NHS Digital Data Release Register - reformatted

Mckinsey & Company projects

29 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

Standard Extract Subscription — DARS-NIC-368233-L2N0W

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012, Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii), , Health and Social Care Act 2012 – s261(2)(a)

Purposes: Yes (Commercial)

Sensitive: Non Sensitive, and Non-Sensitive, and Sensitive

When:DSA runs 2019-12-16 — 2020-12-15 2017.09 — 2024.03.

Access method: Ongoing, One-Off

Data-controller type: MCKINSEY & COMPANY, INC. UNITED KINGDOM

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Critical Care
  4. Hospital Episode Statistics Outpatients
  5. Emergency Care Data Set (ECDS)
  6. HES-ID to MPS-ID HES Accident and Emergency
  7. HES-ID to MPS-ID HES Admitted Patient Care
  8. HES-ID to MPS-ID HES Outpatients
  9. Hospital Episode Statistics Accident and Emergency (HES A and E)
  10. Hospital Episode Statistics Admitted Patient Care (HES APC)
  11. Hospital Episode Statistics Outpatients (HES OP)
  12. Community Services Data Set (CSDS)
  13. Diagnostic Imaging Data Set (DID)
  14. Hospital Episode Statistics Critical Care (HES Critical Care)

Objectives:

NHS organisations, including NHS Trusts and Foundation Trusts, CCGs, CSUs, NHS England, NHS Improvement and Public Health England commission McKinsey & Company, Inc. United Kingdom (referred to as “McKinsey” hereafter) to work on projects which are procured by the NHS organisation within and outside of specific procurement framework agreements.

The scope of this work is developed by the client organisation and covers a broad range as specified by the client including strategy, performance transformation, and organisational development. Examples are listed in the “specific outputs” section.

McKinsey use HES data in order to provide fact-based answers to McKinsey’s NHS clients questions regarding identification, assessment and quantification of opportunities to improve the quality and efficiency of the NHS services that they deliver, or are responsible for overseeing and regulating.

McKinsey have applied for a license renewal for HES data from 2013/14 to the present (ongoing quarterly managed service subscription, with a rolling retention of 3 full years plus latest available) in order to be able to look at trends in performance, expenditure, utilisation and demand.
The specific purposes and types of analysis that McKinsey perform are the following:
(1) Benchmarking and analysis of operational performance
(2) Benchmarking and analysis of variation in utilisation rates and tariff spending
(3) Analysis of historic trends in rates of activity and spending
(4) Analysis of the impact of different service configuration options

HES data will only be used in the context of services by McKinsey in England and will not be used for non-NHS (or social care) organisations or for organisations outside of England.

McKinsey are requesting to maintain access to three years of historical data in order to monitor trends in performance, expenditure, utilisation and demand. Access to three years of data allows for the analysis of trends to identify cyclical patterns in utilisation as well as directional trends in performance, while also allowing for the identification of anomalies in activity. Furthermore this permits the measurement of the effectiveness of performance and cost improvement initiatives such as in tracking activity and expenditure following implementation of a cost improvement plan or QIPP initiative.

Yielded Benefits:

McKinsey has used HES data to support its work with NHS clients for the past 10 years. The benefits of some of this past work is set out in a range of case studies below. 1. Addressing a London health economy deficit of £40m (2017) McKinsey & Company completed a 5 week project in July 2017, working with a London health economy to address a system deficit of £40m. The project team used HES data to drive benchmarking of the CCG and trust’s historic performance against comparators and review internal activity trends such as A&E and ambulatory care sensitive condition attendance rates by GP practice across the borough. The team used these benchmarks to align the different health economy partners (including CCG and trust) around a shared understanding about the drivers of deficit, and identify opportunities to drive improvements to care quality and access. The benchmarking exercise using HES data has helped the local health economy identify geographic areas in need of short-term resource investments to improve access to urgent care. In particular, detailed historic benchmarking indicated that the health system had significantly higher than extended spending on acute care, and non-elective admissions in particular. Both the CCG and the trust agreed that an investment in community services would dramatically improve healthcare access and quality, as well as their deficit. The CCG and trust are now in ongoing discussions to move towards joint ways of working towards improving care and reducing the system deficit. 2. Financial recovery and improvement planning in the Midlands (2017). McKinsey & Company completed a 4 week study in June 2017 to provide financial recovery, improvement and sustainability support to a health economy in the Midlands. The health system had delivered their lowest level of QIPP savings since 2013, and were seeking to develop recurrent and transformational QIPP plans for the current fiscal year. The project team used HES data to benchmark the CCG’s historic performance against comparator CCGs, as well as to compare internal variability in secondary care activity by GP practice. These benchmarks were used to assess the size of the improvement opportunity in the region, evaluate the ambition of current QIPP schemes, and support the development of detailed delivery plans to implement the schemes. The outputs of the financial review, including the use of HES-derived outside-in productivity benchmarks, supported the delivery plans of 15 QIPP initiatives with an expected savings of c. £30m at the end of the fiscal year. 3. Access improvement for elective care at a teaching trust (2017) McKinsey completed an 8-week study in July 2017 to support access improvement to elective care for an NHS teaching trust serving a catchment population of 650,000. The project team used HES data to develop a single version of the truth on planned care performance to align stakeholders to a common understanding on changes in demand and their drivers over time. Analyses included an historical review of elective care activity volumes across inpatient and outpatient settings, compared volume increases to patterns in referral to treatment waiting times, and peer benchmarking on performance measures with other trusts. The analysis revealed that increases in volumes were driven by referrals from out of area CCGs, and by faster than average rise in consultant to consultant referrals. This analysis also supported subsequent prioritisation of improvement initiatives and the quantification of their impact. The anticipated impact of the planned interventions, once fully implemented, include: - Removed outpatient waiting list backlogs across five priority specialities within 12 months - Reversed deterioration in inpatient backlogs across five priority specialities within 12 months - Streamlined and more convenient services, such as faster email and telephone advice, one-stop shops to reduce patient visits, and greater use of patient decision aids and decision making in their treatment 4. Productivity of an ambulance trust (2014) McKinsey has been engaged in ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations. The client was facing deteriorating operational performance and was looking to implement a new operational approach. The project team performed detailed analysis and modelling of patient demand, service capacity and service efficiency. HES data were used to model historic trends in conveyances to A&E. Insights derived from HES analysis formed the basis for discussions with stakeholders and experts to diagnosis drivers of deteriorating performance. HES data was also used to compare historic performance prior to the adoption of a new operational pilot, with trust-supplied data following implementation. Impact of the new operating model was validated using actual observed pilot data. This included a 10% improvement in the proportion of ambulances arriving on scene within 8 minutes. 5. Financial improvement for an acute trust in the North of England (2016) McKinsey completed a 12 week project in July 2016 leading a large acute trust in the North of England through a large-scale financial improvement programme. The client faced an underlying financial challenge of £90m, having historically achieved ~£45m in annual financial improvements. McKinsey worked in consortium with MoorHouse and Four Eyes to review financial improvement opportunities across the whole of the hospital system. The project ran across two phases, with the first 2 weeks dedicated to a rapid baseline assessment to identify top-down opportunities. During this period the McKinsey team used HES data to benchmark productivity KPIs such as case-mix adjusted ALOS and historic changes to activity within key specialties against comparator trust peers to size the overall productivity potential. In the second 10-week delivery phase, the consortium supported hospital divisions to develop and strengthen plans for delivery around the nursing workforce, medical workforce, theatres, outpatients, length of stay and admin and clerical workforce. The team’s work, supported by peer benchmarks using HES data, helped to strengthen 300 existing financial turnaround initiatives and identify an additional 100 plans, for a total achieved in-year savings of £79m (or £100m in annualised savings). 6. Clinical service redesign for an NHS trust (2016) McKinsey completed an 18 week project in clean sheet redesign across six functional and clinical service lines in November 2016. The team used HES data to conduct a diagnostic of orthopaedic productivity metrics including length of stay, operations per consultant, DNA rates and activity rates. The trust’s performance was benchmarked internally across the hospital sites, and nationally against comparable trusts. Metrics were designed to align with national best practice. Analyses of case-mix adjusted length of stay were conducted to confirm that the trust’s higher length of stay post-surgery was related to productivity rather than complexity of cases. McKinsey worked with a triumvirate of consultant, nurse and manager from the service line to develop aspiration targets derived from the benchmarking. HES outputs were presented to a broad range of staff in large design workshops in terms of aggregated PowerPoint tables. The result of the work has been an end to end pathway redesign built around the aspirational productivity metrics and agreed upon by the hospital’s clinicians and non-clinical leads, and a modelled impact of the redesign on the people and infrastructure requirements. . The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks.

Expected Benefits:

Benefits achieved to date are :

1. McKinsey completed an 18 week project in clean sheet redesign across six functional and clinical service lines in November 2016. The team used HES data to conduct a diagnostic of orthopaedic productivity metrics including length of stay, operations per consultant, DNA rates and activity rates. The trust’s performance was benchmarked internally across the hospital sites, and nationally against comparable trusts. Metrics were designed to align with national best practice. Deep dives on case mix adjusted length of stay were conducted to confirm that the trust’s higher length of stay post-surgery was related to productivity rather than complexity of cases. McKinsey worked with a triumvirate of consultant, nurse and manager from the service line to develop aspiration targets derived from the benchmarking. HES outputs were presented to a broad range of staff in large design workshops in terms of aggregated PowerPoint tables. The result of the work has been an end to end pathway redesign built around the aspirational productivity metrics and agreed upon by the hospital’s clinicians and non-clinical leads, and a modelled impact of the redesign on the people and infrastructure requirements. . The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks.

2. McKinsey completed a 13 week project in December 2015 working with a group of CCGs in London and their stakeholder trusts to review productivity savings over the next five years to achieve their planned system transformation. The project ran across three phases to identify and then develop proof of concept projects to achieve these savings. HES data was used to benchmark productivity opportunities in operational performance by comparing case mix adjusted length of stay across the four stakeholder trusts and with their comparator trusts across England. In the first phase, the McKinsey team identified over £500m of potential productivity improvements, drawing on both the HES analysis as well as detailed benchmarking of the trusts’ financial accounts. In the second phase, McKinsey worked with stakeholders to develop ‘proof of concept’ projects across elective orthopaedics, end of life care, and bank and agency staff costs. For each of these categories of spend, the team undertook a productivity diagnosis and developed a joint implementation plan based on the findings. HES data was used to measure historic variation in elective orthopaedics around case-mix adjusted length of stay, procedures per pseudonymised consultant, and other activity metrics. The McKinsey team used this benchmarking analysis, presented in aggregated PowerPoint tables to convene working groups of clinicians and non-clinicians involved in elective orthopaedic care to agree upon a best practice pathway to reduce unwarranted variation. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity.

3. McKinsey completed a 3 month study in March 2016 to develop a single hospital service across a financially challenged health economy in the North of England currently served by three large trusts. The region was facing high levels of health inequalities and poor outcomes. The purpose of the project was to improve clinical care through the reduction of variation in quality, outcomes, patient experience and cost through the consolidation of independent services into a single service. HES data was critical for identifying addressable variations in length of stay, volumes and productivity across the hospitals and through benchmarking with peer trusts. Data was presented in aggregated tables comparing activity across the hospital sites, alongside data on clinical outcomes from the national clinical audits. These were used to align stakeholders around the value of the single hospital service. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised.

4. McKinsey completed a 12 week project in July 2016 leading a large acute trust in the North of England through a large-scale financial improvement programme. The client faced an underlying financial challenge of £90m, having historically achieved ~45m in annual financial improvements. McKinsey worked in consortium with MoorHouse and Four Eyes to review financial improvement opportunities across the whole of the hospital system. The project ran across two phases, with the first 2 weeks dedicated to a rapid baseline assessment to identify top-down opportunities. During this period the McKinsey team used HES data to benchmark productivity KPIs such as case-mix adjusted ALOS and historic changes to activity within key specialties against comparator trust peers to size the overall productivity potential. In the second 10 week delivery phase, the consortium supported hospital divisions to develop a strengthen plans for delivery around the nursing workforce, medical workforce, theatres, outpatients, length of stay and admin and clerical workforce. HES benchmarking against comparator peers was used to assess and strengthen the plans. The team’s work helped to strengthen 300 existing financial turnaround initiatives and identify an additional 100 plans, for a total in-year savings of £79m. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year.

5.McKinsey and Company has been engaged in ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations. The client was facing deteriorating operational performance and was looking to implement a new operational approach. The project team performed detailed analysis and modelling of patient demand, service capacity and service efficiency. HES data were used to model historic trends in conveyances to A&E. Insights derived from HES analysis formed the basis for discussions with stakeholders and experts to diagnosis drivers of deteriorating performance. HES data was also used to compare historic performance prior to the adoption of a new operational pilot, with trust-supplied data following implementation. After successful pilot implementation, impact was validated using actual observed pilot data, confirming operational formats and leading to the roll-out of the operating model across the whole of the ambulance service. The project is set to continue in 2017.

6. McKinsey and Company completed a 14 week project up to December 2016 to provide financial recovery, improvement and sustainability support to an STP footprint in the East of England. The health region was facing a projected deficit of close to £500m by 2021 and is one of the most financially challenged health economies in the country. The project team used HES data to drive operational performance benchmarking of the CCG’s historic performance against comparator CCGs, and to compare internal activity trends, such as rates of A&E attendances and outpatient attendances by GP practice. The team used these benchmarks to assess the size of the total opportunity to improve, evaluate the ambition of current QIPP schemes, identify new opportunities for efficiency savings, and to support development of detailed delivery plans to implement these schemes and address remaining financial gap.

7. At the end of 2016, McKinsey and Company completed an 18 month strategic partnership with a London CCG. The objectives of the work were to ensure the organisational priorities were correct given the changing landscape, local health needs and the quality and performance of local services, as a basis for organisational work to ensure the organisation had the capacity and capability to deliver these priorities. HES data was used at the outset of the programme to conduct a broad and detailed diagnostic of historic 3 year trends in activity across acute and emergency care, and paediatric and maternity care to understand activity growth and service needs across the borough; this included analysis such as identifying potentially avoidable hospitalisations and A&E visits by age group. This diagnostic phase helped facilitate a thorough review of the priorities and draft strategic plans based upon the comprehensive analysis of service utilisation patterns. This work led to the development of 5 year commissioning strategic priorities that address the CCG’s greatest health challenges, and then informed a broader organisational development programme, which included an organisational review of the CCG’s commissioning functions and structure

Expected future benefits for individual projects vary, but in almost all cases involve identification and quantification of opportunities to improve the quality of patient care and population health, and to deliver more effective, efficient care. Target dates (for expected improvements) also vary but in almost all cases are within 3 years and often include within year opportunities for service improvements and/or savings.

Examples of the benefits expected for the projects described above
are:

Examples of the benefits expected for the seven projects described above:

1. The end to end pathway redesign is expected to improve patient care by improving quality of care in line with national best practice guidelines, reducing variation in clinical quality, and reducing referral to treatment times. Referral to treatment times are expected to fall from the current median of >36 weeks to 5 weeks.
2. The outputs of the productivity review are expected to reduced variation in clinical quality and outcomes through the development of a best practice pathway supported by clinicians. The new pathway is also expected to deliver £10m in recurrent efficiency gains through improved clinical productivity.
3. The outputs of the overall Financial Improvement Programme, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of 400 trust initiatives with an expected savings of £79m at the end of the fiscal year.
4. The trusts involved in the single hospital service are in the process of implementing the recommendations from the review (including those underpinned by HES benchmarking) to reconfigure and consolidate services. The reduction in duplication of effort and more streamlined care are expected to produce £20-30m for the health economy when fully realised.
5. The ambulance service has adopted a new operational model developed following a diagnostic of deteriorating operational performance informed by benchmarking using HES A&E data. It is expected that the ambulance service will roll-out the necessary operational changes over the coming months, resulting in a significantly higher productivity of current staff and more consistent achievement of the nationally mandated access targets.
6. The outputs of the overall STP project, including the use of HES-derived outside-in productivity benchmarks supported the delivery plans of >200 STP wide initiatives, include an expected savings of ~£40m at the end of the fiscal year.
7. It is expected that this CCG’s 5 year plan, informed by the HES-derived benchmarks, and the improved capacity and capabilities within the CCG will lead to better allocative efficiency of resources, which should in turn improve access to quality care tailored to the population’s needs.

Outputs:

It is not possible to provide full details of all specific outputs and timings because McKinsey work on multiple projects for a large number of different national, regional and local organisations across the NHS, including providers, commissioners and regulators. Some examples of outputs expected are set out below.

During the course of the projects that McKinsey do with NHS organisations, McKinsey test the data and analysis with McKinsey’s clients, and where necessary update and replace the data with summary data provided by the clients. This is the case with commissioners and providers, but is not always possible due to limitations in analytical capabilities, resources, and their own access to data.

Data is only shared with clients, and only in aggregated, non-patient identifiable formats with small numbers suppressed in line with the HES Analysis Guide’.

McKinsey shares outputs in the following ways with clients:
• McKinsey include aggregated, non-patient identifiable data in line with the small numbers guidance into Excel and Tableau models which McKinsey hand over to the NHS client

• McKinsey publish graphs based on the aggregated, non-patient identifiable results of quantitative analysis in line with the small numbers guidance in reports given to McKinsey’s NHS clients

• McKinsey present the aggregated, non-patient identifiable results in line with the small numbers guidance at meetings with NHS client stakeholders

McKinsey do not directly publish the outputs in any journal articles or other public documents (e.g., white papers) nor do McKinsey directly present any data outputs in the public domain. McKinsey will only share aggregated analysis with its NHS clients in Excel, Tableau or PowerPoint charts, in full compliance with the small numbers guidance in the HES Analysis Guide.

Current projects with NHS clients requiring access to HES include:

• Ongoing 18 month project with a London CCG to review their strategic and organisational development due to end in November 2016. (Complete November 2016)

• Ongoing work since 2014 with an ambulance trust to review productivity opportunities across their operations, due to end in November 2016. (Complete November 2016)

• Current 2 month provision of financial recovery, improvement and sustainability to two STP footprints until mid November 2016. This work entails benchmarking providers across an entire health system, predicting patient flow through different reconfiguration models, and understanding key health activity metrics of their populations. We are in discussions with other STP footprints to commence similar support with their implementation programmes. (Complete November 2016)

• McKinsey expect to begin work in the winter 2017 with a large acute teaching trust in London to review productivity opportunities within specific service lines. Project start date is still under discussion.

• McKinsey have responded to a tender with an NHS region to evaluate their urgent and emergency vanguard programmes. The project would run from January to April 2017. Access to HES would enable analysis to understand historic patient flows for emergency care (A&E and inpatient) to compare with in-year hospital data from NHS trusts participating in the vanguard.

The data or outputs will not be used (directly or indirectly) for sales or marketing purposes by McKinsey & Company Inc. United Kingdom or by any other non-NHS organisation and can only be used for the purposes of the promotion of health.

Processing:

Data is extracted from the SAS (http://www.sas.com) database in which it is stored in the form of data queries. These are analysed further in Excel and Tableau.

McKinsey currently only use SAS and SAS Enterprise Guide to extract the data. Further analysis on extracted data is currently conducted in Excel spreadsheets and in Tableau software.

(1) Benchmarking and analysis of operational performance
McKinsey have a standardised tool which is created annually using HES data. This tool is a "Hospital Diagnostic" which compares all NHS acute Trusts on a range of operational performance metrics (including case-mix adjusted average length of stay, day case and day of surgery admission rates by setting and specialty; proportion of A&E attendances resulting in admission by length of stay of that admission etc) against a peer group (tailored to each individual Trust). This analytical tool is created in Tableau. McKinsey also conduct ad hoc analyses for the same measures to look in more detail at performance, for example at site level, or for specific types of patients (e.g. sub-groups defined by age, gender and diagnosis cluster). Ad hoc analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database.

(2) Benchmarking and analysis of variation in utilisation rates and tariff spending
McKinsey have standardised approaches to measure variation in utilisation rates (by setting, patient type or demographic sub-group, specialty and different activity clusters) and associated tariff expenditure both within (at GP practice level) and between CCG commissioner peer groups (defined using ONS cluster groupings). Utilisation is measured as an activity rate (or associated tariff value) per 1,000 age-needs weighted population (or most appropriate population measure) and compared to CCG (or GP practice) peer group median, quartiles and deciles. This analysis is conducted in excel using subsets of data extracted using standardised data queries from the SAS database.

(3) Analysis of historic trends in rates of activity and spending.
Operational performance and utilisation rates are measured over time at different frequencies, including yearly, monthly and weekly, in order to understand cyclical patterns and directional performance trends. This analysis is conducted in excel or Tableau using subsets of data extracted using standardised data queries from SAS.

(4) Analysis of the impact of different service configuration options
HES data is used to develop best estimates of baseline activity and capacity (defined as bed days for admitted patient care) for commissioners and providers, aggregated at service line level (defined by specialty and point of delivery). This is then forecasted forward using a range of sources of insight, data and triangulation methods (including, but not limited to, local and national historic trends described above), to develop growth assumptions and scenarios. A simulation is created, in excel or Tableau, to analyse how these baseline levels would change over time if service configuration changed.