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Delving into the details
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
For my research project, we are looking to develop a tool that would use data from within 24 hours of a patient’s admission to the hospital to predict whether they will require post-acute care placement after discharge. While I have often been summarizing my project with this broad one-liner, in the last two weeks I have been delving more into the details of what exactly we mean by “data from within 24 hours of a patient’s admission.”
We are going through each of the variables systematically to take into account prior literature on how they were treated in other studies, as well as the practical limitations imposed by the data-gathering within our own system to choose how these values will be selected for each admission. My mentor Dr. Eduard Vasilevskis is helping me with making these decisions, based on the prototype model that was the inspiration for this project. Once we have identified all of the details of each variable we want to track, Dr. Jesse Ehrenfeld will be facilitating our use of the database.
Certainly this project has helped illuminate not only research-specific hurdles, but also underscores the fundamental difficulty of clinical decision-making in the first 24 hours of a patient’s admission. With data changing rapidly and sometimes incomplete data, clinicians need to quickly make care decisions that can impact a lot more than the patient’s post-discharge destination.
We anticipate that once we’ve made these choices, there will be further choices to make about how to treat these variables in the analysis. We hope to have the assistance of an experienced statistician to help guide us in making those decisions.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
For my research project, we are looking to develop a tool that would use data from within 24 hours of a patient’s admission to the hospital to predict whether they will require post-acute care placement after discharge. While I have often been summarizing my project with this broad one-liner, in the last two weeks I have been delving more into the details of what exactly we mean by “data from within 24 hours of a patient’s admission.”
We are going through each of the variables systematically to take into account prior literature on how they were treated in other studies, as well as the practical limitations imposed by the data-gathering within our own system to choose how these values will be selected for each admission. My mentor Dr. Eduard Vasilevskis is helping me with making these decisions, based on the prototype model that was the inspiration for this project. Once we have identified all of the details of each variable we want to track, Dr. Jesse Ehrenfeld will be facilitating our use of the database.
Certainly this project has helped illuminate not only research-specific hurdles, but also underscores the fundamental difficulty of clinical decision-making in the first 24 hours of a patient’s admission. With data changing rapidly and sometimes incomplete data, clinicians need to quickly make care decisions that can impact a lot more than the patient’s post-discharge destination.
We anticipate that once we’ve made these choices, there will be further choices to make about how to treat these variables in the analysis. We hope to have the assistance of an experienced statistician to help guide us in making those decisions.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
For my research project, we are looking to develop a tool that would use data from within 24 hours of a patient’s admission to the hospital to predict whether they will require post-acute care placement after discharge. While I have often been summarizing my project with this broad one-liner, in the last two weeks I have been delving more into the details of what exactly we mean by “data from within 24 hours of a patient’s admission.”
We are going through each of the variables systematically to take into account prior literature on how they were treated in other studies, as well as the practical limitations imposed by the data-gathering within our own system to choose how these values will be selected for each admission. My mentor Dr. Eduard Vasilevskis is helping me with making these decisions, based on the prototype model that was the inspiration for this project. Once we have identified all of the details of each variable we want to track, Dr. Jesse Ehrenfeld will be facilitating our use of the database.
Certainly this project has helped illuminate not only research-specific hurdles, but also underscores the fundamental difficulty of clinical decision-making in the first 24 hours of a patient’s admission. With data changing rapidly and sometimes incomplete data, clinicians need to quickly make care decisions that can impact a lot more than the patient’s post-discharge destination.
We anticipate that once we’ve made these choices, there will be further choices to make about how to treat these variables in the analysis. We hope to have the assistance of an experienced statistician to help guide us in making those decisions.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Identifying the right database
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Vanderbilt University Medical Center will be converting to the most common electronic medical record (EMR) systems used today: Epic. Until that time, Vanderbilt used a homegrown system to keep track of patient data. The “system” was actual comprised of a few separate programs that integrated data, depending on the functions being accessed and who was accessing them.
For many research projects across the hospital, including my own, we are going to be limiting ourselves to data from the time period when our homegrown EMR was functioning. This is thinking a few steps ahead, but it would be interesting to see if our model, once validated, performed similarly in a new EMR environment. Unfortunately, this is thinking a few too many steps ahead for me, as I will have graduated (hopefully) by the time the new EMR is up and running reliably enough for EMR-based research like this project.
The first step in our study was identifying the right database to use, and now the next step will be extracting the data we need. Moving forward, I am continuing to work with my mentors, Dr. Eduard Vasilevskis and Dr. Jesse Ehrenfeld closely. We resubmitted our IRB application now that we have identified how we can pull the data we need, and we identified a few specialized patient populations for whom a separate scoring tool might be useful (e.g., stroke patients). I am looking forward to learning the particulars how our dataset will be built. The potential for finding the answers to many patient-care questions probably lies in the EMR data we already have, but you need to know how to get them to study them.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Vanderbilt University Medical Center will be converting to the most common electronic medical record (EMR) systems used today: Epic. Until that time, Vanderbilt used a homegrown system to keep track of patient data. The “system” was actual comprised of a few separate programs that integrated data, depending on the functions being accessed and who was accessing them.
For many research projects across the hospital, including my own, we are going to be limiting ourselves to data from the time period when our homegrown EMR was functioning. This is thinking a few steps ahead, but it would be interesting to see if our model, once validated, performed similarly in a new EMR environment. Unfortunately, this is thinking a few too many steps ahead for me, as I will have graduated (hopefully) by the time the new EMR is up and running reliably enough for EMR-based research like this project.
The first step in our study was identifying the right database to use, and now the next step will be extracting the data we need. Moving forward, I am continuing to work with my mentors, Dr. Eduard Vasilevskis and Dr. Jesse Ehrenfeld closely. We resubmitted our IRB application now that we have identified how we can pull the data we need, and we identified a few specialized patient populations for whom a separate scoring tool might be useful (e.g., stroke patients). I am looking forward to learning the particulars how our dataset will be built. The potential for finding the answers to many patient-care questions probably lies in the EMR data we already have, but you need to know how to get them to study them.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Vanderbilt University Medical Center will be converting to the most common electronic medical record (EMR) systems used today: Epic. Until that time, Vanderbilt used a homegrown system to keep track of patient data. The “system” was actual comprised of a few separate programs that integrated data, depending on the functions being accessed and who was accessing them.
For many research projects across the hospital, including my own, we are going to be limiting ourselves to data from the time period when our homegrown EMR was functioning. This is thinking a few steps ahead, but it would be interesting to see if our model, once validated, performed similarly in a new EMR environment. Unfortunately, this is thinking a few too many steps ahead for me, as I will have graduated (hopefully) by the time the new EMR is up and running reliably enough for EMR-based research like this project.
The first step in our study was identifying the right database to use, and now the next step will be extracting the data we need. Moving forward, I am continuing to work with my mentors, Dr. Eduard Vasilevskis and Dr. Jesse Ehrenfeld closely. We resubmitted our IRB application now that we have identified how we can pull the data we need, and we identified a few specialized patient populations for whom a separate scoring tool might be useful (e.g., stroke patients). I am looking forward to learning the particulars how our dataset will be built. The potential for finding the answers to many patient-care questions probably lies in the EMR data we already have, but you need to know how to get them to study them.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Improving our approach to discharge planning
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Since finishing up the initial planning phase of our project, my mentors and I have continued with even more planning as we head into the fall. Coming up with a good plan is the first step in making sure everything goes smoothly later on in a project. The same goes for coming up with a well-thought-out discharge plan when sending a patient to the next level of care.
Getting a patient out of the hospital and into their next destination – whether it’s a long-term acute care facility, skilled nursing facility, inpatient rehabilitation, home, or elsewhere – can approach the same level of complexity as the medical care received in the hospital. Getting a patient to any post-acute care facility can be time-consuming because it involves the coordination of two health care entities and their employees.
Discharge planning for post-acute care placement can take many forms and involve many resources. Some studies have shown that certain discharge planning interventions can reduce costs and 30-day readmissions. Many physicians think that discharge planning would help improve outcomes in most groups, but so far the aggregate data do not show that discharge planning account for much improvement in any of these outcomes. Targeting certain groups of hospitalized patients, however, could improve the effect that discharge planning has on these outcomes because more of these scarce resources might be devoted to the right patients earlier in their hospital stays.
A post-acute care placement prediction tool would help hospitalists determine how to allocate their discharge planning resources, including social work, case management, pharmacies, physical therapy, and occupational therapy. While we are working towards integrating this kind of tool in our own institution’s practice, we are also hopeful that we can create a generalizable tool that assists in helping care teams decide how to link patients to the right resources elsewhere.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Since finishing up the initial planning phase of our project, my mentors and I have continued with even more planning as we head into the fall. Coming up with a good plan is the first step in making sure everything goes smoothly later on in a project. The same goes for coming up with a well-thought-out discharge plan when sending a patient to the next level of care.
Getting a patient out of the hospital and into their next destination – whether it’s a long-term acute care facility, skilled nursing facility, inpatient rehabilitation, home, or elsewhere – can approach the same level of complexity as the medical care received in the hospital. Getting a patient to any post-acute care facility can be time-consuming because it involves the coordination of two health care entities and their employees.
Discharge planning for post-acute care placement can take many forms and involve many resources. Some studies have shown that certain discharge planning interventions can reduce costs and 30-day readmissions. Many physicians think that discharge planning would help improve outcomes in most groups, but so far the aggregate data do not show that discharge planning account for much improvement in any of these outcomes. Targeting certain groups of hospitalized patients, however, could improve the effect that discharge planning has on these outcomes because more of these scarce resources might be devoted to the right patients earlier in their hospital stays.
A post-acute care placement prediction tool would help hospitalists determine how to allocate their discharge planning resources, including social work, case management, pharmacies, physical therapy, and occupational therapy. While we are working towards integrating this kind of tool in our own institution’s practice, we are also hopeful that we can create a generalizable tool that assists in helping care teams decide how to link patients to the right resources elsewhere.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
Since finishing up the initial planning phase of our project, my mentors and I have continued with even more planning as we head into the fall. Coming up with a good plan is the first step in making sure everything goes smoothly later on in a project. The same goes for coming up with a well-thought-out discharge plan when sending a patient to the next level of care.
Getting a patient out of the hospital and into their next destination – whether it’s a long-term acute care facility, skilled nursing facility, inpatient rehabilitation, home, or elsewhere – can approach the same level of complexity as the medical care received in the hospital. Getting a patient to any post-acute care facility can be time-consuming because it involves the coordination of two health care entities and their employees.
Discharge planning for post-acute care placement can take many forms and involve many resources. Some studies have shown that certain discharge planning interventions can reduce costs and 30-day readmissions. Many physicians think that discharge planning would help improve outcomes in most groups, but so far the aggregate data do not show that discharge planning account for much improvement in any of these outcomes. Targeting certain groups of hospitalized patients, however, could improve the effect that discharge planning has on these outcomes because more of these scarce resources might be devoted to the right patients earlier in their hospital stays.
A post-acute care placement prediction tool would help hospitalists determine how to allocate their discharge planning resources, including social work, case management, pharmacies, physical therapy, and occupational therapy. While we are working towards integrating this kind of tool in our own institution’s practice, we are also hopeful that we can create a generalizable tool that assists in helping care teams decide how to link patients to the right resources elsewhere.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Using EHR data to predict post-acute care placement
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
When patients are admitted to the hospital, the focus for the first 24 hours is on the work-up: What do the data point values tell you about how sick this patient is, and what will they need to get better? While the goal for this information is to develop the appropriate treatment and management for the patient’s acute problem, it could be leveraged to help with other parts of the patient’s hospital stay as well. In particular, it could help avoid unnecessarily long stays in the hospital caused by patients’ waiting for a bed at a lower level of care.
My research mentor, Eduard Vasilevskis, MD, created a rough scoring system for predicting post-acute care placement using admission data, just based on his clinical gestalt. Even at this preliminary stage, the model has already functioned well without much refinement; however a validated, statistically robust model could potentially transform the way that we initiate the discharge planning process. Jesse Ehrenfeld, MD has helped us develop it further by giving us access to a curated database of deidentified EHR data, which contains all of the variables we would like to assess.
The strengths of this potential model are manifold. First, it relies on data collected early in the patient’s hospital course. Second, it relies on routinely collected information (both at our home institution and elsewhere, making it potentially generalizable). And third, it relies on objective patient data rather than requiring providers use their impressions of the patients’ functional status to guess whether they will require discharge planning services. Although such prediction models have been generated before, this model would be among the first to incorporate information routinely collected by nursing staff, such as the Braden Scale, instead of relying on additional instruments or surveys. In addition to predicting placement destination, the model may also be predictive of in-hospital mortality.
With this information, we hope to give hospital teams an additional tool to help mobilize resources toward patients who need the most attention – not just while they’re in the hospital, but also on their way out.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
When patients are admitted to the hospital, the focus for the first 24 hours is on the work-up: What do the data point values tell you about how sick this patient is, and what will they need to get better? While the goal for this information is to develop the appropriate treatment and management for the patient’s acute problem, it could be leveraged to help with other parts of the patient’s hospital stay as well. In particular, it could help avoid unnecessarily long stays in the hospital caused by patients’ waiting for a bed at a lower level of care.
My research mentor, Eduard Vasilevskis, MD, created a rough scoring system for predicting post-acute care placement using admission data, just based on his clinical gestalt. Even at this preliminary stage, the model has already functioned well without much refinement; however a validated, statistically robust model could potentially transform the way that we initiate the discharge planning process. Jesse Ehrenfeld, MD has helped us develop it further by giving us access to a curated database of deidentified EHR data, which contains all of the variables we would like to assess.
The strengths of this potential model are manifold. First, it relies on data collected early in the patient’s hospital course. Second, it relies on routinely collected information (both at our home institution and elsewhere, making it potentially generalizable). And third, it relies on objective patient data rather than requiring providers use their impressions of the patients’ functional status to guess whether they will require discharge planning services. Although such prediction models have been generated before, this model would be among the first to incorporate information routinely collected by nursing staff, such as the Braden Scale, instead of relying on additional instruments or surveys. In addition to predicting placement destination, the model may also be predictive of in-hospital mortality.
With this information, we hope to give hospital teams an additional tool to help mobilize resources toward patients who need the most attention – not just while they’re in the hospital, but also on their way out.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.
Editor’s Note: The Society of Hospital Medicine’s (SHM’s) Physician in Training Committee launched a scholarship program in 2015 for medical students to help transform health care and revolutionize patient care. The program has been expanded for the 2017-2018 year, offering two options for students to receive funding and engage in scholarly work during their first, second, and third years of medical school. As a part of the longitudinal (18-month) program, recipients are required to write about their experience on a monthly basis.
When patients are admitted to the hospital, the focus for the first 24 hours is on the work-up: What do the data point values tell you about how sick this patient is, and what will they need to get better? While the goal for this information is to develop the appropriate treatment and management for the patient’s acute problem, it could be leveraged to help with other parts of the patient’s hospital stay as well. In particular, it could help avoid unnecessarily long stays in the hospital caused by patients’ waiting for a bed at a lower level of care.
My research mentor, Eduard Vasilevskis, MD, created a rough scoring system for predicting post-acute care placement using admission data, just based on his clinical gestalt. Even at this preliminary stage, the model has already functioned well without much refinement; however a validated, statistically robust model could potentially transform the way that we initiate the discharge planning process. Jesse Ehrenfeld, MD has helped us develop it further by giving us access to a curated database of deidentified EHR data, which contains all of the variables we would like to assess.
The strengths of this potential model are manifold. First, it relies on data collected early in the patient’s hospital course. Second, it relies on routinely collected information (both at our home institution and elsewhere, making it potentially generalizable). And third, it relies on objective patient data rather than requiring providers use their impressions of the patients’ functional status to guess whether they will require discharge planning services. Although such prediction models have been generated before, this model would be among the first to incorporate information routinely collected by nursing staff, such as the Braden Scale, instead of relying on additional instruments or surveys. In addition to predicting placement destination, the model may also be predictive of in-hospital mortality.
With this information, we hope to give hospital teams an additional tool to help mobilize resources toward patients who need the most attention – not just while they’re in the hospital, but also on their way out.
Monisha Bhatia, a native of Nashville, Tenn., is a fourth-year medical student at Vanderbilt University in Nashville. She is hoping to pursue either a residency in internal medicine or a combined internal medicine/emergency medicine program. Prior to medical school, she completed a JD/MPH program at Boston University, and she hopes to use her legal training in working with regulatory authorities to improve access to health care for all Americans.