Outcomes Research

Outcomes Research and HEOR in Health Economics

Outcomes research asks what happens to patients after they receive care, and records it in terms that patients notice, such as survival, symptoms, the ability to carry on daily life and their experience of services. Joined with health economics, the same work is usually called health economics and outcomes research, or HEOR, which is the label used by the professional society ISPOR and by industry and academic teams preparing evidence for payers and HTA bodies. For health economists the link is direct, because a cost-effectiveness model needs quality-of-life data reported by patients, resource use, baseline risks and outcomes seen in routine care, and outcomes research is where much of those inputs come from.

What outcomes research measures

A fact sheet from the US Agency for Healthcare Research and Quality (AHRQ) states that outcomes research "seeks to understand the end results of particular health care practices and interventions". It adds that end results include effects people experience and care about, such as change in the ability to function, and that for people with chronic conditions, where cure is not always possible, they include quality of life as well as mortality.

The fact sheet lists six kinds of health outcome, namely mortality, physiological measures such as blood pressure, clinical events such as stroke, symptoms, functional measures such as the SF-36 health survey, and patients' experiences with care. Each reaches an economic model by a different route. Mortality and clinical events drive survival and transitions, while functional status becomes health-related quality of life and, once valued, the utility weights behind QALYs. Many of these are patient-reported outcomes, collected with a specified outcome measure.

Pharmacoeconomic researchers framed the same idea for medicines. Kozma, Reeder and Schulz proposed the Economic, Clinical and Humanistic Outcomes (ECHO) model, in which the value of a pharmaceutical product or service combines clinical outcomes with measures of economic efficiency and quality, giving a basis for weighing trade-offs among them when health care resources are allocated.

Where outcomes research came from

The AHRQ fact sheet says the urgent need for outcomes research was highlighted in the early 1980s, when researchers found that "geography is destiny". Procedures as common as hysterectomy and hernia repair were done far more often in some areas than in others with no difference in underlying disease, and there was often no information on the end results. Wennberg and Gittelsohn had already reported in 1973, from population data in Vermont, wide differences in use of services and spending among neighbouring communities, and argued that variation in use signalled uncertainty about the effectiveness of health services. That pattern is now studied as clinical practice variation.

Legislation followed. Public Law 101-239, enacted in December 1989, added the provision now codified at 42 U.S.C. 1320b-12, which directs the Secretary of Health and Human Services, acting through the AHRQ Director, to support research on "the outcomes, effectiveness, and appropriateness of health care services and procedures". Priorities are to weigh how many people could benefit, significant variation among physicians in services or outcomes, substantial expenditure, and whether the data are available. The section also calls for research on the use of claims data and data on patients' clinical and functional status.

The first outcomes and effectiveness research centres funded by the Agency for Health Care Policy and Research, the former name of AHRQ, were the Patient Outcomes Research Teams (PORTs). Freund and colleagues credit the PORTs with advances in meta-analysis of non-trial data, the use of administrative data to describe patterns of care, the development of general and disease-specific outcome measures, and the dissemination of outcome information to reduce practice variation.

Health economics and outcomes research as a field

ISPOR was founded in 1995. Its incorporated name remains the International Society for Pharmacoeconomics and Outcomes Research, while it now presents itself as the professional society for health economics and outcomes research, with a mission of advancing HEOR excellence to improve decision making for health globally. A 2020 ISPOR framework set out 41 competencies for HEOR professionals in 13 topic domains, built in part from text analysis of HEOR job postings, so the label names a profession as well as a field.

In the pharmaceutical industry, HEOR is the function that produces the evidence of value that payers and HTA bodies ask for. A 2025 review by Dang describes HEOR work running through the product lifecycle, from early development through launch to the post-marketing generation of real-world evidence, and expects rising demand for economic evidence from HTA agencies and payers to make that work more important. The NICE real-world evidence framework (ECD9, 2022) notes that NICE technology evaluations are usually informed by company submissions, so much of the evidence behind a coverage decision in England starts as HEOR work.

HEOR is a working label more than a single method, joining economic evaluation and health economic models to outcome measurement, utility estimation and observational studies of effectiveness.

How outcomes evidence enters economic evaluation and HTA

Outcomes research supplies inputs at almost every stage of a cost-effectiveness analysis. The NICE manual (PMG36) asks for health effects to be expressed in QALYs, with the EQ-5D as the preferred measure of health-related quality of life in adults, and its reference case says changes in health-related quality of life should be reported directly by patients, or by carers when that is not possible, rather than by healthcare professionals. Resource use and cost data should be identified systematically and may include healthcare service use outcomes such as length of hospital stay and number of hospitalisations.

ECD9 defines real-world data as data relating to patient health or experience or care delivery collected outside a highly controlled clinical trial, with common types that include clinical outcomes, patient-reported outcomes, patient experiences, resource use and costs. Its listed uses in populating economic models read like an inventory of outcomes research, from baseline event rates and transition probabilities to resource use, quality of life and extrapolation.

An ISPOR task force report by Garrison and colleagues defined real-world data as data used for decision making that are not collected in conventional randomised controlled trials, grouped them by type of outcome into clinical, economic and patient-reported, and judged them necessary for coverage and reimbursement decisions while noting a continuing need for modelling. Outcomes research therefore informs health technology assessment both as clinical evidence and through the model.

Worked example of routine-care outcomes changing a cost per QALY (illustrative)

All numbers are illustrative, and discounting is ignored to keep the steps visible. A new treatment for a chronic condition costs GBP 6,000 more per patient over 3 years than current care. The main trial measured an EQ-5D utility gain of 0.10 a year. An outcomes study in routine care, with older patients and more comorbidity, finds a gain of 0.06 a year, and linked hospital records show the treatment saves GBP 1,200 per patient in admissions, which the trial did not record.

$$\text{ICER} = \frac{\Delta C}{\Delta Q} = \frac{c_T - s}{\Delta u \times T}$$

where ICER is the incremental cost-effectiveness ratio in GBP per QALY, $\Delta C$ is net incremental cost per patient, $\Delta Q$ is incremental QALYs per patient, $c_T$ is the extra treatment cost per patient, $s$ is the cost offset from fewer admissions, $\Delta u$ is the annual utility gain and $T$ is the number of years the gain lasts.

Step 1 (trial inputs). With $s = 0$, $\Delta Q = 0.10 \times 3 = 0.30$ and $\text{ICER} = 6000 / 0.30 = 20000$, so GBP 20,000 per QALY.

Step 2 (routine-care utility gain). $\Delta Q = 0.06 \times 3 = 0.18$ and $\text{ICER} = 6000 / 0.18 \approx 33333$, so about GBP 33,333 per QALY.

Step 3 (adding the cost offset). $\Delta C = 6000 - 1200 = 4800$ and $\text{ICER} = 4800 / 0.18 \approx 26667$, so about GBP 26,667 per QALY.

Against an illustrative threshold of GBP 30,000 per QALY, the trial inputs suggest good value, the routine-care utility gain alone suggests poor value, and the full routine-care picture lands under the threshold. Outcomes evidence can therefore move a decision in either direction. The example assumes the routine-care estimates are unbiased, an assumption examined under limitations.

Outcomes research, comparative effectiveness research and real-world evidence

The three terms overlap and are often used loosely. Outcomes research is defined by what it measures, the end results of care that matter to patients, and it can use trials, registries, surveys or routine records.

Comparative effectiveness research is defined by its question, which option works better for whom in real-world settings. The US Federal Coordinating Council for Comparative Effectiveness Research noted in 2009 that such research "has even been called patient-centered health research or patient-centered outcomes research". The Patient-Centered Outcomes Research Institute, authorised by the Affordable Care Act, has a statutory purpose that includes disseminating findings on the relative health outcomes, clinical effectiveness and appropriateness of medical treatments, services and items. Patient-centred outcomes research in this US sense is comparative by design, a narrower use than the older tradition.

Real-world data are a data source, and real-world evidence, in the ECD9 definition, is evidence generated from the analysis of real-world data. Much outcomes research uses real-world data, but an outcomes study can also sit inside a trial, and a real-world study can address questions such as disease epidemiology that are not about the results of care. Effectiveness is the quantity outcomes research usually targets, as distinct from efficacy under the controlled conditions of an explanatory trial.

Limitations and common misreadings

Several limits recur when outcomes evidence is used in economic evaluation. They concern the data behind the estimates and how those estimates are judged.

• Routine data are not free of bias. Patients who receive a treatment in practice differ from those who do not, which creates confounding, in particular confounding by indication. ECD9 asks for statistical methods that address confounding and informational biases, with sensitivity and bias analysis.
• Measures are not interchangeable. PMG36 notes that different methods of measuring health-related quality of life produce different utility values, so results from different instruments cannot always be compared.
• Sponsor evidence is still judged against the reference case. PMG36 requires any departure from reference-case methods to be specified, justified and quantified, and the committee decides how much weight a non-reference-case analysis receives.

Sources

• Agency for Healthcare Research and Quality. Outcomes Research. Fact Sheet. AHRQ Publication No. 00-P011. Rockville, MD: AHRQ; March 2000. https://web.archive.org/web/20170113190822/https://archive.ahrq.gov/research/findings/factsheets/outcomes/outfact/outcomes-and-research.html
• Dang A. Importance of health economics and outcomes research in the product lifecycle. Pharmaceutical Medicine. 2025;39(3):157-170. https://doi.org/10.1007/s40290-025-00564-z
• Federal Coordinating Council for Comparative Effectiveness Research. Report to the President and the Congress. Washington, DC: US Department of Health and Human Services; 30 June 2009. https://web.archive.org/web/20101229055244/http://www.hhs.gov/recovery/programs/cer/cerannualrpt.pdf
• Freund D, Lave J, Clancy C, Hawker G, Hasselblad V, Keller R, et al. Patient Outcomes Research Teams: contribution to outcomes and effectiveness research. Annual Review of Public Health. 1999;20:337-359. https://doi.org/10.1146/annurev.publhealth.20.1.337
• Garrison LP, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real-world data for coverage and payment decisions: the ISPOR Real-World Data Task Force report. Value in Health. 2007;10(5):326-335. https://doi.org/10.1111/j.1524-4733.2007.00186.x
• ISPOR. About ISPOR. Lawrenceville, NJ: ISPOR; accessed 14 October 2026. https://www.ispor.org/about
• Kozma CM, Reeder CE, Schulz RM. Economic, clinical, and humanistic outcomes: a planning model for pharmacoeconomic research. Clinical Therapeutics. 1993;15(6):1121-1132. https://pubmed.ncbi.nlm.nih.gov/8111809/
• National Institute for Health and Care Excellence. NICE real-world evidence framework (ECD9). London: NICE; 23 June 2022. https://www.nice.org.uk/corporate/ecd9
• National Institute for Health and Care Excellence. NICE technology appraisal and highly specialised technologies guidance: the manual (PMG36). London: NICE; 2022, last updated 13 October 2026. Chapter 4. https://www.nice.org.uk/process/pmg36/chapter/economic-evaluation
• Pizzi LT, Onukwugha E, Corey R, Albarmawi H, Murray J. Competencies for professionals in health economics and outcomes research: the ISPOR Health Economics and Outcomes Research Competencies Framework. Value in Health. 2020;23(9):1120-1127. https://doi.org/10.1016/j.jval.2020.04.1834
• United States Code, Title 42, section 1320b-12, Research on outcomes of health care services and procedures, as added by Public Law 101-239 (1989). https://www.law.cornell.edu/uscode/text/42/1320b-12
• United States Code, Title 42, section 1320e, Comparative clinical effectiveness research, as added by Public Law 111-148 (2010), section 6301. https://www.law.cornell.edu/uscode/text/42/1320e
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