Editor's note: This story was updated on July 1, 2019.
The field of precision medicine, while still in its infancy, holds the promise of transforming health care delivery. For example, a recent survey by the Center for Connected Medicine asked 44 health system executives to rank the five emerging health IT categories they expect to have the biggest effect on their business five years from now—the top response was "AI for precision medicine" at 62%, with "genomics'" placing third overall at 54%.
What do we mean by precision medicine?
Precision medicine is not just about genomics. The National Institutes of Health (NIH) defines precision medicine as "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Precision medicine should give clinicians the ability to effectively move away from "one-size-fits-all" care.
Connected health plays a critical role in precision medicine, as health care organizations continue to amass huge quantities of data related to disease diagnosis, treatments, diagnostic testing, and patient behaviors.
By aggregating and analyzing these various streams of data, health care providers and other stakeholders have a better chance of optimally targeting treatments to subpopulations based on their medical history, socioeconomic conditions, current medications, comorbidities, genomic profiles, and other risk factors.
Workflow and IT requirements for a successful precision medicine program
Most health care providers do not have the proper infrastructure or data management policies in place to implement precision medicine programs. While EHRs have helped to compile clinical data, they were not initially designed to incorporate streams of real-time patient-generated health data (PGHD) and patient-specific social determinant and molecular genetic data; nor were they designed to gather and synthesize information from third-party clinical trials. The ability to effectively store and analyze data at scale—and then act on it—will require a reevaluation and likely an upgrade to legacy IT systems. The goal is to utilize IT to help normalize data, discover trends, stratify patients according to their conditions, and develop risk predictions and recommended actions at the point-of-care—with the EHR acting as a central point of integration.
Health care providers will need a range of IT capabilities and specialized skills to successfully launch a precision medicine program, including data repositories, laboratory information management systems, clinical decision support (CDS) technology, and clinical trial research management platforms (the figure below was adapted from a 2017 survey conducted by HIMSS Analytics, on behalf of Intel).
With various tools and emerging technologies at their disposal, CIOs and other IT leaders are likely to focus on a few major hurdles to start: the high volume of data coming in, the need for efficient workflow, scalability requirements, and secure data sharing. This is particularly difficult when ordering genomic tests that can return hundreds of variants of questionable utility that may get lost in the EHR across multiple lab reports in different formats (e.g., PDFs, Word docs). Luckily, the industry has seen recent progress for promoting interoperability and extending clinical systems (particularly around the FHIR data exchange standard), along with the growing use of predictive analytics and AI—although there is still plenty of room for advancement in these areas.
3 key challenges facing precision medicine
Providers and patients have plenty of reasons to be optimistic about the potential for precision medicine. Advancements in IT are bound to create new avenues for precise treatment and improved data analysis, but the field also has a variety of strong barriers to overcome, including:
- Knowledge gaps: Precision medicine research advances at a rapid pace, meaning both patients and their providers are struggling to learn about new treatment options and interpret the results.
- Patient privacy and ethics concerns: There may be instances in which third parties (e.g., insurance companies) can legally gain access to genomic data, negatively influencing a patient's access to care; informed consent will be critical for patients sharing their data for precision medicine initiatives or treatment.
- A lack of standardization: Many hospitals and health systems do not have common or automated processes for ordering genetic or other similar diagnostic tests, working with insurers, accessing test results, comparing results from different labs, and communicating any findings to patients. However, FDA is taking some steps to address these issues, as it recently formally recognized its first public database containing genomic information.
Learn 8 clinical technologies with the potential to transform care delivery
Putting precision medicine into practice will be challenging, but providers can take concrete steps today to prepare for this new era of health care delivery. Health care providers looking to implement their own precision medicine programs will likely need to leverage an ecosystem of vendors and partners to address their full end-to-end needs—heavily leveraging analytics and artificial intelligence for data analysis, cloud for data storage and exchange, and CDS platforms that make unstructured and structured data actionable at the point of care for clinicians. To help harness the power of data, we have outlined five steps to leverage IT in precision medicine initiatives.
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