Models of Resilience – Covid-19 and Non-Covid-19 Contexts

The University of Birmingham is requesting to retain data disseminated by NHS England in order to help them to determine the impact of hospital-level variation in organisational and clinical approaches to acute care delivery at the hospital/community interface during waves of COVID 19 (e.g., prescribing strategies, staff redeployment, integrated community care planning, etc.) on indicators of healthcare resilience such as (a) operational outcomes (e.g. acute care flow, discharge rates), (b) clinical outcomes for COVID-19 related conditions (e.g. mortality, readmission, rates of pulmonary embolism), and (c) clinical outcomes for non-COVID-19 health conditions (e.g. rates of new onset heart failure, stroke). The data requested will make it possible to study five comparative periods of analysis: (i) Pre-COVID-19, no winter pressures, (ii) Pre-COVID-19, winter pressures, (iii) COVID-19 outbreak peaks, (iv) Post-COVID-19 peaks, and (v) Concurrence of COVID-19 and winter pressures (season 2020-2021).

The surge of COVID-19 has had a profound impact on the management and delivery of acute healthcare. To tackle the epidemic, trusts have redesigned organisational models with changes in processes of assessment and care delivery, redeployment of staff, new pathways of care, and different prescribing strategies. These changes have been implemented to provide a rapid increase in acute care assessment and treatment capacity across a system of care for patients with COVID-19-related symptoms, whilst also trying to maintain delivery of care for patients with non-COVID-19 healthcare needs.

The purpose of this agreement is to determine the optimal design of the acute care interface with the community, by correlating hospital-level care delivery approaches elicited by the Society for Acute Medicine Benchmarking Audit data (SAMBA) and hospital and patient outcomes from HES data before, during, and after the COVID-19 periods.

The data requested will support the achievement of the aim of the project through the construction and analysis of indicators of hospital and healthcare resilience, which is defined as (1) the ability to deliver acute care for COVID-19, and (2) the ability to provide standard care for non-COVID-19-related conditions that can present with acute complications. Examples of resilience indicators include readmission rates, length of stay, mortality, intensive care unit admission rates, number of specialist visits, number of elective and emergency hospital admissions (for COVID-19), rates of heart failure (for non-COVID-19-related conditions).

The datasets from NHS England will allow the University of Oxford (University of Birmingham's sole Data Processor) to construct indicators of hospital resilience for COVID-19, and non-COVID-19-related conditions that can evolve and develop complications that require acute care (such as, e.g., heart failure, stroke, cancer) by:

- Following patients across different types of health services that they use before, during, and after COVID-19 outbreak periods;

- Accounting for multiple episodes of hospital attendance/admission and study readmissions for COVID-19 and non-COVID-19-related symptoms;

- Estimate out-of-hospital mortality for patients using data from the Civil Registry (Deaths) - Secondary Cut.

The requested data (years 2018 to 2021) will allow the data processor (University of Oxford) to study five comparative periods of analysis:

(i) Pre-COVID-19, no winter pressures

(ii) Pre-COVID-19, winter pressures

(iii) COVID-19 outbreak peaks

(iv) Post-COVID-19 peaks

(v) Possible interactions between COVID-19 and winter pressures (season 2020-2021).

Due to the novel setting and disease that this project studies, and the as yet unknown COVID-19 and non-COVID-19-related medical complications that the current pandemic may cause, there is a major exploratory element to this study. The uncertainty related to the object of investigation requires access to multiple sources of data such as HES critical care, A&E, Outpatients and Inpatients, emergency care (ECDS), as well as the civil registry of deaths (secondary cut). The data processor, the University of Oxford, used the pseudonymised code provided by NHS England to follow patients across the different NHS England products that were requested for this agreement.

The University of Oxford will use the hospital code in HES to complement the analysis with information at the hospital and catchment area level from publicly available datasets and the Society for Acute Medicine's SAMBA survey of practice, which provides information regarding the size and staffing organisation of each acute medical department in the UK, alongside strategies for care delivery as well as methods of interaction with community care providers. In particular, the project will use SAMBA data from the 2018 and 2019 Winter version, and the 2020 COVID-SAMBA survey.

The Society for Acute Medicine (SAM) is the national representative organisation for acute health care staff. Formed in 2000, the Society now has over 1000 affiliates, the majority of which are doctors training or specialising in acute medicine. SAM delivers annual SAMBA audits to assess acute medicine approaches and the sharing of good practices. These are England-wide surveys at the hospital level and, in the UK, they are recognised by the Healthcare Quality Improvement Partnership.

The study that is subject of this agreement is part of a broader project, which has three operational tiers:

(i) The first part includes literature reviews, engagement with stakeholders and a survey of healthcare delivery practices of UK acute medicine units at the hospital level, based on the Society for Acute Medicine Benchmarking Audit (SAMBA). This part of the project will not use NHS England data.

The Principle Investigator (PI) of the overall project is an active member of the SAM (Society for Acute Medicine) network, has delivered three previous national surveys through the SAMBA network, and has published peer reviewed papers analysing key points from previous audits.

(ii) The second part of the project is the empirical analysis of hospital resilience based on the NHS England data that the University of Birmingham (Data Controller) is requesting in this agreement. This part will rely on developing quantitative econometric analyses of indicators of healthcare resilience for COVID-19 and non-COVID-19 diseases with acute complications constructed from the HES data. Examples of indicators of healthcare resilience include mortality rates, readmission rates, rates of pulmonary embolism, average length of stay in intensive care units, rates of new onset heart failure or stroke, rates of A&E attendances and emergency admissions for heart attack and stroke/transient ischaemic attack (TI), during and after the first COVID-19 wave.

Hospitals will be grouped by common approaches to organisation of care from the SAMBA survey (see (i) above). The trust/hospital/deliverer-level variables that describe care delivery approaches elicited from SAMBA will constitute the main explanatory variables. The analyses will control also for patients' demographics and comorbidities, and data on pre-COVID-19 organisational practices and healthcare needs of the patients and of the population in the trust/hospital/deliverer catchment area. The analysis will deliver aggregate-level results that do not identify individuals, and the publications will not identify hospitals. All outputs will be aggregated with small numbers suppressed in line with the HES analysis guide.

(iii) The third part of the overall project will develop a qualitative study to learn about healthcare seeking behaviour among patients with non-COVID-19 severe disease. For example, this part of the project will develop focus groups to understand the reasons behind the reorganisation/postponement and delay of diagnoses (e.g., for cancer-related screenings and the screening and treatment of heart failure). This final part of the project will also include qualitative work based on site visits (or remote interviews) in well performing systems of care, to understand how novel structures and organisational contexts were successfully implemented and embedded. This part of the study will not involve analyses of NHS England data nor any linkage to NHS England data.

The analysis will control for underlying health conditions, healthcare needs, and characteristics of the population in acute care units and in their catchment areas. Information on different care delivery approaches at the hospital level will be elicited from a national survey of organisation and delivery of acute care, the Society for Acute Medicine Benchmarking Audit (SAMBA). The SAMBA dataset is described below. Importantly, SAMBA contains information at the hospital level and does not entail patient-level linkages.

The findings from this programme of research will enable policy makers within the Department of Health and Social Care and NHS England to determine how best hospitals and community systems should organise and deliver care during and after waves of COVID-19.

The GDPR legal basis for processing data for this research comes under Article 6(1)E – "task in the public interest" The data processing will provide evidence to help (a) policymakers make evidence-based policy decisions, (b) hospital managers to develop evidence-based decisions on the organisation of acute medical services, and (c) acute care clinicians to understand which practices have improved the resilience of acute care services.

The public interest that justifies the processing of this data relates to the improvements that can be made to health-care provision within the NHS as a result of the findings. The University of Birmingham is proposing to process data under point (j) of Article 9(2).

The 2020 version of SAMBA for COVID-19 (COVID-SAMBA) collects hospital-level information about variations in organisational and care delivery approaches during the COVID-19 outbreak, such as the degree of integration across acute/community healthcare providers (e.g., discussion of guidelines and common planning for the referral and management of patients with ambulance services, primary care providers, and care homes), novel care pathways (e.g. prescription and patient screening strategies, staff redeployment), and novel structures/systems of care (e.g. home-based hospitalisation).

With regards to the analysis for which the University of Birmingham is requesting access to NHS England data, the SAMBA surveys will provide information at the hospital level on care delivery approaches. SAMBA data will be linked to HES data using hospital site codes and not at the patient level.

After the onset of the current pandemic, the Department for Health and Social Care (DHSC) asked the project team to analyse pressures as a consequence of COVID-19, as this is an overwhelming national priority in acute care. In particular, following the research focus commissioned by the DHSC, this project defines the concept of hospital resilience as the ability to meet the acute healthcare needs of the population during COVID-19. The researchers will assess which organisational and care delivery practices are associated with improved healthcare delivery performance.

Other parts of the study, which are not based on the requested data and do not include data analysis, involve literature reviews, consultations with stakeholders, health professionals and patients, and qualitative work in a sample of acute hospitals. The University of Birmingham’s data processor, the University of Oxford, will process the NHS England data received and conduct a quantitative analysis for this project.

The University of Birmingham holds the main NIHR research contract for the overall study and has entered into an honorary contract with the Chief Investigator (CI). The CI will formulate hypotheses to be tested and help to interpret the findings of the overall study. Hence, the University of Birmingham is the Data Controller. It will not, however, be involved in processing the data. In its capacity as the University of Birmingham’s data processor, the University Oxford team will hold and process the NHS England data. The University of Birmingham are determining the means and purpose of the processing of the personal data and the University of Oxford are providing their expertise as the data processor but have no role in determining the means and purpose of the processing. The University of Warwick, where the CI now resides, will not be involved in any decisions about the data nor the analysis of the data.

The University of Leicester employs the researchers undertaking the qualitative component of the wider study, that is the third part of the study as described above. The University of Leicester team will not access, process nor control the data.

Department for Health and Social Care (DHSC) has no role in the conduct of the study. It is providing the funding (through the NIHR) and will receive the outputs. It is not involved in deciding which analyses should or should not be conducted.

The overall project, including the collection of SAMBA data and the cross-mapping of SAMBA with HES data, received ethical approval.

Since the study is not an evaluation of a specific intervention, the research approach is not based on a distinction between treated and control groups. Rather, the empirical design relies on correlations between hospital-level care delivery approaches and health outcomes. More specifically, the analysis will correlate indicators based on patient clinical information, mortality data from civil death registry with hospital-level indicators that identify relevant elements in the organisation of acute care delivery during and after COVID-19 outbreaks, elicited from the SAMBA survey.

As the data processor on behalf of the University of Birmingham, Oxford will inform the analysis using data from all attendances at A&E specialist or outpatient clinics or admissions between January 2018 and September 2021. This project requires information on all patients attending/admitted to the hospital, with information on the referral status, the cause of attendance/admission, inpatient/outpatient visits and outcomes, the length of stay, and the clinical health outcome for each episode/service use. The analysis will be conducted with pseudonymised data and no individual patient data will be released.

The purpose of this project is to understand which acute care delivery approaches developed and implemented before, during and after COVID-19 outbreaks translate into better acute care and health outcomes for the population, and to identify the practices best able to make hospitals more resilient when there is an outbreak of a disease such as COVID-19 or the winter flu. Combining SAMBA and HES data will allow the data processor, the University of Oxford, to achieve this aim. While SAMBA contains all the information relating to the processes of care implemented by English hospitals, HES data make it possible to investigate how these processes affect patients and hospitals.

This project requires data from the following data sets:

Emergency Care Data Set (ECDS)

Hospital Episode Statistics Accident and Emergency (HESA&E), non sensitive data

Hospital Episode Statistics Admitted Patient Care (HESAP), non sensitive data

Hospital Episode Statistics Critical Care (HESCC), non sensitive data

Hospital Episode Statistics Outpatients (HESO)

HES: Civil Registration (Deaths) - Secondary Care Cut link

The ECDS data requested is not currently within the TRE dat offering and thus this request can not at this point in time be fulfilled by the NHS England TRE service.

Using the pseudonymised identifiers provided by NHS England to bridge the products requested, the data analysis will connect patient's admission episodes across the HES products (inpatient, outpatient, critical care) and with (i) readmissions, and (ii) out-of hospital mortality (through the Death Civil Registry). As the data processor, the University of Oxford will analyse this information also in conjunction with hospital-level care delivery approaches from the Survey of Acute Medicine Benchmark Audit (COVID-SAMBA and 2019, 2020 SAMBA - please see point 4 of this section and the attached documents for a description) and with aggregate metrics of general health and population characteristics in the acute department's catchment area from publicly available data sources.

Due to the as yet unexplored and as yet unknown context of a novel disease outbreak, and because this project studies how COVID-19-related care as well as non-COVID-19-related conditions relate to different healthcare provision approaches, information on all symptoms and causes of hospital admission is necessary. As features of acute illness are often non-specific (e.g. confusion, generalised functional decline, reduced mobility among older adults), the project requires all available health information without restriction to specific conditions. In addition, there is no guidance yet as to which groups of patients have had fewer admissions due to COVID-19 and its overall effect on hospitals' ability to deliver care: therefore looking at all hospital admissions is the most inclusive and correct approach.

Hospital Episode Statistics Accident and Emergency data will allow the data processor, the University of Oxford, to identify whether patients that attend A&E are discharged or admitted, and to classify the cause of attendance (COVID or non-COVID related).

HES A&E (and the ECDS, once a code will be developed), HES-Outpatient, HES-Inpatient, HES-Critical Care will make it possible to:

- Follow patients that attend A&E/ the hospital/ trust in the subsequent stage (i.e., inpatient / outpatient / discharged), record their process of admission and outcome (e.g., length of stay and clinical health outcome);

- Control for the utilisation of primary care before and after an acute illness that requires A&E attendance or

inpatient/outpatient admission;

- Construct and correlate indicators of acute care resilience with organisational changes and care delivery practices during and after COVID-19 outbreaks (from the SAMBA hospital-level data).

Civil Registration Deaths - Secondary Cut will allow the data processor at the University of Oxford to link attending/admitted patients with out-of-hospital mortality outcomes.

In particular, the University of Birmingham is requesting the following groups of variables:

- Admissions - Period of care (e.g., method, source, date, waiting time) to control for different circumstances and procedures of admission in the analysis of the correlation between hospital-level acute care delivery approaches and average health outcomes, and group patients’ health outcomes by heterogeneous characteristics;

- Augmented/critical care period variables, with information such as time, period, outcome, source, discharge, status, intensive care, high dependency of patient’s admission episodes, to construct outcomes for the analysis (e.g., average time in intensive care, mortality, probability of high dependency case), controlling for further clinical and admission characteristics;

- Clinical information with date of operation, cause of admission, primary and additional diagnosis codes, operation status, and durations, to control for these elements in the analysis, construct health outcomes by specific circumstances/causes/etc. of admission, and duration of the episode(s);

- Clinical information regarding patient classification and consultant/treatment specialty, and Practitioner/Referring organisation codes, to categorise patients’ health outcomes according to specific treatment groups either by own classification or consultant specialty or practitioner;

- Diagnosis codes and Alcohol Attributable Fraction;

- Discharge dates and methods (and flags), to control for length of admissions and cross-validate precision of the duration, and study time lags between readiness for discharge and actual discharge, and their trends before, during, and after peaks of acute care activity;

- Episodes and spells (Period of care) data, such as dates, durations, types, ward types, and Patient Pathway information, to form groups of similar episodes and to control for such characteristics in the analysis of the correlation between care delivery approaches and health, mortality, and readmission outcomes;

- Geographical codes (e.g., CCG, area, region, site code of GP practice, treatment, residence areas, ONS electoral ward codes), Healthcare resource groups (HRG), Organisation codes/information, and Socio-economic indicators (location-based IMD indexes), to control for/group health outcomes by locations, and associate health outcomes to other local-level information from publicly available data at the trust/catchment area level;

- Patient demographic data, to group patients by categories or control for patient characteristics in the empirical analysis of the correlation between hospital-level care delivery approaches and indicators of health care resilience from patient health outcomes;

- System Data to verify validity of assignment of patient/CDS/SUS codes.

The specification of a COVID-19 diagnosis for patients will be based on the ICD-10 code.

This project only requires pseudonymised data, because the analysis will follow patients in the different services/units. The analysis requires patient level records to analyse health outcomes by different patterns of use of the healthcare services and to be able to group/control for demographic characteristics, waiting times, diagnosis, procedures, etc. Furthermore, patient record data will allow the empirical estimations to follow patients/episodes of care across the various NHS England products such as, e.g., deaths registry data, outpatients, etc., to measure healthcare outcomes, before, during and after the pandemic. The University of Birmingham does not request any identifiable or "high risk" variable, and the estimation outcomes and findings of this project will be produced solely in aggregate form, with small numbers suppressed in line with the HES analysis guide.

Nonetheless, the results of the quantitative analyses will only be included in the study outputs and communicated at an aggregate level. There will be no way to identify individual or critically small/selected groups of people from the results of the study all outputs will be aggregated in line with the HES analysis guide. The estimations will only deliver coefficients of correlation between care delivery practices and aggregate categories of health outcomes and indicators (e.g., total A&E admissions, mortality rate, total admissions in cardiology, ICU admissions, average length of stay by non-identifiable demographic characteristics such as age groups).

This data request is limited to the years between 2018 and 2021 inclusive.

This will allow the University of Birmingham to study five comparative periods of analysis:

Pre-COVID-19, no winter pressures (2018-2019, spring-summer)

Pre-COVID-19, winter pressures (2018-2019, winter)

COVID-19 outbreak peaks (2019-2020 winter and spring)

Post-COVID-19 peaks (e.g., July-August 2020)

Possible interactions between COVID-19 and winter pressures in the winter season of 2020-2021.

The quantitative analysis will compare the outcomes of patients in different hospitals and acute care units across England and, as such, it needs data concerning all English hospitals.

There exists no possibility other than via HES to construct and analyse variables that are based on following patients across different units of care, multiple episodes of admission, and out-of-hospital mortality at the hospital/acute care unit aggregate level. This project requires patient-level information also to account for patients' demographic characteristics, and prevalence of as yet not know preconditions and co-morbidities in the reference population that may contribute to determining the success and failure of hospital/acute care unit care delivery organisational approaches and practices in terms of both COVID-19 and non-COVID-19 related care.

The University of Birmingham has minimised the request in the time dimension. In particular, the required data is limited to the years between 2018 and 2021, ending with the release of September 2021.

Due to the exploratory nature of the project and as yet unknown consequences of COVID-19 and care delivery approaches during the current pandemic, the request is not restricted to specific health conditions and causes of admission. The aim of this proposal makes it necessary to request and explore individual-level data because this study is the first of its kind, and the context of the COVID-19 pandemic is as yet unexplored. This analysis will request and explore all possible conditions, causes of admission, and demographic characteristics. It is not possible to pre-aggregate and request health outcomes at the hospital level. This is required to understand the pathways of each individual in the use of the health system, in response to the COVID-19 pandemic, and to group outcomes by (or control for) demographic characteristics, waiting times, diagnosis, and procedures in the analysis.

The ethnic category variable is requested because there is evidence that people from BAME communities are the most affected by the COVID-19 pandemic and the analysis needs to control for this factor. This project requires only pseudonymised data and the request does not include any identifiable or "high risk" variable. The results of the quantitative analyses will only be communicated and included in the study outputs at an aggregate level, further suppressing critically small/selected groups of people.

The request is further minimised by excluding data concerning maternity and psychiatry.

The University of Birmingham is the sole Data Controller. University of Birmingham are determining the means and purpose of the processing of the personal data and the University of Oxford are providing their expertise as the data processor but have no role in determining the means and purpose of the processing. The University of Oxford operates under specific protocols for processing of data directed by the University of Birmingham.

The University of Warwick is not carrying out joint data controllership activities, in light of the Chief Investigator holding an honorary contract with the University of Birmingham, but being a substantive employee of the University of Warwick. The University of Birmingham will remain the only Data Controller, according to its original contract with DSHC and NIHR . University of Leicester employs the researchers undertaking the qualitative component of the wider study. They are not involved in the NHS England data processing.

The Department of Health and Social Care (DHSC) is the commissioner of this project. DHSC has no direct influence over the analysis performed and will have access to a final report of the findings but not the data used. The project funder is National Institute fir Health Research (NIHR).

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract, i.e.: employees, agents and contractors of the Data Recipient who may have access to that data)”

No data is flowing into NHS England. The analysis will employ the requested data solely in its pseudonymised form. The ethnicity data flowing is not identifiable but is a sensitive field. The research team will not publish any individual-level or identifiable information, with patient level HES data used solely to produce a range of aggregate variables for each hospital and to produce information for healthcare resilience indicators. The results of the analyses will be disseminated in aggregate form (e.g., the mean value of a clinical outcome and its standard deviation), with small numbers suppressed in line with the HES analysis guide and the publications will not identify hospitals by name.

The organisation responsible for data processing is the University of Oxford. As the University of Birmingham’s data processor, the University of Oxford will analyse the correlation between hospital-level care delivery approach variables (from the COVID-SAMBA data) and HES-based indicators of healthcare resilience (e.g. readmission rates, mortality for COVID-19 related symptoms, length of stay in Intensive Care Units (ICU), etc.).

This project will develop a dashboard of indicators at the hospital level including information relating to care delivery approaches (from the SAMBA survey) and aggregate baseline acute health and frailty outcomes. These measures will be correlated with healthcare resilience indicators from the NHS England data using site-specific codes (i.e., hospital identifiers). As the University of Birmingham’s data processor, the University of Oxford will process that data. It will not link the requested NHS England data with any other data at individual patient level. The analysis will be based solely on information at hospital level derived from the requested HES data and hospital level information from other data sources, in particular with SAMBA survey data, but this will not involve any linkage at patient level.

HES data will be used exclusively in its pseudonymised form. The analysis will follow patients across the requested NHS England datasets using only pseudonymised codes, such as the Encrypted HESID. The data will not be linked to any other data about individual patients.

As the University of Birmingham’s data processor, the University of Oxford will add to the analyses at hospital level the SAMBA data, which is collected at hospital level from a survey of clinical practice, without any patient data. The project will use COVID-SAMBA and 2019, 2020 regular SAMBA datasets. In addition, the project will add site-level information from a range of publicly available data for hospitals, Commissioners of Health and Social Care and local authorities, using site codes.

The datasets are:

- Office for National Statistics (ONS) data (population aged 65+, IMD of area, rurality index), https://www.ons.gov.uk/;

- NHS workforce statistics (hospital staff, community staff, primary care staff) by CCG, https://digital.nhs.uk/data-and-information/publications/statistical/nhs-workforce-statistics ;

- NHS Digital NHS Outcome Framework (https://digital.nhs.uk/data-and-information/publications/statistical/nhs-outcomes-framework) ;

- Adult Social Care Outcome Framework measures https://digital.nhs.uk/data-and-information/publications/statistical/adult-social-care-outcomes-framework-ascof ;

- Skills for Care data on adult social care staff by LA, https://www.skillsforcare.org.uk/adult-social-care-workforce-data/Workforce-intelligence/publications/Data-and-publications.aspx;

Care Quality Commission (CQC) data on ratings of hospitals, https://www.cqc.org.uk/about-us/transparency/using-cqc-data; and

- NHS England SitRep data on hospital performance, closures, and bed pressures, https://www.england.nhs.uk/statistics/statistical-work-areas/winter-daily-sitreps/ .

The datasets include only variables that are aggregated at the hospital level or catchment areas and there is no identification of any patient.

There is no requirement for the study to re-identify individuals for this project and the University of Birmingham and its data processor, the University of Oxford, confirm that no attempts will be made to re-identify individuals.

As the University of Birmingham’s data processor, the University of Oxford will process the data received. This team resides at the University of Oxford, Nuffield Department of Primary Care Health Sciences (NDPCHS). All researchers and staff at the University of Oxford follow specific protocols for the protection and confidentiality of the data. All team members are also subject to training on these requirements initial upon start at the Department and annually thereafter. This team will retain the data on a secure, network server and limit access to only those researchers approved to access it via an encrypted remote desktop application.

Outputs from the study will include:

(a) Tables of HES-based information aggregated at hospital level with any small numbers suppressed (if there are any), such as number of admissions in period t of patients with condition X;

(b) Correlation or regression coefficients from analyses of hospital level data, such as correlation between operational practice X and proportion of patients with COVID-19 who died within 28 days; and

(c) Possibly a composite resilience index for each hospital calculated as a weighted sum of some of the hospital level data.

The project team will disseminate the research findings to patients, clinicians, professional bodies, and policy makers, and publish the aggregate results of the study in academic journals. The project will produce reports for the Department of Health and Social Care and communicate findings through webinars and conference presentations.

The results of this study will consist of the coefficient of correlation (or effect size) between an organisational or healthcare delivery practice and aggregate outcomes such as:

i. Operational outcomes (e.g. acute care flow, discharge rates);

ii. Indicators of healthcare resilience based on clinical outcomes for patients with COVID-19, e.g.:

• Mortality rates,

• Readmission rates,

• Rates of pulmonary embolism,

• Probability of readmission for suspected COVID-19,

• Average length of stay in intensive care unit;

iii. Indicators of healthcare resilience based on clinical outcomes for patients who do not have COVID-19, e.g:

• Rates of new onset heart failure or stroke,

• Total numbers and rates of A&E attendances and emergency admissions for heart attack and stroke/TI, during and after the first COVID-19 wave.

The researchers will not publish any disaggregated data or information about single individuals or critically small and identifiable groups of individuals. The University of Oxford will ensure that discrete variables cannot be used (either alone or in combination) to identify an individual. Tabulations and summaries of outcomes that may contain very small sample numbers in some cells will not be reported. Tables and other outputs will not be published in a form where the level of geography would threaten the confidentiality of the data.

The project team will disseminate the research findings to patients, clinicians, professional bodies and policy makers, as well as publish in academic journals. The evidence produced by this research will be directly relevant to:

A. Policymakers, planners and decision-makers;

B. Health providers, managers and practitioners.

The dissemination activities are designed with the goal of informing and supporting health and care policy through developing evidence that is crafted and presented with the policy user in mind, rigorous and authoritative, and timely.

The dissemination activities will include a one-day conference for key stakeholders at the end of the project, seeking their responses to study results. The project will ensure that a range of relevant organisations are included at the conference, such as professional societies, CCGs, service users, and carers. This event will be press released.

The project will inform practice at local and national level, leveraging the national roles of co-applicants and collaborators to ensure a wide dissemination to policy makers, relevant Policy Research Units, and professional societies. The project will disseminate findings of hypotheses of health system resilience through practice networks, professional societies and ALBs.

The project will raise public awareness by producing lay summaries of the results in accessible formats, including through webinars and blog entries, which will ensure a broad dissemination thanks to the extensive resonance of the network of universities and stakeholders involved in the project.

The project team will leverage the national roles and visibility of its co-applicants, collaborators, and funding partner to ensure a wide dissemination of the products of the research to policy makers in ALBs (NHS Improvement, Getting It Right First Time, Health Education England) as well as relevant Policy Research Units (Commissioning, Older people and Frailty) and professional societies (British Geriatrics Society, Society for Acute Medicine). The project team will disseminate the findings of this exploration of best practices in acute care delivery and health system resilience in COVID-19 times through practice networks, professional societies and ALBs (Arms Length Bodies)

To raise public awareness, this project will produce a summary of the results in an accessible format with the help of the PPI Panel and distribute it to a range of stakeholders, e.g. the NHS, commissioning groups, policymakers and service users. The University of Birmingham and its data processor, the University of Oxford, will ensure that the research is synthesised and communicated in a meaningful and clear way, such that the results of this study can be employed by all beneficiaries in practice to deliver real healthcare benefits.

The results and outputs of this project do not involve the development of tools, technologies, algorithms, or any similar instruments that may entail issues related to data and knowledge ownership, management, rights, and access.

Target date for the preliminary analysis of acute care delivery during COVID-19 outbreaks: late Summer 2021

Target date for the preliminary analysis of acute care delivery during COVID-19 outbreaks and winter pressures (possibly occurring in winter 2020-2021): Autumn 2021 - Winter 2021/22

Target date for the final analyses, report writing, end of project dissemination meeting, and press release/press coverage: Winter 2022-Spring 2023.

Complications experienced during the project mean that we were unable to complete the project to the timetable originally outlined in our application (below). We have nearly finished the analysis proposed in the application but need to maintain access to the data so that we can complete the final report and publications, make any amendments required by reviewers and meet the data retention policies of the University.

The anticipated evidence produced by this research is hoped will be directly relevant to

a) patients and NHS beneficiaries,

b) policymakers, planners and decision-makers, and

c) health providers, managers and practitioners.

The study hopes to produce findings on what changes in the organisational and cultural approach of hospitals are associated with better coping with Covid-19. These are hoped will be relevant for the development of policy, the organization of NHS acute medical services and the management of patients with COVID-19 and of patients with other conditions during national or local increases in numbers of COVID-19 patients. The dissemination of the anticipated study findings is hoped will enable the NHS to take measures to improve patient care based on evidence gathered on the topics studied.

The project and its dissemination strategy is designed to rapidly inform the Funder (DHSC) and engage in ongoing debates and policy reviews. The University of Birmingham and its project partners have worked with the Patient and Public Involvement (PPI) panel group, study Steering Group and the Funder to agree an engagement and dissemination plan at the start of the project, with activities running throughout its course. The anticipated outputs from the study are centred on informing policy and acute service provision. The University of Birmingham and its team will use its varied professional networks and professional social media presence to raise awareness of the outcomes of this study and maximise engagement with its findings. The external stakeholder group of this project, comprising representatives from the Royal College of Emergency Medicine, the Society for Acute Medicine, NHS Providers and Care England, will consider the anticipated research findings and where it is hoped these will inform potential service improvement or further resources that could help service provision.

It is hoped that the study will identify which organisational and healthcare delivery approaches minimise the impact of COVID-19 in the community, and will identify which practices support the ability to deliver routine care in COVID-19 times. The two focuses of this research project benefit the public interest because they may lead to improved health outcomes, via adoption by the healthcare community. It is hoped that the short-term findings on clinical strategies and organisational approaches associated with high performance in “peak 1” of COVID-19 will inform policy for acute hospitals and acute community providers for any subsequent outbreaks, whether these outbreaks are national or more localised.

Medium term benefits are hoped will be the identification of strategies to maintain ‘business as usual’ healthcare for both acute non-COVID-19 illnesses and serious longer term disease.

Both sets of results will be available to policymakers and health care providers and their guidelines will benefit the public interest and the community.

This project has been solicited by the Department of Health and Social Care (DHSC), to understand how the current pandemic is affecting the delivery of acute care and the delivery of routine care for conditions that may develop into acute complications. By investigating which care delivery approaches entail a better performance for patients with COVID-19 as well as non-COVID-19-related conditions, this project it is hoped will be able to directly inform policy and treatment for future waves of COVID-19, and similar pervasive public health emergencies, and to inform development of new standards of care delivery. The anticipated project outputs and results are hoped will directly feed into policymakers’ decisions. The project team will share the results also with the academic, scientific, and general communities with help from professional networks and by drawing on the team’s personal networks.

The project dissemination plan includes the following list of activities and tentative timeline:

AUTUMN – WINTER 2021:

- Main interim report (draft stage): findings of the COVID-19 related research analysis

- PPI panel meeting

- External Stakeholder Group meeting

WINTER 2021:

- Journal article, first draft: results of the COVID-19 SAMBA questionnaire findings

- Journal article, first draft: results of the quantitative analysis of hospital resilience

SPRING 2022:

- External Stakeholder Group meeting: presentation of the interim results

- PPI panel meeting: presentation of the interim results

- Workshop: presentation of the interim results

- Paper articles: submission to scientific/academic journals

AUTUMN 2022-SPRING 2023

- Final analyses and report writing

- External Stakeholder Group meeting, with a press release

- PPI panel meeting

- End of project dissemination meeting with a press release.

- Press coverage: blog articles, social media-based dissemination activities

It is hoped that with the help of Funder, advisers and stakeholders this project will make findings available to DHSC, NHS England, NHS Acute Trusts and professional organisations so that they can use them to inform their decision-making. The plans for dissemination are set out above.

The University of Birmingham hopes that the findings of this project should lead to improved decision-making by policy-makers, NHS managers and clinicians. While it is not certain in advance of conducting the study what specific decisions will be made as a result, the findings of this project will lead to improvements in the organization and management of acute medical care that will in turn lead to improved quality of care for patients and improved outcomes.

There is potential for large numbers of patients with acute medical conditions to benefit from improvements to their care based on evidence provided by this study. There is also potential for efficiency if improved care leads to better outcomes, including fewer emergency re-admissions and fewer patients experiencing deterioration of their condition resulting in need for more intense and costly treatment. The benefits will accrue to NHS acute services and ultimately to patients.

The benefits could be monitored through future surveys and future analyses of HES and other data sets, if DHSC decides to conduct or fund such monitoring. The University of Birmingham envisages that benefits will start to accrue soon after dissemination of the findings. This may depend on the specifics of the findings and on decisions by DHSC and NHS managers and clinicians informed by the findings.

The study does not support a PhD/post graduate research study.

This project has led to an advancement of methods for measuring and modelling organisational resilience through analysis of the multi-faceted data relating to NHS trusts in England. This includes the development of new statistical models and tools to indicate resilience at the hospital level. Furthermore, the University of Birmingham (UoB) have created a greater knowledge of differences in resilience across NHS hospital trusts in England through analysis of the Resilience data at the organisational level. This provides a broader perspective of variations in resilience across hospitals after a major health shock – in this case COVID-19. This research illuminates factors that contributed to some trusts demonstrating resilience and the ability to recover from the effects of the pandemic more rapidly and effectively than others.

The team have finished one study, examining how these hospital characteristics in acute care are associated with recovery of elective activity performed by hospitals after the height of the Covid-19 pandemic, compared to pre-pandemic levels. Using patient-level data from Hospital Episode Statistics aggregated at monthly-trust level for all English National Health Service (NHS) acute hospital trusts in 2019 and 2021, UoB estimate the associations between hospital recovery rate and hospital pre-pandemic characteristics by employing linear regressions of the proportional change over time in elective activity against a set of explanatory variables related to supply (e.g., hospital size, workforce, type of hospital, regional location) and demand factors (e.g., population need, patient case-mix) and time factors. The results show that the explanatory variables are not systematically associated with hospital recovery rate, except for regional differences. The implication for policy development is that the evolution of hospital recovery rates in elective activity varied across English regions, especially for high-volume and high-risk elective specialties.