AHRM Blog

A near-constant headline over the past several years has been the rising cost of healthcare, whether referring to a hospital visit, a novel (or—in some cases—existing) pharmaceutical compound, or cutting-edge surgical procedures. As medical technologies become more advanced, the cost of research and development increases; while at the same time, the economic impact of interventions on payers (whether governmental, commercial, or individual) is a serious concern. How is it possible to balance these forces pushing and pulling on the economic proposition of a medical intervention?

In nearly every developed market, Health Technology Assessment (HTA) agencies are tasked with trying to understand the clinical and economic impact of healthcare interventions. These agencies may be government bodies (such as NICE in the UK and CADTH in Canada) or private institutions (such as ICER in the USA). In any case, they all seek to develop quantitative evaluations of the clinical benefit and economic impact of healthcare interventions. Their evaluations may be directly or indirectly used by payers to influence access or pricing of interventions. Thus, the economic endpoints of budget holders now represent a significant influence on the market access of a medical intervention in addition to the regulatory bodies that over-see clinical endpoints.

With this in mind, companies working to develop healthcare interventions are now tasked with demonstrating a value proposition focused on defensible data-driven pricing. In practice this means performing research that can establish clinical and real-world evidence in support of the pricing for the intervention, and that the economics of the intervention fit within guidelines of the various HTA and the payers they may represent. Accomplishing this requires consideration of health-economic endpoints earlier in the R&D cycle of an intervention so that data such as patient-reported outcomes or other quality-of-life measures can be collected and used to develop defensible and data-driven pricing. In AHRM’s experience, including these data points in Phase II-IV studies is becoming more and more important. In the end, the goal is to anticipate the findings of HTA agencies and develop an early clinical and economic strategy that will minimize friction. 

Bringing an intervention to market without a serious data-driven pricing strategy presents a difficult situation, and doing so opens the door for significant influence of HTA agencies in how the intervention is priced and in some cases if the intervention is granted access or reimbursement in that market at all. There are no shortage of instances in which budget holders simply say that an intervention is too expensive for the clinical benefit it provides and subsequently limiting access.

It is important to note that there is no upper-limit on absolute cost of an intervention. In repeated discussions with HTA and payers, we have learned that expensive treatments and medications are certainly acceptable, but that they must be worth the cost. Interventions costing over USD $1M are no longer a hypothetical, but in order to warrant that price the intervention must deliver a tremendous improvement over the standard-of-care: interventions seeking a premium price should present research that shows a premium benefit to patients and payers.

AHRM’s familiarity with HTA evaluations can provide insight into what analyses they will be looking to perform, what endpoints may be useful to demonstrate patient impact, and how to correspond with HTA about these matters. The ability for AHRM to predict the likely outcomes of HTA evaluations can provide an edge for companies developing a novel intervention or looking to expand access.

For further information or discussion, please contact:

Raf Magar, MBA

rmagar@ahrminc.com

+1-919-758-8203 

The applications of machine learning (ML) and artificial intelligence (AI) continue to grow each year as more resources are devoted to developing mathematical methodologies and computational hardware that expand the environments in which ML and AI can be used. The most direct interaction that people have with these technologies is with several popular consumer electronics devices and services: Amazon Alexa and Google Home both use AI for speech recognition and natural language processing, streaming services such as Spotify may use ML to generate recommendations for users based on prior media consumption, Amazon leverages ML to offer products in which it believes the user may have interest, and financial institutions utilize ML/AI to predict and detect fraudulent account activity.

Use of ML/AI in healthcare research to improve efficiencies and outcomes is growing as well. At AHRM we are working to tackle problems that may be unique in the healthcare realm, and one “non-engineering” issue that we will consider here is that the output from such ML/AI research is different in scope to the output in a conventional research project. Because of this, a shift in expectations are sometimes required when presenting ML/AI as a research option.

In a conventional research project, a hypothesis is proposed and a protocol is designed to test that hypothesis, including a specific set of statistical analyses that seek to answer whether the hypothesis is true or false within some range of uncertainty. On the other hand, the result of research using ML or AI for healthcare outcomes is typically a predictive or classification model that can be applied to other datasets to generate estimates for future outcomes. Models as outputs for healthcare research isn’t entirely new, as most people are familiar with regression methods that generate equation(s) to describe relationships between exposures and outcomes. However, where regression models start to lose effectiveness and power with many variables or complicated relationships that cannot be defined by explicit equations, ML/AI models can pick up at that point and move forward. At AHRM we often say that ML/AI picks up where conventional statistics lets off. Let’s explore a case study to clarify what this really means.

Suppose we are examining drugs used to treat heart disease in a cohort of patients. Speaking in general terms, with a conventional research project we would establish a hypothesis regarding some important endpoints, then isolate each therapy and perform statistical analyses for each therapy to reach the specified endpoints. We may also examine covariates or exposures across each therapy and perform analyses taking those into consideration. Then the endpoint results would be compared across the treatments and the extent to which the original hypothesis is correct would be determined. So the mean change in some biomarker under therapy A may be 40% greater than the mean change in that same biomarker under therapy B, when controlled for covariates, and if the statistical power and error associated with the change are within the prescribed limits we can say that the therapies achieve different outcomes.

On the other hand, research using ML/AI in a similar situation would take into account all therapies, relevant exposures, and desired endpoints to develop a mathematical model relating all of those aspects. Rather than a single comparison statistic between therapies, we would have a model that describes the likely outcome for many combinations of therapy and covariates. Thus, we would be able to estimate the likely outcomes for patients on therapy A or B and any combination of covariates T, U, V, X, Y, and Z and so on. Because of this, ML/AI research may not necessarily provide a direct comparison between therapies and while there are techniques that can be used with the model to move towards some kind of comparison, if a direct comparison is desired then in many cases it is better to just utilize a conventional hypothesis-driven investigation. 

Those who are familiar with regression models may be able to make the jump to ML/AI more easily, but regardless of research experience this difference in expectation can prove difficult for regulatory agencies, sponsors, IRBs, and decision-makers alike. A co-operative relationship between AHRM and the University at Buffalo Center for Computational Research not only provides nearly 4000 cores and 72 teraflops of supercomputing power for data-intensive ML/AI projects, but also serves as an extension of AHRM’s own knowledge and experience in navigating issues related to ML/AI research projects. Furthermore, at AHRM we also understand that although ML/AI is one of the more revolutionary technologies we have at our disposal, it is not a magic bullet and is not a replacement for other research methods. Nonetheless AHRM sees machine learning and artificial intelligence as an integral part of the future of healthcare and are committed to investigating how and why to use these technologies for healthcare outcomes research.

For further information or discussion, please contact:

Christopher Tyson, Ph.D.

ctyson@ahrminc.com

+1-716-881-7565 

Practicing physicians have consistently cited three major barriers to conducting independent research and/or participating in industry sponsored research: lack of time, lack of money, and lack of research staff. This is especially true for physicians in private practice without direct access to the type of resources available to those practicing in academic-affiliated institutions. 

Practicing physicians hold patient care as their highest priority and often are unable to devote the time necessary to research funding opportunities, develop conceptual research designs, protocols, and CRFs necessary for conducting research.  This is unfortunate because often these physicians have questions or hypotheses regarding treatment comparisons, potential new indications, and other related research ideas based upon what they witness in real world practice. Following through with such research ideas could contribute to a further understanding of medications, diseases, economic impacts, and patient outcomes. 

All research requires either direct or indirect assets in order to complete the necessary steps to bring it from concept to published results that can be shared with the scientific community, patients, payers, and other interested parties. Once a physician has a research concept, potential funding opportunities need to be identified and applications must be made.  When a source of funding has been secured, a protocol must be written, case report forms designed, and a database built and tested.  The research may require an informed consent, along with IRB submission and a statistical analysis plan. All of this while trying to run a successful practice and keeping the care of patients at the forefront?  Clearly this seems virtually impossible for the practicing private physician, regardless of their desire to conduct research.  

 Opportunities to participate in industry Sponsored research can also present limitations.  The processes of subject screening, obtaining informed consent and completion of case report forms is much too time intensive for the typical physician to simply add it to his/her list of current responsibilities.  When you consider all of this, it quickly becomes apparent just how difficult it can be to do so without a team in place.

We here at AHRM Inc. have witnessed this over the past several years and so we have developed a service to assist physicians who have a strong desire to conduct research. We work to collaborate with them to complete all of the necessary steps to bring their research idea to fruition.  We will assist in procurement of funding, completion and submission of applications, development of protocols, CRFs, informed consent documents, etc.  We can build the database, perform the analysis, train the staff and provide monitoring services.  Our services are provided free-of-charge to the physician and are subcontracted through the funding source(s).  This collaboration allows valuable research to be conducted by helping to break down the barriers faced by private practice physicians.   

If you have an idea for research that you are interested in conducting within your practice, or would like to participate in an industry sponsored opportunity that has been made available, please email (ldalfonso@ahrminc.com)  or call +1(716)994-7912 to schedule an initial discussion.