Enterprise-level strategies
Effectively managing labor resources at the enterprise level requires that workforce management technology be robust and sweeping, allowing an organization to view and manage resources at the system level, but be efficient and streamlined, enabling centralized management by a single individual or a few scheduling specialists/analysts. Key to this is the establishment of best-practice polices and business rules that can be embedded into the system, ensuring standardization and providing the framework for predictable and sustainable results.
These principles form the basis of a methodology known as HELM. HELM, or healthcare enterprise labor management, is an Avantas proprietary methodology that combines and embeds the science of workforce planning, demand forecasting and operational best practices into a customizable healthcare scheduling software solution.
All healthcare organizations are different, but all generally struggle with a similar set of issues. The commonality of challenges paired with the uniqueness of individual health systems requires a customizable and scalable solution set. HELM provides this, and the strategies that encompass it are applicable enterprise wide (inpatient, ancillary, clinics, etc.). This methodology has been successfully implemented in virtually every type of healthcare organization: single-site hospitals, academic medical centers, multi-hospital metropolitan systems, large regional systems, systems with extensive clinic operations and Magnet-designated facilities.
Predictive analytics
Key to adjusting the natural ebb and flow of patient demand in a cost-effective manner is the ability to forecast patient volume – and corresponding need for care staff – well in advance of the shift. Predictive analytics provide organizations with the ability to develop better schedules sooner. Utilizing advanced predictive analytics throughout the scheduling and staffing process also results in:
- Accurate, sophisticated budgeting;
- Improved core staff utilization from initial schedule creation – optimizing at-hand resources to meet patient demand; and
- Proactive alignment of float/contingency resources.
Predictive analytics also play a critical role in effective open-shift management. Filling open shifts is generally the most time-intensive, stressful and expensive component of the scheduling process. This is due mainly to the reactive nature of most open shift-management strategies. Reactive approaches to open shifts (posting shifts after ill calls, when there are sudden spikes in census, etc.) can result in last-minute chaos on the unit, time wasted in the recruitment process, high incentive dollars and decreased quality of care. A proactive open shift-management methodology based on accurate forecasts greatly reduces the time and dollars spent in filling open shifts, and makes instances of last-minute chaos much less frequent.
With a strategy involving predictive analytics, once initial schedules are created, any holes in the schedule (gaps between the anticipated/forecasted need and the number of staff scheduled) are automatically posted as open shifts for qualified staff members to pick up. Within some hospital organizations, these open shifts are attached to incentives. The incentives are based on severity of need and decrease in value as the day of the shift approaches, encouraging staff members to sign up sooner. One seven-hospital metropolitan system that utilizes this approach is able to fill 75 percent of open shifts at least two weeks in advance of the shift.
Technology automates best (and worst) practices
A lot of emphasis in healthcare today is put on tools: technology solutions a health system can buy and implement. However, technology without a foundation of sound, efficient and accepted policies and practices will result in little good. Technology does not magically eliminate the variances between policy and practice that exist within all health systems, but it can automate an organization’s business rules to create a more standardized and repeatable process.
Essentially, your software is the tool that automates your plan. Good software and a bad plan only automate a bad plan.
Laying a foundation of best practices, gaining buy-in across the organization to implement the necessary changes to create consistency and then embedding those strategies into a customizable solution are the only ways of ensuring that the changes an organization makes will be permanent.
Change takes time, but the results are worth the effort. The previously mentioned seven-hospital system undertook a massive transformation, going from a collection of individual hospitals operating separately into a truly interconnected organization. This change took five years, but it has had tremendous, sustaining results.
The system’s progression to an enterprise model of resource management and adoption of the forecasting and open shift-management strategies has helped staff members reduce their contingency usage from 33 percent of patient care hours to 12 percent over a five-year period, resulting in a cumulative savings of $23,588,580. Staffing satisfaction had a remarkable improvement over this same time period, as well. Among registered nurses (RNs), satisfaction with staffing levels increased from the 18th percentile in 2006 to the 86th percentile in 2011.
Consolidation needs collaboration
Organizations consolidate to control costs, optimize resources and take advantage of economies of scale. Ensuring that consolidation results in the savings and efficiencies it is intended to provide requires a great deal of organizational communication and collaboration. No single area – HR, finance, nursing or IT, for example – will be successful in the long term without the support and cooperation from the entire organization.
Outcomes achieved were due as much to collaborative culture as to technologies and workforce strategies implemented. All three of these elements must work in tandem to result in sustainable improvements. HMT
About the Author
Chris Fox is CEO, Avantas. For more on Avantas: www.rsleads.com/304ht-202
