Healthcare Town Hall

By jeremy.engdahl-johnson

Milliman today released the results of its 2013 Milliman Medical Index (MMI), which measures the healthcare costs for a typical American family of four receiving healthcare benefits through an employer-sponsored preferred provider organization (PPO) plan. The cost of care for this typical family in 2013 is $22,030.

“For the second consecutive year, the increase over the prior year on a percentage basis was the lowest in the history of the study—and yet the total-dollar increase still exceeded $1,300 for the fourth year in a row,” said Lorraine Mayne, principal and consulting actuary with the Salt Lake City office of Milliman.

“The cost for our typical family is split between employer and employee, with the employer paying about $12,886 in employer subsidy while the employee pays the remaining $9,144 in the form of payroll deductions and out-of-pocket costs,” said Scott Weltz, principal and consulting actuary with the Milwaukee office of Milliman. “While both employers and employees share the burden of financing annual cost increases, our study shows that this year employees continue to take on a growing share of the overall costs.”

In addition to highlighting which costs are borne by employers and which are borne by employees, the MMI also tracks cost increases based on different categories of care, including inpatient care, outpatient care, physician and professional services, and pharmaceuticals.

While the Patient Protection and Affordable Care Act (ACA) has dominated many discussions of healthcare costs since its passage, the law has not materially affected cost of care for families covered by large employer-sponsored plans such as that exemplified by the MMI.

“In addition to the usual discussion of healthcare cost drivers, this year’s MMI includes discussion of how health reform is—and is not—affecting families with these sorts of employer-sponsored plans and how these plans fit in the larger context of a changing healthcare landscape,” said Chris Girod, principal and consulting actuary with the San Diego office of Milliman.

To view the complete MMI, go to www.milliman.com/mmi.

Cost Chris Girod, Lorraine Mayne, Milliman Medical Index, MMI, Scott Weltz

By jeremy.engdahl-johnson

The annual Milliman Medical Index (MMI), which measures the cost of care for the typical American family of four, will be released on Wednesday, May 22. Last year, the cost of care for the MMI family exceeded $20,000 for the first time and was roughly the same as the cost of a midsize sedan. What will this year reveal?

Hint: Costs didn’t go down.

Be sure to check in back at this blog or at www.milliman.com/mmi on Wednesday for the 2013 MMI.

Cost Milliman Medical Index, MMI

By jeremy.engdahl-johnson

Data released today by S&P Dow Jones Indices for the S&P Healthcare Economic Composite Index indicates that the average per capita cost of healthcare services covered by commercial insurance and Medicare programs increased by 3.02% over the 12-months ending March 2013, decelerating from the +3.11% annual growth rate recorded in February. It posted the lowest rate of growth since January 2005.

Seven of the nine S&P Healthcare Economic Indices showed slower annual growth rates for March 2013 compared to February 2013. Annual growth rates for five of the healthcare indices hit their historic lows in March. As measured by the S&P Healthcare Economic Commercial Index, healthcare costs covered by commercial insurance plans rose by 4.46% in March, down from +4.63% reported for February. The Commercial Index rate hit its historic low in March. Annual growth rates in Medicare costs increased by 0.82%, according to the S&P Healthcare Economic Medicare Index, up from +0.78% recorded last month.

The Hospital Index’s growth rate hit its historic low of +1.86% in March, down from +1.92% recorded in February. The Hospital Medicare Index posted a +1.89% annual rate in March, up from +1.73% recorded last month. The Hospital Commercial annual growth rate hit its historic low of +1.81% in March; it posted +2.03% in February.

Read more…

Cost Healthcare, John Cookson, S&P

By Rich Moyer

Over the past several years there has been substantial interest in reducing avoidable emergency department (ED) visits. A wide variety of strategies have been employed to achieve these reductions, including:

• Benefit design changes such as increasing visit copays or putting limits on the reimbursement of number of unnecessary ED visits by a single patient
• Provider incentives through programs such as patient-centered medical homes (PCMHs) to reduce the avoidable ED rate
• Structural delivery system changes to emphasize urgent care facilities and after-hours primary care

 
Many of these interventions rely on analytics based on the avoidable ED algorithm from New York University (NYU), which uses a probabilistic algorithm based on primary diagnosis code to identify the likelihood of avoidable ED visits within populations. Several analyses have now been done that analyze the effectiveness and/or the safety of these interventions.

The first analysis was done by the Washington state Health Care Authority (HCA). It cites an over 10% decrease in ED utilization and ED per member per month (PMPM) costs in the first six months of a program instituting seven best practices for Medicaid enrollees in the state. The best practices included the electronic exchange of information between emergency departments, patient education of ED utilizers, sharing of lists of frequent ED utilizers, development of ED care plans, guidelines and monitoring of narcotic prescribing, and the periodic review of feedback reports. For more information on this program, read HCA’s report, Emergency Department Utilization: Assumed Savings from Best Practices Implementation.

The second is a peer-reviewed study by ED physicians, whose conclusion is that the NYU ED algorithm does a relatively poor job in identifying an individual patient’s need for an ED visit. In this study they compared presenting complaint data with ED discharge diagnosis run through the NYU ED algorithm. They found that the presenting complaint predicted poorly whether the visit should have been avoided and that doing so could have safety consequences. While arguably the NYU ED algorithm wasn’t designed to guide individual patient decisions, the article is thought-provoking and undoubtedly can be cited as an argument against ED visit interventions. Read this recent article in the Journal of the American Medical Association (JAMA), “Comparison of Presenting Compaint vs. Discharge Diagnosis for Identifying ‘Nonemergency’ Emergency Department Visits,” for more information.

I’d expect many more articles to be published about these interventions in the coming months and years. It will be important for informatics to be aware of these evaluations.

This article first appeared at Milliman MedInsight.

Quality of Care avoidable events, healthcare analytics, MedInsight, Rich Moyer

By Javier Sanabria

An analysis based on the composite financial results of a large group of insurers that specialize in medical professional liability (MPL) coverage shows a steady drop in premium but remarkable calendar-year profitability nonetheless. However, despite the strong financial results, it appears that the MPL insurance market is continuing to soften. As the healthcare industry goes through a period of dramatic change, there is significantly more uncertainty in both the future of MPL claim costs and the future of overall MPL insurance market conditions.

Download and read the entire article here.

This article was originally published in the May issue of the Medical Liability Monitor.

Liability, Medmal Bradley Parker, Charles Mitchell, medical professional liability

By Javier Sanabria

The U.S. Department of Health and Human Services has revised applications for people seeking to enroll in a healthcare exchange. The shorter forms are now three pages long for individuals and seven pages long for families.

This Time article provides some background.

Exchanges, Reform enrollment, HHS, state exchanges

By Doug Bates

Developing healthcare quality metrics based on administrative claims data has become increasingly common over the past several years. The National Committee for Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) measures have been a standard for health plan quality reporting for over two decades, and more recently, newer programs such as the Centers for Medicare & Medicaid Services (CMS) Pioneer Accountable Care Organization (ACO) program and Oregon Coordinated Care Organization program have included claims-based quality measures as requirements for program participation.

Most claims-based measures are process-based, evaluating if appropriate services are provided for specified groups of patients, or identifying potential overutilization of services, but claims data are not the sole source of quality measurement. Survey data are often used for patient satisfaction and operational measures, and there is increasing use of lab results and electronic health record (EHR) data to expand the clinical components of quality that can be measured—a topic for another posting.

Despite the expansion of claims-based quality measures, some still question their merit. Those citing concerns point out known limitations associated with analyzing claims data, including:

• Potential errors or inconsistencies in coding.
• Availability of required data sources may be constrained if components of benefits are administered by multiple sources.
• Lack of complete clinical information.
• No diagnostic coding for blood pressure, laboratory results, or pathology results.
• Clinical information is limited to conditions for which the patient was treated and submitted a claim. A noncompliant diabetic may have no claim history of the disease.
• Timeliness of data is impacted by claim lag.

However, the advantages of analyzing claims data greatly outweigh the limitations noted above. The advantages include:

• Data are commonly available and relatively inexpensive to analyze
• Data are available for very large populations, allowing for more robust sample sizes
• Coding accuracy has improved dramatically over the past 20 years
• For some types of measures, claims may produce a more accurate picture than even chart reviews

An example of this last point would be measures focusing on patient compliance with medications. A physician may regularly write refill prescriptions for a patient’s hypertension medication, and those refills may be well documented in the patient’s chart, but those data provide no real evidence that the patient filled those prescriptions. Tracking actual claims for prescription refills is a much better measure. Granted, submitting a claim for a hypertension medication does not prove that the patient actually took the medication at the appropriate frequency, but a regular, ongoing refill pattern is a better proxy of medication adherence than chart review information.

Days supplied is commonly available on claims data, making it easy to calculate “possession ratios” to monitor patient compliance from pharmacy claims. A simplistic way (additional conditions can be added to the calculation) to measure possession ratios is demonstrated in Table 1 below. For patients continuously enrolled during a 180-day period and previously diagnosed with hypertension, the possession ratio for each patient is the sum of all days supplied on their prescriptions during the study period, divided by 180 days.

Although claims data are not perfect for clinical reporting, they will continue to be a valuable and important source of data for quality reporting for a selected set of metrics.

This article first appeared at Milliman MedInsight.

Electronic Health Records Doug Bates, Drug Utilization, healthcare analytics, MedInsight, qualified reporting

By Javier Sanabria

Healthcare reform, mergers and acquisitions, expanding regulatory requirements, and downward pressure on reimbursement and margins create a challenging environment for healthcare management. Although self-insurance can help control total insurance expenses, staying up to date on the financial reporting requirements for this option can be difficult.

This article offers guidance on the key financial reporting issues for medical professional liability (MPL) self-insurance programs. Here is an excerpt:

The following practices will help in keeping on the right course toward full compliance in financial reporting.

• Update the key parties whenever you make changes. Frequent conversations are beneficial. At minimum, you should have annual conversations with the actuary and auditor. If changes occur, in either the program or your loss experience, it is important that all parties understand all of the program changes that have been enacted by management, as soon as possible. Table 1 shows some common questions.

• Create a checklist of requirements. The best way to stay “on top” of the requirements may be to use a single source that lists all of the requirements and indicates when each is due. In addition, it may make sense to determine who will complete each task and to have a strategy in place for efficiently completing the task.

• Seek timely advice. Guidelines are best interpreted by experienced professionals who have the skills needed to understand the current practices and communicate any change from the past. Auditors and actuaries make every effort to update management on a timely basis of any changes that would affect the financial reporting of the entity’s liability, but you can help out by proactively asking for advice for any changes you find out about.

• Request more frequent evaluations. When a program experiences adverse or favorable loss activity or undergoes multiple changes during a fiscal year, you can always ask for an interim actuarial study. You’ll need to determine your comfort level with the program’s current amount of activity, with the goal of reducing year-end “surprises.” Additional analysis may also be helpful during an audit.

Reprinted from the First Quarter 2013 issue of Physician Insurer Magazine, Physician Insurers Association of America.

Medmal medical professional liability, Richard Frese, self-insurance

By jeremy.engdahl-johnson

Data released today by S&P Dow Jones Indices for the S&P Healthcare Economic Composite Index indicates that the average per capita cost of healthcare services covered by commercial insurance and Medicare programs increased by 3.10% over the 12 months ending February 2013, slower than the +3.82% annual growth rate recorded in January.

All nine of S&P Healthcare Economic Indices showed slower annual growth rates for February 2013 compared to January 2013. As measured by the S&P Healthcare Economic Commercial Index, healthcare costs covered by commercial insurance plans rose by 4.62% in February, down from +5.41% reported for January. Annual growth rates in Medicare claim costs increased by 0.78%, according to the S&P Healthcare Economic Medicare Index, down from +1.40% recorded last month.

The Professional Services Index annual growth rate was +4.19% in February 2013, down from the +5.00% January point. The Professional Services Commercial Index decelerated to +6.86% in February, down from +7.61% reported in January. The Professional Services Medicare annual growth rate set a new low of -0.87% in February, down from +0.02% posted in January.

Read more…

Cost Healthcare, John Cookson, S&P

By Javier Sanabria

One of the notable revenue provisions included in the Patient Protection and Affordable Care Act (ACA) is an excise tax on the health insurance industry that will be assessed annually starting in 2014.

This report provides an independent analysis of the ACA health insurer fee provision’s impact on the U.S. health insurance industry.

Reform excise tax, Healthcare Reform