Despite the significant start-up costs and time invested by providers to establish Accountable Care Organizations, initial results have been mixed. Although many have been able to improve quality measures, the number of ACOs that have generated shared savings is disappointing to some. Based upon the recent CMS announcement, 29 percent of MSSP ACOs (97 out of 333) earned shared savings for 2014. That is slightly better than the 24 percent (53 out of 220) that earned shared savings for 2013).
The limited success in shared savings is disappointing because many believed that the shift to preventive, coordinated care would lead not only to healthier populations but reduce the need for higher-cost, episodic care. Achieving shared savings is a critical milestone for ACOs to demonstrate success in the new reimbursement model, the proposed model of care in the future. Many providers also seek to increase the percentage of their revenue that comes from risk-based contracts in order to raise margins and pursue new revenue opportunities through commercial contracts. To execute that strategy, ACOs must be able to demonstrate the ability to lower the cost of care for “at risk” populations.
There may be several reasons why ACOs aren’t able to generate shared savings. First, many ACOs are still trying to find the right care approach that works for their organization. There are many options to choose from such as developing Patient Centered Medical Homes (PCMH), clinical integration, care coordination, transition of care programs, partnerships with community resources, etc. These care approaches take time to implement, optimize and scale. It’s also not uncommon for organizations to have mixed results while experimenting with different approaches before honing in on the right one.
Second, many organizations haven’t acquired health IT solutions needed to support the risk-based care approaches mentioned above in scalable, efficient models. Let’s take a closer look at specific technology challenges ACOs are facing.
Lack of integrated health systems
Many ACOs face the challenge of improving the health of geographically dispersed populations receiving care from a number of hospitals, clinics, skilled nursing facilities and independent specialists. To coordinate care among all of these locations and effectively use analytics, ACOs must integrate data from across the network. The lack of EHR interoperability makes the aggregation and longitudinal presentation of clinical data a significant challenge. The challenge becomes even more difficult when considering the critical need to bring together other types of data (e.g. claims or financial data) from potentially dozens of systems in an efficient and timely manner. Without the ability to leverage sufficient data, providers will have difficulty succeeding in reducing gaps and redundancies in care, standardizing care aligned with evidence-based practice or providing clinical decision support or real-time status alerts.
Need for predictive risk stratification
While payer organizations have been evaluating and managing patient risk for decades, it is a relatively new activity for provider organizations. The most common risk management practice today for providers is to identify their most expensive patients and group them by disease registries such as diabetes, hypertension, etc. Next, providers typically try to identify proactive and preventive care management approaches for those patients to reduce the need for high-cost episodic-care. While this approach may seem logical and fundamentally sound, providers need more sophisticated tools such as predictive analytics in order to achieve better results. The shortcoming of this approach is that it does not provide a multi-dimensional stratification of a targeted population. In addition, having a simple registry of many thousands of the highest cost patients in different disease categories has limited value. Among those thousands, an organization doesn’t know where to focus constrained care management resources in order to achieve the highest return. In fact, it may not be the currently highest cost patients where all the focus should be as providers should also be addressing the next group of patients that are about to become the highest cost patients. It’s these patients who generate unexpected costs that can be a major detriment to the achievement of shared savings.
A more sophisticated risk management approach applies predictive modeling and proprietary algorithms to a much wider array of data and considers factors such as movers risk and patient motivation to generate a manageable list of patients while also forecasting the cost savings potential more accurately. Predictive risk stratification is critical to generate the highest return on intervention.
Inability to scale care management efficiently and effectively
Proactive, preventive care can help reduce the need for high-cost, episodic care. The challenge for organizations is how to scale care management activities and find efficiencies that can enable a care team to deliver that care more efficiently and consistently across a large population. For example, the process to get an untreated diabetic’s glucose under control is fairly simple from a clinical point of view. The challenge arises when a high throughput care team must manage thousands of such patients seeking care in a variety of facilities. Manual processes and a lack of efficiency leads to delays in improved outcomes and inconsistencies in care, which contributes to higher costs. Providers need to streamline care management workflows to achieve the efficiency and scalability that can drive faster results across a population.
Data and analytics aren’t integrated into clinical workflows
ACOs understand the value of using data and analytics to improve the quality of care and lower costs, but many have not yet integrated data and analytics directly into clinical workflows where they can have the greatest impact. ACOs need to take the next step by surfacing valuable data and analytics (e.g. gaps in care, risk scores, full medication histories) into clinical workflows to improve care while a clinician is still in the presence of a patient. Given that the care for a population is a team-based activity involving a variety of clinicians in a variety of different facilities, data and analytics surfaced in clinician workflows helps close gaps faster and improve outcomes for the patient.
Meeting reporting requirements is too time-consuming
Another challenge for ACOs is the time and resources it takes to meet the reporting requirements necessary to qualify for shared savings. When data is housed in multiple systems it becomes a laborious, manual exercise to collect, measure and report on it, which can bog down an entire team of skilled clinical analysts. That team instead could be spending more time on identifying and driving quality improvement initiatives across the organization helping improve patient outcomes. ACOs must be able to measure and report on results efficiently, or it can take up a disproportionate amount of resource time.
How to address these ACO challenges:
Although the overall results are mixed, some ACOs have demonstrated noteworthy results that should give optimism and provide a path to success to other providers. Among the ACOs that did generate shared savings, significant dollars were earned by the top performers. Best practices are beginning to emerge from these ACOs that have had initial success generating shared savings.
Given that, let’s examine the commonalities and the high-level strategies used at top performing ACOs. The key takeaway is that for ACOs to generate greater shared savings, they need to have strong capabilities in each of the following three core strategies.
- Integrate information systems and leverage data across the network to connect all clinicians responsible for a targeted population.
- Stratify populations in order to prioritize limited resources where they will have the greatest impact.
- Increase efforts in targeted care management and primary care for prioritized patients.
Providers should address population health as a series of interconnected activities rather than as distinct, siloed efforts. The weakness of siloed efforts is that deficiencies in any of the activities create deficiencies in the others. For example, being unable to leverage all of your data weakens your ability to use analytics, which weakens your ability to both understand your population and engage in efficient, targeted care management.
Population health is not just solely about data, not solely about analytics and not solely about care management. It’s about all of those activities operating in an integrated manner and augmenting each other. This is known as an enterprise population health approach that integrates data, analytics, workflows, and clinicians across the entire organization. The speed with which providers can drive improved patient outcomes is a critical factor in their ability to achieve shared savings due to the annual time frame parameter. Using this enterprise population health approach can create the synergies and efficiencies that can help ACOs innovate care and generate more shared savings.