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Presented by, Matthew Rusk, D.O. Advisor: Khalid Qazi, M.D.

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Presentation on theme: "Presented by, Matthew Rusk, D.O. Advisor: Khalid Qazi, M.D."— Presentation transcript:

1 Presented by, Matthew Rusk, D.O. Advisor: Khalid Qazi, M.D.

2 Objectives  Introduce a concept that augments the admission process by improving: Admission wait times Patient satisfaction Quality Cost Effective Care  Explain how change was implemented  Discuss results  Compare results to current literature

3 Introduction—Lean Flow  Business concept that is well known and implemented daily by successful businesses  Often ignored in the healthcare industry  Gaining recognition in healthcare  Can make healthcare efficient and improve quality

4 Introduction  ED overcrowding is associated with worse quality of care and service delivery quality (1);  Recent studies have shown clearly that wait time directly affects patient satisfaction (1-9);  Time to evaluation can also influence whether or not a patient is seen at all (1, 2, 10).

5 Hypothesis  Utilizing lean flow will improve the admission process at Sisters of Charity Hospital by: Decreasing the total admission process time Improving patient satisfaction Enhancing quality Improving Cost Effective Care

6 Methodology  Implementation of Lean Flow X32 Healthcare ‘Rapid Improvement 3-day Program’ ○ CHS Staff; ○ Four Residents; ○ Lean Flow Education; ○ ‘Front end’ Improvements; ○ Little focus on admission process

7 Methodology Applied concepts to improve admission process Key Changes: ○ Admission Orders within 30 min; ○ ED Holding Orders in certain situations; ○ Earlier Bed Search; ○ Easier access to order sets, charts and labels

8 Methodology  Outcome measures Time Patient Satisfaction Quality and Safety Cost Effective Care

9 Methodology  Pre-intervention March 1 through October 31, 2008-2011  Intervention November 2011 – February 2012  Post-intervention March 1 through October 31, 2012

10 Methodology-Time Intervals Arrival to Departure (total admission time) Arrival to ED Provider ED Provider to Time Admitting Physician Informed of admission (TAPI) TAPI to Admit Order Admit Order to Departure ArrivalED ProviderTAPIAdmit OrderDeparture

11 Methodology-Patient Satisfaction  Questions: Got help as soon as wanted Quiet around room at night Treated with courtesy and respect by doctors Treated with courtesy and respect by nurses Rate Hospital Would recommend hospital to family  Answers 9 or 10 out of 10 defined as perfect score  8 or below defined as non-perfect (negative response)

12 Methodology-Quality Indicators Inpatient Specific ED Specific  Fall Rate  Core Measure Compliance AMI, HF, PN, SCIP  RRT calls  Inpatient Mortality  Left Without Being Seen (LWBS)  ED Mortality

13 Methodology—Cost Effective Care  Average LOS  ED Volume  Total Admissions

14 Results—Time Variables  Summarized using means and standard deviations.  An independent two-sample t-test using assumption of equal variances was used to test for differences in means.  A multiple regression model was used to test for differences adjusted for baseline variables (age, gender, race, Arr Method, and Bed Type).

15 Time Interval Comparison Time (minutes)

16 (Decrease of 78.8 minutes [417.8 – 339 = 78.8]) Statistically Significant, P-value <.0001 Time (minutes)

17 (Decrease of 35 minutes [168.5 – 133.5 = 35]) Time (minutes) Statistically Significant, P-value <.0001

18 (Decrease of 36.2 minutes [61.9 – 25.7 = 36.2]) Time (minutes) Statistically Significant, P-value 0.0015

19 Summary of Time Variables  Arrival to Departure (Total Admission Time) Decrease of 78.8 minutes 19% reduction in total admission time Most of our overall improvement during TAPI to Dep  TAPI to Departure Decrease of 71.2 minutes 31% reduction of this  TAPI to Admit Order Decrease of 36.2 minutes 58.5% reduction of this interval  Admit Order to Departure Decrease of 35 minutes 21% reduction of this interval

20 Results—Patient Satisfaction  Summarized using frequencies and percentages.  A Pearson chi-square test was used to compare the proportion of satisfaction between pre and post.  Odds ratio and corresponding 95% confidence interval was calculated.

21 Hospital Rating StatisticDFValueProb Chi-Square116.7623<.0001 Chi-square test: Type of StudyValue95% Confidence Limits Case-Control (Odds Ratio) 1.79811.35612.3843 Odds Ratio:

22 Would Recommend Hospital To Family StatisticDFValueProb Chi-Square112.50090.0004 Chi-square test: Type of StudyValue95% Confidence Limits Case-Control (Odds Ratio) 1.69311.26292.2698 Odds Ratio:

23 Treated With Courtesy and Respect By Doctors StatisticDFValueProb Chi-Square110.02760.0015 Chi-square test: Type of StudyValue95% Confidence Limits Case-Control (Odds Ratio) 1.71131.22462.3914 Odds Ratio:

24 Treated With Courtesy and Respect By Nurses StatisticDFValueProb Chi-Square111.02640.0009 Chi-square test: Type of StudyValue95% Confidence Limits Case-Control (Odds Ratio) 1.77031.26062.4861 Odds Ratio:

25 Patient Satisfaction Results  All questions showed significant improvement post-intervention.  Hospital Rating Scores improved to 70.2% (from 56.74%)  Recommend to Family Scores improved to 74.94% (from 63.85%)

26 Results—Quality  Summarized using means and standard deviations  An independent two-sample t-test using assumption of equal variances was used to test for differences in means.

27 Improved Inpatient Fall Rate Falls significantly decreased (p-value < 0.0001 )

28 Improved ED Left Without Being Seen (LWBS) 38% reduction in LWBS p-value is < 0.0001

29 Improved Core Measure Compliance Percentage of Perfect Care Pre (%)Post (%) P-value AMI91.41100.000.0956 HF84.35100.00 <0.0001 PN84.9994.44 0.0293 SCIP84.9892.61 0.0006

30 Decreased Number of Rapid Response Team Calls p-value = < 0.001 Statistically Significant

31 Mortality InpatientED p-value = 0.9053 No significant difference p-value = 0.6264 No significant difference

32 Quality Summary Inpatient Specific ED Specific  Improved Inpatient Fall Rate  Improved Core Measure Compliance AMI, HF, PN, SCIP  Decreased RRT calls  No change in Inpatient Mortality  Improved Left Without Being Seen (LWBS)  No change in ED Mortality

33 Results—Cost Effective Care  Summarized using means and standard deviations  An independent two-sample t-test using assumption of equal variances was used to test for differences in means.

34 Improved Length of Stay Average LOS decreased from 4.68 days to 4.36 days (p-value < 0.0018 )

35 Increased ED Volume and Admissions  ED Volume increased 13.5%: Pre Volume avg = 23,624 Post Volume = 26,799 (March-Oct)  Admissions Increased 3.5%: Pre Admission Avg = 4,002 Post Admission = 4,141 (March-Oct)

36 Cost Effective Care Summary  Improved Average Length of Stay  Increased ED Volume  Increased Admissions

37 Discussion  Yale-New Haven Hospital utilized lean and reduced the time from decision to admit [TAPI] to transfer to floor [departure] by 33% (11) Anecdotal recount We had a 31% reduction of this time frame.  Lack of studies focus on admitted patients. Lack of focus on admission times, affect of overall hospital rating after admission Limited investigation on inpatient quality.

38 Conclusion  Our study fills void ○ focus on how lean affects the admission process and subsequent hospital stay.  Implementing Lean Flow at Sisters Hospital Significantly Improved Admission Times Significantly Improved Patient Satisfaction Significantly Enhanced Quality Facilitated Cost Effective Care

39 Conclusion  Further improvements are possible Focus on specific time intervals Re-evaluate processes  Lean Flow works and is an essential tool implement in healthcare.

40 Acknowledgements  Marylin Boehler, RN, Director of ED and Critical Care  Julie Morgante, Quality Analyst, Quality & Patient Safety Department  Terry Mashtare, PhD, UB Statistics Department  Jingjing Yin, UB Statistics Department  Entire Sisters Medical Records Department  Abid Hussain, MBBS, IM Resident  Sameer Waheed, MBBS, IM Resident  Mohammad Tantray, MBBS, IM Resident  Nancy Roder RN, BSN, Application Analyst, CHS Information Technology  X32 Healthcare—Lean Consulting Firm Chuck Noon, PhD Brian Livingston, MD, MBA Jody Crane, MD, MBA Kim Adams, RN

41 References  1. Eitel DR, et al. Improving Service Quality by Understanding Emergency Department Flow: A White Paper and Position Statement Prepared for the American Academy of Emergency Medicine. The Journal of Emergency Medicine, Vol. 38, No.1, pp.70-79. 2010.  2. Schull MJ, Vermeulen M, Slaughter G, et al. Emergency department crowding and thrombolysis delays in acute myocardial infarction. Ann Emerg Med 2004;11:577– 85.  3. Miro O, Antonio MT, Jimenez S, et al. Decreased healthcare quality associated with emergency department overcrowding. Eur J Emerg Care 1999;6:105–7.  4. Pines JM, Hollander JE, Localio AR, Metlay JP. The association between ED crowding and hospital performance on antibiotic timing for pneumonia and percutaneous intervention for myocardial infarction. Acad Emerg Med 2006;13:873– 8.  5. Boudreaux ED, Ary RD, Mandry CV, McCabe B. Determinants of patient satisfaction in a large, municipal ED: the role of demographic variables, visit characteristics, and patient perceptions. Am J Emerg Med 2000;18:394 –400.  6. Kyriacou DN, Ricketts V, Dyne PL, McCollough MD, Talan DA. A 5-year time study analysis of emergency department patient care efficiency. Ann Emerg Med 1999;34:326 –35.  7. Sun BC, Adams J, Orav EJ, Rucker DW, Brennan TA, Burstin HR. Determinants of patient satisfaction and willingness to return with emergency care. Ann Emerg Med 2000;35:426 –34.  8. Bursch B, Beezy J, Shaw R. Emergency department satisfaction: what matters most? Ann Emerg Med 1993;22:586 –91.  9. Watson WT, Marshall ES, Fosbinder D. Elderly patients’ perceptions of care in the emergency department. J Emerg Nurs 1999; 25:88 –92.  10. Hobbs D, Kunzman SC, Tandberg D, Sklar D. Hospital factors associated with emergency center patients leaving without being seen. Am J Emerg Med 2000;18:767–72.  11. Kulkarni RG. A reader and author respond to “Going Lean in the emergency department: a strategy for addressing emergency department overcrowding.” Medscape J Med. 2008; 10:25.

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