diff --git a/index.Rmd b/index.Rmd index d0e050da..f0a6eaae 100644 --- a/index.Rmd +++ b/index.Rmd @@ -1,11 +1,11 @@ --- -title: "_Modeling to Learn_ 3.7 Consult Manual" +title: "_Modeling to Learn 3.7 Consult Manual_" author: "Team PSD" date: "`r Sys.Date()`" link-citations: yes preview: yes site: bookdown::bookdown_site -description: This is the MTL one-stop shop. +description: This is the _MTL_ one-stop shop. --- # _Why_ use _Modeling to Learn_ @@ -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. +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. ## What gets in the way of meeting patients' needs? @@ -49,23 +49,23 @@ 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. +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. -## Why is the _MTL_ Red Data User Interface useful? +## Why is the _MTL Red_ Data User Interface useful? [Watch the video here.](https://bcove.video/3A6i7Py) [](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. +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 the _MTL_ Blue Simulation User Interface useful? +## Why is the _MTL Blue_ Simulation User Interface useful? [Watch the video here.](https://bcove.video/4fnG5Gb) [](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. +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 applied systems thinking more likely to help us avoid costly mistakes?