HR Advancement Center. What Role Can Workforce Analytics Play in Preventing Turnover?

Transcription

1 HR Advancement Center What Role Can Workforce Analytics Play in Preventing Turnover?

2 2 The Promise of Big Data Compliance Industry: Banking Use Case: Studying patterns of fraud and noncompliance, and can now predict behaviors that will likely result in unethical behavior. Productivity Industry: Auto Use Case: Studying the patterns of unplanned absences; prescheduling extra staff to make up for known periods of absence. Recruiting Industry: Insurance Use Case: Analyzing the profiles of top salespeople; academic pedigree or GPA is no longer considered a strong indicator of future sales performance. Pricing Industry: Retail Use Case: Analyzing consumer behavior to offer targeted discounts, promotions, and segment-based pricing to target different consumers 2017 Advisory Board All Rights Reserved advisory.com 34798B Source: Bersin J, Collins L, Mallon D, Moir J, Straub R, People Analytics: Gaining Speed, Deloitte Insights, LaRiviere J, McAfee P, Rao J, Narayanan VK, Sun W, Where Predictive Analytics is Having the Biggest Impact, Harvard Business Review, HR Advancement Center interviews and analysis.

3 Shifting from Descriptive to Prescriptive Data Analytics 3 Three Progressive Stages of Analytics Degree of Difficulty 1 Descriptive: What happened? 2 Predictive: What might happen? 3 Prescriptive: What should we do? Degree of Competitive Advantage TURNOVER: Who left? Who is likely to leave? How could we stop them from leaving? 2017 Advisory Board All Rights Reserved advisory.com 34798B Source: HR Advancement Center interviews and analysis.

4 A Significant Investment, but Early Results Promising 4 MultiCare Health System Aurora Health Care Results: Reduction in first-year 27% turnover Results: Improvement in first-year 25% turnover since beginning of % 28% RNs recommended by the tool 40% less likely to turn over at the 180 day mark Overall hires recommended by the tool 28% less likely to turn over at the 180 day mark Improvement in RN voluntary 18% turnover since beginning of 2016 Improvement in voluntary turnover 12% overall since beginning of Advisory Board All Rights Reserved advisory.com 34798B Source: MultiCare Health System, Tacoma, WA; Aurora Health Care, Milwaukee, WI; HR Advancement Center interviews and analysis.

5 5 Planning Your Shift Beyond Descriptive Analytics Three Questions to Consider: 1What s your greatest area of opportunity to use predictive analytics to prevent turnover? 2How can you ensure an ROI on pre-hire screens? 3What do you need to be ready to build your own algorithm to predict turnover of in-seat staff? 2017 Advisory Board All Rights Reserved advisory.com 34798B Source: HR Advancement Center interviews and analysis.

6 Using Data and Predictive Analytics in the Healthcare Workforce Kevin B. Dull, ipartner for Health and Healing SVP and Chief of Human Potential For the Advisory Board Retention Roundtable December 17 th, 2017 in Washington, DC

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8 MHS RN annual turnover 14% 12% 10% 8% 6% 4% 2% System RN Annual Turnover 0% Q3

9 Nursing engagement continues to rise MultiCare Nursing Engagement Profile Relative to AB Benchmark 2017 Engagement Mean 2017: 4.97, 37 th percentile 2016: 4.91, 34 th percentile 41.4% 41.0% 41.7% 40.7% 32.0% 34.1% 36.9% 36.4% 18.3% 18.0% 15.2% 15.7% 8.3% 6.9% 7.2% 6.2% Disengaged Ambivalent Content Engaged Pulse 2017 Full 25th %tile Median 75th %tile 90th %tile Engagement for LPNs, PCAs and RNs

10 Brief overview of assessments Arena is focused on improving people, financial, and patient outcomes using data and predictive analytics MHS also uses a psychological assessment for leadership positions with a focus on fit for the role.

11 User experience for job candidate and HR 1 Candidate completes job application 2 Candidate completes Arena screening 3 Arena predicts outcomes for each role * Note: predictions shown in sample ATS

12 The deployment took approximately 8 weeks 1 Analysis 2 Arena Pilot 3 Full Deployment Analyzed employee turnover trends to find patterns Determined which roles to target during the pilot Evaluated effectiveness with nursing roles Reduced 90 day turnover by 36.5% in first 4 months Deployed across both nursing and non-nursing roles Investigated additional outcomes Collected applicant, interaction, and outcome data Built customized models for each location, department, and role Integrated Applicant Tracking System (ATS) Defined applicant, recruiter, and hiring manager workflows and experiences Automated periodic refresh of outcome data to improve accuracy over time

13 The results have been encouraging Qualitative Implementation is not a heavy lift Enables recruiters to have less emotional conversations with hiring managers Quantitative Processed over 20,000 applications Applicant completion rates above 90% Comparison Recommended vs. Non Recommended Results Overall hires have turned over 28% less at 180 days Staff RN hires have turned over 40% less at 180 days Before vs. After Overall first-year turnover is 27% lower

14 The results through Q Employee turnover: all predicted roles Employee turnover: Staff RN roles 12% 10% 8% 6% 27% 31% 28% 16% 12% 8% 7% 13% 40% 4% 2% 4% 0% 60 Day 90 Day 180 Day Not Screened and Non Recommended Recommended 0% 60 Day 90 Day 180 Day Non Recommended Recommended

15 Lessons learned Transition management and adoption is critical Determine whether the assessment is a minimum requirement Involve recruiters and hiring managers early in the process Identify champions to serve as coaches and resources Articulate the value (less time spent hiring and re-hiring) Data can be tricky Work with a partner that has the expertise and experience in healthcare organizations and roles Roles are different Nurse residency role was initially treated as a separate role but learned it was really a combination of many different roles

16 APPENDIX

17 A brief history of data and analytics efforts Started using data & predictive analytics in hiring in May of 2015 Initial focus on predicting employee tenure in nursing roles Recently added a prediction for employee engagement level Predictions are based on MultiCare s outcome data and customized for each department, location, and role

18 It fit seamlessly into our workflow Hiring workflow did not change and recruiters and hiring managers continued to use the same tools ATS: Hiring Manager: Hired Recruiter: Offer

19 Workforce Analytics and the Impact on Turnover Chirag Padalia Director, Workforce Strategy & Analytics Troy Dennhof SVP, Human Resources

20 About Aurora Health Care Private, not-for-profit, integrated health care provider 30 counties, 90 communities 15 hospitals More than 150 clinics 70 pharmacies 33,000 caregivers including more than 1,500 employed physicians Largest home care organization in Wisconsin More than 3.8 million unique patients 7.8 million patient encounters $5.3 billion in revenue Aurora Health Care 2

21 What keeps us up at night? Talent Shortage Talent Shortage Caregiver Retention Talent Shortage Diversity & Inclusion Leadership Aurora Health Care 3

22 Our Journey Basic Reporting Descriptive Analytics / Reporting KPI / Operational reporting from PeopleSoft Advanced People Analytics Supply-Demand Models Forecasted gaps for key roles Advanced recruiting analytics Predictive Analytics Which caregiver is at risk of leaving and potential risk indicators Integration of staffing models with Operations Ad-Hoc reporting Aurora Health Care 4

23 How? Workforce Strategy & Analytics Human resource data (PeopleSoft) - Demographics - Compensation - Performance - Other HR metrics Workforce Capacity Forecasts Productivity data - Patient volumes - Actual hours worked by job code - Non-productive time: Training, in-service, etc. Datamart Other ancillary data - Engagement - Patient Experience - Safety - Ad-hoc data Advanced Analytics Aurora Health Care 5

24 Workforce Analytics Talent Dashboard Aurora Health Care 6

25 Flight Risk Model Predictive & Prescriptive Analytics Aurora Health Care 7

26 Retention Management Process Workforce Strategy & Analytics Why Hire & retain the best talent What Retention management Highly engaged workforce How Actionable Insights Partner with operational leaders to develop retention strategies Aurora Health Care 8

27 Turnover Trends Predictive & Prescriptive Analytics Aurora Health Care 9

28 Outcomes TRANSFORMING HOW WE LOOK AT OUR MOST VALUABLE ASSET! HR TECH OUTLOOK MAGAZINE Aurora Health Care 10

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30 Question 1: What s Your Greatest Area of Opportunity to Use Predictive Analytics to Prevent Turnover? 1 Pre-Hire or In-Seat Pre-Hire versus In-Seat Considerations Pre-Hire Do you experience the most early turnover within the first 90 days? Is your candidate pipeline healthy enough that you can afford to weed some candidates out? Would senior leaders be on board with narrowing the candidate pool? Do operational leaders or tenured frontline staff give you feedback that the candidates who are hired are not a good fit? In-Seat Do you experience the most early turnover after the 90-day mark? Are the reasons new hires are leaving so varied that you cannot identify actionable trends to help you reduce turnover? Do frontline managers lack the skill or time to pinpoint retention risks? 2017 Advisory Board All Rights Reserved advisory.com 34798B Source: HR Advancement Center interviews and analysis.

31 Question 1: What s Your Greatest Area of Opportunity to Use Predictive Analytics to Prevent Turnover? 2 Your First Job Family Considerations for Deciding Which Job Family to Start With Job family has a higher-than-average turnover rate compared to other areas in the organization Organization has sufficient data for staff in this job family; job family has identifiable, role-specific attributes Managers in this job family need more support than others to pinpoint retention risks (either lack skills or time) 2017 Advisory Board All Rights Reserved advisory.com 34798B Source: HR Advancement Center interviews and analysis.

32 Question 2: How Can You Ensure an ROI on Pre-Hire Screens? 3 Pre-Screen Results Only Useful if Leaders Take Action Three Options for Using Pre-Hire Screen Results Use tool as a knock-out during candidate screening Use tool as another factor in the hiring decision Amount of Leader Trust Required Use tool to inform questions hiring managers should emphasize in an interview Candidate Assessment Vendor Guide available at advisory.com/hrac/wintalent 2017 Advisory Board All Rights Reserved advisory.com 34798B Source: HR Advancement Center interviews and analysis.

33 Question 3: What Do You Need to Be Ready to Build Your Own Algorithm to Predict Turnover of In-Seat Staff? 4 Signs You re Ready to Apply Analytics to In-Seat Staff Signs of Readiness You currently have someone who could manage a data analytics team. Or you are willing to hire a Director-level leader externally. HR has sufficient credibility among other leaders in the organization that a predictive analytics tool built by HR will be trusted and used. If you do not have internal talent to fill this role, you know how to hire for the needed skill set. There are other data analytics needs a team could fulfill beyond predicting turnover. Investing in a data analytics team will allow you to inflect a variety of organizational needs Advisory Board All Rights Reserved advisory.com 34798B Source: HR Advancement Center interviews and analysis.