The field of medical product development is constantly evolving, with patient-centered approaches gaining importance. Patients’ experiences and perspectives are invaluable in understanding the efficacy and impact of medical interventions. Traditionally, clinical trials and research studies have relied on clinician-reported data and objective measurements. However, in recent years, inclusion of patient-reported outcomes (PROs) has emerged as a valuable resource to inform all stages of the product development life cycle. What is the significance of patient self-reported data in supporting medical product development and how it can positively impact key stages of the development process?

What are Patient Reported Outcomes (PRO)?

A patient-reported outcome (PRO) is data obtained directly from patients and is based on a patient’s perception of a disease and its treatment. PROs are important because they provide a patient perspective on a disease/treatment that might not be captured by a clinical measurement. This information is collected through validated questionnaires or surveys, where patients self-report their experiences; these responses come directly from the patient, without interpretation of the patient’s response by a clinician or anyone else. Traditionally, PROs have been collected using a paper-based questionnaire, but as with most paper forms, many labs have recently migrated to electronic forms, also known as electronic Patient Reported Outcomes (ePRO). PROs offer a unique perspective as they reflect the patients’ feelings, preferences, and day-to-day experiences with a medical condition or treatment. Some examples of PROs include pain levels, physical functioning, mental health, and overall satisfaction with treatment.

How are Patient Reported Outcomes used in the Product Development Life Cycle?

1. Conceptualization (Pre-clinical): 

The product development life cycle starts with conceptualization. In this phase, ideas for new medical products and treatments are brainstormed, evaluated, and selected to move on to the clinical development phase. By engaging patients early in the process, medical developers can gain valuable insights into patient perspectives, preferences, and priorities, ensuring that the product’s concept aligns with actual patient demands. Incorporating patient self-reported data during this phase can be instrumental in identifying unmet patient needs and provide a good foundation for creating a desirable final product that patients will want.

2. Feasibility Assessment: 

During the planning stage, the design is conceptualized. The concept is based on customer needs and technical requirements. The feasibility of the concept is assessed. Product developers evaluate the practicality and viability of their proposed medical product.  There is a prototype analysis, various go-to-market strategies are developed, and an initial testing takes place in order to collect user feedback. The patient’s perspective regarding the product’s design is taken into consideration before moving on to further development steps.

3. Design and Development:

Patient self-reported data plays a pivotal role in the design and development phase. PROs better capture negative, uncomfortable, and impactful symptoms from the patient’s perspective, such as nausea and anxiety. Therefore, they are better at identifying potential risks and side effects, enabling developers to proactively address safety concerns. Incorporating patient perspectives can enhance the usability, functionality, and overall patient experience of the medical product.

4. Validation/Clinical Trials:

Clinical trials are critical in determining a medical product’s safety and efficacy. By utilizing patient self-reported data in these trials, developers can assess patient outcomes and experiences with the product under real-world conditions. PROs measure the subjective elements of patients’ conditions, including health-related quality of life, pain intensity, activity limitations, participation restrictions, satisfaction or adherence to treatment and help to evaluate the burden of disease and treatment from patients’ perspectives. Integrating PROs into clinical trial data not only enhances the data’s comprehensiveness but also validates the product’s efficacy from a patient’s standpoint.

5. Launch and Regulatory Approval: 

Regulatory bodies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) are increasingly recognizing the significance of patient self-reported data in evaluating product efficacy and safety. They should still be supported by objective or functional outcomes but by including PROs in clinical trial data submissions, companies can provide regulators with a more comprehensive view of the product’s benefits and potential risks.

6. Post-Market Surveillance:

Once a medical product enters the market, the company must continually monitor its safety and continue to gather patient feedback. Once on the market, it will be sold and used by a more diverse population and therefore may lead to unexpected results.  PRO is one kind of data that researchers can use to track adverse events, product shortcomings, and areas for improvement. Analysis of this feedback can be used for continuous product updates that provide better results for unmet patient needs.

By capturing patients’ experiences and perspectives, patient self-reported data can enrich a clinician’s understanding of the patients’ experience with unique information that could not be gained from biomedical outcomes alone. Integrating PROs into the product development life cycle allows for improved clinical outcomes, accelerated regulatory approvals, and faster product improvements. Companies should embrace patient-centric approaches as they will not only lead to more effective and user-friendly treatments, but also foster stronger patient-company relationships built on trust and empathy.

Through Carenity, a dynamic online community encompassing +500,000 patients and caregivers, EvidentIQ offers direct, compliant, and timely access to real-world patient data. Leveraging our Carenity platforms, we deliver a distinctive range of solutions, including Real-World Evidence, Clinical Trial Optimization, and Scientific Communication and Publications.

Carenity creates a supportive space by uniting individuals facing similar conditions, fostering connections through features like newsfeeds, discussion forums, private messaging, and more. We recognize the significance of technology in combating isolation and enhancing information sharing among members.

We have imagined an innovative model based on collaboration and honesty. On Carenity, patients are invited to express their expectations based on their conditions and on their treatment, so that they can be taken into consideration by those who will be developing the enhanced health solutions for patients.

Sources:

https://www.americanpharmaceuticalreview.com/Featured-Articles/594397-The-Lifecycle-from-Drug-Development-Through-Approval-Processes/#:~:text=The%20average%20lifecycle%20of%20drug,about%2010%20to%2012%20years.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1629006/

https://www.americanpharmaceuticalreview.com/Featured-Articles/594397-The-Lifecycle-from-Drug-Development-Through-Approval-Processes/#:~:text=The%20average%20lifecycle%20of%20drug,about%2010%20to%2012%20years.

https://learn.marsdd.com/article/product-development-lifecycle-new-drug-development/

https://toolbox.eupati.eu/resources/patient-reported-outcomes-pros-assessment/

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219423/

https://www.bsigroup.com/en-US/medical-devices/Our-services/product-lifecycle/

https://ep.bmj.com/content/105/3/185

https://toolbox.eupati.eu/resources/patient-reported-outcomes-pros-assessment/

https://jpro.springeropen.com/articles/10.1186/s41687-020-00219-4

In the field of Health Technology Assessment (HTA), understanding the preferences of patients, healthcare professionals, and other stakeholders is crucial for making informed decisions. One valuable tool for capturing these preferences is the Discrete Choice Experiment (DCE). This article aims to explore what a DCE is, its value in HTA, the main steps involved in implementing a DCE, and best practices when building a DCE. By following these steps, researchers can effectively elicit and analyze preferences, leading to better-informed decision-making processes.

What is a DCE?

A Discrete Choice Experiment (DCE) is a quantitative research method used to assess and measure preferences. It presents participants with a set of hypothetical choices between different health interventions or treatment options, each with different attributes, and asks them to state their preferred option. By analyzing these choices, researchers can determine which attributes are most important to patients and stakeholders and how they weigh the trade-offs between different attributes.

What is the value of DCE?

Patient Preference Studies (PPS) like DCE can be implemented in each step of the medical development process life cycle in various ways. For example, DCE provides valuable insights into patient needs during the discovery phase and can also help with trial design during the clinical development phase. Preferences can also be used to evaluate the value of healthcare interventions and during the HTA phase, stakeholders can use these insights to allocate resources more efficiently and effectively to develop treatments with preferred attributes.

Main steps to implementing a DCE:

Step 1. Define the research question: Clearly articulate the research objectives and the specific preferences to be measured. Identify the target population and relevant attributes that influence decision-making.

Step 2. Design the choice sets-choose the attributes and identify levels: Develop choice sets that represent the alternatives. Define the attributes and their levels based on a thorough literature review, expert input, and stakeholder engagement. Ensure that the combinations of attribute levels are realistic and representative of the decision context. Consider the appropriate number of choices to balance the respondent burden and statistical efficiency.

Step 3. Pilot testing: Before conducting the main study, it is essential to pilot test the DCE design. This helps identify any issues with the questionnaire, refine the attribute descriptions, and ensure that the choice sets are understandable and realistic to respondents.

Step 4. Sampling and data collection: Determine the appropriate sample size and sampling strategy based on the research question and target population. Consider the mode of data collection, such as online surveys, face-to-face interviews, or telephone interviews, based on the target population and available resources. Use the appropriate data collection method to minimize biases and maximize response rates.

Step 5. Data analysis and interpretation: Employ appropriate statistical techniques to analyze the data and estimate preference models. There are three main types of models to choose from. The first is a model to estimate preference weights conditional importance of attributes. The second model identifies groups with similar treatment preferences. The last one is an estimation of willingness to pay. After running the various models, interpret the results in the context of the research question. Provide clear and concise summaries of the findings, including the relative importance of attributes.

Best practices when building a preference study using DCE

• It is important to involve stakeholders, such as patients, caregivers, and healthcare professionals, in the design process to ensure that the research question and the attributes of interest are relevant and meaningful. This can be achieved through focus groups, interviews, or surveys.
• The experimental design should be simple, the number of attributes and levels should be kept to a minimum to avoid overwhelming the participants. Generally, the number of attributes to evaluate is between 5 and 8. The main categories of treatment attributes are: Benefits, Risk, and Treatment Modalities.

• The questions should be written in a way that reduces biases.  For example, there should be neutrality in phrasing the questions and each attribute should show up an equal number of times in the DCE.
• Clear instructions and guidance should be provided to the participants to ensure that they understand the purpose of the study and how to complete the DCE. It is important to explain the concept of trade-offs and the hypothetical nature of the choices.

Discrete Choice Experiments (DCEs) are valuable tools in the field of Health Technology Assessment (HTA) for eliciting and measuring preferences. By following the main steps outlined above and adhering to best practices, researchers can effectively design and implement preference studies using DCEs. The insights gained from DCEs contribute to evidence-based decision making, helping policymakers allocate resources and make informed choices that align with the preferences of patients, healthcare professionals, and other stakeholders.

EvidentIQ can provide support when conducting DCE studies. From the experimental design stage to implementation and reporting, EvidentIQ can customize a solution that can help you effectively execute your patient preference study. They offer best-in-class Real World Evidence (RWE) methodologies, such as DCE, for patient studies in multiple diseases and geographical areas thanks to their direct access to a global patient platform. Patient studies can focus on treatment preference, quality of life, value of health, disease/treatment burden, unmet needs, etc. EvidentIQ can help generate unique Real World Data (RWD) to significantly help life sciences customers support the value story of their product for HTA submission, pricing and reimbursement as well as their scientific communication within the clinical community.

Sources: 

https://www.youtube.com/watch?v=IPIkIXWOJ5g
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8546533/#:~:text=A%20discrete%20choice%20experiment%20

https://yhec.co.uk/glossary/discrete-choice-experiment-dce/

https://bmjopen.bmj.com/content/11/3/e045803

https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-021-01647-z

Real-world evidence is being used by the health care sector to support the safe development of new medical interventions including devices, services and biopharmaceuticals. Before, it was only considered in the post-approval phase when bringing a new therapy to market, but now, it is being considered earlier in the clinical trial process. What is real-world evidence, how is it used, why is it important, and how can you start harnessing it?

What is Real World Evidence?

Evidence obtained from analyzing real-world data (RWD) is referred to as real-world evidence (RWE). To generate RWE, the RW data must be analyzed. Different statistical analysis can be done through modeling, or by performing a variety of different studies. Technology, like IBM’s Watson, can also be used for data analysis where it aggregates and reports on insights derived from multifaceted real-world data sets.

This real-world data comes from routine delivery of medical care. For example, RWD can come from clinical data such as electronic health records (EHRs) and electronic Case Report Forms (eCRFs). Patient generated data including medical charts, data gathered from mobile devices or wearables, and Patient Reported Outcomes (PRO). PROs are completed by patients and give researchers firsthand patient perspectives and an understanding of events that go on outside of appointments, operations, and hospital stays. Available public health data such as disease registries, prescription habits, and the use of healthcare services, can give data for the general population.  Lastly, cost data in the form of claims or billing activities can provide a monetary indicator for healthcare spending. Patient identity markers are expressly removed to maintain patient privacy. The RWE extracted from these data sources provide insight into elements that are not included in randomized clinical trials (RCT).

How is RWE used?

Real-world evidence enables researchers to assess the efficacy of medical treatments or interventions while considering other circumstances and variables not always accounted for with RCTs. A randomized controlled trial (RCT) enrolls a small subset of the population and examines how different groups react to a novel therapy in a controlled setting. While RCTs are considered ‘the gold standard’ in clinical testing, they are expensive and take a long time to complete. Rather than using RCTs alone, RWE can provide a more comprehensive insight of how a new treatment option will work in the “real world” and for a more diverse population. By supplementing RCTs with RWE, researchers can gain a better understanding of what works for different sub-populations.

RCT suffer from a narrow pool of trial participants and are not truly representative of the general population. Because RCTs have strict inclusion and exclusion criteria for enrollment, certain populations are expressly not represented in the trial, one such example is pregnant women. It is also the case that trials struggle to recruit candidates from certain ethnic populations, as is common for Hispanic communities. With a lack of diverse representation in clinical trials, the results from RCT do not consider all patient characteristics including age, gender, ethnicity, pregnancy, medical history, etc. However, using RWE can fill in some of these gaps.

Why is RWE important?

Real-world evidence can provide insights into how medicines work in certain patient subgroups that may not have been investigated in RCTs. RWE, for example, can allow researchers to investigate how novel medicines function in patients within specific age ranges, or specific socio-demographic groupings. Real-world evidence can also help researchers comprehend what occurs to a patient over the course of his or her life, not only during the RCT period, to assess the treatment’s effectiveness. How people use a product, what symptoms it does improve, side effects — all of that can be different from what is observed in a controlled trial in a clinic. The healthcare industry is constantly working on improving patient outcomes and reducing hospitalization rates. One way to achieve this is to work on more tailored therapies and personalized medicine. Because RWE insights are so valuable, the FDA employs real-world data and RWE to monitor post-market safety and adverse occurrences, and to support regulatory decisions.

RWE also helps improve healthcare decisions and is used in health economics and outcomes research (HEOR). HEOR measures the outcomes of healthcare interventions and the effect they have on patients to provide data and insights for healthcare decision makers. These decision makers include clinicians, governments, payers, health ministries, patients, to name a few. For these stakeholders, real-world data collection, analysis and reporting is essential.

How to collect and use RWE?

With so many varied data sources (clinical data, patient generated data, public health data, cost data) healthcare stakeholders may choose to use clinical trial and data science services. Evident IQ provides researchers with software to collect real-world data and can provide support to analyze RWD to generate and report on RWE.

As part of clinical trial data collection services, EvidentIQ offers eCRF Setup which simplifies electronic data collection forms, design branching questions, develop in-form and cross-form validations and edit checks to maximize data cleaning at the field level. They also provide configuration support for multilingual eCRF forms, and any localization support needed to configure the application workflow to align with the site’s local processes. Their Patient Reported Outcome (PRO) surveys collect a multitude of data points including treatment preference, quality of life, value of health, disease/treatment burden, and unmet needs. This data is then subject to sophisticated methodologies used to generate unique RWE. Data Linkage is one such method. Data Linkage helps match and merge records from different sources to get a richer dataset and more valuable evidence. EvidentIQ uses significant clinical data expertise, data science analysis, and access to Carenity patient platform, to successfully conduct state of the art data linkage studies. Their innovative data linkage capabilities combine unique self-patient reported data with biomarkers collected by a wearable/sensor through continuous monitoring, bringing RWE to the next level.

Real-world evidence is growing in popularity. Before it was only considered in the post-approval period, but now, it is being considered earlier in the clinical trial process. Using RWE at an early stage in treatment development is a critical consideration for healthcare stakeholders and is driving the trend of gathering more RWE and ultimately improving the safety and effectiveness of medical therapies.

Sources: 

https://veradigm.com/real-world-evidence/

https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence

https://www.arbormetrix.com/blog/9-ways-real-world-evidence-is-changing-healthcare/

https://vial.com/blog/articles/what-is-real-world-evidence-in-clinical-trials/?utm_source=organic

https://viseven.com/what-is-rwe-in-pharma/

https://servier.com/en/newsroom/folders/importance-real-world-studies/

https://rwe-navigator.eu/use-real-world-evidence/rwe-importance-in-medicine-development/

https://www.clinicalleader.com/doc/why-all-the-talk-about-real-world-evidence-0003

https://www.statnews.com/sponsor/2017/02/16/real-world-evidence-need-2/

https://www.ispor.org/heor-resources/about-heor

An evidence generation strategy is an advanced plan for generating evidence necessary for all the steps of the product life cycle. Traditionally, research follows consecutive steps which is more of a reactive process. Using an evidence generation strategy allows for proactive planning and execution of a holistic strategy.

What is evidence generation and what is it used for?

Evidence generation is multifaceted and includes various study designs and data sources. Typically, each department involved in the process (e.g., clinical development, medical affairs, and health economics and outcomes research (HEOR)) works in a siloed structure and independent from one another to come up with evidence to support their specific research topics.

Integrated evidence-generation plan (IEP) is a strategy used where the upfront research plan is developed specifically to find evidence to address the concerns of each stakeholder group (payers, clinicians, patients). Beyond simply proving if a therapy is effective and safe, pharmaceutical companies need to prove that the new treatment is addressing market gaps that the current products do not meet.

Each of these stakeholders have different needs and questions regarding new treatments. Payers, those who pay for the development of the treatment, want to ensure the final product has a value to the healthcare system and will prove to be profitable when launched. Clinicians look for evidence-based research to show how the new treatment is different from existing options. Patients need to know how the new treatment will meet their needs and solve their problems.

An IEP will pinpoint these motivations from each group and strategically set up testing to address each one. Considered in the planning are all functions, geographies, and the entirety of the life cycle to meet the needs of the various stakeholders. Demonstrating the economic, clinical and patient burden of a disease is a key need early in the development process and sets the stage to inform stakeholders regarding the potential value of a product.

Why it is important to have a plan early?

Early evidence-generation planning is key to allowing time to connect all the dots between functions and requirements. It is of vital importance when demonstrating value for the new treatment and it must be demonstrated throughout the lifecycle of the product (early development, clinical research, launch, and post approval) to understand the implications of how the product will address needs at each step along the way.

Impactful data points for each function team (medical, clinical, commercial) can be identified and a roadmap created to ensure the evidence-generation plan includes steps to achieve these data results which will add to the products overall value proposition. A cross-functional and early approach ensures the funding and required expertise is available at all stages.

What are the main steps where you could need evidence?

The ultimate goal of the treatment is to hit the market and provide a solution for the patient, add value to the healthcare community in the form of a differentiated treatment, while being profitable for the developer. Therefore, during the development of the product, it is beneficial to incorporate these factors into each stage of the product lifecycle.

In the early stages of development, there is a need to understand the current state of care for the target patient population. This includes conducting market research, literature reviews and developing a Health Economics and Outcomes Research (HEOR) strategy, identifying possible clinical trial designs, among other tasks that could benefit from evidence-generation. At this point in the product life cycle, the payer stakeholder is interested in the economic burden of disease in order to justify resources required to develop and adopt a new treatment.

During the clinical research and development stage is when patient trials and market access strategies start to be executed. Evidence generation collected from Randomized Clinical Trials (RCT) and patient reported outcomes (PRO) can be paired with Real World Evidence for proof of treatment effectiveness and differentiation of current on-the-market products. Clinicians will be most interested in the data that comes from this stage.

To launch a new product, a medical affairs team may use the evidence to inform their market access strategy and develop communications that target patient needs identified early on in the IEP. The launch stage is where the end prescriber and patient will be convinced of the value of the new treatment based on evidence leveraged through the marketing campaign.

The integrated evidence-generation plan utilizes the research results of all functions: market research, health economics and outcomes research, clinical trials, market access, patient-reported outcomes, literature reviews, etc, and combines them with the strategic objectives and ultimate value proposition. Be proactive with your go-to-market strategy, and plan in advance to address the needs of each stakeholder group. Along with a software suite for clinical trials, EvidentIQ offers the ability to run prospective and direct-to-patient studies with patient-reported outcomes through its Carenity Patient Platform. Carenity part of the EvidentIQ Group is a key partner to consider to support your data generation plan.

Take a look at our RWE offer.

Sources

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047978/

https://www.openhealthgroup.com/news/17-01-2022/key-elements-of-a-real-world-evidence-generation-plan
https://marimacinsight.com/evidence-generation-strategy/
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https://www.evidera.com/wp-content/uploads/2017/05/Strategic-Evidence-Generation-Planning-Product-Positioning.pdf
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