Improving population health management with payer data analytics

May 25, 2016
Saeed Aminzadeh, Chief Executive Officer, Decision Point Healthcare Solutions

As the healthcare industry continues to move into the realm of value-based models, both healthcare providers and payers are adopting consumer marketing tactics to improve prevention, health, and clinical outcomes. Just as a retail company might focus on how to motivate its targeted customer base to acquire more of its products and services, healthcare organizations must figure out the best approaches for engaging patients and motivating them to become more involved in their own care.

More healthcare organizations are augmenting their traditional case management programs with focused population health management programs designed to proactively engage patients to improve quality and loyalty – in essence, to treat patients more like customers, with a focus on keeping them healthy. For example, these programs are asking things like, “How can I motivate a diabetic patient to take control of their blood sugar?” and “How can I convince a healthy patient to get their preventive screenings?”

Retail companies have all sorts of indirect methods to gather customer data: loyalty cards, surveys with rewards. It’s time for healthcare organizations to look beyond traditional claims and EMR data, and leverage non-traditional data sources to better understand how and when to engage their patients.

There are a number of excellent ways to get more information about patients. The easiest is to leverage data that is already available or can be acquired from a third party. For example, data on patients’ consumer buying patterns can be acquired at a relatively low cost from consumer data aggregators. This type of data can provide information on a patient’s interests, household, and other consumer behaviors, which in turn can be used to better understand, predict, and motivate a patient. Other data sources can also provide interesting insights:

  • Call center data provides information on how often a patient calls into the call center and for what purpose, which in turn provides insights into a patient’s overall engagement.
  • Data from patient surveys (such as satisfaction surveys, health risk assessments, etc.) can be aggregated and analyzed to better understand certain drivers of satisfaction and risk.
  • Data from website usage provides information on whether the member is signed up for the patient portal and whether they actually log onto the patient portal, providing additional clues as to that patient’s engagement.

To truly use the outside data, it is important to have existing staff or a vendor analyze the data and determine actionable findings – something that quickly becomes cost prohibitive for some organizations.

Analyzing patient data for better outcomes

From a health plan perspective, once a member joins the plan, it is imperative to know what will keep the member loyal, how to control avoidable costs, and how to engage the member so they are confident, healthy consumers. From a healthcare organization perspective, it is also important to know that most health plans’ networks are vast, and patients always have a choice. Analyzing and acting on patient data leads to smarter decisions, and more loyal and engaged patients.

Healthcare marketers segment patient data in order to identify trends in populations; determining how to segment populations is part science and part art. The science part has to do with ensuring that there are enough patients in the segment for the segment to be meaningful, and to segment patients based on logical categories, such as age or type of insurance. The more nuanced segmentation is in trying to group members based on their “barriers to engagement.” For example, segmenting by health literacy enables the healthcare organization to divert education resources to the patients who need them most.

Effective data analytics includes identification and understanding of group trends, understanding performance across key segments, and identifying individuals or groups of individuals with special circumstances. The question then is, “What can the healthcare organization do about it?”

Making the data actionable

Effective data analytics is a continuous learning experience, enabling healthcare organizations to get smarter and more targeted over time. At a micro level, it is important to understand whether the organization was effective in achieving its clinical and business goals, and in what areas and across which populations it was most effective. This type of understanding is a key part of any analytics platform. This way the organization can continue to deploy the most effective strategies, while modifying the less effective strategies in order to promote continuous improvement. At a macro level, it is also important to understand the relative changes in the population in order to ensure that the services offered are in line with the population’s needs.

Doing the analytics in-house or outsourcing is both a cost/benefit and core competency analysis. The question is, “What are the types of expertise, solutions, and costs associated with bringing the analytics in-house versus acquiring the solution from a vendor?” Also, “Is this the type of core competency that the organization would like to acquire?”

Often, deploying these capabilities successfully hinges not on conducting the analytics, but rather the organization’s ability to act upon the analytics. Analytics can uncover a multitude of actionable issues, but does the organization have the staff and the resources to act upon these issues? Prior to embarking on any analytic project, it is important to understand the budget and resources available, and which issues will potentially yield the highest returns.