Reducing Readmissions and Improving Outcomes at OhioHealth Mansfield Hospital:

Transcription

1 Reducing Readmissions and Improving Outcomes at OhioHealth Mansfield Hospital: Eugenio H. Zabaleta, Ph.D. Clinical Chemist OhioHealth Mansfield Hospital

2 Reducing Readmissions and Improving Outcomes at OhioHealth Mansfield Hospital: Building Effective Lab-Physician Collaboration with more Engagement at the Pre-pre-analytical and Post-post Analytical Stages Eugenio H. Zabaleta, Ph.D. Clinical Chemist OhioHealth Mansfield Hospital

3 Objectives Explain the importance decision support for laboratory testing Describe examples where laboratory decision support impacts patient care. Explain how the laboratory can contribute to improved laboratory test utilization, improved patient safety, decrease in errors, reduction in health care cost, and reduction of length of stay through participation in EHR initiatives. Elaborate on the importance of teamwork in healthcare

4 OhioHealth Mansfield and Shelby Hospitals 351 beds combined (326 & 25) America s 50 Best Hospitals for Cardiac Surgery by Healthgrades Top 5% in the Nation for Cardiac Surgery by Healthgrades Five-star Recipient for Coronary Bypass Surgery, Valve Surgery and Coronary Interventional Procedures by Healthgrades Health Care s Most Wired for Excellence by Hospitals & Health Networks 4

5 The Journey

6 On The Other Side of the Fence Dr. Michael Patterson, D.O. Vice President of Medical Affairs OhioHealth Mansfield and Shelby Hospitals 6

7 EHR LIS Pre-preanalytical Clinical Chemist Post-postanalytical Post-analytical Analytical Pre-analytical George Lundberg (JAMA 1981:245: ) The brain-to-brain turnaround time loop 7

8 Financial System Clinical Decision Support IT Integration (LAB) (Tools) Instrument Clinical System EMR Interface Engine LIS Data Instrument Manager EHR Anatomic Pathology Positive Patient ID Blood Bank Point Of Care Reference Lab 8

9 2010 First Troponin-I increased alert 9

10 Physician Request Identification and alert of clinically significant Troponin values Emergency Department Call (cut-off) Troponin Values (A30B) ng/ml Inpatients 1618 patient results (one month) 323 (ED cut-off) 61 meaningful /19/2007 2/20/2007 2/21/2007 2/22/2007 2/23/2007 2/24/2007 2/25/2007 2/26/2007 2/27/

11 LIS CDS Test Performed Reviewed by Rules engine CPOE DATA Information Tech Notified to call The Floor Nurse Order (Troponin I) Result Posted (no delay) Alert Posted (Doctor role) EHR The Solution Live 3/31/

12 EMR Fisrt Troponin-I= ng/ml (ED) 12

13 First Troponin-I increased alert Time Frame Control Group Study Group Dec 2009-Feb 2010 Dec 2010-Feb 2011 ctn Results First Increase N o Patients Cardiac Services Non Cardiac Services 67 (47.2%) 52 (40%) 75 (52.8%)* 78 (60%)* *Only about 50% were seen by a cardiologist or has diagnosis for Acute Coronary Syndrome at admission or before the first alert 13

14 Clinical Impact (Patient) Patient with no ACS Dx (at admission)* LOS: 7.89 days (Control) 6.17 days (Study) o 14% (no change in Dx) o 86% ACS Dx (Change) 47% (6 Hrs) 60% (12 Hrs) 70% (24 Hrs) Economic Impact (Hospital)** Savings: $300,000+ (Medicare) * Multiples co-morbidities (C.G.: 2.6; S.G.:2.9) ** Data Provided by Brad Peffley, VP Clinical Services OhioHealth Mansfield & Shelby Hospitals

15 1. 99th percentile cut off for troponin. Live 3/31/ Elimination of other cardiac markers 3. Flagging abnormal troponin values based on changes in value over time, Recommendation: "Clinical laboratory reports should indicate whether significant changes in cardiac troponin values for the particular assay have occurred." 15

16 16 Communication

17 2011 CHF-Risk Project 17

18 The Need Hospital Readmissions Reduction Program (3 rd Quarter 2012)- CMS 30-days readmission: Heart Attack Heart Failure Pneumonia 18

19 Heart Failure Rates 25.0% 41.4% 39.4% 11.6% 15.6% 12.9% 29.4% 34.4% 30.0% 33.3% 28.2% 14.3% 13.8% 24.0% 33.3% 17.2% 16.0% 14.3% 27.0% 26.5% 27.8% 25.0% 14.7% 10.5% 9.4% 14.2% 30.0% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% Jul- 09 Aug- 09 Sep- 09 Oct- 09 Nov- 09 Dec- 09 Jan- 10 Feb- 10 Mar- 10 Apr- 10 May- 10 Jun- 10 Jul- 10 Aug- 10 Sep- 10 Oct- 10 Nov- 10 Dec- 10 Jan- 11 Feb- 11 Mar- 11 Apr- 11 May- 11 Jun- 11 Jul- 11 Aug- 11 Sep % 19

20 2011 The Problem % Physician referral % 21.4% Recently diagnosed with HF Location (2 Hospitals) % 15.0 Patient Refusal % 0.0 US National Heart Success (MedCentral) Not Enrolled (MedCentral) Jan-Sep 2011 (MedCentral) Identification Vs. Recruitment 20

21 HF Risk Stratification Circulation 2003; 108; This stratification tool was chosen because ctni & BNP results were already available 21

22 LIS CDS Test Performed Reviewed by Rules engine CPOE DATA Information Order (BNP) Result Posted (no delay) EHR Heart Success (NP) The Solution Live 10/16/

23 Heart Failure Rates 35.0% 30.0% 27.0% 27.8% 30.0% 25.0% 26.5% 25.0% 20.0% 20.0% Go Live CDS 24.8% 15.0% 10.0% 14.7% 10.5% 9.4% 14.2% 13.3% 16.1% 12.9% 5.0% 0.0% Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11 Jan-12 23

24 Communication Resources 24

25 2012 Clostridium difficile Clinical Testing Algorithm IT 25

26 26 CPOE-EHR

27 LIS YES for unformed stools NO for formed stools Always YES (when ileus is suspected, formed stool is an acceptable specimen for testing) 27

28 Clostridium difficile Clinical Testing Algorithm CPOE-EHR RN 30 RN & IC CDS C. Diff orders Testing Algorithm 28

29 Clostridium difficile Project Utilization Impact (90 days) Nursing Time Back to Patient Care Less Samples Received by Lab Both Inpatients Outpatients Pre-Algorithm Sample Collection (CPOE physicians/nursing) Post-Algorithm Reduction (sample collection) (49.6%) 36 (8.2%) 2- Specimen Processing (Lab personnel/it) Received (lab) % Reduction Formed Stools (12.2%) 128 (31.8%) Previous Result (9.2%) 36 (9.0%) Testing Performed (post) (78.6%) 236 (58.7%) 29

30 Clostridium difficile Project Clinical Impact (Patients diagnosed with CDI) The laboratory cost for C. difficile testing decreased 23% (from $ to $ per month) Inpatients with + C. Diff. results LOS (Days) Pre-Algorithm 12.9 Post- Algorithm 8.4 Reduction 4.5 Analytical Performance Nursing/Lab Test of Cure Social Workers Patient Advocacy (To Home ASAP) Resulting in an average total hospital cost savings per patient of $9,849.50; this translates into a total annual savings of approximately $1.1 million per year. 30

31 Communication Resources IT, Teamwork, EBM 31

32 2015 Troponin Order Process Change Observation Unit Pilot (8/3/2015 and 11/1/2015 ) 32

33 New Process: 1. ED physicians order only the initial Troponin (ED orders were modified) 2. The admitting physician is responsible for completing the HEART Scoring tool (Soarian Clinicals documentation) 3. The admitting physician places the proceeding AMI orders (based on the HEART criteria and initial Troponin results) 33

34 New Process: 1. ED physicians order only the initial Troponin (ED orders were modified) 2. The admitting The success physician of this responsible approach hinges for completing on: the HEART Scoring tool 1. Physician education (Soarian Clinicals documentation) 2. Communication between the Emergency Department physician and the admitting physician 3. The admitting physician places the proceeding AMI orders (based on the HEART criteria and initial Troponin results) 34

35 Expected Improvement Outcomes Rule out AMI faster in Observation patient population Improve ED throughput Decreased Length of Stay Decrease potential AMI testing overutilization due to patient screening with HEART tool Decrease cost for patient Reduce unnecessary lab draws 35

36 Expected Improvement Outcomes Rule out AMI faster in Observation patient population Improve ED throughput When addressing overutilization: Decreased Length of Stay Decrease potential AMI testing overutilization due to patient screening with HEART tool Concern over missing something that can lead to litigation and/or patient harm Decrease cost for patient Reduce unnecessary lab draws 36

37 The question was: Can we improve laboratory tests utilization without putting patients at harm or increasing physicians/hospital liability? 37

38 Assuring patient safety by leveraging the EHR 38

39 ED Transfer From ED Orders (Troponin) Admitting Physician LAB (First Troponin) Alert Discontinued 39

40 Between 8/3/2015 and 11/1/2015 ED Transfer From ED NO Orders (Troponin) Two hours Admitting Physician Orders (Troponin) LAB (First Troponin) Alert Discontinued 40

41 Results (Between 8/3/2015 and 11/1/2015) 2123 patients were transferred from ED 603 alerts (pages) 319 (no further Troponins) 284 (further Troponins performed) 32 had clinically significant Troponin increases 527 phone calls Cost containment In 76 no phone call (Doctors Orders entered late) Improved utilization Patient safety Physician s Aid (safety net) 41

42 ED Transfer From ED NO Orders (Troponin) Two hours Orders (Troponin) Admitting Physician LAB (First Troponin) Alert Discontinued 42

43 Results (cont.) 356 patients were discharged from the observation unit or the hospital with no further Troponin testing performed beyond the first Troponin test done in the ED No readmission within 30 days No major adverse cardiac events (MACE) within 30 days No death within 30 days As a direct result of this new process, cardiac marker testing decreased by 28.1% (August-September 2015) 43

44 44

45 Communication Resources IT, Teamwork, EBM Clinical Workflow/Transition of Care 45

46 MU/EHR Lab Opportunities CPOE (CDS/duplicated checking) Implementation of Clinical Decision Support (results) Provide patients electronic copy of their health information Provides patients access to their personal health information (Patient Access Portal) Electronic copy of discharge instruction Protect electronic health information Incorporate clinical lab-test results into EHR as structured data Use certified EHR technology to identify patient-specific education resources and provide those resources to the patient if appropriate Capability to submit electronic data on reportable lab results to public health agencies (ELR/LOINC/SNOMED-CT/LOI/LRI/HIE) 46

47 Things we did and do well Analysis of current process and a commitment to allow change Establish a Strong Lab-Physician team (pillars) Respect Equality Understand each others strength, weakness, workflow, regulations Clear project s goals and objectives Including education when necessary (for lab and clinicians) Analysis of pre- and post-implementation metrics Leverage the best EBM available to us Stakeholders engagement 47

48 Things we could do better Clear and frequent communication among the team members in real-time Monitor: progress, set backs, new problems Learning from our mistakes Understand clinicians needs by specialty/procedures/units Obtain consensus of clinical practice, when EBM is ambiguous 48

49 Questions???? 49