Category: Articles

22 Mar 2018

What predictive and prescriptive modelling is and who it is beneficial to

Mark is diagnosed with stage III colon cancer and is informed of several treatment options by his doctor. As Mark 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 Mark 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 Mark 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” Mark’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 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.

References
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: www.solver.com/files/BAMarathon_DanielFylstra_Feb25.pptx. Accessed on: 15 March 2017.

20 Feb 2018

How important is Real World Evidence (RWE) in building your competitive late phase portfolio of evidence?

Consider the following scenario. Before entering your career, you probably took a course or two, studied for a few years and obtained a degree or even a post-graduate degree. That qualification provided you with a sound theoretical background. However, the real learning only took place when you finally started working in the industry. There is no doubt that real world experience resulted in much more depth and insight into your area of expertise, making you more valuable to your company today. For this reason, most employers secretly tend to prefer experience over qualification. This is true in most industries, hence the well-known term ‘over-qualified, under-experienced’. Think of it this way, when you are faced with a dying cactus, who would you rather call for advice, your friend with a theoretical background in horticulture, or your uncle who has been operating a successful cactus nursery for over 30 years? This doesn’t mean that an academic qualification is not necessary, on the contrary, it provides a much-needed foundation, and not to mention the fact that it eliminates the need for re-occurring trial and error. It just states that, when it comes to decision-making, experience trumps theory.

It is no wonder why “real world” evidence (RWE) has become an increasingly popular topic in the global health market today. As the cost of R&D increases, there is a shift towards looking for answers and quantifying value in new ways.

Even science has seen a shift. If science is the search for truth, where do we start searching, and what is the real truth? Is clinical research, which proves efficacy under prescribed conditions, enough evidence to support commercialisation of products? Or can we compare it to the “theoretical background” concept mentioned earlier? RWE provides outcomes and safety data outside the limited framework of clinical research, providing a deeper insight and depth into a product’s real-world therapeutic effectiveness and side effects. Much in the same way as “work experience” makes you a more valuable employee.

Regulators, payers and providers are all asking new questions regarding value and validity. If the concept of “value” is so subjective, how do you quantify value in a complex and multi-dimensional world sufficiently enough to be convincing to all parties involved? This article in the African Journal of Outcomes Research, attempts to answer these pressing questions and demonstrates how RWE can benefit you in building your competitive product portfolio.

28 Sep 2017

ATTEND OUR TRAINING SESSION – The Funder Mindset: Insight into the funder’s world.

Due to popular demand, TCD Outcomes Research has decided to present a Training Session as a follow up to the Workshop we held in July. We are honoured to invite you to this full day Training Session.

The Funder Mindset: Insight into the funder’s world – Training Session. 

Date: Thursday, 12 October 2017
Time: 09:00 – 16:00
Venue: Radisson Blu Gautrain Hotel, Rivonia Road, Sandton.

Click here for map and directions

WORKSHOP TOPICS

  • The Legal Framework: Regulatory, Suppliers, Providers with a view of future Implications.
  • Strategic imperative for a sustainable business in SA: The Hunger Games & NHI
  • Risk Sharing Funding Principles, Alternate Reimbursement Practices & value Based Contracting: Strategies for the Future
  • The Actuarial Black Box: Understanding “Mission Impossible” (Risk Management, managing contributions/reserves)
  • Conceptual Framework: Integration of how our clients can utilize the knowledge.

Cost to attend: R2,150 per person (ex VAT) – Snacks and refreshments will be provided
Please RSVP by Thursday, 5 October 2017

The objective of this workshop is to create a forum where the pharma and devices industry can have an opportunity to understand the challenges faced by and decisions made by funders. We look forward to seeing you there.

TO REGISTER AND RECEIVE PAYMENT INFORMATION:
Please email amy.stanfley@tcd-global.com.

For more info call Amy Stanfley on 012 664 1622 or via e-mail: amy.stanfley@tcd-global.com

03 Aug 2017

Seed for thought: paper that grows

At the foot of the Paardeberg Mountain, where the early morning mist hovers between the oak trees and vineyards until the sun appears, you’ll hear the bustling sounds of eager hands working to create handmade paper embedded with seeds. On the farm, Wyngaardt, the Growing Paper Company team creates a beautiful collection of seed-paper products. Their products include bookmarks, calendars, gift cards, tags, notebooks as well as customised corporate stationary.

The Growing Paper Company collects post-consumer waste paper from local schools, banks and businesses and then use it to make their iconic handmade seed paper. This paper is then used to make a variety of products and custom corporate stationary.

TCD Outcomes Research recently commissioned Growing Paper to make our promotional corporate stationery. Their helpful team provided excellent customer support throughout the process. We received our stationery within a week from placing the order and were very happy with the end result. In alignment with our vision: “Let our work and actions benefit humanity”, we are proud to support local eco-friendly initiatives such as these.

The growing paper idea was born one day in 2009 when Roxanne Schumann threw out a wedding invitation. She was troubled by the idea of just throwing away a beautifully designed piece of art and wanted to find a way of reusing the paper. She came up with the solution of adding seeds to paper so that it could be sown once read. In 2010 Roxanne and Nileta Knoetzen opened the Growing Paper factory on the Wyngaardt family farm.

The growing paper products are all produced by hand in the factory. Waste paper is processed into pulp by adding water. From this pulp, the papermakers produce handmade sheets. Finally, the paper is dried in a tunnel. All the paper products, embedded with either with flower, herb or vegetable seeds can be planted and nurtured after being used for its original purpose.

The team who forms the heart of the business is made up of 23 individuals from the Malmesbury area and surrounding farms. Carolette, described as “practical and organised”, is the print guru and trimmer. “LOL” Franza, is in charge of admin and “Respectful” Zaid is responsible for quality control, die-cutting and binding. “Ambitious” Abdul is the driver and also the maintenance “guru”. He assists Jackie in the trimming process. “Poshy” Pam is responsible for packing all orders and staff manager Porcia, “always has a plan”. At the core of the team you will also find Cecilia, Meisie, Ceearle, Pierre, Dennwe, Manusca, Desmorien, Beranice, Natalie, Griet and Leena, Chaney, Madeline, Iline and Mercia, carefully producing the paper products by hand. Their products include wholesale items as well as custom promotional material and wedding stationery. Clients can choose from a variety of flower, herb and vegetable seeds. These include Alyssum, Poppies, African Daisies, Vygies, Forget me not, Snapdragon, Rocket, Basil, Parsley, Thyme, Chilli, Lettuce, Carrots and Tomatoes.

To support this innovative initiative and to see what they have to offer, visit http://www.growingpaper.co.za/

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.

 

References

  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 Jun 2017

ATTEND OUR WORKSHOP – The Funder Mindset: Insight into the funder’s world.

TCD Outcomes Research will be hosting a number of workshops over the next few months. The first in the series will be focused on the Medical Scheme Perspective. We are honoured to invite you to the first of this series of workshops, titled:

The Funder Mindset: Insight into the funder’s world. 

Date: Thursday, 13 July 2017
Time: 09:00 – 13:00
Venue: Radisson Blu Gautrain Hotel, Rivonia Road, Sandton.

AGENDA:
9:00 – 9:15 Welcome & opening

9:15 – 10:15 Tebogo Phaleng – Presenting the medical scheme administrator’s role (1hour)
• What services are provided by an administrator?
• What are the roles of actuaries?
• How do administrators manage risk?
• What are health care vs. non-health care costs?
• What are the key challenges facing administrators?
• How should suppliers engage with administrators?
• How can we improve access to new innovations to patients in need?
10:15 – 10:30 Bio break
10:30 – 11:30 Milton Streak – Presenting the medical scheme perspective (1 hour)
• How do medical schemes budget?
• How do medical schemes agree on benefit richness per option?
• How do you manage risk?
• How do you create formularies?
• How do medical schemes make reimbursement decisions?
• What is the relationship between a scheme and its administrator?
• What is the role, responsibilities and authority of the Principal Officer?
• What is the role, responsibilities and authority of the Board of Trustees?
• How does medical industry engage with suppliers (example pharma)?
• What models of engagement can you recommend for suppliers?
• How can we improve access to new innovations to patients in need?
11:30 – 12:00 Tienie Stander – Presenting the role of health outcomes research in this environment (30 mins)
• Moving away from price arguments to value arguments
• Breaking down the silo-thinking inherent in the industry
• From clinical evidence to real-world evidence to quantify value
• A framework for value quantification
12:30 – 12:30 Panel discussion (30 mins)
• Questions from the audience
12:30 – 12:35 Closure
12:35 – 13:00 Refreshments

SPEAKERS:
Milton Streak: Milton has 21 years experience serving in senior leadership positions at leading South African Medical Scheme Administrators/Managed Care Organisations and Medical Schemes, including Discovery Health Medical Scheme, as Principal Officer/CEO from 2009 to 2016. He is currently an independent Healthcare Strategy Adviser and has spent the last three months in India working with India’s 3rd largest standalone private health insurer.

Dr. Tebogo Phaleng: Tebogo was Deputy General Manager of Strategy and Risk Management at Discovery Health and Managing Director of Coalesce, a Strategy and Risk Advisory consultancy. He is currently the Managing Director of EOH Health.

Dr. Tienie Stander: Tienie is the managing director of TCD Outcomes Research. He is a member of ISPOR International, International Aids Society and Health Financial Management Association. He collaborates extensively with international academic organisations such as Harvard Medical School and British Columbia University. International experience includes consulting work in the SADC, Mauritius, Ghana, Libya, Egypt, Sudan, India, Thailand, UAE, Oman and China related to policy, health systems and health economics and outcomes research. 

Cost to attend: R1100
Please RSVP by Friday 7 July 2017

The objective of this workshop is to create a forum where the pharma and devices industry can have an opportunity to understand the challenges faced by and decisions made by funders. We look forward to seeing you there.

TO REGISTER AND RECEIVE PAYMENT INFORMATION:
Please email amy.stanfley@tcd-global.com

For more info call: +27 12 664 1622 or email: kumen.chetty@tcd-global.com
or.tcd-global.com
05 Jun 2017

Improving women’s health across the Kurdistan region through the Centre for Research and Education in Women’s Health (CREWH)

The inauguration of the Center for Research and Education in Women’s Health (CREWH) took place in Erbil, Iraq – Kurdistan on the 24th of May 2017 under the patronage of Hawler Medical University and the ministry of health (MoH). The CREWH is part of Hawler Medical University and works towards improving women’s health across the Kurdistan region.

Dr. Hamdia Ahmed, president of the CREWH, outlined the need for this centre during her speech: “[The] CREWH will advocate women’s health and development through education, research, capacity building and community projects. [The] CREWH was established to be a leading center dedicated to advocate women’s health and development with the academic institution and across the community.

Several speakers representing academics, official and international organisations, as well as the chairman of the party, president and professors of the university, the ministry of health, and the CREWH’s staff outlined the importance of such a centre and its role to improve the health and well-being of women in the community.

Professor Ibrahim Labouta, president of TCD Outcomes Research MENA region, also attended the event. During his speech, Prof. Labouta, addressed the critical importance of the development of a 5-year strategy, building the capacity of the staff in outcomes research and introducing information technology such as registries into this strategy.

Speeches by the representatives of UN Women and WHO further appraised the opening of the centre and emphasised its future role in enhancing the health and status of women in Kurdistan.

This event highlighted the importance of bringing together researchers, clinicians, outcomes research experts, international organisations and communities to promote women’s health through research, training, advocacy and community research projects. It also covered the following topics/issues:
• Identifying strategies for improving women’s health
• Exploring future business opportunities
• Exploiting the competitive pharmaceutical market
• Getting an insight of the needs of Kurdistan region in terms of outcomes and clinical research and training
• Getting to know the latest regulatory developments, key indicators and major corporate developments

To find out more about the CREWH, visit http://hmu.edu.krd/WomensHealth.aspx or contact crewh@hmu.edu.krd

 

Professor Ibrahim Labouta, addressed the critical importance of the development of a 5-year strategy, building the capacity of the staff in outcomes research and introducing information technology such as registries into this strategy during the inauguration of the CREWH (Photo:http://hmu.edu.krd/HMUNews/tabid/343/articleId/101/articlesListTabId/40/articlesListModId/734/articleDetailsModId/734/listType/templateBased/Default.aspx)

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.

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.

References

  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: solver.com/files/BAMarathon_DanielFylstra_Feb25.pptx. 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.