Creating a clinical data culture

Healthcare organisations are facing five key challenges in a post Covid-19 environment.

Due to Covid-19, elective care and procedures (and to an extent, selected urgent care aspects) were severely impacted. The current elective waiting list in England is 7.47m¹. After a period of investment and spending during the pandemic, there is now a return to focus on cost efficiency. Integrated Care Boards have been asked to save 30% of running costs by FY25/6, with at least 20% by FY24/5². Meanwhile, the secondary care system is being increasingly digitised with large spends on Electronic Patient Records (EPRs).

We experienced the potential of and started to uncover the assumed benefits of virtual care delivery during the pandemic. Now we need to confirm those assumptions and identify the best elements of virtual and face-to-face care delivery, to design and develop hybrid care delivery models. Our recent research with citizens shows that ‘human touch’ – empathy and compassion – is also important for patients to feel satisfied with digital services they receive.³

With a renewed focus on prevention and population health management, data-driven insights are needed to identify the patient cohorts and the cohort at risk of becoming patients, appropriate interventions, and to monitor the outcomes. All integrated Care Systems (ICS) need to set up Intelligence Functions, multi-disciplinary collaborations, research and prevention teams across an ICS, bringing together the analytical skills to enable decision making based on locally tailored evidence and population-based approach to deliver prevention interventions, care planning and delivery.

One key limitation is that we haven’t a large enough or skilled enough workforce. In December 2022, there were 124,000 vacancies in the NHS, with 10.8% nursing and 5.9% medical posts vacant⁴. And those that remain are often tired, demotivated. Junior doctors, nurses, consultants, radiographers have all undertaken recent strikes over pay and working conditions, for the first time in decades. A survey conducted by the BMA with junior doctors in late 2022 revealed that 79% often thought about leaving the NHS and that 65% have actively researched doing so in the past 12 months⁵.

Data and data insights can help with many of the above. With operational recovery, data can help understand the specialties and activities on waiting lists that require more focus and can help us tackle waiting lists in a more intelligent, risk managed and clinical needs driven basis rather than just from ‘top to bottom’ of the list. In driving cost efficiencies, data analysis is crucial to identify the baseline position, conduct benchmarking and quantify opportunities to save money and undertake research on clinical and cost effectiveness of novel workload management innovations. By combining datasets, often siloed to date, it allows us to build up much richer scenarios and helps us identify corelations between metrics that helps focus valuable resources and more importantly not merely shift system ‘bottle necks’ by not considering the overall pathway.

Data is not only about the quantitative metrics. By considering the qualitative elements using data, for example patient and staff user experience, we can start developing more accurate understanding of how best to deliver care and what is most effective in improving patient outcomes. Data, linked across the appropriate care settings, is crucial in identifying the patient cohorts, appropriate interventions, and to monitor the outcomes during Population Health Management. Linking activity, budget and workforce datasets enable you to accurately understand the current position as well as model scenarios of what may occur in the future so you can understand gaps in your workforce plan.

Many healthcare organisations are setting out their data strategies, outlining their ambitions and how they want to make the best use of data. They are investing in data visualisation capabilities such as Power BI and in upgrading their data platform capabilities to support the collation, standardisation and analysing of data.

One key enabler that should be part of all organisations’ foundational efforts is to develop and foster the right data culture among its clinical workforces.

A supportive data culture is needed within healthcare organisations to maximise the value from the data and data insights.

Clinicians fully engaging with data and exploring further insights will use those insights fully to inform their decision making. For example, to take a preventative approach in care delivery (in the context of Population health Management) or to take a risk-based approach (prioritising the care of patients on a waiting list or taking a clinical need-based approach to following up patients in an outpatient setting).

One of the key ingredients to enabling data driven decision making in healthcare is (near) real time data that is of good quality. The majority of data capture is still manual and inputted by frontline staff. Clinicians motivated to invest time in inputting data and taking part in data quality initiatives, working within an enabling environment (enough time and headspace, user friendly systems and tool, etc), will have a massive impact on uplifting the quality of real time data flows and providing clinical satisfaction on efforts invested to improve care pathways using data driven decision making.

There are a number of challenges.

Clinicians may not be fully engaged, or face time restrictions based on their job plan to be engaged, in the organisation’s vision for data and not full bought into or believe in the value data can deliver to their clinical practice, and how it can make their work easier. Dr Devesh Sinha, CCIO and Stroke consultant at Barking, Havering and Redbridge University Hospitals NHS Trust believes that sometimes we can be too focused on the bad news and data breaches, and not enough time is spent spreading news where data has been put to good use. A delicate balance needs to be struck between data governance and risk of not sharing data for direct care and improving patient outcomes.

Clinicians who want to engage may be put off by the poor quality and accuracy of the data and therefore not trust the insights derived from the data. This could lead to disengagement as well as kick off a vicious cycle where it becomes increasing difficult to address the data quality issues.

Other clinicians who want to engage and use insights derived from data to inform their practice and improve patient outcomes face a different challenge. Due to traditional clinical undergraduate courses and post-graduate training structures, they may lack the technical skills to analyse and interpret data – data literacy - and therefore the confidence to ask data related questions and to derive the right insights to inform their decision making. For example, they may lack the skills to deeply interrogate data to identify data biases, data gaps or missingness influences outputs and how can these be rectified to maintain integrity in the analytical outputs.

Difficult to use systems further complicates matters and combined with data literacy challenges can lead to further data quality issues, including data being collected outside of systems, on paper or in non-standard formats.

We believe a data strategy aligned to organisational strategic objectives, with a clear case for change to include use cases and early wins helps develop the required data culture, trust in the data and a mindset that promotes data driven decision making. The language and how benefits are articulated (for example, through appropriate user personas such as ‘Community district nurse’ or ‘Consultant physician’) helps make it easy for clinicians to understand ‘what it means to me’.

A clinical workforce that understands and believes in the value of data will engage and take care in entering relevant and accurate data.

Mistrust in data quality may stem from not having visibility of the processed followed to collate, analyse and check the data. Clear communication on steps followed, including any limitations in the data will help provide transparency and inform clinicians on what they are working with. The reality is that data quality improvement is an iterative process, and it is difficult to aim for ‘perfect’ data from the start. A more practical approach would be to clearly communicate current situation and processes as above, and then to make data available to clinicians and work with them to identify opportunities to improve data. Apart from assessing systems in use in the organisation (and the related user experience in using these to input data), other potential opportunities could be in identifying the data that is currently being inputted to prioritise subsets to focus quality improvement initiatives on and to identify opportunities to automate data input to free up clinician’s time to focus on priority subsets.

Technologies such as EPRs, used in the right way, can improve data quality and patient outcomes.

However, poor usability of EPRs can burden already stretched frontline staff and take up valuable clinical time. A survey done in 2019 of EPRs in the Emergency Department suggested that none of the 25 systems included met the acceptable usability standards.

As an addition to what Peter describes above, AI technologies that provide ability to translate speech (i.e. dictations) into written text can automate the text entry first step further making the process easier for clinicians.

Data literacy is essential. Training on data (and statistics principles) as applied to healthcare should be embedded into undergraduate curriculums and made available to the existing workforce to complement ‘on the job’ training. The training should include how new technologies and data analysis techniques can support research and evaluation of the health economics of interventions. A learning framework which develops and maintains data literacy and capability is helpful and this needs to be supplemented by developing communities and encouraging champions that will support others and promote data literacy. As advanced technologies such as Artificial Intelligence becomes more prevalent and has the potential to improve patient outcomes and healthcare operations, training on these technologies also must be considered. This will create a more confident clinical workforce when it comes to data and technology and will support the informed and safe use of data and AI solutions, where the workforce understands both the potential and limitations, and is able to derive insights appropriately and work effectively alongside technologies. Investment should be made in creating hybrid clinical roles with dedicated time to data and health technology solution development and implementation which could also help engage and retain a clinical workforce by providing variety and a means to pursue their areas of interest without leaving the NHS.