without adding hours in our day? Watch that video to find out. +We all want to meet Veterans’ needs for timely, high-quality care. So, what gets in our way? _Modeling to Learn_ is a way to upgrade care decisions by understanding the dynamics of common care problems over time. Demanding clinical days mean we don't often have bandwidth to gain new insights, particularly in our understanding of interdependent clinic dynamics over time. We're often flying blind to the impacts that our daily decisions have on the overall community that relies on us. As clinicians, we look at the patient in front of us and decide when we think they should be seen again. But have you ever been told you cannot see a patient as soon as you would like due to the constraints of the clinic? For example, an evidence-based course of care requires starting treatment without delay and keeping the Veteran engaged in a therapeutic dose of care over time to meet their needs. If we only emphasize Veterans starting care, but the time between visits extends way out, this interferes with evidence-based care and Veterans getting better. How can we get more Veterans better without adding hours in our day? Watch that video to find out. ## If we keep making the same care decisions, will things get better or worse? @@ -41,7 +41,7 @@ Hi, I'm Lindsey and this is David. We all want to meet Veterans’ needs for tim [](https://bcove.video/4cZpLJk) -Hi, I'm Lindsey and this is Debbie. What if we keep making the same care decisions? Will things get better or worse? This is almost a trick question. If we keep making the same care decisions, more than likely things will stay the same in the clinic. Very rarely do clinical teams _not_ have any local improvements that they would like to make. So, what do we mean by staying the same? We know that things in the clinic are not static but are always dynamic and always changing. We'll describe how start delays affect treatment decisions to explain what we mean. The _Modeling to Learn_ Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) is designed to help a team see what is likely to happen over the next two years if they continue to make the same daily clinical decisions that were made over the past two years. We call this the base case of no new decisions. Any number of feasible alternative decisions a team or site could make can be compared against this base case to find the best options for improving care over time. Because feedback dynamics produce non-linear trends or system behaviors, this means even the base case run can be surprising for some teams. The base case may show even more undesirable likely futures which teams would need to make new decisions to avoid. An example is the way start delays affect treatment decisions through balancing feedback. As the number of patients waiting to start a specific service grows, clinicians must adjust and try to find another way to meet their Veterans’ needs. Over time, as the clinicians learn about a local delay, it starts to affect what they think the most ethical, clinically appropriate treatment for the Veteran will be. An analogy from daily life will help. If there's a traffic jam outside your VA, you may try to find another route home. As more drivers choose alternative routes, traffic returns to normal. Of course, when drivers don't know about the jam, it will get worse over time. And whether drivers choose alternative routes or not—with balancing feedback like a traffic jam, where more cars are getting on the highway than are getting off—cars will build up and slow down the rate of traffic for everyone, whether we like it or not. We have these kinds of experiences in our clinics, too. Balancing feedback can be a blessing or curse. A blessing in that the balancing feedback causes the clinical system to return to its status quo. A curse if the status quo is undesirable for quality of patient care or provider quality of work life. When you start _Modeling to Learn_, it is surprising to see how powerful feedback is in driving care dynamics in any clinical care setting—VA or non-VA, VAMC or CBOC, urban and rural, BHIP, or PCT. General system insights, like the link between balancing feedbacks and nonlinear system behaviors, can be understood quickly from _Modeling to Learn_. You can understand _exactly_ how many patients will be waiting and when. And then discover options for addressing the number of patients waiting for specific services with your existing staff mix. If you'd like specific recommendations for your local site or team based on these dynamics, request a _Modeling to Learn_ consult or use the guides and videos at [mtl.how](https://github.com/lzim/mtl). Clinical oscillations can really whipsaw clinicians and patients around, which is stressful and can increase potential clinical risks. If the number of patients waiting to start grows for a long time, it's overwhelming. But it can also be exhausting when things go up and down quickly and clinics feel like they're chasing their tails trying to find improvements. Especially given these dynamics can vary service by service, even within the same team. If you want to understand this principle, go to [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) and check out the Team Care module. Or are you interested in another common balancing care problem, how the appointment backlog extends the weeks between visits? Watch that video to find out. +What if we keep making the same care decisions? Will things get better or worse? This is almost a trick question. If we keep making the same care decisions, more than likely things will stay the same in the clinic. Very rarely do clinical teams _not_ have any local improvements that they would like to make. So, what do we mean by staying the same? We know that things in the clinic are not static but are always dynamic and always changing. We'll describe how start delays affect treatment decisions to explain what we mean. The _Modeling to Learn_ Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) is designed to help a team see what is likely to happen over the next two years if they continue to make the same daily clinical decisions that were made over the past two years. We call this the base case of no new decisions. Any number of feasible alternative decisions a team or site could make can be compared against this base case to find the best options for improving care over time. Because feedback dynamics produce non-linear trends or system behaviors, this means even the base case run can be surprising for some teams. The base case may show even more undesirable likely futures which teams would need to make new decisions to avoid. An example is the way start delays affect treatment decisions through balancing feedback. As the number of patients waiting to start a specific service grows, clinicians must adjust and try to find another way to meet their Veterans’ needs. Over time, as the clinicians learn about a local delay, it starts to affect what they think the most ethical, clinically appropriate treatment for the Veteran will be. An analogy from daily life will help. If there's a traffic jam outside your VA, you may try to find another route home. As more drivers choose alternative routes, traffic returns to normal. Of course, when drivers don't know about the jam, it will get worse over time. And whether drivers choose alternative routes or not—with balancing feedback like a traffic jam, where more cars are getting on the highway than are getting off—cars will build up and slow down the rate of traffic for everyone, whether we like it or not. We have these kinds of experiences in our clinics, too. Balancing feedback can be a blessing or curse. A blessing in that the balancing feedback causes the clinical system to return to its status quo. A curse if the status quo is undesirable for quality of patient care or provider quality of work life. When you start _Modeling to Learn_, it is surprising to see how powerful feedback is in driving care dynamics in any clinical care setting—VA or non-VA, VAMC or CBOC, urban and rural, BHIP, or PCT. General system insights, like the link between balancing feedbacks and nonlinear system behaviors, can be understood quickly from _Modeling to Learn_. You can understand _exactly_ how many patients will be waiting and when. And then discover options for addressing the number of patients waiting for specific services with your existing staff mix. If you'd like specific recommendations for your local site or team based on these dynamics, request a _Modeling to Learn_ consult or use the guides and videos at [mtl.how](https://github.com/lzim/mtl). Clinical oscillations can really whipsaw clinicians and patients around, which is stressful and can increase potential clinical risks. If the number of patients waiting to start grows for a long time, it's overwhelming. But it can also be exhausting when things go up and down quickly and clinics feel like they're chasing their tails trying to find improvements. Especially given these dynamics can vary service by service, even within the same team. If you want to understand this principle, go to [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html) and check out the Team Care module. Or are you interested in another common balancing care problem, how the appointment backlog extends the weeks between visits? Watch that video to find out. ## Why is _Modeling to Learn_ useful when we have critical staffing and hiring needs? @@ -49,7 +49,7 @@ Hi, I'm Lindsey and this is Debbie. What if we keep making the same care decisio [](https://bcove.video/4f7OfSL) -Hi, I'm Lindsey and this is Debbie. Leaders and clinicians often ask how can _Modeling to Learn_ be useful when facing critical staffing and hiring needs? With limited staff coverage, the need to hire is priority number one, but even then there isn't a magic wand that produces staff where they may not exist today. What could help now? From the beginning, _Modeling to Learn_ prioritized evidence-based episodes of care within existing staff time. When _Modeling to Learn_ launched nationally in the VA in March of 2020, it was to empower staff to find the highest yield local improvements without asking staff to do more with less. As a result, when staff coverage may not change quickly, _Modeling to Learn_ will find options for improving quality of care for Veterans and quality of work life for providers. How? What does Debbie mean by high yield local improvements? Because evidence-based behavioral healthcare is delivered over time, there are many possibilities. Small decisions made all day, every day by the clinicians, when compounded over time, can be surprisingly powerful. Think of your savings account or your waistline. In _Modeling to Learn_, we look for the lightest clinical lift teams and VAs can make that have the biggest payoff for Veterans in terms of timely, high-quality care. We work hard to avoid big difficult changes with limited benefit. When working with the _Modeling to Learn_-read data user interface, clinical teams are often motivated when they see trends that reflect hard-won efforts to implement high quality episodes of care which may not show up in other data systems for some time. The _Modeling to Learn Blue_ simulation user interface saves staff time because alternatives can quickly be assessed during a modeling consultation. Change is hard, but we no longer have to learn by trial and error, wearing out already burdened staff. Does that sound too good to be true? If so, you may be wondering about examples of _Modeling to Learn_ use cases for the pain points you face in your specific team or program. As an example, how does _Modeling to Learn_ benefit substance use disorder, or SUD programs? Watch that video to find out. +Leaders and clinicians often ask how can _Modeling to Learn_ be useful when facing critical staffing and hiring needs? With limited staff coverage, the need to hire is priority number one, but even then there isn't a magic wand that produces staff where they may not exist today. What could help now? From the beginning, _Modeling to Learn_ prioritized evidence-based episodes of care within existing staff time. When _Modeling to Learn_ launched nationally in the VA in March of 2020, it was to empower staff to find the highest yield local improvements without asking staff to do more with less. As a result, when staff coverage may not change quickly, _Modeling to Learn_ will find options for improving quality of care for Veterans and quality of work life for providers. How? What does Debbie mean by high yield local improvements? Because evidence-based behavioral healthcare is delivered over time, there are many possibilities. Small decisions made all day, every day by the clinicians, when compounded over time, can be surprisingly powerful. Think of your savings account or your waistline. In _Modeling to Learn_, we look for the lightest clinical lift teams and VAs can make that have the biggest payoff for Veterans in terms of timely, high-quality care. We work hard to avoid big difficult changes with limited benefit. When working with the _Modeling to Learn_-read data user interface, clinical teams are often motivated when they see trends that reflect hard-won efforts to implement high quality episodes of care which may not show up in other data systems for some time. The _Modeling to Learn Blue_ simulation user interface saves staff time because alternatives can quickly be assessed during a modeling consultation. Change is hard, but we no longer have to learn by trial and error, wearing out already burdened staff. Does that sound too good to be true? If so, you may be wondering about examples of _Modeling to Learn_ use cases for the pain points you face in your specific team or program. As an example, how does _Modeling to Learn_ benefit substance use disorder, or SUD programs? Watch that video to find out. ## Why is the _MTL Red_ Data User Interface useful? @@ -57,7 +57,7 @@ Hi, I'm Lindsey and this is Debbie. Leaders and clinicians often ask how can _Mo [](https://bcove.video/3A6i7Py) -Hi, I'm Lindsey and this is Debbie. Why is _Modeling to Learn Red_ useful and how does the _Modeling to Learn_ Data User Interface provide new insights? The primary value of _MTL_ Red is its power to efficiently query the VA Corporate Data Warehouse directly, and we've come a long way over the years. Yes we have. When we first began, we were using an Excel workbook so that frontline teams could carefully review the clinic selections that define their _Modeling to Learn_ team data sets. Fast forward to the present and now teams have real time data available to them from within the VA domain from any computer with PIV badge access. Since the Data UI includes PHI, if you go to [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), you will be able to see the same data you have permissions to access in the electronic health record. But given that clinicians, managers, data leads, quality improvement staff, evaluators … well, basically everyone is so busy, no one has time to review another data dashboard unless it offers something of really high value that distinguishes it from other resources. When you request a _Modeling to Learn Red_ consultation, we work with your team or VA to explore care data that can address your locally identified priority. Clinics or scheduling grids are often changing. For that reason, we wanted a user interface where clinic selections can include active or inactive clinics over the last two years. That way, the team can filter the information to find the most appropriate clinics to include in their data set to gain new insights. And in Team Flow, clinic selections can be used to evaluate transitions between an episode of care in one team and the start of another episode of care in a higher or lower intensity care setting. _Modeling to Learn Red_ also enables zooming in to check on the care of an individual patient at the start of the clinical day or during case reviews at a team meeting. But with the _MTL_ Red Data User Interface, you can also zoom out to view teen care trends, bringing patient-level care coordination and trend-level process improvement decisions together. And that's where things start to get interesting. Based on the clinic selections, the next set of tabs to find local data values for common care problems, including care coordination, psychotherapy, medication management, team care, and team flow. Each tab features simple definitions of how data were estimated for the common care problem. Detailed definitions with technical specifications are also provided to allow valid comparison of these data to other VA dashboards. That said, a focus on data details could be frustrating and add limited value. _Modeling to Learn_ emphasizes understanding system problems in care flow over time. These care flow problems can be defined accurately with just five key time-based variables that drive care quality. How do five key variables drive care quality? Watch that video to find out. +Why is _Modeling to Learn Red_ useful and how does the _Modeling to Learn_ Data User Interface provide new insights? The primary value of _MTL_ Red is its power to efficiently query the VA Corporate Data Warehouse directly, and we've come a long way over the years. Yes we have. When we first began, we were using an Excel workbook so that frontline teams could carefully review the clinic selections that define their _Modeling to Learn_ team data sets. Fast forward to the present and now teams have real time data available to them from within the VA domain from any computer with PIV badge access. Since the Data UI includes PHI, if you go to [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), you will be able to see the same data you have permissions to access in the electronic health record. But given that clinicians, managers, data leads, quality improvement staff, evaluators … well, basically everyone is so busy, no one has time to review another data dashboard unless it offers something of really high value that distinguishes it from other resources. When you request a _Modeling to Learn Red_ consultation, we work with your team or VA to explore care data that can address your locally identified priority. Clinics or scheduling grids are often changing. For that reason, we wanted a user interface where clinic selections can include active or inactive clinics over the last two years. That way, the team can filter the information to find the most appropriate clinics to include in their data set to gain new insights. And in Team Flow, clinic selections can be used to evaluate transitions between an episode of care in one team and the start of another episode of care in a higher or lower intensity care setting. _Modeling to Learn Red_ also enables zooming in to check on the care of an individual patient at the start of the clinical day or during case reviews at a team meeting. But with the _MTL_ Red Data User Interface, you can also zoom out to view teen care trends, bringing patient-level care coordination and trend-level process improvement decisions together. And that's where things start to get interesting. Based on the clinic selections, the next set of tabs to find local data values for common care problems, including care coordination, psychotherapy, medication management, team care, and team flow. Each tab features simple definitions of how data were estimated for the common care problem. Detailed definitions with technical specifications are also provided to allow valid comparison of these data to other VA dashboards. That said, a focus on data details could be frustrating and add limited value. _Modeling to Learn_ emphasizes understanding system problems in care flow over time. These care flow problems can be defined accurately with just five key time-based variables that drive care quality. How do five key variables drive care quality? Watch that video to find out. ## Why is the _MTL Blue_ Simulation User Interface useful? @@ -65,7 +65,7 @@ Hi, I'm Lindsey and this is Debbie. Why is _Modeling to Learn Red_ useful and ho [](https://bcove.video/4fnG5Gb) -Hi, I'm Lindsey and this is Debbie. Why is _Modeling to Learn Blue_ useful? Well, why is it wisest to focus on the dynamics of care over time? The short answer is that clinical and improvement teams cannot adjust one part of the care equation without everything else changing. In _Modeling to Learn Blue_, we zoom out to see how care variables are locked in relationship with one another over time. The key variables that define either a poor quality or high quality episode of care must be understood together. Building from _MTL_ Red, you can export your local data set created in the _Modeling to Learn_ Data User Interface at [mtl.how/data](https://app.powerbigov.us/groups/me/apps/b9686a29-6857-46c9-bdf9-043ca2b29138?ctid=e95f1b23-abaf-45ee-821d-b7ab251ab3bf). Then, if you navigate to [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), you can find the _Modeling to Learn Blue_ Simulation User Interface, which is a dynamic and interactive way to understand why problems with care coordination, medication management, psychotherapy, team care, and team flow persist over time. The Simulation User Interface is a way to see how adjustments in one part of an episode of care explain subsequent impacts in the care system. For any of the common care problems, the simulation saves you time and energy by accounting for the local new patient start rate in patients per week and the local appointment supply and appointments per week. The simulation also keeps track of the local new patient wait time in weeks, time between visits and weeks, and the engagement duration over time, again in weeks. All are calculated for you automatically in the Data User Interface, but their interdependence is accounted for in the Simulation User Interface. The _Modeling to Learn Blue_ Simulation User Interface empowers teams to avoid ineffective strategies because you very quickly learn to develop new insights that would be inefficient, if not impossible, to figure out in your head or by hand. Learning from simulation is designed to help upgrade local decision-making. Teams develop new rules of thumb and insights in which the dependent dynamics among these variables that define care are all taken into account. With a _Modeling to Learn_ consult, we come alongside with partners mid stride in their daily clinical activities who may have limited insight into what is likely to happen over the near future if they keep making the same decisions every day. With _Modeling to Learn Blue_ simulation learning, sites and teams can safely see the impact of new decisions while building new capacities for systems thinking. Why is applied systems thinking more likely to help us avoid costly mistakes? Watch that video to find out. +Why is _Modeling to Learn Blue_ useful? Well, why is it wisest to focus on the dynamics of care over time? The short answer is that clinical and improvement teams cannot adjust one part of the care equation without everything else changing. In _Modeling to Learn Blue_, we zoom out to see how care variables are locked in relationship with one another over time. The key variables that define either a poor quality or high quality episode of care must be understood together. Building from _MTL_ Red, you can export your local data set created in the _Modeling to Learn_ Data User Interface at [mtl.how/data](https://app.powerbigov.us/groups/me/apps/b9686a29-6857-46c9-bdf9-043ca2b29138?ctid=e95f1b23-abaf-45ee-821d-b7ab251ab3bf). Then, if you navigate to [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), you can find the _Modeling to Learn Blue_ Simulation User Interface, which is a dynamic and interactive way to understand why problems with care coordination, medication management, psychotherapy, team care, and team flow persist over time. The Simulation User Interface is a way to see how adjustments in one part of an episode of care explain subsequent impacts in the care system. For any of the common care problems, the simulation saves you time and energy by accounting for the local new patient start rate in patients per week and the local appointment supply and appointments per week. The simulation also keeps track of the local new patient wait time in weeks, time between visits and weeks, and the engagement duration over time, again in weeks. All are calculated for you automatically in the Data User Interface, but their interdependence is accounted for in the Simulation User Interface. The _Modeling to Learn Blue_ Simulation User Interface empowers teams to avoid ineffective strategies because you very quickly learn to develop new insights that would be inefficient, if not impossible, to figure out in your head or by hand. Learning from simulation is designed to help upgrade local decision-making. Teams develop new rules of thumb and insights in which the dependent dynamics among these variables that define care are all taken into account. With a _Modeling to Learn_ consult, we come alongside with partners mid stride in their daily clinical activities who may have limited insight into what is likely to happen over the near future if they keep making the same decisions every day. With _Modeling to Learn Blue_ simulation learning, sites and teams can safely see the impact of new decisions while building new capacities for systems thinking. Why is applied systems thinking more likely to help us avoid costly mistakes? Watch that video to find out. ## Why is applied systems thinking more likely to help us avoid costly mistakes? @@ -73,7 +73,7 @@ Hi, I'm Lindsey and this is Debbie. Why is _Modeling to Learn Blue_ useful? Well [](https://bcove.video/3AelyUg) -Hi, I'm Lindsey and this is David. Why is applied systems thinking more likely to help us avoid costly mistakes? Unless we understand interdependent system effects over time, we're very likely to choose ineffective strategies for our clinical work. And when chronic impairment, relapse, suicide, and overdose are the critical concerns for our patients, we need system insights about how to best organize our clinical practice. Applied systems thinking includes understanding complexity, feedback, and system behaviors over time. By complexity, we mean understanding the relationship or the interaction among two or more variables, such as wait times and the improvement rate. By feedback, we mean moving beyond simple cause and effect events to consider how some effects are reinforced and get stronger over time and other effects are reduced over time. To explain feedback using David's example, it's not just that if you have long wait times then the patient improvement rate will go down because patients aren't starting care. It's also that as the improvement rate goes up, then at a system level wait times will go down as Veterans get their needs met and graduate from care. Our systems are perpetually causing themselves over time through feedback. Causal feedbacks explain trends over time, called system behaviors. Given that feedback can be a reinforcing effect or a balancing effect over time, _Modeling to Learn_ provides better insights about short- and long-term understanding of change over time. This can include decisions that may make things worse before they get better, or may make things better before they get worse. For example, longer wait times for new patients may reduce their improvement rate in the short term, but if this enables higher quality care engagement for them and existing patients over time, then more Veterans can get better and graduate from care and wait times will go down for the entire community of patients. It is through improving our systems thinking that _Modeling to Learn_ really helps us move beyond making costly mistakes. If you'd like to learn more about how _Modeling to Learn_ helps us improve the dynamics driving where Veterans get stuck or drop out of care, watch that video to find out. +Why is applied systems thinking more likely to help us avoid costly mistakes? Unless we understand interdependent system effects over time, we're very likely to choose ineffective strategies for our clinical work. And when chronic impairment, relapse, suicide, and overdose are the critical concerns for our patients, we need system insights about how to best organize our clinical practice. Applied systems thinking includes understanding complexity, feedback, and system behaviors over time. By complexity, we mean understanding the relationship or the interaction among two or more variables, such as wait times and the improvement rate. By feedback, we mean moving beyond simple cause and effect events to consider how some effects are reinforced and get stronger over time and other effects are reduced over time. To explain feedback using David's example, it's not just that if you have long wait times then the patient improvement rate will go down because patients aren't starting care. It's also that as the improvement rate goes up, then at a system level wait times will go down as Veterans get their needs met and graduate from care. Our systems are perpetually causing themselves over time through feedback. Causal feedbacks explain trends over time, called system behaviors. Given that feedback can be a reinforcing effect or a balancing effect over time, _Modeling to Learn_ provides better insights about short- and long-term understanding of change over time. This can include decisions that may make things worse before they get better, or may make things better before they get worse. For example, longer wait times for new patients may reduce their improvement rate in the short term, but if this enables higher quality care engagement for them and existing patients over time, then more Veterans can get better and graduate from care and wait times will go down for the entire community of patients. It is through improving our systems thinking that _Modeling to Learn_ really helps us move beyond making costly mistakes. If you'd like to learn more about how _Modeling to Learn_ helps us improve the dynamics driving where Veterans get stuck or drop out of care, watch that video to find out. ## Why is _Modeling to Learn_ able to provide new insights? @@ -81,7 +81,7 @@ Hi, I'm Lindsey and this is David. Why is applied systems thinking more likely t [](https://bcove.video/3XEKDAY) -Hi, I'm Lindsey and this is Debbie. Why is _Modeling to Learn_ able to provide new insights about where Veterans get stuck or drop out of care? Because the _Modeling to Learn_ Simulation User Interface depicts the states of care as stocks based on the flows in and out of that state. Take for example, patients waiting to start. We depict patients waiting to start as a stock, a rectangle with an accumulated volume indicator. The number of Veterans waiting to start in a typical week is determined by the difference between the number of Veterans that flow into that state and the number of Veterans who flow out of that state each week. The rate dials depict the flows in and out of stocks, and the primary rates in _Modeling to Learn_ are patients per week, appointments per week, and episodes of care per week. The initial values for any team or site depicted in the Simulation User Interface are based on the last two years of data exported from the Data User Interface. The important idea is that in any dynamic system, things get to be the way they are over time. Things either stay the same or improve decision by decision over time. For example, in a clinic or site, the number of Veterans engaged in a given state of care, such as medication management or psychotherapy, reflects decisions that lead to accumulation in that state over time. The dynamic stock and flow diagram provides a way of understanding where Veterans get stuck. As clinicians know, often there is more than one way out of a state of care. With psychotherapy, you may flow from the first session to the second session, but you may drop out at any point in that flow. Why does _Modeling to Learn_ emphasize flows through care over time? Watch that video to find out. +Why is _Modeling to Learn_ able to provide new insights about where Veterans get stuck or drop out of care? Because the _Modeling to Learn_ Simulation User Interface depicts the states of care as stocks based on the flows in and out of that state. Take for example, patients waiting to start. We depict patients waiting to start as a stock, a rectangle with an accumulated volume indicator. The number of Veterans waiting to start in a typical week is determined by the difference between the number of Veterans that flow into that state and the number of Veterans who flow out of that state each week. The rate dials depict the flows in and out of stocks, and the primary rates in _Modeling to Learn_ are patients per week, appointments per week, and episodes of care per week. The initial values for any team or site depicted in the Simulation User Interface are based on the last two years of data exported from the Data User Interface. The important idea is that in any dynamic system, things get to be the way they are over time. Things either stay the same or improve decision by decision over time. For example, in a clinic or site, the number of Veterans engaged in a given state of care, such as medication management or psychotherapy, reflects decisions that lead to accumulation in that state over time. The dynamic stock and flow diagram provides a way of understanding where Veterans get stuck. As clinicians know, often there is more than one way out of a state of care. With psychotherapy, you may flow from the first session to the second session, but you may drop out at any point in that flow. Why does _Modeling to Learn_ emphasize flows through care over time? Watch that video to find out. ## Why does _Modeling to Learn_ emphasize flows through care? @@ -89,4 +89,4 @@ Hi, I'm Lindsey and this is Debbie. Why is _Modeling to Learn_ able to provide n [](https://bcove.video/3ME6cvm) -Hi, I'm Lindsey, and this is David. Why does _Modeling to Learn_ emphasize flows through care over time? Our goal is to ensure Veterans flow through our addiction and mental health care services to recovery. A basic idea is that care occurs over time. Flows are powerful because they express a rate of change, which means they give us key information about where we will be in the future. Imagine your next trip in the car. We need the miles per hour to know when we will arrive at our destination. But knowing the number of miles to our destination is not enough. We must also know how fast we're traveling to know when we will get there. In other words, miles per hour. The same is true in our clinical care. It's not enough to know how many new medication management appointments we have on the books today. We need to know how quickly those appointments were added to the books or the booking rate, in appointments per week, as well as how quickly those appointments are completed or the completing rate, in appointments per week. When the inflow is greater than the outflow, the level of the appointment or patient stock will rise. When the outflow is greater than the inflow, then the level of the appointment or patient stock will fall. Opening the _Modeling to Learn_ Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), you can see where all the Veterans are accumulating in stocks. You can understand the inflows and outflows for each stock and which accumulations are likely to get better or worse over time. Many clinical teams talk about feeling inundated by the addiction and mental health needs of the communities that rely on them. They can feel like they're drowning, providing the volume of care needed, especially when short staffed. We understand this kind of flooding from the natural world. To stay safe, it's critical to know how fast the waters are rising and where. We understand this by understanding flows. You may relate to this feeling of being overwhelmed and want to know more about how _Modeling to Learn_ can help. For example, how does _Modeling to Learn_ help improve medication management? Watch that video to find out. +Why does _Modeling to Learn_ emphasize flows through care over time? Our goal is to ensure Veterans flow through our addiction and mental health care services to recovery. A basic idea is that care occurs over time. Flows are powerful because they express a rate of change, which means they give us key information about where we will be in the future. Imagine your next trip in the car. We need the miles per hour to know when we will arrive at our destination. But knowing the number of miles to our destination is not enough. We must also know how fast we're traveling to know when we will get there. In other words, miles per hour. The same is true in our clinical care. It's not enough to know how many new medication management appointments we have on the books today. We need to know how quickly those appointments were added to the books or the booking rate, in appointments per week, as well as how quickly those appointments are completed or the completing rate, in appointments per week. When the inflow is greater than the outflow, the level of the appointment or patient stock will rise. When the outflow is greater than the inflow, then the level of the appointment or patient stock will fall. Opening the _Modeling to Learn_ Simulation User Interface at [mtl.how/sim](https://forio.com/app/va/va-psd-sim/login.html), you can see where all the Veterans are accumulating in stocks. You can understand the inflows and outflows for each stock and which accumulations are likely to get better or worse over time. Many clinical teams talk about feeling inundated by the addiction and mental health needs of the communities that rely on them. They can feel like they're drowning, providing the volume of care needed, especially when short staffed. We understand this kind of flooding from the natural world. To stay safe, it's critical to know how fast the waters are rising and where. We understand this by understanding flows. You may relate to this feeling of being overwhelmed and want to know more about how _Modeling to Learn_ can help. For example, how does _Modeling to Learn_ help improve medication management? Watch that video to find out.