Real-World Data in the Current Health Care Environment

In this guest blog post, Dr. Eberechukwu Onukwugha of the University of Maryland School of Pharmacy's Department of Pharmaceutical Health Services Research explores the value of real-world data.

By Eberechukwu Onukwugha, MS, PhD
Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy

Improve patient care, increase patient satisfaction, reduce costs: These calls for change are common in today’s health care environment. What is often less common is access to the appropriate data for providing timely information on outcomes and patient satisfaction for a target population. To this end, government agencies, hospitals, clinicians and payers are investing in developing a mix of human, information technology and data resources to be able to quantify and track results related to health outcomes and quality (Wang 2017).

Many institutions (i.e., hospitals, government agencies, accreditation bodies, payers, etc.) depend on the ability to quantify and track outcomes and quality metrics associated with health care delivery (Berger 2017, Garrison 2007). The hope is that if we are able to contain costs of care while achieving improvements in outcomes and quality of care, then everybody wins.

To demonstrate changes in these metrics, institutions and governmental agencies are increasingly turning to real-world data (RWD). Most recently, the U.S. Food and Drug Administration is developing a framework for how it will use RWD in regulatory decision-making (USFDA 2017, Dreyer 2018).

Real-world data, such as hospital discharge datasets, medical and pharmacy claims data, patient registries, patient surveys, public health surveillance data, medical charts and electronic health records, are collected in a routine manner outside of a controlled, experimental setting (e.g., outside of a clinical trial) for a variety of reasons (Makady 2017, Berger 2017). Using these data, investigators can study patterns of health care resource utilization and health outcomes for individuals receiving health care services in the real-world environment. Depending on the dataset, sample sizes can reach tens of millions of data points or more.

The ability to generate findings related to health outcomes and quality that apply to a cross-section of people is important for public health decision-making, regulatory oversight and, in some cases, provider reimbursements. In the policy arena, RWD have been used to examine the impact of federal regulations such as the Affordable Care Act (Equia 2018, Ngo-Metzger Q 2018, VanGarde A 2018), guideline recommendations such as those released by the United States Preventive Services Task Force (Ngo-Metzger Q 2018, Gurgle HE 2017, Khairnar 2018) and state policies (Horny 2018, Haegerich 2014, Goold 2018).

Tools like administrative medical and pharmacy claims data also provide a wealth of information on health care resource utilization and reimbursements by insurers, but they have their own sets of challenges. In particular, this data is usually collected for specific administrative purposes and without a defined scientific research question in mind. Other challenges encountered using administrative RWD are: 1) the measures will often require subsequent analysis and re-categorization in order to be suitable for analysis and reporting, 2) the datasets will require a non-trivial investment of time and expertise to develop the files into a structure that is suitable for statistical analysis, and 3) there are a number of black box decisions (Wang 2017) that will be needed, and at the same time, there is limited work to understand the implications of such decisions for the research study conclusions (Bjarnadottir, 2017).

Despite these challenges, there is immense value to RWD. They offer a unique opportunity to characterize health care utilization, describe the patient experience with health care, identify areas for improvement in health care delivery and quantify the effects of public policies. Opportunities to learn more about RWD, such as the University of Maryland School of Pharmacy’s upcoming September 2018 training course providing a general introduction to RWD and administrative claims data, can help make this useful tool more accessible to broader audiences. There are many ways to improve the health care environment, and with the right training, RWD can provide valuable guidance regarding the most promising pathways.

Register for the course today via the University of Maryland’s website.

This is a guest blog post by Eberechukwu Onukwugha, MS, PhD, of the Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy. The views in this post are entirely her own.


  1. Berger ML, Sox H, Willke RJ et al. Good practices for real‐world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR‐ISPE Special Task Force on real‐world evidence in health care decision-making. Pharmacoepidemiol Drug Saf. 2017; Sep; 26(9): 1033–1039.
  2. Bjarnadottir MV, Czerwinski D, Onukwugha E. Sensitivity of the Medication Possession Ratio to Modelling Decisions in Large Claims Databases. Pharmacoeconomics. 2018 Mar;36(3):369-380. doi: 10.1007/s40273-017-0597-y.
  3. Dreyer NA. Advancing a Framework for Regulatory Use of Real-World Evidence When Real Is Reliable. Therapeutic Innovation & Regulatory Science 2018; Vol. 52(3) 362-368.
  4. Equia E, Cobb AN, Kothari AN et al. Impact of the Affordable Care Act (ACA) Medicaid Expansion on Cancer Admissions and Surgeries. Ann Surg. 2018; Jul 12. doi: 10.1097/SLA.0000000000002952.
  5. Garrison LP, Newmann PJ, Erickson P et al. Using Real-World Data for Coverage and Payment Decisions:
  6. The ISPOR Real-World Data Task Force Report. Value in Health 2007; 10(5): 326-335.
  7. Goold SD, Tipirneni R, Kieffer E. Primary Care Clinicians' Views About the Impact of Medicaid Expansion in Michigan: A Mixed Methods Study. J Gen Intern Med. 2018 Jun 12. doi: 10.1007/s11606-018-4487-6.
  8. Gurgle HE, Schauerhamer MB, Rodriguez SA, McAdam-Marx C. Impact of statin guidelines on statin utilization and costs in an employer-based primary care clinic. Am J Manag Care. 2017; Dec 1;23(12):e387-e393.
  9. Haegerich TM, Paulozzi LJ, Manns BJ. What we know, and don't know, about the impact of state policy and systems-level interventions on prescription drug overdose. Drug Alcohol Depend. 2014; Dec 1;145:34-47. doi: 10.1016/j.drugalcdep.2014.10.001. Epub 2014 Oct 14.
  10. Horný M, Shwartz M, Duszak R Jr, et al. Characteristics of State Policies Impact Health Care Delivery: An Analysis of Mammographic Dense Breast Notification and Insurance Legislation. Med Care. 2018; Jul 20. doi: 10.1097/MLR.0000000000000967. [Epub ahead of print]
  11. Khairnar R, Mishra MV, Onukwugha E. Impact of United States Preventive Services Task Force Recommendations on Utilization of Prostate-specific Antigen Screening in Medicare Beneficiaries. Am J Clin Oncol. 2018; Feb 16. doi: 10.1097/COC.0000000000000431. [Epub ahead of print]
  12. Makady A, de Boer A, Hillege H et al. What Is Real-World Data? A Review of Definitions Based on Literature and Stakeholder Interviews. Value in Health 2017; 20(7): 858-86.
  13. Ngo-Metzger Q, Zuvekas SH, Bierman AS. Estimated Impact of US Preventive Services Task Force Recommendations on Use and Cost of Statins for Cardiovascular Disease Prevention. J Gen Intern Med. 2018 May 31. doi: 10.1007/s11606-018-4497-4.
  14. United States Food and Drug Administration. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices. August 2017.
  15. VanGarde A, Yoon J, Luck J, Mendez-Luck CA. Racial/Ethnic Variation in the Impact of the Affordable Care Act on Insurance Coverage and Access Among Young Adults. Am J Public Health. 2018 Apr;108(4):544-549. doi: 10.2105/AJPH.2017.304276. Epub 2018 Feb 22.
  16. Wang SV, Schneeweiss S, Berger M, et al. Reporting to Improve Reproducibility and Facilitate Validity
  17. Assessment for Healthcare Database Studies V1.0. Value in Health 2017; 20: 1009-1022.