Using Predictive Analytics and AI for PROACTIVE RESOURCE ...€¦ · appropriately deploying its...

Using Predictive Analytics and AI for PROACTIVE RESOURCE PLANNING AND MANAGEMENT

www.hospiq.com | [email protected] | 617.960.8600

Using Predictive Analytics and AI for Proactive Resource Planning and Management 1

And yet, at the most fundamental level, hospitals—from the smallest community hospital to the largest academic medical center—are places where we rely on people to take care of other people; places where the most important resource isn’t beds, operating rooms, pharmacies, and imaging machines, it’s doctors, nurses, aides, technicians, orderlies, administrators, and support staff.

A hospital’s ability to effectively and appropriately treat patients depends directly on effectively and appropriately deploying its human talent. Because its workforce is usually a hospital’s single biggest cost and because putting the right staff in place at the right time has a direct impact on patient outcomes, aligning staffing to meet true demand is one of the single biggest challenges that hospitals confront.

THE CHALLENGE OF ALIGNING CAPACITY TO MEET DEMANDHospitals are being squeezed. Healthcare expenditures continue to rise, outpacing overall economic growth as insurance reimbursements fail to keep up with healthcare inflation and insurers place new restrictions on which treatments and services are available to patients.

Well-known and highly regarded hospitals including Houston’s MD Anderson Cancer Center, Partners HealthCare in Boston,1 and the University of Massachusetts Medical Center2 have reported significant financial losses in recent years. In a 2017 article in the Harvard Business Review, health industry analyst Jeff Goldsmith referenced a litany of factors explaining these losses, ranging from declining inpatient admissions to difficult IT installations to Medicare reimbursement reductions.3 But he placed the most blame on how hospitals deploy their most important resource: their workforces. “The main cause of the operating losses,” he wrote, “. . . has been organizations’ lack of discipline in managing the size of their workforces, which account for roughly half of all hospital expenses.”

We live in an era of seemingly miraculous medical discovery, where new procedures, medicines, and technologies hold the promise of more effectively preventing, better treating, and even curing some of humanity’s deadliest, most painful, and most unrelenting maladies.

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Indeed, labor costs eat up nearly sixty percent of a hospital’s budget according to a 2016 study published in the journal Health Affairs.4 To survive in this increasingly challenging healthcare environment and deliver the highest quality care for the fewest dollars, hospitals must align their staffing to meet their true demand.

Appropriate staffing is, of course, a priority for all industries. The principle is simple: schedule too many workers and you waste money, schedule too few and you fail to deliver the appropriate care that patients expect. And over the last few decades, we have seen industries from manufacturing to retail adopt a variety of technologies to help them manage the challenge of placing the right number of people on the floor at the right time.

The inherent unpredictability of delivering healthcare makes that kind of alignment particularly challenging, however. Although routine patient visits and elective surgeries are typically scheduled days, weeks, and even months in advance, emergency room visits, many inpatient admissions, and non-elective surgeries are generally unpredictable: there is, it seems, little way of knowing in advance who will get sick or injured on any given day.

How do managers know how many emergency room physicians, nurses and aides to schedule when they don’t know how many patients will show up in the emergency room? And what happens when they don’t schedule enough of them? Wait times extend, ambulances are diverted to other hospitals, patients wait for beds on gurneys in hallways, and those who are well enough to walk away often do—without being seen by a provider.

And, conversely, what happens when the hospital schedules too many workers? It pays people it doesn’t need, wasting scarce hospital resources for that day and potentially limiting its ability to staff appropriately in subsequent days when the census may require more staff. In both scenarios, nurse managers are forced to make sometimes frantic last-minute adjustments to either find additional staff to care for patients on the units or to find work for surplus staff, impacting the delivery of care, productivity and workplace satisfaction.

Although routine patient visits and elective surgeries are typically scheduled days, weeks, and even months in advance, emergency room visits, many inpatient admissions, and non-elective surgeries are generally unpredictable.





The inherent chaos of the hospital environment can stymie managers’ ability to accurately predict their staffing needs on any given day. Hospital leaders struggle to find the right answers to important staffing questions, including:

• Do we have the correct staffing to stay within our budget?

• Do we have enough staff to achieve good clinical outcomes and high patient satisfaction scores?

• Will we be able to share underutilized staff from other units across the hospital?

• How will we know we can confidently make the decision to close an underutilized unit and share its resources or, alternatively, safely open and staff new beds?

TRADITIONAL STAFFING MODELS ARE INADEQUATE Hospital staffing models have remained unchanged for decades and leaders know that hospital staff-management models are antiquated, inefficient, and ripe for change. For example:

• Future staffing plans are often based on past staffing models and census levels and don’t account for variable demand, which hamstrings hospitals when they need to adjust bed capacity to meet that demand. In fact, many staffing plans seem to involve merely “cutting and pasting” the previous month’s model.

• Staffing decisions and processes often vary randomly across departments.

• Overstaffing and “staff hoarding” is commonplace because managers fear they won’t have enough staff if patient demand unexpectedly increases.

• Staff skill level is misaligned with patient need when staff members are assigned to areas in which they lack adequate experience or, conversely, when highly specialized – and expensive—staff are assigned to work below their skill level. This skill-level misalignment threatens effective patient care and weakens staff morale.

• Senior executives are often unaware of how staffing processes are managed—and of the full impact when staffing and demand are misaligned.





THE PROMISE OF PREDICTIVE ANALYTICS

How can hospitals resolve these workforce dilemmas? The answer, it turns out, lies in their own data and the predictive analytics solutions that can harness that data for their use. Effective advanced data analytics products for healthcare can mine and analyze the hospital’s EHR and other IT systems to help hospitals redesign their staffing models, build their staff schedules, and more proactively respond to changes in, for example, ED volume and inpatient census. The result is a workforce aligned to meet true patient demand and a better-run, more efficient, and more responsive hospital.

Predictive analytics solutions can help the hospital more effectively design its staffing schedules for the long term (weekly, monthly, seasonally, annually) and help managers adjust staffing in the near term—daily and even hourly—as well. They start by analyzing past patterns, integrated with knowledge of the hospital’s staffing policies, to arrive at the most efficient staff schedule for each area of the hospital. Next, they integrate current internal data (surgical schedules, inpatient census, typical length of stay by diagnoses) as well as external data (weather, air quality, seasonal health trends) to forecast shifts in demand to make both near-term adjustments and improve long-term planning.

Near-term adjustments: Advanced analytics systems synthesize data from the hospital’s IT infrastructure

to forecast shifts in demand days or hours in advance, helping hospitals predict, for

example, how a spike in ED visits or a drop in ICU patients will ripple across

the hospital and affect staffing needs. This enables leadership to

address staffing shortfalls and over coverage before they

arise, ensuring both a productive workforce

and that patients are receiving the right care from the most

appropriate caregiver, which can directly impact

both patient satisfaction scores and clinical outcomes.

Long-term planning: These solutions analyze past patterns, integrated with knowledge of the hospital’s staffing policies, to arrive at the most efficient staff schedule for each unit of the hospital. Over time, this improves the accuracy and reliability of the schedules created four to six weeks in advance, necessitating fewer near-term schedule changes and improving staff satisfaction.

“What-if” scenario testing: Effective solutions take it one step further—they use simulation technology to help hospitals test “what-if” scenarios, which is critical if hospitals are going to “right size” their workforce for the long-term, as Goldsmith recommended. These simulations allow hospitals to test a range of possible staffing scenarios under a range of conditions. What if the hospital across town shuts down and ours is the only ED? What if we open a new surgical center? Would it help us to manage shifting demand if we created a flex team of nurses and techs, who could work in a variety of areas, as needed? Do we have the right kind of shift schedule? Should we adjust the length and timing of our shifts?

These are the kind of questions hospitals should be asking and need to answer as they face the challenges of the current healthcare environment and strive to serve more patients more effectively using fewer resources. Predictive analytics solutions can help them build and deploy the right workforce in the right places and at the right time so that they can serve more patients with better results at a lower cost—for the benefit of everyone.

Predictive analytics solutions can help the hospital more effectively design its staffing schedules for the long term and help managers adjust staffing in the near term.





HOSPITAL IQ: POWERFUL, PRECISE, CUSTOMIZED PREDICTIVE ANALYTICS FOR WORKFORCE MANAGEMENT

Hospital IQ’s Census Solution helps health systems align staffing strategies to better meet forecasted patient demand. Its recommendations help managers make better workforce decisions for both clinical and non-clinical teams (i.e. internal medicine, nursing, case management, and various support services). Better workforce alignment improves quality of care, increases productivity, enhances staff satisfaction, reduces turnover, and decreases labor costs for the entire health system.

Staffing Gap Identification: Identify and Correct Staffing Misalignments. On a daily basis, Hospital IQ helps hospitals to predict ED volume, census, admissions, discharges, and transfers across the health system and to assign the appropriate staff members according to the skills required. Hospital leaders can more accurately adjust department and float pool staffing before a labor crisis develops, changing staffing levels and opening or closing beds to meet patient demand when and where it arises—to staff up the locations that need the most support and avoid overstaffing elsewhere, minimizing the use of agencies and reducing overtime costs.

Demand Pattern Alignment: Align Initial Schedules with Patient Demand. As managers build their weekly and monthly staff schedules, Hospital IQ helps them identify potential overstaffing and understaffing risks and stay within budget by proactively designing both clinical and non-clinical schedules. Managers can more effectively align staff to patient care requirements and do so within—or even under—budget.

Shift Optimization: Build A Long-Term Shift Strategy. Hospital IQ can also guide long-term staff planning—its “what-if” scenario planning can help hospital leaders strategically test and analyze the impact of a range of structural changes to staff schedules and bed capacity.





FOR MORE INFORMATION on predictive analytics to align capacity to meet true demand, visit

www.hospiq.com/workforceor call 617.960.8600

1 Pearl, R. Why major hospitals are losing money by the millions. Forbes: November 7, 2017. www.forbes.com/sites/robertpearl/2017/11/07/hospitals-losing-millions/#2dbf703e7b50

2 Eckelbecker, L. Despite cuts, UMass Memorial Health Care posts operating loss for 2018. Worcester Telegram and Gazette: December 30, 2108. www.telegram.com/news/20181230/despite-cuts-umass-memorial-health-care-posts-operating-loss-for-2018

3 Goldsmith, J. How U.S. hospitals and health systems can reverse their sliding financial performance. Harvard Business Review: October 05, 2017. hbr.org/2017/10/how-u-s-hospitals-and-health-systems-can-reverse-their-sliding-financial-performance

4 American Hospital Association. The Cost of Caring: February 2018. www.aha.org/system/files/content/17/costofcaringfactsheet.pdf.

These scenarios integrate the hospital’s policies with data about available resources and patient demand to simulate the impact of changes to staffing models, including changes to shift lengths and start times. Scenario planning can help hospitals recruit the right number of new hires for the right long-term roles, reduce labor costs, and enhance employee satisfaction.

Predictive analytics can reduce the hours that hospital leaders spend “putting out the fires” that can consume so much of their time enabling leaders and front-line managers to make better-informed decisions faster and more accurately than ever before. Better decision-making improves patient care, increases staff productivity, creates a healthier work environment, and enables hospital leaders to focus their attention on longer-term strategic growth issues.

Powerful predictive and prescriptive analytics combined with robust simulation capabilities, such as those developed by Hospital IQ, can help hospitals and health systems unlock the potential of their own data to align staffing to meet true demand and put the right people in place at the right time for the benefit of the patients they serve as well as the dedicated professionals those patients count on.



https://www.hospiq.com/workforce

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