The Evolution to a Learning Health Care System in the United States:

The US health care system faces increasing pressure to improve quality while curbing costs, reducing inefficiency, and maintaining patient satisfaction. A Learning Health Care System is one solution to help solve these problems.  However, fundamentally transforming the health care system towards a LHCS model presents numerous ethical challenges.

US-based health systems are moving to a Learning Health Care System:

Public and private systems throughout the United States have begun the transition to a Learning Health Care System model.   Health systems like Geisinger Health System in Pennsylvania, Group Health Cooperative in Washington, and Kaiser Permanente Colorado have made system-wide reforms that reflect elements of a Learning Health Care System. Central to these reforms has been the creation of large data systems to collect information from routine clinical care that can be used for research and operations activities to improve health care.

Similar transition efforts are being taken up by the Veteran’s Health Administration (VA), the largest integrated health care system in the US.  The VA is increasingly studying the ongoing care that patients receive through both observational designs and randomized and embedded “pragmatic” trials (Rosenthal, 2014). The goal of this work is to accelerate collection and incorporation of evidence into care to both more quickly improve care and to reduce the cost associated with traditional clinical trials (D’Avolio et al., 2014).

The Ethical Challenges within a Learning Health Care System:

The Johns Hopkins University research team has identified several key ethical issues that Learning Health Care Systems will face.

Continuous Data Generation

A foundational piece of a LHCS system is the need to collect and analyze data that cuts across different areas within the LHCS: providers, patients, health system performance, etc. One data collection concern is the ability to identify poorer performing providers. How should health institutions respond to such information? Do they have an ethical obligation to disclose information about specific provider performance to patients?


Whether it is collecting data that identifies under-performing providers or on the relative costs and benefits of comparative treatment options, LHCS will raise questions about transparency. A LHCS will have to determine how transparent to be with their patients and other stakeholders about which types of data are being analyzed, how decisions are made based on those data, and whether and how stakeholders should be engaged in these decisions.

Data Interoperability

Another set of ethical challenges is related to data interoperability in the US. Individual institutions can deepen the integration of research and care to improve the quality of care for their patients, but full realization of a LHCS model will require extensive sharing of data and insights from other learning activities across providers and different systems.  However, market forces and other features of the current system may impede the sharing necessary for full realization of the LHCS ideal.


The Research-Practice Distinction and Ethics Oversight in a Learning Health Care System

Another set of ethical challenges relates to the continued separation of research and practice distinction for ethical oversight.  As health care institutions begin to move toward a learning health care system model, the same patient care activity may aim both at providing patient treatment and gathering data on outcomes to improve knowledge and future performance.  In such contexts, the reliance on the research-practice distinction becomes increasingly problematic, creating a regulatory environment plagued by “delays, confusions, and frustrations.” (Kass et al., 2013; Selker et al., 2011; Cassarett, Karlawish, and Sugarman, 2000; Baily, 2008; Fiscella et al., 2015)    IRBs struggle to ascertain when an activity should properly be classified as research, and LHCSs struggle with how to develop ethical oversight for activities that are intentionally both practice and research (Kass et al., 2013).