The future of healthcare administration lies in the insights that experts find through careful health data analytics.
To fully understand what health data analytics is, Phillip James McGuckin explains its three related components:
· Electronic Health Records (EHRs)
· Population Health Management (PHM)
· Analytics adoption model
The world of health data analytics owes its existence to electronic health records. Moved from paper to digital file, words within these files are searchable and assimilated for the sake of research.
According to Phillip James McGuckin, health data analytics is further necessary for advancements in population health management. Putting health data analytics to practice for the sake of population health management requires healthcare experts to adopt an analytics adoption model.
Electronic Health Records (EHRs)
EHRs are just as they sound — handwritten medical records transferred to searchable digital files that are easily shared between doctors and other healthcare workers. Healthcare professionals with authorized access can retrieve these records as necessary to treat their patients and/or to perform clinical research.
The advent of EHRs has lowered healthcare costs across the board. More importantly, EHRs make it possible for patients to receive faster and more accurate medical care.
Population Health Management (PHM)
With more data available on a macro level, healthcare experts are better able to dictate and manage social initiatives that increase the quality of healthcare and healthcare delivery for an entire group of people.
PHM in communities, regions, and entire countries focus on disease prevention and treatment. From a process and affordability standpoint, PHM continually looks for ways to streamline the proliferation of healthcare services for all groups of people.
Healthcare Analytics Adoption Model
Phillip James McGuckin explains that with more healthcare data available than ever before, it can be difficult to know where to begin and make sense of it all. Thus, healthcare experts must adopt a sustainable model of organizing all the data.
In order to espouse the right analytics adoption model, new data must include some of the old data, as well. This old — or “traditional” — data analytics is more often known as claims-based analytics.
Claims Based Analytics
When administering medical care to patients, the most widely accepted data contribution pertained to insurance claims processing. Medical coders and billers (still) decipher treatment notes and attribute a particular treatment to a cost category to facilitate medical claims processing.
By and large, claims based analytics is an outmoded predecessor to clinical, or health data analytics. Still, using the best analytics adoption model should still factor in claims-based analytics, particularly where more recent health data analytics lacks relevant information.
Clinical/Health Data Analytics
The mountain of health and clinical data continues to grow due to the use of EHRs. These analytics can inform decision-makers toward more affordable and effective healthcare. They help to provide a basic structure from which to analyze a hospital and create a plan that has measurable results.
Phillip James McGuckin concludes that the future of health data analytics is bright. With the industry expected to grow to over $34 billion by the end of 2022, there is a lot of anticipated growth that the medical industry awaits.