NHS Digital Data Release Register - reformatted
Imperial College London
Opt outs honoured: N
Basis: Section 251 approval is in place for the flow of identifiable data
Format: Anonymised - ICO code compliant Sensitive
How often: Ongoing
When: unknown — 11/2016
HSCIC Id: DARS-NIC-345991-H2F5N-v0.0
Data: Hospital Episode Statistics Accident and Emergency
Data: Hospital Episode Statistics Admitted Patient Care
Data: Hospital Episode Statistics Critical Care
Data: Hospital Episode Statistics Outpatients
Data: Hospital Episode Statistics Accident and Emergency
Data: Hospital Episode Statistics Admitted Patient Care
Data: Hospital Episode Statistics Critical Care
Data: Hospital Episode Statistics Outpatients
Data: MRIS - Scottish NHS / Registration
Data: MRIS - Cause of Death Report
Data: MRIS - Scottish NHS / Registration
Data: MRIS - Cohort Event Notification Report
Data: MRIS - Members and Postings Report
Data: MRIS - Cause of Death Report
Data: MRIS - Cause of Death Report
Data: MRIS - Cohort Event Notification Report
Data: Hospital Episode Statistics Accident and Emergency
Data: Hospital Episode Statistics Admitted Patient Care
Data: Hospital Episode Statistics Critical Care
Data: MRIS - Flagging Current Status Report
Data: MRIS - Scottish NHS / Registration
Data: MRIS - Cause of Death Report
Data: MRIS - Cause of Death Report
Data: MRIS - Cohort Event Notification Report
Data: MRIS - Cause of Death Report
Data: MRIS - Cohort Event Notification Report
Data: MRIS - Cause of Death Report
Data: Hospital Episode Statistics Admitted Patient Care
Data: Hospital Episode Statistics Outpatients
Data: Hospital Episode Statistics Accident and Emergency
Data: Hospital Episode Statistics Critical Care
Data: Hospital Episode Statistics Admitted Patient Care
Data: Office for National Statistics Mortality Data
Data: MRIS - Members and Postings Report
Data: Hospital Episode Statistics Critical Care
Data: Hospital Episode Statistics Admitted Patient Care
Data: MRIS - Scottish NHS / Registration
Data: Hospital Episode Statistics Accident and Emergency
Data: Hospital Episode Statistics Admitted Patient Care
Data: Hospital Episode Statistics Outpatients
Output: -Specific outputs expected, including target dates:
All outputs will contain only aggregate data with small numbers suppressed in line with the HES Analysis Guide.
The study will yield a PhD Thesis between October 2019 and October 2020 in addition to published academic papers as follows:
1. The structure of interhospital transfers in the NHS.
2. The impact of patient choice in maternity care on local service supply and demand.
3. The structure of care networks for patients following trauma, stroke and cardiovascular events, comparing regions with established care networks to those without.
4. A network analysis demonstrating the interdependence of secondary care providers in the NHS.
5. Predictive modelling of patient flows to secondary care providers in response to changing organizational capacity.
Each of these papers will be targeted at health policy or health informatics journals including;
The British Medical Journal
Annals of Surgery
The Lancet
British Journal of Obstetrics and Gynaecology
Health Affairs
International Journal of Systems Science.
It is intended that all work will be presented at academic conferences prior to publication, including;
Health Systems Global Symposium – UK
International Health Policy Conference – UK.
Results will also be directly reported to NHS England and NHS Improvement where appropriate.
Activities: The Department of Surgery and Cancer confirms that the data under this application would only be used for the project described in this document. Individuals working on this project would only be permitted to access data relating to that project, as identified within the application. Access is granted to the data only to named individuals working on the project under authorized user names. Such access is password controlled (with a password reset required on a regular refresh). Only substantive employees of Imperial College London will use the disseminated data and only for the purposes described in this document.
The raw data will be handled only within the Department of Surgery and Cancer to support academic research.
The data will be received from NHS Digital and stored on a secure server hosted at the South Kensington campus of Imperial College. Access to data on this server is restricted to authorized individuals only. The data is accessed and processed by researchers who are based in rooms with keyless combination locks that are always locked when not in use.
This access is password-based and permitted solely to registered users logging on via permitted IP addresses. Record level data will not be distributed to different parts of the organization. The data will not be made available to third party individuals, institutions or companies.
No other data will be linked to this data though data will be compared at aggregate levels if required.
The data will be processed as part of the above mentioned research project within the Department of Surgery and Cancer. It will be queried using data analytical tools such as SPSS, STATA, SAS, Microsoft Excel, Matlab, Python etc. to aid in answering specific research questions. Data visualizations will be done to present insights gained using suitable tools including Tableau, Inkscape and others.
The specific processing activities will be as follows:
1. A patient level database for interhospital transfers of patients will be constructed, in addition to a range of utilization and outcome variables (e.g. length of stay, additional procedures, readmissions). A patient level database of maternity care will be constructed to examine patient choice in relation to delivery location. In both cases, directed unipartite networks of care transitions from one provider to another will be constructed and the characteristics of the network, providers and patients will be analysed using linear and logistic regression.
2. A patient level database of presentations to acute hospitals will be constructed. This database will be used to identify the probability of presentation of a patient in a particular geographic location to a particular centre with a particular diagnosis. These values will be used to undertake computational community detection algorithms to identify geographically nested networks of care providers to compare to existing predetermined care networks and guide the implementation of novel, more efficient networks of care.
3. A patient level database of outpatient, inpatient and A&E presentations will be created. The aggregate interaction across datasets between a particular geographic region (e.g. postcode, LSOA or primary care provider) and a secondary care institution will be used to generate a unipartite network of acute hospitals linked to one another by the strength of their shared patient activities. The network will then be interrogated to identify how patterns of patient flow will change in response to increased patient demand and altered provider capacity. Clusters of vulnerability in the network will be identified and optimal avenues for intervention will be suggested.
Objective: The Department of Surgery and Cancer, based at Imperial College London, is requesting data for use in the following research project:
The Power of Connections: Mapping the Behaviour of Health Care Networks
The purpose of this study is to examine how care providers in England are connected by virtue of the patients that flow between them. This request for data will, through the application of network analysis, provide insights into the factors determining how patients flow through the network and where the network may be particularly vulnerable will be identified.
Strategies to improve the efficiency, equity and safety of the network may be developed and tested using predictive modelling in order to identify to optimal routes for investment and restructuring of the health care providers.
This project will use the following data: HES OP 2011/12-2014/15, HES A&E 2011/12-2014/15 and HES APC 2011/12-2014/15. These four years of data are necessary to provide an adequate picture of health care utilization and capture less common events.
Benefits: The measurable benefits to health and social care are expected to be as follows:
1. The structure of interhospital transfers in the NHS.
The transfer of a patient from one hospital to another often occurs at critical periods in a patient’s journey where they can no longer be optimally cared for by their current hospital. Recent centralization of specialist services has increased the need for transfer to another hospital to receive specialist care.
This transfer process is a period of increased patient risk, where an often critically ill patient is transferred by ambulance over significant distances and whose care is handed over to an entirely new team of clinicians. Which patients need to be transferred, when and to which hospital remains poorly understood, as do their health outcomes relative to those who do not need to be transferred to receive the same specialist care.
Through the publication of this work in relevant academic journals, insights into the movement of patients from one hospital to another may be achieved by clinicians and commissioners. This knowledge may be used to both understand the factors which influence patient and physician choice, and also incorporate these factors into future service design. By understanding the circumstances that lead to patient transfer the aim is to identify patient groups that are particularly likely to undergo interhospital transfer and to focus on the development of local and national strategies to ensure optimal transfer of care for these specific groups.
It is expected that this work will be completed within three months of receipt of the data.
2. The impact of patient choice in maternity care on local service supply and demand.
Patient choice is an increasingly important factor of care delivery in the NHS. The factors underlying patient choice remain poorly understood, in part because of the many patient and provider factors that influence decision making.
Expectant mothers can freely choose which hospital they would like to deliver their maternity care. Maternity care is delivered frequently across the country and as it is generally focused solely on the process of giving birth, the variability in patient and provider factors is far less than for other clinical scenarios. This therefore serves as an excellent setting to model the factors which underlie patient choice.
In the context of maternity care, where patients can freely choose where their care is delivered, certain providers may be repeatedly favoured or avoided by expectant mothers in response to a range of factors including individual previous experience, geography or waiting times. This may lead to demand for certain providers becoming too great to be met, while others have unused capacity. Identifying and predicting these factors allows providers locally and nationally to correct imbalance in the supply and demand relationship for maternity care, thereby optimising the effectiveness of maternity provision nationally.
It is expected that this work will be completed within three months of receipt of the data.
3. The structure of care networks for patients following trauma, stroke and cardiovascular events, comparing regions with established care networks to those without.
The introduction of defined care networks for the treatment of trauma, stroke and cardiovascular disease in parts of the NHS have demonstrated significant improvements in patient outcomes where they have been implemented. In the case of stroke care, networks have been extremely successful in London and Manchester where they have been introduced. The rest of the country currently does not have the same effective network structure. Using the principles of community detection analysis and Markov models is would be possible to identify for the London and Manchester stroke networks whether their structure optimally reflects the distribution of disease and pattern of clinical practice in the geographic areas they cover.
Outside of these two networks it would be possible to examine whether similar network structures already informally exist elsewhere in the country, and develop a nationwide stroke network, in a manner like that which was created for the highly successful national trauma network. This knowledge would inform the development of a national stroke network so that the benefits already obtained from its implementation in London and Manchester may be available nationally. Publication of these findings in high impact health policy journals will bring this work to the attention of key stakeholders nationally and locally.
It is expected that this work will be completed within nine months of receipt of the data.
4. A network analysis demonstrating the interdependence of secondary care providers in the NHS followed by predictive modelling of patient flows to secondary care providers in response to changing organizational capacity.
Demand for health care within the National Health Service continues to rise, and does so in a stochastic fashion. Each hospital has a finite capacity to provide safe care, and therefore a threshold over which harm is more likely to result. The likelihood of the demand being placed on a hospital exceeding the care it can safely provide is dependent upon the local incidence of disease and its intrinsic capacity to provide care, but is also critically dependent on the performance of its neighbouring hospitals.
If a hospital is unable to meet the demands placed on it, the burden of care provision falls to its neighbouring hospitals, which therefore see an increase in the demands placed on their services. Hospitals with many nearby hospitals may be less vulnerable to this pattern of behaviour and would therefore be said to have a low degree of interdependence, while a pair of hospitals with no nearby neighbours would be highly interdependent on the behaviour of one another.
This principle when applied across hospitals the National Health Service will identify areas of high interdependence within the health care network. Areas of high interdependence of care providers are expected to be less resilient to increases in demand for care or reduction in the capacity to provide care. Identifying these vulnerabilities will assist NHS England in identifying hospitals who require additional investment to ensure the ongoing delivery of high quality patient care.
It is intended that the predictive models developed from this work will be published in high impact health policy or general medical journals to reach the widest possible interested audience. Additionally, the methodological insights from this work will be disseminated either in the form of a further journal article or white paper for NHS Improvement and to detail the application of these techniques. It is expected that this work will be completed within 12 months of receipt of data.
The proposed work in focusing on the interconnectedness of healthcare providers, represents an exciting, novel and important means by which the efficiency and equity of health care provision may be examined in a new light, with a high likelihood of lasting improvement to the NHS as a whole.
Source: NHS Digital.