The government’s objectives to “Achieve Rapid Learning and Technological Advancement” are: A.Lead the creation of a learning health system to support quality, research, and public and population health B.Broaden the capacity of health IT through innovation and researchSpotlight on Health Outcomes A learning health system can lead to earlier detection and better risk management The use of health IT can enable a learning health system to help prevent and monitor adverse events caused by new drugs. As George begins therapy using a new drug, information about his health can be captured in the EHR and findings may be automatically transmitted from the EHRs in which they are collected and reported in aggregate via a standard format to oversight agencies, such as the FDA, or to the company which manufactures the drug. By making this information available quickly in the learning health system, population-level data could be aggregated for earlier signaling of possible adverse reactions to new drugs. If a potential adverse reaction is identified, EHRs can serve another role in helping providers to quickly identify each of their patients taking a certain medication and notify the patients via their preferred communication channel about the potential risks. These “Spotlight on Health Outcomes” are intended to highlight exemplary ways that widespread adoption and use of health IT and electronic health information exchange could help transform and improve health care. Some of these examples are only aspirational today or only exist in select communities or health systems. However, these examples illustrate the type of transformed health care that could be possible with the achievement of the goals of this Plan. The nationwide adoption and meaningful use of EHRs could facilitate the collection of clinical and research data from disparate sources including hospital systems, provider offices, laboratories, biorepositories, registries, and other research databases. Some stakeholders within the health care industry – such as researchers – are currently on the cutting edge of analyzing EHR-generated data to identify patient populations that are at-risk for preventable hospitalizations. EHR-generated data combined with analytical systems can be a boon to predictive modeling and knowledge generation. As an elderly woman with multiple medical conditions, Jane is part of an at-risk population. As these populations are identified earlier and risk factors are better understood, the learning health system could also enable resources, such as disease management and case management, to be deployed earlier to help avoid preventable hospitalizations. Currently, good clinical information regarding patient-centered outcomes on the best treatment options for patients with multiple diagnoses is lacking.
You've reached the end of your free preview.
Want to read all 80 pages?
- Fall '15
- Electronic health record, health information