What is Outcomes Research?
Outcomes research, also known as health outcomes research, is the study of the end results of specific healthcare practices and interventions. It focuses on understanding what happens in the real world to patients when they receive a certain treatment or method of care. By analysing data from real-world settings, outcomes research helps to assess the effectiveness, safety, and value of these interventions. These studies are crucial for improving the quality of care and ensuring that healthcare practices provide the best possible value for patients. As a key component of Health Economics and Outcomes Research (HEOR), outcomes research provides insights that inform decision-making on healthcare policies, clinical guidelines, and resource allocation.
What is HEOR?
Health Economics and Outcomes Research (HEOR) evaluates the value of healthcare treatments by examining their costs, benefits, and impacts on patients. It combines data from various fields, such as economics and patient experiences, to guide healthcare decisions, develop policies, and assess treatment cost-effectiveness. HEOR helps determine whether a new drug offers better value than existing treatments by considering direct medical expenses, indirect costs like lost income, and intangible costs such as pain.
HEOR analysis has significantly increased in recent years, with numerous organisations now leveraging it to guide strategic decision-making. By leveraging real-world evidence and analysing these factors, HEOR helps determine whether new treatments offer better value compared to existing options, ultimately supporting more informed decisions about healthcare investments and resource allocation.
HEOR's Influence on Drug Development, Pricing Decisions, and Marketing
HEOR plays a critical role throughout the entire lifecycle of drug development, from clinical trials to pricing and marketing strategies. HEOR provides comprehensive insights into the impact of healthcare interventions on patient outcomes. It helps clinical research by guiding decision-makers on patient access to specific drugs and services whereas traditional economic evaluations focus solely on cost-effectiveness.
Clinical trials are performed to evaluate the safety and effectiveness of new drugs in comparison to current treatments or placebos. The information gathered from these trials enables researchers to determine the Cost-Effectiveness Ratio, which measures the value of a treatment relative to other available alternatives. If a new drug shows greater effectiveness, improved safety, or more favourable cost implications, it is then considered suitable for further development.
HEOR plays a crucial role in this process by helping to establish optimal dosing strategies, pinpointing patient groups that are most likely to benefit from a treatment and creating methods for evaluating health outcomes. HEOR also offers essential guidance for estimating prices, analysing cost-effectiveness, and setting appropriate reimbursement rates. These insights are vital for supporting commercialisation efforts, such as determining product positioning and planning launch strategies. By offering a comprehensive perspective on the value of healthcare interventions, HEOR empowers companies to make more informed decisions in drug development, pricing, and marketing.
Various organisations use HEOR to guide strategic decisions such as:
- Pharmaceutical and medical device companies use HEOR to decide which drugs to develop, how to price them, and how to effectively market them
- Healthcare providers, such as hospitals and clinics, apply HEOR insights to improve care delivery and patient outcomes.
- Health insurance companies rely on HEOR to determine which treatments to cover and at what reimbursement levels.
- Government agencies at local, state, and federal levels, including programs like Medicare and Medicaid, utilize HEOR to manage healthcare access while balancing budgetary constraints.
Working with Longitudinal Databases, Big Data & Teradata SQL
To assess what is happening in the real world, rather than using clinical trials to collect data, outcomes research often uses retrospective, non-interventional studies performed on huge databases which contain de-identified medical records. These databases are often called longitudinal databases as they are able to track patients over multiple years.
Examples include administrative medical and pharmacy claims databases, such as the Truven Health MarketScan® Research Database, which is the largest available for licensing with over 170 million patients since 1995, and the Clinformatics Data Mart from Optum, encompassing over 114 million lives since 1993. Clinical databases, like The Health Improvement Network (THIN) Database in the UK, which gathers data from over 550 general practitioners, and the Premier Healthcare Database, from more than 700 U.S. hospitals.
All these databases are regularly updated—from quarterly to annually—allowing researchers to investigate trends in treatment patterns using current data.
Due to their huge size and differing structures, working with these databases presents many challenges. In particular, data extraction times and the resulting extracted datasets can become unmanageably large. With traditional SAS programming techniques, the process of querying, extracting, and processing data can be inefficient and slow, leading to long turnaround times.
Therefore it requires an appropriate environment which would make programming on these large datasets more efficient. Raw data obtained from vendors are pre-processed to correct inconsistencies, transform data into a usable format, and integrate information from various sources. This pre-processed data is then loaded into local systems (data warehouse) designed to manage big data.
At Quanticate we have been using the Teradata database and find it to be more efficient than the standard SAS datasets as it is designed in a way to handle the large volumes of big data compared to typical SAS data structure. Teradata is a data warehouse solution that offers scalable, high-performance data management and analytics, making it well-suited for handling extensive datasets and complex queries.
A programmer needs to demonstrate a technical proficiency of handling large datasets and be familiar with Teradata SQL syntax in order to process the data directly into the database. This way, handling huge datasets is less time consuming. Only final extracts are downloaded into SAS datasets for further analysis. Other notable companies offering environments for programming big data include Oracle, with solutions like Oracle Exadata and Oracle Big Data Service; IBM, featuring products such as IBM Db2 and IBM Netezza; and Microsoft, with offerings like Azure Synapse Analytics and SQL Server.
Outcomes Research Study Design
When programming an outcomes research project, a sponsor may be looking for experienced programming resource with an understanding of statistics and database design. Data needs to be handled carefully, keeping in mind all limitations. Outcomes Research programmers provide all required information and advise on best approaches to reach the realisation of the client request.
Data Usage
Instead of the typical clinical trial structure where you are testing against a predicted hypothesis to validate the efficacy of a drug, Outcomes Research study design requires less specific terms and a more general approach that looks to monitor the real world data to find patterns and health outcomes. You can perform this type of research even before a clinical trial has started to assess if the potential study population is suitable for the desired trial.
An Outcomes Research study can consist of any number of the following parts:
- Measuring presence of a disease in a population
- Tracking usage of given drug among selected population across years
- Comparing selected drugs
- Analysing Health Care Resource Utilizations and costs
- Analysing potential clinical trial population
- Comparing case and control cohorts
- Analysing treatment patterns (persistence on given drug, switches to competitors, uses of generics)
- Conducting sponsor’s specific analysis
Requested analysis depends on the study design. In addition to descriptive statistics, more sophisticated analysis can be performed (i.e. logistic regressions and survival analysis).
Three Phases of Outcomes Research Studies
Typical Outcomes Research studies consist of 3 phases:
- Defining/extracting cohort (e.g. list of patients meeting inclusion/exclusion criteria)
- Performing requested analyses for selected patients. Most common are: demographic characteristics, comorbidities analysis, drug persistence, cost and HCRU
- Extracting required data for the patients in cohort at the request of the sponsor’s hypothesis: inpatient/outpatient claims, drug claims etc.
Usually there is one main programmer and one quality control programmer which means that a single programmer is independently taking care of all the phases. For more complicated studies, input from the main programmer has an impact on the final shape of the study. It requires close cooperation with the sponsor and statistician.
Conclusion
In conclusion, outcomes research programming and Health Economics and Outcomes Research (HEOR) are essential for understanding the value of healthcare treatments. Outcomes research looks at how treatments work in real life and their costs, while HEOR evaluates whether these treatments are worth the investment. Together, they help healthcare providers and policymakers make better decisions to improve patient care and use resources wisely. Knowing about both helps ensure that treatments are effective and cost-efficient, benefiting both patients and the healthcare system.
