NHS Digital Data Release Register - reformatted
McKinsey & Company, Inc. United Kingdom
🚩 McKinsey & Company, Inc. United Kingdom received multiple copies of from the same dataset, in the same month, both with optouts respected and with optouts ignored. McKinsey & Company, Inc. United Kingdom may not have compared the two datasets, but the identifiers are consistent between datasets and NHS Digital does not know what their recipients actually do.
Project 1 — DARS-NIC-90989-D6T1T
Opt outs honoured: Yes - patient objections upheld (Section 251)
Sensitive: Non Sensitive, and Sensitive
When: 2019/04 — 2019/05.
Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)
Categories: Anonymised - ICO code compliant
- Hospital Episode Statistics Admitted Patient Care
- HES:Civil Registration (Deaths) bridge
- Civil Registration - Deaths
Hospice UK and NHS England are running a service evaluation to improve the provision of end of life care in England: Hospice-led innovations for end of life care (HOLISTIC). A major component of this service evaluation is a quantitative comparison of health services utilisation by patients in the last 90 days of life, across different end-of-life care models, to which this agreement relates. Medical and statistical purposes The medical purpose of Hospice UK and NHS England’s service evaluation is to improve the provision of care and treatment and the management of health and social care services for end of life care, in terms of the quality and cost of care, as well as the alignment with patients’ preferences over care location. The study is a statistical evaluation of different care models, using quantitative data to identify significant differences in various outcome measures, including the number of hospital bed-days in the last 90 days of life, that identify the best interventions for improving end-of-life care in the UK. Background It matters where people spend the end of their lives. People prefer to die at home (of those who express preferences, 80% prefer to die at home and 5% in hospital), but the reality is very different (50% die in hospital, and 25% at home). The costs of end-of-life care are high, amounting to over 20% of the NHS budget, and there are many home, community, hospice and care-home based end of care settings that provide high-quality, appropriate care at substantially lower costs (in comparison to admitted patient care in NHS acute hospitals). Project overview and objective NHS England and Hospice UK are running a service evaluation on the effectiveness of end of life care programmes in moving patients out of hospitals at the end of their lives and into other locations of care, such as their homes, care homes and hospices, and in their ability to prevent avoidable hospital admissions for these patients. Twenty-four hospices have identified recently-introduced care models that may reduce patients’ usage of hospital-based end-of-life care. HospiceUK’s study will evaluate these models using a quantitative and qualitative methodology. The results will be presented in academic literature, publicly available reports and as guides on how to implement the most effective models nationwide. The quantitative analysis will use aggregated and de-identified historical secondary care records of deceased patients, from the linked HES-mortality dataset, to measure the impact created by the introduction of these recent care innovations on the average amount of time patients spend in hospital. This will allow the effectiveness of each care model to be compared. The comparison measures include the (reduction in) number of hospital bed days in the last 90 days of life, the number of A&E and inpatient admissions in the same timeframe, the probability of dying in hospital, and the number of discharges into palliative care. The qualitative phase will consist of interviews of hospice and care home staff, patients and carers, to understand the experience of care within each new care initiative, and to gain sufficient information to provide detailed guidance to other hospices on how to implement these new programmes. This phase will be performed in collaboration with St Giles Medical. Whilst it does not relate to this agreement, it is referenced here for completeness. The study as a whole will therefore both provide a statistical examination of the most effective care innovations in reducing hospital usage at the end of life, as well as detailed implementation guides to enable care providers nationwide to adopt these practices. The quantitative phase uses pseudonymised historical secondary care records. The Health Research Authority have confirmed that their ethical review and approval is not required for this phase. Note on project design, funding, and parties involved The project is joint funded by NHS England and Hospice UK. The HOLISTIC project, and the quantitative analyses that it includes, was designed by Hospice UK and NHS England. As part of this process, Hospice UK and NHS England took advice on the design of the project and analyses from McKinsey & Company, Inc. United Kingdom, (referred to from hereon as "McKinsey"). Based in part on this advice, the final decisions on study design were taken by Hospice UK and NHS England, who act as data controllers for this project. McKinsey has further been commissioned by Hospice UK and NHS England to be their data processor, running analyses on the linked HES-mortality datasets to test the effectiveness of the different care models. Patient record data will be processed by McKinsey alone, and the outputs will be aggregated with small numbers suppressed in line with the HES Analysis Guide. McKinsey will store the data onsite at their London office in a locked server room compliant with industry best practices. Data will only be accessed for the specific project by a named analyst, and will be destroyed within 14 days of study completion. NHS England and Hospice UK will only view aggregated results of the analysis with small numbers suppressed, and will not be storing or processing record-level data. McKinsey's support to Hospice UK and NHS England, both in the provision of advice before the project design was confirmed, and in their provision of data processing activities, is on a pro-bono basis. McKinsey has no commercial interest in any aspect of this study, and in particular has no commercial interest in the processing of the data or publication of results relating to this project. St Giles Medical have been contracted to perform qualitative data collection and analysis related to the qualitative research. St Giles medical will also assist with final report writing, but will only view aggregated outputs with small numbers suppressed.
The overarching objectives of the proposed analysis of end-of-life care innovations is to improve care for patients at the end of life, in terms of the quality and cost of care, as well as the alignment with patients’ preferences over care location. Analysis of the linked HES-ONS data will allow Hospice UK and NHS England to test the ability of recent care interventions to reduce the number of bed days spent in hospital by patients at the end of life, to determine how to scale up the best approaches, and to develop evidence-based end of life pathways for commissioners. The expected benefits from the recommended care innovations to patients include: - Reduced lengths of stay for end-of-life patients in hospital - A shift of up to 30,000 patients to end of life care in other settings than hospital: supported at home, in care homes, or in hospices. (This represents a movement of 10% of the patients who currently end their lives in hospital.) - Improved patient and caregiver/family satisfaction, as more end-of-life care is provided in situations that align with patient preferences (only 5% of surveyed patients that expressed a preference over place of care at the end of life said that they preferred to die in hospital). The expected benefits to commissioners and NHS England include: - Clear evidence-based guidance on the number of hospital bed days saved by the surveyed interventions, and the potential for the number of saved bed days nationally if the best interventions are adopted - An understanding, based on this evidence, which end-of-life care innovations reduce hospital costs, align with patient preferences, and are scalable to regional and national levels - Guidance on how best to commission and sustain the recommended models - With this evidence, understanding and guidance, commissioners will change end of life care in the UK. This could realise savings of around £500 per patient in the last days of life by following alternatives to hospital palliative care, and provide more care that satisfies the NHS Patient Experience Framework’s respect for patient preferences The expected benefits to hospices and care homes include: - Evidence-based recommendations on new care innovations that increase the usage of alternatives to hospital services at the end of life - Detailed guidance on how to interact with local commissioners and other funding sources, and on how to operate the recommended models at scale - As a result of this evidence and guidance, hospices and care homes will be enabled to implement the most effective care innovations over the next 1-4 years.
Outputs from the analysis The output from the analysis of the HES-ONS data will contain only aggregate data with small numbers suppressed in line with HES analysis guidance. The use of these data will be solely to produce the outputs for NHS England and Hospice UK’s service evaluation, and not for any other purpose, organisation, company or project. Outputs from NHS England’s and Hospice UK’s service evaluation In disseminating the findings, NHS England and Hospice Uk will seek input and guidance from key stakeholders, including hospice services, commissioners, and patients and families. Alongside a final report to NHS England (by November 2019) and publication of the quantitative evaluation as an open-access article through submission to a peer-reviewed academic journal (by November 2019), Hospice UK will also be developing ‘how to’ guides for hospices and for commissioners (written and presented by November 2019) to help them put the findings in to practice. These guides will draw out the practical steps that those key stakeholders can take based upon the study conclusions, and will be developed in partnership with representatives of those key audiences. The guides will be supplemented by workshops and webinars, and will sit alongside other resources developed by Hospice UK to help support service improvement based on population need, including our Population Needs Assessment Tool (PopNAT). The project team will also be exploring the use of Project ECHO methodology to establish communities to practice to implement the findings. Hospice UK will also discuss the findings with our People in Partnership group, which is made up of people with lived experience of terminal illness. They will ask the group to advise us on how best to communicate the findings of the study to patients and families, for example via resources that help describe the role that hospice care can play in supporting people and their families.
The data requested for this study relates specifically to the following cohorts: 1) Intervention cohorts 2) Pre-treat cohort 3) Control cohorts. Each of these is described in detail below. All data relates to the deceased only. 1) INTERVENTION COHORTS The following cohorts all contain deceased people who were affected by an intervention of end of life care. They have been divided in to four cohorts as the most appropriate method of evaluation depends on which type of intervention influenced their end of life care. • Hospice service users • Care home residents • Patients that received hospital-based interventions • Patients in the hospice catchment area All cohorts will be minimised to patients who were aged over 18 years of age as well as to episodes no greater than 90 days prior to the date of their death. Intervention cohort 1- Hospice service users This cohort includes 7 hospices. The individuals included in this cohort are all those who used the hospice service from its start date (range January 2013-November 2016) to April 2018 and died prior to April 2018. Hospices will only upload identifiers of those individuals who are deceased. This cohort includes intervention types: hospice at home, community services for non-cancer patients and nurse-led beds for less complex patients. These services are in this cohort because the individuals in receipt of these interventions can be identified through records kept by the hospices. To select records associated with the intervention cohort the 7 hospices in the evaluation will collect a set of patient identifiable information (the NHS number, sex, date of birth and home postcode of each patient) indicating the complete cohort of patients that have been involved in the studied care intervention, over the past 6 years. The individuals included in this cohort are all those who used the hospice service from its start date (range January 2013-November 2016) to April 2018 and died prior to April 2018. Hospices will only upload identifiers of those individuals who are deceased. Each hospice will securely upload the above identifiers to NHS Digital of any individuals referred to their intervention from the point of service implementation to NHS Digital. NHS Digital will associate these data with the appropriate APC HES and mortality data and flag these patients as belonging to a specific hospice in the pseudonymised dataset provided to McKinsey & Company, Inc. United Kingdom, for further data processing. This will enable McKinsey to identify which Hospice the patient is associated with. Intervention cohort 2- care home residents This cohort relates to 5 hospices and includes the 80 care homes that they have worked with. The individuals in this cohort are residents living at the care homes since hospice service start date (range April 2015-July 2016), and who died before April 2018. This cohort includes the hospice interventions that have involved working with care homes (termed care/nursing home education in this study). To select the individuals in this cohort, Hospice UK will provide the names and addresses of the care homes. NHS Digital will associate the relevant APC HES and mortality data based on recorded addresses of the care home residents. Intervention cohort 3- patients that received hospital-based interventions In total this cohort relates to 5 hospices and involves 8 hospitals (some of the hospices work with more than one local hospital). The individuals in this cohort are those who died prior to April 2018 and interacted with one of these 8 hospitals in their last 90 days of life, after the hospice implemented their service (range January 2013-October 2016). This cohort includes the hospice interventions that involved working with a local hospital. These are: hospital-based discharge service, hospital-based discharge service delivering social care and nurse-led beds for less complex patients. Hospice UK will provide NHS Digital with the name, address and NHS trust code of the hospital. They will use this information to extract APC HES and mortality data of people who have interacted with this hospital in the 90 days prior to death. It should be noted for one of the interventions in this cohort , nurse-led beds for less complex patients, involves two hospices and is being measured through both intervention cohort 3 and intervention cohort 1. This is because, due to hospice records, it is possible to measure at the level of identifier-upload by the hospice (cohort 1). However, in terms of the wider system, the impact is likely to be seen within the hospital population as less-complex patients will now be transferred to these beds instead of perhaps remaining in hospital. It is for this reason that these two interventions will be measured through two cohorts. Triangulating the data between the two cohorts also presents an opportunity to critically assess whether variation in data is due to the method of evaluation. Intervention cohort 4- patients in the hospice catchment area This cohort involves data from 9 hospices relating to decedents who resided in the hospice catchment from date of service implementation (range April 2011-December 2016) until April 2018 and died prior to April 2018. Hospice UK are requesting control cohorts This cohort includes these intervention types: single point of access (SPA), 24/7 helpline for service users, ambulance staff education, palliative care outreach to rural areas and hospice inpatient capacity expansion. These intervention types are included in this cohort because they impact the entire of the hospice catchment area and there is no way of knowing which individuals benefitted from these interventions. Hospices do not keep records on who rings an advice line for example, however there is likely to be a cumulative impact on hospital usage at the population level. Hospice UK will provide NHS Digital with postcodes at the sector level (e.g. SW9 6) that represent the catchment of the hospice. NHS Digital will select individuals from within this catchment based on recorded home addresses. 2) PRE-TREAT COHORT To obtain baseline data, Hospice UK are looking at the catchment areas (as described for intervention cohort 4) for all 24 hospices prior to 2010. The individuals included will be those living in the catchment area of the hospice April 2009-April 2010, and who died in the same period. 3) CONTROL COHORTS For the control cohort 11 control cohorts will be created and their size will range from 1,000 to 15,000, with an average size of 9275. There are three cohorts; appropriate to the method of evaluation for the intervention cohorts. These three cohorts allow us to control for any general changes in palliative care that may have happened during the period being looked at in the study: April 2010-April 2018. For the controls hospices we do need something from them to include in our analysis and the postcode lists are least sensitive as they are not patient-identifiable (sector level rather than full postcode) and we will be looking at an aggregated locality level. Control cohort 1- the control for intervention cohorts 1 and 4 (Hospice service users and Patients in the hospice catchment area) This control cohort is for both intervention cohorts 1 and 4 and follows the same method described for intervention cohort 4. This cohort involves data from 11 hospices relating to decedents who resided in the hospices catchments in April 2010-April 2018 and died in the same period. This group involves data from 11 hospices relating to decedents who resided in the hospices catchments in April 2010-April 2018 and died in the same period. These hospices were selected through two routes. 1. In response to an email sent about service provision stability over the study period to a potential longlist of 66 hospices suitable to be controls as identified by Hospice UK leadership and 2. Hospices that had applied to be in the intervention arm, but were excluded due to their intervention start date. These two methods resulted in 14 control hospices and of those we received data from 11 Control cohort 2- the control for intervention cohort 2 (Care home residents) This cohort is the control for intervention cohort 2 and relates to the same 5 hospices. In addition to supplying details of the homes that they had worked with (intervention cohort 2), they also provided names of care homes in their locality with which they had not worked. This control cohort includes those who resided at these 25 control care homes between April 2010-April 2018, and who died prior to April 2018. To select the individuals in this cohort, Hospice UK will provide the names and addresses of the care homes to NHS Digital and NHS Digital will associate the relevant HES and mortality data based on recorded addresses. Control cohort 3- the control for intervention cohort 3 (Patients that received hospital-based interventions ) The 8 hospitals in intervention cohort 3 were matched by deprivation score and size (number of beds used as a proxy) to 5 hospitals appropriate to be controls. Hospice UK will provide NHS Digital with the name, address and NHS trust code of the hospital. NHS Digital will use this information to extract HES and mortality data of individuals who interacted with this hospital in their 90 days of life. The individuals in this cohort are those who died prior to April 2018 and interacted with one of these 5 hospitals in their last 90 days of life, between April 2010-April 2018. For each patient cohort, NHS Digital will provide a pseudonymised extract from the HES APC linked with mortality data, labelled by cohort. No direct patient identifiers will flow from NHS Digital. No direct patient identifiers will be revealed outside the organizational boundaries of the care providers or of NHS Digital. The data flow between care providers and NHS Digital is covered by section 251 support. Each record will be given a unique pseudonymised ID. To enable association of each record with a hospice and intervention, the year of death, month of death, and the first half of the patient’s postcode (or other indicator of which hospice and intervention the patient links to), will also be recorded. OUTCOME VARIABLES The principal outcome measure for this study is the reduction in the number of hospital bed days for an average patient involved in an intervention. This is measured as the reduction in the number of nights spent in hospital during the last 90 days of life. Additionally, secondary outcome measures are being requested form NHS Digital. These are: service of place of death (hospital / hospice / home), number of A&E admissions in last 90 days of life, number of emergency admissions in last 90 days of life, number of inpatient admissions in last 90 days of life, number of outpatient admissions in last 90 days of life, number of transfers to palliative care and social care services in the last 90 days of life ADDITIONAL EXPLANATORY VARIABLES Each record will include the following control variables: ICD-10 code for diagnosis at death, ICD-10 code for additional diagnoses (including cancer), age, sex, ethnicity, Index of Multiple Deprivation to measure socio-economic deprivation at the home postcode, an urbanisation indicator based on home postcode, distance from home postcode to nearest hospital, distance from home postcode to nearest hospice. It is the intention that each of these variables should be included as patient demographics within the model. USAGE OF THE DATA SUPPLIED BY NHS DIGITAL As data processors, McKinsey will test the effects of the various care interventions on the patient cohorts in the final 90 days of life. The quantitative analysis at McKinsey will consist of a separate quasi-experimental longitudinal study for each end of life care initiative being evaluated. These analyses will test the differences in hospital utilization in the last 90 days of life for similar cohorts of patients, both before and after the introduction of each initiative. These differences will be compared to control cohorts of patients, sampled over similar time points, that did not have access to the new care initiatives. This is, in effect, a “difference of differences” analysis. Other outcome measures will include numbers of A&E admissions, together with the place of death (such as hospital, home or hospice). No direct flow of identifiers will flow from NHS Digital. The results of the analysis will be a statistical comparison of the impact of each care initiative on the hospice utilisation of patients involved in these care programmes, controlling for site-specific differences and for other trends in hospice utilisation over time. The assessment of hospital utilisation will be made using pseudonymised patient secondary care records, drawn from the HES and mortality databases. These linked records will provide a detailed picture of patients’ interactions with the hospital system in the last 90 days of their lives and are matched to dates and places of death. Only results constructed from aggregate data with small numbers suppressed in line with the HES Analysis Guide will be shared with Hospice UK or St. Giles Medical for the purposes of writing the final report. For example, these outputs might effect size estimates with standard errors; or mean and variance for the number of bed days patients spend in hospital in the last 90 days of life, by hospice location and year, after removing the variability attributable to patient demographics.) OTHER DATA FLOW CONSIDERATIONS No data will be shared with any 3rd party organisations other than outputs that are aggregated with small numbers suppressed in line with the HES Analysis Guide. The requested data cover a sensible time-period for testing recent hospice-led care innovations: 2009-2018, with most records relating to patients who died in the last 3-4 years. Hospice UK is an umbrella organisation that represents hospices in the UK. While 7 Hospices are sending in the identifiers to NHS Digital to facilitate the linkage of datasets, no identifiable or record-level data associated with this application will flow to or from Hospice UK. All outputs and publications contain only aggregated data with small numbers suppressed in line with the HES Analysis Guide. By signing the Data Sharing Agreement, the Data Controller confirms that the Data Processors listed within this agreement have each: - Confirmed that they understand their roles and responsibilities on behalf of the Data Controller as defined within the Data Sharing Agreement. - Confirmed that the Processing Activities described within the Data Sharing Agreement are accurate and achievable in terms of the particular Data Processors’ processing.
Project 2 — DARS-NIC-368233-L2N0W
Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)
Sensitive: Non Sensitive
When: 2016/04 (or before) — 2019/10.
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 Accident and Emergency
- Hospital Episode Statistics Admitted Patient Care
- Hospital Episode Statistics Critical Care
- Hospital Episode Statistics Outpatients
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.
NHS organisations, including NHS Trusts and Foundation Trusts, CCGs, CSUs, and NHS England, commission McKinsey and Company 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 and Company use HES and PbR 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 and Company have applied for permission to use both HES and PbR datasets because, though derived from the same source, they each support different aspects of advanced analytics: (a) PbR allows the most accurate assessment of income and expenditure under the activity-based funding model, Payment by Results, used in the NHS, while, (b) HES allows for the most granular analysis of operational performance, capacity, utilisation and demand. McKinsey and Company have applied for a license renewal for HES and PbR data from 2012/13 to the present (ongoing monthly managed service subscription) 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 and PbR 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 and Company 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.
Benefits achieved to date are: 1. McKinsey and Company completed a project with a large acute Trust to support them in developing a reconfiguration strategy, affecting a number of adjacent NHS acute Trusts. The purpose of the work was to improve clinical care by organising services in such a way that national clinical standards, in particular for 7 day working and 24/7 consultant cover in critical care, could be met; and to address a forecast deficit of £150m by 2019/20 through identifying efficiency opportunities and new sources of income. The outputs of the work included operating models by specialty (and setting), showing workforce requirements, activity, costs and income, for a range of different configuration options. Models were developed using Hospital Episode Statistics and Payment by Results data as the source for baseline activity and income assumptions and involved the analysis of the impact of different service configuration options as described above. McKinsey and Company prepared a set of written reports setting out the implications of each option and supporting documents for use in stakeholder engagement and alignment activities. The work lasted approximately 4 months finishing in May 2015. Following the work, the hospital has transitioned from being an amalgamation of four discrete sites, into a cohesive hospital with a clear identity, vision and strategy shared across management, front line staff and service lines. Furthermore, the hospital has moved to the front of the pack in being clear and assertive about its vision, and is now busy implementing it, working in a very complex external environment. (2) McKinsey and Company worked with a large acute Trust for approximately four months to support them to improve overall performance in their A&E department and, in particular, to increase the proportion of patients treated within 4 hours in line with national quality targets (of 95% achievement), and to improve clinical care for all admitted and non-admitted emergency patients. Outputs included a detailed modelling of patient flows through the Emergency Department; analysis of patterns of demand for A&E services nationally, sub-nationally and locally; identification of the most useful performance metrics to support management of the emergency pathway; analysis of the impact of A&E demand and performance on the rest of the hospital (in terms of non-elective admissions; bed occupancy rates; length of stay; delayed discharges) for all medical and surgical specialties relevant to the emergency pathway. Benchmarks of operational performance (as described above) compared the trust against size and activity profile for similar NHS trusts across England. Benchmarking analysis was used to develop targets and conclusions about demand factors (demand factors measured using the types of analysis described in analysis of variation in utilisation and analysis of historic trends as described above) affecting emergency care performance. The work concluded in February 2015. Since then the trust has seen an improvement of 11.5% percentage points in the achievement of their waiting times standard (with the proportion of patients waiting less than 4 hours in A&E averaging at 95.9% in June 2015). (3) McKinsey and Company worked with a group of NHS organisations across a large local health economy to benchmark operational performance in A&E and non-elective inpatient care looking at demand, capacity and productivity. The outputs included analysis of trends in demand overall and for specific segments including out-of-area patients, elderly patients and other sub-groups; detailed analysis by admission type and method, case mix, treatment received and length of stay. This involved analysis of operational performance, variation in utilisation and historic trends as described above. The output resulted in a written report including an agreed set of performance indicators. The project took 12 weeks and completed in February 2015. Since then the participating trusts have seen an average improvement of 5.5% from their Q3 waiting time standards achievement to the end of June 2015, compared to a national improvement of 2.6 percentage points. In addition to impact on the headline waiting times in A&E units, a number of other direct benefits were realised from this work, including: • Reduced length of stay for long-waiting patients including those experiencing delayed transfers of care (DTOC) • Improved real time data across the whole urgent care system enabling more effective escalation and therefore preventing risks associated with “surge” impacts on A&E departments or assessment units • Improved understanding of system wide capacity and demand, allowing Systems Resilience Groups to plan interventions to re-balance capacity 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: (1) The outputs of the analysis of different configuration options which will support the reconfiguration programme are expected to improve patient care by supporting the delivery of 7 day services and 24/7 consultant-led care in critical care. The absence of 7 day services and 24/7 consultant-led services have contributed to observed excess mortality at nights and weekends in NHS hospitals. The reconfiguration programme is also expected to deliver £26-36m of cost savings through more efficient use of estates and workforce; and to support the delivery of a further £111m in identified cost improvement programme (CIP) savings opportunities over the next two years. (2) The outputs of the operational performance benchmarking, variation in utilisation benchmarking and analysis of historic trends (described above) undertaken for this project looking at the performance of the A&E department in a large acute Trust, are expected to increase the proportion of patients in A&E treated within 4 hours from just over 80% (rolling weekly average prior to this work) to over 95% within a four month period. This will improve patient care and experience. Associated objectives include improving health outcomes by quicker placement of patients into appropriate wards and on swifter discharge placement in alternative care settings. (3) The outputs of the operational performance benchmarking, variation in utilisation benchmarking and analysis of historic trends (described above) undertaken for this project looking at how to improve the delivery of non-elective health services across a large local health economy, are expected to support NHS Trusts to improve care for patients arriving at A&E by increasing the proportion of patients treated within 4 hours and to allow providers and commissioners to develop the most appropriate range of services for people in need of urgent care. Improvements are expected within a time-frame of 6-12 months.
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 them. 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 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 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 Current projects with NHS clients requiring access to HES and SUS PbR data include: • Ongoing 18 month project with a London CCG to review their strategic and organisational development due to end in November 2016 • Ongoing 3.5 month strategy review with an acute trust in Northern England, due to end in December 2015 • 2 month urgent care pathway study with a London acute trust scheduled to start in September 2015 and run through to mid November 2015. • 2 month health system reconfiguration study with a CCG collaborative in Midlands and East scheduled to start in September 2015 and run through to mid November 2015. • 10 week hospital productivity study with 8 London trusts McKinsey are currently planning to start in September 2015 and run through to December 2015. • McKinsey are currently responding to a tender for the provision of Health Economy Success Regime support for a large CCG in the South of England which would last 3 months and is estimated to end in December 2015. The data or outputs may not be used (directly or indirectly) for sales or marketing purposes by McKinsey Ltd or by any other non NHS organisation and can only be used for the purposes of the promotion of health.
Data is extracted from the SAS (http://www.sas.com) database in which it is stored in the form of data queries and these are analysed further in Excel. McKinsey and Company currently only use SAS and SAS Enterprise Guide to extract the data. Further analysis on extracted data is currently conducted in Excel spreadsheets. (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 Excel. 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 ageneeds 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 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, to analyse how these baseline levels would change over time if service configuration changed.