Predictive Analytics in

Transcription

1 Predictive Analytics in Emergency Medicine: Improving Patient Flow, Access, Experience and Profitability Timothy Reeder, MD, Vidant Medical Center Tommy Bohrmann, PhD, Roundtable Analytics

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3 Vidant Medical Center Emergency Departments Adult Children Minor 77,000 visits 67 Beds 38% admission rate 27,000 visits 16 beds 11% admission rate 34,000 visits 10 beds ~ 15 relocations/day

4 Vidant Medical Center Emergency Departments Medical Director responsible for operations One of top 25% busiest academic EDs Complex staffing needs Like a medium sized hospital Throughput is a critical quality measure

5 My Data Journey

6 My Data Journey Promise of EMR has not materialized Too much data in and not much information out Data is not available to inform Important management metrics Volume Throughput Measures Left Without Being Seen (LWBS) Length of Stay (LOS) Finances I looked at crowding measures and wrote paper in 2001!! There has to be a better way ENTER Roundtable Analytics

7 Copyright 2017 Roundtable Analytics, Inc. The Challenge DATA IMPROVEMENT

8 The Partners Copyright 2017 Roundtable Analytics, Inc.

9 The Stakeholders ED Administrators Performance Improvement Statisticians EM Physicians Information Technology EM Nurses Hospital Administrators Copyright 2017 Roundtable Analytics, Inc.

10 Saunders et al 1989

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12 Copyright 2017 Roundtable Analytics, Inc.

13 Copyright 2017 Roundtable Analytics, Inc.

14 Copyright 2017 Roundtable Analytics, Inc.

15 Copyright 2017 Roundtable Analytics, Inc.

16 Copyright 2017 Roundtable Analytics, Inc.

17 Copyright 2017 Roundtable Analytics, Inc.

18 Copyright 2017 Roundtable Analytics, Inc.

19 Case Study - Aim Redesign nurse staffing model of the main ED in costeffective manner using simulation to provide the most efficient, effective, and valuable care

20 Historical Data: Patient count by hour by day of week Copyright 2017 Roundtable Analytics, Inc.

21 Will the Change Be an Improvement? Match staffing to patient demand Minimize patients leaving without being seen (LWBS) Decrease patient length of stay Increasing value of care by decreasing cost Ideal use of simulation to answer complex question

22 Simulation Results

23 BUDGETED PROPOSED

24 Comparison of Metrics Measure Budgeted Proposed Delta LWBS 11.69% 2.05% 9.64% decrease Patients Treated 1,207 1, increase Door to Provider 43 minutes 29 minutes 14 minute decrease Door to Disposition 252 minutes 213 minutes 39 minute decrease Door to Exit 332 minutes 316 minutes 16 minute decrease Collections labor $34,304,153 $37,527,655 $3,223,502 increase Approval of 17 new nursing FTE s Middle of budget cycle at time of other cuts

25 Descriptive Analytics Measure past and current performance Identify strengths, weaknesses and opportunities Manage adaptively Copyright 2017 Roundtable Analytics, Inc.

26 Daily Analytics Copyright 2017 Roundtable Analytics, Inc.

27 Copyright 2017 Roundtable Analytics, Inc.

28 Daily Analytics Copyright 2017 Roundtable Analytics, Inc.

29 Historical Analytics Copyright 2017 Roundtable Analytics, Inc.

30 Data Extract All analytics leverage an automated, daily extract from EMR backup database (e.g. Clarity for Epic) Automatic processing Analysis-ready (e.g. simulation) Analytics-ready (e.g. daily front page) No need to integrate with live EMR environments Copyright 2017 Roundtable Analytics, Inc.

31 Simulation: Planning to build capacity Who? What? When? How?

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34 Simulation: Managing through the renovation Multi-year, multi-phase Address future challenges today Adapt quickly (things don t always go as planned ) Copyright 2017 Roundtable Analytics, Inc.

35 Current State 14 Beds Copyright 2017 Roundtable Analytics, Inc.

36 Interim Phase I 10 Beds Copyright 2017 Roundtable Analytics, Inc.

37 Interim Phase II 17 beds Copyright 2017 Roundtable Analytics, Inc.

38 Final State 21 beds Copyright 2017 Roundtable Analytics, Inc.

39 Thank you! For more information: roundtableanalytics.com