Patient engagement remains an elusive goal in the healthcare industry. Though providers understand the importance of the patient experience in a consumer-based healthcare landscape, the question remains: How can they effectively drive – and promote – patient engagement?
To engage patients who are searching for personalized experiences from their providers, health systems are looking to data analytics. In the 2016 Health IT Industry Outlook Survey, one-third of respondents ranked data analytics as the biggest trend in the industry.
Enterprises that are able to collect and analyze data, and effectively implement insights, can create the types of experiences that improve patient engagement, lead to patient retention and proactive care, and lower providers’ overhead costs.
One example of the use of big data analytics in healthcare is with chronically ill patients. This patient segment accounts for 78 percent of all healthcare spending, 81 percent of in-patient stays, 91 percent of prescriptions, and 76 percent of physical visits.
That’s a significant amount of resources dedicated to a population that also has a problem with compliance: 24 percent of all prescriptions for chronic conditions are never filled, which can lead to larger health problems and even death.
This is where technology comes into play.
Providers can use big data insights to identify individuals who have chronic illnesses and ensure that they are receiving the information they need to stay healthy. Health systems can use health records data, coupled with patient data from a variety of other sources (such as wearables), to predict future health problems. If these problems are identified early enough, providers can offer high-risk patients the right tools to reduce their risk before complications occur.
Once critical patients have been identified, health systems can use analytics to deliver personalized messaging to patients. Personal patient information used in conjunction with health-record insights helps providers target individuals with messages that are highly relevant to their situation.
The idea is to have this message reach patients on their preferred communication channels. Some patients prefer healthcare communication to come through a patient portal, for example, where others prefer phone calls or even text messages about appointments and follow-up information.
To craft relevant messaging, providers need to be aware of each individual patient’s journey. This is another example of where they can – and should – leverage data analytics. If, when a patient calls a healthcare organization, the call-center representative is already aware of the patient’s health history, communication preferences, and where the patient is on his healthcare journey, their conversation will be more informed and productive.
Especially in the case of chronic patients, targeted and relevant communication on the right channels can improve engagement and reduce the health risks associated with non-compliance with treatment plans, non-adherence to a medication regimen, and missed follow-up appointments. Providers can deepen the relationship between any patient and the health system by improving the quality of communication.
How to Get Started
Unfortunately, implementing big data analytics for healthcare has its challenges. According to the Health IT Industry Outlook Survey, one-third of respondents reported that they struggle with correlating data from diverse and dissimilar sources.
The first step in getting started with data analytics is finding the right technology to aggregate and store data from these disparate sources. A CRM system, for example, collects and segments out patient information so healthcare organizations can understand more about patient behavior and target ongoing messaging accordingly.
Data lakes are an alternate type of data hub that permits the same data to be used in different scenarios. A horizontally scalable platform allows data scientists to store and access data at any time, for any circumstance. This flexibility means data scientists are able to configure and reconfigure models and queries during the data-analysis process in near real time.
Once the technology has been implemented, providers should aim to collect patient data from a multitude of sources, including health records, demographics, consumer behavior, and even social media. Other information that may help to inform patient-engagement strategies includes financial data, health surveys, buying habits, administrative data, and physician notes.
The end goal of collecting and analyzing data is to create a 360-degree view of the patient that results in a personalized engagement plan – not just during a single patient encounter, but throughout the entire patient lifecycle.
Big data analytics in healthcare can help limit patient non-adherence, build trust, and improve engagement. Ultimately, this will help to improve a patient’s health and reduce the risk of future complications. These tactics not only create better experiences for patients, but also reduce costs associated with healthcare operations and improve ROI.
As Director of Analytics at Evariant, Bill’s focus is on design, execution, and implementation of optimal analytics. Maximizing ROI as a result of multivariate predictive modeling is a primary goal. Bill has been a presenter at major marketing, analytics, and academic conferences, including the DMA, AMA, APA, APHA, and GSA. His background as an experimental and health psychologist has included teaching as well as several years of clinical work. His specialty areas include older adults, community-based research, HIV/AIDS, sexual behavior and risk, environmental stress, influenza and pneumonia vaccination, depression and mental health, medication and health literacy. Dr. Disch earned his Ph.D. in experimental psychology from the University of Rhode Island, with a specialty in quality of life and well-being, and mixed-methods (quant, qual, mixed).
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