At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM)...
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At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM) conference in National Harbor, Maryland. Our team is presenting work on five recent projects. Data is central to our mission, and as the CSO of Delfina, I love thinking about how different data sources can uniquely be used to drive improvements in maternal healthcare. Each data source has its own pros and cons—some have breadth, and some have depth—and can be utilized in different ways. Let’s take a peek behind the data curtain at all of the data sources that have been central to each research project.
We’ll start at the macro-level.
We utilized the National Vital Surveillance Statistics (NVSS) Birth Data to develop a prediction model for small-for-gestational age infants and investigate trends in co-diagnosis with hypertensive disorders of pregnancy and gestational diabetes. Our team loves this dataset because of the breadth. It contains information on all births in the United States—that’s over 3 million births per year! However, the NVSS data lacks depth due to the challenges in collecting data on every single birth. So, although NVSS contains pregnancy-level information on a variety of key health factors and outcomes, it does not contain the richness of information typically found in our pregnancy care platform or an electronic health record (e.g. lab results, social history, vitals).
However, the lack of depth is not necessarily a negative. As we will showcase at SMFM 2024, NVSS data is useful for identifying population-level trends and associations, which can then guide the development of specific interventions or prediction models for application to specific patient populations.
Now, let’s zoom in to Houston, Texas.
We leveraged our Delfina Care platform data to assess differences in patient engagement by type of blood pressure cuff. At a clinic in Houston, we introduced connected blood pressure devices that automatically sync to the Delfina Care app as part of a quality improvement initiative. It was important to our team to understand if the introduction of these devices significantly improve patient engagement compared to their unconnected counterparts that require manual entry of measures. As a byproduct of implementation, we collect patient characteristics that may impact engagement with remote blood pressure monitoring, such as patient age, number of prior births, or preferred language, which we can account for in our analyses to ensure the accuracy of our findings. This project highlights an important aspect of Delfina Care — using our care platform to rigorously evaluate the impact of technological solutions on patient care experiences.
And, let’s end at the micro-level.
Continuous Glucose Monitoring (CGM) has been shown to improve maternal and neonatal outcomes among pregnancies complicated by diabetes. CGM devices measure glucose every 1-5 minutes – that’s over 400,000 measurements over the course of one pregnancy! Unlike the NVSS data, the CGM data has a lot of depth (frequency of measures), but may lack breadth (number of patients) due to the relatively low incidence of pregestational diabetes and the fact CGM is not widely used among pregnant patients—though CGM use was approved by the FDA in December 2022. Once again, the lack of breadth is not necessarily a negative. As we will showcase at SMFM 2024, CGM data from hundreds of patients can still be utilized to identify discrete glucose profiles and their associations with adverse maternal and neonatal outcomes.
Please reach out to research@delfina.com if you would like to meet with one of our team members at SMFM 2024. You can also find us at the following sessions below:
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM)...
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM) conference in National Harbor, Maryland. Our team is presenting work on five recent projects. Data is central to our mission, and as the CSO of Delfina, I love thinking about how different data sources can uniquely be used to drive improvements in maternal healthcare. Each data source has its own pros and cons—some have breadth, and some have depth—and can be utilized in different ways. Let’s take a peek behind the data curtain at all of the data sources that have been central to each research project.
We’ll start at the macro-level.
We utilized the National Vital Surveillance Statistics (NVSS) Birth Data to develop a prediction model for small-for-gestational age infants and investigate trends in co-diagnosis with hypertensive disorders of pregnancy and gestational diabetes. Our team loves this dataset because of the breadth. It contains information on all births in the United States—that’s over 3 million births per year! However, the NVSS data lacks depth due to the challenges in collecting data on every single birth. So, although NVSS contains pregnancy-level information on a variety of key health factors and outcomes, it does not contain the richness of information typically found in our pregnancy care platform or an electronic health record (e.g. lab results, social history, vitals).
However, the lack of depth is not necessarily a negative. As we will showcase at SMFM 2024, NVSS data is useful for identifying population-level trends and associations, which can then guide the development of specific interventions or prediction models for application to specific patient populations.
Now, let’s zoom in to Houston, Texas.
We leveraged our Delfina Care platform data to assess differences in patient engagement by type of blood pressure cuff. At a clinic in Houston, we introduced connected blood pressure devices that automatically sync to the Delfina Care app as part of a quality improvement initiative. It was important to our team to understand if the introduction of these devices significantly improve patient engagement compared to their unconnected counterparts that require manual entry of measures. As a byproduct of implementation, we collect patient characteristics that may impact engagement with remote blood pressure monitoring, such as patient age, number of prior births, or preferred language, which we can account for in our analyses to ensure the accuracy of our findings. This project highlights an important aspect of Delfina Care — using our care platform to rigorously evaluate the impact of technological solutions on patient care experiences.
And, let’s end at the micro-level.
Continuous Glucose Monitoring (CGM) has been shown to improve maternal and neonatal outcomes among pregnancies complicated by diabetes. CGM devices measure glucose every 1-5 minutes – that’s over 400,000 measurements over the course of one pregnancy! Unlike the NVSS data, the CGM data has a lot of depth (frequency of measures), but may lack breadth (number of patients) due to the relatively low incidence of pregestational diabetes and the fact CGM is not widely used among pregnant patients—though CGM use was approved by the FDA in December 2022. Once again, the lack of breadth is not necessarily a negative. As we will showcase at SMFM 2024, CGM data from hundreds of patients can still be utilized to identify discrete glucose profiles and their associations with adverse maternal and neonatal outcomes.
Please reach out to research@delfina.com if you would like to meet with one of our team members at SMFM 2024. You can also find us at the following sessions below:
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM)...
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM) conference in National Harbor, Maryland. Our team is presenting work on five recent projects. Data is central to our mission, and as the CSO of Delfina, I love thinking about how different data sources can uniquely be used to drive improvements in maternal healthcare. Each data source has its own pros and cons—some have breadth, and some have depth—and can be utilized in different ways. Let’s take a peek behind the data curtain at all of the data sources that have been central to each research project.
We’ll start at the macro-level.
We utilized the National Vital Surveillance Statistics (NVSS) Birth Data to develop a prediction model for small-for-gestational age infants and investigate trends in co-diagnosis with hypertensive disorders of pregnancy and gestational diabetes. Our team loves this dataset because of the breadth. It contains information on all births in the United States—that’s over 3 million births per year! However, the NVSS data lacks depth due to the challenges in collecting data on every single birth. So, although NVSS contains pregnancy-level information on a variety of key health factors and outcomes, it does not contain the richness of information typically found in our pregnancy care platform or an electronic health record (e.g. lab results, social history, vitals).
However, the lack of depth is not necessarily a negative. As we will showcase at SMFM 2024, NVSS data is useful for identifying population-level trends and associations, which can then guide the development of specific interventions or prediction models for application to specific patient populations.
Now, let’s zoom in to Houston, Texas.
We leveraged our Delfina Care platform data to assess differences in patient engagement by type of blood pressure cuff. At a clinic in Houston, we introduced connected blood pressure devices that automatically sync to the Delfina Care app as part of a quality improvement initiative. It was important to our team to understand if the introduction of these devices significantly improve patient engagement compared to their unconnected counterparts that require manual entry of measures. As a byproduct of implementation, we collect patient characteristics that may impact engagement with remote blood pressure monitoring, such as patient age, number of prior births, or preferred language, which we can account for in our analyses to ensure the accuracy of our findings. This project highlights an important aspect of Delfina Care — using our care platform to rigorously evaluate the impact of technological solutions on patient care experiences.
And, let’s end at the micro-level.
Continuous Glucose Monitoring (CGM) has been shown to improve maternal and neonatal outcomes among pregnancies complicated by diabetes. CGM devices measure glucose every 1-5 minutes – that’s over 400,000 measurements over the course of one pregnancy! Unlike the NVSS data, the CGM data has a lot of depth (frequency of measures), but may lack breadth (number of patients) due to the relatively low incidence of pregestational diabetes and the fact CGM is not widely used among pregnant patients—though CGM use was approved by the FDA in December 2022. Once again, the lack of breadth is not necessarily a negative. As we will showcase at SMFM 2024, CGM data from hundreds of patients can still be utilized to identify discrete glucose profiles and their associations with adverse maternal and neonatal outcomes.
Please reach out to research@delfina.com if you would like to meet with one of our team members at SMFM 2024. You can also find us at the following sessions below:
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM)...
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM) conference in National Harbor, Maryland. Our team is presenting work on five recent projects. Data is central to our mission, and as the CSO of Delfina, I love thinking about how different data sources can uniquely be used to drive improvements in maternal healthcare. Each data source has its own pros and cons—some have breadth, and some have depth—and can be utilized in different ways. Let’s take a peek behind the data curtain at all of the data sources that have been central to each research project.
We’ll start at the macro-level.
We utilized the National Vital Surveillance Statistics (NVSS) Birth Data to develop a prediction model for small-for-gestational age infants and investigate trends in co-diagnosis with hypertensive disorders of pregnancy and gestational diabetes. Our team loves this dataset because of the breadth. It contains information on all births in the United States—that’s over 3 million births per year! However, the NVSS data lacks depth due to the challenges in collecting data on every single birth. So, although NVSS contains pregnancy-level information on a variety of key health factors and outcomes, it does not contain the richness of information typically found in our pregnancy care platform or an electronic health record (e.g. lab results, social history, vitals).
However, the lack of depth is not necessarily a negative. As we will showcase at SMFM 2024, NVSS data is useful for identifying population-level trends and associations, which can then guide the development of specific interventions or prediction models for application to specific patient populations.
Now, let’s zoom in to Houston, Texas.
We leveraged our Delfina Care platform data to assess differences in patient engagement by type of blood pressure cuff. At a clinic in Houston, we introduced connected blood pressure devices that automatically sync to the Delfina Care app as part of a quality improvement initiative. It was important to our team to understand if the introduction of these devices significantly improve patient engagement compared to their unconnected counterparts that require manual entry of measures. As a byproduct of implementation, we collect patient characteristics that may impact engagement with remote blood pressure monitoring, such as patient age, number of prior births, or preferred language, which we can account for in our analyses to ensure the accuracy of our findings. This project highlights an important aspect of Delfina Care — using our care platform to rigorously evaluate the impact of technological solutions on patient care experiences.
And, let’s end at the micro-level.
Continuous Glucose Monitoring (CGM) has been shown to improve maternal and neonatal outcomes among pregnancies complicated by diabetes. CGM devices measure glucose every 1-5 minutes – that’s over 400,000 measurements over the course of one pregnancy! Unlike the NVSS data, the CGM data has a lot of depth (frequency of measures), but may lack breadth (number of patients) due to the relatively low incidence of pregestational diabetes and the fact CGM is not widely used among pregnant patients—though CGM use was approved by the FDA in December 2022. Once again, the lack of breadth is not necessarily a negative. As we will showcase at SMFM 2024, CGM data from hundreds of patients can still be utilized to identify discrete glucose profiles and their associations with adverse maternal and neonatal outcomes.
Please reach out to research@delfina.com if you would like to meet with one of our team members at SMFM 2024. You can also find us at the following sessions below:
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM)...
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM) conference in National Harbor, Maryland. Our team is presenting work on five recent projects. Data is central to our mission, and as the CSO of Delfina, I love thinking about how different data sources can uniquely be used to drive improvements in maternal healthcare. Each data source has its own pros and cons—some have breadth, and some have depth—and can be utilized in different ways. Let’s take a peek behind the data curtain at all of the data sources that have been central to each research project.
We’ll start at the macro-level.
We utilized the National Vital Surveillance Statistics (NVSS) Birth Data to develop a prediction model for small-for-gestational age infants and investigate trends in co-diagnosis with hypertensive disorders of pregnancy and gestational diabetes. Our team loves this dataset because of the breadth. It contains information on all births in the United States—that’s over 3 million births per year! However, the NVSS data lacks depth due to the challenges in collecting data on every single birth. So, although NVSS contains pregnancy-level information on a variety of key health factors and outcomes, it does not contain the richness of information typically found in our pregnancy care platform or an electronic health record (e.g. lab results, social history, vitals).
However, the lack of depth is not necessarily a negative. As we will showcase at SMFM 2024, NVSS data is useful for identifying population-level trends and associations, which can then guide the development of specific interventions or prediction models for application to specific patient populations.
Now, let’s zoom in to Houston, Texas.
We leveraged our Delfina Care platform data to assess differences in patient engagement by type of blood pressure cuff. At a clinic in Houston, we introduced connected blood pressure devices that automatically sync to the Delfina Care app as part of a quality improvement initiative. It was important to our team to understand if the introduction of these devices significantly improve patient engagement compared to their unconnected counterparts that require manual entry of measures. As a byproduct of implementation, we collect patient characteristics that may impact engagement with remote blood pressure monitoring, such as patient age, number of prior births, or preferred language, which we can account for in our analyses to ensure the accuracy of our findings. This project highlights an important aspect of Delfina Care — using our care platform to rigorously evaluate the impact of technological solutions on patient care experiences.
And, let’s end at the micro-level.
Continuous Glucose Monitoring (CGM) has been shown to improve maternal and neonatal outcomes among pregnancies complicated by diabetes. CGM devices measure glucose every 1-5 minutes – that’s over 400,000 measurements over the course of one pregnancy! Unlike the NVSS data, the CGM data has a lot of depth (frequency of measures), but may lack breadth (number of patients) due to the relatively low incidence of pregestational diabetes and the fact CGM is not widely used among pregnant patients—though CGM use was approved by the FDA in December 2022. Once again, the lack of breadth is not necessarily a negative. As we will showcase at SMFM 2024, CGM data from hundreds of patients can still be utilized to identify discrete glucose profiles and their associations with adverse maternal and neonatal outcomes.
Please reach out to research@delfina.com if you would like to meet with one of our team members at SMFM 2024. You can also find us at the following sessions below:
At Delfina, we’re gearing up for the annual Society of Maternal and Fetal Medicine (SMFM)...
