NHS Digital Data Release Register - reformatted
Department Of Health (dh)
Project 1 — DARS-NIC-09122-R1S1D
Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)
Sensitive: Non Sensitive
When: 2017/09 — 2019/01.
Repeats: Ongoing, System Access
Legal basis: Health and Social Care Act 2012, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)
Categories: Anonymised - ICO code compliant
- Hospital Episode Statistics Admitted Patient Care
- Hospital Episode Statistics Accident and Emergency
- Hospital Episode Statistics Outpatients
- Hospital Episode Statistics Critical Care
Access to the data helps to inform national policy development aimed at the improvement of patient outcomes more generally. Since the Health and Social Care Act, DHSC delivers most operational improvement and policy development through arms length bodies such as NHS England. This means that analysis if often used to identify and better understand emerging issues - such as demand pressures on acute hospitals including those waiting for extended lengths of stay and/or suffering from delayed discharges - and to inform strategic thinking and initial policy phases. It is also used to advise and brief ministers. Examples of outputs are included in relevant section but include, for example, development of robust 7 day services policy to improve outcomes for patients needing emergency admissions on a weekend.
The HDIS system enables organisations to access HES data for a wide range of data analytical purposes. The system is an online analytical processing tool through which the users of this organisation data has access to a wide range of analytical, graphical, statistical and reporting functions. Access is provided to the entire HES dataset (non-identifiable) for the specific purposes as listed below. The Department of Health will use the HDIS system through the analysis of HES data for the following purposes: - Benchmarking; - Provision of support services; - Producing publications including contributing to national and regional publications such as A7E reports; - Supporting the Government in the development and monitoring of policy; - Early analysis for projects and programmes to support commissioning and policy decisions; - Commissioning decisions; - Responding to and answering of parliamentary questions in a timely fashion as part of statutory duties. The analysis conducted by DH is wide ranging and will most often be used for internal DH purposes. DH analysts do however also provide support to other agencies including NHS England, PHE, NHS Blood and Transplant etc. Department of Health analytics team is sometimes under great pressure from No. 10 / Secretary of State (SofS) to provide statistics such as: • How many more operations (FCEs with a procedure or intervention) the NHS or individual providers are doing now compared to earlier years • Waiting times for common procedures such as hips, knees, cataracts in England compared to another devolved administration, usually Wales (with PEDW being the Welsh equivalent of HES) The analytics team carry out a project for the OECD to provide information on volumes and costs of specific procedures and groups of patients. The criteria used to determine which individual cases should or should not be included is fairly rigid and HES allows the criteria to be set to meet the requirements exactly. The data the team provide is used to create indicators of efficiency and productivity which are comparable on an international basis and are used for the “Health at a Glance” publication. A Recent example of HDIS use has been used to explore the determinants of emergency admissions from A&E from 2010 onwards. The research question was: are non-elective admissions from A&E driven only by demand-side factors (type and severity of condition)? Do supply-side factors (hospital capacity) matter? The team conducted this analysis as part of the value maps project: a piece of analysis HM Treasury commissioned from every central government Department in order to assess their understanding of current and potential efficiency and effectiveness. In terms of methodology, a logit model was used where the unit of observation was an A&E episode from 2010 onwards and the binary dependent variable described whether the episode ended up in a non-elective admission or not. This research project is currently on hold due to other emerging priorities however it is scheduled to be finalised after DH2020. The above project is an important example for the following reasons: (1) it was fundamental to have patient level data (as it was the only way to control for observable demand-side factors); (2) it was part of a high-profile piece of work (commissioned by (Director General of public spending and finance at HMT) and (Chief Economic Adviser at HMT), and presented to a panel of senior officials from prestigious organisations (Deputy National Statistician and Director General for Population and Public Policy at ONS) and (Chief Executive of the Behavioural Insights Team and Board Director)] Two further examples of how data are being used: a. Analysis of acute care data including bed days and emergency admissions to support the New Models of Care and Transformation programmes (both SofS priorities). Department Of Health rely on HES data to analyse time trends and local variation to feed into SoS Transformation meetings and other needs. b. Analysis of referrals to Outpatients – this has informed a range of policy work including extending the ability of AHPs to refer directly to Outpatient clinics, the savings potentially achievable by key interventions such as GP One Stop, local patterns of referral by demographics. Accident and Emergency is one of several compartments in the Model Hospital (MH). It has been developed by combining key indicators recommended by the Royal College of Emergency Medicine (RCEM) with productivity metrics recommended by Lord Carter operational productivity team. One of the purpose of the MH is to serve as a platform to enable Trusts to compare resource and associated clinical output, level of responsiveness as well as their overall financial productivity to that of their peers. Some of the indicators created using the data you provided are below: - % waiting <6 hours: RCEM opinion is that four other flow metrics in combination with the four hours standard waiting time performance metric are essential to optimizing the productivity of the emergency department. The ‘A&E 6hrs waiting time performance’ is one of the four metrics. - Aggregated Patient Delay (APD): This adds granularity to the 4hrs target and removes the false dichotomy in which 3 hrs. 59 minutes is regarded as a success and 4 hrs. 1 minute a failure. - Inpatient Daily Discharge Ratio (DDR): This enables hospitals to predict capacity shortfalls and allows the wider healthcare system to intervene to ameliorate such situations. Low ratios are known to be associated with increased A&E waits the next day. - Using HES to assess length of stay for elective and non-elective patients by day of the week to form a key benefit in the 7DS in hospital impact assessment – this is key analysis would have been impossible without HES. This will feed into the Impact Assessment on 7 Day Services. Data will only ever be used for purposes relating to healthcare or the promotion of health in line with the requirements of the Health and Social Care Act 2012 as amended by the Care Act 2014.
The use of HDIS allows DH analysts to have a secure access to a remotely hosted software application for the analysis of HES data. Having access to record level downloads will permit the following activities which are not possible/practical within the HDIS system itself: - following individual patient pathways through each of the datasets - following individual patient pathways chronologically - permits linkage of HES data to anonymous data (e.g. Health Resource Group tariff information) The provision of this tool enables rapid analysis to be performed on the most recent version of the data. The availability of this function is crucial to DH in circumstances where speedy analysis is required to react to either local public health, commissioning or research requirements. Access to the data helps to Inform national policy development aimed at the improvement of patient outcomes generally.
Due to the nature of the organisation, outputs are often unknown in advance and these will be driven by changing policy and ministerial priorities. Any outputs that are produced from the system 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 in line with the HES analysis guide. Users are not permitted to link data extracted from the system to any other data items which make the data identifiable. Below are some recent examples of the uses of HES data within DH: • Input to the quality assurance of denominator data derived from KH03 (quarterly bed availability and occupancy) used by PHE in annual publication of Healthcare Associated Infections (HCAI) rates. • Development of Alcohol Attributable fractions. It is anticipated that a similar approach might be used in future for new developing public health analyses. Through analysis of the data it is possible to calculate the cost of alcohol to the NHS which are carried out annually to support DH policy teams business case. A similar approach has been taken for smoking. • Research into areas of current policy interest, eg pneumonia. • As part of the New Models of Care and Transformation agendas (both SoS priorities), a key efficiency metric that will be used to measure success is bed days. DH has utilised HES data to understand this metric further, i.e. what variables in HES are used to calculate bed days, how good is the measure, etc. DH are currently using the data to explore some possible hypotheses such as: - Whether there are more bed days for patients admitted in the week vs. at the weekend; and - Under what treatment specialties are bed days very high, etc. None of this work so far has been used for official briefings or publications, but it is very likely that HES will be needed in the near future for briefings and QA. DH intend to utilise the HES data for other metrics for new models of care (NMC) and Transformation, for example A&E attendances and performance against the A&E 4-hour waiting standard. • DH works closely with DfE on policy for hospital schools. A new model of funding for hospital schools is being developed and HES data is playing an important role in this. • OECD research into Purchasing Power Parity in healthcare provision – An analysis is being carried out on the activity and prices for delivery of certain specific healthcare services (inpatient and day case basis). To do this access to HES data is required which details this at HRG level. • Cross sectional and time series analysis to understand efficiency and productivity of healthcare providers – This analysis is to be used for work relating to the Lord Carter report on efficiency, reporting on measures of efficiency and productivity for Secretary of State and HMT • Ministerial briefings - On-going work to understanding the link between activity/workload and staffing levels, work to understand impact upon safety and quality of care. • Internal analysis to provide management information required for the spending review.
This application is for online access to the record level HES database via the HDIS2 system. The system is hosted and audited by NHS Digital meaning that large transfers of data to on-site servers is reduced and NHS Digital has the ability to audit the use and access to the data. HDIS is accessed via a two-factor secure authentication method to approved users who are in receipt of an encryption token ID. Users have to attend training before the account is set up and users are only permitted to access the datasets that are agreed within this agreement. Users log onto the HDIS system and are presented with a SAS software application called Enterprise Guide which presents the users with a list of available data sets and available reference data tables so that they can return appropriate descriptions to the coded data. The access and use of the system is fully auditable and all users have to comply with the use of the data as specified in this agreement. The software tool also provides users with the ability to perform full data minimisation and filtering of the HES data as part of processing activities. Users are not permitted to upload data into the system. Users of HDIS are able to produce outputs from the system in a number of formats. The system has the ability to be able to produce small row count extracts for local analysis in Excel or other local analysis software. Users are also able to produce tabulations, aggregations, reports, charts, graphs and statistical outputs for viewing on screen or export to a local system. Any record level data extracted from the system will not be processed outside of the analytics team. Only registered HDIS users will have access to record level or aggregate data containing small numbers downloaded from the HDIS system. All HDIS users with access to the HDIS system are substantive employees of DH. Following completion of the analysis the record level data will be securely destroyed. DH currently has 24 licenses for access to HDIS and have the option to apply for further licenses if required. Approval for additional licences will be managed by the NHS Digital.
Project 2 — HDIS_Department of Health
Opt outs honoured: N
Sensitive: Non Sensitive
When: 2016/04 (or before) — 2016/08.
Legal basis: Health and Social Care Act 2012
Categories: Anonymised - ICO code compliant
- Access to HES Data Interrogation system
The HES (Hospital Episode Statistics) Data Interrogation System (HDIS) allows users to securely access HES, interrogate the data, perform aggregations, statistical analysis, and produce a range of different outputs. Access to HDIS is only provided to organisations who work within the public sector with a specific interest in public health. There is a strict information governance applications process in place to protect and control how the data is managed.