This book includes a plain text version that is designed for high accessibility. To use this version please follow this link.
● Staffing and Scheduling


Consolidating T


healthcare How migrating to enterprise strategies helps manage resources. By Chris Fox


he predominant trend in healthcare today is “consol- idation” – medical groups are consolidating, health


systems are consolidating and account- able care organizations are forming – all in an eff ort to control costs, optimize resources and take advantage of econo- mies of scale. As part of this mindset, more and more healthcare organizations are migrating to an enterprise approach to managing resources – specifi cally labor. As labor accounts for roughly 60 percent of every organization’s operat- ing budget, even small improvements in this area can have signifi cant savings potential.


Enterprise-level strategies Eff ectively managing labor resources at the enterprise level requires that work- force management technology be robust and sweeping, allowing an organization to view and manage resources at the system level, but be effi cient and stream- lined, enabling centralized management by a single individual or a few scheduling specialists/analysts. Key to this is the es- tablishment of best-practice polices and business rules that can be embedded into the system, ensuring standardization and


14 April 2013


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, de- mand forecasting and operational best practices into a customizable healthcare scheduling software solution. All healthcare organizations are dif-


ferent, but all generally struggle with a similar set of issues. T e commonality of challenges paired with the uniqueness of individual health systems requires a customizable and scalable solution set. HELM provides this, and the strate- gies that encompass it are applicable enterprise wide (inpatient, ancillary, clinics, etc.). T is 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


HEALTH MANAGEMENT TECHNOLOGY


Chris Fox is CEO, Avantas.


For more on Avantas: www.rsleads. com/304ht-202


and fl ow of patient demand in a cost- eff ective 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 ad- vanced predictive analytics throughout the scheduling and staffi ng 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 fl oat/con- tingency resources.


Predictive analytics also play a critical


role in eff ective open-shift management. Filling open shifts is generally the most time-intensive, stressful and expensive component of the scheduling process. T is is due mainly to the reactive nature of most open shift-management strate- gies. 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-


www.healthmgttech.com


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28