The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
University of Oxford · Academic
In term In term in the September 2026 edition: the latest version runs to 24 September 2027.
- Reference
- DARS-NIC-712819-X8G2J
- Current version
- v3.4
- Term of current version
- 24 October 2025 to 24 September 2027
- Start date
- 26 July 2024
- Data controller
- Sole Data Controller
- Commercial purposes
- No
- Sublicensing
- No
- Files released to date
- 0
Why the data was released
Objective for processing
University of Oxford requires access to NHS England data for the purpose of the following research project:
The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
The following is a summary of the aims of the research project provided on behalf of University of Oxford:
The purpose of this project is to analyse maternity services & secondary care data to determine how different operational interventions impact maternity care pathways and consequently, different health outcomes.
The NHS has set out several priorities in addressing health inequalities. One of the main areas which is consistent within national and local planning are inequalities in maternity and neonatal care, as set out within the annual NHS Priorities and Operational Planning Guidance. These priorities are largely in response to the findings of reports such as those by Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries across the UK (MBRRACE-UK), revealing disparaging outcomes in maternity and neonatal care for socioeconomic deprived individuals and ethnic minorities.
This research project aims to support these priorities by addressing this topical issue through:
- Developing tools to understand how the study can use the mother’s demographic data and ante-natal care pathways to segregate the population in a meaningful way.
- Investigating the impact of operational interventions such as continuity of care for the different sub-populations.
- Proposing personalised pathways to address health inequalities and improve the operational efficiency of the public health system based on the characteristics of mothers.
The operational interventions refer to changes in the way the patient is treated throughout their interaction with healthcare services. The study will look to consider:
1. Continuity of Care: This refers to providing a consistent healthcare experience for the patient by minimizing changes in healthcare providers throughout the maternity care pathway. Continuity of care ensures that patients are treated by the same healthcare professionals (like midwives or obstetricians) during prenatal visits, labor, delivery, and postnatal care. This consistency fosters a better understanding of the patient's history, preferences, and needs, leading to improved communication, trust, and personalized care. On the other hand, continuity of care may result in increased system congestion.
2. Frequency of Visits: This intervention addresses the number of scheduled appointments or check-ups during the pregnancy. A tailored approach to visit frequency can identify potential risks or complications early, ensure proper monitoring of the baby's development, and provide adequate support to the mother. By adjusting the frequency of visits based on individual needs, healthcare providers aim to enhance the quality of care while managing resources efficiently.
3. Frequency of Scans: Ultrasound scans are an essential component of maternity care, used to monitor fetal development, identify abnormalities, and assess pregnancy progress. Operational interventions may involve adjusting the number and timing of scans to ensure they align with patient health conditions, while avoiding unnecessary scans to minimize risks and reduce costs.
4. Staffing Levels and Skill Mix: Ensuring that maternity care units are appropriately staffed with qualified professionals, with the right balance of experience and expertise, can significantly impact care quality and outcomes. This intervention involves adjusting staffing levels to meet patient demands and ensure safe care delivery.
5. Integration of Technology: The use of telemedicine, and other digital tools can improve communication among healthcare providers, streamline care processes, and enhance patient engagement. Operational interventions in this area might focus on implementing or optimizing technology to support continuity of care and better patient outcomes.
6. Collaborative Care Models: These models encourage collaboration between various healthcare providers involved in maternity care, such as midwives, obstetricians, pediatricians, and primary care physicians. By fostering teamwork and communication, these interventions can lead to more coordinated care pathways and improved outcomes for mothers and newborns.
This project has the following aims:
1. Compare outcomes across different sub-groups of patients to determine disparities in health outcomes of those subject to different socioeconomic factors.
2. Compare care cluster pathways across different sub-groups of patients to provide intelligence on how varying care pathways can impact the health outcomes of patients, including time under care. Specifically, to perform analysis to cluster the ante-natal pathways and examine what are the significant distinguishing factors between the pathways.
3. Evaluate the impact of operational interventions on health outcomes. This will aim to assess the effectiveness of interventions such as continuity of care, frequency of visits, frequency of scans and whether these have a meaningful impact on health outcomes. Further to this, to assess whether the impact of these interventions for socioeconomic deprived patients is more significant. For example, the ability to see the same provider who they have built a relationship with at each appointment for deprived patients.
4. Prescriptive analysis - Based on the analyses in aims 1-3, the study then aims to develop personalised pathways based on the clinical and socioeconomic characteristics of the mother. The study will develop and validate novel ante-natal pathways that account for both clinical and socioeconomic patient characteristics as well as operational constraints of the NHS.
The following NHS England Data will be accessed:
• Hospital Episode Statistics
o Admitted Patient Care – necessary to provide information on patients use of secondary care services that may not be recorded within the Maternity Services Dataset (MSDS), but will still contribute to accurately determining the care pathways and health outcomes of patients & provide insight into any acute and relevant chronic conditions that may confound pregnancy outcomes.
o Accident & Emergency & Uncurated Low Latency Hospital Data Sets - Emergency Care – necessary to provide information on patients use of emergency care services, who have or are currently receiving maternity care. This data will allow the study to consider the full picture of these patients care pathways and account for emergency care events which can contribute to health outcomes during pregnancy within the analysis.
• Maternity Services Dataset (MSDS) v2– necessary to provide information on the care pathways of maternity, natal & neonatal patients whilst also helping the study to build a picture the socio-economic characteristics of these patients for analysis addressing the aims of the study.
The level of the Data will be pseudonymised. The Data contains multiple sensitive fields which have been selected for the purpose of this analysis. The sensitive data items are key to ensure the the effectiveness of the analysis. In order to reconstruct maternity pathways effectively, it is imperative to understand the circumstances under which patients accessed emergency care, which requires data on start dates and times, and expiry dates and times. This information is essential for creating a chronological timeline for each patient, allowing for a comprehensive understanding of their healthcare journey. The inclusion of ethnic categories aligns with the aims and objectives of the study. Specifically, the study aims to identify and investigate potential disparities in maternity pathways and health outcomes across different ethnic groups and explore recommendations for improvements.
Access to consultant codes and referrer codes within the HES APC data is vital for tracking which professionals are involved in each patient’s care along their maternity journey. This information allows the study to explore the continuity of care, collaboration among healthcare providers, and any variations in care delivery. Understanding these patterns can provide insights into best practices and areas needing improvement, so that the study can conduct a comprehensive analysis of maternity pathways and associated outcomes.
Several factors pertaining to the mother's health and social circumstances may significantly impact her pregnancy journey and subsequent outcomes. Variables within the MSDS dataset corresponding to complex social factors, mental health conditions, adherence to folic acid supplementation, smoking and alcohol consumption, are crucial for identifying vulnerable groups and tailoring interventions accordingly. Additionally, previous pregnancy outcomes serve as important controls to account for in the analysis. The outcome of the current pregnancy is of central importance in our study, since the study hypothesizes that maternity pathways influence outcomes of the pregnancy. Moreover, factors such as the support network and employment status of the mother can affect her overall well-being and mental health, which are also known to impact pregnancy outcomes.”
The Data requested from the Maternity Services Dataset (MSDS) will be minimised as follows:
• Limited to data between 2018/19 to the latest available complete financial year.
• The Data will cover patients nationally & will include all pregnancies recorded within the above time frame.
• The Data will look at all patients episodes, including the unborn child and neonatal records of patients.
The Data requested from Hospital Episodes Statistics will be:
• Limited to data between 2016/17 to the latest available complete financial year - the study requires access to patients secondary care data from the period preceding their appearance within the MSDS datasets in order to analyse any acute and relevant chronic conditions and associated care delivered which may impact a pregnancy.
• The Data will be limited to a subset of the total available fields available within the datasets as selected by University of Oxford.
University of Oxford is the research sponsor and the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under the UK GDPR is:
Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller;
The lawful basis for processing special category data under the UK GDPR is:
Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.
This processing is in the public interest as it aims to support improved intelligence in the development of policy and guidance on provision of health care regarding maternity care decisions. Through this improved research, the study aims to help address the key priorities set by the NHS focused on improving the health outcomes of maternity patients & reducing inequalities that a large proportion of the population may be subject to. This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding is provided by the John Fell Fund at University of Oxford. The funding is specifically for the study described. The funder(s) will have no ability to suppress or otherwise limit the publication of findings.
Data will be accessed by researchers from the University of Oxford and individuals with an honorary contract with University of Oxford – a collaborating researcher from University of Cambridge working within the study and with the Data and an individual from London Business School specialising in statistical analysis & interpretation of results for stochastic modelling and optimization of service systems focused on by the study. The individuals have completed mandatory data protection and confidentiality training and is subject to University of Oxford’s policies on data protection and confidentiality. The individuals accessing the data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by the individuals. The honorary contractors would only work on the data for the purposes described in this Data Sharing Agreement (DSA).
The study has engaged with two different healthcare professionals prior to the initiation of the study. The input hoped to be gained from their involvement were for the purpose of:
- Providing insights into outcomes
- Associate data driven findings with clinical knowledge and real world impacts
- Ground proposed solutions from these outcomes into practise
Upon initially reviewing the proposed study, the individuals supported the study overall and the use of the data for the purposes described above.
The study has set out an ongoing Patient & Public Involvement & Engagement plan for the duration of the study. The plan includes engagement with patient & public representative groups such as Women's Voice & Maternity Action. The groups will be consulted on their views of the research hypothesis & questions posed by the study, the processing of the Data for the purpose of the study & interpretation of the outputs generated by the study. Engagement is planned throughout 2026 & 2027 for the term of the study and the review of study outcomes.
Processing activities
No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).
NHS England will provide access to the relevant records from the Data to authorised users from the University of Oxford. The Data will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.
NHS England will grant access to the Data via the Secure Data Environment (SDE). The SDE is a secure data and research analysis platform. It allows approved researchers with approved projects access to pseudonymised data and industry leading analytics tools.
The Data will not be transferred to any other location.
SDE users can request exportation of aggregated analysis results (suppressed and summarised according to the NHSE SDE Disclosure Control rules) subject to review and approval by the NHS England SDE Output Checking team. The SDE Output Checking team will ensure that no output contains information which could be used either on its own or in conjunction with other data to breach an individual's privacy.
Users must identify themselves via a multi-factor authentication mechanism and are only able to access the datasets detailed within this DSA. The access and use of the system is fully auditable and all users must comply with the use of the Data as specified in this DSA.
Users will be authorised to access the data specified in this DSA and can utilise a variety of analytical tools available within the SDE platform. Users are not permitted to export record-level data from the SDE.
Remote processing will be from secure locations within England.
The Data will not leave England at any time.
Access is restricted to employees or agents of the University of Oxford.
All personnel accessing the Data have been appropriately trained in data protection and confidentiality.
The Data will not be linked with any other data.
There will be no requirement and no attempt to reidentify individuals when using the Data.
The Data will be used solely for the purposes described above.
Expected output
The expected outputs of the processing will be:
1. Submissions to peer review journals, including publications in reputable health journals such as British Medical Journal, as well as Management Science, Manufacturing & Service Operations Management journals among others.
2. Presentations at relevant international conferences, such as the Institute for Operations Research and the Management Sciences (INFORMS)
3. Presentations, conferences, and partnerships with clinical partners, experts in the field, and collaborators in Oxford, Cambridge and London, e.g., maternity units in the Oxford university Hospitals and Cambridge university Hospitals to ensure that the results are disseminated widely among the maternity services community. To maximize the impact of the findings, the research team plans to share the results with key stakeholders and decision-makers in maternity services, including NHS executives, healthcare policymakers, and professional bodies.
4. Publications on social media outlets such as business reviews in universities.
The outputs will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset from which the information was derived. No data that identifies an individual consultant or their performance is permitted to be published.
The output will be communicated to relevant recipients through the following dissemination channels:
1. Peer review journals
2. Social media: The study will promote results through social media platforms and business school magazines/websites, such as Think at London Business School. The research team will work with the media team at the institution to write media pieces about the research findings, including newspapers, online media platforms, and other media outlets.
The target time frame for achieving these outputs is 2025 and will be ongoing throughout access to the data.
This research project examining the impact of operational interventions on maternity care pathways and health outcomes offers a range of significant benefits across multiple dimensions. Below is a more detailed description of these benefits, including plans for dissemination and the broader impact on maternity services and healthcare policy.
Expected measurable benefits
The findings of this research study are expected to contribute to evidence-based decision-making for policy-makers, local decision-makers such as doctors, and patients to inform best practice to improve the care, treatment and experience of health care users relevant to the subject matter of the study.
The use of the data could:
• Help the system to better understand the health and care needs of populations.
• Advance understanding of regional and national trends in health and social care needs.
• Advance understanding of the need for, or effectiveness of, preventative health and care measures for particular populations or conditions that may cause concern during pregnancy such as cardiovascular problems or diabetes.
• Inform planning health services and programmes, for example to improve equity of access, experience and outcomes.
It is hoped this project will yield valuable insights on the impact of operational interventions on maternity care pathways and health outcomes, to inform potential policy changes made by NHS around both ante-natal and post-natal operational interventions on maternity pathways. By focusing on these operational interventions, this research project aims to explore how changes in these areas can affect maternity care pathways and health outcomes, ultimately providing insights into best practices for the UK NHS.
The project hopes to provide detailed understanding of the maternity care pathways and health outcomes of different sub-groups, which informs social care and health policy making and intervention design. In particular, the findings “the findings will include recommendations on how to effectively reduce the existing health inequality in the field of maternal services, better support patients from deprived areas in the provision of health services and social care, and facilitate local maternity systems in implementing evidence-based and targeted interventions for different sub-groups and risk-factors of patients. The Data will be used to measure the effect of currently established pathways of care on quality and access to care, to estimate the impact of operational intervention on the population and the healthcare system, and ultimately to develop and analyse the performance of new personalised pathways of maternal care for the national healthcare system.
The project seeks to deliver these benefits through application of its outputs, specifically:
• Informing NHS Policy and Operations: By analysing the effects of various operational interventions on maternity care pathways, this study provides data-driven insights to inform NHS policy decisions. The findings can guide both antenatal and postnatal interventions, helping to shape operational strategies that improve care quality and patient outcomes. The results may lead to enhanced protocols, better resource allocation, and improved operational decision-making within maternity units.
• Reducing Health Inequalities: The project's focus on sub-groups within maternity care pathways has the potential to address health disparities. By identifying patterns and outcomes across different demographic groups, including those from deprived areas, the study can inform social care and health policy. This insight will allow policymakers to design targeted interventions to reduce health inequalities, thereby improving access to quality maternity care for underrepresented or marginalized groups.
• Academic Contribution and Dissemination: This study seeks to address a gap in the current academic literature on healthcare operations management in maternity services. To date, the sole relevant paper in the field of healthcare operations management is the work by Freeman et al. (2016), published in Management Science, which demonstrated the significant influence of resource availability on patient-care pathways in the context of maternity care, drawing on data from a maternity hospital in the UK. Given the limited research in this specific field, the project has the potential to drive further academic interest and research.
Academically, it is noteworthy that there is a noticeable lack of studies that specifically focus on the operational implications of maternal services. To date, the sole relevant paper in the field of healthcare operations management is the work by Freeman et al. (2016), published in Management Science, which demonstrated the significant influence of resource availability on patient-care pathways in the context of maternity care, drawing on data from a maternity hospital in the UK. As such, this research project looks to drive more operations related research in maternity services.
The aims of the study seek to yield operationally valuable insights in the field of maternal services and to effectively reduce the existing gap between patients from deprived areas and facilitate local maternity systems in implementing evidence-based and targeted interventions for different sub-groups of patients. It is hoped that through publication of findings in appropriate media, the findings of this research will add to the body of evidence that is considered by the bodies, organisations and individual care practitioners charged with making policy decisions for or within the NHS or treatment decisions in relation to specific patients.
Benefits reported so far
The University of Oxford have produced a series of preliminary models that have allowed them to verify using descriptive techniques some of the primary hypotheses and take a more holistic view over the data.
Datasets on the current version
Legal basis for provision: Health and Social Care Act 2012 – s261(2)(a)
| Dataset | Type of data | Sensitivity | Frequency | Confidential data |
|---|---|---|---|---|
| Hospital Episode Statistics Accident and Emergency (HES A and E) | Anonymised - ICO Code Compliant | Non-Sensitive | System Access | Does not include the flow of confidential data |
| Hospital Episode Statistics Admitted Patient Care (HES APC) | Anonymised - ICO Code Compliant | Sensitive | System Access | Does not include the flow of confidential data |
| Maternity Services Data Set (MSDS) v2 | Anonymised - ICO Code Compliant | Non-Sensitive | System Access | Does not include the flow of confidential data |
| Uncurated Low Latency Hospital Data Sets - Emergency Care | Anonymised - ICO Code Compliant | Sensitive | System Access | Does not include the flow of confidential data |
Files released
Files released counts only files released externally by DARS. Access granted in NHS England's own systems, such as its Secure Data Environment, is not included.
No files recorded as released under this agreement.
Version history
The register lists each renewal of this agreement as a separate row. This site has 4 versions.
DARS-NIC-712819-X8G2J-v3.4 24 October 2025 to 24 September 2027
- Title
- The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
- Commercial
- No
- Sublicensing
- No
- Datasets
- 4
- Files released
- 0
Datasets: Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Maternity Services Data Set (MSDS) v2; Uncurated Low Latency Hospital Data Sets - Emergency Care
What changed from DARS-NIC-712819-X8G2J-v2.3
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2025-10-24 |
Objective for processing
[30 paragraphs unchanged]
• Limited to data between 2018/19 to
2022/23
the latest available complete
financial
years.
year.
[3 paragraphs unchanged]
• Limited to data between 2016/17 to
2022/23
the latest available complete
financial
years
year
- the study requires access to patients secondary care data from the
[15 words unchanged]
relevant chronic conditions and associated care delivered which may impact a pregnancy.
[14 paragraphs unchanged]
The study has set out an ongoing Patient & Public Involvement &
[55 words unchanged]
interpretation of the outputs generated by the study. Engagement is planned throughout
2024
2026
&
2025
2027
for the term of the study and the review of study outcomes.
Expected output
[9 paragraphs unchanged]
The target time frame for achieving these outputs is
the early
2025 and will be ongoing throughout access to the data.
[1 paragraph unchanged]
Unchanged: Processing activities, Expected measurable benefits, Benefits reported.
DARS-NIC-712819-X8G2J-v2.3 13 June 2025 to 24 September 2027
- Title
- The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
- Commercial
- No
- Sublicensing
- No
- Datasets
- 4
- Files released
- 0
Datasets: Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Maternity Services Data Set (MSDS) v2; Uncurated Low Latency Hospital Data Sets - Emergency Care
What changed from DARS-NIC-712819-X8G2J-v1.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2025-06-13 | |
| End date | 2027-09-24 |
Processing activities
This data sharing agreement is for online access to the record level datasets via the NHS England Secure Data Environment (SDE). The system is hosted and audited by NHS England meaning that large transfers of data to on-site servers is limited and NHS England has the ability to audit the use and access to the data. NHS England will grant University of Oxford access to the relevant records from the HES APC, HES A&E, Uncurated Low Latency Hospital Data Sets - Emergency Care & MSDS v2 datasets via NHS England’s Secure Data Environment (SDE). The Data will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.
No data will flow to NHS England for the purposes of this Data Sharing Agreement (DSA).
The Secure Data Environment (SDE) is a data storage and access platform that enables approved users to access de-identified data and analytical tools for approved projects. Users must identify themselves via a multi-factor authentication mechanism and are only able to access the datasets detailed within this agreement. Users can request that aggregated outputs are exported from the system following approval by trained NHSE staff. The access and use of the system is fully auditable, and all users must comply with the use of the data as specified in this agreement.
NHS England will provide access to the relevant records from the Data to authorised users from the University of Oxford. The Data will contain no direct identifying data items. The Data will be pseudonymised and individuals cannot be reidentified through linkage with other data in the possession of the recipient.
Users can produce aggregate outputs from the system, however, record level extracts are not permitted. As record level data cannot be extracted from SDE. The system accommodates a variety of technical tools for data analysis.
NHS England will grant access to the Data via the Secure Data Environment (SDE). The SDE is a secure data and research analysis platform. It allows approved researchers with approved projects access to pseudonymised data and industry leading analytics tools.
Researchers will conduct analyses with various levels of complexity, some will be interested in simple summary statistics, some will look at trend analysis, others will apply more complex analysis techniques.
The Data will not be transferred to any other location.
Data processing will be carried out by substantive employees of the University of Oxford who have been appropriately trained in data protection and confidentiality. Additional data processing may be carried out by University of Oxford honorary contractors who have signed a contract with the University ensuring they abide by the University's statutes and regulations encompassing data protection and confidentiality. NHS England are listed as a data processor as they host the SDE environment in which the data will be accessed.
SDE users can request exportation of aggregated analysis results (suppressed and summarised according to the NHSE SDE Disclosure Control rules) subject to review and approval by the NHS England SDE Output Checking team. The SDE Output Checking team will ensure that no output contains information which could be used either on its own or in conjunction with other data to breach an individual's privacy.
Only registered users will have access to record level or aggregate data containing small numbers downloaded from the system. All users with access to the data are restricted to substantive employees or honorary contractors of the University of Oxford. Following completion of the study & analysis, access to the record-level data will be closed.
Users must identify themselves via a multi-factor authentication mechanism and are only able to access the datasets detailed within this DSA. The access and use of the system is fully auditable and all users must comply with the use of the Data as specified in this DSA.
The Data will not leave England at any time. The Data will not be linked with any other data.
Users will be authorised to access the data specified in this DSA and can utilise a variety of analytical tools available within the SDE platform. Users are not permitted to export record-level data from the SDE.
Any outputs that are produced from the data that are to be published or shared with a third party (individuals or organisations outside of the analytical team) will be aggregated with small number suppressed, as set out within NHS England guidance applicable to each data set (to note, no pseudonymised data will be downloaded from the SDE; this refers to aggregated outputs only).
Remote processing will be from secure locations within England.
Following similar principles to those adopted by the SAIL Databank for Wales (https://saildatabank.com/saildata/data-privacy-security/#secure-access) and the Scottish National Data Safe Haven (https://www.isdscotland.org/Products-and-Services/EDRIS/Use-of-the-National-Safe-Haven/), only summary, aggregate results data are exported from the SDE by Department of Health and Social Care, subject to the approval of NHS England’s trained output checkers. This ensures that no output contains information which could be used either on its own or in conjunction with other data to breach an individual's privacy.
The Data will not leave England at any time.
Researchers from the University of Oxford will analyse the Data for the purposes described above
Access is restricted to employees or agents of the University of Oxford.
All personnel accessing the Data have been appropriately trained in data protection and confidentiality.
The Data will not be linked with any other data.
There will be no requirement and no attempt to reidentify individuals when using the Data.
The Data will be used solely for the purposes described above.
Benefits reported
Not stated in the previous version; added here.
The University of Oxford have produced a series of preliminary models that have allowed them to verify using descriptive techniques some of the primary hypotheses and take a more holistic view over the data.
Unchanged: Objective for processing, Expected output, Expected measurable benefits.
Objective for processing
University of Oxford requires access to NHS England data for the purpose of the following research project:
The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
The following is a summary of the aims of the research project provided on behalf of University of Oxford:
The purpose of this project is to analyse maternity services & secondary care data to determine how different operational interventions impact maternity care pathways and consequently, different health outcomes.
The NHS has set out several priorities in addressing health inequalities. One of the main areas which is consistent within national and local planning are inequalities in maternity and neonatal care, as set out within the annual NHS Priorities and Operational Planning Guidance. These priorities are largely in response to the findings of reports such as those by Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries across the UK (MBRRACE-UK), revealing disparaging outcomes in maternity and neonatal care for socioeconomic deprived individuals and ethnic minorities.
This research project aims to support these priorities by addressing this topical issue through:
- Developing tools to understand how the study can use the mother’s demographic data and ante-natal care pathways to segregate the population in a meaningful way.
- Investigating the impact of operational interventions such as continuity of care for the different sub-populations.
- Proposing personalised pathways to address health inequalities and improve the operational efficiency of the public health system based on the characteristics of mothers.
The operational interventions refer to changes in the way the patient is treated throughout their interaction with healthcare services. The study will look to consider:
1. Continuity of Care: This refers to providing a consistent healthcare experience for the patient by minimizing changes in healthcare providers throughout the maternity care pathway. Continuity of care ensures that patients are treated by the same healthcare professionals (like midwives or obstetricians) during prenatal visits, labor, delivery, and postnatal care. This consistency fosters a better understanding of the patient's history, preferences, and needs, leading to improved communication, trust, and personalized care. On the other hand, continuity of care may result in increased system congestion.
2. Frequency of Visits: This intervention addresses the number of scheduled appointments or check-ups during the pregnancy. A tailored approach to visit frequency can identify potential risks or complications early, ensure proper monitoring of the baby's development, and provide adequate support to the mother. By adjusting the frequency of visits based on individual needs, healthcare providers aim to enhance the quality of care while managing resources efficiently.
3. Frequency of Scans: Ultrasound scans are an essential component of maternity care, used to monitor fetal development, identify abnormalities, and assess pregnancy progress. Operational interventions may involve adjusting the number and timing of scans to ensure they align with patient health conditions, while avoiding unnecessary scans to minimize risks and reduce costs.
4. Staffing Levels and Skill Mix: Ensuring that maternity care units are appropriately staffed with qualified professionals, with the right balance of experience and expertise, can significantly impact care quality and outcomes. This intervention involves adjusting staffing levels to meet patient demands and ensure safe care delivery.
5. Integration of Technology: The use of telemedicine, and other digital tools can improve communication among healthcare providers, streamline care processes, and enhance patient engagement. Operational interventions in this area might focus on implementing or optimizing technology to support continuity of care and better patient outcomes.
6. Collaborative Care Models: These models encourage collaboration between various healthcare providers involved in maternity care, such as midwives, obstetricians, pediatricians, and primary care physicians. By fostering teamwork and communication, these interventions can lead to more coordinated care pathways and improved outcomes for mothers and newborns.
This project has the following aims:
1. Compare outcomes across different sub-groups of patients to determine disparities in health outcomes of those subject to different socioeconomic factors.
2. Compare care cluster pathways across different sub-groups of patients to provide intelligence on how varying care pathways can impact the health outcomes of patients, including time under care. Specifically, to perform analysis to cluster the ante-natal pathways and examine what are the significant distinguishing factors between the pathways.
3. Evaluate the impact of operational interventions on health outcomes. This will aim to assess the effectiveness of interventions such as continuity of care, frequency of visits, frequency of scans and whether these have a meaningful impact on health outcomes. Further to this, to assess whether the impact of these interventions for socioeconomic deprived patients is more significant. For example, the ability to see the same provider who they have built a relationship with at each appointment for deprived patients.
4. Prescriptive analysis - Based on the analyses in aims 1-3, the study then aims to develop personalised pathways based on the clinical and socioeconomic characteristics of the mother. The study will develop and validate novel ante-natal pathways that account for both clinical and socioeconomic patient characteristics as well as operational constraints of the NHS.
The following NHS England Data will be accessed:
• Hospital Episode Statistics
o Admitted Patient Care – necessary to provide information on patients use of secondary care services that may not be recorded within the Maternity Services Dataset (MSDS), but will still contribute to accurately determining the care pathways and health outcomes of patients & provide insight into any acute and relevant chronic conditions that may confound pregnancy outcomes.
o Accident & Emergency & Uncurated Low Latency Hospital Data Sets - Emergency Care – necessary to provide information on patients use of emergency care services, who have or are currently receiving maternity care. This data will allow the study to consider the full picture of these patients care pathways and account for emergency care events which can contribute to health outcomes during pregnancy within the analysis.
• Maternity Services Dataset (MSDS) v2– necessary to provide information on the care pathways of maternity, natal & neonatal patients whilst also helping the study to build a picture the socio-economic characteristics of these patients for analysis addressing the aims of the study.
The level of the Data will be pseudonymised. The Data contains multiple sensitive fields which have been selected for the purpose of this analysis. The sensitive data items are key to ensure the the effectiveness of the analysis. In order to reconstruct maternity pathways effectively, it is imperative to understand the circumstances under which patients accessed emergency care, which requires data on start dates and times, and expiry dates and times. This information is essential for creating a chronological timeline for each patient, allowing for a comprehensive understanding of their healthcare journey. The inclusion of ethnic categories aligns with the aims and objectives of the study. Specifically, the study aims to identify and investigate potential disparities in maternity pathways and health outcomes across different ethnic groups and explore recommendations for improvements.
Access to consultant codes and referrer codes within the HES APC data is vital for tracking which professionals are involved in each patient’s care along their maternity journey. This information allows the study to explore the continuity of care, collaboration among healthcare providers, and any variations in care delivery. Understanding these patterns can provide insights into best practices and areas needing improvement, so that the study can conduct a comprehensive analysis of maternity pathways and associated outcomes.
Several factors pertaining to the mother's health and social circumstances may significantly impact her pregnancy journey and subsequent outcomes. Variables within the MSDS dataset corresponding to complex social factors, mental health conditions, adherence to folic acid supplementation, smoking and alcohol consumption, are crucial for identifying vulnerable groups and tailoring interventions accordingly. Additionally, previous pregnancy outcomes serve as important controls to account for in the analysis. The outcome of the current pregnancy is of central importance in our study, since the study hypothesizes that maternity pathways influence outcomes of the pregnancy. Moreover, factors such as the support network and employment status of the mother can affect her overall well-being and mental health, which are also known to impact pregnancy outcomes.”
The Data requested from the Maternity Services Dataset (MSDS) will be minimised as follows:
• Limited to data between 2018/19 to 2022/23 financial years.
• The Data will cover patients nationally & will include all pregnancies recorded within the above time frame.
• The Data will look at all patients episodes, including the unborn child and neonatal records of patients.
The Data requested from Hospital Episodes Statistics will be:
• Limited to data between 2016/17 to 2022/23 financial years - the study requires access to patients secondary care data from the period preceding their appearance within the MSDS datasets in order to analyse any acute and relevant chronic conditions and associated care delivered which may impact a pregnancy.
• The Data will be limited to a subset of the total available fields available within the datasets as selected by University of Oxford.
University of Oxford is the research sponsor and the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under the UK GDPR is:
Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller;
The lawful basis for processing special category data under the UK GDPR is:
Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.
This processing is in the public interest as it aims to support improved intelligence in the development of policy and guidance on provision of health care regarding maternity care decisions. Through this improved research, the study aims to help address the key priorities set by the NHS focused on improving the health outcomes of maternity patients & reducing inequalities that a large proportion of the population may be subject to. This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding is provided by the John Fell Fund at University of Oxford. The funding is specifically for the study described. The funder(s) will have no ability to suppress or otherwise limit the publication of findings.
Data will be accessed by researchers from the University of Oxford and individuals with an honorary contract with University of Oxford – a collaborating researcher from University of Cambridge working within the study and with the Data and an individual from London Business School specialising in statistical analysis & interpretation of results for stochastic modelling and optimization of service systems focused on by the study. The individuals have completed mandatory data protection and confidentiality training and is subject to University of Oxford’s policies on data protection and confidentiality. The individuals accessing the data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by the individuals. The honorary contractors would only work on the data for the purposes described in this Data Sharing Agreement (DSA).
The study has engaged with two different healthcare professionals prior to the initiation of the study. The input hoped to be gained from their involvement were for the purpose of:
- Providing insights into outcomes
- Associate data driven findings with clinical knowledge and real world impacts
- Ground proposed solutions from these outcomes into practise
Upon initially reviewing the proposed study, the individuals supported the study overall and the use of the data for the purposes described above.
The study has set out an ongoing Patient & Public Involvement & Engagement plan for the duration of the study. The plan includes engagement with patient & public representative groups such as Women's Voice & Maternity Action. The groups will be consulted on their views of the research hypothesis & questions posed by the study, the processing of the Data for the purpose of the study & interpretation of the outputs generated by the study. Engagement is planned throughout 2024 & 2025 for the term of the study and the review of study outcomes.
Expected output
The expected outputs of the processing will be:
1. Submissions to peer review journals, including publications in reputable health journals such as British Medical Journal, as well as Management Science, Manufacturing & Service Operations Management journals among others.
2. Presentations at relevant international conferences, such as the Institute for Operations Research and the Management Sciences (INFORMS)
3. Presentations, conferences, and partnerships with clinical partners, experts in the field, and collaborators in Oxford, Cambridge and London, e.g., maternity units in the Oxford university Hospitals and Cambridge university Hospitals to ensure that the results are disseminated widely among the maternity services community. To maximize the impact of the findings, the research team plans to share the results with key stakeholders and decision-makers in maternity services, including NHS executives, healthcare policymakers, and professional bodies.
4. Publications on social media outlets such as business reviews in universities.
The outputs will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset from which the information was derived. No data that identifies an individual consultant or their performance is permitted to be published.
The output will be communicated to relevant recipients through the following dissemination channels:
1. Peer review journals
2. Social media: The study will promote results through social media platforms and business school magazines/websites, such as Think at London Business School. The research team will work with the media team at the institution to write media pieces about the research findings, including newspapers, online media platforms, and other media outlets.
The target time frame for achieving these outputs is the early 2025 and will be ongoing throughout access to the data.
This research project examining the impact of operational interventions on maternity care pathways and health outcomes offers a range of significant benefits across multiple dimensions. Below is a more detailed description of these benefits, including plans for dissemination and the broader impact on maternity services and healthcare policy.
Benefits reported
The University of Oxford have produced a series of preliminary models that have allowed them to verify using descriptive techniques some of the primary hypotheses and take a more holistic view over the data.
DARS-NIC-712819-X8G2J-v1.2 25 September 2024 to 24 September 2025
- Title
- The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
- Commercial
- No
- Sublicensing
- No
- Datasets
- 4
- Files released
- 0
Datasets: Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Maternity Services Data Set (MSDS) v2; Uncurated Low Latency Hospital Data Sets - Emergency Care
What changed from DARS-NIC-712819-X8G2J-v0.9
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2024-09-25 | |
| End date | 2025-09-24 |
Datasets:
− Emergency Care Data Set (ECDS); − MSDS (Maternity Services Data Set) v1.5
Objective for processing
[24 paragraphs unchanged]
o Accident & Emergency
/ Emergency Care Data Set (ECDS)
& Uncurated Low Latency Hospital Data Sets - Emergency Care – necessary
[39 words unchanged]
events which can contribute to health outcomes during pregnancy within the analysis.
• Maternity Services Dataset (MSDS)
–
v2–
necessary to provide information on the care pathways of maternity, natal &
[13 words unchanged]
characteristics of these patients for analysis addressing the aims of the study.
[7 paragraphs unchanged]
• The Data will be limited to a subset of the total available fields available within the datasets as selected by University of Oxford.
The Data requested from Hospital Episodes Statistics will be:
The Data requested from Hospital Episodes Statistics & Emergency Care Datasets (ECDS) will be limited to only include individuals who appear in the MSDS Data requested under this agreement, in addition to:
[16 paragraphs unchanged]
Processing activities
This data sharing agreement is for online access to the record level
[46 words unchanged]
will grant University of Oxford access to the relevant records from the
HES, ECDS
HES APC, HES A&E, Uncurated Low Latency Hospital Data Sets - Emergency Care
& MSDS
v2
datasets via NHS England’s Secure Data Environment (SDE). The Data will contain
[14 words unchanged]
reidentified through linkage with other data in the possession of the recipient.
[9 paragraphs unchanged]
Benefits reported
Stated in the previous version and removed here.
Yielded Benefits is not a requirement for new applications.
Unchanged: Expected output, Expected measurable benefits.
Objective for processing
University of Oxford requires access to NHS England data for the purpose of the following research project:
The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
The following is a summary of the aims of the research project provided on behalf of University of Oxford:
The purpose of this project is to analyse maternity services & secondary care data to determine how different operational interventions impact maternity care pathways and consequently, different health outcomes.
The NHS has set out several priorities in addressing health inequalities. One of the main areas which is consistent within national and local planning are inequalities in maternity and neonatal care, as set out within the annual NHS Priorities and Operational Planning Guidance. These priorities are largely in response to the findings of reports such as those by Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries across the UK (MBRRACE-UK), revealing disparaging outcomes in maternity and neonatal care for socioeconomic deprived individuals and ethnic minorities.
This research project aims to support these priorities by addressing this topical issue through:
- Developing tools to understand how the study can use the mother’s demographic data and ante-natal care pathways to segregate the population in a meaningful way.
- Investigating the impact of operational interventions such as continuity of care for the different sub-populations.
- Proposing personalised pathways to address health inequalities and improve the operational efficiency of the public health system based on the characteristics of mothers.
The operational interventions refer to changes in the way the patient is treated throughout their interaction with healthcare services. The study will look to consider:
1. Continuity of Care: This refers to providing a consistent healthcare experience for the patient by minimizing changes in healthcare providers throughout the maternity care pathway. Continuity of care ensures that patients are treated by the same healthcare professionals (like midwives or obstetricians) during prenatal visits, labor, delivery, and postnatal care. This consistency fosters a better understanding of the patient's history, preferences, and needs, leading to improved communication, trust, and personalized care. On the other hand, continuity of care may result in increased system congestion.
2. Frequency of Visits: This intervention addresses the number of scheduled appointments or check-ups during the pregnancy. A tailored approach to visit frequency can identify potential risks or complications early, ensure proper monitoring of the baby's development, and provide adequate support to the mother. By adjusting the frequency of visits based on individual needs, healthcare providers aim to enhance the quality of care while managing resources efficiently.
3. Frequency of Scans: Ultrasound scans are an essential component of maternity care, used to monitor fetal development, identify abnormalities, and assess pregnancy progress. Operational interventions may involve adjusting the number and timing of scans to ensure they align with patient health conditions, while avoiding unnecessary scans to minimize risks and reduce costs.
4. Staffing Levels and Skill Mix: Ensuring that maternity care units are appropriately staffed with qualified professionals, with the right balance of experience and expertise, can significantly impact care quality and outcomes. This intervention involves adjusting staffing levels to meet patient demands and ensure safe care delivery.
5. Integration of Technology: The use of telemedicine, and other digital tools can improve communication among healthcare providers, streamline care processes, and enhance patient engagement. Operational interventions in this area might focus on implementing or optimizing technology to support continuity of care and better patient outcomes.
6. Collaborative Care Models: These models encourage collaboration between various healthcare providers involved in maternity care, such as midwives, obstetricians, pediatricians, and primary care physicians. By fostering teamwork and communication, these interventions can lead to more coordinated care pathways and improved outcomes for mothers and newborns.
This project has the following aims:
1. Compare outcomes across different sub-groups of patients to determine disparities in health outcomes of those subject to different socioeconomic factors.
2. Compare care cluster pathways across different sub-groups of patients to provide intelligence on how varying care pathways can impact the health outcomes of patients, including time under care. Specifically, to perform analysis to cluster the ante-natal pathways and examine what are the significant distinguishing factors between the pathways.
3. Evaluate the impact of operational interventions on health outcomes. This will aim to assess the effectiveness of interventions such as continuity of care, frequency of visits, frequency of scans and whether these have a meaningful impact on health outcomes. Further to this, to assess whether the impact of these interventions for socioeconomic deprived patients is more significant. For example, the ability to see the same provider who they have built a relationship with at each appointment for deprived patients.
4. Prescriptive analysis - Based on the analyses in aims 1-3, the study then aims to develop personalised pathways based on the clinical and socioeconomic characteristics of the mother. The study will develop and validate novel ante-natal pathways that account for both clinical and socioeconomic patient characteristics as well as operational constraints of the NHS.
The following NHS England Data will be accessed:
• Hospital Episode Statistics
o Admitted Patient Care – necessary to provide information on patients use of secondary care services that may not be recorded within the Maternity Services Dataset (MSDS), but will still contribute to accurately determining the care pathways and health outcomes of patients & provide insight into any acute and relevant chronic conditions that may confound pregnancy outcomes.
o Accident & Emergency & Uncurated Low Latency Hospital Data Sets - Emergency Care – necessary to provide information on patients use of emergency care services, who have or are currently receiving maternity care. This data will allow the study to consider the full picture of these patients care pathways and account for emergency care events which can contribute to health outcomes during pregnancy within the analysis.
• Maternity Services Dataset (MSDS) v2– necessary to provide information on the care pathways of maternity, natal & neonatal patients whilst also helping the study to build a picture the socio-economic characteristics of these patients for analysis addressing the aims of the study.
The level of the Data will be pseudonymised. The Data contains multiple sensitive fields which have been selected for the purpose of this analysis. The sensitive data items are key to ensure the the effectiveness of the analysis. In order to reconstruct maternity pathways effectively, it is imperative to understand the circumstances under which patients accessed emergency care, which requires data on start dates and times, and expiry dates and times. This information is essential for creating a chronological timeline for each patient, allowing for a comprehensive understanding of their healthcare journey. The inclusion of ethnic categories aligns with the aims and objectives of the study. Specifically, the study aims to identify and investigate potential disparities in maternity pathways and health outcomes across different ethnic groups and explore recommendations for improvements.
Access to consultant codes and referrer codes within the HES APC data is vital for tracking which professionals are involved in each patient’s care along their maternity journey. This information allows the study to explore the continuity of care, collaboration among healthcare providers, and any variations in care delivery. Understanding these patterns can provide insights into best practices and areas needing improvement, so that the study can conduct a comprehensive analysis of maternity pathways and associated outcomes.
Several factors pertaining to the mother's health and social circumstances may significantly impact her pregnancy journey and subsequent outcomes. Variables within the MSDS dataset corresponding to complex social factors, mental health conditions, adherence to folic acid supplementation, smoking and alcohol consumption, are crucial for identifying vulnerable groups and tailoring interventions accordingly. Additionally, previous pregnancy outcomes serve as important controls to account for in the analysis. The outcome of the current pregnancy is of central importance in our study, since the study hypothesizes that maternity pathways influence outcomes of the pregnancy. Moreover, factors such as the support network and employment status of the mother can affect her overall well-being and mental health, which are also known to impact pregnancy outcomes.”
The Data requested from the Maternity Services Dataset (MSDS) will be minimised as follows:
• Limited to data between 2018/19 to 2022/23 financial years.
• The Data will cover patients nationally & will include all pregnancies recorded within the above time frame.
• The Data will look at all patients episodes, including the unborn child and neonatal records of patients.
The Data requested from Hospital Episodes Statistics will be:
• Limited to data between 2016/17 to 2022/23 financial years - the study requires access to patients secondary care data from the period preceding their appearance within the MSDS datasets in order to analyse any acute and relevant chronic conditions and associated care delivered which may impact a pregnancy.
• The Data will be limited to a subset of the total available fields available within the datasets as selected by University of Oxford.
University of Oxford is the research sponsor and the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under the UK GDPR is:
Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller;
The lawful basis for processing special category data under the UK GDPR is:
Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.
This processing is in the public interest as it aims to support improved intelligence in the development of policy and guidance on provision of health care regarding maternity care decisions. Through this improved research, the study aims to help address the key priorities set by the NHS focused on improving the health outcomes of maternity patients & reducing inequalities that a large proportion of the population may be subject to. This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding is provided by the John Fell Fund at University of Oxford. The funding is specifically for the study described. The funder(s) will have no ability to suppress or otherwise limit the publication of findings.
Data will be accessed by researchers from the University of Oxford and individuals with an honorary contract with University of Oxford – a collaborating researcher from University of Cambridge working within the study and with the Data and an individual from London Business School specialising in statistical analysis & interpretation of results for stochastic modelling and optimization of service systems focused on by the study. The individuals have completed mandatory data protection and confidentiality training and is subject to University of Oxford’s policies on data protection and confidentiality. The individuals accessing the data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by the individuals. The honorary contractors would only work on the data for the purposes described in this Data Sharing Agreement (DSA).
The study has engaged with two different healthcare professionals prior to the initiation of the study. The input hoped to be gained from their involvement were for the purpose of:
- Providing insights into outcomes
- Associate data driven findings with clinical knowledge and real world impacts
- Ground proposed solutions from these outcomes into practise
Upon initially reviewing the proposed study, the individuals supported the study overall and the use of the data for the purposes described above.
The study has set out an ongoing Patient & Public Involvement & Engagement plan for the duration of the study. The plan includes engagement with patient & public representative groups such as Women's Voice & Maternity Action. The groups will be consulted on their views of the research hypothesis & questions posed by the study, the processing of the Data for the purpose of the study & interpretation of the outputs generated by the study. Engagement is planned throughout 2024 & 2025 for the term of the study and the review of study outcomes.
Expected output
The expected outputs of the processing will be:
1. Submissions to peer review journals, including publications in reputable health journals such as British Medical Journal, as well as Management Science, Manufacturing & Service Operations Management journals among others.
2. Presentations at relevant international conferences, such as the Institute for Operations Research and the Management Sciences (INFORMS)
3. Presentations, conferences, and partnerships with clinical partners, experts in the field, and collaborators in Oxford, Cambridge and London, e.g., maternity units in the Oxford university Hospitals and Cambridge university Hospitals to ensure that the results are disseminated widely among the maternity services community. To maximize the impact of the findings, the research team plans to share the results with key stakeholders and decision-makers in maternity services, including NHS executives, healthcare policymakers, and professional bodies.
4. Publications on social media outlets such as business reviews in universities.
The outputs will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset from which the information was derived. No data that identifies an individual consultant or their performance is permitted to be published.
The output will be communicated to relevant recipients through the following dissemination channels:
1. Peer review journals
2. Social media: The study will promote results through social media platforms and business school magazines/websites, such as Think at London Business School. The research team will work with the media team at the institution to write media pieces about the research findings, including newspapers, online media platforms, and other media outlets.
The target time frame for achieving these outputs is the early 2025 and will be ongoing throughout access to the data.
This research project examining the impact of operational interventions on maternity care pathways and health outcomes offers a range of significant benefits across multiple dimensions. Below is a more detailed description of these benefits, including plans for dissemination and the broader impact on maternity services and healthcare policy.
DARS-NIC-712819-X8G2J-v0.9 26 July 2024 to 31 December 2026
- Title
- The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
- Commercial
- No
- Sublicensing
- No
- Datasets
- 6
- Files released
- 0
Datasets: Emergency Care Data Set (ECDS); Hospital Episode Statistics Accident and Emergency (HES A and E); Hospital Episode Statistics Admitted Patient Care (HES APC); Maternity Services Data Set (MSDS) v1.5; Maternity Services Data Set (MSDS) v2; Uncurated Low Latency Hospital Data Sets - Emergency Care
Objective for processing
University of Oxford requires access to NHS England data for the purpose of the following research project:
The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes
The following is a summary of the aims of the research project provided on behalf of University of Oxford:
The purpose of this project is to analyse maternity services & secondary care data to determine how different operational interventions impact maternity care pathways and consequently, different health outcomes.
The NHS has set out several priorities in addressing health inequalities. One of the main areas which is consistent within national and local planning are inequalities in maternity and neonatal care, as set out within the annual NHS Priorities and Operational Planning Guidance. These priorities are largely in response to the findings of reports such as those by Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries across the UK (MBRRACE-UK), revealing disparaging outcomes in maternity and neonatal care for socioeconomic deprived individuals and ethnic minorities.
This research project aims to support these priorities by addressing this topical issue through:
- Developing tools to understand how the study can use the mother’s demographic data and ante-natal care pathways to segregate the population in a meaningful way.
- Investigating the impact of operational interventions such as continuity of care for the different sub-populations.
- Proposing personalised pathways to address health inequalities and improve the operational efficiency of the public health system based on the characteristics of mothers.
The operational interventions refer to changes in the way the patient is treated throughout their interaction with healthcare services. The study will look to consider:
1. Continuity of Care: This refers to providing a consistent healthcare experience for the patient by minimizing changes in healthcare providers throughout the maternity care pathway. Continuity of care ensures that patients are treated by the same healthcare professionals (like midwives or obstetricians) during prenatal visits, labor, delivery, and postnatal care. This consistency fosters a better understanding of the patient's history, preferences, and needs, leading to improved communication, trust, and personalized care. On the other hand, continuity of care may result in increased system congestion.
2. Frequency of Visits: This intervention addresses the number of scheduled appointments or check-ups during the pregnancy. A tailored approach to visit frequency can identify potential risks or complications early, ensure proper monitoring of the baby's development, and provide adequate support to the mother. By adjusting the frequency of visits based on individual needs, healthcare providers aim to enhance the quality of care while managing resources efficiently.
3. Frequency of Scans: Ultrasound scans are an essential component of maternity care, used to monitor fetal development, identify abnormalities, and assess pregnancy progress. Operational interventions may involve adjusting the number and timing of scans to ensure they align with patient health conditions, while avoiding unnecessary scans to minimize risks and reduce costs.
4. Staffing Levels and Skill Mix: Ensuring that maternity care units are appropriately staffed with qualified professionals, with the right balance of experience and expertise, can significantly impact care quality and outcomes. This intervention involves adjusting staffing levels to meet patient demands and ensure safe care delivery.
5. Integration of Technology: The use of telemedicine, and other digital tools can improve communication among healthcare providers, streamline care processes, and enhance patient engagement. Operational interventions in this area might focus on implementing or optimizing technology to support continuity of care and better patient outcomes.
6. Collaborative Care Models: These models encourage collaboration between various healthcare providers involved in maternity care, such as midwives, obstetricians, pediatricians, and primary care physicians. By fostering teamwork and communication, these interventions can lead to more coordinated care pathways and improved outcomes for mothers and newborns.
This project has the following aims:
1. Compare outcomes across different sub-groups of patients to determine disparities in health outcomes of those subject to different socioeconomic factors.
2. Compare care cluster pathways across different sub-groups of patients to provide intelligence on how varying care pathways can impact the health outcomes of patients, including time under care. Specifically, to perform analysis to cluster the ante-natal pathways and examine what are the significant distinguishing factors between the pathways.
3. Evaluate the impact of operational interventions on health outcomes. This will aim to assess the effectiveness of interventions such as continuity of care, frequency of visits, frequency of scans and whether these have a meaningful impact on health outcomes. Further to this, to assess whether the impact of these interventions for socioeconomic deprived patients is more significant. For example, the ability to see the same provider who they have built a relationship with at each appointment for deprived patients.
4. Prescriptive analysis - Based on the analyses in aims 1-3, the study then aims to develop personalised pathways based on the clinical and socioeconomic characteristics of the mother. The study will develop and validate novel ante-natal pathways that account for both clinical and socioeconomic patient characteristics as well as operational constraints of the NHS.
The following NHS England Data will be accessed:
• Hospital Episode Statistics
o Admitted Patient Care – necessary to provide information on patients use of secondary care services that may not be recorded within the Maternity Services Dataset (MSDS), but will still contribute to accurately determining the care pathways and health outcomes of patients & provide insight into any acute and relevant chronic conditions that may confound pregnancy outcomes.
o Accident & Emergency / Emergency Care Data Set (ECDS) & Uncurated Low Latency Hospital Data Sets - Emergency Care – necessary to provide information on patients use of emergency care services, who have or are currently receiving maternity care. This data will allow the study to consider the full picture of these patients care pathways and account for emergency care events which can contribute to health outcomes during pregnancy within the analysis.
• Maternity Services Dataset (MSDS) – necessary to provide information on the care pathways of maternity, natal & neonatal patients whilst also helping the study to build a picture the socio-economic characteristics of these patients for analysis addressing the aims of the study.
The level of the Data will be pseudonymised. The Data contains multiple sensitive fields which have been selected for the purpose of this analysis. The sensitive data items are key to ensure the the effectiveness of the analysis. In order to reconstruct maternity pathways effectively, it is imperative to understand the circumstances under which patients accessed emergency care, which requires data on start dates and times, and expiry dates and times. This information is essential for creating a chronological timeline for each patient, allowing for a comprehensive understanding of their healthcare journey. The inclusion of ethnic categories aligns with the aims and objectives of the study. Specifically, the study aims to identify and investigate potential disparities in maternity pathways and health outcomes across different ethnic groups and explore recommendations for improvements.
Access to consultant codes and referrer codes within the HES APC data is vital for tracking which professionals are involved in each patient’s care along their maternity journey. This information allows the study to explore the continuity of care, collaboration among healthcare providers, and any variations in care delivery. Understanding these patterns can provide insights into best practices and areas needing improvement, so that the study can conduct a comprehensive analysis of maternity pathways and associated outcomes.
Several factors pertaining to the mother's health and social circumstances may significantly impact her pregnancy journey and subsequent outcomes. Variables within the MSDS dataset corresponding to complex social factors, mental health conditions, adherence to folic acid supplementation, smoking and alcohol consumption, are crucial for identifying vulnerable groups and tailoring interventions accordingly. Additionally, previous pregnancy outcomes serve as important controls to account for in the analysis. The outcome of the current pregnancy is of central importance in our study, since the study hypothesizes that maternity pathways influence outcomes of the pregnancy. Moreover, factors such as the support network and employment status of the mother can affect her overall well-being and mental health, which are also known to impact pregnancy outcomes.”
The Data requested from the Maternity Services Dataset (MSDS) will be minimised as follows:
• Limited to data between 2018/19 to 2022/23 financial years.
• The Data will cover patients nationally & will include all pregnancies recorded within the above time frame.
• The Data will look at all patients episodes, including the unborn child and neonatal records of patients.
• The Data will be limited to a subset of the total available fields available within the datasets as selected by University of Oxford.
The Data requested from Hospital Episodes Statistics & Emergency Care Datasets (ECDS) will be limited to only include individuals who appear in the MSDS Data requested under this agreement, in addition to:
• Limited to data between 2016/17 to 2022/23 financial years - the study requires access to patients secondary care data from the period preceding their appearance within the MSDS datasets in order to analyse any acute and relevant chronic conditions and associated care delivered which may impact a pregnancy.
• The Data will be limited to a subset of the total available fields available within the datasets as selected by University of Oxford.
University of Oxford is the research sponsor and the controller as the organisation responsible for ensuring that the Data will only be processed for the purpose described above.
The lawful basis for processing personal data under the UK GDPR is:
Article 6(1)(e) - processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller;
The lawful basis for processing special category data under the UK GDPR is:
Article 9(2)(j) - processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.
This processing is in the public interest as it aims to support improved intelligence in the development of policy and guidance on provision of health care regarding maternity care decisions. Through this improved research, the study aims to help address the key priorities set by the NHS focused on improving the health outcomes of maternity patients & reducing inequalities that a large proportion of the population may be subject to. This processing is in the public interest because it adheres to the UK Policy Framework for Health and Social Care Research, which protects and promotes the interests of patients, service users and the public, and aims to produce generalisable and publicly available information to inform future decisions over patients’ treatments or care.
The funding is provided by the John Fell Fund at University of Oxford. The funding is specifically for the study described. The funder(s) will have no ability to suppress or otherwise limit the publication of findings.
Data will be accessed by researchers from the University of Oxford and individuals with an honorary contract with University of Oxford – a collaborating researcher from University of Cambridge working within the study and with the Data and an individual from London Business School specialising in statistical analysis & interpretation of results for stochastic modelling and optimization of service systems focused on by the study. The individuals have completed mandatory data protection and confidentiality training and is subject to University of Oxford’s policies on data protection and confidentiality. The individuals accessing the data will do so under the supervision of a substantive employee of University of Oxford. University of Oxford would be responsible and liable for any work carried out by the individuals. The honorary contractors would only work on the data for the purposes described in this Data Sharing Agreement (DSA).
The study has engaged with two different healthcare professionals prior to the initiation of the study. The input hoped to be gained from their involvement were for the purpose of:
- Providing insights into outcomes
- Associate data driven findings with clinical knowledge and real world impacts
- Ground proposed solutions from these outcomes into practise
Upon initially reviewing the proposed study, the individuals supported the study overall and the use of the data for the purposes described above.
The study has set out an ongoing Patient & Public Involvement & Engagement plan for the duration of the study. The plan includes engagement with patient & public representative groups such as Women's Voice & Maternity Action. The groups will be consulted on their views of the research hypothesis & questions posed by the study, the processing of the Data for the purpose of the study & interpretation of the outputs generated by the study. Engagement is planned throughout 2024 & 2025 for the term of the study and the review of study outcomes.
Expected output
The expected outputs of the processing will be:
1. Submissions to peer review journals, including publications in reputable health journals such as British Medical Journal, as well as Management Science, Manufacturing & Service Operations Management journals among others.
2. Presentations at relevant international conferences, such as the Institute for Operations Research and the Management Sciences (INFORMS)
3. Presentations, conferences, and partnerships with clinical partners, experts in the field, and collaborators in Oxford, Cambridge and London, e.g., maternity units in the Oxford university Hospitals and Cambridge university Hospitals to ensure that the results are disseminated widely among the maternity services community. To maximize the impact of the findings, the research team plans to share the results with key stakeholders and decision-makers in maternity services, including NHS executives, healthcare policymakers, and professional bodies.
4. Publications on social media outlets such as business reviews in universities.
The outputs will only contain aggregated information with small numbers suppressed as appropriate in line with the relevant disclosure rules for the dataset from which the information was derived. No data that identifies an individual consultant or their performance is permitted to be published.
The output will be communicated to relevant recipients through the following dissemination channels:
1. Peer review journals
2. Social media: The study will promote results through social media platforms and business school magazines/websites, such as Think at London Business School. The research team will work with the media team at the institution to write media pieces about the research findings, including newspapers, online media platforms, and other media outlets.
The target time frame for achieving these outputs is the early 2025 and will be ongoing throughout access to the data.
This research project examining the impact of operational interventions on maternity care pathways and health outcomes offers a range of significant benefits across multiple dimensions. Below is a more detailed description of these benefits, including plans for dissemination and the broader impact on maternity services and healthcare policy.
Benefits reported
Yielded Benefits is not a requirement for new applications.
Register history
When this agreement appeared in, or was edited in, each monthly edition of the register. Built by comparing every edition this site holds.
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September 2024 —
first listed. 1 version: DARS-NIC-712819-X8G2J-v0.9
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November 2024
1 version added: DARS-NIC-712819-X8G2J-v1.2
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July 2025
1 version added: DARS-NIC-712819-X8G2J-v2.3
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November 2025
1 version added: DARS-NIC-712819-X8G2J-v3.4
Cite this page
NHS England (2026) Data Uses Register, September 2026 edition, agreement DARS-NIC-712819-X8G2J, “The Effect of Operational Interventions on Maternity Care Pathways and Health Outcomes”. Read via NHS Data Access Explorer (unofficial), https://healthdatauses.uk/agreements/dars-nic-712819-x8g2j/ (accessed [date]).
This address stays the same, but the page is rebuilt with each monthly edition, so the citation names the edition it shows. Every edition's data is kept in the facts store.
Source: datausesregister_september2026.xlsx, September 2026 edition of the NHS England Data Uses Register. Search that workbook for DARS-NIC-712819-X8G2J to see the original rows.