Four ways demand for healthcare data will grow in 2017

http://managedhealthcareexecutive.modernmedicine.com/managed-healthcare-executive/news/four-ways-demand-healthcare-data-will-grow-2017

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The New Year presents challenges on many fronts, including questions surrounding how President-elect Donald Trump will change healthcare policy. Yet, “repeal and replace” or “replace and improve” activities on The Hill, though not “business as usual,” won’t necessarily slow down data-driven focus areas in healthcare that will continue in 2017.

Here are four key ways demand for data will grow in the year ahead:

1. Increased demand for insight into discharge gaps, risks and exposures.

As delivery and payment models continue placing risk within the care setting, increased insight into the member’s (the patient’s) likelihood of adherence or compliance is critical in evaluating expected outcomes and coordination of care post-discharge. Socioeconomic data surrounding the patient and their caregiver can complete the picture of their expected behavior.

2. Maximizing identity management capabilities.

Identity insight and management solutions will be critical to ensure the right approach for the right member but, more importantly, to securely house and validate identity data. While a national patient identifier may become closer to reality at some point, for now, identity management techniques can be critical to ensuring all operational processes and players within the care payment and delivery setting can link the right information for each individual.

3. Integration of health-tracking wearables into care analytics.

The market for wearable fitness and health devices has grown exponentially. Integration of health tracking wearables into the care analytic systems creates opportunities for using wearable metrics as a basis for member rewards but also in risk scoring for compliance augmentation for new targets, for member engagement, and for prediction of medical complications or improvement.

4. Evaluation of provider performance.

While the release of MACRA benchmarks has gotten considerable attention the past month, it is really only a beginning. Commercial plans have attempted various P4P approaches over the years with one missing ingredient, now shared with MACRA: Insight into patient profiles and behaviors and their influence and impact on ultimate outcomes. Socioeconomic data augmenting existing measurement sources can serve a critical role in tiering performance measures with patient make-up to arrive at a more mutually accepted performance structure.

Healthcare organizations and payers should reach out to new data sources, augment their thinking with them, and redefine how their day is focused on insights into their most valuable player: the customer, the member and the patient.

Asking the Right Questions: Why Healthcare Predictive Analytics Often Don’t Predict Anything Meaningful at All.

https://www.linkedin.com/pulse/asking-right-questions-why-healthcare-predictive-dont-cousins-phd?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BgC0bModjFFl7I4auAFAWBQ%3D%3D

Healthcare organizations collectively waste billions of dollars every year by focusing on the wrong problem to solve. This isn’t unique to healthcare, of course. A recent, must-read article in Harvard Business Review, “Are You Solving the Right Problems” by Thomas Wedell explains how organizations that are good at problem solving often focus on the wrong ones to solve. It’s often human nature.

Healthcare analytics offers a particularly good example. Today, health plans, hospital systems, post-acute care companies and other provider organizations are keenly focused on identifying those patients who are at highest risk of adverse events, such as readmissions and post op complications. They then invest precious resources trying to reduce that risk through home visits, additional pre-op or post-operative care, and so on.

Yet, despite the apparent logic, according to our research, 50% or more of patients identified as high risk cannot be impacted by the interventions provided to reduce that risk.

Yet, despite the apparent logic, according to our research, 50% or more of patients identified as high risk cannot be impacted by the interventions provided to reduce that risk.

On the face of it, this seems like a logical approach to improving care and reducing costs. Yet, despite the apparent logic, according to our research, 50% or more of patients identified as high risk cannot be impacted by the interventions provided to reduce that risk. In some cases, they can’t be impacted at all. Our research is consistent with that conducted by others, including a randomized controlled study of telephone care management and study on nurse-led home-based intervention. In short, a lot of the money spent trying to avoid adverse events is wasted.

https://hbr.org/2017/01/are-you-solving-the-right-problems

 

Will data analytics in healthcare take until 2040 to be fully realized? And other highlights from Health IT Summit

Will data analytics in healthcare take until 2040 to be fully realized? And other highlights from Health IT Summit

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