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Understanding the past, measuring the now, predicting the future
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Journal of Health & Medical Informatics

ISSN: 2157-7420

Open Access

Understanding the past, measuring the now, predicting the future


3rd International Conference on Health Informatics and Technology

June 29-30, 2016 New Orleans, USA

Danny Rathgeber

Health-e Workforce Solutions, Australia

Scientific Tracks Abstracts: J Health Med Inform

Abstract :

One of the greatest challenges to a Healthcare Service in achieving its mission is the effective management of the professional workforce. Issues of workforce shortage, widely varying skill levels, shift work, staff retention and the mix of fulltime, parttime and casual workers compound the management challenge for a cohort that represents the biggest single cost of doing business. Failure to meet the challenge has impacts on financial performance, quality of service delivery and the overall reputation of the Healthcare Service. In today�s health care, the following Human Resource systems exist: Rostering systems to enable staff scheduling; Time and attendance systems integrated with award interpreters; Payroll systems to generate staff pays; Budgeting tools to estimate budgets; and Patient acuity systems to measure work demand for nurses. Existing Human Resource Management (HRM) systems concentrate on roster and payroll functions, but do not contain the kinds of information required to facilitate strategic, proactive workforce management practices. The �Predictive Modeller� does not duplicate the functionality of existing HRM systems; instead, it has the capacity to build virtual hospitals that are able to model and evaluate multiple workforce configurations. Although these software packages appear to enable proactive workforce analysis by allowing staff to directly input via self-scheduling, shift bidding, swapping, and processing payrolls, they are limited by their reliance on the �now� and the �past�. The �Predictive Modeller�, in contrast, uses past data to better understand the relationship between staff supply and work demand however, its sole focus is on the �future�. The Predictive Modeller is an Innovative Knowledge Base System, that integrates and works on the strengths of existing HRM software to enable hospitals to build virtual organisations of the future populated with real staff from today. A myriad of workforce scenarios can be auto-generated in search of the most cost efficient staffing model.

Biography :

Danny Rathgeber has an extensive experience in senior healthcare leadership positions and has been directly involved in software development for many years. Danny is the founding director of Health-e Workforce Solutions (2007), a software development and consultancy company that is making a significant impact on workforce management. As a former Executive Director of Nursing at Melbourne Health, Danny is well acquainted with the kinds of problems faced by managers in dealing with workforce issues. He is also aware of the enormous potential for service improvement and cost savings. Prior to executive roles Danny had more than twenty years experience working as a qualified Registered Nurse, with fourteen of those years working in critical care settings and ten years as Nurse Manager.

Email: danny@healthewfs.com.au

Google Scholar citation report
Citations: 2128

Journal of Health & Medical Informatics received 2128 citations as per Google Scholar report

Journal of Health & Medical Informatics peer review process verified at publons

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