A value-add approach to workforce planning in healthcare

A value-add approach to workforce planning in healthcareBetter patient outcomes driven by predictive staffing analytics

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For most healthcare systems, the largest line item is people-related — salaries and benefits. As such, “human capital” is often seen as a cost to be managed rather than an asset to be grown and nurtured. Most workforce-planning approaches show return on changes in the workforce model with cost savings — how to make the workforce leaner, based on a benchmark; or cheaper, based on a financial model.

Move beyond cost managementWith the advent of digitized, standardized administrative data, however, leading healthcare systems have found ways to move beyond cost management to link workforce practices directly to measurable patient outcomes. In doing so, they expand their investments in people and practices, adding value where the payoff is greatest. By marrying the cost-based and “value-add” approaches, managers can easily see the tradeoffs between cost savings and better outcomes. No health system can survive without keeping cost at the center of its focus, but now organizations can quantify the impact of cost-based workforce interventions on the primary mission of healthcare — patient care.

Using data you have already collected for administrative purposes, you can balance your cost model to universally tracked value-based outcomes, such as your healthcare acquired infection (HAI) rates, patient satisfaction and quality of care. At Mercer, we have learned that this value-add approach does not require expensive new data-collection systems or transformations to workflow. The process by which we arrive at these links is Business Impact Modeling™, a proprietary statistical technique derived from the disciplines of labor economics and industrial organizational psychology.

Unlock your talent “best practices” with your own dataOur research has shown consistent and actionable links between human capital practices — staffing, scheduling, career management, compensation — and outcomes like patient satisfaction and quality of care. Although we have seen some constants — for example, the more experienced your workforce, the better your outcomes — benchmarking can be misleading due to the complexity of healthcare delivery. Our 20 years of experience have taught us that:

1. The relationships between human capital practices and outcomes are fairly unique to each facility. Healthcare delivery is supported by many checklists and standard protocols … but there are no such things when it comes to managing healthcare workforces for optimal outcomes. The best way of doing so changes from facility to facility, and Mercer has best practices for discovering the ideal mix of workforce characteristics and practices for you.

2. Our data-driven approach allows you to determine not only the predictors of quality of care but also how strongly each factor influences healthcare outcomes. This insight helps prioritize actions in terms of their expected payoffs.

3. Ninety-nine percent of the data needed is already tracked in your systems — HRIS, ICD-10 and clinical administrative systems. You can use the data you already have on hand and, on average, can predict 75% to 85% of the variance in outcomes from these sources.

4. Not only does this data-based approach reveal the links between your workforce practices and quality of care but it also allows you to leverage your existing reporting tools to track these indicators going forward.

5. It’s crucial to remember the influence of your external market — more accurate predictions of who will come in the door, and when, have an almost immediate payoff.

At Mercer, we believe this approach provides the opportunity for hospital systems to drive significant value through data analytics as it pertains to three unique imperatives.

In this paper, we do a deep dive into some specific examples to illustrate how hospitals can leverage the data they already have in unique and impactful ways — controlling cost and/or improving results. This discussion will showcase how data can drive meaningful outcomes through a reduction in healthcare-acquired infection rates and increases in patient satisfaction.

A value-add approach to workforce planning in healthcare: Better patient outcomes driven by predictive staffing analytics

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Case study 1 “More employees” beats “more overtime”Hospital system ABC was facing a common operational issue: To ensure adequate staffing as demand increased, should it hire more employees, or expand overtime? Financially, the outcome pointed to more overtime — as the premium for it was less than the cost of a new employee. However, an analysis at the discharge-unit level combining HR data, patient safety, case mix and patient-census data showed that 85% of infection occurrence rate could be predicted by how the ABC staffed its units.

The link between overtime usage and patient care was strong — but in unexpected ways. More overtime predicted lower patient safety in one part of the hospital and better patient safety somewhere else. When controlling for other factors, the amount of overtime used in each unit was a very strong predictor of HAI. In one facility, more overtime (above 7% of all hours worked) led to an overall doubling of HAI probability. In an adjoining facility (ICU), however, a similar overtime level led to lower HAI occurrence.

The intervention was simple: in ICU units where more overtime was good, overtime guidance was relaxed; for the rest of the facility, overtime was capped at 6%–7%. Overall, this led to more hiring. Looking at cost (overtime versus regular hours) in parallel with results (patient safety) led to an investment that was unique to the system.

10%

8%

6%

4%

2%

0%0% 2% 4% 6% 8% 10%

Distribution of nursing overtime hours

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14% 16% 18% 20%12%

0% 5% 10% 15% 20% 25%

Overtime as a percentage of total hours

Hig

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Average overtime usage = 7%

18% seems to be the turning point for overtime usage.

In this facility, when overtime usage went from 6% to 10%, bacterial infection (C. diff) incidence increased by 23%, controlling for all other factors.

Figure 1. Percentage of discharge units with increased overtime

Figure 2. Likelihood of C. diff infections with higher overtime

Yet for a peer facility, increased overtime actually led to decreased incidence of bacterial infections (C. diff), controlling for other factors.

Masked and disguised sample results.

Clostridioides difficile (also known as C. diff) is a bacterium that causes diarrhea and colitis (an inflammation of the colon).

The strategic framework of Business Impact Modeling™

In 20 years of practice, we’ve found that in healthcare, the key predictors of value-add outcomes are employee attributes, organizational practices and external influences.

Employee attributes are the characteristics of an organization’s workforce: Each one is unique. These factors include qualifications, length of service and flow (hiring, promotions and attrition) — all are actionable from a human capital perspective.

Organizational practices are the policies and norms of the organization: They include staffing mix, distribution of workloads, organizational structure and salary management. These are highly predictive of healthcare outcomes and are items over which organizations have control.

Finally, external influences are the characteristics of the community served (for example, area demographics, case complexity index, patient census) and the labor markets in which the organization competes for talent. Although these are not actionable, we need to control for these factors when determining predictors.


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Case study 2 Poor shift scheduling costs more than just moneyA regional hospital system was looking for ways to optimize shifts to maintain the leanest-possible staffing model across discharge units. It adopted a hybrid system of employees on 12-hour shifts to cover the minimum staffing requirements plus additional employees on eight-hour shifts to account for patient ebbs and flows on a day-to day basis. This led to a wide distribution of different “shifts” working simultaneously in the same units. While there were financial advantages to the flexibility of an overlay of eight-hour shift employees, the influence on two key outcomes — patient satisfaction and HAI — was strong. Adding shorter shifts was saving money but having a negative impact on results. Controlling for a multitude of factors (including diagnostic area, case complexity, nurse-to-patient ratio, length of service, pay and time of year), those areas that were able to maintain a higher balance of employees on 12-hour shifts showed better results.

The effects were not subtle. When the proportion of employees on 12-hour shifts increased from 30% to 40%, HAI dropped by a third, and patient satisfaction rose. This was not a function of “how busy” a discharge unit was — the result controlled for the number of patients treated and the complexity of cases — but purely of scheduling. The hospital system quickly decided that the financial benefit of the “leaner-staffing” model was outweighed by the marked decrease in patient safety. It adopted more widespread use of 12-hour shifts, and results improved.

-20.7% C. diff

CLABSI

MRSA

HCAHPS overall rating

% answering “nurses always listen”

-32.4%

-32.3%

0.06

1.9%

Change in HAI occurrence likelihood in the next month

Change in HCAHPS scores in the next few months

Figure 3. Predicted change in outcomes when moving workforce from 28% to 38% on a 12-hour shift

Figure 4. Average shift composition in nursing department

No shift information

Rotating shift

8-hour third shift

8-hour second shift

8-hour first shift

12-hour day shift

12-hour night shift

4%8%

4%3%

31%

24%

28%

Return on investment is achieved by taking action based on the findings of what works best for your facility — and merging the insights for common cost-based models with a value-based approach. To achieve this, follow these four key principles:

Think holistically. This was one example of how analytics can drive business results. There are several other areas that can have a meaningful impact as well, such as pay equity, DEI, and health and productivity.

Act intelligently. Tracking tools and systems are only as good as the people using them and interpreting their data — don’t get fooled into thinking a new system will solve your problems. An ECG is only as good as the clinician that interprets the results.

Be proactive. The data is readily available and there are meaningful insights to gather using predictive analysis. Don’t get caught up in a self-repeating cycle of reaction.

Be strategic. Data can be a wonderful tool for organizations, helping them to answer questions and create new models; however, it is imperative that employers follow through with the data to turn insights into meaningful action. Data can be powerless unless employers determine what to do with it and what comes next.


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Case study 3 Better predictions = better staffing

Figure 5. Predicted change in HAI occurrence likelihood in the next month when increasing inpatient days from 661 to 954

54.3%

51.1%

61.3%

76.9%

85.4%

CAUTI

C. diff

CLABSI

MRSA

VAE

Key findings Higher patient days in current month predicts higher HAI next month. Recommendations Consider a more optimal scheduling and staffing model to reduce HAI occurrence.

Consequently, the organization scrambled to staff and care for patients during spikes of visits — and this was showing up in patient safety. The solution was threefold. First, better predictions of patient visits were required so that the organization wasn’t always in “reaction mode.” Second, XYZ put an allowance in place to expand the staffing model in anticipation of change. Finally, the system added refreshers on patient safety measures in advance of predicted increases in patient volume.

Mercer found that even when controlling for other factors, such as nurse/patient ratios, case complexity, diagnostic type, employee characteristics and organizational practices, spikes in patient visits had a disproportionate impact on HAI incidence. A 50% increase in patient visits led to an increase in HAI occurrence of up to 84% — far beyond expectation.

In a typical year, flu season leads to fairly predictable spikes in patient visits. However, being able to better predict when people will show up for care can lead to cost savings (less overtime and agency staffing) and dramatically better patient safety. This is an inherent risk in many of the “lean-staffing” models adopted for financial reasons: They tend to be less resilient in the face of change. In hospital system XYZ, it was not uncommon to see increases in patient visits of more than 33% month-to-month, usually predicated by flu season.

Each of the case studies show unique connections between patient outcomes and workforce practices in healthcare settings. These can be learned through systematic analyses customized to each healthcare system. This underlines two important points. First, the data used to make these connections in each system was not new — it just wasn’t being used to its potential. Second, what works for one system — such as boosting overtime in system ABC — does not work for a different system. In fact, the influence of a specific practice may have the opposite effect.

In summary, Mercer’s value-add approach is an exciting opportunity for employers and hospital systems to reinvent data analytics not just to understand a problem but to create a roadmap for long-term business growth. Better results come from balancing the cost models with staffing and management practices that improve patient care. The data is all there for making the right decisions — you just need to use it. As the patient population ages and a greater proportion are covered by Medicare and Medicaid, understanding the “people” drivers of quality and outcomes will have a larger reputational and financial impact. Those organizations that do not use this data to improve care will be left behind.


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Copyright 2021 Mercer LLC. All rights reserved. 6011469-CRA business of Marsh McLennan

For more information, please contact:

Brian Stucky [email protected]

Uniko Chen [email protected]

Dan [email protected]

Matt Stevenson [email protected]

Rick Guzzo [email protected]

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A value-add approach to workforce planning in healthcare