Case study

Development of Sensyne Health's first clinical support algorithm to help clinicians improve the care of women with diabetes during pregnancy

October 7, 2019

Overview

Up to 20% of women are diagnosed with diabetes during pregnancy. Known as gestational diabetes mellitus (GDM), it is a serious condition that can endanger both mother and child and affect their future health. The number of women being diagnosed with GDM is rising globally, yet monitoring the condition often remains an inefficient and time-consuming process for women and their clinicians.

Purpose

In 2018 Sensyne Health launched GDm-Health, an app designed to help women and clinicians monitor blood glucose levels more effectively during pregnancy. The app helps to safely monitor, manage and reduce the risks associated with diabetes in pregnancy which include induced labour, caesarean section, premature birth, high blood pressure for women and low blood glucose and jaundice for the baby.

The work

GDm-Health is used in clinical care and creates a growing set of anonymised data for research. As more NHS Trusts adopted GDm-Health during 2018–19, the number of patients using the app increased and this rich, real-world anonymised database reached a size whereby it was large enough to enable the development and training of a new software algorithm. The dataset, collected from 15 NHS Trusts, included anonymised data from 4,025 women and a set of 411,785 blood glucose readings.

Research on this database enabled Sensyne Health to develop a clinically valuable software algorithm using AI (SYNE001), the first such clinical support algorithm developed by the Company. SYNE001 is designed as a new clinical support tool to help clinicians decide whether to prescribe medication to better regulate blood glucose levels.

Using GDm-Health’s longitudinal data to extract characteristic features of blood glucose readings (e.g. maximum, minimum and mean values) and demographic and clinical information (e.g. age and previous GDM pregnancies) provided the dataset to develop the algorithm to identify women belonging to one of two groups, those who do not transition to medication and those who do.

To avoid analysis bias (e.g. selecting an easy to identify group of women), Sensyne Health has repeated its experiment 100 times considering a random subgroup of women at each repetition.

Outcomes

The Clinical AI SYNE001 algorithm deployed in GDm-Health has the potential to allowearlier intervention by identifying women at risk, enabling prompt clinical review and initiation of medication. This AI-driven technology may lead to improved pregnancy outcomes for both women with GDM and their babies.

After the initial success of its first clinical algorithm in GDM, Sensyne Health is developing further clinically valuable AI algorithms. The algorithms will use research databases from Sensyne Health’s other digital health products to provide AI-guided decision support tools for clinicians and improve patient care in the areas of cardiovascular and respiratory disease.

Case study

Development of Sensyne Health's first clinical support algorithm to help clinicians improve the care of women with diabetes during pregnancy

October 7, 2019

Overview

Up to 20% of women are diagnosed with diabetes during pregnancy. Known as gestational diabetes mellitus (GDM), it is a serious condition that can endanger both mother and child and affect their future health. The number of women being diagnosed with GDM is rising globally, yet monitoring the condition often remains an inefficient and time-consuming process for women and their clinicians.

Purpose

In 2018 Sensyne Health launched GDm-Health, an app designed to help women and clinicians monitor blood glucose levels more effectively during pregnancy. The app helps to safely monitor, manage and reduce the risks associated with diabetes in pregnancy which include induced labour, caesarean section, premature birth, high blood pressure for women and low blood glucose and jaundice for the baby.

The work

GDm-Health is used in clinical care and creates a growing set of anonymised data for research. As more NHS Trusts adopted GDm-Health during 2018–19, the number of patients using the app increased and this rich, real-world anonymised database reached a size whereby it was large enough to enable the development and training of a new software algorithm. The dataset, collected from 15 NHS Trusts, included anonymised data from 4,025 women and a set of 411,785 blood glucose readings.

Research on this database enabled Sensyne Health to develop a clinically valuable software algorithm using AI (SYNE001), the first such clinical support algorithm developed by the Company. SYNE001 is designed as a new clinical support tool to help clinicians decide whether to prescribe medication to better regulate blood glucose levels.

Using GDm-Health’s longitudinal data to extract characteristic features of blood glucose readings (e.g. maximum, minimum and mean values) and demographic and clinical information (e.g. age and previous GDM pregnancies) provided the dataset to develop the algorithm to identify women belonging to one of two groups, those who do not transition to medication and those who do.

To avoid analysis bias (e.g. selecting an easy to identify group of women), Sensyne Health has repeated its experiment 100 times considering a random subgroup of women at each repetition.

Outcomes

The Clinical AI SYNE001 algorithm deployed in GDm-Health has the potential to allowearlier intervention by identifying women at risk, enabling prompt clinical review and initiation of medication. This AI-driven technology may lead to improved pregnancy outcomes for both women with GDM and their babies.

After the initial success of its first clinical algorithm in GDM, Sensyne Health is developing further clinically valuable AI algorithms. The algorithms will use research databases from Sensyne Health’s other digital health products to provide AI-guided decision support tools for clinicians and improve patient care in the areas of cardiovascular and respiratory disease.

Case study

Development of Sensyne Health's first clinical support algorithm

Development of Sensyne Health's first clinical support algorithm to help clinicians improve the care of women with diabetes during pregnancy

October 7, 2019

Overview

Up to 20% of women are diagnosed with diabetes during pregnancy. Known as gestational diabetes mellitus (GDM), it is a serious condition that can endanger both mother and child and affect their future health. The number of women being diagnosed with GDM is rising globally, yet monitoring the condition often remains an inefficient and time-consuming process for women and their clinicians.

Purpose

In 2018 Sensyne Health launched GDm-Health, an app designed to help women and clinicians monitor blood glucose levels more effectively during pregnancy. The app helps to safely monitor, manage and reduce the risks associated with diabetes in pregnancy which include induced labour, caesarean section, premature birth, high blood pressure for women and low blood glucose and jaundice for the baby.

The work

GDm-Health is used in clinical care and creates a growing set of anonymised data for research. As more NHS Trusts adopted GDm-Health during 2018–19, the number of patients using the app increased and this rich, real-world anonymised database reached a size whereby it was large enough to enable the development and training of a new software algorithm. The dataset, collected from 15 NHS Trusts, included anonymised data from 4,025 women and a set of 411,785 blood glucose readings.

Research on this database enabled Sensyne Health to develop a clinically valuable software algorithm using AI (SYNE001), the first such clinical support algorithm developed by the Company. SYNE001 is designed as a new clinical support tool to help clinicians decide whether to prescribe medication to better regulate blood glucose levels.

Using GDm-Health’s longitudinal data to extract characteristic features of blood glucose readings (e.g. maximum, minimum and mean values) and demographic and clinical information (e.g. age and previous GDM pregnancies) provided the dataset to develop the algorithm to identify women belonging to one of two groups, those who do not transition to medication and those who do.

To avoid analysis bias (e.g. selecting an easy to identify group of women), Sensyne Health has repeated its experiment 100 times considering a random subgroup of women at each repetition.

Outcomes

The Clinical AI SYNE001 algorithm deployed in GDm-Health has the potential to allowearlier intervention by identifying women at risk, enabling prompt clinical review and initiation of medication. This AI-driven technology may lead to improved pregnancy outcomes for both women with GDM and their babies.

After the initial success of its first clinical algorithm in GDM, Sensyne Health is developing further clinically valuable AI algorithms. The algorithms will use research databases from Sensyne Health’s other digital health products to provide AI-guided decision support tools for clinicians and improve patient care in the areas of cardiovascular and respiratory disease.

Case study

Development of Sensyne Health's first clinical support algorithm

Development of Sensyne Health's first clinical support algorithm to help clinicians improve the care of women with diabetes during pregnancy

Overview

Up to 20% of women are diagnosed with diabetes during pregnancy. Known as gestational diabetes mellitus (GDM), it is a serious condition that can endanger both mother and child and affect their future health. The number of women being diagnosed with GDM is rising globally, yet monitoring the condition often remains an inefficient and time-consuming process for women and their clinicians.

Purpose

In 2018 Sensyne Health launched GDm-Health, an app designed to help women and clinicians monitor blood glucose levels more effectively during pregnancy. The app helps to safely monitor, manage and reduce the risks associated with diabetes in pregnancy which include induced labour, caesarean section, premature birth, high blood pressure for women and low blood glucose and jaundice for the baby.

The work

GDm-Health is used in clinical care and creates a growing set of anonymised data for research. As more NHS Trusts adopted GDm-Health during 2018–19, the number of patients using the app increased and this rich, real-world anonymised database reached a size whereby it was large enough to enable the development and training of a new software algorithm. The dataset, collected from 15 NHS Trusts, included anonymised data from 4,025 women and a set of 411,785 blood glucose readings.

Research on this database enabled Sensyne Health to develop a clinically valuable software algorithm using AI (SYNE001), the first such clinical support algorithm developed by the Company. SYNE001 is designed as a new clinical support tool to help clinicians decide whether to prescribe medication to better regulate blood glucose levels.

Using GDm-Health’s longitudinal data to extract characteristic features of blood glucose readings (e.g. maximum, minimum and mean values) and demographic and clinical information (e.g. age and previous GDM pregnancies) provided the dataset to develop the algorithm to identify women belonging to one of two groups, those who do not transition to medication and those who do.

To avoid analysis bias (e.g. selecting an easy to identify group of women), Sensyne Health has repeated its experiment 100 times considering a random subgroup of women at each repetition.

Outcomes

The Clinical AI SYNE001 algorithm deployed in GDm-Health has the potential to allowearlier intervention by identifying women at risk, enabling prompt clinical review and initiation of medication. This AI-driven technology may lead to improved pregnancy outcomes for both women with GDM and their babies.

After the initial success of its first clinical algorithm in GDM, Sensyne Health is developing further clinically valuable AI algorithms. The algorithms will use research databases from Sensyne Health’s other digital health products to provide AI-guided decision support tools for clinicians and improve patient care in the areas of cardiovascular and respiratory disease.

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Case study

Development of Sensyne Health's first clinical support algorithm

October 7, 2019

Overview

Up to 20% of women are diagnosed with diabetes during pregnancy. Known as gestational diabetes mellitus (GDM), it is a serious condition that can endanger both mother and child and affect their future health. The number of women being diagnosed with GDM is rising globally, yet monitoring the condition often remains an inefficient and time-consuming process for women and their clinicians.

Purpose

In 2018 Sensyne Health launched GDm-Health, an app designed to help women and clinicians monitor blood glucose levels more effectively during pregnancy. The app helps to safely monitor, manage and reduce the risks associated with diabetes in pregnancy which include induced labour, caesarean section, premature birth, high blood pressure for women and low blood glucose and jaundice for the baby.

The work

GDm-Health is used in clinical care and creates a growing set of anonymised data for research. As more NHS Trusts adopted GDm-Health during 2018–19, the number of patients using the app increased and this rich, real-world anonymised database reached a size whereby it was large enough to enable the development and training of a new software algorithm. The dataset, collected from 15 NHS Trusts, included anonymised data from 4,025 women and a set of 411,785 blood glucose readings.

Research on this database enabled Sensyne Health to develop a clinically valuable software algorithm using AI (SYNE001), the first such clinical support algorithm developed by the Company. SYNE001 is designed as a new clinical support tool to help clinicians decide whether to prescribe medication to better regulate blood glucose levels.

Using GDm-Health’s longitudinal data to extract characteristic features of blood glucose readings (e.g. maximum, minimum and mean values) and demographic and clinical information (e.g. age and previous GDM pregnancies) provided the dataset to develop the algorithm to identify women belonging to one of two groups, those who do not transition to medication and those who do.

To avoid analysis bias (e.g. selecting an easy to identify group of women), Sensyne Health has repeated its experiment 100 times considering a random subgroup of women at each repetition.

Outcomes

The Clinical AI SYNE001 algorithm deployed in GDm-Health has the potential to allowearlier intervention by identifying women at risk, enabling prompt clinical review and initiation of medication. This AI-driven technology may lead to improved pregnancy outcomes for both women with GDM and their babies.

After the initial success of its first clinical algorithm in GDM, Sensyne Health is developing further clinically valuable AI algorithms. The algorithms will use research databases from Sensyne Health’s other digital health products to provide AI-guided decision support tools for clinicians and improve patient care in the areas of cardiovascular and respiratory disease.