Predictive Analytics can Help Solve Healthcare Workforce Crisis

But the Technology Remains Unknown among Nurse Managers Struggling to Cover Shifts

Growing shortages of healthcare professionals, and particularly registered nurses, are now being felt throughout our nation’s hospitals and health systems, threatening not only staff morale but also patient satisfaction and care quality. Without adequate staff, healthcare providers must optimize the workforce they already have. Predictive analytics is one of the most important – and underutilized – methods to assure that hospital shifts and healthcare enterprises have the right nurses in the right places at the right times.

Predictive analytics is a technology-enabled solution that is relatively unknown among nurse managers, who struggle each day to cover nursing shifts and provide the highest quality of care for patients. A survey of nurse managers, including chief nursing officers, directors of nursing and vice presidents of nursing, by AMN Healthcare and Avantas, found that 80% of nurse managers are unaware of available technology that can accurately forecast patient demand and staffing needs. Nearly 90% said that such technology would be helpful in the daily scheduling and staffing of nurses.

Survey: Problems Hurt Staff Morale, Impact Care Quality

The survey, Predictive Analytics in Healthcare 2016: Optimizing Nursing Staffing in an Era of Workforce Shortages, included a questionnaire completed by 85 nurse managers and interviews with 35 nurse managers, registered nurses and finance managers.

The survey found that nearly 94% of nurse managers say that under-staffing caused by scheduling and staffing problems hurts staff morale. Nearly 70% say they are very concerned about the impact on patient satisfaction, and more than half say they are very concerned about the effect on quality of care.

Said one Registered Nurse interviewed as part of the survey, “I feel like my patients are getting the short end of the stick, but I always try to give my all no matter what…” A nurse manager added, “Morale is affected adversely any time there are not sufficient staff to take care of patient needs.”

However, nurse managers are not using advanced methods to solve these problems: 24% use paper-based scheduling and staffing tools, 19% use simple digital spreadsheets, and 23% don’t use any scheduling tools at all. The apparent reason is that a large majority of nurse managers do not know about the availability of advanced scheduling tools that can manage and solve scheduling and staffing problems up to 120 days in the future.

While some healthcare enterprises engage in long-range workforce planning, this survey shows that most actions taken are short-term, reactive responses to immediate scheduling and staffing problems. Since these problems are prevalent at most patient care facilities, it can be surmised that the usual responses are not broadly successful.

Balancing Staffing Needs, Budget Concerns and Patient Care

The survey also included interviews with nurse managers, registered nurses and finance managers on scheduling and staffing problems. Many of these interviews reiterated the problems that registered nurses, nurse managers and finance managers face daily in balancing staffing needs and budget concerns.

Said one nurse manager, “When staffing is low, staff doesn’t have faith that it will ever get better. They don’t feel like anyone cares how hard they work, and I see how this impacts patient care.”

A Registered Nurse concurred, adding, “When we are understaffed, patients do not get the quality care they deserve. Nurses rarely have time to really connect with patients on a human level, because they have so many patients, tasks, and charting.”

Finance managers also share the frustrations of staffing problems, with one respondent stating, “It’s very difficult to maintain budgeted productivity levels when there aren’t sufficient people to staff the shifts.”

Predictive Analytics Offer New Approach

Predictive analytics is already used successfully for resource management in industries such as manufacturing, transportation and financial services. The same approach can be applied to healthcare scheduling and staffing through the analysis of “big data” about patient census, public health threats, business information about the enterprise, and other material.

In the predictive analytics processes, staffing data are first processed with standard algorithms, then forecasting models are created and validated, allowing workforce projections up to 120 days prior to the shift. The forecast is updated weekly, and by 30 days in advance of the shift, the forecast of staffing need is 97% accurate. The analytics are then applied to workforce scheduling and staffing through advanced labor management strategies such as an open-shift management process that allows hospitals and other healthcare organizations to fill 75% of open shift hours more than two weeks ahead of the shift.

Healthcare enterprises that have adopted predictive analytics combined with advanced labor management strategies have realized outcomes that include reductions in agency nursing, increased staff satisfaction scores, improved nurse retention, reductions in open shift incentives and bonus pay, and significant annual savings in labor spending. Hospitals are routinely saving 4% to 7% of labor budgets, which is considerable since labor typically represents over half the annual costs for healthcare organizations. Predictive analytics can transform scheduling and staffing from guesswork to science -- and staff, patients and healthcare organizations reap the rewards.

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