Inventory and Prioritization of Measures To Support the Growing Effort in Transparency Using All-Payer Claims Databases

With nearly 18 States that have or are readying legislation for APCDs and with more than 30 States showing increasing interest in setting up APCDs, they are potentially a rich data source that captures unique data points and presents unique measurement opportunities. Unlike other data sources derived from a sliver of the health care system, APCDs allow more comprehensive evaluations of care across the health care system and the integration of cost of processes of care.²

These data include a wider range of individuals than other data sources; namely, they include a large proportion of covered encounters for commercially insured individuals and in some cases Medicare and Medicaid populations.² Theoretically, information on individuals is not lost when switching providers or insurance coverage.

Because of these unique attributes, APCDs offer several unique opportunities for measurement:

1. Ambulatory care. Although Medicare fee-for-service utilization is widely studied across the inpatient and outpatient setting, data on all-payer ambulatory care are limited. APCDs can capture process measures occurring in the outpatient setting, such as appropriate ordering of laboratory testing or imaging, outpatient visits for chronic disease, and ambulatory care for adverse events following ambulatory surgery or hospitalization.^8,9
2. Pediatric care. This area has been particularly difficult to measure, because most children receive all of their care in outpatient settings. Pediatric measures such as immunizations, filling of prescriptions for asthma, and appropriate antibiotic prescriptions already are defined in other data sources.
3. Cross-cutting, episodic care. Measures that capture the spectrum of care can help us learn more about our health care system and effective care transitions. In addition, measures that capture care over an episode of several encounters are less susceptible to variation in the locations where patients seek or receive care. APCDs can capture followup care and care coordination and identify more entities involved in a patient's care.⁹
4. Population health. Because APCD data capture large portions of the population and are relatively inexpensive to collect, they are prime candidates for use in population health measures. Utilization measures are already used as proxies for population health, and APCDs can aid in expanding that view to different types of care, capturing routine care utilization and acute care that does not result in hospitalization. Some processes can also be captured in APCD data that are relevant to population health or the measures can be used to assess policy changes or disparities.^5,8-10 From a resource use perspective, APCD-based measures can provide a window into the health care system, understanding the value of care and how health care costs are tied to the local practice patterns and population.⁹
5. Value measure suites. Although claims data have been tagged a source of health care price transparency, when cost data are paired with quality data, value measure suites can provide more nuanced information to both consumers and payers. These measure sets can be used to learn about more efficient ways to achieve better outcomes.

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Limitations of APCD Data for Measurement

Missing populations. Although APCDs do include a large number of health care claims, some populations are omitted.^2,8,9,11 If the data from these populations differ systematically from the data included in the APCD, the omission will result in biased measures. APCDs typically do not include:

• Uninsured patients.
• Some substance use, mental health, and HIV populations, depending on State regulations.
• Worker's compensation patients.
• Tricare or Veterans Affairs data.
• Federal Employee Health Benefit Plan patients.
• Indian Health Service patients.⁹

A recent Supreme Court case (Gobeille v. Liberty Mutual) emphasized that certain plans are exempt from data submission requirements, although some will choose to submit anyway.¹¹ In this case, the Court found that the Federal Employee Retirement Income Security Act statute that preempts State legislation pertains primarily to self-insured health plans.¹² Because many employer-sponsored plans across the Nation are self-insured, this potential gap is being watched closely.

APCDs also have different rules regarding which patients should be included in the dataset.^1,8,12 Some States require all care for patients residing in the State, while others require those employed within a State or those employed by a company within the State.¹ Because these are all slightly different definitions, users must be clear about the desired population for their use and clean the data appropriately.^1,9

Data quality. APCDs are prone to several data quality concerns. Claims data do not have details included in electronic health record data, including laboratory and biometric values. Most importantly, they typically do not contain patient outcomes.⁷ In this case, linking when possible to hospital administrative data records, electronic health records, or State mortality data add data.^13-15 The data also may not capture bundled payments.⁸

Many of the critical data elements were well populated and were defined using uniform billing standards or other standardized data collection methods. However, some data elements for the validity or reliability of the populated values were not assessed. Two critical data elements in particular create data quality challenges.

First, the patient identifier is often based on a combination of static patient numbers (e.g., Social Security number, date of birth) and contract plan number.⁷ If patients switch from one plan to another, even if not switching providers, the unique identifier may change.⁷ In addition, if patients switch providers, employers, or claims from carve-out coverage, it may result in additional identifiers for the same individual.⁷ This can create difficulty linking encounters over months when plan changes are likely to take place (e.g., open enrollment, first of the fiscal or calendar year).

In a similar manner, there is evidence that payers and providers are not fully consolidated.⁷ In this case, administrative features may lead to a provider using different identifiers.

Delay in data versus data completeness. In the evaluation and on State APCD data Web sites, the struggle between data timeliness and completeness is highlighted. Some submitters fail to provide complete or cleaned data to the APCD in a timely manner, leading to claims being omitted. Therefore, one sees drops in utilization or data completeness until those data are received.

Although late data are eventually folded into the data, the delay in receipt of the full complement of claims can limit the usability of APCDs. In particular, cost is a factor that can change quickly. If APCDs are to provide cost transparency to consumers, the data must be available quickly. Because of this limitation, consumer use cases may be the most challenging use case for APCD measures.

Measure development gaps. Although hundreds of potential measures were identified that might be applied to APCDs, a few major measurement gaps exist. Specifically, measures of behavioral health, potentially due to data limitations, are not well developed or cannot be applied to the data.⁸ Pediatric measures focus primarily on a few clinical topics and could capture additional care.

Measure sets are likely to provide the best picture of health care value. Often, however, for the same population, few measures are available for cost, quality, and utilization.⁸ In particular, our review showed that quality measures were lacking. Again, data limitations may hamper the development of quality measures that assess outcomes using only APCD data, but linking to other datasets may allow stronger measures.

Finally, many measures were not rigorously tested for validity and reliability. Others may have been tested for reliability and validity for a specific level of analysis (e.g., health plan) but not another (e.g., physician group). Testing is required before a measure can be fully implemented.

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Ongoing Efforts To Improve APCD Data

In speaking with States and reviewing State Web sites, the team identified a large number of ongoing activities related to improving data.^10,12 These included:

• Ongoing evaluation of documentation and the release of validation reports by States.
• Work with researchers or other interested parties to assess the data against other data sources (e.g., mortality vital records).
• Creation of reports based on APCD data.

Some States are isolating different methods for categorizing cost data that will best accommodate and present resource use information.⁸ Finally, some States have active user communities that provide an effective feedback loop on both usability and data quality issues to State organizations.

APCD core set of data elements. In 2009, the APCD Council and AHRQ began to develop a common core set of data elements that most of the APCD States were capturing. The result of that work was the APCD Core, a set of data elements common to most APCDs, which would provide a foundation for new States to grow their APCDs. Since then, the APCD Council has worked with two data standards maintenance organizations to develop standards based on electronic transactions used for claims adjudication. States have varied in their uptake of the APCD Core, with some adopting a unique format or, more commonly, using the core as a starting place for submission requirements.

In light of the 2015 Gobeille v. Liberty Mutual ruling, the APCD Council and the National Academy of State Health Policy have convened States to consider options for working with the U.S. Department of Labor (DOL), including development of a uniform dataset standard referred to as the common data layout (CDL). Although it remains in draft form, once finalized, the CDL can be referred to in work with DOL to identify a process for submitting self-funded plan data to State APCDs.

Federal interest in standards. Recognizing the emerging need for national standards, the National Committee on Vital and Health Statistics (NCVHS) convened a day-long public meeting, "Hearing on Claims-based Databases for Policy Development and Evaluation:- Overview and Emerging Issues," in Washington, DC, on June 17, 2016. The purpose of this meeting was to highlight the current state of development, challenges, issues, and opportunities faced by claim-based databases, including State-based APCD initiatives and private-sector multipayer claim-based database efforts.

The meeting had the following priorities:

• Engage stakeholders on key issues related to claims-based databases.
• Identify priority areas and opportunities for recommendations to the Secretary of Health and Human Services and the health care industry.

Of particular interest to the Committee was to identify benefits, efficiencies, and barriers to the adoption of a common claim-based database and APCD reporting standard, to outline a roadmap for achieving standardization, and to determine how NCVHS could provide support.

NCVHS is currently reviewing the results of the meeting and formulating recommendations to the Secretary that are within the scope of the Committee's statutory role and priorities identified at the June meeting.

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