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4 Ways Healthcare Data Analytics Tools Prepare You for the Future


doctor-with-stehiscopeAs healthcare consumerism gains momentum, value-based care is shifting from the exception to the rule. As hospitals and other healthcare providers adjust to new pay-for-performance models, they must develop strategies to manage the increased financial risk these models engender. Most industry experts agree that effective use of data will be critical to succeeding in a value-based world. What are the most critical healthcare data analytics tools hospitals will need?

1. Using Predictive Analytics to Manage Financial Risk

Thanks to the adoption of healthcare data solutions in recent years, hospitals — like The Cleveland Clinic, a recognized innovator in the healthcare industry — are taking advantage of administrative and clinical data to better manage costs while delivering value.

Dr. Joseph Cacchione, the chair of operations and strategy at Cleveland Clinic’s Heart and Vascular Institute, told Health Analytics “We’re looking at using predictive analytics to help move us more into the realm of bundled payments or payment for episodes of care, and using administrative and clinical data analytics to create models for clinical outcomes and predicting costs.”

He explained that visibility into claims data, for example, helps them anticipate resource use and identify predictors of positive clinical outcomes.

2. Reducing Unnecessary Variations in Care

Healthcare data analytics also offer insights into where variations in care occur and which variations could be eliminated to increase cost effectiveness without negatively impacting quality.

The Cleveland Clinic’s Dr. Cacchione offered an example in which they might look at the why more echocardiograms are ordered following a particular procedure. By evaluating the clinical and non-clinical drivers for echocardiograms, the Clinic can better determine best practices and share those findings with clinicians. Dr. Cacchione said, “We’re not trying to impugn anybody, but we’re trying to find out why that variability occurs and how we can use clinical data, which is available at the time that a physician sees a patient, to add to our view of things rather than just trying to think about things from administrative data set, which is always historical.”

This enhances the Clinic’s ability to make the changes need to meet the demands of pay-for-performances reimbursement models.

3. Addressing Population Health with Healthcare Data Analytics

One of the key components of healthcare reform is the call for healthcare providers to positively impact population health.

Health Analytics also spoke with Dr. Anil Jain, Chief Medical Officer at Explorys and Consulting Staff at Cleveland Clinic who said, “If you don’t start with the right data, and you don’t start with the right analytics, it’s really difficult to know which segment of the population you need to manage. Population health management absolutely requires a platform that can use predictive models to help health systems address some of the changes that they’re undergoing as they try to cope with the needs of value-based care or accountable care.”

4. Developing More Effective Engagement Strategies with Psychographic Segmentation

Despite the volumes of data that hospitals and health systems are collecting, high levels of true patient engagement psyremain elusive, when one considers patient engagement a two-way interaction fulfilling the needs of both parties. The reason? Often, the strategies and tactics used, such as tools for education and engagement for a particular segment of the population — like patients with diabetes — are treated like one cohesive group.

Healthcare consumers are more than a diagnosis, and the data typically found in EHRs and other clinical systems often fail to recognize important differences. While meaningful use is driving the inclusion of data related to typical consumer segmentation like demographic and socio-economic markers, clinical data does not capture variations within similar populations that can make — or break — activation among individuals. This includes insights and understanding about healthcare consumer motivations and behavioral triggers.

The proprietary psychographic segmentation model developed by PatientBond empowers healthcare organizations to take a more strategic approach by creating communications that are customized to meet the various needs of different psychographic patient segments to improve outcomes. This is because psychographics focus on similar or shared beliefs, attitudes and motivations among groups of patients.

A “one-size-fits-all” approach to a patient population will result in a small Return on Engagement.  Respecting the diverse needs and motivations of a patient population will yield a large Return on Engagement.

If you’d like to see how healthcare data in your clinical and administrative systems can be enriched with deeper consumer insights, read our whitepaper on patient activation or contact PatientBond today.

Psychographic Segmentation and its Practical Application in Patient Engagement and Behavior Change


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