Currently Viewing:
The American Journal of Managed Care August 2018
Impact of a Medical Home Model on Costs and Utilization Among Comorbid HIV-Positive Medicaid Patients
Paul Crits-Christoph, PhD; Robert Gallop, PhD; Elizabeth Noll, PhD; Aileen Rothbard, ScD; Caroline K. Diehl, BS; Mary Beth Connolly Gibbons, PhD; Robert Gross, MD, MSCE; and Karin V. Rhodes, MD, MS
Choosing Wisely Clinical Decision Support Adherence and Associated Inpatient Outcomes
Andrew M. Heekin, PhD; John Kontor, MD; Harry C. Sax, MD; Michelle S. Keller, MPH; Anne Wellington, BA; and Scott Weingarten, MD
Currently Reading
Precision Medicine and Sharing Medical Data in Real Time: Opportunities and Barriers
Y. Tony Yang, ScD, and Brian Chen, PhD, JD
From the Editorial Board: Michael E. Chernew, PhD
Michael E. Chernew, PhD
Optimizing Number and Timing of Appointment Reminders: A Randomized Trial
John F. Steiner, MD, MPH; Michael R. Shainline, MS, MBA; Jennifer Z. Dahlgren, MS; Alan Kroll, MSPT, MBA; and Stan Xu, PhD
Impact of After-Hours Telemedicine on Hospitalizations in a Skilled Nursing Facility
David Chess, MD; John J. Whitman, MBA; Diane Croll, DNP; and Richard Stefanacci, DO
Baseline and Postfusion Opioid Burden for Patients With Low Back Pain
Kevin L. Ong, PhD; Kirsten E. Stoner, PhD; B. Min Yun, PhD; Edmund Lau, MS; and Avram A. Edidin, PhD
Patient and Physician Predictors of Hyperlipidemia Screening and Statin Prescription
Sneha Kannan, MD; David A. Asch, MD, MBA; Gregory W. Kurtzman, BA; Steve Honeywell Jr, BS; Susan C. Day, MD, MPH; and Mitesh S. Patel, MD, MBA, MS
Evaluating HCV Screening, Linkage to Care, and Treatment Across Insurers
Karen Mulligan, PhD; Jeffrey Sullivan, MS; Lara Yoon, MPH; Jacki Chou, MPP, MPL; and Karen Van Nuys, PhD
Reducing Coprescriptions of Benzodiazepines and Opioids in a Veteran Population
Ramona Shayegani, PharmD; Mary Jo Pugh, PhD; William Kazanis, MS; and G. Lucy Wilkening, PharmD
Medicare Advantage Enrollees’ Use of Nursing Homes: Trends and Nursing Home Characteristics
Hye-Young Jung, PhD; Qijuan Li, PhD; Momotazur Rahman, PhD; and Vincent Mor, PhD

Precision Medicine and Sharing Medical Data in Real Time: Opportunities and Barriers

Y. Tony Yang, ScD, and Brian Chen, PhD, JD
Facilitating real-time data sharing while protecting individual privacy, reducing the risk of data misuse, and enhancing public trust becomes critical as precision medicine moves forward.
ABSTRACT

Sharing massive amounts of medical data is critical to precision medicine. The California Department of Public Health recently started to partner with certain hospitals in the state to better understand cancer trends by collecting and securely sending standardized cancer data directly to the California Cancer Registry. This initiative is the first of its kind in the United States. This has afforded the cancer registry the opportunity to perform real-time surveillance on data reported via participating hospitals, and researchers can use advanced methods to analyze these data. Other states are likely to follow California’s lead. However, there are barriers to increased data-sharing efforts. How these barriers can be addressed to facilitate data sharing while protecting individual privacy, reducing the risk of data misuse, and enhancing public trust becomes critical as precision medicine moves forward.

Am J Manag Care. 2018;24(8):356-358
Takeaway Points
  • The California Department of Public Health recently started to partner with hospitals to better understand cancer trends by collecting and sending standardized cancer data directly to the California Cancer Registry.
  • This has afforded the registry the opportunity to perform real-time surveillance on data reported via participating hospitals, and researchers can use advanced methods to analyze these massive amounts of data.
  • There are barriers to increased data-sharing efforts. How these barriers can be addressed to facilitate data sharing while protecting individual privacy, reducing the risk of data misuse, and enhancing public trust becomes critical as precision medicine moves forward.
The growing use of big data and the ability to share data across entities and fields have been called the “biggest medical innovation in recent years” and hold great promise for the future of population health and precision medicine.1 Big data are characterized by “volume, variety, and velocity,” or the ability to store massive amounts of data, to analyze differently formatted data structures, and to do so quickly and efficiently.1 Big data can be used to deliver personalized medicine, help identify population health patterns in communities, and predict and track disease.2 California recently introduced a real-time cancer tracker, which allows pathologists and oncologists to upload current patient data into a central database.3 Researchers and providers will be able to use the database to recommend treatments for patients based on real-time information and outcomes.3 This model is promising, but there are barriers that may impede the system’s adoption elsewhere.

The California Model

In 2014, the California Department of Public Health (CDPH) and the College of American Pathologists (CAP) began a pilot project to track cancer data in real time in a central database that will be accessible to providers across the state.3 One of the reasons California’s model is so novel is because of how the information is uploaded. The CDPH and CAP worked together to develop a standardized checklist into which physicians and providers enter patient data.3 Currently, pathologists write brief paragraphs about their observations and outcomes and send them to the California Cancer Registry. A researcher then reads the paragraphs and enters the information into the system.3

A real-time registry offers many benefits. If enough hospitals adopt the same format, physicians will be able to use all of the standardized data to help determine treatment plans for their patients, including recommending clinical trials. The new format will also allow cancer centers to better conduct internal evaluations to detect problems with their laboratories or pathology departments. Currently, only a few of the hundreds of hospitals in California are utilizing the new format.3 As of today, there remain many barriers that impede speedy adoption.

Barriers

Technical. Most hospitals use an electronic health record (EHR) system, which, in theory, should give researchers immediate access to big data, but barriers exist. First, these systems vary and may not be compatible with other systems.2 Second, clinical trial and other research data are not standardized. Researchers work around these limitations by extracting, transforming, and loading data from EHRs into a “data lake,” which is a massive, easily accessible, centralized repository of large volumes of structured and unstructured data.1 Researchers can then create 1 large data schema, but this can be very expensive in time and resources, requiring intensive and customized data coding.4

Also, some consumers may feel uncomfortable with their data being stored and accessed by multiple users, even when deidentified.1 There are also questions about whether anonymization truly deidentifies data. One study found, for example, that 40% of the participants in a deidentified DNA study were reidentifiable.1

Legal. Data sharing is regulated by federal and state laws. The content of the data, their identifiability, and the context of use determine how these laws apply.5 The Public Health Service Act limits the use of identifiable data and requires the secretary of HHS to ensure that data are protected from inappropriate use.6 The Health Insurance Portability and Accountability Act of 1996 (HIPAA) regulates what types of identifiable data can be disclosed by healthcare plans and providers.7 The HIPAA Privacy Rule, which applies to identifiable health information only, protects such data from being improperly disclosed by health plans, providers, clearinghouses, and business associates.8 The HIPAA Security Rule requires covered entities to establish structures to protect data from being breached or inappropriately disclosed.8 In addition, tax considerations restrict nonprofit organizations from entering into certain types of data-sharing arrangements.5 Many governmental agencies also have their own privacy and data-sharing policies.9

The ability to collect public health data, as well as to disclose and share such data, varies by state.10 Some states have privacy protections that are stronger than those required under HIPAA10 or specific protections for certain diseases, such as HIV.5 Other state laws are comprehensive and apply generally to system participants who collect, acquire, use, or disclose information within the state.5 Federal and state antitrust laws may also regulate contractual information exchange and sharing arrangements to promote competition.5 Providers may have to consider state licensing requirements.5 Some states create legal obligations and requirements on government entities regarding government data, and others utilize broader privacy laws.10 Mandatory data-reporting laws also vary across states.10 Such variations in state laws may present barriers to information sharing.

Institutionalized. Institutionalized barriers must also be overcome. Of particular significance are patients’ privacy concerns and reluctance to share data.1 Many consumers are concerned that their data may be used by third parties, even though they frequently and voluntarily upload identifiable data on unsecure websites and social media.2 Consumers also fear that their data will be used in unethical or harmful ways,2 such as to discriminate against minorities.5 Patients want data to be shared if it benefits them, but they want to protect their data and keep them to themselves if they do not have a vested interest in sharing the data.5 In order to bring consumers on board, the government, health systems, and research institutions will have to prove that they can be trusted with sensitive health information and explain the exact purpose of data collection.5

In addition, health systems are worried that their competitors will be able to use their data against them when competing for customers.1 Providers fear that if their health statistics are publicly available, they will lose patients or be sanctioned if they do not measure up in performance.2 The cost of implementation of data management and sharing software is also a barrier. Companies are reluctant to implement large data systems requiring high front-end costs if they do not see a clear financial benefit.4


 
Copyright AJMC 2006-2018 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up
×

Sign In

Not a member? Sign up now!