The IntegrityM Blog


Five Best Practices for Repairing Your Customer Relationship (Pt 2)

There are times, despite your best efforts, things may go wrong, impacting customer satisfaction. When conflict or uncomfortable situations arise, act swiftly to reduce tension and begin repairing your customer relationship using these best practices: Apologize. Regardless of who caused the issue step up and offer an apology. Acknowledge any oversights or mistakes, and focus […]

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  • Author: IntegrityM
  • Date: November 26, 2018
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Five Customer Service Best Practices to Maximize Retention (Pt 1)

Most businesses have two basic goals: to maintain profitability and to grow revenue. As simple as these goals are, some companies are so focused on growing new business pursuits, they tend to overlook the importance of keeping their current customers satisfied. Generating and retaining loyal, longtime customers should be a priority for every business. While some […]

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  • Author: IntegrityM
  • Date: November 14, 2018
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Network Adequacy: Meeting Requirements and the Impact of the ACA

The Affordable Care Act directed the Secretary of Health and Human Services to establish criteria for certification of qualified health plans, to include (1) ensuring a sufficient choice of providers and (2) providing information to enrollees and prospective enrollees on the availability of in-network and out-of-network providers.  45 CFR 156.230, entitled network adequacy standards, requires qualified […]

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How Effective are Criminal Background Checks Really?

A U.S. Department of Health and Human Services Office of Inspector General (OIG) report from October, 2012 offers some interesting insights into the usefulness of criminal background checks for nurse aides. To get to these insights, some setup is needed. Nurse aides work in long-term-care facilities. To work as a nurse aide, individuals must complete […]

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  • Author: IntegrityM
  • Date: October 14, 2015
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Healthcare Data Mining for Fraud Detection: Identify and Reduce False Positives to Build Stronger Models

One of the key goals of healthcare data mining is to reduce false positives.  In health care fraud data mining, a false positive occurs when your model identifies a provider that is not engaging in fraudulent activity and that has legitimate reasons for having seemingly aberrant billing. Pursuing false positives can be expensive and time […]

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  • Author: IntegrityM
  • Date: May 7, 2015
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