Tag: patient registries

05 Jul 2017

Personalised (precision) medicine: what is it, and how does it work?

John received chemotherapy for colon cancer. The chemotherapy drug that he used, however, was not effective for him. He also experienced severe nausea and diarrhoea when using this chemotherapy.

He speaks to another patient treated at the same hospital, who has the same type of colon cancer and who received the same chemotherapy. For this patient, the chemotherapy was very effective and the patient only experienced mild nausea. What causes these differences between patients and what approaches are needed to overcome them?

Patients have different responses to different treatments, as well as different adverse reactions[1]. These differences could be caused by genetic or other factors. The solution here lies in personalised, or precision, medicine.

What is personalised medicine?

Personalised medicine is the concept of adapting patient treatment (drug and dosage) to a specific patient, based on that patient’s specific characteristics, such as genomic information and environment[1].

The aim of personalised medicine is to provide a patient with the correct treatment regimen that will potentially result in better treatment of a disease, as all patients do not have the same characteristics and do not respond the same to the same treatment.

What is required for personalised medicine to work?

Personalised medicine typically consists of two parts: firstly, using diagnostic medical devices to identify specific characteristics of a patient. Diagnostic devices or tests could include, amongst others, genetic tests, or imaging equipment. Secondly, therapeutic products (drugs, devices or other treatments) are provided based on the results of the diagnostic test[1].

This implies that there could be a synergy or collaboration between device and pharmaceutical companies in providing personalised medicine to a patient.

For personalised medicine to be successful, data on past patients should be available. This can be in the form of patient registries or clinical trials. This data should include the various characteristics of the patients (for instance, biomarkers, genetic information, age, gender, family history, etc.) and disease (especially if the disease can present in different ways). This data should also provide information on the outcomes for patients using specific drugs.

Predictive and prescriptive modelling can be used to analyse this data and provide insights into the relationships between patient characteristics (biomarkers, genetic information, etc.) and the outcome (cure or survival using a specific drug). Predictive and prescriptive modelling can also be used to determine if patient characteristics have a significant impact on the outcome, whereas this can become a focus point or objective for further research. This is done to further improve knowledge and treatment habits for a given patient.

As mentioned in a previous blog post, “Predictive and prescriptive modelling in health care”, TCD Outcomes Research can assist you in your prescriptive and predictive modelling requirements. We can also assist in positioning your product, using this information, for the correct market segment. This will not only assist the pharmaceutical or medical device company, but also the patient, for whom funding for the correct treatment would more likely be available from the medical aid. A patient would also know that treatment with a specific drug is catered to their needs and that the patient receives the best treatment based on his/her medical records.



  1. S. Food and Drug Administration. Paving the Way for Personalized Medicine – FDA’s Role in a New Era of Medical Product Development. October 2013. Available from: https://www.fda.gov/downloads/ScienceResearch/SpecialTopics/PersonalizedMedicine/UCM372421.pdf. Accessed on: 15 March 2017.
23 May 2017

How data influences decision-making in the health care industry

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 data help John to make an informed decision on which treatment option to select?

Pharmaceutical company A has a new treatment for colon cancer, drug X, which they want to launch. They also want to apply for funding from the medical schemes for this drug. How can data help pharmaceutical company A to show to the medical schemes that drug X is a cost-effective option compared to the current standard of care, and that drug X should, therefore, be funded by the medical schemes? Further to this, how can data help pharmaceutical company A to know in which regions to place the bulk of their sales representatives?

Pharmaceutical company B has launched a new treatment, drug Y, and wants to broaden the indication and simultaneously broaden access to patients on a lower option medical scheme. In conjunction with this, they want to show that the drug is safe and effective in real-world practice. How can data help pharmaceutical company B to reach these objectives?

Many diverse data sets are created in the health care industry. These include patient-reported outcomes, patient registries, medical schemes claims data, clinical trial data, sales data, and more.

How can this data be used to assist John and pharmaceutical companies A and B to make informed decisions?

Firstly, the outcomes that can be extracted from health care data should be considered.


Outcomes that can be extracted from health care data

A diverse set of outcomes can be extracted from health care data:

  1. From patient-reported outcomes: The subjective level of joint pain experienced because of, for instance, rheumatoid arthritis, measured via a scale such as the visual analogue scale (VAS).
  2. From patient registries: The efficacy of a drug for treating a specific patient population could be determined, considering specific patient characteristics, such as age, gender, whether the patient has diabetes, as in John’s case, etc.
  3. From medical schemes claims data: Patient journeys, for instance, the in-hospital cost of treating a patient with colon cancer. Further to this, what specific areas of costs contribute most to in-hospital cost (for instance, ICU vs general ward vs medicine cost), for patients treated with drug X compared to patients treated with drug Y?
  4. From clinical trial data: The efficacy of one drug compared to another to treat a disease. This data can further be used, in conjunction with cost data and safety (adverse event) data, to ascertain whether a specific drug, such as drug X, is cost-effective, compared to another drug.
  5. From sales data: The total number of units of a drug sold per year, and factors influencing sales, such as seasonality and the location and effort of sales staff.


Methods used to extract outcomes from health care data

A variety of methods are used to extract outcomes from health care data:

  1. From medical schemes claims data and patient-reported outcomes: Descriptive and inferential statistical analysis using tools such as Microsoft Excel or SAS.
  2. From patient registries: Big data analysis methods using statistical modelling and machine learning, such as descriptive, predictive and prescriptive analytics.
  3. From clinical trial data: A pharmacoeconomic model can be developed that uses the efficacy data, together with cost data from amongst others medical schemes claims data, to determine whether a drug or device is cost-effective, compared to a comparable drug or device (comparator). Methods used in modelling can include Markov modelling (where a disease or treatment of a disease is broken into different states, with different utilities/weights and costs related to each state), as well as newer techniques such as discrete event simulation. Further to this, clinical trial data can be analysed using biostatistics, to prove efficacy in terms of predefined primary and secondary endpoints.
  4. From sales data: Depending on the size of the data set, different methods can be used, including big data analysis methods.


How can value be extracted for the patient and pharmaceutical or device company?

Methods one to three above can be used to inform John of the best drug to use for his specific circumstances, considering both the cost and efficacy of the drug and considering his age, diabetes status and family history of colon cancer (personalised medicine).

For pharmaceutical company A, methods one to four can help to provide evidence to the medical schemes of the cost-effectiveness of drug X, and to determine where to best place different sales staff members to achieve the optimal number of sales in a specific region.

For pharmaceutical company B, methods one, two and four can help to provide evidence to broaden the indication or access for lower option scheme members. These methods can also be used to prove efficacy and safety in a real-world setting, compared to a clinical trial setting.

These examples demonstrate how value can be extracted for the patient, the pharmaceutical (or device) companies as well as the medical schemes companies. However, the scientific evidence by itself is insufficient to convince them of the value of the drug. It requires a subtle combination of science, art and communication to convert these abstract concepts into value stories that will inspire them. By combining science with art, one can communicate the value of products and/or treatments in a language that appeals to each of these stakeholders. At TCD Outcomes Research, we have termed this process as “Dynamic Solutions to Dynamic QuestionsTM”.

Follow our blog for more information and case studies on data in the health care industry. Contact us to find out how TCD Outcomes Research can assist in providing you with valuable insights from your data. Visit our website for more information.

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.