A complete guide to HEOR project costs

Every new treatment in healthcare requires evidence. From developing a novel drug to introducing a groundbreaking treatment for a rare disease, real-world evidence must support clinical trials, inform new distribution relationships, and help monitor post-market performance.

However, the cost of real-world evidence generation through health economics and outcomes research (HEOR) is ever-rising. There are many variables to consider when developing a new drug, and for most pharmaceutical companies, planning appropriately for observational HEOR costs can help increase the chances of the study’s success.

This blog covers the main HEOR project and cost variables to consider before starting a new study.

What is the goal of the research?

The research question posed can affect the overall cost of the study, and there are many questions research teams can ask themselves to solidify their focus area. For example:

• Is the research being conducted for observational research, commercial analytics, or to understand standard of care benchmarks and real-world baselines?
• Is it a descriptive HEOR project question or does it include adjusted analysis or multiple outcomes of interest?
• Is the research focused on understanding burden of illness?
• What are the conditions being studied?
• What data source or sources are needed to answer the research question?
• How long is the study expected to last?
• What time period will be used in the study?
• Is the study protocol driven?

The answers to these questions will help determine the complexity of the study design and the time needed to organize and analyze the data, and ultimately, influence the cost of the project.

What condition is being studied?

HEOR studies can focus on more common therapeutic areas, specialty conditions, or rare diseases.

Common conditions are typically more cost-effective to research, as more data is available on these patient populations which can decrease the time (and investment) it takes to gather an appropriate amount of data for analysis.

On the other hand, the rarer the disease, the higher the cost to study based on the scarcity of available data. A specialty or rare disease category will have smaller patient populations, so it can take longer to generate sufficient data for analysis, especially considering that these patients often change payers over the course of their disease. This means a single dataset alone likely won’t capture the full picture; research teams need a dataset that can follow patients across payer transitions to create a more complete population and per-patient profile. One data source that combines multiple payers is preferable, as it saves time and resources to the alternative of gathering individual datasets to manually standardize and aggregate.

What data sources are required?

In addition to considering the payer types necessary for a representative, longitudinal dataset, research teams must determine whether their objective requires open claims, closed claims, EHR data, registry data, social drivers of health (SDOH) data, or some combination of these sources. Special linkages, such as mother and infant pairs, may also be required to establish a representative and comprehensive view of the population(s) in question.

For most therapeutic areas, a closed claim source is needed to understand both diagnosis and medication. It helps depict if a prescription was actually filled, not just ordered during the physician encounter. When a study calls for clinical information – vitals, test scores, etc. – EHR data and clinical notes provide that context.

Each study will require a unique payer mix and combination of data sources. These are fundamental to the validity of the research, though more data typically means a higher study cost. Still, diversifying data is critical, as it helps make a study more applicable to the greater population.

Working with Inovalon makes it easy to access, standardize, and analyze large amounts of data for HEOR studies. Our industry-leading primary source dataset is a real-world database that includes medical and pharmacy claims from over 160 deidentified data sources. And through our partnership with OMNY Health, our Linked Dataset provides a connected view across closed claims, EHR data, and clinical notes in one de-identified dataset ready for HEOR and RWE research.

Data linking for HEOR

Data from other sources can also be linked to enhance study objectives and outcomes – and our team of research experts can help securely connect data via tokenization that meets HIPAA de-identification standards through Safe Harbor or Expert Determination.

Claims and EHR data show what happens inside the healthcare system. Many HEOR questions also depend on outcomes and context recorded somewhere else, and patient-level linkage brings that information into the same study.

Mortality is a common example. Claims capture deaths unevenly, and deaths outside a facility are easy to miss, which weakens survival analyses and end-of-life cost estimates. Linked mortality data gives those studies a more reliable endpoint.

Additionally, social drivers of health rarely show up in claims at all, yet income, housing, food access, and neighborhood conditions shape how patients use care and stay on treatment, so having them in the data helps researchers adjust for confounding and compare outcomes across populations. For maternal and pediatric research, the gap is structural: a newborn usually gets a separate record from the mother. Mother-infant linkage reconnects the two, so prenatal exposures, treatments, and conditions can be studied alongside infant outcomes.

And in clinical trials, linking trial records, registry data, or other sponsor data to real-world data lets a study follow the same patients in the real world, reaching beyond the trial end or the registry’s reporting window to deeper understand the population being observed. That follow-up can show how treatment patterns, adherence, healthcare utilization, and costs change once patients leave a controlled setting, and whether outcomes seen in a trial hold up in practice.

Determining the study lookback period

The further back a study intends to go, the more data there is to process and analyze – and the higher the total cost of the project.

How many years of longitudinally linked data does the project need to include to provide value? Some studies may get all the data they need with just a three-year data window, while others need to go back five years or more to establish a truly comprehensive and statistically representative view.

Additional HEOR project costs to consider

Will there be a need to develop an abstract or manuscript? Will the findings disseminate via a conference proceeding or publication?

The time and experience required to produce these deliverables will add to the total spend of the project, but the value of dedicated support at every point from study design through final deliverables often outweighs the cost. Additionally, conducting a study externally allows for more targeted expertise and frees up valuable time for companies by outsourcing.

HEOR costs vary, and the better that a team can identify exactly the kind of data they need, the data sources required, and who will be leading the study – the better off their cost (and timing!) expectations will be.

Answer your HEOR project questions with seasoned research experts

With over 500 publications amongst our team across HEOR, public health, outcomes research, epidemiology, clinical analytics, commercial analytics, and biostatistics, our Outcomes and Evidence Solutions team is here to help answer your healthcare research questions.