When skilled nursing facility staff members have patient insights at their fingertips, their patients win. They do too, because healthier patients lead to happier staff members. That can help reduce turnover, all of which adds up to a sturdier bottom line. For Dr. Steve Buslovich, VP — Senior Care, VBC of PointClickCare, that’s an equation that works.
Whether mid-level providers and specialists, nursing staff, care managers and coordinators or executives, everyone at a SNF benefits from the cutting-edge insights of a top-flight clinical dashboard. In this Clinical Dashboard Series interview, Buslovich shares the capabilities of his ideal clinical dashboard, the top patient care decisions that care providers are making with their dashboards, and more.
Skilled Nursing News: What are the three most important data points for an ideal skilled nursing clinical dashboard, and why?
Dr. Steve Buslovich: The most important data points are those that encourage clear action with a particular workflow. I like to think about three areas. First is patient risk assessment: identify patients who are at the highest risk. This includes individuals with chronic conditions, instability and a heightened likelihood of requiring hospitalization. Providers then have to understand which patients fall into those categories for efficiently prioritizing care and resources.
The second important element would be prioritizing patient issues. Not all issues are treated equally. A summary of the highest priority patient issues could include medical conditions, symptoms or care interventions that need to be addressed promptly. Knowing the top priority issues helps in providing timely and effective care to those who need it most.
The third is to understand admissions and regulatory compliance areas of focus. This data point would involve tracking things like new admissions, readmissions and regulatory compliant visits that we need to address for closing quality gaps or for just regulatory purposes. Having that data available would help ensure that all necessary assessments and documentation are completed for these new patients and that any regulatory obligations are met in a timely fashion.
How does an ideal clinical dashboard drive patient care decisions for operators?
An ideal clinical dashboard would drive patient care decisions by facilitating informative, proactive approaches to care, helping the care team pinpoint where to focus their limited time span and attention. This ensures that resources and efforts are directed toward addressing the most critical aspects of patient care.
The second is predictive insights. The dashboard goes beyond retrospective data and allows for the prediction of future events. We want to be able to predict what’s going to happen around the corner. This includes anticipating potential declines in the patient’s condition, unfavorable outcomes or situations involving dissatisfied or unrealistic family members. By having access to predictive data, the care team can then take a preemptive action to mitigate risks and improve patient outcomes.
The third is enhanced communication and collaboration. The dashboard would serve as a tool for enhancing communication and collaboration among health care providers and facilities. It enables the care team to practically identify solutions where they need to engage with family members to align and address concerns. This proactive communication can lead to improved patient and family satisfaction, which is a growing important component of clinical services.
The last item is streamlined information. The clinical dashboard compiles and presents data in a clear, accessible format, making it easier for the care team to interpret and act upon the information efficiently. This ensures that critical information is readily available and reduces the risk of overlooking important details.
One of my personal favorites is frailty risk stratification. The dashboard would include a stratification system based on clinical frailty utilizing a validated mathematical model of risk as an index. This allows for a deeper understanding of which residents are physiologically at risk for declining outcomes. By incorporating data related to frailty, the care team can tailor interventions and care plans to the specific needs of each resident, their deficits, ensuring a much more personalized approach to care.
How would the ideal clinical dashboard help optimize reimbursement for operators?
The ideal clinical dashboard plays a pivotal role in optimizing reimbursement within the health care provider organization, particularly in the context of evolving reimbursement models such as PDPM and the shift towards value-based care. The first area would be enhancing clinical accuracy. Under programs like PDPM, where providers take on financial risk, having a clinically accurate representation of a patient’s condition is essential to match the level of care provided with the reimbursement required. Engaging medical practitioners such as myself to help with this level of accuracy is very important, as is alignment between the facility coding and clinical assessment with the licensed medical provider at that particular setting.
Compliance and quality measures also matter. Right now, a lot of data is being documented, but it’s not all validated by the medical team. From a compliance standpoint, those elements need to be aligned. What’s more, compliance and accuracy are essential for securing reimbursement and avoiding penalties. The dashboard identifies any gaps in coding and quality measures, allowing the facility to address them properly.
Then transitioning reimbursement models. We’re seeing a wave of change. One of the challenges in health care reimbursement is transitioning from one payment mechanism to another. The dashboard provides insights into when these transitions occur. This is crucial for ensuring a seamless shift from one reimbursement model to another as patients’ needs and payment mechanisms change. Understanding these transitions is vital for maintaining revenue streams and also different requirements around compliance.
How can the clinical dashboard improve staffing efficiency?
The first thought is patient acuity-based staffing. The clinical dashboard allows for a more systematic and data-driven approach to patient care by helping identify high-risk individuals and organizing them into higher acuity settings where staffing skill sets and ratios are appropriately matched to the patient’s needs. This approach optimizes staffing resources by centralizing higher acuity patients in specific areas of facilities, ensuring that the right level of care is provided without over-allocating resources to lower acuity units.
The second area is workflow automation. Currently, staff may underutilize technology due to lack of awareness, training or suboptimal technology implementation. By integrating workflow automation tools into the clinical dashboard, tasks such as documentation, data entry can be streamlined, reducing manual labor, bringing up staff time for more direct patient care at the bedside.
Clinical dashboards can also help identify patients who may be experiencing polypharmacy. The dashboard can provide insights into medication management and help prioritize medication administration times.
How would you like to see the clinical dashboard integrated with predictive analytics tools?
The integration of the clinical dashboard with predictive analytic tools holds immense potential for health care organizations, particularly as they grow and are more centralized. Here’s how I envision this integration. First is the enterprise-level predictive insights. The integration extends the use of a clinical dashboard from the facility level to the enterprise level. This broader perspective, more of a bird’s-eye view, allows health care organizations to predict emerging trends and risks on a larger scale.
By harnessing predictive analytics to basically predict these areas, the dashboard can anticipate what challenges are on the horizon, whether they’re related to patient care, resource allocation, or other critical factors. This means that a high-scale staff can triage, intervene where it matters most, reducing the likelihood of unnecessary hospitalizations or poor outcomes.
The second area of integration would be for preventative care and resource optimization. As the saying goes: prevention is better than cure. The integration of predictive analytics empowers health care organizations to adopt a proactive approach to care. By foreseeing potential issues, addressing them early, they can avoid the need for resource-intensive interventions down the line.
The last point would be interdisciplinary collaboration. Predictive analytics fosters interdisciplinary collaboration within the health care team. It facilitates communication coordination among various health care professionals, ensuring that everyone is aligned in their efforts to address emerging issues and risks.
What are some things that you think skilled nursing facilities could do to improve the collection of patient health data?
To improve the collection of patient health data several critical steps can be taken. First is establishing a longitudinal patient record. It’s crucial to create a comprehensive and continuous longitudinal record that spans the entirety of the patient’s health care journey. This record should include data from various care settings and transitions, allowing health care providers to access a complete medical history. Such a record can significantly reduce the time and effort required to recollect data at each encounter and provide a more holistic view of the patient’s health. It’s also critical to ensure accurate diagnosis and documentation capture.
The second is standardizing data across care settings. Standardization of data collection and storage practices across different care settings is imperative. Currently, data fragmentation non-standardized practices lead to inefficiencies and potential errors. Improved interoperability efforts can help unify data standards and facilitate seamless data sharing between health care delivery organizations.
The third point is engaging families and caregivers. We want to see collaboration with families and caregivers which can enhance data accuracy. These caregivers often possess vital information about the patients, such as important medication histories or over-the-counter meds they might be taking that may not be part of the record. Involving them in the data collection process and validation of available data can help reconcile discrepancies and ensure that the records are up-to-date and accurate.
The last thing that’s related to that is really a focus on medication management, which is a critical aspect of a patient health data record. Ensuring that medication records are actively maintained across care settings is vital. The focus on better longitudinal medication data management can significantly reduce errors, rework and inefficiencies. This improvement can have a profound impact on patient outcomes.
