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    Hawkesbury General Hospital

    Workforce Analytics: Diagnostic Assessment

    May 15, 2009

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    Diagnostic Assessment: UTHCT CONFIDENTIAL Page 2 of 17

    TABLE OF CONTENTS

    1 SUMMARY 3

    2 EXCESS LABOR COST 4

    2.1 OVERTIME 42.1.1 TRENDHOURS PEREMPLOYEE, PERINVOLVED 42.1.2 TRENDMONTHLYCONTRIBUTION TO TOTAL OTHOURS 52.1.3 OUTLIERS 62.1.4 CORRELATION OF OVERTIME TO OTHERMETRICS 72.2 TIME PAID NOT WORKED (ROUNDING RULES) 82.2.1 TRENDTIME PAID NOT WORKED (SPLIT BY IN/OUT PUNCHES) 82.2.2 OUTLIERS 9

    3 WORKFORCE ANALYTICS PLATFORM 10

    4 ASSUMPTIONS

    5 APPENDIX ATIME PAID NOT WORKED OUTLIERS 12

    6 APPENDIX BPAY CATEGORY MAPPING

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    1 SUMMARYOverall, the Hawkesbury General Hospital (HGH) has the potential to save $197,251 (annual,

    based on April 1, 2008 March 31, 2009).

    Kronos used Workforce Analytics, its proprietary labor analytics solution to prepare this diagnosticassessment. In general, labor analytic measures and KPIs provide micro-level visibility into the trends,drivers and outliers that create excess labor cost and lost productivity. In addition, Workforce Analyticsprovides: organizations with the ability to hold supervisors and employees accountable, managers withactionable information and data to support process improvements, and the corporation the ability tocontrol costs.

    Below is a summary of potential cost savings for HGH:

    $142,188 if overtime costs for outlier occupations are brought in line with company averageOvertime Hours as % of Total Paid Hours. $55,333 if rounding rule abuse is addressed.

    Both of these are expanded on in detail in the sections that follow.

    The cost savings analysis was based on wages for limited Occupation Types; overtime assumed to be 1.5

    times average regular for the purposes of the cost analysis included in this document.

    NOTE: The analysis contained in this document is meant solely to identify potential areas for costreduction. Kronos does not guarantee any labor cost reductions by delivering this document;

    savings may only result from actions taken by HGH. This assessment is blind to quality of careconsiderations that may impact HGHs ability to optimize labor scheduling and utilization.

    Cost-based measures are available in Workforce Analytics when wages are stored in WFC; useraccess to wages in WFC may be controlled by the Wage Access Profile Switch, i.e. the datacould be stored in WFC without anyone having access to it in WFC, access would only be formanagers with access to Analytics.

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    2 EXCESS LABOR COSTIn the Workforce Analytics platform, excess labor cost is comprised of several metric categories, including (but nTime Paid Not Worked (i.e. rounding rule abuse).

    2.1 OVERTIME2.1.1 TRENDHOURS PEREMPLOYEE, PERINVOLVEDThis Overtime Hours trend allows for very high-level comparison across the total organization; Workforce Analytic

    filter results by any Labor Level and/or Employee.

    The graph above shows that for employees who incurred overtime, a high was reached in August 2008, when 13.Involved (number of OT hours / number of employees who incurred overtime). Lower per Involved value means incurring the overtime (hours are distributed across more employees). The next section shows that total Overtimyear over year 24-month period.

    11.8410.29

    11.09 11.27

    13.29

    10.80 10.369.72 9.33 9.41

    02468

    1012

    14

    April May June July August September October November December January

    2008

    OT Hours per Employee, per Involved

    Hours Hours per Employee Hours per Involved

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    2.1.2 TRENDMONTHLYCONTRIBUTION TO TOTAL OTHOURSThis trend shows that Overtime has been steadily increasing at HGH, with significant peaks in March and August o2590.60 hours respectively.

    Considering this in context of the previous section: there are more total hours (upward trend) being distribnumber of employees incurring the overtime.

    This leads to the following questions for further consideration:

    Are there opportunities to replace overtime labor with regular labor? Does staffing need to be increased? Are there outside factors, e.g. closing of surrounding/competitive facilities that have contributed to influx

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    Overtime Hours: Trend

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    2.1.3 OUTLIERSHGH provided regular wage rate for select, non-salaried Occupation Types. Those occupations wereevaluated, and the following outliers are performing above the company average of 2.71% OvertimeHours as Percent of Paid Hours.

    Occupation Type Overtime HRS as a %of Worked HRS

    Overtime HRS as a %of Paid HRS

    Commis Clinique Consultant 7.79% 6.35%

    Orderly 5.55% 5.42%

    Reg Nurse 5.94% 5.11%

    RPN 4.94% 4.39%

    Senior Tech Ultrasound 5.41% 4.05%

    Senior Tech 4.25% 3.61%

    Security Guard with HOOPP - 1 yr plus 3.63% 3.58%

    Porter 3.17% 2.93%

    If overtime for these outliers is brought in line with average, i.e. reduced to 2.71%, HGH has the

    potential to save $142,187.60, based on April 1, 2008 March 31, 2009 data. This equates toreducing Overtime by 9179.95 hours, to be replaced by Regular hours.

    NOTE: Average regular wage per occupation x 1.5 = average OT wage per occupation. This costsavings example is based on wage information for a limited number of occupations; actualsavings may be greater (i.e. if there are additional outliers exceeding 2.71% overtimeperformance for which cost information was not available at time of this analysis).

    Another way to look at Outliers is by organization breakout, where the following 2 organizationssignificantly exceeded the company average 2.71% Overtime Hours as Percent of Paid Hours for the April1, 2008 March 31, 2009 time frame:

    Organization

    Overtime HRS as a %

    of Worked HRS

    Overtime HRS as a %

    of Paid HRS

    Hawkesbury General Hospital 3.19% 2.71%

    Ambulatory Services 5.41% 4.76%Nursing In-Patient Services 5.18% 4.40%Diagnostic & Therapeutic Services 1.32% 1.08%Administration & Support Services 1.16% 0.98%Community Services 0.30% 0.23%Mental Health Services 0.25% 0.22%Marketing Services 0.19% 0.17%In Service 0.04% 0.04%

    NOTE: This outlier information (section 2.1.3) was analyzed on the 12 months from April 1, 2008 March 31, 2009.

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    2.1.4 CORRELATION OF OVERTIME TO OTHERMETRICSLastly, Overtime may be taken in the context of other metrics. The following shows a trend of Overtime vs. Unexwords: Scheduled Not Worked / non-approved absences.

    As is clearly demonstrated, the increasing trend in overtime is paralleled by the increasing trend in Unexcused Abs

    NOTE: The accuracy of the Absenteeism category is dependent on schedules, which were present in the HGH da

    Absenteeism (displayed above), Excused Absenteeism requires manager approval of the exception (in Wo

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    O t b

    2007 2008

    All Paid OT All Absenteeism - Unexcused Absen

    Linear (All Paid OT) Linear (All Absenteeism - Unexcused

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    2.2 TIME PAID NOT WORKED (ROUNDING RULES)Good news HGH is benefiting significantly and consistently from the current rounding ruleconfiguration, a negative differential is benefit to the organization (positive would be benefit to theemployee).

    All occupations were included in the Rounding Rule differential hour analysis above.

    2.2.1 TRENDTIME PAID NOT WORKED (SPLIT BY IN/OUT PUNCHES)Regardless of the observation in the preceding section, the 12-month trend below shows that there issignificantly more abuse on the out punch. Thought of in a business context, this is perhaps people

    leaving early knowing that the out punch on their time card is rounded forward.

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