How Providence St Joseph Health devised a Value-Oriented Architecture to guide physician practice. Rather than blaming "good doctors" or "bad doctors" they have developed a method for simultaneously measuring cost and outcomes and "drilling down" into the specific practices that drive variation.
In health care, variation is the state of nature. Despite efforts to introduce protocols, care pathways, order sets, checklists, and standardized quality measures, providers vary in their practice to a remarkable degree. Differences in outcomes and cost of care are visible when comparing countries, geographical regions, hospitals within a single city, and providers in the same practice. Some of this variation is doubtless benign. In other cases, it contributes to poor outcomes. And sometimes, perhaps frequently, it results in waste — greater cost without better outcomes.
Selective Pressure On Practice
The situation that healthcare practitioners find ourselves in might be described with an evolutionary analogy. In nature, random mutations in DNA sequences create genotypic variation, which results in phenotypic variation (observable differences in species). In the presence of selective pressure, some phenotypes propagate their genes more successfully than others. In medical practice, “genotypes” are the many individual decisions that constitute a provider’s method of practice, such as the selection of a medication, a surgical approach, a communication style. “Phenotypes” are the cost and clinical outcomes of care. Controlling for patient differences, it is variation in these many elements of practice (genotype) that drives differences in cost and outcomes (phenotype). Contemporary health care is characterized by abundant variation in practice and very little selective pressure to weed out those that don’t add value. Over a century and a half of modern medicine, a great variety of practices have developed, largely in the absence of forces that reward practices leading to good outcomes and low cost.
Administrators want physicians to lower cost. Physicians want to optimize patient outcomes. While these conversations can go badly, they also have the potential to result in something very good — the selective pressure that health care has long lacked.”
Good Doctors, Bad Doctors — A False Dichotomy
At Providence St. Joseph Health, the third-largest nonprofit health system in America, with 24,000 physicians in seven states, we have developed a method for simultaneously measuring cost and outcomes and “drilling down” into the specific practices that drive variation. We refer to this as a Value-Oriented Architecture (VOA). The ability to drill down is essential. Consider the example of cost and outcomes variation for total knee replacements. Figure 1a is a Value Plot, displaying what our evolutionary analogy would call phenotypic variation. Each circle represents the performance of one high-volume orthopedic surgeon for primary elective unilateral knee replacement. The y-axis represents average direct cost per case (plotted in reverse order so that lower costs are higher on the graph); the x-axis is a composite outcome score (better scores are further to the right). Circles in the right upper quadrant thus seem to represent “high-value” practitioners.
At this summary level of analysis, where in the past our conversation may have ended, there is a tendency to overinterpret the results. Misleadingly, the graphic makes us think of “good doctors” and “bad doctors,” and suggests that the best solution might be to pick out “bad doctors” for remediation or removal. While that might be possible, it would be a very incomplete solution to the problem of health care waste. A more productive activity is to dive deeper and identify the drivers of variation.
Figure 1b simplifies the view by displaying only the cost dimension of total knee replacements. A different story emerges when cost is exploded into its primary component parts: implants, OR/anesthesia time, pharmacy room and board, and supplies. Figure 1c plots each individual as a ratio of his or her costs within each of those categories to the system average (marks above 1 indicate higher than average costs; marks below 1 indicate lower than average). A “good doctor” in this view would be someone with low costs in all categories. Figure 1d shows that while such physicians exist, they are rare. So are “bad doctors” — providers with high costs across the board. In fact, nearly all surgeons display “crossover” in this graph; that is, their cost is relatively high in some categories and relatively low in others.
This phenomenon becomes even clearer as we move more deeply into the data. Figure 1d seems to indicate that within the category of pharmaceuticals, some physicians are high cost and other are low cost. However, even that distinction hides important variation. Figure 1e is a deeper view of pharmacy cost. Here, overall pharmacy costs are broken into subcategories that group similar agents together: in this case, analgesic and anesthetic agents and hematological agents (overall, the two largest pharmaceutical cost drivers in total knee replacement). The y-axis again expresses each surgeon’s cost as a ratio to the system average. Again, we see that some physicians cross over between categories — that is, they are low cost in some things and high cost in others. With respect to cost, “good practice” and “bad practice” often exist within the same doctors.
Everyone Has Something To Learn
Underneath the pharmaceutical agent categories lies the genotypic view. Figure 1f shows the breakdown of costs for one pharmaceutical subcategory, hematological agents. Here we discover that the use of Tisseel, a branded fibrin sealant, and tranexamic acid, an agent used to control blood loss, are the underlying practice variants. These practice differences are driving variation in hematological cost, which in turn contributes to variation in pharmaceutical cost and eventually to overall cost.
For each clinical area that we have explored using VOA, there are many small differences in practice like this one, which together have a large cumulative impact on cost. For example, Table 1 shows that for total knee replacements, the cost per case of a hypothetical surgeon whose practice consisted only of the most cost-effective practice variations is 35% lower than the system average. Today, Providence St. Joseph’s most cost-effective provider is 20% lower than the system average, meaning that even he has opportunity for improvement.
Table 1. Click To Enlarge.
The work of uncovering the specific elements of practice that drive cost differences has been illuminating and at times surprising. We have uncovered many examples of administrative quirks (“I have no idea how that got into my preference card”); cost ignorance (“This costs how much?”); misleading information (“The rep told me this was cheap”); and a (more......
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