Medicare and the New Guidelines– Where do you fit in?

Medicare Guidelines…

It’s a pain.  A royal pain.

And, now there are the new guidelines!

First, they are confusing.  Second, it is difficult to find where there is an improvement.  But, something positive has to be found in all of the mess so you can reap a benefit from the changes.

You can find benefit when you take a moment and review the effects of Medicare, and the new Medicare guidelines.  Let’s take a moment to see where you fit in.

August 2013 marked the date of the most recent change in Medicare guidelines beginning with the “two-midnight rule” regarding hospitalization or Medicare Part A (78 Fed. Reg. 50495, 50906-954, 2013).  The Centers for Medicare & Medicaid Services (CMS), Federal regulators who create Medicare rules, decided that as of October of that year the definition of Observation Status for Medicare recipients was any hospitalization that lasted less than two midnights.  There was a revision in July 2015 permitting a physician to order inpatient status, but Medicare granted this only on a “case by case basis” (Revisions to “Two-Midnight Rule”…, 2015).   Thus, until a person exceeds a two night stay of hospitalization Observation Status is still considered outpatient (Medicare Part B) and only qualifies for outpatient benefits (Observation Status, 2013).

What is the effect of remaining under Observation Status and receiving only outpatient benefits?  Medicare recipients with outpatient status have greatly reduced benefits.  medicare-2The limited benefits include access prevention to many of the post-hospital nursing home care resources.  The limitations also change the way public health information is collected and reported about inpatient hospitalizations or readmissions, put a higher burden on all Part B (outpatient status) expenses, and create excessive delays on appeals processed against Medicare claims (Weaver, Mathews & McGinty, 2015).

The next major Medicare guideline change

happened when the Consolidated Omnibus Budget Reconciliation Act of 1985 (COBRA) was adjusted, which created gaps in coverage when switching to Medicare.  Individuals who pay COBRA must still enroll in Medicare Part B or risk a gap in coverage if the COBRA coverage expires before arrival of the next window of opportunity (Medicare & You! 2015).  A window of opportunity is referred to as a special enrollment period (SEP).

Medicare Part D (prescription coverage) considers COBRA as qualified coverage, however (The Medicare Modernization Act, 2003).    As far as Part D is concerned, Medicare grants “creditable coverage” for prescription drugs to individuals with a COBRA plan; the SEP extends to the end of COBRA for registration of Medicare’s Part D (COBRA and Medicare, 2015).

Access to medications remains a problem with the new guidelines as ethnicity and race, combined with lower socio-economic status create an unresolved barrier (McGarry, Strawderman & Li, 2014).  Similarly, Registered Nurses (RN) and Certified Nurse Assistants (CAN) were found to receive dramatically fewer work hour assignments in nursing facilities that were dedicated to serving the predominantly ethnic and racial minority populations (Li, et al., 2015).

February 2016 marked another development when the Centers for Medicare & Medicaid Services (CMS) began a prior authorization system for limiting funds to several benefits citing paperwork reduction efforts (81 Fed Reg 6275, 2016).  The CMS mentioned it was trying to mirror the practices of the military’s Tricare insurance fund, Medicaid programs of specific states, and private insurance carriers (Medicare’s Home Health Benefit Under Threat, 2016).

The impetus behind Medicare’s new guidelines is computer advances in the field of machine learning.  Machine learning put simply is using computers to perform tasks that are not originally part of their programming, a practice similar to what most employers do once they hire a new staff member (Machine Learning: Stanford University, 2016).  Machine learning includes data mining, and statistical pattern recognition, all components of census work used by Medicare for tallying trends and patterns to cut costs.  Unfortunately, where machine sees a cost savings man often pays the price.

Now, publications describe how machine learning, data mining, and statistical pattern recognition solve “prediction policy problems.” Health policy is becoming defined by independent machine thinking (Kleinberg, Ludwig, Mullainathan & Obermeyer, 2015).

Diabetes, the second most common chronic disease behind high blood pressure, has benefited from some elements of Medicare’s machine learning paradigm.  diabetes-medicareResearchers compared the clinical and economic outcomes of early treatment with insulin versus delayed initiation of the diabetes type 2 treatment.  Prescription data (Medicare Part D) was used to identify patterns of oral antidiabetic use in almost 15,000 elderly Medicare recipients (Bhattacharya, Zhou, Wei, Ajmera & Sambamoorthi, 2015).

Machine learning calculated that an early start in insulin use by older adults improves type 2 diabetes blood sugar control without increasing overall direct costs to health care or adding to a hypoglycemia risk (Bhattacharya, Zhou, Wei, Ajmera & Sambamoorthi, 2015).  Medicare’s machine learning function automatically modified its policy to authorize higher and early expenditure for insulin and related type 2 diabetes therapies.

Another study is evaluating mail order prescription practices and its effect on diabetes medication adherence of Medicare plan members.  The analysis of 6 years of data (to be completed in 2020) seeks to determine the effects of diabetes medication adherence in those with Medicare coverage versus those who experience a gap in coverage (Forristal, 2015).  Once the findings are completed Medicare’s machine learning function will automatically modify its policy to authorize proportionate funding for mail order services.

Not all data mining adventures created by Medicare’s new guidelines and background machine learning system have had stable results.  Pain was added as the “fifth” mandatory vital sign for physician reimbursement behind temperature, pulse (heart beat), respiration (breaths per minute), and blood pressure as part of the new Medicare machine learning oversight (Quality Measures, 2013-2015; Measure #131…, 2015).

Immediately, physicians and health care practitioners were monitored for satisfying pain thresholds.  Medicare policy continues to underfund effective pain therapies such as physical therapy, osteopathic manipulation, chiropractic therapy, massage, and pool therapy leaving inexpensive but effective opioid medications (morphine, OxyContin, Dilaudid) as the only funded Medicare tool (McCarberg, 2015).  Currently, the number of Medicare elderly who misuse pain relievers is almost three times what it was ten years ago (Pearson, Burton, Harhaj & Graziano, 2015).

Physicians have been placed in the middle between policymaker and patient as the designated Medicare cost-saving officer (other health care professionals are not able to prescribe pain medications).  Physicians were first cast in the role of using inexpensive opioid tools to solve the federal pain measurement mandate (Pacula, Powell & Taylor, 2015).  Then, their unwanted, second role (as the result of machine learning oversight and automatic Medicare policy) was being labeled as the source of supply for illegal, prescription drugs (Yarbrough, 2015).

The U.S. Senate also has approved the Comprehensive Addiction and Recovery Act (CARA), which curbs access to pain medication for surgically related conditions and authorizes $600 million for grants to control and monitor the nation’s prescribing practices of opioid medication (Senate approves CARA…, 2016).  In the end, policymakers achieved personal, political gain both from the new pain measurement mandates and the highly public regulation of pain treatment; it was something that used to be managed between the physician and the patient (CDC Guideline for Prescribing Opioids for Chronic Pain, 2016).

What are your thoughts? Leave me a comment and share your opinion or experience. I hope you found this helpful and informative. Be sure to scroll down below the references for even more on the Impacts of the new Medicare Guidelines. 

 

References:

78 Fed. Reg. 50495, 50906-954 (Aug. 19, 2013).

81 Fed Reg 6275 (February 5, 2016).

Bhattacharya, R., Zhou, S., Wei, W., Ajmera, M., & Sambamoorthi, U. (2015). A Real‐World Study of the Effect of Timing of Insulin Initiation on Outcomes in Older Medicare Beneficiaries with Type 2 Diabetes Mellitus. Journal of the American Geriatrics Society, 63(5), 893-901. DOI: 10.1111/jgs.13388

CDC Guideline for Prescribing Opioids for Chronic Pain. (2016). Early Release, Volume 65, March 15, 2016. Centers for Disease Control and Prevention.  Retrieved March 15, 2016 from http://freepdfhosting.com/c6749f69de.pdf  Retrieved March 14, 2016 from http://www.cdc.gov/drugoverdose/prescribing/guideline.html

COBRA and Medicare, Part II. (2015). Center for Medicare Advocacy.  Retrieved March 13, 2016 from http://www.medicareadvocacy.org/cobra-and-medicare-part-ii/

Forristal, J. A. (2015).  The effect of the patient protection and Affordable Care Act on the Medicare Part D coverage gap as reflected in diabetes medication adherence. Electronic Theses and Dissertations. Paper 2315.  DOI:10.18297/etd/2315

Kleinberg, J., Ludwig, J., Mullainathan, S., & Obermeyer, Z. (2015). Prediction policy problems. The American Economic Review, 105(5), 491-495. DOI: http://dx.doi.org/10.1257/aer.p20151023

Li, Y., Harrington, C., Mukamel, D.B., Cen, X., Cai, X. and Temkin-Greener, H. (2015). Nurse Staffing Hours At Nursing Homes With High Concentrations Of Minority Residents, 2001–11. Health Affairs. 34(12), 2129-2137. DOI: 10.1377/hlthaff.2015.0422

Machine Learning: Stanford University. (2016). Coursera. Retrieved March 13, 2016 from https://www.coursera.org/learn/machine-learning

McCarberg, B. (2015). Washington State Opioid Prescribing Guidelines. Pain Medicine, 16(8), 1455-1456. DOI: 10.1111/pme.12851

McGarry, B.E., Strawderman, R.L. & Li, Y. (2014).  Lower Hispanic Participation In Medicare Part D May Reflect Program Barriers. Health Aff (Millwood). 33(5), 856–862. doi:  10.1377/hlthaff.2013.0671

Measure #131 (NQF 0420): Pain Assessment and Follow-Up – National Quality Strategy Domain: Community/Population Health. (2015).   Retrieved March 13, 2016 from https://www.apta.org/uploadedFiles/APTAorg/Payment/Medicare/Pay_for_Performance/PQRS/2015/PQRS2015Measure131PainAssessmentandFollowUp.pdf

Medicare & You! (2015). Centers for Medicare & Medicaid Services.  Retrieved March 13, 2016 from https://www.medicare.gov/Pubs/pdf/10050.pdf

Medicare’s Home Health Benefit Under Threat. (2016).  Center for Medicare Advocacy.  Retrieved March 13, 2016 from http://www.medicareadvocacy.org/medicares-home-health-benefit-under-threat/#_edn1

Observation Status: New Final Rules from CMS Do Not Help Medicare Beneficiaries. (2013). Center for Medicare Advocacy.  Retrieved March 13, 2016 from http://www.medicareadvocacy.org/observation-status-new-final-rules-from-cms-do-not-help-medicare-beneficiaries/#_edn1

Pacula, R. L., Powell, D., & Taylor, E. (2015). Does Prescription Drug Coverage Increase Opioid Abuse? Evidence from Medicare Part D (No. w21072). National Bureau of Economic Research.  Retrieved March 13, 2016 from http://www.nber.org/papers/w21072.pdf

Pearson, C. F., Burton, C., Harhaj, C., & Graziano, J. A. (2015). Opioid Abuse and the Development of Abuse-Deterrent Drugs: Trends and Coverage in the Medicare Part D Program.  Retrieved March 13, 2016 from http://avalere-health-production.s3.amazonaws.com/uploads/pdfs/1433848970_Medicare_Drug_Plans_Favor_Generic_Opioids_that_Lack_Abuse_Deterrent_Properties.pdf

Quality Measures. (2013-2015). Centers for Medicare and Medicaid Services. Retrieved March 13, 2016 from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-instruments/NursingHomeQualityInits/NHQIQualityMeasures.html

Revisions to “Two-Midnight Rule” Do Not Help Hospitalized Medicare Patients in Observation Status. (2015). Center for Medicare Advocacy.  Retrieved March 13, 2016 from http://www.medicareadvocacy.org/revisions-to-two-midnight-rule-do-not-help-hospitalized-medicare-patients-in-observation-status/

Senate approves CARA to address national opioid epidemic, BCHC encourages passage of TREAT. (2016).  Healio Psychiatric Annals.  Retrieved March 13, 2016 from http://www.healio.com/psychiatry/addiction/news/online/%7B99491eee-9ef2-4ecb-9b26-d77f78f9c241%7D/senate-approves-cara-to-address-national-opioid-epidemic-bchc-encourages-passage-of-treat?utm_source=maestro&utm_medium=email&utm_campaign=psychiatry%20news

The Medicare Modernization Act (MMA). (2003). Creditable coverage, 42 CFR §423.56.  Retrieved March 13, 2016 from  http://www.cms.gov/Medicare/Prescription-Drug-Coverage/CreditableCoverage/index.html?redirect=/CreditableCoverage/

Weaver, C., Mathews, A.W. & McGinty, T. (2015). Medicare Rules Reshape Hospital Admissions: Return-visit rate drops, but change in billing tactics skews numbers.  The Wall Street Journal.  Retrieved March 13, 2016 from http://www.wsj.com/articles/medicare-rules-reshape-hospital-admissions-1449024342

Yarbrough, C. R. (2015, November). The Impact of State Prescription Drug Monitoring Programs on Opioid Prescribing Among Medicare Part D Patients. In 2015 Fall Conference: The Golden Age of Evidence-Based Policy. Appam. Retrieved March 13, 2016 from https://appam.confex.com/appam/2015/webprogram/Paper14530.html

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