{"id":1733,"date":"2019-08-11T18:56:43","date_gmt":"2019-08-11T18:56:43","guid":{"rendered":"https:\/\/blogs.bmj.com\/ebn\/?p=1733"},"modified":"2019-08-11T18:56:43","modified_gmt":"2019-08-11T18:56:43","slug":"using-big-data-to-improve-the-patient-journey-in-cancer-care","status":"publish","type":"post","link":"https:\/\/blogs.bmj.com\/ebn\/2019\/08\/11\/using-big-data-to-improve-the-patient-journey-in-cancer-care\/","title":{"rendered":"Using big data to improve the patient journey in cancer care"},"content":{"rendered":"

Dr Amanda Lee; Associate Dean (International), Faculty of Health Sciences, University of Hull (Twitter @amandaleehull)<\/p>\n

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Over recent years, healthcare has been collecting a variety of \u2018big data\u2019 \u2013 sets of information on a variety of diseases, treatments, and outcomes. These are useful to inform research and clinical interventions, to provide an overview of emerging \u2018things\u2019 in healthcare. Datasets can be used to predict, to model, to understand and explore diseases and treatments \u2013 to show how (and where) they affect patients. However, because they often lie behind a firewall of confidentiality, big datasets can be difficult to access; because they are complex, they need careful application and evaluation to assert clinical focus. We collect information on everything, but do we know how to use data so that it is meaningful for clinical practice?<\/p>\n

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Take cancer for example – survival outcomes for many cancers are increasingly improving, and there are many governmental initiatives which aim to encourage people to present earlier in their disease processes, so they receive appropriate care. One exemplar is breast cancer: because of data-driven campaigns, survival outcomes have improved so significantly, that 78% of patients diagnosed will survive for more than 10 years (CRUK, 2019).<\/p>\n

Many healthcare professionals use data to identify a host of things, which potentially affect cancer outcomes. We have big datasets to inform our care and treatments; we can now identify factors that may cause or exacerbate cancer; we can offer information on how best to survive with cancer and can identify particular groups of patients who are more likely to be diagnosed.<\/p>\n

But do these datasets really tell us what we need to know? Each cancer is different, but many cancers are still overlooked. The less common cancers are often grouped alongside other – more prevalent \u2013 forms. Take gastroesophageal cancer for example: it is the eighth most commonly diagnosed cancer in the world, yet data are often merged with head and neck cancers, or gastric cancers (Lee, Khulusi, Watson, 2017).<\/p>\n

As evidence-based practitioners, it is now time to challenge the usual assumptions we have about \u2018generally\u2019 applied statistics. For example, a recent survival study published in the European Journal of Oncology (Lee et al, 2019) identified a disparity in survival statistics. The \u20181-year\u2019 survival statistic (a routinely applied measurement of cancer survival outcomes) \u2013 misses the fact that up to 39% of diagnosed patients die within the first 6 months.\u00a0 Apportioning 1-year survival statistics doesn\u2019t provide the full picture \u2013 this is clinically relevant, because it shows that the proportion of people with palliative care needs is likely to be high.<\/p>\n

Using a 6-month survival parameter suggests that not all – but many – patients diagnosed with gastroesophageal cancer need a different approach. Their treatment options would be more aligned to palliative care, so that appropriate healthcare professionals could ensure these patients are treated with the intent to reduce suffering, rather than further aggressive diagnostic testing and interventions. However, not all cancer patients are ready for aggressive investigations, surgery, or treatments. Many patients just need earlier signposting to supportive interventions and palliative care.<\/p>\n

It is important to challenge assumptions we make to group variables in any research, but it is more important to consider what these actually mean to our clinical nursing practice. These statistics help us to inform practice and allocate scarce resources effectively and in the best interests of our patients. We must continue to challenge big data so they inform our practices in the most relevant way and so we can drive forwards initiatives which best suit our patient groups.<\/p>\n

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References<\/strong><\/p>\n

Cancer Research UK (2019) Breast Cancer Statisticshttps:\/\/www.cancerresearchuk.org\/health-professional\/cancer-statistics\/statistics-by-cancer-type\/breast-cancer#heading-Eight<\/a>, [Accessed May, 2019].<\/p>\n

Lee, A., Khulusi, S., Watson, R. (2017) Which interval is most crucial to presentation and survival in gasroesophageal cancer: A Systematic review. Journal of Advanced Nursing 73. 2270-2282<\/p>\n

Lee, A., Khulusi, S., Watson, R (2019) Gastroesophageal cancer patients need earlier palliative intervention \u2013 using data to inform appropriate care. European Journal of Oncology Nursing 40 126-130 https:\/\/www.sciencedirect.com\/science\/article\/pii\/S146238891930047X<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Dr Amanda Lee; Associate Dean (International), Faculty of Health Sciences, University of Hull (Twitter @amandaleehull)           Over recent years, healthcare has been collecting a variety of \u2018big data\u2019 \u2013 sets of information on a variety of diseases, treatments, and outcomes. These are useful to inform research and clinical interventions, to provide […]<\/p>\n

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