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What’s next for Population Health and IT Technology in 2017?

One of the major themes I’ve noticed around healthcare IT and population health is the precedence of quality over quantity.  That is, how can healthcare providers work smarter, rather than harder, when it comes to managing and analysing population health?
Population HealthAccording to the University of Illinois at Chicago, the new focus is centered upon improving population health in order to reduce the required service levels.  In other words, preventative care is being increasingly recognized as important, both because it reduces overall healthcare costs and it catches health problems before they have a chance to get off the proverbial ground.

In order to continue providing effective treatment, all care giving organizations must participate in the information sharing networks made possible by technology and newly mandated health care reforms; however, only about 25 percent of medical institutions have implemented the technology required to process population health analytics.

Many public health technologies that are currently in use are grounded in preventative medicine.  For example, virtual check-ups ensure that patients who might not otherwise be physically able to visit a doctor are able to do so remotely, via mobile health (a.k.a. mhealth).

Another example of technology in public health monitoring is the use of Twitter to gauge the development of infectious diseases like the flu.  This ability to monitor public outbreaks allows doctors to make better treatment decisions during health care epidemics.

Lastly, geospatial technology has the ability to collect information, analyse data, and display the results via maps—offering healthcare professionals the ability to make more informed decisions about, say, the need for a public vaccine or whether to declare a state of emergency.

Other technological tools, such as electronic health records (a.k.a. EHRs), have allowed for better communication and deeper insight about patient health, as well as offering up valuable information that can potentially improve research efforts and boost overall population health.

EHRs can reveal trends, minimize the spread of disease, identify test subjects for drug trials, and determine the best treatment plans for different conditions. Online forums for medical researchers and healthcare professionals can also provide a convenient way to collaborate and share data, tools, and other resources.

Currently, a substantial gap exists between care management and population health.  However, population health strategies are being considered as ways to reduce the overall cost of care, as the U.S. healthcare system moves toward value-based care.  One way this gap can be minimized, according to MedCity News, is through optimizing care managers’ time while making remote communication with patients easier.

In this way, providers can access current data about high- and rising-risk patients and immediately enact treatment in order to prevent increased health risks among their patient populations.  One concrete way to increase remote communication is for hospitals and clinics to invest in and utilize remote patient monitoring.

Population health goals have come about, over the past decade, as a result of prominent development of what’s known as the “Triple Aim,” which advocates three linked goals: improving individual care experience; reducing the per capita cost of care; and improving the health of populations.  David Kindig and Greg Stoddard, via Health Catalyst, propose that “Population health is concerned with both the definition of measurement of health outcomes and the pattern of determinant.

Determinants include medical care, public health interventions, genetics, and individual behaviour, along with components of the social (e.g. income, education, employment, culture) and physical (e.g. urban design, clean air, water) environments.”

According to Chet Stagnaro via Healthcare Informatics, the five current best practices in technology include making a think “big picture” technology roadmap, creating a data management foundation with data governance and metadata standards, instituting sufficient data profiling, evaluating technology needs and advances, and ensuring sufficient amounts of population health data storage.

Additionally, three big ways technology can assist population health is through the use of analytics, technology that supports care paths (e.g. automated text messages with reminders to take medication) and patient engagement (e.g. telehealth).

There are a number of practical, real-world applications that are relatively easy to implement and should be considered essential to population management and digital health technology.  A patient-facing portal allows for convenient online access to medical data and other patient-related information.  A mobile platform is simply essential, in this era of smartphone-connectedness.

Care tools, furthermore, should all be connected to each other via objective and subjective data, patient-reported outcomes, and patient-derived data tools.  Moreover, all this data should be evaluated via real-time analytics.  Lastly, social interaction—via online patient communities, for example—are critical to patients’ ability to feel connected and part of a larger community.

Though many of these data luxuries may feel extravagant, being overly concerned with cost can cut into the information that might be gleaned for a better customer experience.  The other major concern on the table?  Privacy, says Sriram Vishwanath, professor of electrical and computer engineering at the University of Texas at Austin.  Beyond data, moreover, is the need to translate all that data into actionable information.  The goal is to be better able to understand characteristics of patients and populations.

According to a practicing medical doctor in the field, Dr. Michael Blackman, however, there are three data analysis challenges to keep in mind when attempting to extract all that information from patient records and statistics:

“gathering data in one place from a myriad of digital sources, sharing data and collaborating with payers, and understanding the value-based care/CMS roadmap.”  Along with those challenges, there are a few best practices to keep in mind, as well.  One guideline is to focus on data’s ease of usability.  There’s also the need to normalize data with standards, maintain focus, and remain realistic.

What’s important to your particular healthcare community, for example?  Keeping your community’s priorities in mind can help you address the right issues at the right time. One example of data analysis in action, with successful results, can be seen in the Health Catalyst data warehouse, which initially focused on improving its ability to manage and analyse individuals with heart failure.  In examining this specific patient population, clinic leaders were able to develop interventions and best practices based on evidence tied specifically to these patients.

The primary goals included reduction in the frequency of health crises, reduction in the cost per service, improvement of the overall patient experience, and enablement of patients to better manage their own health.  The accumulation of near real-time data has enabled the medical centre’s care coordinators to drive preventive care and lower health care costs.

The utilization of data must not only become more thorough but also smarter and more strategic, with an eye toward not so much quantity and quality and a certain kind of practicality of information application with very concrete end goals, for the user: not only should data be translated into useful information; it should also be accessible to a larger community of interested parties, including patients, doctors, nurses, and healthcare data analysts who will be able to analyse the data for additional uses and applications, in order to help streamline and improve future health care scenarios.

The future of healthcare data analysis is quicker, more thorough, and easier to understand than ever before.  Ideally, current progress in data collection and analysis experiments will allow for greater ease of access and interpretation, so as to allow for better data sharing and interpretation.  Data analysis need not be difficult to access or interpret.

We simply must keep in mind that people are social creatures that need their data bytes to be chewable and digestible, so as to allow for multiple readings and applications. Remember: data should translate into information that is usable by humans in the real world of 2017, rather than mere numbers for academic reports.

Author Bio:

Daphne Stanford hosts The Poetry Show! on KRBX, her local community radio station, every Sunday at 5 p.m. A writer of poetry, nonfiction, and lyric essays, her favorite pastimes include hiking, bicycling, and good conversation with friends and family.  Follow her on Twitter @TPS_on_KRBX.

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