Presentation is loading. Please wait.

Presentation is loading. Please wait.

Pursuing High Value Healthcare

Similar presentations


Presentation on theme: "Pursuing High Value Healthcare"— Presentation transcript:

1 Pursuing High Value Healthcare
Optimizing Laboratory Testing Learning Session 3 April 2, 2015 Central Vermont Hospital Conference Center

2 Few Reminders Please silence your cell phones, pagers, etc
Restroom location Collaborative Materials Agenda Worksheets

3 Morning Agenda Welcome and Collaborative Timeline Team Reports Break
Research Findings- Optimizing Laboratory Testing PDSA- Putting Ideas into Action Lunch

4 Afternoon Agenda Data Update NORC Data Upload Report Reviews
Data Comparisons Team Working Session -Design at least one PDSA to implement -Determine Process Measures and create a data collection plan -Discuss potential barriers Team Report Out from Working Session Next Steps

5 Welcome Allen repp

6 Collaborative Timeline
Learning Session 1 Oct. 22 8:30 to 3:30 Session 4 Jun. 4 DHMC Session 2 Feb. 12 UVM Kick-Off Week 1 Sept 11, 2PM Session 3 Apr. 2 CVH Pre-work 5-6 weeks Conference Call / Webinar Nov 6, 2PM Dec 4, 2PM Jan. 8, 2PM Continuous Coaching/Faculty Support Collaborative Timeline Conference Call / Webinar Mar 5, 2PM Conference Call / Webinar May 7, 2PM

7 Our Vision Continuous Quality Improvement
State-wide learning, sharing & support system Patient Centered Improvement Efforts Foundation: Common Tools & Techniques Quality Improvement Tools Model for Improvement Common Lab Improvements & Site Specific Improvements

8 Our First Collaborative: Global Aim
We aim to reduce harm to patients and conserve system resources by optimizing the use of laboratory tests for patients cared for in our region’s hospitals. We will use a collaborative approach considering the best medical evidence and quality improvement science. It begins with an evaluation of current test ordering profiles and patterns followed by an organized plan to optimize testing and ends with a plan to sustain these practices. By doing this we expect to reduce cost and improve satisfaction and quality of care for patients and the health system. It is important to work on this now because as health care professionals we can play an important role in health care reform by designing more patient-centered, efficient and high value inpatient care.

9 Team Reports Learning Session 3 April 2, 2015
Optimizing Laboratory Testing Collaborative

10 Our Team Report Team: UVMHN CVMC Learning Session 3 April 2, 2015
Optimizing Laboratory Testing Collaborative

11 Our Team Team Members – List Names and Institutional Roles:
Don Weinberg, MD Kristin Calcagni, Lab Judy Perdue, Lab Justin Stinnett-Donnelly, MD Kevin Knapp, IT Tommie Murray, QM Team Meeting Frequency and members who attend: Monthly meetings

12 Our Data Status of Baseline Data Query:
Run Please see next Slide Specific Institutional Aim(s) and other data reviewed to support your aims: (Please use the AIM template language on the QI Monitoring Worksheet) Decrease the number of laboratory draws / day Patients will not getting woken up at 6am with a needle Reduce the possibility of anemia Reducing patient pain and discomfort

13 Baseline Data 1/2013 – 2/2015 (# tests/1000 pt days)

14 CVMC March Data -2.2 stDev -5 stDev 0.3 stDev

15 Our Ideas/Theories for Change
Change Ideas/Theories to meet Specific Institutional Aim: Modification of standardized admission orders to eliminate recurrent orders for CBC, BMP and CMP Continue ability for MDs to add recurrent labs when clinically indicated Action Plan and Timeline for each Change Idea: March 2, 2015

16 Our Next Steps Data review monthly
Calculation of total numbers of each test / patient / day

17 Optimizing Laboratory Testing Collaborative
Our Team Report Brattleboro Memorial Hospital Learning Session 3 April 2, 2015 Optimizing Laboratory Testing Collaborative

18 Our Team Team Members – List Names and Institutional Roles:
Aida Avdic – Hospitalist A. Shams Helminski – Hospitalist Michele Rowland, Charmaine Winton – Quality Frank Field , Doreen Lincoln, John Farina – IT Carolyn Allen - Laboratory Team Meeting Frequency and members who attend: Monthly meetings exchange

19 Our Data Status of Baseline Data Query:
Extracts completed – data uploaded to NORC this week Specific Institutional Aim(s) and other data reviewed to support your aims: (Please use the AIM template language on the QI Monitoring Worksheet) Minimizing routine morning laboratories (CBC and BMP) for patients admitted within 24 hours (evening and night admissions) Optimizing CBC ordering for stable patients (every hours)

20 Our Ideas/Theories for Change
Change Ideas/Theories to meet Specific Institutional Aim: Hospitalist meeting (monthly update and reminders) Limitations of the electronic health record (no easy way to build in reminders or ‘hard stops’) Effect of shift change / service change Action Plan and Timeline for each Change Idea: Test period March and April Reminders on Hospitalist work stations

21 Our Next Steps Data analysis and comparison of pre / post intervention
Awaiting data for March Feedback to Hospitalists Other tests to address?

22 Optimizing Laboratory Testing Collaborative
Our Team Report The University of Vermont Medical Center Learning Session 3 April 2, 2015 Optimizing Laboratory Testing Collaborative

23 Our Team Team Members : Meeting Frequency:
Allen Repp MD, Chief, Primary Care Internal Medicine Trace Barrett MD, Medicine Resident Mark Fung MD, Medical Director Lab and Pathology Steven Jarzembowski MD, Medicine Resident Mark Pasanen MD, Program Director, Internal Medicine Hospitalist Program and Internal Medicine Residency Program LeAnna Burgess MD, Medicine Resident Melissa Holman RHIA, CHDA, Senior Measurement Analyst, Jeffords Institute for Quality Jill Warrington MD, Pathology Lauren Pearson MD, Pathology Resident Allison Kaigle Holm PhD, Senior Research Specialist, Jeffords Institute for Quality Maria Burnett MD, Medicine Resident Heidi Guevin RN, Quality Improvement Consultant, Jeffords Institute for Quality Meeting Frequency: Standing weekly meeting Meeting approximately 1x/month since our last collaborative session. Work accomplished in sub-groups

24 Our Data Status of Baseline Data Query:
Preliminary data retrieved for 2013 Required baseline data accessed from UVMMC shared drive Specific Institutional Aim(s) and other data reviewed to support your aims: Decrease unnecessary venipunctures and blood loss Retrieved and reviewed All Service Medicine Admissions with standing daily lab orders Family Practice, Medicine, PCIM

25 Our Ideas/Theories for Change
Change Ideas/Theories to meet Specific Institutional Aim: Education for healthcare providers Modify admission order sets with daily labs Modify “a la carte” orders with daily option Patient lab needs discussed on rounds with rounding tool/checklist Lab orders to identify last three results Lab orders to identify costs of labs Hard stop to indicate/identify need for daily lab order Eliminate lab testing on day of discharge Incentives for not ordering labs Action Plan and Timeline for each Change Idea: MD Education- presentations at education forums: 2/2, 2/4, 3/3 Modify Gen Med/Family Med Admission order sets: PRISM re-engaged, meet Modify Admission order sets from other services Modify “a la carte” lab orders: TBD Resident (medicine/pathology) Survey surrounding ordering practices-draft Chart Audit Tool- draft

26 Our Team Report Porter Hospital Learning Session 3 April 2, 2015
Optimizing Laboratory Testing Collaborative

27 Our Team Team Members – List Names and Institutional Roles:
Amber Bailey, Information Technology Marianne Collins, Quality Director David Rand, Hospitalist Julie Vest, Lab Director Rebecca Woods, Information Technology Director Team Meeting Frequency and members who attend: We will meet as a team to review our data in the next week or two

28 Our Data Status of Baseline Data Query:
Data submitted. Looking forward to the results Specific Institutional Aim(s) and other data reviewed to support your aims: Create a QI culture Decrease blood draws, patient discomfort, anemia

29 Our Ideas/Theories for Change
Change Ideas/Theories to meet Specific Institutional Aim: -Decrease frequency of reflex platelet checks when on lovenox -Cease phlebotomy practice of from drawing extra “hold” tubes ….More to come once data is reviewed Action Plan and Timeline for each Change Idea: -Not yet defined

30 Our Next Steps Review data
Invite all stakeholders in the next two weeks to meet

31 Optimizing Laboratory Testing Collaborative
Our Team Report NVRH Lab Collaborative Learning Session 3 April 2, 2015 Optimizing Laboratory Testing Collaborative

32 Our Team Team Members – List Names and Institutional Roles:
Michael Rousse, MD, MPH Hospitalist Director Bonnie Torres, MLS (ASCP) Director of Laboratory Services Andrea Dinneen, MBA, MPH CIO, VP Information Services Jim Coulson Infection Control Officer Ryan Cloutier Application Support Analyst Team Meeting Frequency and members who attends: Currently developing team strategy

33 Our Data Status of Baseline Data Query:
All Baseline Data has been collected and uploaded to NORC. Specific Institutional Aim(s) and other data reviewed to support your aims: Reduce the number of unnecessary labs Target CBC/CBCD, BMP/CMP, Albumin, and Troponins (w and w/o reflex testing) Study Number of Tests, Number of Tests per Avg Daily Census, Number of Tests per Patient per day. Compare the total Number of Phlebotomies, Avg Daily Census, Avg LOS, and Annual Discharges for 18 yo+.

34 Our Ideas/Theories for Change
Change Ideas/Theories to meet Specific Institutional Aim: Develop a safe method to reduce unnecessary labs as identified by our data Possible Solutions Modify Workflows Modify Order Sets Incorporate Order Level Decision Support Etc. Action Plan and Timeline for each Change Idea: TBD after receiving initial results from data analysis

35 Our Next Steps Work with the Collaborative’s Data Analysts
Begin collecting monthly data Finalize Team Strategy Begin developing strategies to decrease the unnecessary labs

36 Univ of Vermont Medical Center
Am J Clin Pathol 143: March 2015 issue Presentation By Mark Fung, MD PHD Univ of Vermont Medical Center

37 Study Design 2 month intervention in general medicine units at 400 bed hospital Educational flyers in offices Periodic reminders of using daily lab tests only if results changed patient care 2 month pre-intervention data (982 patients) 2 month post-intervention data (988 patients) Measured # of daily blood tests/patient/day Included CBC, BMP, CMP, PT/INR, PTT

38 Messaging Interactive educational sessions with providers and nurses
Discussions at division meetings and noon conferences Educational flyers in provider and nurse work areas Weekly communication to all providers and nurses

39

40 3 Consistent Points Covered
Providers should: Question the utility of very blood test and order only if result will affect patient care Think about the impact that costs of blood tests have on health care expenditures Consider “adding on” test to blood samples already collected whenever possible

41 Results Am J Clin Pathol 143:

42

43 Pros and Cons to their approach
Low cost intervention – talks, flyers, s, awareness Did not compromise provider work flow or efficiency Con Unknown how much of the improvements will persist long term

44 (in press)

45 Study Design – QI intervention
53 providers across 4 inpatient facilities Focused on common labs ordered by a large community hospitalist group Academic detailing, audit, feedback, and transparent reporting of performance Pre-analysis was 10 month baseline (n=7824) 7 month intervention period (n=5759)

46 Messaging Introductory email recommending 2 changes:
Immediate stop to practice of open-ended ordering of common labs as daily Assessing the need for common labs in the next 24 hours, and ordering based on that need, but no further into the future

47 Messaging (continued)
Providers were informed that number of common labs ordered daily would be monitored prospectively Monthly reports given to individual providers Top 5 hospitalists with highest frequently ordered labs sent a personal notification of their “top five” status

48 Messaging (continued)
Monthly report included: Reminder of the recommendations and reasoning in the original List of all members and their frequency of lab ordering as daily (open ended) per month Recommendation to discontinue daily ordering of common labs At least one example of a patient with 5 days of daily labs with no mention in the progress notes

49

50

51 Limitations of this study
Patient characteristics were statistically different between pre-analysis and intervention period (but small) Choosing Wisely Report was made public during same time of start of intervention period Baseline and Intervention time periods were not seasonally matched.

52 Quality Improvement Process
PDSA SDSA 1 2 3 Success Measure Global Aim Assessment Theme Global Aim Specific Aim Change Ideas Measure(s) Step 8- Test Your Changes Step 7 –Determine Process Measures Step 6- Brainstorm Change Ideas Step 5 –Determine Cause and Effects Step 4- Create Specific Aim Step 3- Create Global Aim We are here! Step 2- Get Baseline Data Step 1- Form Team Modified from Quality By Design, A Clinical Microsystems Approach Nelson, Batalden and Godfrey© Trustees of Dartmouth College

53 Model for Improvement Setting Aims Establishing Measures Selecting
1. What are we trying to accomplish? Setting Aims Establishing Measures 2. How will we know that a change is an improvement? Selecting Changes 3. What changes can we make that will result in an improvement? Avoid this turning into a viscous cycle. Small short term tests of change. TAKE ACTION! Nolan’s model--three questions and PDSA, but not just one PDSA. Difference between this and Juran is the emphasis on repeated PDSA cycles Testing Changes

54 Important Things to Know about the Model for Improvement
Can be used for most types of changes Focus on small tests of change New tests are based on what you learn Approach should not create chaos Only spread to others when you are ready

55 Why Test? Increase your belief (and others!) that the change will result in improvement Document how much improvement can be expected from the change Understand the effects – upstream & downstream Minimize resistance to change 17

56 Tips for Testing Changes
Team approach (interdisciplinary) Understand where you are today – baseline Do not try to get buy-in, consensus, etc. Strive for Ownership not buy-in! Test with engaged volunteers Scale down size of test # of patients, location, # of providers Run tests in a small period of time Test over a wide range of conditions Different days, ages, genders, locations 25

57 Small RAPID Tests of Change
Act Plan Adopt, adapt or abandon based on what was learned Build knowledge into next PDSA cycle State objectives Make predictions Who will do what by when Study Do Carry out the test Document problems, surprises, observations Complete analysis Compare data to prediction Summaries what was learned

58 Reminder: Balanced Cycles
Too Much Doing Too Much Studying Too Much Acting Too Much Planning Act Act Act Plan ACT Plan Plan PLAN Study Study Too Much Studying STUDY Do DO Study Do Do

59 Reminder: Push Yourself & Your Team
If you think you can test it in… 1 Day……..few hours 2 Weeks….few days 1 Month….1 week 1 Year……wrong model!

60 Form Team-Rutland Regional Hospital
Step 1- Form Team Team Members Name Role Dr. Rick Hildebrant MD Lead Joe Walker Project Admin and Lab Lead Wendy Bixby Admin Support Sherry Ravlin IT Data Mgr Deborah Roy IT Project Mgr Daniel Michel IT Report Writer Rob Mcginness IT Interface Elizabeth Mahar-Kyhill Nursing Lead Dr. Susan Blish MD Alternate Angela Murphy Performance Improvement/QM

61 Assessment Step 2- Baseline Data- Inpatients > 18 yrs. of age calendar yr. 2013 Baseline Data Hemo Lytes BUN Creat PTT TBD # of Tests #Tests/Avg. Daily Census # Tests/Patient Day Total Inpatient Lab Tests # Annual Inpatient Phlebs AVG Daily Census AVG Length of Stay (LOS) Annual # of discharges 18yr+ EMR Based Order Protocols?

62 Institutional Aim Step 3- Create Institutional Aim
We aim: To improve the quality and value of the care we provide to our patients by ensuring we order the appropriate laboratory tests during their stay. In/at: Rutland Regional Medical Center The process begins with: Analyzing how our laboratory ordering and collection data compares to the rest of the collaborative. The process ends with: A workflow to minimize unnecessary laboratory testing. By working on the process, we expect: To reduce unnecessary testing and phlebotomy and to improve patient satisfaction with their care. It is important to work on this now because: We have an opportunity to improve the quality and value of the care we provide to our patients.

63 Specific Aim Step 4 –Create Specific Aim We will: Decrease
30% of the contracted hospitalists will reduce the percentage of day 2 CBC’s ordered on patients with a normal admission CBC for COPD. From: our current value to be provided by the collaborative To: less than X% of the current value By: June 1, 2005

64 Fishbone/Cause and Effect Diagram

65 Report Monitoring

66 Report Monitoring

67 Change Ideas List Possible Change Ideas:
Step 6- Brainstorm Change Ideas List Possible Change Ideas:

68 Plan Step 7- Determine Process Measures/Plan What will be Measured
Who will Measure? How will it be measured? When

69 Plan Step 7- Determine Process Measures/Plan What will be Measured
Who will Measure? How will it be measured? When

70 DO Step 8- Test Your Change Cycle 1 Date: February 16, 2015 What Who
What Who How When

71 Study What was learned? What will be changed?

72 ACT Cycle 2 Date: February 23, 2015 What was learned?
What Who How When What was learned? What will be changed?

73 Lunch!!

74 Data Update

75 Data Definitions Patient
Patient Days: (“date/time discharge – date/time admit”)/24 Patient Stays Hospital Days: 0 (admission), 1, 2, 3, 4, 5, ….

76 Data Initial Measures: Admissions
Number of Patient Days: sum((“date/time discharge – date/time admit”)/24) Number of Unique Patient Stays Number of Unique Patients Length of Stay: average, standard deviation, median, 25th & 75 percentiles 15 Most Frequent DRGs

77 Data Initial Measures: Labs Number of Unique Date/Time Collections
Total Number of Date/Time Collections 15 Most Frequent Lab Test Codes

78 Data Initial Measures: Refining (HELP!)
What DRGs do we want to exclude? 775 vaginal delivery w/o complications 765 cesarean section with complications 766 cesarean section w/o complications others? What Lab Test Codes should we focus on? collaborative vs. institution specific distinguishing lab panels vs. individual tests – is there a need? do we know what these test codes mean? are these all blood tests?

79 Data Pending Measures Distribution of Hospital Days
Day 0 (day of admission) Day 1 (starts 12am after day of admission) Day 2 And so on…. Number of Unique Date/Time Collections by Hospital Day Day 0 Day 1 Day discharge Day discharge -1, etc

80 Data Pending Measures Sequential Date/Time Collections
Do patients get the same labs every day? Normal lab results How can we define if a value is within a standard normal range? If you have two normal values in a row, what is the probability the third draw will be abnormal? Stable lab results If values are not within a normal range, how we can tell if there is change over time? Delta checks Focus on the abnormal values

81 Data Collaborative Comparison Measure
The number of unique date/time collections per patient day # Unique date/time collections in a given month Total Number of Patients Days in a given month

82 Data Collaborative Comparison Measure
The number of unique date/time collections per patient day UVM MC: 25,223/8, = 2.91 (Oct 2014) CVMC: 931/1,568.21=0.59 (Oct 2014) RRMC: 2,305/2,687.31= 0.86 (Oct 2014) PMC: 101,422/25,755.15=3.94 (All Baseline) NVRH: 13,804/14,232.37=0.97 (All Baseline) BMH: 557/471=1.18 (Dec 2014) Bennington: pending DHMC: pending

83 Working Session All Teams & Organizations

84 Supported Team Working Session
Review Tracking Tool & Action Plan Review Institutional/Specific Aims Create at least one PDSA to implement Determine Process Measures and data collection plan Consider possible barriers

85 Team Report Outs PDSA Plan Process Measures Data Collection plan
Barriers What might limit your ability to change? What support will you need?

86 Shared Team Documents We wanted to make it easy for teams across institutions to easily share documents with each other Protected, but not difficult to access Admin approval needed before documents can be seen Created a section on the website where team members can upload, and download files Each institution has its own username and password Used across the institution Will provide username and passwords to team leaders

87 Shared Team Documents How do you use the Shared Team Document function? Start at VMSFoundation.org Click on the Optimizing Laboratory Testing Collaborative image (home for all Collaborative information) Click on Shared Team Documents Option to Upload or Download files, make choice and enter username and password

88 Shared Team Documents Uploading a file: File Title - Give it a name
File Description - Summary paragraph Click Browse and then select the file on your computer Click upload Categorize document Institution: Select your institution Document Category: Data, Interventions, Measurement, Other, References Click preview and/or save It will not appear in the database immediately. An admin will have to approve. Five of us, so should happen quickly.

89 Shared Team Documents Downloading a file: Click Download Documents
Documents are sorted by Document Category and Project Click parameters to sort Click on desired file name and then apply to download.

90 Next Steps Keep meeting with your team!
Determine frequency time and location of future meetings- Schedule them! Collect and/or Validate Baseline data Finalize your site specific improvement aims Finalize your change ideas Finalize your plans to measure your success Plan your PDSA cycles Check out our website Work in progress VMSfoundation.org: click on High Value Care for Vermonters box We will be contacting you to set up a time for one of us to join a team meeting at your location.


Download ppt "Pursuing High Value Healthcare"

Similar presentations


Ads by Google