Predictive and prescriptive modelling in health care

Following from our previous blog “How data influences decision-making in the health care industry”, we are relooking the scenario of John.

John is diagnosed with stage III colon cancer and is informed of several treatment options by his doctor. As John is not medically-trained, he does not have the medical knowledge or knowledge of new trends in treatment to decide what treatment option is the best for his specific circumstances (family history of colon cancer, 55 years of age, diabetic patient), or to decide if he needs a second opinion on treatment options. How can John make an informed decision on which treatment option to select, also considering costs of treatment which he can afford?

Further to this, was there perhaps a chance that John could have been warned ahead of time of his increased risk of developing colon cancer, given his family history, older age and the fact that he is a diabetic patient, and therefore could have been screened earlier?

Newer big data analysis techniques (predictive and prescriptive modelling) can be used to “predict” John’s risk of developing colon cancer, given his risk factors, as well as “prescribe” the most suitable treatment.

What is predictive and prescriptive modelling and how does it work?

Predictive modelling can be used to predict, for instance, the risk of developing colon cancer for a specific patient with a specific set of risk factors. This is based on past data for patients with different risk factors (predictors). Statistical regression or machine learning techniques can be used to predict a risk for a specific patient, based on the diagnosis and outcomes of other patients with similar characteristics/risk factors. The model can be updated when more data becomes available. Risk factors could also include molecular biomarkers or gene expression[1].

Prescriptive modelling is aimed at helping people make better decisions with the data at hand[2]. Prescriptive modelling uses optimisation and simulation techniques to determine all possible outcomes, as well as the best or optimal outcome[2]. Prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters (characteristics/risk factors). Therefore, clinical outcomes (e.g. survival due to colon cancer) can be optimised by changing inputs such as treatment provided and considering the cost of available treatments.

Who could benefit from predictive and prescriptive modelling?

As demonstrated in the example above, the patient could benefit from the insights provided by predictive and prescriptive modelling by, firstly, knowing their risk of a specific disease, and therefore getting diagnosed early, due to regular screening, if they know they are at increased risk for that disease. Secondly, the patient could benefit, by knowing what treatment will work best for him/her, also considering the cost-effectiveness of the specific treatment.

This same rationale can be used in the field of preventive medicine. Screening programmes could be aimed at those patients at most risk of acquiring a specific disease, to use the available health care budget optimally. This is also the case with vaccines. If a patient knows he/she is part of a high-risk population group, or in a high-risk phase in their life cycle, they can prevent certain diseases, like meningitis, by getting vaccinated against the disease.

Predictive and prescriptive modelling could also be used to assist pharmaceutical companies in the positioning of their products for the correct market. For instance, a specific cancer treatment may be more effective or only effective in a small subgroup of patients; for instance, for patients with a specific biomarker or gene expressed. A pharmaceutical company can use the insights provided by modelling to position their drug (especially relevant if treatment is very expensive) to only that patient group for which it is the most effective (and the most cost-effective) – a term called personalised medicine. This could help in convincing medical schemes to provide cover for that subset of patients who have the best chances of survival or cure with that drug, instead of not providing cover to any patients for that specific drug, due to the treatment being so expensive.

TCD Outcomes Research can assist you in your prescriptive and predictive modelling requirements. We can pre-process your data (if required) and then import into our modelling/machine learning platform. This data can then be analysed descriptively, predictively and prescriptively to assist with visualising the status quo, predicting new outcomes and making better decisions to benefit patients, pharmaceuticals and device companies.


  1. Kourou, K, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2014 Nov 15;13:8-17.
  2. Fylstra, D. PASS Business Analytics Conference 2016. Available from: Accessed on: 15 March 2017.

TCD Outcomes Research is a fully fledged, full service, health economics and outcomes research (HEOR) company serving healthcare companies globally and forms part of the TCD Group. We specialise in late phase health outcomes research by studying the real world value of healthcare solutions and its economic and financial impact. Partner with us to receive a skill set on a continuum of your needs, be it market access, medical, clinical, regulatory, sales or marketing. Convert scientific evidence related to efficacy, safety and quality into a market approach that focuses on real world evidence (RWE) to communicate the value of your product to your stakeholders.

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