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Showing posts with the label Ankura

The Document Few May Read and Why It Is the Most Important Thing in Your SNP

I was sitting with a model of care (MOC) recently — not skimming it, actually reading it — and it brought back a conversation I had with a colleague that I have not been able to shake. We were talking about our years leading case management teams inside Special Needs Plans (SNPs). Both of us held senior roles. Both of us supervised the people responsible for coordinating some of the most medically complex, socially fragile members in managed care. When we asked each other the same question, we got the same answer: We did not remember ever having the MOC in our line of sight. Not pinned to the wall. Not referenced in team huddles. Not pulled out when a new case manager was onboarded or when a difficult member situation required a reset. The policies and procedures? Yes. The workflows? Certainly. But the MOC — the document that defines why all those policies exist, whom they were designed to serve, and how the health plan envisioned care being coordinated for its most vulnerable enrolle...

Demystifying Statistical Sampling: What Litigators Should Know About Statistical Sampling in Labor and Employment Disputes

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With statistical sampling, counsel can simplify damage analyses, avoid potential issues with incomplete or missing data, and minimize the risk of error. Questions Counsel Should Ask After the Decision to Sample is Made: What is statistical inference? What does the margin of error (MoE) and confidence level mean? What types of sampling methods are there? How can I be confident in the results of the sample? Recap From Last Time In  our prior article , we discussed how and why you should sample because statistical sampling is a generally accepted methodology used to make inferences about populations. When done correctly, statistical samples can produce valid and reliable results. Courts and regulatory agencies alike have acknowledged and allowed for the use of statistical sampling in situations where data may be incomplete, too unorganized, or costly to analyze in its entirety. Additionally, sampling can provide distinct benefits over evaluating all available data. Those benefits in...