Evolution of Healthcare Is Increasingly Data-Driven and Patient-Focused
Creating a truly accessible and complete patient health record that “connects” with the actual healthcare delivery system is far from an easy proposition, as healthcare data exists in disparate forms – disconnected from itself from the get-go. While providers, payers and other stakeholders continue to develop technologies to bridge these data silos, the patient, who should be at the center of this initiative is the final beneficiary of this alignment.
Giving patients more direct access to their healthcare data will be fueled and shaped through the recent actions of the U.S. Department of Health and Human Services through the Trusted Exchange Framework, which hopes to create national interoperability as required by the 21st Century Cures Act of 2016. The goal is for patients to have access to their health information electronically. This sets the stage for the ongoing evolution of the PHR.
According to a survey from Deloitte on behalf of NEJM Catalyst, many healthcare providers are committed to using data to promote patient-centered care and dedicating resources to enable insights for clinical care. Such dedication serves to drive greater delivery of patient-centric healthcare, increasing transparency and improving understanding of quality and cost information to empower the patient to take an active role in the decision-making process for his or her care. For healthcare organizations, this patient-centric focus will ideally improve personal health responsibility, promote disease prevention, drive better health outcomes and result in greater patient satisfaction rates. At this time, it is unknown the level at which patients will engage.
A Patient-Centric Convergence of the Payer and Provider
As patients take on more healthcare costs, payers and providers can expect continued demand for greater access to data on both quality and cost. Payers and providers understand they need to provide both outcomes and pricing information so that patients can make informed decisions about their health. Providing patients with greater transparency into estimated economics associated with their care protocols supports greater engagement in their care, which can improve outcomes and potentially help providers mitigate bad debt associated with patients unprepared to cover healthcare costs. This is part of a broader industry drive toward value-based healthcare that nearly every sector is experiencing including payers, health systems and pharmaceutical and medical device companies.
The goal: find data-driven approaches that demonstrate value in healthcare and do so with the engagement and support of both the provider and the patient.
For the past decade, the financial and regulatory pressures on payers, hospitals and providers to demonstrate a particular level of healthcare quality outcomes has driven a significant effort to combine provider clinical data with non-clinical or administrative payer data, as well as patient data from PBMs, labs and other disparate sources. Organizations are making significant investments in underlying “big data” technologies and partnerships with companies that can acquire and cleanly integrate that clinical data to meet this need and drive greater transparency, care continuity and clinical quality outcomes for their members. This effort requires investments and expertise in EHR HL7 clinical data messages, typically Continuity of Care Documents (CCDs) and increasingly now FHIR resources.
The combination of this disparate data, including data originating from the patient (albeit that’s a fractional component at this point), requires organizations to employ “smart” data management practices, particularly in the areas of patient and provider matching and linking, anonymization where needed and the use of more fluid data models as found in a Hadoop data lake. The desire to extract important clinical insights that could be highly valuable for calculating quality and attaining desired outcomes is driving investments in Natural Language Processing (NLP) and Machine Learning (ML).
Building the Ideal Patient Profile
Patients, as well as payers and providers, understand that an incomplete or untimely “data picture,” especially as it relates to monitoring one’s own health, is far from ideal and can often result in ill-informed healthcare decision-making. The focus on consumer-centric healthcare, deploying the latest data-driven analytics coupled with sophisticated technologies such as NLP and ML to drive greater accuracy and insight, will shift the role of the patient from one of “a patient” to that of “decision-maker.”