Stephanie Alexander
Colleen Vetere

Engaging physicians' performance measurement can help improve service line margin.


At a Glance

Effective data-driven analyses of service-line performance require:
 

  • Buy-in and agreement at the outset from all parties (hospital and physicians) on the validity of the data used to evaluate service-line performance  
  • Actionable data and metrics relevant to physicians, with financial goals tangibly linked to clinical improvement  
  • Transparent sharing of data with physicians to build their trust and support the case for change  
  • A physician champion who can help validate findings and guide how data are presented  
  • Willingness of physicians to acknowledge that the opportunity for improved margin depends largely on the variable costs that they control as individuals  

The converging forces of decreased payment and changing payment models are causing providers to focus intensively on reducing the cost of care delivery. To overcome the pressures of squeezing margins, health systems need to identify and normalize the cost variations of care delivery within service lines. Doing so is a critical catalyst to improving quality, efficiency, and bottom-line performance.

Unfortunately, providers can still spend considerable time manually collecting and aggregating service-line-specific data without gaining short- and long-term improvements. Data in hand are not enough. Both hospitals and physicians need to agree on which performance metrics to use, and appropriate analysis of the relevant data is required to accurately depict service-line operations, including the true cost of supplies. The data also need to be packaged correctly so physicians can easily interpret the data and change practice patterns as needed.

The experiences of several healthcare organizations that have used best-practice, data-driven analysis to improve service line management and margin point to two key success factors: establishing expectations up-front and getting physicians to understand and accept that the opportunity for improved margin depends on the variable costs that they control. These lessons provide valuable insight for oganizations seeking their own road map for using data to determine and manage service-line margin.

Establishing Metrics

A key starting point for appropriate analysis is agreeing upon relevant metrics-measures that indicate whether a service line is within acceptable range. Striking the right balance among financial, clinical, and operational metrics to gain consensus is important. From a financial viewpoint, metrics can include cost per case, supply cost per case, or overall margin.

When evaluating supply-cost metrics and setting expected expenses, hospitals should examine patient and case mixes in the right context. A high ratio of surgical patients with implants, for example, would be expected to generate significant supply expense, but supply costs should not be higher than expected simply for a large number of medical patients.

Although a healthy bottom line is crucial for the organization, physicians focus on initiatives that also benefit patient care and clinical outcomes. Focusing the analysis first on clinical aspects encourages physician input and support that are crucial to overall service-line improvement. Clinical improvements should be linked with financial improvements where possible. Physicians need to see how containing costs can also help patients.

For example, cost-reduction initiatives across a service line should consider physician concerns about efficiency of processes. The same data that show savings opportunities also can show areas of improvement for turnaround times and highlight obstacles to optimal operating room (OR) usage. Clinicians may benefit from seeing their volumes per service line, the number of cases and turnaround times in the OR, outcomes relating to mortality and infection rates, and relative performance in patient-safety indicators.

One health system's heart and vascular cost-reduction program received a jump-start when the system reinvested a portion of savings to purchase new equipment for its cardiovascular center. By creating shared incentives to meet both financial and clinical goals, the system drove physician support to reduce spending for pacemakers and defibrillators while shifting utilization patterns for premium and standard devices.

Exhibit 1

f_alexander_exh1

Hospitals that engage physicians in discussions around contribution margin and overall service-line profitability often are able to create a compelling case for change. These discussions become the "burning platform" for cost-reduction efforts.

Trusting Data

Reaching agreement on metrics is an important step toward another critical success factor: gaining trust in the data. At the outset, all parties should recognize the validity of information gathered in evaluating service-line performance, understand where it originates, and support its use as a baseline for recommending changes.

Time should be spent up front educating stakeholders on reasons behind the preferred metrics, or more effort will be needed later to defend data validity than to act upon the data. This "analysis paralysis" can result in a repetitious cycle of pulling more data to prove or disprove initial findings. A narrative story should be shared with stakeholders that includes a "wow" factor to illustrate service-line improvement and help them keep the end result in mind.

It is often best to include transparent sharing of detailed financial data when presenting such a narrative to physicians. In some instances, where there may be competitive reasons for not sharing openly, the story can be told through trends rather than numbers.

Expense reductions, however, happen more readily when physicians understand the true cost of their practice patterns, supply costs, and acquisition costs (what the hospital actually pays to acquire the product, as opposed to an allocated accounting cost). It's essential to drill down and show individual physicians how their service-line practice patterns compare against averages and the practice patterns of their peers.

Transparent data sharing builds support for improvement efforts and allows physicians to make informed choices. Physicians typically are willing to change patterns only if they understand the true relative value of one product compared with another and whether choosing higher-priced options over similar products is worth the extra expense for similar patient outcomes.

Exhibit 2

f_alexander_exh2

Championing Data

Gaining support for service-line improvements requires an internal champion-someone who can guide how data are presented, shape the direction of practices, and communicate what can be done differently. Ideally, the champion should be a high-volume, highly respected physician, a key service-line leader, or a physician administrator. Such a leader knows the issues that concern his or her colleagues and ensures that data are packaged appropriately. The internal champion should test findings before they are presented formally.

If a qualified clinically oriented champion is not available, a senior administrator should lead the effort, working with individual physicians to garner their support of the initiative.

Presenting the Data

Many hospitals make a misstep by communicating in terms of diagnosis-related groups and product descriptions existing in underlying information systems-that is, by using language of the hospital and finance that does not resonate with physicians.

Data should be presented using terminology tailored for its intended audience. For physicians, rather than a diagnostic code, the procedure name should be used, such as total joint, knee, or hip. Catalog numbers or descriptions in item files should be avoided. Physicians respond to product names or systems they know through vendors.

Accessing the Data

Hospitals often cannot easily obtain detailed information about which specific products individual physicians are using for their patients, such as catalog number, size, brand, and price.

Hospitals frequently rely upon their decision support systems as the primary source of information for their analytical needs. These decision support systems utilize hospital billing and coding information as their data sources. Charge-based information is typically based on generic charge descriptions.

An example of this would be a charge code with the description "hip implant." Consequently, identifying products by physician and determining the acquisition costs can be a time-consuming process that involves a substantial manual effort.

Exhibit 3

f_alexander_exh3

Hospitals that practice due diligence and develop more specificity in their chargemasters are ahead of the game. An example of this would be the use of charge codes with a description such as "femoral stem porous" or "femoral stem cemented." Charge codes with this type of specificity would allow for better analysis in the use of hip replacement technology utilizing the decision-support data.

Hospitals that link their chargemasters to the supply chain item master are in an even better position, because as mentioned previously, readily available data for service-line analyses are often based on cost-accounting and decision-support systems already in use. Hospitals that practice this type of diligence in the maintenance of their chargemasters would have descriptions such as "Vendor X Brand Y femoral stem," often including the actual catalog number in the description, thereby allowing dynamic linking of the charge code to the supply chain item master to identify true acquisition costs.

Although hospitals can readily access diagnostic-coded, charge-based, and supply-cost data, albeit often in disparate systems, they have a limited ability to report on clinical outcomes. Hospital-based information primarily addresses length of stay, mortality rates in the facility, postoperative complications on-site, and patient-discharge disposition. Information on other factors determining outcomes, such as complications treated in physicians' offices or time required to return to work, is not so readily available and requires assumptions based on proxy measures. One example is the distribution of patients discharged to home versus a skilled nursing facility. The assumption is those discharged to home can return to work quicker.

Evaluating the Product Mix

A key component in evaluating service-line performance is identifying the product mix used to treat patient populations and educating physicians on the financial implications. Choosing advanced or standard technologies can have a significant financial impact.

Agreeing on advanced and standard technologies usage requires physician input. For example, data for a total joint replacement may show usage patterns between hard-bearing and standard porous technology for hip procedures or flex/mobile-bearing and standard cemented technology for knees. Data sharing encourages changes to the product mix while not conveying judgment on what should be used for a case.

The findings on product mix and discussions with physicians can lead to new strategies that recognize the reality of practices prevalent in a hospital. For example, national averages may indicate a 50/50 usage of high-flex versus standard knees, but data for a provider may show an 80/20 or 90/10 use of high-flex knees. It's important to discuss why the advanced technology is required when data for similar organizations indicate it's not the standard.

If an advanced technology has become the default "standard" for that service line, the hospitals should acknowledge that reality to physicians, but ask for their help in narrowing the variability in pricing. Narrowing the field of suppliers, for example, for high-flex knees can result in increased market share for the suppliers, which can be a negotiating tool to receive favorable vendor pricing.

In working with physicians to define an appropriate product mix, physician choice over products and vendors is important, but price points for products used in the mix should be set at levels that help drive a positive margin, if the service line is to remain able to provide patient care. Service-line improvements occur when provider and physician interests converge.

Uncovering Hidden Costs

A full analysis of service-line costs should dig deep behind the scenes, looking not only at high-profile items such as implants, but also at other choices made in the OR and how they affect the total cost of care.

For example, vendors may be promoting the use of custom cutting guides as part of a knee procedure in an orthopedic service line. The use of these cutting guides requires a preprocedure MRI, and they add $900 to $1,200 per case, which is not separately reimbursable. The extra variation adds to the cost of care.

As another example, in spinal fusions, physicians perform specialized surgical approaches such as lateral access with the incision on the side, instead of the back, which requires multiple expensive disposables per case. Still other procedure costs that vary with physician usage and practice are the use of antibiotic bone versus standard bone cement at a cost that's often four times higher, or the selection of thigh-high versus knee-high T.E.D. hose.

Data-driven analysis helps tie usage to individual physicians and presents an opportunity for dialogue with physicians on how a shift in utilization practices can improve service-line performance. Comparing notes on practice patterns for a procedure, without attaching judgment to its validity, can yield benefits.

Determining Margin

A few key service lines can drive substantial costs for procedures performed in hospitals. For example, choices of implants for orthopedics can consume such a large percentage of costs that money is lost before the patient leaves the OR.

To help determine margin, it is necessary to know the true cost of acquisition for implants. Data typically pulled from decision-support systems report the allocated cost from a cost-accounting perspective, not the actual acquisition cost as reflected in the materials management system. The challenge is to accurately reflect the true cost by linking it to the charge, and tying the charge to the physician and patient.

One health system discovered while analyzing total joint replacements that the true acquisition cost of its implants constituted 56 percent of total variable costs for a procedure-much higher than a median average of 46 percent. Because variable costs represent what providers control directly, the cost of implants became a target for savings.

By working with physicians, the system developed a formulary of price targets for implants that reflected system-specific needs-and negotiated accordingly with vendors. This initiative took approximately seven months and resulted in lower average implant costs per case, saving the health system more than $1 million annually in adjusted annual spend for total joint replacements.

Managing service lines to break even for Medicare payments can be a predictor for future overall segment margin. Analyzing details such as cost of implants as a percentage of overall Medicare payment per case, when measured against national medians, helps paint a more accurate picture of service-line health.

It's not unusual for a program with positive contribution margins to show an overall net loss when fixed costs are balanced against revenues and direct expenses. When this occurs, the hospital's relationships with its physicians come into play in evaluating how variable versus fixed costs are used to frame discussions with physicians.

Some surgeons view their part of the overall contribution margin as the only factor to consider when evaluating a service line, regardless of fixed costs. Other physicians recognize that fixed costs are a part of doing business and must be considered in weighing the health of a program. The key factor is recognition from physicians that margin is highly dependent on the variable costs they control.

Setting Expectations

A service-line analysis gains impetus when stakeholders understand everything is on the table for discussion-from supply use in the OR and how supplies are packaged to physician orders and clinical pathways for patients. Providers rely on data to tell the story and define opportunities for improvement, and serve as the basis for dialogue to address all aspects of business and clinical performance.

Data collection efforts should include workflow methodologies to manage and monitor on an ongoing basis. If it took four months to start the data gathering process, it's not feasible to spend that much effort for each snapshot in time in the future. Lessons learned about gaps in data may require solutions as simple as maintaining an implant log linked to catalog numbers and pricing.

Finally, the hospital should be ready to act if slippage occurs in agreed-upon service-line improvements. For example, practice patterns may shift for some reason to more expensive products, causing suboptimal utilization management of advanced versus standard technologies. Rigorous monitoring is essential to reverse undesirable trends. The initial wave of success from a service-line analysis is most meaningful with a commitment to ongoing data sharing and transparency in reporting results.


Stephanie Alexander is president, performance analytics, MedAssets, Inc., Atlanta (stalexander@medassets.com).

Colleen Vetere is vice president, Aspen Healthcare Metrics, a MedAssets company (cvetere@medassets.com).




 

Publication Date: Monday, October 03, 2011

Login Required

If you are an existing member, please log in below. Username and password are required.

Username:

Password:

Forgot User Name?
Forgot Password?

If you are not an HFMA member and would like to access portions of our content for 30 days, please fill out the following.

First Name:

Last Name:

Email:

   Become an HFMA member instead