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The American Journal of Managed Care Special Issue: Health Information Technology
Improving Adherence to Cardiovascular Disease Medications With Information Technology
William M. Vollmer, PhD; Ashli A. Owen-Smith, PhD; Jeffrey O. Tom, MD, MS; Reesa Laws, BS; Diane G. Ditmer, PharmD; David H. Smith, PhD; Amy C. Waterbury, MPH; Jennifer L. Schneider, MPH; Cyndee H. Yonehara, BS; Andrew Williams, PhD; Suma Vupputuri, PhD; and Cynthia S. Rand, PhD
Information Retrieval Pathways for Health Information Exchange in Multiple Care Settings
Patrick Kierkegaard, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
The 3 Key Themes in Health Information Technology
Julia Adler-Milstein, PhD
Leveraging EHRs to Improve Hospital Performance: The Role of Management
Julia Adler-Milstein, PhD; Kirstin Woody Scott, MPhil; and Ashish K. Jha, MD, MPH
Electronic Alerts and Clinician Turnover: The Influence of User Acceptance
Sylvia J. Hysong, PhD; Christiane Spitzmuller, PhD; Donna Espadas, BS; Dean F. Sittig, PhD; and Hardeep Singh, MD, MPH
Cost Implications of Human and Automated Follow-up in Ambulatory Care
Eta S. Berner, EdD; Jeffrey H. Burkhardt, PhD; Anantachai Panjamapirom, PhD; and Midge N. Ray, MSN, RN
Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology
Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Adoption of Electronic Prescribing for Controlled Substances Among Providers and Pharmacies
Meghan Hufstader Gabriel, PhD; Yi Yang, MD, PhD; Varun Vaidya, PhD; and Tricia Lee Wilkins, PharmD, PhD
Health Information Exchange and the Frequency of Repeat Medical Imaging
Joshua R. Vest, PhD, MPH; Rainu Kaushal, MD, MPH; Michael D. Silver, MS; Keith Hentel, MD, MS; and Lisa M. Kern, MD
Information Technology and Hospital Patient Safety: A Cross-Sectional Study of US Acute Care Hospitals
Ajit Appari, PhD; M. Eric Johnson, PhD; and Denise L. Anthony, PhD
Automated Detection of Retinal Disease
Lorens A. Helmchen, PhD; Harold P. Lehmann, MD, PhD; and Michael D. Abràmoff, MD, PhD
Trending Health Information Technology Adoption Among New York Nursing Homes
Erika L. Abramson, MD, MS; Alison Edwards, MS; Michael Silver, MS; Rainu Kaushal, MD, MPH; and the HITEC investigators
Electronic Health Record Availability Among Advanced Practice Registered Nurses and Physicians
Janet M. Coffman, PhD, MPP, MA; Joanne Spetz, PhD; Kevin Grumbach, MD; Margaret Fix, MPH; and Andrew B. Bindman, MD
The Value of Health Information Technology: Filling the Knowledge Gap
Robert S. Rudin, PhD; Spencer S. Jones, PhD; Paul Shekelle, MD, PhD; Richard J. Hillestad, PhD; and Emmett B. Keeler, PhD
Currently Reading
Overcoming Barriers to a Research-Ready National Commercial Claims Database
David Newman, JD, PhD; Carolina-Nicole Herrera, MA; and Stephen T. Parente, PhD

Overcoming Barriers to a Research-Ready National Commercial Claims Database

David Newman, JD, PhD; Carolina-Nicole Herrera, MA; and Stephen T. Parente, PhD
Lessons learned about data governance and distribution from a voluntary healthcare claims repository, the Health Care Cost Institute, a nonprofit research organization
Billions of dollars have been spent on the goal of making healthcare data available to clinicians and researchers in the hopes of improving healthcare and lowering costs. However, the problems of data governance, distribution, and accessibility remain challenges for the healthcare system to overcome.

Study Design
In this study, we discuss some of the issues around holding, reporting, and distributing data, including the newest “big data” challenge: making the data accessible to researchers and policy makers.

This article presents a case study in “big healthcare data” involving the Health Care Cost Institute (HCCI). HCCI is a nonprofit, nonpartisan, independent research institute that serves as a voluntary repository of national commercial healthcare claims data.

Governance of large healthcare databases is complicated by the data-holding model and further complicated by issues related to distribution to research teams. For multi-payer healthcare claims databases, the 2 most common models of data holding (mandatory and voluntary) have different data security requirements. Furthermore, data transport and accessibility may require technological investment.

HCCI’s efforts offer insights from which other data managers and healthcare leaders may benefit when contemplating a data collaborative.

Am J Manag Care. 2014;20(11 Spec No. 17):eSP25-eSP30
Research-focused data organizations face challenges in holding and distributing healthcare data. Using the experience of the Health Care Cost Institute as a model, the authors explore approaches and obstacles to making claims available to researchers.
  • There are 2 common models for holding multi-payer health claims data.
  • Date security requirements will vary depending on the model.
  • Data licensing requirements will be complicated by the Health Insurance Portability and Accountability Act, anti-trust regulation, and other issues.
  • Two external issues, data transport and access, may require more technological investment to make data research-ready.
Big healthcare data have become ubiquitous, and discussions about such data often focus on outcome metrics and healthcare costs. However, the jump from a raw database to an analytical file is fraught with issues. There are problems of governance, data distribution, and accessibility that data holders must overcome before researchers and policy makers can benefit from the data.

In this study, we discuss how one research-oriented, data-holding organization, the Health Care Cost Institute (HCCI), has addressed some of the barriers to effective data use.1 HCCI is a nonprofit, nonpartisan, independent research institute that serves as the repository of healthcare claims for more than 50 million Americans per year (for 2007 through 2013) from 3 of the nation’s largest insurers. HCCI holds individual insurance, group insurance, and MedicareAdvantage data in a manner compliant with the Health Insurance Portability and Accountability Act (HIPAA) and antitrust law, and in a manner that addresses insurers’ concerns about company confidentiality. HCCI licenses and distributes data to research institutions. HCCI’s current challenge is building a scalable, secure data distribution system to support timely, independent healthcare research. Below, we describe HCCI’s approach to data governance and distribution, and we explore ways in which technology may help data holders promote public research.


As recently as 6 years ago, data on healthcare costs were relatively scarce. Some states, through either local initiatives or national efforts, had hospital reporting on healthcare utilization.2 Other states had launched efforts to mandate the reporting of healthcare claims from private insurers.3 Some organizations, such as Blue Cross Blue Shield or Thompson Reuters (now Truven Health Informatics), had commercialized private healthcare data from a limited set of insurers and/or employers.4

Today, healthcare data, in general, are more available. The federal government has a number of ongoing initiatives. HHS began an effort to build a national multi-payer claims database to support comparative effectiveness research.5 CMS embarked on efforts to make Medicare data more available to the states and launched the Qualified Entity program.6 CMS also invested in the creation of a virtual research data center to make Medicare data more accessible to researchers. Additionally, the Affordable Care Act and the American Relief and Recovery Act increased provider use of electronic health records.7,8 Some states mandated all-payer claims databases (APCDs) to support insurance regulation and inform public health policies.4 Other states, in particular Hawaii and Arkansas, have used federal dollars available from the Center for Consumer Information and Insurance Oversight (CCIIO) to initiate public reporting efforts based on claims data.9 Employers and communities are also collecting and sharing data on local healthcare markets.10,11 The Midwest Health Initiative holds health data on some 1.8 million residents of St. Louis, Missouri, and 18 nearby counties.6 The California Healthcare Performance Information System (CHPIS) collects data and reports on physicians.7

There is more reporting of healthcare prices to consumers through the Internet. FAIR Health, a nonprofit, “offers unbiased data products and services to consumers, the healthcare community, employers, unions, government agencies, policymakers, and researchers.”12 Castlight Health, Inc, offers transparency services to the employees of businesses enrolled in their system “to enable employers and health plans to lower the cost of healthcare and provide individuals unbiased pricing and quality information to make smart healthcare purchase decisions.”13,14 Though both these initiatives hold multi-state data from multiple data suppliers, their respective scopes limit access to their pricing information. When data are available for the patient’s location, FAIR Health provides healthcare cost information to the public via billing codes. Castlight provides their clients’ employees with price transparency services but does not provide this information to the public.

Perhaps unique among these efforts is HCCI, a nonprofit research institute. HCCI was launched in 2011 with the support of 4 of the nation’s largest national health insurance companies: Aetna, Humana, UnitedHealthcare, and Kaiser Permanente.1 Overseen by an independent governing board principally composed of academic economists, the institute’s public mission is to report on trends in cost and utilization, to make commerical claims data available for research and, most recently, to help states.1 To support these missions, HCCI assembled a national multi-payer claims database with allowed amounts (actual prices paid to providers for services). HCCI has released several reports describing national trends in healthcare spending, prices, and utilization, and has provided statistically de-identified databases to research institutions for noncommercial purposes. To do so, HCCI has addressed 2 of the key barriers to using big healthcare data: governance and distribution.


Health data organizations, whether they are public or private entities, face challenges in holding healthcare data. Foremost is getting permission from the owners of data (healthcare payers, providers, and patients) to assemble a database (data contribution). Then, organizations face a series of challenges related to keeping the information private and useful. The way an organization collects data changes the way it responds to these challenges.14

Mandatory Contribution Models

Most holders of multi-payer health data receive their data through a mandatory contribution model (MCM). MCMs occur when a state government requires that insurers, providers, and/or employers provide healthcare data for statutory purposes (such as insurance or provider regulation).4 A common form of MCM is the mandated state all-payer claims database. Many MCMs require data owners to provide healthcare claims using unique data extraction rules, such that many states operate with different data specifications.15 Not surprisingly, this sort of effort is costly. States face significant costs as they develop customized solutions and analytic results.16 For example, for fiscal year 2015, Maine’s MCM-governed ACPD may cost the state about $1.66 per Maine resident.17 Moreover, multiple data feeds and multiple reporting systems burden providers and payers who cross state boundaries. How much MCM compliance costs providers and payers has not been documented.

Voluntary Contribution Models

An alternative to the MCM is the voluntary contribution model (VCM). VCMs are a contractual approach in which data owners voluntarily contribute information to a data collaborative. Many VCMs are associated with not-for-profit entities such as HCCI, the Wisconsin Health Information Organization, and the Midwest Health Initiative.6,18 A growing number of states are considering a VCM, and in at least 1 (Virginia), the Commissioner of Insurance has negotiated insurer participation in a statewide data-sharing effort.19 VCMs require greater confidence-building than MCMs, as the entities providing the data voluntarily relinquish some control of their data. For example, HCCI and its data contributors entered into a series of agreements that govern how the research institute can use these data and the terms under which it can license these data. HCCI maintains an internal data integrity committee whose mandate is to ensure that HCCI’s activities conform to the law and to its contractual obligations concerning the data. Even with the cost of coordination, this type of effort could be less costly than an MCM. A national or multi-state VCM is very scalable for both the VCM and for data owners as long as each contributor sends 1 feed for all geographies with a common set of data definitions and requirements. Scalability for a VCM declines if it is restricted to the state or sub-state level, or if the VCM has not sufficiently invested in data standards. However, VCMs may not have the utility for some state purposes as MCMs do, in part because consensus, not statute, dictates data use.

Transitional Contribution Models

A number of states are also contemplating developing hybrid contribution models wherein data contribution would begin as a VCM and then transition to an MCM. These transitional contribution models (TCMs) would operate initially as VCMs and, after a start-up period, transition into MCMs. Helping drive the emergence of the TCMs are CCIIO grants to support rate reviews and price transparency.20 Both Arkansas and Hawaii have indicated they are interested in developing a TCM using the Cycle III grant funds. Arkansas has released a request-for-proposal to support building its data center as a VCM and then transitioning it to an MCM.9 In December 2013, Hawaii asked commenters to detail potential issues involved with moving from a VCM to an MCM, including questions regarding sustainability, data owner relationships, and data standards. One key governance issue with TCMs is data licensing. When contemplating a TCM, one should be aware that data licenses are not necessarily transferable if the data holding organization’s legal structure changes. For example, if the model begins as a nonprofit effort (like HCCI) and is then integrated with a state or federal agency, the data licenses will likely need to be renegotiated, and currently held data may have to be destroyed. If a VCM becomes an MCM without changing legal structure, a data holder may avoid this potential legal hazard.

Data Privacy and Confidentiality

Depending on the data contribution model, the data holder may have different protected health information requirements. To ensure privacy, VCMs often face more restrictions than do MCMs. Qualified entities and state agencies are not as restricted by HIPAA as other data holders.

HIPAA provides federal protections for personal health information and constrains the data available for research.2 However, HIPAA generally provides that health information is not individually identifiable if someone, with appropriate training and accepted statistical and scientific methods, determines that the risks of identifying an individual in the data are small. Using this framework, HCCI uses and distributes “statistically de-identified databases.” HCCI currently distributes 2 statistically de-identified claims “data views,” distinguished by the protected health information allowed in each. For example, one view has year of birth, whereas the other has patient zip code. To maintain statistical de-identification, research teams may not combine or merge the data views. This approach allows researchers to receive the richest claims data possible in an HIPAA-compliant manner.

In addition to HIPAA, everything that an MCM, a VCM, or a TCM does must conform to applicable antitrust law. In the case of HCCI, there is a 1-way flow of the data from the data contributors to HCCI. The data contributors have no rights to the combined database or any access to the combined database. Every research product generated by HCCI undergoes a legal review for antitrust issues. Finally, HCCI does not perform any proprietary or confidential research using its data on behalf of the data contributors.

Copyright AJMC 2006-2020 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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