The following is a guest article by Jason Harber, EVP of Operations at Hospital IQ.
Years before the pandemic, many hospitals were already facing a crisis. They struggled to manage operational inefficiencies and, as a result, unnecessary expenses, overworked and stressed staff, and less effective, potentially unsafe, patient care.
“We always seem surprised that patients are boarding in our emergency department and operate in crisis mode daily,” according to one emergency room director. Staff turnover was an ongoing challenge.
“One of my biggest concerns is nurse turnover; too many nurses are leaving the bedside as a result of being overworked without adequate time off,” says a chief nursing officer. Now, amid a once-in-a-century pandemic, the cost of these inefficiencies has been magnified, and hospitals – and the human beings who deliver care within them – are now at a breaking point.
Finding solutions to eliminate these inefficiencies has always been important. But now, the human cost associated with these operational problems makes finding and implementing solutions that fully optimize operations more critical than ever.
The Need for ‘Systemness’ in Healthcare
Hospital operations management requires a holistic approach to optimization, including managing capacity, throughput, and workforce in unison, while simultaneously looking across the continuum of care. In rethinking the standard hospital operational model, cost reduction and quality improvement imperatives are two key pillars of the current healthcare landscape that are driving hospital leaders to pursue optimizing operations by improving coordination, integration, and productivity – or “systemness,” as defined by Advisory Board.
Technology that streamlines standard operating procedures while using artificial intelligence to detect and mitigate risk, both in real time and the future, can transform a health system’s operations. This combination of technologies, also known as intelligent automation, ensures that the right information is delivered to the right person at the right time so they can take the right action, giving leaders and frontline staff the ability to prevent problems before they occur.
Emphasizing Interconnectivity in Optimizing Patient Flow
Hospital patient flow is extraordinarily complex and optimizing it is one of the greatest operational challenges that hospitals face. Many hours have been devoted to developing various playbooks and protocols attempting to improve patient flow. But these efforts are stymied by several deep-rooted inefficiencies, including lack of awareness across clinical and non-clinical teams, inadequate discharge processes, and siloed staffing practices.
It is vital to recognize that the different parts of a hospital are deeply interconnected and achieving meaningful optimization requires each area and their associated teams to achieve systemness. Leveraging intelligent technology, such as predictive analytics and AI-enabled workflows, enables hospital leaders to predict and prevent patient flow problems before they arise.
Like many of their peers across the country, the hospital leaders and care teams at Health First, one of Florida’s leading health systems, struggled to efficiently manage patient flow. The traditional processes care teams used to manage discharges (such as manually updating paperwork) were creating siloed information among different teams, delaying discharges and creating patient flow bottlenecks.
Recognizing the urgent need to solve these inefficiencies, Health First turned to intelligent automation. Converting manual processes to digital allowed multi-functional teams who traditionally operated independently to be connected with streamlined workflows and enhanced collaboration. In turn, Health First eliminated 517 avoidable hospital days per month, reduced length of stay by 6 hours per patient, and cut the time spent collecting data by 200 hours per week.
Health First’s experience shows that intelligent automation is already playing a significant role in helping hospitals and health systems move away from a reactive approach to patient flow and mitigate bottlenecks before they happen. Data and analytics, combined with process improvements, help organizations make critical patient flow decisions that support better patient care, while also enabling happier, healthier staff – and more profitable operations.
Bolstering Staffing Efficiency
Traditional staffing models are antiquated, inefficient, and reactive. Future staffing plans are often based on budgeted staff and don’t account for variability in patient demand, and staffing decisions and processes often differ across different groups. Hospitals across the country continue to struggle with not having enough nurses to meet patient care needs. As a result, understaffed hospital units require managers to scramble daily to ensure appropriate coverage by implementing temporary, often costly, solutions such as frequent use of overtime pay, incentive pay programs, and supplemental agency staff. Managers take overstaffing measures when possible because they fear they won’t have enough staff if patient demand unexpectedly increases. All this uncertainty and variability, on top of the incredibly demanding nature of the profession, leads to significant turnover among the nursing staff.
Fortunately, the availability of predictive analytics solutions has created the opportunity to significantly improve daily staffing activities. These solutions can mine and analyze the valuable information within the hospital’s EHR and other IT systems to develop accurate predictions of future patient care needs which can be used to transform the staffing process from reactive to proactive.
MercyOne Des Moines Medical Center regularly operated in crisis mode due to staff shortages and manual staffing processes, until they deployed a predictive staffing solution that combined data science, machine learning, and workflow automation. This solution allowed MercyOne Des Moines to institute proactive staff planning based on 7-day forecasts and automate manual staffing processes while reducing the time required to allocate staff daily. MercyOne Des Moines realized a 50 percent reduction in premium pay-based shifts, eliminated over 80 hours of manual work per week, and gave over 10 hours of time back to nursing managers to focus on patient care activities each week. MercyOne Des Moines’s transition to proactive staff planning through intelligent automation has enabled the hospital to maximize the use of its existing staff to meet patient care, nurse productivity, and staff satisfaction goals.
The Path Forward
One key takeaway from the pandemic is that hospitals need to transform the way they run their operations to be more proactive, agile, and flexible. Relying on previous methods will only result in additional waste and inefficiency on the operational side, and a further drain of human capital on the resource side. Intelligent automation will play a foundational role in transforming hospitals as they look to evolve from the complexities of traditional, inefficient processes.