Infusion Center Throughput Phase 1

Transcription

1 Infusion Center Throughput Phase 1 Green Belt: Ranjeeta Kumar, MBA, Luanne Sims, RN, FNP & Dawn Shelton Champion: Alice Issai Date March 21,

2 DEFINE PHASE What is the problem? LSS: Infusion Center Throughput 2

3 DEFINE Problem Statement Wait time is a significant problem impacting Infusion Centers (IC) nationwide What is our problem? Patients arrived for scheduled infusion treatment incur an average of 24 minute wait before being roomed (from the time patient arrives to the time patient is roomed) 3

4 DEFINE Problem Statement How do we know it is a problem? Survey conducted from 11 April to 15 April with a sample size of 326 patient visits. Data demonstrated 10 minutes to 3 hours wait time before patient is roomed 27% of sample data indicated that the wait resulted from absence or lack of necessary documents that enable treatment to begin Lack of Patient Readiness 4

5 DEFINE Problem Statement What pain does it cause? (impact to patient and/or bottom line) Waiting creates: - Lack of unit control and patient flow - Patients arrive at unscheduled times - Patients and staff alike experience an increase level of anxiety and stress - Loss of confidence in UCI s ability to offer good patient care - Loss of future patient referral when patient satisfaction diminishes - UCI commitment to patient satisfaction - DSRIP is a significant component of Healthcare reform - Loss of revenue - Lack of throughput, increase in overhead (staff over-time, lack of utilization of unit, etc) 5

6 DEFINE Project Name: Infusion Center Patient Flow Optimization Phase 1 Belt: Ranjeeta Kumar, MBA & Luanne Sims, RN Project Charter Champion: Alice Issai, COO Master Black Belt: Henry Alvarez & Laura Winner Problem Statement: Infusion Center pts incur an average of 24 minute wait from the time patient arrives to the time patient is roomed, resulting in poor patient/staff satisfaction, negative impact on patient referral process (loss in revenue) Project Y / Path-Y: Project Y= Total Time of Arrival for Infusion Center Appointment to Discharged Path Y1 = Patient arrival time to time patient is roomed Path Y2 = Pharmacy Time required to prepare medications Path Y3 = Wait time from end of preparation to start infusion Path Y4 = How long Patient is in chair receiving infusion Team Members: Claudette Bettis, RN Gema De La Cruz Nancy Eagan, RN Daniel Hoang, Pharm.D. Ranjeeta Kumar, MBA Marie Polito, RN Tara Seery, MD Luanne Sims, RN Dawn Shelton Julie Smith, RN Raja Zeitany, Pharm D Project Goal: Reduce number of defects that are causing the ~24 min delay by 25%. Total soft savings - $105K (Xs = absence of chart, orders, labs, and consents) Scope: Reduce wait time for chemotherapy patients in the infusion center. Exclusions: 1 st time chemo, IM, IV, SQ injections, hydrations, symptom management/acuity, same day appointment, walk-ins (unscheduled) and non-oncology patients Benefits: Ensure patient safety Increase patient, staff and physician satisfaction Increase volume, capacity and revenue Increase likelihood to recommend by patients Timeline: Define/Measure March April 2011 Analyze May Dec 2011 Improve/Control Dec March

7 DEFINE Why is this important? Health Care is dedicated to quality and perfection of outcomes We have been entrusted with Oncology patients lives Understanding what is important to a patient is critical to achieving quality Stable processes minimizes variations in outcomes Design is optimal when we listen to our customers 7

8 DEFINE Why is this important? - VOC Voice of the Customer (VOC) Assumptions 5 questions over 5 days 44 total responses - Patients were dissatisfied with IC experience - Patients preferred to see Oncologist same day - Patients preferred to get labs drawn same day - IC staff did not see the value of informing patients of delay 8

9 DEFINE Why is this important? VOC Results - used to defined Critical to Quality Would you recommend this infusion center to your friends and family? - 89% of patients would refer UCI IC to Friends and Family Do you prefer to see your oncologist on the same day as your infusion appt. even if it can cause delays in getting your treatment? - 50% of patient would not see their Oncologist on the same day of their IC appointment if it reduces wait time How valuable would it be for you if you could have your blood work and other tests taken one or two days before your appointment in order to prevent any delays during your treatment? - 65% of patients would get labs drawn 48 hrs prior to IC appointment to reduce waiting time VOC graphs are in appendices 9

10 DEFINE Why is this important? VOC Results - used to defined Critical to Quality Would you have your blood work and other tests done several days before your appointment in order to shorten the length of time you spend in the infusion center on the day of treatment? - 64% of patients would get labs drawn 48 hrs prior to IC appointment to reduce waiting time If you encounter a delay while waiting for your treatment, how important is it to your to be given a reason for the delay? - 64% of patients would get labs drawn 48 hrs prior to IC appointment to reduce waiting time VOC graphs are in appendices 10

11 DEFINE Waiting is an Epidemic Problem!! Perception of ineffective care are directly proportional to a patients wait time Survey of 200 patients suggested wait time affects perception of quality, satisfaction and likeability, as well as likelihood of recommendation and repeat visits * HealthMarkQ, 2005: 23(2);69-87 Visited multiple IC UCSD, UCLA, UCSF and City of Hope All are experiencing wait issues (no labs, change in pts acuity, etc) Article in appendices 11

12 DEFINE Why is this important? Why this, why now? ( Burning Platform ) The pain of continuing with the status quo process of unchecked charts, missing orders and consents was creating a burden for Infusion Center staff and increasing delay in patient start times. Domino effect from late morning starts created afternoon bottleneck effect for the remainder of the day which increased the patient s expected wait time. Average wait time was 24 minutes after patient registration to room assignment due to incomplete required medical record data. A new patient flow was needed for the unit to function efficiently because the current process became more inefficient as patient volume increased. 12

13 DEFINE Why is this important? What will happen if we don t fix this? Inconsistent wait times creates anxiety for patients and the health care team. Prolonged delays for medical records, physician orders, labs or consents impacts customer service ratings and recommendation for future referrals. Patient acuity becomes the driver of the process instead of the patients having proper scheduling and avoiding delays of treatment. Maximum patient capacity can not be fully achieved in the Infusion Center due to our current process/design. 13

14 DEFINE SIPOC S Suppliers Physicians Pharmacy RN Medical Records Authorization Unit Scheduler Orders I P O C Inputs Processes Outputs Customers Medical Records Drugs Consent Labs Chair/Room Patient Information Staffing Medical Supplies EVS Nutrition 1 st Step Patient arrives Patient is roomed Infusion prepared by pharmacy Infusion treatment Patient is discharged Registration Info Drug Information Chemical Data Billing - Encounters Patients Patient Family/ Friends Physicians Pharmacy Staff Clinic EVS Nutrition Revenue Audit Last Step 14

15 DEFINE Value Stream Map 15

16 DEFINE Swim Lanes 16

17 DEFINE Stakeholder Analysis Purpose: Understand the influences that may ultimately determine project success Step 1 Step 2 Step 3 Step 4 Identify all stakeholders (refer to SIPOC diagram) Seek to understand their perspective/interests Determine the risk or benefit to the project Leverage the learning to develop a strategy for project success Stakeholder(s) Level of Influence Interest in project 2 Current Level of Support Strategy Likert 1-5 High = 3 Medium = 2 Low = 1 Gain = 3 Neutral = 2 Loss = 1 Very supportive = 5 Slightly Supportive =4 Neutral = 3 Slightly Resistant =2 Very Resistant = 1 Name and Title could they determine project success? do they perceive a gain or loss? how supportive are they currently? how can we engage their support? Dr. Sender, Clinical Director High Gain Very supportive Dr. Seery, Medical Director High Gain Very supportive schedule weekly meeting to discuss barriers Alice Issai, COO High Gain Very supportive Cancer Center, Medicine High Loss, believe process belongs to infusion center Slightly Resistant Schedule meetings with managers and staff Pharmacy Medium Loss, believe all problems are operational Neutral Meet with managers Cancer Center, Neuro High Loss, believe process belongs to infusion center Slightly Resistant Schedule meetings with managers and staff Cancer Center, GynOnc High Loss, believe process belongs to infusion center Slightly Resistant Schedule meetings with managers and staff Infusion Center RNs Medical Records Medium Low Gain, eliminating muda (waste) will enable happier staff with less room for error Very supportive Engage staff by providing status and results of project Neutral, want to see improvement in charts being returned Neutral Meet with MR manager Meet with scheduler to understand barriers. Meet Scheduler Low Neutral Very supportive with Quest Authorization Unit Low Gain Very supportive Meet with unit 17

18 DEFINE Early Waste Identification DOMOWIT Defects Incomplete medical records Chemotherapy order missing Consents and laboratory result not in chart Overprocessing Re-writing lost chemotherapy orders Repeating labs and obtaining consents Providing customer service recovery efforts Double/triple chart check review Double check of schedule Motion Poor design excessive walking Poor flow Charts moving from clinic to infusion center Movement between RN and pharmacy Movement between registration and patient room Supplies removed from unit 18

19 DEFINE Early Waste Identification DOMOWIT Over production Re-adjusting and evaluating patient schedule and nurse assignments Re-assignment of patients Contact clinic for missing items Calling MD/Fellows for signature Waiting Chart arrival from clinic (bldg 23) to Infusion Center (Douglas Hospital) Arrival for chemo orders, labs, & consents Receipt of chemotherapy drugs Pharmacy drug production flow for same day/research patients Waiting for patients to be discharged Waiting for lab results Waiting for MD/Fellow to answer questions Wait for Nurse practitioner for symptom management Wait for MA to take patient to be roomed and vital Wait for RN to evaluate patient Clinic waiting for IC scheduler to schedule patient Clinic waiting for IC Nurse manager to return call 19

20 DEFINE Early Waste Identification DOMOWIT Patients in lobby Excess chemotherapy drug (patient evaluated and sent home) Inventory Transportation Paper movement charts, labs, orders, etc Moving patients from hallway to bed/chair Transporting medical records Transporting patients from clinic (bldg 23) to Infusion Center (Douglas Hospital) 20

21 MEASURE PHASE What are the causes? LSS: Infusion Center Throughput 21

22 MEASURE Baseline Data for Y Data was collected on individual oncological patients. Data is comprised of a sample size of 326 patient visits from 11 April to 15 April In order to reduce the time measurement variation the data was collected using server time tide to all computers Data was collected via a time study. Time study form was created to capture time for each step of an infusion visit process Time study collection tool in appendices 22

23 MEASURE Baseline Data for Y Project Y is the Total time from Arrival time for infusion center appointment to discharge Multiple Path Ys indentified within the process Phase 1 focus on Path Y1 = Patient arrival time to time patient is roomed 23

24 MEASURE Normality Graphical summary of patients actual arrival time to their scheduled appt Early Late Understand arrival patterns of IC patients On average our patients arrived ~14 min late paste 24

25 MEASURE Normality Interpretation of Graphical Summary Patient arrived vs. Patient scheduled appointment Graph to look at patients arrival pattern P-value is <.005. Data set is not behaving normally, therefore data is non-normal Non-normal data is expected when using time as your data set approaches 0 (Time data in this case usually forms a weibull distribution) With 95% confidence we can state that on avg. patients arrive mins late to an appointment Used data outcome to assess how to educate patients 25

26 MEASURE Normality Data distribution of actual patient arrival time to actual roomed time Showed significant outliers On average the process took 24 mins to get patient roomed paste 26

27 P-value is <.005 Normality Interpretation of Graphical Summary Patient Arrived vs. Patient roomed Data set is not behaving normally, therefore data is non-normal Non-normal data is expected when using time as data set approaches 0 (Time data in this case usually forms a weibull distribution) With 95% confidence we can state that on avg. it took mins for a patient to get roomed Outliers helped identify Patient Readiness issues paste 27

28 MEASURE Capability Capability goal is to room a patient in 15 mins or less (Reflects UCI MC goal of rooming patient within 15 mins) DPMO expected overall performance is 625,191 Plenty of opportunity for improvement paste 28

29 MEASURE Stability Process is beyond control limits Upper limits = 15 mins Lower limits = 0 mins Process is extremely unstable 29

30 MEASURE Baseline Process Map NVA wait time is created by defects before patients are in the chair 30

31 MEASURE Process Flow Majority of Non Value Added wait is found between the time patient arrive to the time patient is roomed A separate process flow was developed to understand the steps the IC goes through when necessary documents are missing 31

32 MEASURE Process Flow 32

33 MEASURE Measure Ishikawa Diagram ALL potential X s Infusion Center Throughput Project- Phase 1: Fishbone Diagram 33

34 MEASURE Filter X s C & E Matrix in appendices 34

35 MEASURE Sequence of Events Total annual cost for daily chart check review for six missing; - Charts - Labs - Consents - Orders $105, Sequence of Events in appendices 35

36 MEASURE Measure Data highlighted significant defects (Xs) via time study - Missing medical records - Missing or outdated lab results - Missing or incomplete MD orders - Missing patient consent Additional data was collected to validate assumed Xs Data is comprised of a sample size of 85 (excluding chart checks) 513 defects from 13 June to 21 June 36

37 MEASURE Measure 37

38 MEASURE Measure Pareto Charts 38

39 MEASURE Measure Pareto Charts 39

40 MEASURE Measure Pareto Charts 40

41 MEASURE Measure Spaghetti Diagram data comprised of 1 day (2 shifts) - 1 st shift = 1 RN (Fast-track), 2 RN, 1 MA - 2 nd Shift = 1 RN, 1 MA Spaghetti illustrated following in the GEMBA OVER PROCESSING MOTION TRANSPORTATION OVER-PRODUCTION 41

42 MEASURE Spaghetti Diagram 42

43 MEASURE Spaghetti Diagram 43

44 MEASURE Spaghetti Diagram 44

45 ANALYZE PHASE What is the root cause? LSS: Infusion Center Throughput 45

46 ANALYZE Analyze Analyzed data using following tools: 5-Why s To determine root cause To rule out non-value added steps Fault-Tree 5S Identify potential causes of a problem Streamline the process Benefit and Effort Matrix Prioritized the DOMOWIT Pareto chart check Impact of missing documents on infusion center 46

47 ANALYZE Analyze 5-Why s Why? Medical records and required documents are missing on the day of the Infusion treatment Why? No one is reviewing Infusion center roster the day before tx to prepare charts (required documents) Why? Clinic RN is not held accountable to ensue charts are reviewed day before their patients tx Project Y Infusion Center appointments are delayed due to incomplete medical records Why? There is no standardize process for chart check review on the day before tx Why? Issue has not been identified by Infusion Center as a cause for delay 47

48 ANALYZE Analyze Fault - Tree 48

49 ANALYZE Benefit & Effort Matrix Benefit & Effort Matrix in appendices 49

50 ANALYZE Analyze 5S Sort Straightened Scrub Standardize Sustain Create 3 colored labeled bins to separate the medical records to easily identify what is missing Re-organize layout of bed/chairs to organize flow Organize medical records by terminal digit when preparing charts for next day tx Clean up medical records and infusion center to make room for new colored bins Create standardized clinical teams assigned to new pods within new layout Create procedure to notify clinic manager if chart check process is not being followed Daily page reminders to nursing staff that charts are ready to be reviewed in medical record room in clinic Daily report to track missing items by MD/RN to monitor compliance 50

51 Analyze Chart check by Clinic Based on the collected chart check data: HemOnc represented the highest volume of defects (missing charts, no labs, no consents, no orders) 51

52 Analyze Chart check by Clinic 52

53 ANALYZE Analyze HemOnc couldn t find the charts. We identify missing chart locations Data collected 14 June to 21 June. Sample size of 85 defects 53

54 ANALYZE Analyze Impact on Infusion Center if these Xs are missing No Charts No Labs No Orders No Consents No reference documentation Unauthorized lab draws loss of rev. Patient safety Lack of confidence in continuity of care Patient cancelled loss of revenue Delay in care Rx delay Patient trust is compromised Loss of confidence Patient safety Impact to patient flow/delay Failure to follow COPE Negative customer service Lack of confidence 54

55 IMPROVE PHASE Trial Interventions LSS: Infusion Center Throughput 55

56 IMPROVE Improve Plan Improve plan in appendices 56

57 IMPROVE Improve Created clinic RN chart check review process Kanban (3 colored bins) Green Compliant = Current orders/labs/consents in chart Yellow Red Non-compliant = missing labs ONLY Non-compliant = missing orders/labs/consents Red bin will contain missing chart list Improvement doc in appendices 57

58 IMPROVE Improve Developed educational handout for clinic Helpful hints for IC patients Highlight key services at the IC Educational Handout in appendices 58

59 IMPROVE Reorganized IC layout Improve Created 3 teams Teams consist of 3 RNs and 1 MA Increases ability to cross cover Revised layout in appendices 59

60 IMPROVE Improve Spaghetti Diagram data comprised of 1 day (2 shifts) - 1 st shift = 1 RN, 1 MA - 2 nd Shift = 1 RN, 1 MA Minimized distance and movement Eliminated wasted steps Facilitate one-piece flow to pharmacy and front desk Improve communication 60

61 IMPROVE Improve - Spaghetti 61

62 IMPROVE Improve - Spaghetti 62

63 IMPROVE Improve Staff comments. Revised layout in appendices 63

64 IMPROVE Quality Results/Benefits Increased Patient Safety by allowing double checks (layout) Reduction in rework/defects by having clinic RN review chart to ensure proper documentation is in the IC before patient s scheduled appointment Efficiency Reduced wait time by creating a process to ensure all documents are in place before patient s schedule Increase capacity by starting patients as scheduled in Quest Increased throughput by starting infusions on time Financial Increased revenue by maximizing utilization 64

65 CONTROL PHASE Establish and follow up LSS: Infusion Center Throughput 65

66 CONTROL Final Capability Initial capability: 10.54% defects Final capability: ONLY 0.81% defects Attribute Capability Plan in appendices 66

67 CONTROL Control Plan Control Plan in appendices 67

68 Control- Poka-Yoke BEST Elimination: Eliminate possibility of error All chemo consents are now scanned into EMR Facilitation: Make distinctions more obvious Created colored kanbans to identify items needing attention Detection: Identified defects before further processing occurs Implemented chart check process 24 hrs before appointment to identify defects in advance to decrease delay of infusion start time 68

69 Appendices 1. Voice of the Customer Results 2. The Oncology Report article 3. Time Study Tool 4. Graphical Summary Pt arrival to pt schedule 5. Graphical Summary Pt arrival to pt roomed 6. Process Capability Pt arrival to pt roomed 7. Stability Pt arrival to pt roomed 8. Cause & Effect Matrix 9. Sequence of Events 10. Benefit & Effort Matrix 11. Improvement Plan 12. Improvement Documentation - Kanban 13. Education Handout 14. Revised Layout 15. Capability by Attribute Data 16. Control Plan 69