Month: May 2017

31 May 2017

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.

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.

17 May 2017

Patient-reported outcomes: A smart way of collecting powerful datasets

This blog post highlights the importance and the benefits of researching complementary and alternative practices by using Patient Reported Outcomes. 

In November 2013, South Africa’s Medicines Control Council (MCC) released official guidelines for Complementary and Alternative medicines (CAMs) that could effectively see between 60 – 85% of products being removed from shelves. Currently, this industry is estimated to be worth a whopping R7-billion, with more than ±40 000 products on the market. The regulations have now defined a new category of medicines under law, Category D: Complementary Medicines. Products that do not fit the description of a Category D Medicine will automatically move into Category A: General medicines, which are classified as, and subject to, pharmaceutical drug regulations.

As a result of increasing popularity by general consumers, the need for data collection on CAMs has become increasingly highlighted by the release and hype of these new guidelines. Due to the exorbitant costs involved in clinical research, required for Category A medicines, CAM companies are forced to seek alternate ways of collecting powerful datasets that could be used in support of their claims, registrations and used as powerful marketing material for their product portfolios.

Although medical technology allows us to measure physical, physiological or biochemical parameters of data for a patient; it is not able to give all the data about the treatment or the disease itself. Some data can be obtained only from the patients themselves.

In short, patients can provide much more insightful data about things like thoughts, complaints or opinions that technology or any observer can’t, which would be considered as valuable data. Furthermore, quality of life also plays an essential role in the treatment as in some diseases, survival is not the ultimate goal of the treatment.

Patient-reported outcomes research (PROs) that stresses the importance of research “informed by the perspectives, interests and values of patients” throughout the research process has become increasingly more attractive to the CAMs industry as a powerful way of collecting valuable data. Aiming to provide practical evidence to support real-world decision-making – PROs particularly emphasises the patient perspective and are commonly used as outcome measures in clinical trials and observational studies. Identifying valuable information on the benefits and harms of interventions in a real-world setting can be valuable to a company and their consumers.

Compared to the older paper-based process of data collection (being expensive, labour intensive, error-inducing and lengthy), recent technological developments that facilitate the electronic collection process of PROs and the linkage of PRO data with other clinical data offer new opportunities for faster more effective ways of data collection. Across all levels of PRO application, the collection of PRO data is difficult – albeit not impossible – without electronic data methods. Electronic data capturing improves feasibility of collection, decreases burden and enables sophisticated survey administration.

The outcomes are broadly classified into- clinical (e.g. cure, survival), humanistic (e.g. emotional status) and economical (e.g. expenses, saving).

Ideal properties of a PRO instrument:

  • As seen in literature, the following ideal properties can be extracted:
    • It should be specific to the concept being measured;
    • It should be based on end-point model;
    • It should have conceptual equivalence;
    • It should be based on the conceptual framework;
    • It should have proper evidence for the conceptual framework;
    • It should contain optimum number of items;
    • It should have easy and specific measurement properties i.e. use of the scales which is easiest for the intended population to understand;
    • It should maintain the confidentiality.


PRO measurements can provide critically important information to CAM companies about the quality of improvement initiatives, enabling them to focus on what is most important to patients: how they feel and function. PRO measurement is advancing quickly and is currently the subject of international attention. With increasing pressure being applied to the CAMs industry, PROs could be the alternative option many companies are searching for in aiding them to meet the MCC’s requirements to gather real-world data in support of their registrations.

TCD Outcomes Research specialises in late phase research and is well equipped for undertaking such data collection, where focus is given to the patient. Combining our pharmaceutical experience together with our “High Tech, Low Touch” philosophy, we provided creative solutions that are tailored specifically to the project’s objectives. TCD Outcomes Research regards PROs as a powerful tool and aims to make high use of its impact with future endeavours.


15 May 2017

“In South Africa, one out of four people suffer from mental health issues”

This is according to Dr Renata Schoeman, who spoke on the impact of mental illness on the workforce at the Corporate Wellness Day hosted by the University of Stellenbosch Business School on Friday 12 May 2017 in Port Elizabeth.

South Africa still has a culture of silence surrounding mental health problems in the workplace. Employers and employees are unwilling to talk openly about conditions such as anxiety and depression for fear of association with weakness and failure. However, healthy employees are productive employees. Investing in a mentally healthy workforce is good business. It can curb medical costs, increase productivity, decrease absenteeism, and prevent and decrease disability costs. Investing in mental health also improves employee motivation, staff retention and competitiveness. The following experts shared their insights at the seminar:

  • Prof Piet Naudé, Director, USB: The ethical responsibility of leadership to enable multi-dimensional staff wellness.
  • Dr Renata Schoeman, Private Psychiatrist; Part-time Senior Lecturer: Leadership, USB: Clinical aspects of common mental health problems such as burnout and the importance of self-care.
  • Dr Tienie Stander, CEO, TCD Outcomes: The value of technology in corporate wellness assessments, interventions and outcome measurements.
  • Prof Christoffel Grobler, Associate professor, Walter Sisulu University; Clinical Head, Elizabeth Donkin hospital: The prevention and assessment of disability.

The following article from by Estelle Ellis, provides more insights into the seminar.

High cost of mental problems

11 May 2017


We are delighted to share the following significant changes and positive developments which took place recently within the TCD Group.

New Ownership

EOH, a JSE-listed South African based Knowledge Management group, have been in negotiation with the TCD Group since the last quarter of 2015, sharing the strategic objective to expand in the field of healthcare. EOH consists of a diverse portfolio of specialised industries in 21 countries across four continents.

The groups came to agreement in October 2016 and the transaction was completed in February 2017. EOH Holdings is now the 100% shareholder of the TCD Group of companies. We are excited by this positive development which opens many new horizons through the global profile of our new proudly South African owners.

The EOH philosophy involves sustaining successful acquisitions by building upon the inherent culture. The TCD Group will remain easily recognisable, building on our rich legacy, striving as always for continuous improvement.

Expansion of the TCD Group

The rapid evolution of the TCD Group expanded the original Triclinium into a full-service CRO (Contract Research Organisation), with a growing range of services and geographical footprint. Triclinium’s sister companies, within the TCD Group now consists of:

  • TCD Outcomes Research: The TCD Group developed a Health Economics and Outcomes Research (HEOR) organisation, through an acquisition of HeXor (a late phase development and pharmacoeconomics company), combined with the Late Phase Clinical Development expertise of TCD.
  • TCD e-Clinical Solutions: The TCD Group created an e-Clinical Solutions division which includes Software as a Service solution. TCD e-Clinical Solutions is focused around improving the outcome of clinical trials from a quality and efficiency perspective. Services include Electronic Data Capture (EDC), Electronic Medical Records (EMR), Electronic Patient Reported Outcomes (ePRO), Electronic Clinical Outcome Assessments (eCOA) and Mobile Health (mHealth), etc.
  • TCD Global Data Services: The TCD Group established and consolidated its Data Management and Biostatistics division under the TCD GDS brand, which is spearheaded out of South Africa and Bangalore, India.
  • TCD-MENA: The TCD Group established a full-service CRO based in Cairo, Egypt through a Joint Venture to focus on the Middle East and North Africa region.
  • PharmaLTX: The TCD Group established a software development company which specialises in software solutions in the wider health care sector such as partnering with Ministries of Health, Regulatory agencies, Institutional Review Boards, Universities, Clinical Research Centres, etc.


New TCD Group Chairman

The mandate to meld the member companies into a synergistic group has been entrusted to Dr Tienie Stander, founder of HeXor Pty Ltd., current Managing Director of TCD Outcomes Research, and now also the inaugural Chairman of the TCD Group.

Change in Managing Director of Triclinium Clinical Development

To those who know the company well, the most visible change is that after 17 years at the helm, founder Victor Strugo will relinquish the position of Managing Director to Abraham van Wyk, effective 1st May, 2017. Abraham has been increasingly involved in TCD executive functions since 2014 and his promotion is fully endorsed by TCD’s Board of Directors.

Victor will nevertheless remain active and visible in the new role of TCD Group Strategic Adviser, facilitating a smooth management transition, supporting key projects and developing new horizons for the company that he created in February 2000.

Our electronic media will be updated accordingly over time. We look forward to maintaining and strengthening our association with your organisation through this transition and to offer an ever-improving and broadening range of services in the years ahead.

For queries or more information, contact us at

11 May 2017

First ISPOR SA Chapter Workshop for 2017

Topic: “What’s the evidence?”- A practical approach on the HTA process in the UK: From the NICE appraisal to reimbursement

Date: 16 May 2017
Time: 08:45 – 15:00
Venue: Protea Hotel Marriot Balalaika Maude Street, Sandown, Sandton
Faculty: Nan Oliver

Nan Oliver has worked as a health economist in the UK over the past eight years. While working for Novartis Oncology she was responsible for 13 health technology appraisals, including 6 submissions to NICE, 5 to the Scottish Medicines Consortium and 2 to the AWMSG in Wales. These submissions were for drugs used to treat various rare cancers and tumours in areas including chronic myeloid leukaemia, gastrointestinal stromal tumours, pancreatic neuroendocrine tumours, myelofibrosis and acromegaly. In her last year in the UK, she was Head of Outcomes Research for Novartis Oncology. She has a B Pharm degree from Rhodes, MBA from UCT and a post-graduate diploma in health economics (PGDip HE) from the University of York. Having always worked in the pharmaceutical industry, Nan gained extensive experience in marketing, new product development and general management in South Africa before spending the last 14 years in the UK. She initially worked in marketing In the UK but it soon became evident that market access was the key to achieving access for patients to appropriate treatments and so she retrained in health economics. In the workshop, Nan will draw on her personal experience to highlight the processes and issues involved in making a submission to NICE, from planning the HTA strategy to developing the submission and managing the process through to final decision. The workshop will be interactive with plenty of time for discussion and questions.
08:45 – 09:00 Registration
09:00 – 09:10 Welcome and background
09:10 – 09:40 Brief overview of NICE process
09:40 – 10:10 Submission strategy development and timelines for developing submission
10:10 – 10:30 TEA
10:30 – 10:45 Developing the submission: ICER
10:45 – 11:05 Developing the submission: Costs
11:05 – 11:30 Developing the submission: Effectiveness
11:30 – 12:15 Developing the submission: Results interpretation
13:00 – 13:45 Lunch
13:45 – 14:00 Preparing submission
14:00 – 14:45 Interactions with NICE
15:00 Close of meeting

Registration fee: R2500 (Member), R3250 (Non Member) RSVP to or