Archive for category Disease Management
In a previous post, I commented on a study that examined the correlation between medication compliance and use of other healthcare services over three years for a large population of commercially insured members with a diabetes or cardiovascular-related diagnosis. The study found a strong association between greater medication adherence (defined as a medication possession ratio of 0.80 or higher) and lower utilization of other medical services, primarily hospitalizations. Considering all healthcare costs, not just disease-related, they reported an ROI of 3:1 for medication adherence in dyslipidemia and 10:1 for hypertension.
While my previous post highlighted a key methodological concern about the study, one of the most powerful tools for quickly stress-testing a study’s findings is to conduct a plausibility test to see if the results match up with what other rigorous research would suggest. For this plausibility comparison, I selected a meta-analysis of data from 90,056 individuals in 14 randomized trials of statins. There are other meta-analyses and randomized controlled trials that I could have chosen, which would have led to similar conclusions.
The adherence study included all patients with dyslipidemia as evidenced by a diagnosis in the medical claims. To be conservative, I selected patients from the meta-analysis who were taking statins for secondary prevention and looked at 5-year effectiveness, as shown in the table below. Given the absolute risk reductions observed for hospitalizations for MI, revascularization, and strokes, the estimated number of hospital days avoided across all the patients was 0.33. In contrast, the adherence study reported an average of 1.18 fewer hospital days for adherent patients versus non-adherent patients with dyslipidemia.
It is not plausible that the nearly 4-fold greater hospital reduction reported in the adherence study (1.18 versus 0.33 days) was due to greater medication compliance. The implausibility is compounded by the fact that I included more severe patients, followed them for a much longer period of time, and examined the full effect of statins versus placebo rather than the effect of differences in adherence, all of which inflated our hospital days avoided.
|Patients with Previous MI or CAD|
|Event||Statin||No Statin||Absolute Reduction||Length of Stay||Hospital days avoided|
|Total hospital days avoided||0.33|
Plausibility tests are quick and powerful and can be used to test the ROI claims from disease management vendors, medication compliance programs, and many other healthcare services. Recognizing the need for such tools and plan sponsors’ limited time to examine vendors’ savings claims, we designed plausibility calculators specific to disease management and value-based insurance to help plan sponsors discern fact from fiction. They are free so the next time you are listening to a vendor’s sales pitch, you can do a quick reality check.
The honeymoon for disease management (DM) has clearly passed. Health plans and employers are frustrated with the lack of value provided by their DM vendors but they often struggle to understand why their DM program isn’t working as intended or what to do to correct it. The combination of frustration and confusion has resulted in a wave of market experimentation fueled by vendors who claim to have discovered the allusive secret ingredient that makes DM work.
Published today in the American Journal of Managed Care is a paper I wrote to help plan sponsors better understand what is really known about savings from DM, why telephone-based DM does not save money and what it takes to generate real savings. This work is based on my market experiences, an extensive review of the literature, both in DM and other related disciplines, and a detailed assessment of what is known about cost-saving healthcare services and treatments.
The answer to why DM does not provide short-term savings lies partly in the myriad of cost-effectiveness assessments that have been conducted on the treatment for chronic disease over the last 30 years. Cohen and Neumann reported that less than 20 percent of preventive measures or treatments for chronic conditions are cost-saving, even for a 30-year time horizon. Kahn found that aggressive implementation of nationally recommended medical activities would increase costs over a 30-year period for all activities except smoking cessation. Looking more specifically at the individual components of DM programs, the evidence is equally compelling. At the level of the individual program activity, cost-savings has not been shown for treatment of these chronic conditions, the exception being heart failure. Accordingly, one should not necessarily expect cost savings for the program as a whole, absent a belief that DM can independently improve the outcomes of patients with chronic disease without affecting the key clinical goals for each disease.
Cost-Effectiveness of Common Disease Management Activities/Goals
|Diabetes||A1C < 7%||No|
|LDL Cholesterol < 100 mg/dl||No|
|Blood pressure < 130/80 mmHG||No|
|CAD||Antihyperlipidemics (LDL < 100 mg/dl)||No|
|Asthma||Inhaled anti-inflammatory use||No|
|Asthma education on symptom monitoring and/or trigger avoidance||Unknown|
|HF||ACE inhibitor use||Yes|
|Beta blocker use||Yes|
|Structured remote monitoring (weight, blood pressure, etc.)||Yes|
While the cost-effectiveness literature does not bode well for the future of telephone-based DM as currently designed, cost-effectiveness research suggests that better targeting of treatment activities and patients may provide opportunity for cost-savings for some disease states. Although a targeted approach is both intuitive and supported by the evidence, it has two practical problems that will prevent it from being widely adopted in the marketplace.
- First, a more targeted approach is not a revenue-optimizing model for DM vendors. For example, only about 5% of asthmatics would be targeted for intervention if the criteria were real cost-savings, hardly a desirable approach for DM vendors whose revenue model is based on volume due to their large fixed cost structure.
- A second barrier to adoption is the marketability of the more realistic return-on-investment (ROI). An employer is unlikely to select a vendor that is offering a 1.85 ROI over a vendor with an inflated ROI (that also includes a much larger group of patients since it is not targeted). The same problem holds true for health plans—even though they often understand the methodological problems of the vendors’ ROIs, ultimately they too must market their program to the employers who sometimes believe all ROIs are created equal.
To learn more about proven strategies for short-term savings, take a look at the full published article.
The disease management industry appeared to receive some good news in September when Health Dialogue published a randomized controlled trial of its care management program in The New England Journal of Medicine, an unprecedented achievement for an industry that has been plagued by questions of value. The study has been widely touted at health conferences in the last few years, but its recent publication represents the first chance to better understand their care model and evaluation approach.
The study was a randomized trial that compared usual care to enhanced care support, the key differences being the number and type of patients targeted as well as the number of contact attempts. Patients with heart failure, CAD, COPD, diabetes, or asthma represented less than twenty-five percent of the targeted population. The other 75% of targeted patients included those at high risk for hospitalization for preference-sensitive conditions (defined as a condition for which at least two valid, alternative treatments strategies are available, such as hip replacement) and patients with other high-risk conditions (e.g., back and neck pain).
How Much Did the Program Save?
- Overall savings were 3.6% ($8 PMPM) or 2.7% ($6 PMPM) after subtracting out program fees of $2 PMPM.
- Savings were driven by reductions in hospitalizations. After one year, the hospital admission rate was 10.1% lower for the enhanced-support group, a reduction which was almost entirely accounted for by a reduction in admissions for preference-sensitive and high variation medical conditions (never defined in paper).
- Hospital admissions and medical expenditures actually increased for the cohort of patients with chronic conditions and gaps in care.
- No difference was found between the enhanced and usual care groups for lab tests or pharmaceuticals.
How Did The Program Achieve These Medical Savings?
These findings show that Health Dialog’s care management model is generating at least 20% of its savings through its unique feature of shared decision-making for preference-sensitive conditions and that the program is having minimal, if any, impact on traditional quality of care measures. Accordingly, the label “disease management” may be a misnomer—note that the authors never referred to the program as such.
How might the program be delivering the savings for patients without preference-sensitive conditions? The authors indicate they had access to real-time discharge notifications. This feature, in and of itself, could explain Health Dialog’s success for traditional DM patients in the absence of notable quality improvements. Research has shown that timely intervention at discharge and beyond (a.k.a., transitional care model), particularly for patients with heart failure, can provide real short-term savings. Over the years, the DM industry has struggled to identify these hospitalized patients in a timely fashion. One clue that this rapid discharge notification may be an explanation is the large reduction in hospitalizations for patients with heart failure. Heart failure is the condition for which transitional care has the strongest evidence base and in this study, heart failure patients experienced a reduction in hospitalizations that was more than three times any of the other chronic conditions. However, this hypothesis is mere speculation—the authors acknowledge that their purpose was not to determine which specific components accounted for the savings; and given the program’s commercial focus, these details are not likely to be revealed.
What Are the Implications for Disease Management Purchasing?
Certainly Health Dialog’s research has notable limitations, perhaps the biggest being how will the program perform over time–did patients who decided against surgery in the first year stick to that decision in the following year? Despite the unknowns related to potential quality improvements and how the program generates saving, definitive answers to these questions may not be a necessary requirement to purchase. In the near term, the study’s methodology is stronger than what we have seen in the commercial market to date, its theoretical basis for savings for preference-sensitive conditions is strong, and the reach rate exceeded 50 percent for its traditional disease management patients, better than the market norm. While this study may not be a great advertisement for traditional disease management, it provides compelling support for Health Dialog’s potentially unique model.