NHS Digital Data Release Register - reformatted

Wilmington Healthcare

Project 1 — DARS-NIC-16016-Y9H1D

Opt outs honoured: N

Sensitive: Non Sensitive, and Sensitive

When: 2016/04 (or before) — 2018/05.

Repeats: Ongoing

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Critical Care
  • Diagnostic Imaging Dataset
  • Mental Health and Learning Disabilities Data Set
  • Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset

Benefits:

PURPOSE 1: Disease Insight Reports (DIRs) The following groups will be an end beneficiary of DIRs: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients • DIRs aims to assist health and social care through one or several of the following: o Creating a national platform from which to roll out local analysis and improvements o Identifying both current performance and themes around problems affecting individual diseases o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources An example of the benefits DIRs have already achieved is below: A report has been published on the MS Trust and NHiS websites. This report was also shared by Public Health England and the National Clinical Director for Neurology, and been mentioned at all NHiS Neurology commissioning network events during 2015/16 to several hundred senior stakeholders in the NHS. At a neurology commissioning meeting NHiS held on 19 November in Haydock, an MS Nurse Consultant in Salford highlighted how she is now using this data to support their service redesign in MS. One such report (the Neurology Intelligence Report) is the result of close working between a number of organisations including the National Institute for Health Research, which is the research arm of the NHS. The report authors found that in many cases, minor illnesses, which could potentially have been assessed and treated and managed proactively, were responsible for admissions in people with neurological conditions. In addition overall they identified a significant rise in the number of people with neurological conditions who were admitted to hospital, or seen as outpatients. PURPOSE 2: Q-PASS • At a macro level the broad benefits to health and social care of using Q-PASS are: o A reduction in inappropriate hospital activity and cost – avoidable admissions, readmissions, excessive length of stay (LOS) o An overall improvement in patient outcomes – reduced comorbid disease, mortality, LOS, hospital acquired infection; move patient treatment from inpatient to outpatient or primary care o A reduction in the burden on social care – effectively designed clinical pathways and services using Q-PASS stop patients from leaving healthcare and becoming a burden on social care • Q-PASS achieves the benefits by : o Assisting local health and social care environments in identifying where service efficiencies and patient outcomes can be improved before monitoring the impact of any intervention o Studying disease progression, over time, both locally and nationally. Process map patient cohort journeys through data to show the cost of ineffective disease management and the consequences to patients and the social system o Showing healthcare activity and cost, comparing like-for-like organisations and trending data over time o Mapping performance locally and nationally where specialist teams or resources are in place o Providing a reliable evidence baseline for performance to inform key decisions and to enable measurement of impact on the condition o Addressing health inequalities o Providing transferable collaborative service solutions o Measuring success and effectiveness post implementation of a new pathway or service • Examples of where HES outputs being used by health and social care from Q-PASS: o MS Pathway in Hull via the Hull Royal Infirmary used NHiS HES data to understand service need and create a new MS pathway : https://cmscactrims.confex.com/cmscactrims/2014/webprogram/Paper2416.html o Neurology admission and cost analysis across the SE Coast Strategic Clinical Network. This work formed the audit aspect of the commissioning cycle from which service design recommendations were then created. The analysis identified in one CCG alone, for one neurological condition, £237k of potentially avoidable UTI admissions could be saved: http://www.secscn.nhs.uk/files/1114/0360/1253/130614_NHS_South_Kent_Coast_CCG_data_intelligence_report.pdf An example of the benefits Q-PASS has already achieved is below: A life science company in Lancashire has used Q-PASS dashboards to help the CCGs identify issues around variation in the management of type 2 diabetes across the CCGs and their constituent GP practices, develop a new treatment guideline, and support practices with the highest need with training and mentorship to improve their confidence to manage patients appropriately in accordance with the new guidance. PURPOSE 3: Tabulations • Tabulations assist health and social care through rapidly being able to assist with one or several of the following: o Identifying both current performance and identifying problems affecting individual disease management o Improving the management of specific diseases o Prioritising areas to improve, identify and address variation in services, improving value o Improving efficiency and effectiveness to maximise resources • The following groups will be the end beneficiary and user of Tabulations: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) An example of the benefits the tabulations have already achieved is below: Life Science companies have used HES data tabulations to help NICE better understand the current management of Dupuytren’s contracture, including the range of treatment approaches and the costs associated with these. This has enabled NICE to review their guidance to support a more patient-friendly and cost effective treatment approach to be approved. NHiS’ Commissioning Excellence Directorate has used HES data tabulations to enable understanding of patient management in neurology for Redditch and Bromsgrove, Worcestshire and Wyre Forest CCGs. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions and provided recommendations based on consultation with Paris and carers. All recommendations accepted by the 3 CCGs and service transformation is underway. In addition, HES data tabulations have been used to enable understanding of patient management in neurology for Walsall CCG. This has enabled understanding of which patient pathways should change to address common issues relating to emergency hospital admissions. The CCG is currently utilising the data to enable service transformation.

Outputs:

PURPOSE 1- Disease Insight Reports Published outputs from DIRs are peer reviewed, aggregated data which can either be: • A written report in Word summarising analysed data • An Excel sheet containing aggregated HES data, complete with accompanying commentary • A PDF of either of the above • Any equivalent medium available for printing or web publishing PURPOSE 2 - Q-PASS The following outputs are available within respective elements of Q-PASS. Outputs will be generated by aggregating the data and applying suppression in line with the HES Analysis Guide and other policies as stated within the Data Sharing Agreement (DSA). Secondary suppression^ is also applied to prevent users being able to calculate the number within a suppressed field. Disease Management Dashboards & Maps (Dashboards): • Display dashboards and/or maps produced by disease type and/or geographic area • Display medium can be online and offline (Excel dashboards and Tablet) • PDF export report option available Disease Management Analyser (Analyst) • Excel based system to analyse any aggregated, suppressed, non-sensitive, non-identifiable ICD10/OPCS4 or HRG code for any non-sensitive, non-identifiable fact or dimension in any clinical pathway or organisation • An online system • Export function available Patient Pathway and Service Design Modeller (Modeller) o Offline Excel based system to model the design and cost of one clinical pathway or service against alternatives to establish the most efficient option o Uses the data outputs from Analyst to power the model o Export available PURPOSE 3 - Tabulations Published outputs from tabulations are peer checked, aggregated, double suppressed^ data which is provided: • On an Excel sheet (or PDF version) • For mandatory publishing on the Web

Processing:

General Processing Activities: All outputs are published at an aggregated level using non-sensitive, non-identifiable HES, MHMDS and DIDs data and in line with the required legislation, guidelines plus policy documentation listed within NHiS’s existing Data Re-Use Agreement (DRA) with the HSCIC. This includes suppressing numbers 1 to 5 with either primary or double suppression (* = double suppression is the suppression of any field(s) that would allow imputation of a small number). ^Double Suppression Example: In primary suppression the following is displayed: Total Admissions = 10, Elective Admissions = 9, Non Elective Admissions = * Day Case = 0; in double suppression the following is displayed: Total Admissions = 10; Elective Admissions **, Non Elective Admissions = **; Day Case = 0). This highly secure approach to small number suppression was recognised by the HSCIC’s audit team as an area of good practice, stating in their report, “Double suppression of Small Numbers provides extra assurance of security around patient identification.” NHiS match organisation level (aggregated) data from HES to publicly available GP Prescribing, QOF and ODS data, but only to meet the objectives listed and not for the purposes of re-identifying any individual. For clarity, no record level data is supplied by NHiS to third parties and therefore no identifiable data is either available nor can be inferred. This was confirmed by The Information Commissioners Office (ICO), who performed an independent review of NHiS’s management of HES data in August 2014. The ICO confirmed, in a published letter, that NHiS neither handles nor creates personal data when using Hospital Episode Statistics and that the ICO was satisfied with NHiS’s use of HES data in relation to managing personal data. Pseudonymised HES, MHDMS and DIDs data are securely downloaded via the HSCIC SEFT server and stored on a secure network drive in one location in England. Record level data are loaded into a data warehouse, on a dedicated private non-external facing server, prior to aggregation into a separate database (both of which are stored independently in the same location in England). The final aggregated, non-sensitive, non-identifiable outputs are uploaded to professionally hosted user facing servers in England. Only the final aggregated database links to user interfaces, meaning record level data is inaccessible via any user interface. Access to the network drive and servers that contain the pseudonymised record level data and aggregated database are restricted to named, fully trained members of NHiS staff with internal audits carried out (and documented) to ensure that only the appropriate, trained personnel within the organisation have access to these datasets. Specific Processing Activities – PURPOSE1: Disease Insight Reports (DIRs) What specifically are Disease Insight Reports? DIRs are an analysis of a disease and/or its management, predominately in secondary care. The methodology of the analysis is based on in-house research undertaken by NHiS using non-sensitive, non-identifiable, record level data. Published outputs will be based on peer reviewed, aggregated, double suppressed^ data and can be exported in the form a PDF, Excel Workbook, written document or equivalent medium available for printing or web publishing. For clarity, NHiS has complete and independent editorial control over the outputs of Disease Insight Reports. NHiS commits to as part of this Purpose statement to: • Publishing all aggregated results, irrespective of outcomes and independent of external influence. • Having outputs reviewed by a member or members of the NHiS advisory group consisting of Medics (GP & Hospital Consultant), Statistician, NHS Health Service Data Analysts, NHiS Insight Consultant, Former head of UK policy and practice adviser for Long Term Conditions at the Royal College of Nursing. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES, MHMDS or DIDs extract? Please note: Only NHiS will require pseudonymised, record level, non-sensitive, non-identifiable data. Users will view only aggregated, non-sensitive, non-identifiable data which has all suppression rules applied. Record level data is stored separate to aggregated data. There is no possibility of users accessing record level data. DIR’s use patient cohort analysis which requires an aggregation of pseudonymised, non-sensitive, non-identifiable record level data. The ability to be able to see diagnosis, procedures and HRGs by multiple individual episodes at record level is imperative to being able to undertake the analysis for DIRs. The diverse and intricate episode level data is not available through an extract. Specific Processing Activities – PURPOSE 2: Q-PASS What specifically is Q-PASS? Q-PASS is an electronic on and offline commissioning support solution which uses aggregated, double suppressed^ , non-sensitive, non-identifiable record level HES, MHMDS or DIDs data as the outputs. The service is used by registered, authenticated users who have access under licence, over a sustained period, typically of one year. There are three main elements to Q-PASS: • Disease Management Dashboards & Maps (Dashboards) • Disease Management Analyser (Analyst) • Patient Pathway and Service Design Modeller (Modeller) Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES, MHMDS or DIDs extract for Q-PASS? Please note: Only NHiS will require access to record level, non-sensitive, non-identifiable record data for the reasons listed below. All other users will receive aggregated, non-sensitive, non-identifiable data which has all suppression rules applied in line with the HES Analysis guide and the guidance within Part 2, section 3.5 of the Data Sharing Framework contract. Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the NHiS HES Data Flow v1r0 and HES Protocol v1r0 documents). Pseudonymised non-sensitive, non-identifiable record level data is required so NHiS registered users can: • Comprehend how spells break into episodes at record level, to enable all non-sensitive comorbid conditions and procedures to be rolled into aggregated, non-identifiable, patient cohorts. This allows users to analyse the existing pathway against modelled pathways in detail, to portray disease progression over time at patient cohort level, and to study the impact of poorly managed disease over time. • Establish what HRGs are being applied to each episode in a spell at record level, prior to aggregating into user output data. This allows users to view whether a more efficient tariff or route of treatment could be used. • Provide an aggregated way of demonstrating how the pathway, disease or service being analysed intricately fits with related inter related pathways. For example in diabetes pathway and disease analysis, the areas of obesity, cardiovascular disease, ophthalmology, renal etc. will also require analysing. For each health & social care geography, interrelated pathways are different, meaning the complete spectrum of ICD10, OPCS4 and HRG codes are required. • Analyse patient outcomes such as comorbid disease, procedures and unnecessary hospital activity (admissions, excess bed days, readmissions) at an individual level prior to aggregating into predefined cohorts for use by users in the Modeller system. • Produce a system that allows users to compare at aggregated level one organisation, geography or patient cohort against another with similar characteristics (socio, demographic and ethnicity). This allows users to understand what best practice can look like. The ability to provide feedback in relative real time on the success of a new pathway or new service, is critical to the realisation of the redesign project. This means a monthly breakdown and routine data refresh at record level will be required. How does access and use of the Q-PASS system work? For all user groups access works as follows: • Each user organisation agrees a legal contract with NHiS stipulating terms and conditions (T&Cs) and contains a sub licence with the HSCIC. This contract contains but is not limited to: o Purpose of data access – as defined in this Purpose Statement between NHiS and the HSCIC o Restrictions on use of data outputs o Duration of contract o Number of users - with an addendum that lists user name and job function o Requirement to publish and reference (where possible) any work which uses the outputs of the HES, MHMDS or DIDs data within Q-PASS o Confirmation that failure to apply with the above will result in NHiS removing the organisation from the approved user list and notifying the HSCIC of the organisation and reason as to removal • On contract signing NHiS provides HES Protocol training to all registered users, which is an on or offline assessment that demonstrates that users know the regulations plus T&Cs relating to use of HES, MHMDS or DIDs data outputs. These users known as Registered, Approved users or RAs also receive an RA Certification. • RAs provided with secure login details (username and password) that they must authenticate to access. • NHiS train authenticated RAs. • RAs use the Q-PASS for the purposes defined the T&Cs. • RA login details to be active for restricted time before expiry and the reissue of new details. • New potential RAs to follow procedure 2 – 4 above. • NHiS Customer Service team responsible for tracking, with the organisation’s commitment, all existing leavers and removing from the system. NB – NHiS will require access to Q-PASS for the purposes of: • Quality assuring updates or changes, and to contribute to ongoing Q-PASS improvement • To train and provide ongoing support to users • To demonstrate to existing and potential users in all Groups 1,3,4 & 5 • Assist any of the user Groups 1,3,4 & 5 NHiS users, with the exception of HSCIC registered users, do not have access to record level data; these users only have access to aggregated data via NHiS standard interfaces. This separation is achievable because record level data is stored separate to aggregated data. Access to record level data in NHiS is limited to the small team who are responsible for technical development, data loading and carrying out data aggregation. Specific Processing Activities – PURPOSE 3: Tabulations What specifically are Tabulations? Tabulations are aggregated with small number suppression in line with the HES Analysis Guide and will use HES, MHMDS or DIDs outputs of hospital activity and/or cost at organisational or patient cohort level. They are used by health and social care to quickly find out insight relating to the management of specific diseases or procedures so that effective decisions can be made in real time. Why are pseudonymised record level, non-sensitive, non-identifiable data required in preference to a pre-aggregated HES, MHMDS or DIDs extract? A tabulation cannot be created without pseudonymised record level data being available to NHiS. Please note: Record level data is stored separate to aggregated data. All pseudonymised record level data is stored in one location in England, with their being no possibility of users accessing record level data (refer to Section B, 3C of the Data Sharing Framework contract and the NHiS HES Data Flow v1r0 and HES Protocol v1r0 documents). How does access to and use of Tabulations work? 1. Signed contract by NHiS with the 3rd party stipulating what the Tabulation can and cannot be used for. Copy of signed contract distributed to all parties. 2. Post contract signing NHiS undertake tabulation production 3. Tabulation output peer reviewed and quality assured by NHiS in line with HSCIC guidance and suppression 4. Tabulation released to 3rd party 5. Tabulation simultaneously published on NHiS’s website and any other website the HSCIC require it to be published on

Objectives:

NHiS is an information intermediary which specialises in applying healthcare data to produce outputs that are used in health and social care to: 1. Raise disease awareness, management and diagnosis through analysing data and publishing reports and tabulations which are available in the public domain 2. Support the commissioning cycle and enhance patient outcome through understanding disease progression and applying to continual service development improvement A directorate within NHiS is the Neurology Commissioning Service (NCS), an official NHS England (Ref: Map of Medicines), niche commissioning support unit. NHiS and its Commissioning Excellence directorate (formerly NCS) have provided aggregated HES data outputs for use in report production and commissioning support. NHiS has used (and wishes to continue to use) record level pseudonymised, and non-sensitive: • HES data since 2008, using data from 2000/01 till current • MHMDS and DIDs data since 2013, using data from 2011/12 till current NHiS will use the data solely for the following purposes (any other requirement will be subject to a further application) :- PURPOSE 1) Disease Insight Reports (DIRs) • DIRs are reports which publish aggregated, double suppressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data, and seek to: o Increase the appropriate diagnosis of a disease and minimise misdiagnosis o Raise awareness of a specific disease o Analyse the management of disease • For the avoidance of doubt, DIRs will not: o Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of pharmaceutical products o Include any analysis on the impact of commercially available products. An example being pharmaceutical products • Reports are made publically available. For example the “State of the Nation” on Parkinsons used by Newsnight – see example in the Expected Measurable Benefit section • A full program of reports is scheduled for 2015 • In addition to the publication of national DIRs, local reports are available for sub national health and social care organisations to view data at their level of interest The potential users of DIRs are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) Group 6 – General Public Group 7 – Other commercial organisations NHiS have complete editorial control over the DIRs meaning that the reports are developed completely independently of the commissioner of the work. Below is a summary of who the commissioner could be, their editorial influence and the potential publishing channels: Commissioner 1 - NHIS: Pro-Active Internal research reports of interest, independently produced by NHiS and published on the NHiS, NCS or NHS websites, or via hard-copy production Commissioner 2 - Group 1: Research reports in which NHiS has complete editorial control, without external influence. All reports published on either the NHiS, NCS, NHS or other website or via hard-copy production. Commissioner 3 - Group 2, 3, 4, 5, 7: Research reports in which NHiS has complete editorial control, without external influence. All reports published on either the NHiS, NCS, NHS or other website or via hard-copy production. It is proposed that a legally binding contract between NHiS and the commissioner of the report be signed by both parties. The contract will stipulate what the DIR and consequential aggregated HES, MHMDS or DIDs outputs can and cannot be used for. PURPOSE 2) Q-PASS – Quantis Pathway and Service System Q-PASS is a series of tools that uses aggregated, double supressed^, non-sensitive, non-identifiable HES, MHMDS or DIDs data to aid the commissioning cycle. Q-PASS assists health and social care in creating and delivering The Quality, Innovation, Productivity and Prevention (QIPP) priorities , Five Year Forward View Planning, implementation of NICE HTAs, local Five Year Commissioning Plans and Joint Strategic Needs Assessment (between health and social care). Q-PASS allows users to: • Identify where local health and social care organisations should focus their planning • Understand the efficiency of existing clinical pathways and services • Model more efficient, integrated (between health and social care) pathways and services by understanding patient cohort journeys and the progression of poorly managed disease • Monitor the success of a newly implemented pathway and/or service The potential users of Q-PASS are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of QPASS are all of the above groups, the data can only be used for the purposes listed above (with the ultimate beneficiary being the NHS and Social Care). This will be agreed through a contract between NHiS and the 3rd party. NHiS have been, and are currently using the MHMDS data by producing a performance dashboard that combines HES and the MHMDS datasets (using the linkage file supplied by the HSCIC) to get a holistic view on the overall secondary care impact of patients with severe mental health difficulties. Many commissioners are unaware of the impact of Psychosis on the standard acute setting and this highlights additional costs and associated patient implications. The performance dashboard also looks at the effectiveness of clustering and the proportion of patients that are assigned to a cluster. This is vital for the future of Mental Health commissioning and enables both provider and commissioner to see where process improvements need to occur in the patient monitoring system. This will help to improve planning across the commissioning sphere. HES, MHMDS or DIDs outputs will be used by the ultimate beneficiary, as they have been for five years (one year in the case of MHMDS and DIDs), in the following elements of the Commissioning Cycle: • Analysis Phase: o Dashboards & Analyst - to assess a pathway’s and/or organisation’s performance against similar comparisons and to understand where change could be required to achieve QIPP planning. • Planning Phase: o Dashboards – to communicate with all NHS stakeholders in explaining the rationale for change and to create engagement with users to understand their needs in the commissioning process. o Analyst – to identify local health economies which are managing a specific disease effectively. To use this data to quantify what success will look like in terms of reduced inappropriate hospital activity & cost plus decreased comorbid patient disease. o Modeller – to apply predictive modelling to understand the potential impact for patients plus the health and social care system by adopting a clinical pathway or service design which is optimal. • Implementation Phase: o Dashboards – to enable continual communication and education with all NHS stakeholders as to the rationale and requirements for a new clinical pathway or service. • Review Phase: o Dashboards, Analyst, Modeller to review progress on a frequent basis and to make any necessary, close to real-time, changes to the pathway or service to optimise efficiency. Life Science Companies are a user of the aggregated outputs exclusively for the purpose of providing Q-PASS to benefit the health and social care organisations listed in Group 1 in England. Group 5 users will be highly restricted in their use of Q-PASS to ensure aggregated HES, MHMDS or DIDs data outputs are not used for their own commercial purpose such as targeting sales resource. These restrictions are to be underpinned through a signed legal contract between the 3rd party and NHiS. Measures which NHiS recommend are placed on the 3rd party via contractual obligation include but are not limited to: • The system to be used exclusively for the purpose of provision of outputs to assist health and social care organisations listed in Group 1 in England • The system not to be used for commercial purpose • Where appropriate, the system to be governed and resourced by the non-promotional medical department • Where appropriate, an official NHS/industry joint working contract to be put in place • The same aggregated HES, MHMDS or DIDs data outputs to be made available, if requested, to all organisations in Group 1, irrespective of their value to the company • The system only to be provided to a restricted number of named Group 5 users, who have undergone and passed NHiS’s HES Protocol training (audited by the HSCIC) plus the NHiS Data Reuse Protocol (an specific addendum to Group 5 underlining the need for non-commercial reuse) • All named users to authenticate sign on through unique password protection • Passwords to be changed routinely • Life Science Companies to abide by the established PMCPA Code of Practice and DH governance on the use of healthcare data by Life Science Companies with health and social care PURPOSE 3) Tabulations NHiS receives unsolicited requests for suppressed, aggregated, non-sensitive, non-identifiable tabulated data both on a random basis and as part of wider commissioning projects. The tabulations NHiS wishes to provide are those which are complicated in nature and are required in rapid timeframes to achieve NHS and social care project objectives. Specifically, NHIS does not wish to provide simple tabulations of activity data such as admissions by Trust by ICD10. Instead the purpose for use is to provide tabulations that are complicated in nature, requiring in-depth understanding of the patient pathway and coding practices. Two examples would be: • Unbundling tariffs to understand high cost activity from the core HRG tariff to infer additional information on cost of procedures to provide an actual total charge of a service. • Strategic Clinical Network (SCN) wishing to view, for each of the CCGs within the SCN, an analysis which understands for one particular operation the comorbid conditions patients had pre and post event, how the operation was coded, whether a site of operation was record and the effect on tariff and how these factors influenced outcomes plus cost. The construction of these tabulations are highly dynamic in nature requiring NHiS to work in an iterative fashion to analyse data, assess outputs, refine search and resubmit until the exact answer to the initial problem has been resolved. Frequently, the iterative process can take between five to ten iterations to achieve the required outputs. It is for the combination of the knowledge which NHiS applies to the data and the restraints associated with working remotely that NHiS requires the ability to provide tabulations. For the avoidance of doubt, Tabulations do not: • Relate HES, MHMDS or DIDs data outputs to the use of commercially available products. An example being the prescribing of an individual pharmaceutical products • Include any analysis on the impact of commercially available products. An example being an individual pharmaceutical products The potential users of Tabulations are: Group 1 – NHS- GPs, Commissioners, Trusts (Acute and Mental Health), Area & Regional Teams, Strategic Clinical networks Government & government aligned groups: DH, NHS England, NICE and Academic Health Science Networks (AHSN) Social Care: Local Authorities, Health and Wellbeing Boards NHS England Commissioning Support Units (CSUs) Group 2- Patients Group 3- Companies that specialise in providing commissioning support services to the NHS Group 4- Charity not-for-profit organisations Group 5 – Life Science Companies (pharmaceutical, medical technology, and medical biotechnology) For clarity, even though the potential users of Tabulations are all of the above groups, the ultimate beneficiary is restricted to Group 1 only. This will be agreed through a contract between NHiS and the 3rd party for each tabulation. As part of the contract it will be mandatory for NHiS to publish all tabulations on the NHiS and/or suitable alternative website for public access. NHiS will not solicit requests for Tabulations through web advertising or promotional activity. NHiS will only provide Tabulations based upon inward requests that benefit Group 1. All Tabulation outputs will include double suppression^ and will be in line with the HES Analysis Guide, thus classifying the data as anonymised.