Self-Reported Health Care Resource Utilization in Clinical Trials

J Clin Pathways. 2021;7(10):10-12. doi: 10.25270/jcp.2021.12.1

When engaging in health care research, clinical data are a primary source of evidence, and the clinical trial setting is the “gold standard” for evidence collection. Clinical outcomes, however, are not the only outcome that should be of interest; increasingly, health care resource utilization (HCRU) and its associated costs, are of interest to payers, providers, and patients. Utilization types can be broadly categorized as direct medical costs to a private or public payer (eg, hospital admissions, emergency department (ED) visits, clinician visits, laboratory and imaging tests) and, separately, as costs that are indirectly associated with a disease or treatment, such as travel time, out-of-pocket expenses, caregiver burden, and others.¹ HCRU data can be used to determine the economic burden of a specific disease through cost-effectiveness models, budget impact models, or other analyses.

Real-world data, such as those collected by hospitals, insurers, or governments, are important sources within HCRU research, but these databases often are repurposed for this use rather than custom designed. Some researchers are beginning to collect HCRU data within the context of clinical trials, with the intention to address research questions of interest, rather than relying solely on data salvaged from insurance claims or electronic health records. Here, we will explore the benefits and limitations of collecting HCRU within a clinical trial, as well as recommendations for how to do so in a way that will yield the most useful data.

Benefits

Instead of using administrative data, HCRU values can be reported by patients (typically) through questionnaires.^1,2 The primary advantages of patient-reported HCRU over administrative databases is that questionnaires can be tailored to the research question, allowing for targeted information gathering. Electronic systems that hold administrative HCRU data are usually designed by independent contractors, specifically in the interest of administrative personnel.³ However well-designed questionnaires can take into account the particular circumstances of a given patient population and disease.¹ Relatedly, self-reported HCRU can provide additional information that is not collected in administrative databases, such as indirect costs.² Expenses that accompany absenteeism or reduced work productivity are not routinely collected from administrative data. When these values are collected, HCRU can be thoroughly captured, recording costs in a patient’s life that are often unreported, but still experienced by the individual. When it is time to analyze the data, HCRU can be linked to clinical outcomes, providing a richer, more complex understanding of patient experiences. Unlike clinical trial data, administrative data may be unobtainable in a timely, cost-efficient manner.¹ It is also a possibility that trial participants may not even have their respective HCRU values captured within various administrative databases, or that those databases do not have the appropriate means to link data to participants.³ The direct interaction with patients in clinical trials allows for this access gap to be avoided. Gathering self-reported HCRU as part of a clinical trial can provide valuable data on treatment outcomes beyond the strictly clinical outcomes that trials must focus on.

Limitations

Gathering HCRU as part of a clinical trial has some potential shortcomings that can ultimately detract from the data’s value. While these shortcomings are not unique to self-reported HCRU in a clinical trial, the relative novelty and secondary status of the practice merits it special attention. First and foremost, self-reported questionnaires are inherently at risk of distortion and bias, and situational factors may affect the accuracy of the data.^1,4 These factors can include the type of service a patient sought, the time required for recall, utilization frequency, the mode of data collection, and even the questionnaire design.⁴ When using a data source that is potentially biased, inaccurate, or both, a study’s findings are consequently under threat of those same characteristics as well. Other limitations of gathering HCRU in a clinical trial generally include the cost, time, and other resources required to plan, carry out, and analyze the data collection.  Especially in a prospective study, the actual collection of self-reported HCRU can be  particularly labor intensive, due to the amount of time needed to examine initial findings that have not yet been refined. To improve the validity of HCRU data collected in a clinical trial and to encourage the practice in future research, there are best practices that can be followed to avoid common pitfalls.

Recommendations

Clarify the research question and remain mindful of the collected data’s purpose

At its core, research questions drive study objectives, design, and analysis. Data obtained in a study ultimately provide evidence that either support or refute a study hypothesis. To successfully use HCRU, the investigator must keep in mind the purpose of data that are collected. If the initial research questions are poorly defined, the outcomes will be as well. With poorly defined outcomes, researchers are often left to use their own discretion and experience to come to a clear consensus of their interpretation.  This creates inconsistencies that hinder the ability to effectively compare study results.⁵ Self-reported HCRU data are especially vulnerable to these inconsistencies, due to their ability to accommodate non-standard responses which may be interpreted arbitrarily.³  Are direct or indirect health care costs the priority of the study? Should the study consider time use and family/caregiver resources? An investigator must determine which categories of resource use are of interest early in the planning process.

Consider the specifics of the investigated disease

Expanding on the prior recommendation, an investigator must stay mindful of the purpose and appropriateness of the collected data. Diseases are not created equally and thus their manifestations and impact on HCRU are expressed differently. Do patients with the targeted disease tend to end up in the ED? Do patients with this disease experience regular interruptions in their daily life? Collecting data irrelevant to a particular disease can add noise to a study and even confuse the patient’s self-report, all of which have the potential of compromising HCRU validity.

Use consistent measurements across clinical and self-reported HCRU data

Standardized instruments for data collection allow for scientific rigor to be preserved within research. The standardization of patient-reported outcome measurements in economic evaluation has been accepted as a principle for some time, and even enables the cross-comparison of outcomes across trials.³ Consistent measurement should not be exclusive to conventional trials, but instead extended to self-reported HCRU questionnaires. For example, when considering something as fundamental to HCRU as determining hospitalization rates, asking a patient to merely report the number of visits may be insufficient for analysis without capturing hospitalization dates.

Systematically gather baseline measurements

Although adjusting for baseline differences in resource use is recommended as part of statistical analyses associated with trials, this protocol is not consistently followed.³ Collecting data by patient recall at baseline involves an additional burden on patients, and is typically not undertaken.³ To actually measure an effect and ultimately establish causality, an initial measurement is necessary for comparison and adjustment. Having baseline measurements as an expectation of self-reported HCRU can improve the validity of the data.

Actively prevent recall bias

Self-reported HCRU is contingent on a patient’s recall, a significant limitation of these data. Patients tend not to be able to recall their resource use accurately, with recall accuracy decreasing as the period over which they must remember increases.³ Ultimately this leads to both underreporting and overreporting of values, compromising the strength of a study and its results.² Investigators must take a proactive approach in limiting the influence of recall bias for self-reported data. For example, by using shorter periods, if possible, within the study context, or by considering the age of the patient–as age increases, accurate recall decreases. There should also be safeguards for patients recalling routine health visits. In contrast to inpatient stays that are typically associated with significant events or exacerbations, routine visits tend to be immemorable and are less likely to be recounted correctly.

Preserve data granularity

It is essential that when creating the collection tool for self-reported HCRU, the actual resources that are used are differentiated. Hospitalizations and office visits, for example, are very different types of HCRU and should not be combined. Additionally, ED visits that result in admission should be noted as hospitalizations. This approach should extend to both medications and indirect utilizations. By collecting data that have a level to granularity to it, interpretations and applications of the values can allow for nuance and detail that is often sought after in self-reported data. The significance of differentiating between HCRU also extends to determining the costs associated with HCRU, where specific values are needed for accurate costs. If the investigator is unfamiliar with distinguishing between various HCRU, it can be helpful to include a clinical or administrative professional in the initial process of establishing granularity for appropriate guidance.

Consult experts

Finally, each of the above recommendations can be improved upon by the involvement of a team of experts. Statisticians, epidemiologists, database programmers, and health services researchers, for example, may all be able to contribute to the design and implementation of self-reported HCRU collection tools, as well as the collection and interpretation of the data.

Conclusion

In partnership, both self-reported HCRU and clinical trial data can provide a comprehensive overview of outcomes within research. As HCRU data gains ground in research, ensuring its validity is important. Self-reported HCRU is an invaluable source of data, the rigor of studies utilizing it can be maintained and improved by following appropriate recommendations moving forward.

References

1. Leggett LE, Khadaroo RG, Holroyd-Leduc J, et al. Measuring resource utilization: a systematic review of validated self-reported questionnaires. Medicine (Baltimore). 2016;95(10):e2759. doi:10.1097/MD.0000000000002759

2. Janssen LMM, Drost R, Paulus ATG, et al. Aspects and challenges of resource use measurement in health economics: towards a comprehensive measurement framework. Pharmacoeconomics. 2021;39(9):983-993. doi:10.1007/s40273-021-01048-z

3. Franklin M, Thorn J. Self-reported and routinely collected electronic healthcare resource-use data for trial-based economic evaluations: the current state of play in England and considerations for the future. BMC Med Res Methodol. 2019;19(1):8. doi:10.1186/s12874-018-0649-9ø

4. Short ME, Goetzel RZ, Pei X, et al. How accurate are self-reports? Analysis of self-reported health care utilization and absence when compared with administrative data. J Occup Environ Med. 2009;51(7):786-796. doi:10.1097/JOM.0b013e3181a86671

5. Freundlich RE, Boncyk CS. Clearly-defined outcomes improve the quality of health outcomes research. Br J Anaesth. 2019;122(1):14-16. doi:10.1016/j.bja.2018.11.009

Author Information

Authors: Ronaé McLin, MPH; Marissa Baker-Wagner, MPH; Bridget Healey, MPH; Anh Thy H. Nguyen, MSPH

Affiliation: PRECISIONheor

Disclosures: The authors have no disclosures to report.