NHS Digital Data Release Register - reformatted

Imperial College London Business School

Project 1 — DARS-NIC-14360-S9G2Y

Opt outs honoured: N

Sensitive: Non Sensitive

When: 2016/04 (or before) — 2016/08.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Benefits:

Understanding the drivers of cost-effective innovation is important, both for individual patients and the healthcare system as a whole. By 2030, the prevalence of colorectal and breast cancer is projected to be 474,000 and 1.2 million respectively in the UK. The cost of hospital treatment is estimated to be in the order of £540 million for colorectal and £500 million for breast cancer per annum. Hence, the NHS could achieve large benefits for patients and also cost savings for tax payers by accelerating the diffusion of cost-effective innovation and the implementation of voluntary guidelines into medical practice. Both innovations that will be examined in this study have been shown to be cost-effective. Hence there is a clear social gain from understanding what makes surgeons adhere to voluntary guidelines on treatment in these two fields. This research will quantify the impact of clinical networks on determining variation in the take-up of innovation in treatment of two common forms of cancer. The research will compare and contrast the impact of these formal networks with the social networks of consultants formed during their training and spatial competition. This has not previously been done: to date analysis has simply focused on the time series of uptake of innovation rather than an assessment of the benefit of networks relative to no networks. These outputs will help policy makers to decide where to invest resources. Understanding the relative importance of formal, top down driven networks, compared to organic ones formed during or after training will allow more cost-effective decisions to be made about the best place to invest scarce resources to expedite the uptake of proven innovations. It will provide guidance to policy makers as to whether it is more important to target doctors during their medical training period or later on, and whether formal networks are more or less effective than informal ones. The ultimate beneficiaries are those patients who receive more effective treatment sooner. More broadly, this research focuses on understanding clinical practice variation which is a major concern for the NHS. The Department of Health and NICE are currently investing large amounts of resources in producing evidence for best practice in hospital care in the form of NICE Pathways. Further, the Quality, Innovation, Productivity and Prevention programme has recently been established to realise an ambitious plan of quality improvements and cost savings though the implementation of evidence based recommendations to best practice. If successful, the programme was expected to deliver up to £20billion efficiency savings to be reinvested in frontline services in 2014-15. The Department of Health has recently introduced Best Practice Tariffs that link reimbursement to adherence to best practice guidelines in several clinical areas. Best practice guidelines that inform the adoption of cost effective innovations are considered essential instruments for reducing variations in clinical practice, improving quality and containing costs, provided they are implemented and adhered to by decision makers in hospitals. The results of this research are intended as an input to help UK policy makers to target activities and funding to promote a decision making culture in hospitals that is more in line with the Department of Health's stated objectives. Data from the wider HES dataset will be used to construct variables at the level of the trust, including hospitals’ patient casemix such as socioeconomic characteristics, the impact of spatial competition between nearby NHS Trusts, and the culture of the hospital in general in the adoption of new innovations. Controlling for these measures will allow for more robust results and provide policymakers with more causal understanding on the factors determining diffusion of best practice innovations. This research project has greatly benefitted from the input of clinicians and bodies involved in the diffusion of the procedures of interest. Thus far, we have been in contact with a number of leading bodies including Lapco (the training unit for laparoscopy surgery in England),and other colorectal cancer surgeons, and formal cancer networks. We also plan to interview other leading people in this area including the Royal College of Surgeons. In our discussions we will not only gather crucial information concerning innovation in the NHS but also explore ways of making our research accessible to practitioners.

Outputs:

One set of outputs will be statistical analyses to be submitted to peer reviewed academic papers. These outputs will not contain any data which enables identification of individual patients, consultants, sites or NHS trusts. The journals being targeted are health service research journals (e.g. Social Science and Medicine and the BMJ) and economics journals (e.g. Journal of Health Economics). Papers will be submitted during in the final two years of the research (i.e. between 2016 and 2017). The stakeholders interested in this research are diverse and extend beyond the academic community. Given the nature of the benefits, the outputs intend to inform policy making. None of these outputs will contain any data which permit identification of individual patients, consultants, sites or NHS Trusts. Several strategies will be adopted to inform. (i) The project will report regularly to a steering group that includes the Chief Analyst at NHS England. (ii) the funder of the research, the Health Foundation will be used to publish research highlights (iii) individual discussions will be held with policy makers and to make presentations to NHS England and other regulatory bodies. More broadly, the dissemination machinery of Imperial College Business School will be used to disseminate research highlights alumni, the general media and through this, to members of the public. The full set of outputs will be available by the end of the research in 2018, but will be disseminated earlier than this date for laparoscopic surgery for colorectal cancer. All outputs will be published in accordance with the HES Analysis guide and will be aggregated outputs with small numbers suppressed.

Processing:

The object of this analysis is the time to adoption of the innovative procedure by consultants employed in the NHS in the relevant speciality. The study’s focus is the impact on time to adoption of (a) elapsed time plus the factors our analysis is focused upon, these being:- (b) NHS ‘top down’ mandated tumour specific cancer networks (c) consultant initial professional networks (where and with whom the consultant trained) (d) publication of guidelines on adoption of the innovative procedure (e) spatial location of hospital (whether located near other hospitals that provide similar services). This study will estimate models of time to adoption using primarily a generalised hazard approach (time to failure models allowing for heterogeneity), with a focus on the impact of factors (b)-(e). This study will also examine geographical variation in the diffusion of innovation across consultants and hospitals using Arc-GIS heat maps. In each part of the analysis, statistical regression models will be used to disentangle the contribution of different factors, including formal and informal networks of physician, to the diffusion of innovation and potential confounders which may independently affect the speed of adoption and which may be correlated with the factors (b)-(e) will be controlled for. From this analysis our primary output is estimates of the absolute and relative impact of the factors (b)-(e) on the rate of adoption of innovative surgery for the two cancer types. Data requirements These analyses require bringing together data from several sources, some of which is from administrative records and some of which will be collected as part of the research. These data will need to be matched at the consultant and/or site and/or trust level – the level determined by the type of data to be collected. Two sources of data are held by HSCIC (HES and Workforce data). One is published by the GMC. One is a survey of consultants to be carried out by IPSOS for the research project. The final sources are various publicly available data. The type of data, the sources, the variables the HSCIC will construct from each source and the time period are detailed below – Workforce data - Current and past employment of consultants in the relevant specialties in NHS trusts. The variables used will be the trust and specialty in which each relevant consultant is employed in each year, covering the period 1992-1993 to 2014-2015. Published GMC data - Qualifications and date of qualifications of consultants The variables used will be the year of qualification; age band; gender, place, covering the period from 2005-2006 onwards. HES Admitted Patient Care data - The procedures undertaken by the target consultant population (those undertaking breast and colorectal cancer surgery), covering the period from 2000-2001 to 2014-2015 Survey to be carried out by IPSOS on behalf of the researchers - Survey of target consultant population. Medium Super Lower Output characteristics, publicly available data on Trust financial performance, details of cancer networks and guidelines on use the of laparoscopic surgery in treatment of colorectal cancer and SLN in breast cancer will also be utilised. The relevant procedures used for both the older treatment and the innovation of the two new tumour treatments. These are (a) Open Surgery (b) Laparoscopic Surgery (the innovation) in colorectal cancer surgery and (a) ALND and (b) SLN (the innovation) in breast cancer surgery. The term “target consultant population” is all those consultants employed as surgeons in breast or colorectal cancer treatment and any other consultants that perform the “relevant procedures”. The data will be linked using the clear consultant code from the HES and workforce data, and the GMC number from the GMC dataset. Once the linkage has taken place, the consultant code/GMC number will be converted into a study ID that permits Imperial to use the linked data without needing sensitive data. The linkage will take place at the HSCIC and Imperial will only receive a unique study ID that will be created and present in the HES dataset together with a limited number of fields from both the GMC file and the Workforce file. Fields to be provided from those datasets include Age Band of the Consultant (as opposed to actual age) and recent workplace, training and current trust. These will be appended to the HES dataset using the same unique study ID. Imperial will then use the linked data to identify a control group of consultants to carry out follow up survey questions with. Imperial will send a list of study ID's to the HSCIC where the HSCIC will look up the Name and Trust from the GMC file already provided (public data). HSCIC will pass the Name, Trust and study ID to IPSOS who will carry out a survey. IPSOS will then append the survey results to the Trust and study ID, and submit this data directly back to Imperial College so that Imperial can link this to previously provided data and conclude the analysis to conclude the research topic. Imperial will not have need to refer to the GMC file, nor will they have the ability to determine the Pseudo version from the information provided. IPSOS will not get the Consultant Code, only the name and trust to allow them to make contact to carry out the survey (alongside the study ID for matching purposes). The HSCIC will be performing all of the linkage and will only be providing pseudo files back to Imperial. Furthermore, the HSCIC will be the ones who are receiving and submitting the files through to IPSOS for the purposes of the survey.

Objectives:

Diffusion of innovation in cancer treatment plays a key role in improving the survival rates and the quality of life of patients affected by cancer. The English National Health System has experienced a slower diffusion of innovation than health systems in other countries. To speed up the diffusion of innovation the Department of Health instituted formal cancer networks covering all NHS Trusts in 2001 and NICE issued guidelines intended to increase adoption of innovative procedures with proven benefits. Our aim is to understand the role of the government introduced formal networks and the less formal professional networks of consultant surgeons in determining adherence to these voluntary guidelines. This project focuses on surgical treatment of two specific types of cancer. In both cases surgical treatment of these cancers is covered by clinical guidance issued by NICE. This guidance is intended to increase the rate of diffusion of innovation. 1. Colorectal Cancer: Patients undergoing surgery can undergo two alternative procedures (a) Open Surgery (b) Laparoscopic Surgery (the innovation). Laparoscopic surgery has been shown to result in significant improvements in outcomes for a range of surgical procedures including colon cancer resection compared to open resection, in terms of mortality, and secondary outcomes such as shorter length of stay, reduced surgical complications, reduced bleeding and pain, and lower hospital costs. 2. Breast Cancer Patients who need to have a lymph node dissected have two possible treatments (a) Axillary lymph node dissection (ALND) (b) Sentinel lymph node dissection (SLN) (the innovation). ALND is the procedure traditionally used to assess whether cancer has spread from the breast to neighbouring lymph nodes. SLN is the surgical removal of one or more small lymph glands from the axilla. ALND is associated with significant more side effects and morbidity relative to SLN, yet the use of SLN has plateaued in the NHS.. This project will examine the impact on diffusion of these innovations of (a) professional networks of consultants (b) the formal spatially based cancer networks of NHS Trusts. The former are examined because of their importance in shaping individual and firm behaviour. The long training period of consultants, the importance of the Royal Colleges and the dominance of the NHS as an employer means that professional networks are potentially very important in the NHS in determining clinical practice. The latter are examined as they are important policy instruments intended to increase collaboration between hospitals in cancer care. However, they are in operation at the same time as there were, perhaps conflicting, and policies to allow competition for patients between hospitals (the right of patients, with their GPs, to choose the hospital of treatment from 2006 onwards). Therefore the impact of spatial competition between nearby NHS Trusts will also be studied. Imperial are modelling cancer innovation uptake as a function of a number of factors that are considered to be important in the uptake of innovation literature as well as ones that are specific to the treatments examined. These include: (a) the work history of the consultants (hence the need for workforce data) (b) the nature of the hospital they work in, including measures of size; patient mix (severity, SES status, age, gender) across a range of specialties (not just oncology or surgery); the extent to which laproscopic techniques are used in other specialties; resources that may be complementary to the innovation (e.g. Use of imaging); decisions to invest in areas other than cancer or general surgery; the extent of innovation in other areas; the position of the hospital as a training facility and the position of the hospital in various networks for specialist care (e.g. Stroke networks). What is important is often not just whether a hospital does something but how much it does in absolute and relative terms. (c) The rest of HES is useful in that it allows issues with missing data that come from coding and recording practices. For example, it may appear that some consultants do vey low volume in one year preceded and followed by very high volumes. The low volume may in fact not be correct and may reflect HES coding practices in a hospital. Imperial will be able to examine this by looking at the patterns of consultant recording in other specialties. It means sophisticated Monte Carlo methods to deal with missing data under various assumptions can be utilised. This is very important when using administrative data. (d) Finally, the approach has the added benefit that rapid progress in cancer treatment can be analysed, Imperial can look at spillovers from these into other innovations in related areas such as surgery. This will increase the value to the patients and user community of the research. Most of these variables in (b), (c) and (d) will be measured by use of the HES data outside oncology and surgery. For that reason the whole HES data set is required (excluding maternity and mental health). The literature on innovation shows that context matters and therefore how much the rest of the HES data set is needed to examine this context as well as address a range of statistical issues. The whole purpose of the study would be seriously invalidated if Imperial were not able to have the data to establish this and to allow for miscoding and recording.