Telling a 46 year health care cost growth story in one graph

In a recent post to the Health Affairs Blog, Charles Roehrig, an economist who serves as VP and director of Altarum’s Center for Sustainable Health Spending, presented some very interesting graphics of long term health care cost growth in the U.S.  He shows the often-presented statistic of health care cost as a percent of Gross Domestic Product (GDP) over the 46 year period since Medicare was established in 1965.  The climbing graph is bumpy due to the effect of recessions and recoveries on the GDP measure in the denominator.  To see the underlying health care cost growth more clearly, Roehrig calculates what the GDP would have been during each time period if the economy was at a full employment state, called the “potential GDP.”  He then calculates health care cost as a percentage of potential GDP.  This creates a nice, steady upward ramp from 6% to almost 17%.

Then, using the potential GDP as a base, Roehrig created a great graphic showing how fast hospital, physician, prescription drug and other cost components grew in excess of the potential GDP.  In his blog post, Roehrig tells the story in graphs and words.  I created the following version of Roehrig’s graph to try to incorporate more of the story into the graph itself.

Roehrig concluded that the “policy responses to excess growth for hospital services, physician services, and prescription drugs seem to have been fairly successful.”  But, he referenced Tom Getzen, who warns against assuming that the recent lower growth rate is the “new normal.”  Rather, it may be temporarily low due to the lagged effects of the recent recession.  So, it may be too early to break out the champagne.

I really like showing such a long time horizon and breaking down health care costs into these five categories.  And, I am convinced that using potential GDP represents an improvement over the conventional GDP measure as a denominator for cost growth statistics.  But, I’ve never understood the popularity of expressing costs as a percentage of GDP in the first place.  In my opinion, it is more relevant to just show growth in real (inflation-adjusted) per capita costs, or the insurance industry variant of that, “per member per month” (PMPM) costs. Using GDP or potential GDP in the denominator seems to imply that, as our country gets more populous and richer, we should increase health care costs accordingly.  I agree with the idea that population growth should logically lead to increasing health care expenditures.  Expressing costs on a per capita basis handles that issue.  But, if we are prioritizing health care services as essential services, we would not necessarily need to spend that much more on health care if we got richer.

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Hospitalists have been focused on reducing hospital length of stay, but not so much on smooth transitions to ambulatory care

A new study published in the  Annals of Internal Medicine compared the economic outcomes of hospital episodes managed by hospitalists to those managed by the patients’ primary care physicians in a Medicare population. Previous studies focused only on the cost of the hospital stay itself, and showed that hospitalists were able to reduce length of stay and hospital cost. These economic savings accrue primarily to hospitals who are reimbursed with a fixed DRG-based payment for most hospital stays. These hospital savings have motivated hospitals to hire more hospitalist physicians. According to the Society of Hospital Medicine, 80 percent of hospitals with more than 200 beds now have hospitalists.  There are now 30,000 hospitalist physicians, and the specialty continues to grow more rapidly than any other.

But, the new study measures the economic outcomes of the entire hospital episode, including care received after the patient is discharged from the initial hospital stay.   The study shows that hospital stays managed by hospitalists had an average length of stay that was 0.64 days shorter, saving an average of $282. But, those patients were more likely to return to the emergency department and more likely to be readmitted to the hospital, leading to post-discharge costs that averaged $332 higher than for hospital episodes managed by the patients’ own primary care physicians. Thus, the use of hospitalists ends up costing Medicare $50 more per episode, increasing overall costs by $1.1 billion annually.

The study authors, Yong-Fang Kuo, PhD and James S. Goodwin, MD from the University of Texas Medical Branch in Galveston, hypothesized that ”hospitalists, who typically are employed or subsidized by hospitals, may be more susceptible to behaviors that promote cost shifting.” The implication is that, if hospitalists were employed by primary care-based Accountable Care Organizations (ACOs) that were being held responsible for the total cost of care for a defined population of patients, they might be more strongly encouraged to focus on improving care transitions to reduce downstream complications and associated emergency department visits and hospital re-admissions.

Even without ACOs, there has been a great deal of effort over the last few years to improve transitions of care for patients discharged from acute care hospitals.  Most of these efforts attempt to improve both the “pitch” by the hospital-based team and the “catch” by ambulatory care providers.  But, some efforts, such as the BOOST program funded by the Hartford Foundation, have a primary emphasis on the pitch.  Other efforts, such as the STAAR program of the Commonwealth Fund and the Institute for Healthcare Improvement (IHI), tend to emphasize the catch. Hopefully such programs will lead to wide-spread improvements in quality of care and reductions in total cost of hospital episodes.  ACOs could catalyze and accelerate those improvements by linking hospital care more tightly to primary care and supporting this linkage through investments in health information exchange (HIE) capabilities designed to foster thoughtful, smooth transitions of care.

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How to get a clearer picture of economic performance: Standard Cost and Payer Neutral Revenue

This morning, I read a set of slides published by the American Hospital Association (AHA) and the Health Research and Education Trust (HRET) called “Striving for Top Box: Hospitals Increasing Quality and Efficiency.”

The report showcased the clinical integration and performance improvement efforts of three hospital-based health systems: Novant (North Carolina), Piedmont (Georgia) and Banner (Arizona). The report identified some common cultural characteristics and strategies responsible for their success, including precise execution, accountability in performance improvement, engaged physicians, focused action plans, consistent communication, team-oriented approaches, transparent data sharing, data dependent atmosphere and standardization in processes.   The slides are clearly written and are a worthwhile read.  I agree with essentially all the points made.  A concise summary based on actual case studies is a good thing.

One point, however, went beyond stating the obvious and got me thinking.   Novant, a health system with 12 hospitals serving the Charlotte and Winston-Salem metropolitan areas, described one of its two key strategies as “moving toward a payer-neutral revenue (PNR) system.” Novant considers all payers as if they were Medicare to prepare for a day when lower payments could be a reality.  They do this by running claims to be submitted to other health plans through a payment algorithm based on Medicare reimbursement rules and rates.  They use the resulting data to prepare pro forma financial statements showing which services lines would be profitable if all health plans paid like Medicare.

How does Payer Neutral Revenue relate to standard cost and standard revenue?

On the health plan side, clinical utilization efficiency measures have traditionally been based on “units” on paid claims.  But, units for different procedures codes within a single category of utilization can have vastly different economic implications.  Three MRIs plus one chest x-ray equal four units of radiology utilization.  If I eliminate the inexpensive chest x-ray, is it meaningful to say I improved clinical efficiency by 25%?

In response to these concerns, financial analysts in health plans have traditionally focused on per-member-per-month (PMPM) paid claim dollars.  But, if the health plan negotiated different fees for different providers, then the PMPM paid claims measures reflect a mixture of the providers’ clinical prudence and the health plan’s effectiveness in negotiating lower fees.  If a provider organization in the health plan’s provider network has a high paid claims PMPM cost, it could be because of unwarranted clinical utilization, or it could be that the provider organization successfully negotiated a higher price per service.

To create a measure that more clearly reflects only clinical efficiency, I have used what managerial accountants would call a “standard cost” approach.  This involves multiplying units of utilization of different procedures by a standardized dollar amount for each procedure.  The result is a utilization measure which is weighted by the economic value of each unit of utilization, after removing the effects of fee variation.   By using standard cost PMPM metrics, with proper risk-adjustment, the health plan can compare the clinical efficiency of different providers in its network on a more level playing field from the perspective of the individual clinician, who usually does not feel responsible for negotiating higher or lower fees.

In a sense, Novant’s PNR approach represents the inverse of health plans’ standard cost PMPM metrics. Just as a health plan can use standard cost to remove the variation in reimbursement of different providers, Novant is using PNR to remove the variation in reimbursement from different payers.  As such, I would suggest that a more generic term for PNR would be “standard revenue.”  After all, every organization’s cost is another’s revenue.

Is Novant’s Payer Neutral Revenue really neutral?

But there is one difference between Novant’s PNR approach and the standard cost approach that I typically use.  I typically set the standard cost for each procedure to the average fee paid for that procedure code during a prior time period.  That way, the standard cost PMPM metrics are measured in dollars, and overall standard cost will roughly equal overall actual cost.  There is no upward or downward bias in standard cost vs. actual cost.

But, Novant’s PNR approach does not try to be “neutral” in this sense.  They picked Medicare to be the basis of their standard.  Medicare generally pays substantially less than private health plans.  I presume this is because Novant is trying to create a sense of urgency.  They use Payer Neutral Revenue as the banner over their initiatives to reduce cost and unnecessary healthcare utilization, including their efforts to create a data-driven culture and improve their analytic capabilities.  I assume they are trying to emphasize the bottom-line connection of those efforts.  They currently have relatively rosy financials, based largely on the currently-available opportunity to shift costs from public to private payers.  By showing their financials in the more pessimistic future scenario where all reimbursement was at Medicare level, when cost-shifting is no longer possible, they are apparently trying to convey a greater sense of urgency.  They want people’s heads to be in the future, so they are more motivated to get started with preparations and make difficult choices today.

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Improving Total Hip Replacement Surgery

Background

Joint replacement is a costly and frequently performed inpatient procedure.  In 1995, Henry Ford Hospital carried out 280 total hip replacements and 225 total knee replacements.  To decrease unnecessary inpatient utilization and enhance functional outcomes of joint replacement surgery, Henry Ford Hospital established a multi-disciplinary improvement team, including representatives from Orthopedic Surgery, Physical Therapy, Home Health Care,  Social Work, Utilization Management, the Center for Clinical Effectiveness, and Marketing.

Methods

The team developed and implemented a best practice guideline in the form of a “care-map”, describing the default plan of care for delivering multi-disciplinary services to the patient for each day of the planned length of stay.   These interventions included outcomes assessments, tests & diagnostics, consults, treatments, procedures, medications, diet & nutrition, elimination goals, activity goals, and safety goals, skin condition goals, educational interventions and learning goals, and discharge planning.

To support the evaluation of the work of the team, comparative data was used.   These data included comparative outcomes data obtained through a collaborative Outcomes Measurement Consortium organized through the American Medical Group Association (AMGA).  Comparative process data was obtained through the Group Practice Improvement Network (GPIN).  In addition, a periodic audit process was used to measure variance from the care-map.  Additional process and outcomes data were collected and managed using software applications developed by the Center for Clinical Effectiveness:

  • Complications Tracking System used to enter and report on trends in various joint-replacement complications. This system was used to support the Department of Orthopedic Surgery morbidity and mortality conferences.
  • Outcomes data acquisition was accomplished using the “Flexi-Scan” forms scanning and study management software.
  • Cross-institutional pooling of outcomes data data pooling and quarterly analysis was accomplished by staff of the American Group Practice Association usuing the Flexi-Scan analytic dataset builder and other tools developed by the Center for Clinical Effectiveness.

Finally, patient satisfaction and subjective feedback data was obtained using patient focus groups arranged by Center for Clinical Effectiveness and staff from the Marketing Department.

Results

The implementation of the care-map led to a further one-day reduction of the length-of-stay, as illustrated in the following figure.

Functional outcomes data revealed that hip replacement surgery led to rapid reduction in bodily pain to normal age and sex-adjusted levels. (Note that in the following graph, pain is expressed on the SF-36 pain scale, in which higher numbers represent better functional status, or less pain).

As shown in the following figure, physical function is also improved, although not as rapidly nor as dramatically as bodily pain.

The following figure shows the distribution in bodily pain and physical function, showing that although the average improvement is favorable, 15% of patients have worse pain one year after surgery, and 22% have worse physical function after surgery.

An analysis was conducted to identify baseline variables (collected prior to the surgery) which could predict failure to acheive improvement in pain and function.   As shown in the following table, mild pre-operative pain was among the strongest predictors of failure to achieve an improvement in pain.

In order to optimize the ability to predict which patients would fail to achieve a pain reduction from hip replacement, a neural network was trained based on 13 baseline variables collected from the patient before surgery.  The neural network was then used to calculate a predictive score for each of the patients.  The frequency distribution of predictive scores for patients that did achieve a pain benefit, as well as the distribution for those that did not experience a pain reduction are shown in the following figure.

If the neural network was perfectly predictive, these two distributions would not overlap at all.  A threshold predictive score was selected such that the model could be said to be 85% sure about making a prediction that a given patient would not achieve a pain reduction from surgery.  Of the 185 patients that experieced an improvement, only 2 had a neural network predictive score below 0.3.  Of the 36 patients that experienced an improvement, 13 of them had scores under 0.3.  So, for the 15 patients with a score below 0.3, 85% failed to experience a pain reduction.   The sensitivity of the model in predicting this type of treatment failure was 35%.   In other words, of 100 patients considering hip replacement therapy, about 15 of these will fail to achieve a pain reduction.  Of the 15, about 5 of these patients can be identified in advance using this neural network and using a cut-off threshold of 0.3.  If implemented in clinical practice, this model could lead to the elimination of the treatment cost and risk of complications associated with these 5 patients.  On the down side, approximately one patient who would have benefited from the surgery would have been incorrectly told that they were unlikely to benefit.  Such a patient would unnecessarily suffer pain that could have been relieved by the surgery.

As shown in the following figure, analysis also revealed that patients had unrealistic expectations of the time it would take for them to feel fully recovered after the surgery.

Finally, the data revealed that, compared to other institutions, Henry Ford experienced an unusually long physical function recovery period among patient receiving revision total hip replacement (operations on patients that had previously received hip replacement surgery).  Such data, shown in the following figure, led to a re-evaluation of Henry Ford’s approach to post-operative rehabilitation.

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