Strategies such as practicing to top of license, focusing on risk, and data mining can be used to transform care management.
“Precision health” is a hot topic in healthcare today as a strategy for managing high-risk, high-cost patients. For example, two physicians who wrote in The American Journal of Managed Care last year argued creating “comprehensive data networks with information on medical claims, clinical factors (including genomics), and social determinants of health” would be required to more effectively manage higher-risk patients.
Such data networks would be enormously helpful in generating optimal outcomes for these challenging patients, but truly transforming care requires more than data and technology alone.
Care coordinators or care managers are the foundation for any accountable care organization (ACO) or other such integrated delivery networks for managing patient populations. Robust, integrated and artificial intelligence-powered information technology is certainly essential for helping these teams work efficiently and productively.
At the same time, integrated delivery networks need to configure their care management teams and work flows to ensure the highest quality care while controlling labor costs.
In that light, the following are three strategies organizations can use to transform care management.
While care managers are crucial to healthcare organizations in achieving their care quality and cost reduction goals, some organizations may be staffing their teams inefficiently.
For example, an ACO may still require its most skilled, highly licensed and experienced clinicians to perform care tasks that could be delegated to a lower-licensed, but highly competent clinician. In physician-led teams, having a medical assistant take a patient’s vitals and conduct basic intake duties while the physician performs more complex assessments and examinations is common.
With population health management care teams, which are typically led by nurses, staffing structures are less standardized and highly variable due to the evolving nature of this type of care delivery. Nonetheless, appropriate workflows based on licensure and experience levels need to be in place so clinicians can practice to the top of their licenses.
A red flag would be if a less-experienced clinician is conducting outreach and discovers a patient care complexity that is outside of his or her comfort level, then the team needs to have protocols in place for the more senior clinician to assume management of the patient.
“Better to be safe than sorry” is a saying that is perhaps most applicable to healthcare delivery.
As stated in the AJMC article, robust networks of varied clinical, financial and social determinants of health (SDoH) information are helpful to manage high-need patients, but care management teams also require easy-to-use analytics to identify highest-need patients and intervene. Using AI-embedded and machine learning population health management technology would also help organizations in delegating duties among care management teams.
Such technology can ensure that the clinical, social, and claims data is current, aggregated and normalized with duplicate information removed. Once cleansed, the technology would assign a risk level to each patient, identifying those who have the highest predicted resource utilization over the next 12 months.
Additionally, a second layer of risk stratification would further segment the population to help prioritize care management efforts based on those patients who are most likely to respond to an intervention. Such insight does not replace the skills and experience of a qualified care manager, but rather helps him or her practice to the top of one’s license.
While a comprehensive view of the population’s risk levels is enormously helpful, it is only through careful interviews and screening tools that organizations can learn about the SDoH challenges that may be standing in the way of the patient’s care goals. For example, a caregiver who recently lost a spouse may be unable to care for themselves, requiring in-home assistance or increased monitoring from a care manager.
A similar example was cited in a recent report from HHS’ Office of Inspector General report about 20 high-performing ACOs who each earned well-deserved recognition for their savings and improved health outcomes.
In a high-performing ACO, care coordinators called high-risk patients every day to check on their status and report any changes in their health to the appropriate provider. In these follow-up conversations, if there were any significant changes, the care coordinator could dispatch a nurse or any other needs they required immediately. With these practices in place, the ACO achieved a 43% reduction in emergency visits and a 47% reduction in hospital readmissions.
Leveraging technology for population health management continues to evolve as new capabilities are developed to help patients achieve optimal clinical outcomes and improve their quality of life. The one constant through all these exciting changes, however, is people.
The goal for ACOs and other integrated delivery networks is to select, implement, and configure the technology that will best help their employees use their skills and experience to identify, intervene and manage the care for the people they serve.
Jessica Scruton, BSN, RN, CCM, is the clinical transformation advisor for Lightbeam Health Solutions.